f.hoffstaedter/ds003416-mriqc
1
0
Fork 0
Code Pull requests Packages Activity
ds003416-mriqc/logs/sub-cIIs04_10066487.err
Felix Hoffstaedter bfde166847 add logs
2025-10-10 23:58:36 +02:00

305 lines
42 KiB (Stored with Git Annex)
Text
Raw Permalink Blame History

+ dssource=ria+file:///data/project/QC_workflow/TMP/RIA_QCworkflow/inputstore#aae8905a-985f-46fb-91f5-35c772654ddd
+ pushgitremote=file:///data/project/QC_workflow/TMP/RIA_QCworkflow/aae/8905a-985f-46fb-91f5-35c772654ddd
+ subid=sub-cIIs04
+ export 'DUCT_OUTPUT_PREFIX=logs/duct/sub-cIIs04_{datetime_filesafe}-{pid}_'
+ DUCT_OUTPUT_PREFIX='logs/duct/sub-cIIs04_{datetime_filesafe}-{pid}_'
+ datalad clone ria+file:///data/project/QC_workflow/TMP/RIA_QCworkflow/inputstore#aae8905a-985f-46fb-91f5-35c772654ddd ds
[INFO] Attempting a clone into /var/lib/condor/execute/dir_3951209/ds
[INFO] Attempting to clone from file:///data/project/QC_workflow/TMP/RIA_QCworkflow/inputstore/aae/8905a-985f-46fb-91f5-35c772654ddd to /var/lib/condor/execute/dir_3951209/ds
[INFO] Completed clone attempts for Dataset(/var/lib/condor/execute/dir_3951209/ds)
+ cd ds
+ git remote add outputstore file:///data/project/QC_workflow/TMP/RIA_QCworkflow/aae/8905a-985f-46fb-91f5-35c772654ddd
+ git checkout -b job_sub-cIIs04_10066487
Switched to a new branch 'job_sub-cIIs04_10066487'
+ datalad get -n sourcedata/raw/
[INFO] Attempting a clone into /var/lib/condor/execute/dir_3951209/ds/sourcedata/raw
[INFO] Attempting to clone from file:///data/project/QC_workflow/TMP/RIA_QCworkflow/inputstore/9e5/e9b46-fb3d-48cf-b766-89923247370d to /var/lib/condor/execute/dir_3951209/ds/sourcedata/raw
[INFO] Attempting to clone from https://github.com/OpenNeuroDatasets/ds003416.git to /var/lib/condor/execute/dir_3951209/ds/sourcedata/raw
[INFO] Start enumerating objects
[INFO] Start counting objects
[INFO] Start compressing objects
[INFO] Start receiving objects
[INFO] Start resolving deltas
[INFO] Completed clone attempts for Dataset(/var/lib/condor/execute/dir_3951209/ds/sourcedata/raw)
[INFO] Remote origin not usable by git-annex; setting annex-ignore
[INFO] https://github.com/OpenNeuroDatasets/ds003416.git/config download failed: Not Found
[INFO] access to 1 dataset sibling s3-PRIVATE not auto-enabled, enable with:
| datalad siblings -d "/var/lib/condor/execute/dir_3951209/ds/sourcedata/raw" enable -s s3-PRIVATE
+ datalad containers-run -m 'Compute MRIQC for sub-cIIs04' -n bids-mriqc -i sourcedata/raw/sub-cIIs04 -i sourcedata/raw/dataset_description.json mriqc sourcedata/raw . participant --participant-label sub-cIIs04 --no-datalad-get --no-sub --verbose --nprocs 1 --mem 3000 --work-dir /tmp --float32 --verbose-reports
[INFO] Making sure inputs are available (this may take some time)
[INFO] Attempting a clone into /var/lib/condor/execute/dir_3951209/ds/code/containers
[INFO] Attempting to clone from file:///data/project/QC_workflow/TMP/RIA_QCworkflow/inputstore/b02/e63c2-62c1-11e9-82b0-52540040489c to /var/lib/condor/execute/dir_3951209/ds/code/containers
[INFO] Attempting to clone from https://github.com/ReproNim/containers.git to /var/lib/condor/execute/dir_3951209/ds/code/containers
[INFO] Start enumerating objects
[INFO] Start counting objects
[INFO] Start compressing objects
[INFO] Start receiving objects
[INFO] Start resolving deltas
[INFO] Completed clone attempts for Dataset(/var/lib/condor/execute/dir_3951209/ds/code/containers)
[INFO] Remote origin not usable by git-annex; setting annex-ignore
[INFO] https://github.com/ReproNim/containers.git/config download failed: Not Found
[INFO] == Command start (output follows) =====
2025-10-09T23:59:23+0200 [INFO ] con-duct: duct 0.16.0 is executing 'singularity exec -W /tmp/singtmp.bHIMLn -B /var/lib/condor/execute/dir_3951209/ds/code/containers/binds/zoneinfo/UTC:/etc/localtime -B /tmp/singtmp.bHIMLn/tmp:/tmp -B /tmp/singtmp.bHIMLn/var/tmp:/var/tmp -e -B /var/lib/condor/execute/dir_3951209/ds -H /var/lib/condor/execute/dir_3951209/ds/code/containers/binds/HOME --pwd /var/lib/condor/execute/dir_3951209/ds code/containers/images/bids/bids-mriqc--24.0.2.sing mriqc sourcedata/raw . participant --participant-label sub-cIIs04 --no-datalad-get --no-sub --verbose --nprocs 1 --mem 3000 --work-dir /tmp --float32 --verbose-reports'...
2025-10-09T23:59:23+0200 [INFO ] con-duct: Log files will be written to logs/duct/sub-cIIs04_2025.10.09T23.59.23-4048006_
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
Fontconfig error: No writable cache directories
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: divide by zero encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: invalid value encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: divide by zero encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: invalid value encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: divide by zero encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: invalid value encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: divide by zero encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: invalid value encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: divide by zero encountered in divide
s = sum_m2 / (2 * K * sigma**2)
/opt/conda/lib/python3.11/site-packages/dipy/denoise/noise_estimate.py:252: RuntimeWarning: invalid value encountered in divide
s = sum_m2 / (2 * K * sigma**2)
0%| | 0/106292 [00:00<?, ?it/s]
0%| | 372/106292 [00:00<00:28, 3715.11it/s]
1%| | 868/106292 [00:00<00:23, 4443.07it/s]
1%|▏ | 1366/106292 [00:00<00:22, 4684.33it/s]
2%|▏ | 1870/106292 [00:00<00:21, 4823.26it/s]
2%|▏ | 2367/106292 [00:00<00:21, 4873.19it/s]
3%|▎ | 2869/106292 [00:00<00:21, 4920.76it/s]
3%|▎ | 3367/106292 [00:00<00:20, 4937.81it/s]
4%|▎ | 3861/106292 [00:00<00:21, 4684.72it/s]
4%|▍ | 4357/106292 [00:00<00:21, 4766.37it/s]
5%|▍ | 4859/106292 [00:01<00:20, 4841.48it/s]
5%|▌ | 5363/106292 [00:01<00:20, 4899.36it/s]
6%|▌ | 5861/106292 [00:01<00:20, 4923.52it/s]
6%|▌ | 6365/106292 [00:01<00:20, 4957.94it/s]
6%|▋ | 6862/106292 [00:01<00:20, 4944.95it/s]
7%|▋ | 7360/106292 [00:01<00:19, 4954.62it/s]
7%|▋ | 7863/106292 [00:01<00:19, 4977.11it/s]
8%|▊ | 8367/106292 [00:01<00:19, 4995.33it/s]
8%|▊ | 8867/106292 [00:01<00:20, 4742.01it/s]
9%|▉ | 9366/106292 [00:01<00:20, 4811.71it/s]
9%|▉ | 9872/106292 [00:02<00:19, 4881.66it/s]
10%|▉ | 10369/106292 [00:02<00:19, 4907.64it/s]
10%|█ | 10868/106292 [00:02<00:19, 4930.51it/s]
11%|█ | 11371/106292 [00:02<00:19, 4959.88it/s]
11%|█ | 11877/106292 [00:02<00:18, 4988.36it/s]
12%|█▏ | 12382/106292 [00:02<00:18, 5004.04it/s]
12%|█▏ | 12883/106292 [00:02<00:18, 4999.63it/s]
13%|█▎ | 13388/106292 [00:02<00:18, 5012.87it/s]
13%|█▎ | 13890/106292 [00:02<00:19, 4761.01it/s]
14%|█▎ | 14392/106292 [00:02<00:19, 4835.67it/s]
14%|█▍ | 14892/106292 [00:03<00:18, 4882.27it/s]
14%|█▍ | 15397/106292 [00:03<00:18, 4930.29it/s]
15%|█▍ | 15904/106292 [00:03<00:18, 4969.98it/s]
15%|█▌ | 16409/106292 [00:03<00:18, 4992.47it/s]
16%|█▌ | 16909/106292 [00:03<00:18, 4937.61it/s]
16%|█▋ | 17407/106292 [00:03<00:17, 4948.77it/s]
17%|█▋ | 17907/106292 [00:03<00:17, 4963.36it/s]
17%|█▋ | 18406/106292 [00:03<00:17, 4970.97it/s]
18%|█▊ | 18904/106292 [00:03<00:18, 4689.60it/s]
18%|█▊ | 19399/106292 [00:03<00:18, 4763.77it/s]
19%|█▊ | 19899/106292 [00:04<00:17, 4832.19it/s]
19%|█▉ | 20403/106292 [00:04<00:17, 4892.34it/s]
20%|█▉ | 20905/106292 [00:04<00:17, 4927.94it/s]
20%|██ | 21406/106292 [00:04<00:17, 4951.86it/s]
21%|██ | 21912/106292 [00:04<00:16, 4983.57it/s]
21%|██ | 22420/106292 [00:04<00:16, 5010.16it/s]
22%|██▏ | 22928/106292 [00:04<00:16, 5028.97it/s]
22%|██▏ | 23433/106292 [00:04<00:16, 5034.90it/s]
23%|██▎ | 23937/106292 [00:04<00:17, 4757.19it/s]
23%|██▎ | 24442/106292 [00:05<00:16, 4839.18it/s]
23%|██▎ | 24948/106292 [00:05<00:16, 4902.29it/s]
24%|██▍ | 25453/106292 [00:05<00:16, 4944.61it/s]
24%|██▍ | 25955/106292 [00:05<00:16, 4965.50it/s]
25%|██▍ | 26453/106292 [00:05<00:16, 4932.69it/s]
25%|██▌ | 26957/106292 [00:05<00:15, 4963.44it/s]
26%|██▌ | 27458/106292 [00:05<00:15, 4975.99it/s]
26%|██▋ | 27960/106292 [00:05<00:15, 4988.13it/s]
27%|██▋ | 28460/106292 [00:05<00:15, 4987.12it/s]
27%|██▋ | 28959/106292 [00:05<00:16, 4733.05it/s]
28%|██▊ | 29465/106292 [00:06<00:15, 4826.85it/s]
28%|██▊ | 29972/106292 [00:06<00:15, 4897.01it/s]
29%|██▊ | 30477/106292 [00:06<00:15, 4939.40it/s]
29%|██▉ | 30979/106292 [00:06<00:15, 4962.03it/s]
30%|██▉ | 31484/106292 [00:06<00:14, 4987.98it/s]
30%|███ | 31992/106292 [00:06<00:14, 5012.44it/s]
31%|███ | 32500/106292 [00:06<00:14, 5032.07it/s]
31%|███ | 33006/106292 [00:06<00:14, 5037.73it/s]
32%|███▏ | 33511/106292 [00:06<00:14, 5035.59it/s]
32%|███▏ | 34015/106292 [00:06<00:14, 5009.39it/s]
32%|███▏ | 34517/106292 [00:07<00:14, 4787.56it/s]
33%|███▎ | 35024/106292 [00:07<00:14, 4867.45it/s]
33%|███▎ | 35526/106292 [00:07<00:14, 4910.40it/s]
34%|███▍ | 36034/106292 [00:07<00:14, 4957.67it/s]
34%|███▍ | 36531/106292 [00:07<00:14, 4934.22it/s]
35%|███▍ | 37037/106292 [00:07<00:13, 4969.16it/s]
35%|███▌ | 37545/106292 [00:07<00:13, 5001.25it/s]
36%|███▌ | 38052/106292 [00:07<00:13, 5020.50it/s]
36%|███▋ | 38561/106292 [00:07<00:13, 5040.15it/s]
37%|███▋ | 39066/106292 [00:07<00:13, 5029.64it/s]
37%|███▋ | 39570/106292 [00:08<00:13, 4775.57it/s]
38%|███▊ | 40078/106292 [00:08<00:13, 4860.99it/s]
38%|███▊ | 40586/106292 [00:08<00:13, 4923.13it/s]
39%|███▊ | 41093/106292 [00:08<00:13, 4965.78it/s]
39%|███▉ | 41596/106292 [00:08<00:12, 4983.03it/s]
40%|███▉ | 42101/106292 [00:08<00:12, 5001.94it/s]
40%|████ | 42610/106292 [00:08<00:12, 5026.49it/s]
41%|████ | 43117/106292 [00:08<00:12, 5038.19it/s]
41%|████ | 43623/106292 [00:08<00:12, 5043.07it/s]
42%|████▏ | 44128/106292 [00:08<00:12, 5017.75it/s]
42%|████▏ | 44630/106292 [00:09<00:12, 4757.43it/s]
42%|████▏ | 45137/106292 [00:09<00:12, 4846.09it/s]
43%|████▎ | 45645/106292 [00:09<00:12, 4911.47it/s]
43%|████▎ | 46139/106292 [00:09<00:12, 4917.99it/s]
44%|████▍ | 46640/106292 [00:09<00:12, 4943.33it/s]
44%|████▍ | 47144/106292 [00:09<00:11, 4969.73it/s]
45%|████▍ | 47652/106292 [00:09<00:11, 5001.64it/s]
45%|████▌ | 48160/106292 [00:09<00:11, 5023.83it/s]
46%|████▌ | 48667/106292 [00:09<00:11, 5036.14it/s]
46%|████▋ | 49171/106292 [00:09<00:11, 5027.00it/s]
47%|████▋ | 49674/106292 [00:10<00:11, 4760.29it/s]
47%|████▋ | 50178/106292 [00:10<00:11, 4839.44it/s]
48%|████▊ | 50685/106292 [00:10<00:11, 4905.69it/s]
48%|████▊ | 51189/106292 [00:10<00:11, 4945.01it/s]
49%|████▊ | 51692/106292 [00:10<00:10, 4968.54it/s]
49%|████▉ | 52190/106292 [00:10<00:10, 4960.10it/s]
50%|████▉ | 52693/106292 [00:10<00:10, 4979.00it/s]
50%|█████ | 53197/106292 [00:10<00:10, 4995.96it/s]
51%|█████ | 53700/106292 [00:10<00:10, 5003.59it/s]
51%|█████ | 54203/106292 [00:11<00:10, 5009.37it/s]
51%|█████▏ | 54705/106292 [00:11<00:10, 4742.65it/s]
52%|█████▏ | 55207/106292 [00:11<00:10, 4820.40it/s]
52%|█████▏ | 55713/106292 [00:11<00:10, 4887.83it/s]
53%|█████▎ | 56205/106292 [00:11<00:10, 4895.51it/s]
53%|█████▎ | 56708/106292 [00:11<00:10, 4933.89it/s]
54%|█████▍ | 57207/106292 [00:11<00:09, 4948.34it/s]
54%|█████▍ | 57710/106292 [00:11<00:09, 4971.38it/s]
55%|█████▍ | 58217/106292 [00:11<00:09, 4997.89it/s]
55%|█████▌ | 58724/106292 [00:11<00:09, 5016.84it/s]
56%|█████▌ | 59229/106292 [00:12<00:09, 5026.39it/s]
56%|█████▌ | 59732/106292 [00:12<00:09, 4755.72it/s]
57%|█████▋ | 60236/106292 [00:12<00:09, 4837.28it/s]
57%|█████▋ | 60739/106292 [00:12<00:09, 4891.41it/s]
58%|█████▊ | 61246/106292 [00:12<00:09, 4943.26it/s]
58%|█████▊ | 61754/106292 [00:12<00:08, 4982.15it/s]
59%|█████▊ | 62257/106292 [00:12<00:08, 4994.55it/s]
59%|█████▉ | 62763/106292 [00:12<00:08, 5011.80it/s]
60%|█████▉ | 63271/106292 [00:12<00:08, 5031.95it/s]
60%|██████ | 63780/106292 [00:12<00:08, 5048.78it/s]
60%|██████ | 64288/106292 [00:13<00:08, 5058.01it/s]
61%|██████ | 64795/106292 [00:13<00:08, 4807.15it/s]
61%|██████▏ | 65291/106292 [00:13<00:08, 4849.66it/s]
62%|██████▏ | 65795/106292 [00:13<00:08, 4904.53it/s]
62%|██████▏ | 66288/106292 [00:13<00:08, 4908.03it/s]
63%|██████▎ | 66795/106292 [00:13<00:07, 4954.62it/s]
63%|██████▎ | 67301/106292 [00:13<00:07, 4984.81it/s]
64%|██████▍ | 67802/106292 [00:13<00:07, 4992.14it/s]
64%|██████▍ | 68309/106292 [00:13<00:07, 5012.71it/s]
65%|██████▍ | 68816/106292 [00:13<00:07, 5026.70it/s]
65%|██████▌ | 69319/106292 [00:14<00:07, 5020.84it/s]
66%|██████▌ | 69826/106292 [00:14<00:07, 5035.00it/s]
66%|██████▌ | 70330/106292 [00:14<00:07, 4761.11it/s]
67%|██████▋ | 70835/106292 [00:14<00:07, 4841.93it/s]
67%|██████▋ | 71341/106292 [00:14<00:07, 4903.47it/s]
68%|██████▊ | 71846/106292 [00:14<00:06, 4944.78it/s]
68%|██████▊ | 72351/106292 [00:14<00:06, 4974.55it/s]
69%|██████▊ | 72851/106292 [00:14<00:06, 4981.56it/s]
69%|██████▉ | 73357/106292 [00:14<00:06, 5003.27it/s]
69%|██████▉ | 73862/106292 [00:14<00:06, 5015.79it/s]
70%|██████▉ | 74366/106292 [00:15<00:06, 5022.00it/s]
70%|███████ | 74870/106292 [00:15<00:06, 5024.91it/s]
71%|███████ | 75373/106292 [00:15<00:06, 4762.67it/s]
71%|███████▏ | 75865/106292 [00:15<00:06, 4806.18it/s]
72%|███████▏ | 76372/106292 [00:15<00:06, 4881.67it/s]
72%|███████▏ | 76876/106292 [00:15<00:05, 4928.17it/s]
73%|███████▎ | 77373/106292 [00:15<00:05, 4938.66it/s]
73%|███████▎ | 77873/106292 [00:15<00:05, 4956.78it/s]
74%|███████▎ | 78380/106292 [00:15<00:05, 4989.09it/s]
74%|███████▍ | 78887/106292 [00:16<00:05, 5011.02it/s]
75%|███████▍ | 79394/106292 [00:16<00:05, 5027.41it/s]
75%|███████▌ | 79897/106292 [00:16<00:05, 5023.27it/s]
76%|███████▌ | 80400/106292 [00:16<00:05, 4764.85it/s]
76%|███████▌ | 80904/106292 [00:16<00:05, 4843.83it/s]
77%|███████▋ | 81409/106292 [00:16<00:05, 4902.94it/s]
77%|███████▋ | 81913/106292 [00:16<00:04, 4940.92it/s]
78%|███████▊ | 82411/106292 [00:16<00:04, 4951.81it/s]
78%|███████▊ | 82914/106292 [00:16<00:04, 4974.81it/s]
78%|███████▊ | 83419/106292 [00:16<00:04, 4996.60it/s]
79%|███████▉ | 83924/106292 [00:17<00:04, 5011.19it/s]
79%|███████▉ | 84428/106292 [00:17<00:04, 5017.94it/s]
80%|███████▉ | 84931/106292 [00:17<00:04, 5009.14it/s]
80%|████████ | 85433/106292 [00:17<00:04, 4758.27it/s]
81%|████████ | 85920/106292 [00:17<00:04, 4788.12it/s]
81%|████████▏ | 86425/106292 [00:17<00:04, 4863.07it/s]
82%|████████▏ | 86924/106292 [00:17<00:03, 4899.87it/s]
82%|████████▏ | 87427/106292 [00:17<00:03, 4936.98it/s]
83%|████████▎ | 87932/106292 [00:17<00:03, 4969.66it/s]
83%|████████▎ | 88437/106292 [00:17<00:03, 4991.53it/s]
84%|████████▎ | 88939/106292 [00:18<00:03, 4999.71it/s]
84%|████████▍ | 89440/106292 [00:18<00:03, 4995.51it/s]
85%|████████▍ | 89944/106292 [00:18<00:03, 5007.04it/s]
85%|████████▌ | 90445/106292 [00:18<00:03, 4752.88it/s]
86%|████████▌ | 90947/106292 [00:18<00:03, 4829.66it/s]
86%|████████▌ | 91441/106292 [00:18<00:03, 4859.41it/s]
86%|████████▋ | 91937/106292 [00:18<00:02, 4888.37it/s]
87%|████████▋ | 92433/106292 [00:18<00:02, 4909.01it/s]
87%|████████▋ | 92931/106292 [00:18<00:02, 4927.23it/s]
88%|████████▊ | 93425/106292 [00:18<00:02, 4930.06it/s]
88%|████████▊ | 93924/106292 [00:19<00:02, 4945.38it/s]
89%|████████▉ | 94419/106292 [00:19<00:02, 4945.34it/s]
89%|████████▉ | 94920/106292 [00:19<00:02, 4961.90it/s]
90%|████████▉ | 95417/106292 [00:19<00:02, 4763.76it/s]
90%|█████████ | 95896/106292 [00:19<00:02, 4719.73it/s]
91%|█████████ | 96400/106292 [00:19<00:02, 4810.94it/s]
91%|█████████ | 96901/106292 [00:19<00:01, 4869.01it/s]
92%|█████████▏| 97397/106292 [00:19<00:01, 4894.75it/s]
92%|█████████▏| 97900/106292 [00:19<00:01, 4934.83it/s]
93%|█████████▎| 98403/106292 [00:19<00:01, 4961.99it/s]
93%|█████████▎| 98901/106292 [00:20<00:01, 4965.15it/s]
94%|█████████▎| 99404/106292 [00:20<00:01, 4982.74it/s]
94%|█████████▍| 99908/106292 [00:20<00:01, 4997.61it/s]
94%|█████████▍| 100408/106292 [00:20<00:01, 4989.32it/s]
95%|█████████▍| 100908/106292 [00:20<00:01, 4732.44it/s]
95%|█████████▌| 101409/106292 [00:20<00:01, 4809.72it/s]
96%|█████████▌| 101905/106292 [00:20<00:00, 4852.22it/s]
96%|█████████▋| 102402/106292 [00:20<00:00, 4884.72it/s]
97%|█████████▋| 102899/106292 [00:20<00:00, 4909.71it/s]
97%|█████████▋| 103391/106292 [00:21<00:00, 3156.41it/s]
98%|█████████▊| 103890/106292 [00:21<00:00, 3549.06it/s]
98%|█████████▊| 104385/106292 [00:21<00:00, 3876.16it/s]
99%|█████████▊| 104832/106292 [00:21<00:00, 3908.55it/s]
99%|█████████▉| 105331/106292 [00:21<00:00, 4187.21it/s]
100%|█████████▉| 105831/106292 [00:21<00:00, 4404.45it/s]
100%|██████████| 106292/106292 [00:21<00:00, 4871.68it/s]
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/mosaic.py:565: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
fig = plt.figure(layout=None)
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/mosaic.py:565: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
fig = plt.figure(layout=None)
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/modality/dwi.py:114: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
fig, axs = plt.subplots(
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/modality/dwi.py:162: RuntimeWarning: divide by zero encountered in scalar divide
max_snr = imax / sigma
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/modality/dwi.py:164: RuntimeWarning: invalid value encountered in divide
labels_bins_position = bins[0] * np.array(labels_bins) / max_snr
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/modality/dwi.py:162: RuntimeWarning: divide by zero encountered in scalar divide
max_snr = imax / sigma
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/modality/dwi.py:164: RuntimeWarning: invalid value encountered in divide
labels_bins_position = bins[0] * np.array(labels_bins) / max_snr
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/modality/dwi.py:162: RuntimeWarning: divide by zero encountered in scalar divide
max_snr = imax / sigma
/opt/conda/lib/python3.11/site-packages/nireports/reportlets/modality/dwi.py:164: RuntimeWarning: invalid value encountered in divide
labels_bins_position = bins[0] * np.array(labels_bins) / max_snr
/opt/conda/lib/python3.11/site-packages/mriqc/interfaces/anatomical.py:490: RuntimeWarning: divide by zero encountered in divide
bg_data[bg_data > 0] = bg_data[bg_data > 0] / bg_spread
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:420: UserWarning: empty mask
xmin_, xmax_, ymin_, ymax_, zmin_, zmax_ = get_mask_bounds(
/opt/conda/lib/python3.11/site-packages/mriqc/interfaces/anatomical.py:490: RuntimeWarning: divide by zero encountered in divide
bg_data[bg_data > 0] = bg_data[bg_data > 0] / bg_spread
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:420: UserWarning: empty mask
xmin_, xmax_, ymin_, ymax_, zmin_, zmax_ = get_mask_bounds(
/opt/conda/lib/python3.11/site-packages/mriqc/interfaces/anatomical.py:490: RuntimeWarning: divide by zero encountered in divide
bg_data[bg_data > 0] = bg_data[bg_data > 0] / bg_spread
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:420: UserWarning: empty mask
xmin_, xmax_, ymin_, ymax_, zmin_, zmax_ = get_mask_bounds(
/opt/conda/lib/python3.11/site-packages/mriqc/interfaces/anatomical.py:490: RuntimeWarning: divide by zero encountered in divide
bg_data[bg_data > 0] = bg_data[bg_data > 0] / bg_spread
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:420: UserWarning: empty mask
xmin_, xmax_, ymin_, ymax_, zmin_, zmax_ = get_mask_bounds(
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:420: UserWarning: empty mask
xmin_, xmax_, ymin_, ymax_, zmin_, zmax_ = get_mask_bounds(
/opt/conda/lib/python3.11/site-packages/mriqc/interfaces/anatomical.py:490: RuntimeWarning: divide by zero encountered in divide
bg_data[bg_data > 0] = bg_data[bg_data > 0] / bg_spread
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:420: UserWarning: empty mask
xmin_, xmax_, ymin_, ymax_, zmin_, zmax_ = get_mask_bounds(
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: divide by zero encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:234: RuntimeWarning: divide by zero encountered in scalar divide
cc_snr_estimates['shell0'] = round(float(in_b0[cc_mask].mean() / std_signal), decimals)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:257: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_worst / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:260: RuntimeWarning: invalid value encountered in scalar divide
float(np.mean(mean_signal_best / std_signal)), decimals
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/nilearn/plotting/img_plotting.py:557: RuntimeWarning: overflow encountered in scalar subtract
ptp = 0.5 * (vmax - vmin)
/opt/conda/lib/python3.11/site-packages/nireports/interfaces/reporting/base.py:105: UserWarning: The given float value must not exceed 0. But, you have given threshold=0.001.
mask_nii = threshold_img(fixed_image_nii, 1e-3)
/opt/conda/lib/python3.11/site-packages/mriqc/qc/diffusion.py:220: ExtremeValueWarning: CC mask is too small (0 voxels)
warn(f'CC mask is too small ({nvox_cc} voxels)', ExtremeValueWarning, stacklevel=1)
2025-10-10T09:25:39+0200 [INFO ] con-duct: Summary:
Exit Code: 0
Command: singularity exec -W /tmp/singtmp.bHIMLn -B /var/lib/condor/execute/dir_3951209/ds/code/containers/binds/zoneinfo/UTC:/etc/localtime -B /tmp/singtmp.bHIMLn/tmp:/tmp -B /tmp/singtmp.bHIMLn/var/tmp:/var/tmp -e -B /var/lib/condor/execute/dir_3951209/ds -H /var/lib/condor/execute/dir_3951209/ds/code/containers/binds/HOME --pwd /var/lib/condor/execute/dir_3951209/ds code/containers/images/bids/bids-mriqc--24.0.2.sing mriqc sourcedata/raw . participant --participant-label sub-cIIs04 --no-datalad-get --no-sub --verbose --nprocs 1 --mem 3000 --work-dir /tmp --float32 --verbose-reports
Log files location: logs/duct/sub-cIIs04_2025.10.09T23.59.23-4048006_
Wall Clock Time: 33975.985 sec
Memory Peak Usage (RSS): 13.6 GB
Memory Average Usage (RSS): 2.1 GB
Virtual Memory Peak Usage (VSZ): 19.2 GB
Virtual Memory Average Usage (VSZ): 4.9 GB
Memory Peak Percentage: 2.40%
Memory Average Percentage: 0.23%
CPU Peak Usage: 238.40%
Average CPU Usage: 92.35%
[INFO] == Command exit (modification check follows) =====
+ datalad push --to mriqc_out-storage
[INFO] Determine push target
[INFO] Push refspecs
[INFO] Transfer data
[INFO] Finished push of Dataset(/var/lib/condor/execute/dir_3951209/ds)
+ flock --verbose /data/project/QC_workflow/TMP/ds003416-mriqc/.condor_datalad_lock git push outputstore
To file:///data/project/QC_workflow/TMP/RIA_QCworkflow/aae/8905a-985f-46fb-91f5-35c772654ddd
* [new branch] job_sub-cIIs04_10066487 -> job_sub-cIIs04_10066487
+ echo SUCCESS
View git blame Copy permalink