Nipype-SPM fMRI Analysis

Subject and Group Level Analysis Workflows

Author: Monika Doerig

Date: 13 June 2024

License:

MIT License

Note: If this notebook uses neuroimaging tools from Neurocontainers, those tools retain their original licenses. Please see Neurodesk citation guidelines for details.

Citation and Resources:

Tools included in this workflow

Nipype:

• Esteban, O., Markiewicz, C. J., Burns, C., Goncalves, M., Jarecka, D., Ziegler, E., Berleant, S., Ellis, D. G., Pinsard, B., Madison, C., Waskom, M., Notter, M. P., Clark, D., Manhães-Savio, A., Clark, D., Jordan, K., Dayan, M., Halchenko, Y. O., Loney, F., … Ghosh, S. (2025). nipy/nipype: 1.8.6 (1.8.6). Zenodo. https://doi.org/10.5281/zenodo.15054147

SPM12:

• Friston, K. J. (2007). Statistical parametric mapping: The analysis of functional brain images (1st ed). Elsevier / Academic Press.

• Online Book

Dataset

• Wakeman, DG and Henson, RN (2021). Multisubject, multimodal face processing. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds000117.v1.0.5

• Wakeman, D.G. & Henson, R.N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Sci. Data 2:150001 doi: 10.1038/sdata.2015.1

Educational resources:

• SPM Documentation: Event-related fMRI

• SPM Documentation: Face group fMRI data

• SPM Documentation: Group Analysis

• Wikibooks: SPM Group analysis

• Nipype Documentation: fMRI: Famous vs non-famous faces in SPM

Introduction

The fMRI dataset used for this example is part of a multi-subject, multi-modal (sMRI, fMRI, MEG, EEG) neuroimaging dataset on face processing. It contains data in BIDS format on sixteen healthy volunteers. The data was recoreded while the volunteers performed multiple runs of hundreds of trials of a simple perceptual task on pictures of familiar, unfamiliar and scrambled faces during two visits to the laboratory.

The facial stimuli consisted of two groups of 300 greyscale photos, half of which were of famous people and half of which were of non-famous people (unknown to the participants). Each scrambled face was created either from the famous face or the non-famous face of the same stimulus number. Additionally, each image was presented twice to the participants. The second presentation occurred either immediately after the first presentation (Immediate Repeats) or after 5–15 intervening stimuli (Delayed Repeats), with 50% of each type of repeat. To ensure that each stimulus received equal attention, participants were instructed to use their left or right index finger to press one of two keys (assignment counter-balanced across participants). They determined the symmetry of each image by pressing a key based on whether they perceived it to be ‘more’ or ‘less symmetric’ than average.

In the original paper (Wakeman & Henson, 2015), the repetition manipulation was not distinguished, meaning that initial and repeated presentations were treated identically without considering the timing of the repeats.

To illustrate the setup of a 3x2 factorial design analysis (familiar vs. unfamiliar vs. scrambled faces) x (1st vs. 2nd presentation) in an SPM Nipype workflow, the event files will be adapted accordingly. Each stimulus type will be labeled as either the first or second presentation. However, for simplicity, no distinction is made between immediate and delayed repetitions, resulting in 6 stimulus types (conditions): Familiar-Rep1 (F1), Familiar-Rep2 (F2), Unfamiliar-Rep1 (U1), Unfamiliar-Rep2 (U2), Scrambled-Rep1 (S1), and Scrambled-Rep2 (S2).

Examples of a familiar, unfamiliar and scrambled face:

PATTERN_STIMULI = "stimuli/func/*001.bmp"
!datalad install https://github.com/OpenNeuroDatasets/ds000117.git
!cd ds000117 && git checkout 1.0.5 && datalad get $PATTERN_STIMULI

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[INFO   ] Remote origin not usable by git-annex; setting annex-ignore           
[INFO   ] https://github.com/OpenNeuroDatasets/ds000117.git/config download failed: Not Found 
install(ok): /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/ds000117 (dataset)
Note: switching to '1.0.5'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

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get(ok): stimuli/func/s001.bmp (file) [from s3-PUBLIC...]
get(ok): stimuli/func/pu001.bmp (file) [from s3-PUBLIC...]
get(ok): stimuli/func/pf001.bmp (file) [from s3-PUBLIC...]
get(ok): stimuli/func/u001.bmp (file) [from s3-PUBLIC...]
get(ok): stimuli/func/ps001.bmp (file) [from s3-PUBLIC...]
get(ok): stimuli/func/f001.bmp (file) [from s3-PUBLIC...]
action summary:
  get (ok: 6)

import matplotlib.pyplot as plt
from matplotlib.image import imread

# Load the .bmp images
familiar = imread('ds000117/stimuli/func/f001.bmp')
unfamiliar = imread('ds000117/stimuli/func/u001.bmp')
scrambled = imread('ds000117/stimuli/func/s001.bmp')

# Create a Matplotlib figure with subplots
fig, axes = plt.subplots(1, 3, figsize=(12, 4))

# Plot each image on a subplot
axes[0].imshow(familiar, cmap='gray')
axes[0].set_title('Familiar face')
axes[0].axis('off')

axes[1].imshow(unfamiliar, cmap='gray')
axes[1].set_title('Unfamiliar face')
axes[1].axis('off')

axes[2].imshow(scrambled, cmap='gray')
axes[2].set_title('Scrambled face')
axes[2].axis('off')

plt.tight_layout()
plt.show()

Download Data and install Python modules

# get func data of the mri session of 9 individuals  
PATTERN = "sub-0*/ses-mri/func"

!datalad install https://github.com/OpenNeuroDatasets/ds000117.git
!cd ds000117 && git checkout 1.0.5 && datalad get $PATTERN

install(error): /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/ds000117 (dataset) [target path already exists and not empty, refuse to clone into target path]
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Get sub-06/s .. _bold.nii.gz:  28%|▊  | 10.3M/37.0M [00:00<00:01, 20.6M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  39%|█▏ | 14.4M/37.0M [00:00<00:01, 20.4M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  50%|█▍ | 18.5M/37.0M [00:00<00:00, 20.4M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  61%|█▊ | 22.5M/37.0M [00:01<00:00, 20.4M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  72%|██▏| 26.6M/37.0M [00:01<00:00, 20.3M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  82%|██▍| 30.5M/37.0M [00:01<00:00, 18.9M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  88%|██▋| 32.6M/37.0M [00:01<00:00, 19.3M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  94%|██▊| 34.8M/37.0M [00:01<00:00, 19.8M Bytes/s]
                                                                                
Get sub-06/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Total:   9%|██▍                        | 258M/2.93G [00:16<02:47, 16.0M Bytes/s]
Get sub-06/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Get sub-06/s .. _bold.nii.gz:   5%|▏  | 1.96M/37.0M [00:00<00:01, 18.0M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  13%|▍  | 4.79M/37.0M [00:00<00:02, 15.1M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  18%|▌  | 6.74M/37.0M [00:00<00:01, 16.6M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  27%|▊  | 9.95M/37.0M [00:00<00:01, 16.2M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  32%|▉  | 11.8M/37.0M [00:00<00:01, 16.8M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  41%|█▏ | 15.1M/37.0M [00:00<00:01, 15.4M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  45%|█▎ | 16.7M/37.0M [00:01<00:01, 15.6M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  50%|█▍ | 18.4M/37.0M [00:01<00:01, 15.7M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  54%|█▌ | 20.0M/37.0M [00:01<00:01, 15.8M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  59%|█▊ | 21.7M/37.0M [00:01<00:00, 15.9M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  63%|█▉ | 23.5M/37.0M [00:01<00:00, 16.5M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  68%|██ | 25.2M/37.0M [00:01<00:00, 16.8M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  77%|██▎| 28.6M/37.0M [00:01<00:00, 16.7M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  86%|██▌| 31.9M/37.0M [00:01<00:00, 16.6M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  91%|██▋| 33.8M/37.0M [00:02<00:00, 17.1M Bytes/s]
Get sub-06/s .. _bold.nii.gz: 100%|██▉| 37.0M/37.0M [00:02<00:00, 16.7M Bytes/s]
                                                                                
Get sub-06/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Total:  10%|██▋                        | 295M/2.93G [00:18<02:48, 15.6M Bytes/s]
Get sub-06/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Get sub-06/s .. _bold.nii.gz:   5%|▏  | 1.70M/37.1M [00:00<00:02, 17.0M Bytes/s]
Get sub-06/s .. _bold.nii.gz:   9%|▎  | 3.52M/37.1M [00:00<00:01, 17.7M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  14%|▍  | 5.33M/37.1M [00:00<00:01, 17.8M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  24%|▋  | 8.72M/37.1M [00:00<00:01, 17.2M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  29%|▊  | 10.6M/37.1M [00:00<00:01, 17.7M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  34%|█  | 12.6M/37.1M [00:00<00:01, 18.2M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  44%|█▎ | 16.1M/37.1M [00:00<00:01, 18.0M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  53%|█▌ | 19.7M/37.1M [00:01<00:00, 17.9M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  63%|█▉ | 23.3M/37.1M [00:01<00:00, 18.0M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  73%|██▏| 27.2M/37.1M [00:01<00:00, 18.3M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  83%|██▍| 30.6M/37.1M [00:01<00:00, 17.5M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  88%|██▋| 32.5M/37.1M [00:01<00:00, 17.2M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  93%|██▊| 34.4M/37.1M [00:01<00:00, 17.5M Bytes/s]
Get sub-06/s .. _bold.nii.gz:  98%|██▉| 36.2M/37.1M [00:02<00:00, 17.8M Bytes/s]
                                                                                
Get sub-06/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Total:  11%|███                        | 333M/2.93G [00:21<02:46, 15.7M Bytes/s]
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:  10%|▎  | 3.65M/35.3M [00:00<00:01, 18.3M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  16%|▍  | 5.58M/35.3M [00:00<00:01, 18.7M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  26%|▊  | 9.26M/35.3M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  32%|▉  | 11.1M/35.3M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  42%|█▎ | 14.8M/35.3M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  53%|█▌ | 18.6M/35.3M [00:01<00:00, 18.7M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  64%|█▉ | 22.5M/35.3M [00:01<00:00, 18.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  69%|██ | 24.5M/35.3M [00:01<00:00, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  80%|██▍| 28.2M/35.3M [00:01<00:00, 18.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  90%|██▋| 31.9M/35.3M [00:01<00:00, 18.2M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.4M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:   5%|▏  | 1.93M/35.4M [00:00<00:01, 19.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  16%|▍  | 5.67M/35.4M [00:00<00:01, 18.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  21%|▋  | 7.59M/35.4M [00:00<00:01, 18.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  32%|▉  | 11.4M/35.4M [00:00<00:01, 19.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  43%|█▎ | 15.3M/35.4M [00:00<00:01, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  54%|█▌ | 18.9M/35.4M [00:01<00:00, 18.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  59%|█▊ | 20.9M/35.4M [00:01<00:00, 18.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  70%|██ | 24.7M/35.4M [00:01<00:00, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  81%|██▍| 28.6M/35.4M [00:01<00:00, 19.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  91%|██▋| 32.1M/35.4M [00:01<00:00, 18.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  96%|██▉| 34.0M/35.4M [00:01<00:00, 18.0M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.4M [00:00<?, ? Bytes/s]
Total:  14%|███▋                       | 403M/2.93G [00:25<02:40, 15.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.4M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:   5%|▏  | 1.90M/35.4M [00:00<00:01, 18.3M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  16%|▍  | 5.60M/35.4M [00:00<00:01, 18.4M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  26%|▊  | 9.33M/35.4M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  32%|▉  | 11.2M/35.4M [00:00<00:01, 18.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  42%|█▎ | 14.8M/35.4M [00:00<00:01, 18.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  47%|█▍ | 16.8M/35.4M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  53%|█▌ | 18.7M/35.4M [00:01<00:00, 18.7M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  58%|█▊ | 20.7M/35.4M [00:01<00:00, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  69%|██ | 24.5M/35.4M [00:01<00:00, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  80%|██▍| 28.4M/35.4M [00:01<00:00, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  90%|██▋| 32.1M/35.4M [00:01<00:00, 18.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  96%|██▉| 34.1M/35.4M [00:01<00:00, 19.1M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.4M [00:00<?, ? Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.5M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:   5%|▏  | 1.85M/35.5M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  16%|▍  | 5.74M/35.5M [00:00<00:01, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  27%|▊  | 9.51M/35.5M [00:00<00:01, 19.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  36%|█  | 12.9M/35.5M [00:00<00:01, 18.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  42%|█▏ | 14.8M/35.5M [00:00<00:01, 18.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  47%|█▍ | 16.8M/35.5M [00:00<00:00, 18.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  53%|█▌ | 18.8M/35.5M [00:01<00:00, 18.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  63%|█▉ | 22.5M/35.5M [00:01<00:00, 18.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  69%|██ | 24.5M/35.5M [00:01<00:00, 19.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  80%|██▍| 28.4M/35.5M [00:01<00:00, 19.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  90%|██▋| 31.9M/35.5M [00:01<00:00, 18.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  96%|██▉| 34.2M/35.5M [00:01<00:00, 19.3M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.5M [00:00<?, ? Bytes/s]
Total:  16%|████▎                      | 474M/2.93G [00:29<02:34, 15.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:  10%|▎  | 3.70M/35.6M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  16%|▍  | 5.60M/35.6M [00:00<00:01, 18.7M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  21%|▋  | 7.55M/35.6M [00:00<00:01, 19.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  32%|▉  | 11.4M/35.6M [00:00<00:01, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  43%|█▎ | 15.2M/35.6M [00:00<00:01, 19.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  48%|█▍ | 17.1M/35.6M [00:00<00:00, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  59%|█▊ | 21.0M/35.6M [00:01<00:00, 19.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  65%|█▉ | 23.0M/35.6M [00:01<00:00, 19.3M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  70%|██ | 24.9M/35.6M [00:01<00:00, 19.3M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  81%|██▍| 28.9M/35.6M [00:01<00:00, 19.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  91%|██▋| 32.5M/35.6M [00:01<00:00, 19.0M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:   5%|▏  | 1.94M/35.6M [00:00<00:01, 19.3M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  16%|▍  | 5.87M/35.6M [00:00<00:01, 19.4M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  22%|▋  | 7.92M/35.6M [00:00<00:01, 19.7M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  33%|▉  | 11.8M/35.6M [00:00<00:01, 19.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  39%|█▏ | 13.8M/35.6M [00:00<00:01, 19.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  45%|█▎ | 15.9M/35.6M [00:00<00:00, 19.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  50%|█▌ | 17.9M/35.6M [00:00<00:00, 20.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  62%|█▊ | 21.9M/35.6M [00:01<00:00, 20.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  73%|██▏| 25.9M/35.6M [00:01<00:00, 20.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  79%|██▎| 28.0M/35.6M [00:01<00:00, 20.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  90%|██▋| 32.0M/35.6M [00:01<00:00, 20.1M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
Total:  19%|█████                      | 545M/2.93G [00:34<02:29, 16.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:   6%|▏  | 2.08M/35.7M [00:00<00:01, 20.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  17%|▌  | 6.22M/35.7M [00:00<00:01, 20.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  24%|▋  | 8.42M/35.7M [00:00<00:01, 21.0M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  30%|▉  | 10.5M/35.7M [00:00<00:01, 21.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  42%|█▏ | 14.8M/35.7M [00:00<00:00, 21.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  53%|█▌ | 19.1M/35.7M [00:00<00:00, 21.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  65%|█▉ | 23.4M/35.7M [00:01<00:00, 21.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  72%|██▏| 25.7M/35.7M [00:01<00:00, 21.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  84%|██▌| 30.0M/35.7M [00:01<00:00, 21.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  90%|██▋| 32.2M/35.7M [00:01<00:00, 21.7M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:  12%|▎  | 4.32M/35.7M [00:00<00:01, 21.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  18%|▌  | 6.56M/35.7M [00:00<00:01, 21.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  30%|▉  | 10.8M/35.7M [00:00<00:01, 21.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  43%|█▎ | 15.2M/35.7M [00:00<00:00, 21.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  55%|█▋ | 19.5M/35.7M [00:00<00:00, 21.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  66%|█▉ | 23.7M/35.7M [00:01<00:00, 21.4M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  77%|██▎| 27.4M/35.7M [00:01<00:00, 20.4M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  86%|██▌| 30.8M/35.7M [00:01<00:00, 19.3M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  96%|██▉| 34.3M/35.7M [00:01<00:00, 18.0M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
Total:  21%|█████▋                     | 617M/2.93G [00:38<02:22, 16.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
Get sub-05/s .. _bold.nii.gz:  10%|▎  | 3.63M/35.7M [00:00<00:01, 18.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  15%|▍  | 5.51M/35.7M [00:00<00:01, 18.4M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  26%|▊  | 9.12M/35.7M [00:00<00:01, 18.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  31%|▉  | 11.1M/35.7M [00:00<00:01, 18.5M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  41%|█▏ | 14.8M/35.7M [00:00<00:01, 18.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  47%|█▍ | 16.8M/35.7M [00:00<00:01, 18.9M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  52%|█▌ | 18.8M/35.7M [00:01<00:00, 19.1M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  58%|█▋ | 20.7M/35.7M [00:01<00:00, 19.2M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  64%|█▉ | 22.7M/35.7M [00:01<00:00, 19.4M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  75%|██▏| 26.7M/35.7M [00:01<00:00, 19.6M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  80%|██▍| 28.7M/35.7M [00:01<00:00, 19.8M Bytes/s]
Get sub-05/s .. _bold.nii.gz:  91%|██▋| 32.7M/35.7M [00:01<00:00, 19.8M Bytes/s]
                                                                                
Get sub-05/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.0M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:  12%|▎  | 4.15M/36.0M [00:00<00:01, 20.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  23%|▋  | 8.33M/36.0M [00:00<00:01, 20.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  35%|█  | 12.6M/36.0M [00:00<00:01, 20.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  41%|█▏ | 14.7M/36.0M [00:00<00:01, 20.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  47%|█▍ | 16.8M/36.0M [00:00<00:00, 21.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  53%|█▌ | 19.0M/36.0M [00:00<00:00, 21.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  65%|█▉ | 23.3M/36.0M [00:01<00:00, 21.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  71%|██▏| 25.6M/36.0M [00:01<00:00, 21.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  83%|██▍| 29.8M/36.0M [00:01<00:00, 21.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  94%|██▊| 33.9M/36.0M [00:01<00:00, 21.0M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.0M [00:00<?, ? Bytes/s]
Total:  23%|██████▎                    | 688M/2.93G [00:42<02:17, 16.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:  11%|▎  | 4.06M/36.1M [00:00<00:01, 20.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  23%|▋  | 8.20M/36.1M [00:00<00:01, 20.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  35%|█  | 12.6M/36.1M [00:00<00:01, 21.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  47%|█▍ | 16.9M/36.1M [00:00<00:00, 21.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  58%|█▊ | 21.1M/36.1M [00:01<00:00, 21.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  70%|██ | 25.4M/36.1M [00:01<00:00, 21.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  83%|██▍| 30.0M/36.1M [00:01<00:00, 21.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  95%|██▊| 34.2M/36.1M [00:01<00:00, 21.4M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:   6%|▏  | 2.33M/36.2M [00:00<00:01, 23.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  17%|▌  | 6.32M/36.2M [00:00<00:01, 20.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  29%|▊  | 10.3M/36.2M [00:00<00:01, 20.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  35%|█  | 12.5M/36.2M [00:00<00:01, 20.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  46%|█▎ | 16.5M/36.2M [00:00<00:00, 20.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  57%|█▋ | 20.5M/36.2M [00:01<00:00, 20.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  67%|██ | 24.3M/36.2M [00:01<00:00, 19.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  73%|██▏| 26.4M/36.2M [00:01<00:00, 20.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  84%|██▌| 30.5M/36.2M [00:01<00:00, 20.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  95%|██▊| 34.3M/36.2M [00:01<00:00, 19.8M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Total:  26%|███████                    | 761M/2.93G [00:46<02:12, 16.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:  12%|▎  | 4.33M/36.2M [00:00<00:01, 21.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  23%|▋  | 8.38M/36.2M [00:00<00:01, 20.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  29%|▉  | 10.7M/36.2M [00:00<00:01, 21.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  41%|█▏ | 14.7M/36.2M [00:00<00:01, 20.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  47%|█▍ | 16.9M/36.2M [00:00<00:00, 21.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  58%|█▋ | 21.0M/36.2M [00:01<00:00, 20.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  70%|██ | 25.2M/36.2M [00:01<00:00, 20.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  82%|██▍| 29.5M/36.2M [00:01<00:00, 21.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  88%|██▋| 31.7M/36.2M [00:01<00:00, 21.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  94%|██▊| 33.8M/36.2M [00:01<00:00, 21.3M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:  12%|▎  | 4.32M/36.2M [00:00<00:01, 21.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  18%|▌  | 6.65M/36.2M [00:00<00:01, 22.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  29%|▉  | 10.6M/36.2M [00:00<00:01, 20.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  40%|█▏ | 14.7M/36.2M [00:00<00:01, 20.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  52%|█▌ | 18.8M/36.2M [00:00<00:00, 20.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  64%|█▉ | 23.1M/36.2M [00:01<00:00, 21.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  75%|██▏| 27.1M/36.2M [00:01<00:00, 20.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  81%|██▍| 29.3M/36.2M [00:01<00:00, 20.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  87%|██▌| 31.5M/36.2M [00:01<00:00, 20.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  98%|██▉| 35.7M/36.2M [00:01<00:00, 20.9M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Total:  28%|███████▋                   | 833M/2.93G [00:50<02:06, 16.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:  12%|▎  | 4.27M/36.3M [00:00<00:01, 21.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  18%|▌  | 6.55M/36.3M [00:00<00:01, 21.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  29%|▊  | 10.4M/36.3M [00:00<00:01, 17.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  40%|█▏ | 14.6M/36.3M [00:00<00:01, 18.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  46%|█▍ | 16.7M/36.3M [00:00<00:01, 18.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  56%|█▋ | 20.2M/36.3M [00:01<00:00, 18.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  65%|█▉ | 23.6M/36.3M [00:01<00:00, 17.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  70%|██ | 25.4M/36.3M [00:01<00:00, 17.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  75%|██▎| 27.4M/36.3M [00:01<00:00, 18.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  86%|██▌| 31.2M/36.3M [00:01<00:00, 18.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  96%|██▉| 34.8M/36.3M [00:01<00:00, 18.3M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:  11%|▎  | 3.87M/36.3M [00:00<00:01, 19.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  22%|▋  | 7.81M/36.3M [00:00<00:01, 19.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  27%|▊  | 9.83M/36.3M [00:00<00:01, 19.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  38%|█▏ | 13.8M/36.3M [00:00<00:01, 19.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  49%|█▍ | 17.9M/36.3M [00:01<00:01, 15.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  56%|█▋ | 20.4M/36.3M [00:01<00:00, 17.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  65%|█▉ | 23.5M/36.3M [00:01<00:00, 16.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  73%|██▏| 26.6M/36.3M [00:01<00:00, 16.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  82%|██▍| 29.7M/36.3M [00:01<00:00, 16.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  94%|██▊| 34.0M/36.3M [00:02<00:00, 14.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz: 100%|██▉| 36.2M/36.3M [00:02<00:00, 13.3M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
Total:  31%|████████▎                  | 906M/2.93G [00:55<02:03, 16.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:   3%|   | 1.16M/36.3M [00:00<00:03, 11.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:   7%|▏  | 2.42M/36.3M [00:00<00:02, 12.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  13%|▍  | 4.76M/36.3M [00:00<00:02, 11.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  19%|▌  | 6.72M/36.3M [00:00<00:02, 10.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  22%|▋  | 7.94M/36.3M [00:00<00:02, 11.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  26%|▊  | 9.35M/36.3M [00:00<00:02, 11.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  32%|▉  | 11.7M/36.3M [00:01<00:02, 11.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  39%|█▏ | 14.0M/36.3M [00:01<00:02, 10.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  42%|█▏ | 15.1M/36.3M [00:01<00:01, 10.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  45%|█▎ | 16.3M/36.3M [00:01<00:01, 11.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  48%|█▍ | 17.5M/36.3M [00:01<00:01, 11.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  52%|█▌ | 18.7M/36.3M [00:01<00:01, 11.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  55%|█▋ | 19.9M/36.3M [00:01<00:01, 10.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  59%|█▊ | 21.5M/36.3M [00:01<00:01, 11.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  66%|█▉ | 24.1M/36.3M [00:02<00:01, 12.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  70%|██ | 25.4M/36.3M [00:02<00:00, 12.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  76%|██▎| 27.7M/36.3M [00:02<00:00, 11.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  80%|██▍| 28.9M/36.3M [00:02<00:00, 11.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  83%|██▍| 30.2M/36.3M [00:02<00:00, 11.6M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  87%|██▌| 31.4M/36.3M [00:02<00:00, 11.8M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  90%|██▋| 32.7M/36.3M [00:02<00:00, 12.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  94%|██▊| 34.0M/36.3M [00:02<00:00, 12.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  97%|██▉| 35.3M/36.3M [00:03<00:00, 12.5M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
Get sub-09/s .. _bold.nii.gz:   7%|▏  | 2.50M/36.3M [00:00<00:02, 12.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  11%|▎  | 3.92M/36.3M [00:00<00:02, 13.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  18%|▌  | 6.51M/36.3M [00:00<00:02, 13.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  25%|▊  | 9.16M/36.3M [00:00<00:02, 13.1M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  29%|▊  | 10.5M/36.3M [00:00<00:01, 13.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  35%|█  | 12.7M/36.3M [00:01<00:01, 12.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  40%|█▏ | 14.4M/36.3M [00:01<00:01, 12.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  43%|█▎ | 15.7M/36.3M [00:01<00:01, 12.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  47%|█▍ | 17.0M/36.3M [00:01<00:01, 12.4M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  53%|█▌ | 19.4M/36.3M [00:01<00:01, 11.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  57%|█▋ | 20.9M/36.3M [00:01<00:01, 12.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  61%|█▊ | 22.2M/36.3M [00:01<00:01, 12.5M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  65%|█▉ | 23.5M/36.3M [00:01<00:01, 12.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  68%|██ | 24.9M/36.3M [00:01<00:00, 12.7M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  73%|██▏| 26.3M/36.3M [00:02<00:00, 13.3M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  80%|██▍| 29.1M/36.3M [00:02<00:00, 13.0M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  87%|██▌| 31.6M/36.3M [00:02<00:00, 11.9M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  91%|██▋| 32.9M/36.3M [00:02<00:00, 12.2M Bytes/s]
Get sub-09/s .. _bold.nii.gz:  94%|██▊| 34.2M/36.3M [00:02<00:00, 12.4M Bytes/s]
                                                                                
Get sub-09/s .. _bold.nii.gz:   0%|            | 0.00/36.3M [00:00<?, ? Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   3%|   | 1.24M/37.0M [00:00<00:02, 12.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   7%|▏  | 2.50M/37.0M [00:00<00:02, 12.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  10%|▎  | 3.82M/37.0M [00:00<00:02, 12.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  14%|▍  | 5.18M/37.0M [00:00<00:02, 13.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  18%|▌  | 6.56M/37.0M [00:00<00:02, 13.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  25%|▋  | 9.10M/37.0M [00:00<00:02, 13.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  31%|▉  | 11.6M/37.0M [00:00<00:02, 12.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  40%|█▏ | 14.7M/37.0M [00:01<00:01, 12.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  43%|█▎ | 16.0M/37.0M [00:01<00:01, 12.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  47%|█▍ | 17.4M/37.0M [00:01<00:01, 12.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  54%|█▋ | 20.1M/37.0M [00:01<00:01, 12.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  58%|█▋ | 21.4M/37.0M [00:01<00:01, 13.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  62%|█▊ | 22.7M/37.0M [00:01<00:01, 13.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  65%|█▉ | 24.1M/37.0M [00:01<00:00, 13.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  71%|██▏| 26.3M/37.0M [00:02<00:00, 12.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  75%|██▏| 27.7M/37.0M [00:02<00:00, 12.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  78%|██▎| 29.0M/37.0M [00:02<00:00, 12.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  82%|██▍| 30.3M/37.0M [00:02<00:00, 12.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  89%|██▋| 32.9M/37.0M [00:02<00:00, 12.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  93%|██▊| 34.2M/37.0M [00:02<00:00, 12.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  96%|██▉| 35.5M/37.0M [00:02<00:00, 12.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz: 100%|██▉| 36.9M/37.0M [00:02<00:00, 12.5M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Total:  35%|████████▉                 | 1.02G/2.93G [01:05<02:03, 15.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   7%|▏  | 2.62M/37.0M [00:00<00:02, 13.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  11%|▎  | 3.96M/37.0M [00:00<00:02, 13.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  18%|▌  | 6.60M/37.0M [00:00<00:02, 13.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  21%|▋  | 7.93M/37.0M [00:00<00:02, 13.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  25%|▊  | 9.34M/37.0M [00:00<00:02, 13.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  29%|▊  | 10.7M/37.0M [00:00<00:01, 13.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  33%|▉  | 12.1M/37.0M [00:00<00:01, 13.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  40%|█▏ | 14.8M/37.0M [00:01<00:01, 13.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  47%|█▍ | 17.3M/37.0M [00:01<00:01, 12.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  53%|█▌ | 19.7M/37.0M [00:01<00:01, 12.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  57%|█▋ | 21.0M/37.0M [00:01<00:01, 12.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  60%|█▊ | 22.3M/37.0M [00:01<00:01, 11.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  64%|█▉ | 23.6M/37.0M [00:01<00:01, 11.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  67%|██ | 24.9M/37.0M [00:01<00:01, 11.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  71%|██▏| 26.2M/37.0M [00:02<00:00, 12.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  75%|██▏| 27.6M/37.0M [00:02<00:00, 12.8M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  78%|██▎| 29.0M/37.0M [00:02<00:00, 13.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  85%|██▌| 31.4M/37.0M [00:02<00:00, 12.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  89%|██▋| 32.9M/37.0M [00:02<00:00, 13.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  93%|██▊| 34.3M/37.0M [00:02<00:00, 13.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  99%|██▉| 36.7M/37.0M [00:02<00:00, 12.8M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Total:  36%|█████████▎                | 1.05G/2.93G [01:08<02:02, 15.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   7%|▏  | 2.78M/37.1M [00:00<00:02, 13.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  15%|▍  | 5.61M/37.1M [00:00<00:02, 13.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  19%|▌  | 7.04M/37.1M [00:00<00:02, 14.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  23%|▋  | 8.47M/37.1M [00:00<00:02, 14.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  31%|▉  | 11.3M/37.1M [00:00<00:01, 14.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  37%|█  | 13.6M/37.1M [00:01<00:01, 12.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  41%|█▏ | 15.1M/37.1M [00:01<00:01, 13.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  49%|█▍ | 18.3M/37.1M [00:01<00:01, 13.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  53%|█▌ | 19.7M/37.1M [00:01<00:01, 12.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  57%|█▋ | 21.0M/37.1M [00:01<00:01, 12.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  61%|█▊ | 22.5M/37.1M [00:01<00:01, 13.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  65%|█▉ | 24.0M/37.1M [00:01<00:00, 13.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  69%|██ | 25.5M/37.1M [00:01<00:00, 13.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  76%|██▎| 28.2M/37.1M [00:02<00:00, 13.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  80%|██▍| 29.7M/37.1M [00:02<00:00, 14.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  84%|██▌| 31.2M/37.1M [00:02<00:00, 14.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  92%|██▊| 34.3M/37.1M [00:02<00:00, 14.8M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  97%|██▉| 35.9M/37.1M [00:02<00:00, 15.0M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Total:  37%|█████████▋                | 1.09G/2.93G [01:11<02:01, 15.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.2M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   4%|   | 1.44M/37.2M [00:00<00:02, 12.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   8%|▏  | 3.03M/37.2M [00:00<00:02, 14.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  12%|▎  | 4.59M/37.2M [00:00<00:02, 14.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  17%|▍  | 6.18M/37.2M [00:00<00:02, 15.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  21%|▋  | 7.77M/37.2M [00:00<00:01, 15.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  25%|▊  | 9.36M/37.2M [00:00<00:01, 15.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  30%|▉  | 11.0M/37.2M [00:00<00:01, 15.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  36%|█  | 13.5M/37.2M [00:00<00:01, 14.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  41%|█▏ | 15.2M/37.2M [00:01<00:01, 14.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  47%|█▍ | 17.5M/37.2M [00:01<00:01, 16.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  55%|█▋ | 20.6M/37.2M [00:01<00:01, 16.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  61%|█▊ | 22.6M/37.2M [00:01<00:00, 17.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  66%|█▉ | 24.7M/37.2M [00:01<00:00, 14.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  72%|██▏| 26.8M/37.2M [00:01<00:00, 15.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  77%|██▎| 28.5M/37.2M [00:01<00:00, 16.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  82%|██▍| 30.3M/37.2M [00:01<00:00, 16.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  86%|██▌| 32.1M/37.2M [00:02<00:00, 17.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  96%|██▊| 35.5M/37.2M [00:02<00:00, 17.0M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.2M [00:00<?, ? Bytes/s]
Total:  38%|█████████▉                | 1.13G/2.93G [01:14<01:59, 15.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.2M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   5%|▏  | 1.84M/37.2M [00:00<00:01, 18.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  14%|▍  | 5.25M/37.2M [00:00<00:01, 17.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  19%|▌  | 7.18M/37.2M [00:00<00:01, 18.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  29%|▊  | 10.8M/37.2M [00:00<00:01, 18.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  34%|█  | 12.8M/37.2M [00:00<00:01, 18.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  40%|█▏ | 14.7M/37.2M [00:00<00:01, 18.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  49%|█▍ | 18.2M/37.2M [00:01<00:01, 17.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  54%|█▌ | 20.0M/37.2M [00:01<00:00, 17.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  61%|█▊ | 22.8M/37.2M [00:01<00:01, 14.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  69%|██ | 25.6M/37.2M [00:01<00:00, 16.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  77%|██▎| 28.5M/37.2M [00:01<00:00, 15.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  85%|██▌| 31.7M/37.2M [00:01<00:00, 15.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  94%|██▊| 34.8M/37.2M [00:02<00:00, 15.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  98%|██▉| 36.4M/37.2M [00:02<00:00, 15.6M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.2M [00:00<?, ? Bytes/s]
Total:  40%|██████████▎               | 1.16G/2.93G [01:16<01:56, 15.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   9%|▎  | 3.23M/37.1M [00:00<00:02, 16.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  13%|▍  | 4.89M/37.1M [00:00<00:01, 16.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  22%|▋  | 8.33M/37.1M [00:00<00:01, 16.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  30%|▉  | 11.1M/37.1M [00:00<00:01, 15.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  36%|█  | 13.4M/37.1M [00:00<00:01, 17.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  45%|█▎ | 16.7M/37.1M [00:01<00:01, 16.8M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  55%|█▋ | 20.3M/37.1M [00:01<00:00, 17.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  64%|█▉ | 23.6M/37.1M [00:01<00:00, 17.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  72%|██▏| 26.6M/37.1M [00:01<00:00, 16.3M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  77%|██▎| 28.7M/37.1M [00:01<00:00, 16.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  82%|██▍| 30.4M/37.1M [00:01<00:00, 16.8M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  87%|██▌| 32.2M/37.1M [00:01<00:00, 17.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  92%|██▊| 34.0M/37.1M [00:02<00:00, 17.2M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   9%|▎  | 3.51M/37.1M [00:00<00:01, 17.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  15%|▍  | 5.40M/37.1M [00:00<00:01, 18.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  24%|▋  | 8.88M/37.1M [00:00<00:01, 17.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  29%|▊  | 10.7M/37.1M [00:00<00:01, 17.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  34%|█  | 12.7M/37.1M [00:00<00:01, 18.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  44%|█▎ | 16.3M/37.1M [00:00<00:01, 18.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  55%|█▋ | 20.3M/37.1M [00:01<00:00, 18.8M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  64%|█▉ | 23.8M/37.1M [00:01<00:00, 18.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  69%|██ | 25.8M/37.1M [00:01<00:00, 18.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  79%|██▎| 29.3M/37.1M [00:01<00:00, 18.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  85%|██▌| 31.5M/37.1M [00:01<00:00, 19.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  90%|██▋| 33.5M/37.1M [00:01<00:00, 19.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  96%|██▉| 35.5M/37.1M [00:01<00:00, 19.5M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Total:  42%|██████████▉               | 1.24G/2.93G [01:21<01:52, 15.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:   5%|▏  | 1.71M/37.1M [00:00<00:02, 16.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  14%|▍  | 5.35M/37.1M [00:00<00:01, 17.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  21%|▌  | 7.68M/37.1M [00:00<00:01, 19.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  26%|▊  | 9.71M/37.1M [00:00<00:01, 19.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  37%|█  | 13.6M/37.1M [00:00<00:01, 19.7M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  48%|█▍ | 17.6M/37.1M [00:00<00:00, 19.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  58%|█▊ | 21.7M/37.1M [00:01<00:00, 20.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  68%|██ | 25.2M/37.1M [00:01<00:00, 19.0M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  74%|██▏| 27.5M/37.1M [00:01<00:00, 19.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  85%|██▌| 31.6M/37.1M [00:01<00:00, 19.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  96%|██▊| 35.5M/37.1M [00:01<00:00, 19.3M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.1M [00:00<?, ? Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
Get sub-01/s .. _bold.nii.gz:  11%|▎  | 3.91M/37.0M [00:00<00:01, 19.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  16%|▍  | 5.87M/37.0M [00:00<00:01, 19.6M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  21%|▋  | 7.92M/37.0M [00:00<00:01, 19.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  27%|▊  | 9.92M/37.0M [00:00<00:01, 19.9M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  32%|▉  | 12.0M/37.0M [00:00<00:01, 20.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  43%|█▎ | 15.7M/37.0M [00:00<00:01, 19.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  53%|█▌ | 19.5M/37.0M [00:01<00:00, 19.2M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  58%|█▋ | 21.6M/37.0M [00:01<00:00, 19.4M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  64%|█▉ | 23.8M/37.0M [00:01<00:00, 20.1M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  76%|██▎| 28.2M/37.0M [00:01<00:00, 20.8M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  87%|██▌| 32.2M/37.0M [00:01<00:00, 20.5M Bytes/s]
Get sub-01/s .. _bold.nii.gz:  98%|██▉| 36.2M/37.0M [00:01<00:00, 19.8M Bytes/s]
                                                                                
Get sub-01/s .. _bold.nii.gz:   0%|            | 0.00/37.0M [00:00<?, ? Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/35.8M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:   6%|▏  | 2.01M/35.8M [00:00<00:01, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  16%|▍  | 5.72M/35.8M [00:00<00:01, 18.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  26%|▊  | 9.15M/35.8M [00:00<00:01, 14.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  34%|█  | 12.1M/35.8M [00:00<00:01, 14.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  38%|█▏ | 13.7M/35.8M [00:00<00:01, 14.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  46%|█▍ | 16.5M/35.8M [00:01<00:01, 14.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  51%|█▌ | 18.1M/35.8M [00:01<00:01, 14.8M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  55%|█▋ | 19.8M/35.8M [00:01<00:01, 15.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  63%|█▉ | 22.6M/35.8M [00:01<00:00, 14.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  68%|██ | 24.3M/35.8M [00:01<00:00, 15.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  72%|██▏| 26.0M/35.8M [00:01<00:00, 15.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  81%|██▍| 29.1M/35.8M [00:01<00:00, 15.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  86%|██▌| 30.9M/35.8M [00:02<00:00, 15.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  91%|██▋| 32.5M/35.8M [00:02<00:00, 15.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  95%|██▊| 34.1M/35.8M [00:02<00:00, 15.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz: 100%|██▉| 35.7M/35.8M [00:02<00:00, 15.7M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/35.8M [00:00<?, ? Bytes/s]
Total:  46%|███████████▉              | 1.35G/2.93G [01:28<01:44, 15.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/35.8M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:   5%|▏  | 1.64M/35.8M [00:00<00:02, 16.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:   9%|▎  | 3.39M/35.8M [00:00<00:01, 17.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  14%|▍  | 5.10M/35.8M [00:00<00:01, 17.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  19%|▌  | 6.94M/35.8M [00:00<00:01, 17.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  29%|▊  | 10.3M/35.8M [00:00<00:01, 17.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  34%|█  | 12.2M/35.8M [00:00<00:01, 17.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  39%|█▏ | 14.0M/35.8M [00:00<00:01, 17.8M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  49%|█▍ | 17.5M/35.8M [00:01<00:01, 17.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  59%|█▊ | 21.2M/35.8M [00:01<00:00, 17.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  68%|██ | 24.4M/35.8M [00:01<00:00, 17.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  74%|██▏| 26.6M/35.8M [00:01<00:00, 18.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  84%|██▌| 30.2M/35.8M [00:01<00:00, 17.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  90%|██▋| 32.1M/35.8M [00:01<00:00, 17.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  95%|██▊| 34.0M/35.8M [00:01<00:00, 17.7M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/35.8M [00:00<?, ? Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.0M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:  10%|▎  | 3.72M/36.0M [00:00<00:01, 18.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  21%|▋  | 7.55M/36.0M [00:00<00:01, 18.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  31%|▉  | 11.0M/36.0M [00:00<00:01, 18.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  36%|█  | 13.0M/36.0M [00:00<00:01, 18.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  42%|█▎ | 15.1M/36.0M [00:00<00:01, 18.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  47%|█▍ | 17.0M/36.0M [00:00<00:01, 18.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  53%|█▌ | 19.1M/36.0M [00:01<00:00, 19.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  64%|█▉ | 23.0M/36.0M [00:01<00:00, 19.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  75%|██▎| 27.0M/36.0M [00:01<00:00, 19.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  86%|██▌| 30.8M/36.0M [00:01<00:00, 19.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  96%|██▉| 34.7M/36.0M [00:01<00:00, 19.3M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.0M [00:00<?, ? Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:  11%|▎  | 3.97M/36.1M [00:00<00:01, 20.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  17%|▍  | 5.97M/36.1M [00:00<00:01, 20.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  28%|▊  | 10.0M/36.1M [00:00<00:01, 20.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  33%|▉  | 12.0M/36.1M [00:00<00:01, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  45%|█▎ | 16.1M/36.1M [00:00<00:00, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  55%|█▋ | 19.7M/36.1M [00:01<00:00, 19.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  66%|█▉ | 23.9M/36.1M [00:01<00:00, 19.8M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  72%|██▏| 26.1M/36.1M [00:01<00:00, 20.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  84%|██▌| 30.2M/36.1M [00:01<00:00, 20.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  94%|██▊| 34.1M/36.1M [00:01<00:00, 19.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz: 100%|██▉| 36.1M/36.1M [00:01<00:00, 19.2M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:  11%|▎  | 4.03M/36.1M [00:00<00:01, 19.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  17%|▌  | 6.15M/36.1M [00:00<00:01, 19.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  23%|▋  | 8.28M/36.1M [00:00<00:01, 20.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  34%|█  | 12.4M/36.1M [00:00<00:01, 20.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  46%|█▍ | 16.7M/36.1M [00:00<00:00, 20.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  58%|█▋ | 20.8M/36.1M [00:01<00:00, 20.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  69%|██ | 24.8M/36.1M [00:01<00:00, 20.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  80%|██▍| 28.7M/36.1M [00:01<00:00, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  90%|██▋| 32.6M/36.1M [00:01<00:00, 19.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  96%|██▉| 34.6M/36.1M [00:01<00:00, 19.5M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
Total:  51%|█████████████▏            | 1.49G/2.93G [01:37<01:34, 15.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:  11%|▎  | 3.99M/36.1M [00:00<00:01, 20.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  17%|▌  | 6.04M/36.1M [00:00<00:01, 20.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  28%|▊  | 9.97M/36.1M [00:00<00:01, 19.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  33%|█  | 12.1M/36.1M [00:00<00:01, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  45%|█▎ | 16.2M/36.1M [00:00<00:00, 20.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  55%|█▋ | 20.0M/36.1M [00:01<00:00, 19.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  66%|█▉ | 23.8M/36.1M [00:01<00:00, 19.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  73%|██▏| 26.3M/36.1M [00:01<00:00, 20.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  83%|██▍| 30.1M/36.1M [00:01<00:00, 19.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  90%|██▋| 32.6M/36.1M [00:01<00:00, 19.8M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:   6%|▏  | 2.01M/36.1M [00:00<00:01, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  11%|▎  | 4.03M/36.1M [00:00<00:01, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  17%|▌  | 6.08M/36.1M [00:00<00:01, 20.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  28%|▊  | 10.1M/36.1M [00:00<00:01, 20.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  34%|█  | 12.2M/36.1M [00:00<00:01, 20.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  45%|█▎ | 16.3M/36.1M [00:00<00:00, 20.2M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  51%|█▌ | 18.6M/36.1M [00:00<00:00, 20.8M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  62%|█▊ | 22.3M/36.1M [00:01<00:00, 19.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  68%|██ | 24.4M/36.1M [00:01<00:00, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  74%|██▏| 26.6M/36.1M [00:01<00:00, 20.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  84%|██▌| 30.4M/36.1M [00:01<00:00, 19.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  93%|██▊| 33.5M/36.1M [00:01<00:00, 18.2M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.1M [00:00<?, ? Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:   5%|▏  | 1.93M/36.2M [00:00<00:01, 19.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  11%|▎  | 3.90M/36.2M [00:00<00:01, 19.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  16%|▍  | 5.96M/36.2M [00:00<00:01, 20.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  28%|▊  | 10.1M/36.2M [00:00<00:01, 20.3M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  38%|█▏ | 13.9M/36.2M [00:00<00:01, 19.6M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  44%|█▎ | 16.1M/36.2M [00:00<00:01, 20.1M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  56%|█▋ | 20.4M/36.2M [00:01<00:00, 20.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  68%|██ | 24.5M/36.2M [00:01<00:00, 20.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  79%|██▎| 28.5M/36.2M [00:01<00:00, 20.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  84%|██▌| 30.6M/36.2M [00:01<00:00, 20.4M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  90%|██▋| 32.7M/36.2M [00:01<00:00, 20.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  96%|██▉| 34.8M/36.2M [00:01<00:00, 20.7M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Get sub-04/s .. _bold.nii.gz:  11%|▎  | 3.95M/36.2M [00:00<00:01, 19.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  17%|▌  | 6.18M/36.2M [00:00<00:01, 20.8M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  29%|▊  | 10.4M/36.2M [00:00<00:01, 20.8M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  40%|█▏ | 14.4M/36.2M [00:00<00:01, 20.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  46%|█▎ | 16.5M/36.2M [00:00<00:00, 20.7M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  52%|█▌ | 18.7M/36.2M [00:00<00:00, 20.8M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  58%|█▋ | 20.8M/36.2M [00:01<00:00, 21.0M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  69%|██ | 25.0M/36.2M [00:01<00:00, 20.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  75%|██▏| 27.1M/36.2M [00:01<00:00, 20.9M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  81%|██▍| 29.5M/36.2M [00:01<00:00, 21.5M Bytes/s]
Get sub-04/s .. _bold.nii.gz:  93%|██▊| 33.7M/36.2M [00:01<00:00, 20.5M Bytes/s]
                                                                                
Get sub-04/s .. _bold.nii.gz:   0%|            | 0.00/36.2M [00:00<?, ? Bytes/s]
Total:  56%|██████████████▌           | 1.64G/2.93G [01:45<01:23, 15.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.4M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:   6%|▏  | 2.00M/36.4M [00:00<00:03, 10.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  13%|▍  | 4.65M/36.4M [00:00<00:01, 17.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  19%|▌  | 6.74M/36.4M [00:00<00:01, 18.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  27%|▊  | 9.96M/36.4M [00:00<00:01, 17.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  33%|█  | 12.2M/36.4M [00:00<00:01, 14.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  43%|█▎ | 15.5M/36.4M [00:01<00:01, 12.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  50%|█▍ | 18.1M/36.4M [00:01<00:01, 12.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  56%|█▋ | 20.5M/36.4M [00:01<00:01, 12.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  60%|█▊ | 21.8M/36.4M [00:01<00:01, 12.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  66%|█▉ | 24.2M/36.4M [00:01<00:00, 12.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  73%|██▏| 26.6M/36.4M [00:02<00:00, 11.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  80%|██▍| 29.1M/36.4M [00:02<00:00, 11.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  83%|██▍| 30.4M/36.4M [00:02<00:00, 11.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  87%|██▌| 31.6M/36.4M [00:02<00:00, 12.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  90%|██▋| 32.9M/36.4M [00:02<00:00, 12.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  94%|██▊| 34.2M/36.4M [00:02<00:00, 12.4M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.4M [00:00<?, ? Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:   7%|▏  | 2.68M/36.5M [00:00<00:02, 13.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  11%|▎  | 4.03M/36.5M [00:00<00:02, 13.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  15%|▍  | 5.40M/36.5M [00:00<00:02, 13.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  22%|▋  | 7.98M/36.5M [00:00<00:02, 13.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  30%|▉  | 10.8M/36.5M [00:00<00:01, 13.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  37%|█  | 13.4M/36.5M [00:01<00:01, 13.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  41%|█▏ | 14.9M/36.5M [00:01<00:01, 12.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  44%|█▎ | 16.2M/36.5M [00:01<00:01, 12.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  48%|█▍ | 17.5M/36.5M [00:01<00:01, 12.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  51%|█▌ | 18.8M/36.5M [00:01<00:01, 12.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  55%|█▋ | 20.1M/36.5M [00:01<00:01, 12.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  59%|█▊ | 21.5M/36.5M [00:01<00:01, 13.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  63%|█▉ | 23.0M/36.5M [00:01<00:01, 13.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  67%|██ | 24.4M/36.5M [00:01<00:00, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  73%|██▏| 26.8M/36.5M [00:02<00:00, 12.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  78%|██▎| 28.5M/36.5M [00:02<00:00, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  86%|██▌| 31.3M/36.5M [00:02<00:00, 13.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  90%|██▋| 32.8M/36.5M [00:02<00:00, 14.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  97%|██▉| 35.4M/36.5M [00:02<00:00, 13.3M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:   8%|▏  | 2.84M/36.6M [00:00<00:02, 14.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  12%|▎  | 4.30M/36.6M [00:00<00:02, 14.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  19%|▌  | 7.09M/36.6M [00:00<00:02, 14.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  27%|▊  | 10.0M/36.6M [00:00<00:01, 14.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  35%|█  | 13.0M/36.6M [00:00<00:01, 14.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  39%|█▏ | 14.4M/36.6M [00:01<00:01, 13.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  43%|█▎ | 15.8M/36.6M [00:01<00:01, 13.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  47%|█▍ | 17.3M/36.6M [00:01<00:01, 13.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  51%|█▌ | 18.7M/36.6M [00:01<00:01, 13.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  55%|█▋ | 20.1M/36.6M [00:01<00:01, 13.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  59%|█▊ | 21.5M/36.6M [00:01<00:01, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  63%|█▉ | 22.9M/36.6M [00:01<00:00, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  70%|██ | 25.7M/36.6M [00:01<00:00, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  74%|██▏| 27.2M/36.6M [00:01<00:00, 14.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  78%|██▎| 28.7M/36.6M [00:02<00:00, 14.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  86%|██▌| 31.5M/36.6M [00:02<00:00, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  93%|██▊| 34.2M/36.6M [00:02<00:00, 13.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  97%|██▉| 35.6M/36.6M [00:02<00:00, 13.3M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:   4%|   | 1.45M/36.6M [00:00<00:02, 14.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:   8%|▏  | 2.91M/36.6M [00:00<00:02, 14.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  12%|▎  | 4.38M/36.6M [00:00<00:02, 14.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  20%|▌  | 7.33M/36.6M [00:00<00:02, 14.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  24%|▋  | 8.79M/36.6M [00:00<00:01, 14.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  31%|▉  | 11.4M/36.6M [00:00<00:01, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  35%|█  | 12.8M/36.6M [00:00<00:01, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  40%|█▏ | 14.5M/36.6M [00:01<00:01, 13.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  43%|█▎ | 15.9M/36.6M [00:01<00:01, 13.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  47%|█▍ | 17.2M/36.6M [00:01<00:01, 13.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  51%|█▌ | 18.6M/36.6M [00:01<00:01, 13.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  55%|█▋ | 20.0M/36.6M [00:01<00:01, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  59%|█▊ | 21.5M/36.6M [00:01<00:01, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  63%|█▉ | 22.9M/36.6M [00:01<00:00, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  66%|█▉ | 24.3M/36.6M [00:01<00:00, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  70%|██ | 25.7M/36.6M [00:01<00:00, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  74%|██▏| 27.1M/36.6M [00:01<00:00, 14.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  78%|██▎| 28.6M/36.6M [00:02<00:00, 14.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  86%|██▌| 31.5M/36.6M [00:02<00:00, 14.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  94%|██▊| 34.3M/36.6M [00:02<00:00, 13.5M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Total:  61%|███████████████▊          | 1.78G/2.93G [01:58<01:16, 15.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:   1%|    | 491k/36.5M [00:00<00:07, 4.86M Bytes/s]
Get sub-03/s .. _bold.nii.gz:   8%|▏  | 2.74M/36.5M [00:00<00:02, 14.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  15%|▍  | 5.62M/36.5M [00:00<00:02, 14.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  23%|▋  | 8.57M/36.5M [00:00<00:01, 14.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  31%|▉  | 11.4M/36.5M [00:00<00:01, 14.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  36%|█  | 13.0M/36.5M [00:00<00:01, 14.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  43%|█▎ | 15.5M/36.5M [00:01<00:01, 13.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  47%|█▍ | 17.3M/36.5M [00:01<00:01, 14.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  55%|█▋ | 20.0M/36.5M [00:01<00:01, 13.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  62%|█▊ | 22.7M/36.5M [00:01<00:01, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  70%|██ | 25.5M/36.5M [00:01<00:00, 13.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  74%|██▏| 26.9M/36.5M [00:01<00:00, 13.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  78%|██▎| 28.4M/36.5M [00:02<00:00, 13.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  82%|██▍| 29.8M/36.5M [00:02<00:00, 14.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  90%|██▋| 32.7M/36.5M [00:02<00:00, 14.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  94%|██▊| 34.3M/36.5M [00:02<00:00, 14.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  98%|██▉| 35.7M/36.5M [00:02<00:00, 14.5M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Total:  62%|████████████████          | 1.82G/2.93G [02:00<01:14, 15.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:   4%|   | 1.52M/36.5M [00:00<00:02, 15.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  12%|▎  | 4.48M/36.5M [00:00<00:02, 14.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  20%|▌  | 7.40M/36.5M [00:00<00:01, 14.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  24%|▋  | 8.92M/36.5M [00:00<00:01, 14.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  29%|▊  | 10.4M/36.5M [00:00<00:01, 14.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  33%|▉  | 12.0M/36.5M [00:00<00:01, 15.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  37%|█  | 13.5M/36.5M [00:00<00:01, 15.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  41%|█▏ | 15.0M/36.5M [00:01<00:01, 15.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  49%|█▍ | 18.0M/36.5M [00:01<00:01, 14.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  54%|█▌ | 19.7M/36.5M [00:01<00:01, 15.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  62%|█▊ | 22.6M/36.5M [00:01<00:00, 14.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  66%|█▉ | 24.2M/36.5M [00:01<00:00, 14.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  74%|██▏| 27.2M/36.5M [00:01<00:00, 14.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  79%|██▎| 28.8M/36.5M [00:01<00:00, 14.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  83%|██▍| 30.4M/36.5M [00:02<00:00, 15.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  88%|██▋| 32.0M/36.5M [00:02<00:00, 15.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  96%|██▊| 35.0M/36.5M [00:02<00:00, 15.0M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Total:  63%|████████████████▍         | 1.86G/2.93G [02:03<01:11, 15.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:   9%|▎  | 3.12M/36.5M [00:00<00:02, 15.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  13%|▍  | 4.77M/36.5M [00:00<00:02, 15.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  18%|▌  | 6.57M/36.5M [00:00<00:01, 16.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  27%|▊  | 9.76M/36.5M [00:00<00:01, 16.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  36%|█  | 13.0M/36.5M [00:00<00:01, 16.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  45%|█▎ | 16.5M/36.5M [00:01<00:01, 16.5M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  55%|█▋ | 20.0M/36.5M [00:01<00:00, 17.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  64%|█▉ | 23.4M/36.5M [00:01<00:00, 16.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  74%|██▏| 27.0M/36.5M [00:01<00:00, 17.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  79%|██▎| 28.8M/36.5M [00:01<00:00, 17.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  84%|██▌| 30.7M/36.5M [00:01<00:00, 17.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  89%|██▋| 32.4M/36.5M [00:01<00:00, 17.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  94%|██▊| 34.2M/36.5M [00:02<00:00, 17.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  99%|██▉| 36.0M/36.5M [00:02<00:00, 17.4M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:  10%|▎  | 3.73M/36.5M [00:00<00:01, 18.6M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  16%|▍  | 5.73M/36.5M [00:00<00:01, 19.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  26%|▊  | 9.57M/36.5M [00:00<00:01, 19.1M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  32%|▉  | 11.6M/36.5M [00:00<00:01, 19.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  37%|█  | 13.6M/36.5M [00:00<00:01, 19.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  43%|█▎ | 15.6M/36.5M [00:00<00:01, 19.8M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  48%|█▍ | 17.7M/36.5M [00:00<00:00, 19.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  59%|█▊ | 21.7M/36.5M [00:01<00:00, 19.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  71%|██ | 25.8M/36.5M [00:01<00:00, 20.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  81%|██▍| 29.5M/36.5M [00:01<00:00, 19.4M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  87%|██▌| 31.9M/36.5M [00:01<00:00, 20.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  94%|██▊| 34.3M/36.5M [00:01<00:00, 20.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz: 100%|██▉| 36.5M/36.5M [00:01<00:00, 20.7M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Total:  66%|█████████████████         | 1.93G/2.93G [02:08<01:06, 15.0M Bytes/s]
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-03/s .. _bold.nii.gz:  11%|▎  | 4.01M/36.5M [00:00<00:01, 20.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  17%|▍  | 6.07M/36.5M [00:00<00:01, 20.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  28%|▊  | 10.1M/36.5M [00:00<00:01, 20.2M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  39%|█▏ | 14.3M/36.5M [00:00<00:01, 20.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  50%|█▌ | 18.3M/36.5M [00:00<00:00, 20.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  56%|█▋ | 20.4M/36.5M [00:01<00:00, 20.3M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  62%|█▊ | 22.6M/36.5M [00:01<00:00, 20.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  68%|██ | 24.8M/36.5M [00:01<00:00, 20.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  79%|██▎| 28.9M/36.5M [00:01<00:00, 20.9M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  85%|██▌| 31.0M/36.5M [00:01<00:00, 20.7M Bytes/s]
Get sub-03/s .. _bold.nii.gz:  96%|██▊| 34.9M/36.5M [00:01<00:00, 20.5M Bytes/s]
                                                                                
Get sub-03/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.4M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   7%|▏  | 2.52M/36.4M [00:00<00:01, 25.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  18%|▌  | 6.46M/36.4M [00:00<00:01, 20.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  24%|▋  | 8.66M/36.4M [00:00<00:01, 21.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  35%|█  | 12.8M/36.4M [00:00<00:01, 20.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  46%|█▎ | 16.6M/36.4M [00:00<00:00, 20.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  52%|█▌ | 18.8M/36.4M [00:00<00:00, 20.6M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  62%|█▊ | 22.6M/36.4M [00:01<00:00, 19.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  69%|██ | 25.0M/36.4M [00:01<00:00, 20.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  79%|██▎| 28.8M/36.4M [00:01<00:00, 20.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  90%|██▋| 32.9M/36.4M [00:01<00:00, 20.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  96%|██▉| 35.1M/36.4M [00:01<00:00, 20.4M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.4M [00:00<?, ? Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   6%|▏  | 2.03M/36.5M [00:00<00:03, 10.6M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  12%|▎  | 4.21M/36.5M [00:00<00:02, 15.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  18%|▌  | 6.50M/36.5M [00:00<00:01, 18.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  24%|▋  | 8.81M/36.5M [00:00<00:01, 19.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  30%|▉  | 11.0M/36.5M [00:00<00:01, 20.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  37%|█  | 13.5M/36.5M [00:00<00:01, 21.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  43%|█▎ | 15.7M/36.5M [00:00<00:00, 22.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  50%|█▌ | 18.3M/36.5M [00:00<00:00, 23.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  57%|█▋ | 20.7M/36.5M [00:00<00:00, 23.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  70%|██ | 25.5M/36.5M [00:01<00:00, 23.6M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  77%|██▎| 28.0M/36.5M [00:01<00:00, 23.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  90%|██▋| 32.9M/36.5M [00:01<00:00, 24.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  97%|██▉| 35.4M/36.5M [00:01<00:00, 24.3M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Total:  69%|██████████████████        | 2.04G/2.93G [02:14<00:59, 15.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   7%|▏  | 2.50M/36.5M [00:00<00:01, 25.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  15%|▍  | 5.63M/36.5M [00:00<00:02, 13.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  22%|▋  | 8.16M/36.5M [00:00<00:01, 17.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  32%|▉  | 11.9M/36.5M [00:00<00:01, 17.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  38%|█▏ | 13.9M/36.5M [00:00<00:01, 18.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  48%|█▍ | 17.6M/36.5M [00:00<00:01, 18.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  59%|█▊ | 21.5M/36.5M [00:01<00:00, 18.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  64%|█▉ | 23.5M/36.5M [00:01<00:00, 19.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  70%|██ | 25.6M/36.5M [00:01<00:00, 19.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  80%|██▍| 29.4M/36.5M [00:01<00:00, 19.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  91%|██▋| 33.1M/36.5M [00:01<00:00, 18.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  96%|██▉| 35.2M/36.5M [00:01<00:00, 19.4M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.5M [00:00<?, ? Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   5%|▏  | 2.01M/36.6M [00:00<00:01, 20.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  11%|▎  | 4.03M/36.6M [00:00<00:01, 19.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  17%|▌  | 6.15M/36.6M [00:00<00:01, 20.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  23%|▋  | 8.32M/36.6M [00:00<00:01, 20.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  29%|▊  | 10.4M/36.6M [00:00<00:01, 20.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  34%|█  | 12.5M/36.6M [00:00<00:01, 20.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  46%|█▎ | 16.7M/36.6M [00:00<00:00, 20.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  56%|█▋ | 20.3M/36.6M [00:01<00:00, 19.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  63%|█▉ | 22.9M/36.6M [00:01<00:00, 21.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  69%|██ | 25.1M/36.6M [00:01<00:00, 21.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  79%|██▎| 28.7M/36.6M [00:01<00:00, 19.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  90%|██▋| 32.9M/36.6M [00:01<00:00, 19.6M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  96%|██▉| 35.1M/36.6M [00:01<00:00, 20.0M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   6%|▏  | 2.11M/36.6M [00:00<00:01, 21.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  17%|▌  | 6.27M/36.6M [00:00<00:01, 20.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  28%|▊  | 10.4M/36.6M [00:00<00:01, 20.6M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  40%|█▏ | 14.6M/36.6M [00:00<00:01, 20.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  51%|█▌ | 18.8M/36.6M [00:00<00:00, 20.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  57%|█▋ | 20.8M/36.6M [00:01<00:00, 20.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  69%|██ | 25.2M/36.6M [00:01<00:00, 21.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  80%|██▍| 29.3M/36.6M [00:01<00:00, 20.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  87%|██▌| 31.7M/36.6M [00:01<00:00, 21.5M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  98%|██▉| 36.0M/36.6M [00:01<00:00, 20.4M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Total:  73%|███████████████████       | 2.15G/2.93G [02:21<00:51, 15.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   6%|▏  | 2.04M/36.6M [00:00<00:01, 20.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  15%|▍  | 5.65M/36.6M [00:00<00:02, 14.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  23%|▋  | 8.30M/36.6M [00:00<00:01, 17.6M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  33%|▉  | 11.9M/36.6M [00:00<00:01, 17.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  41%|█▏ | 14.9M/36.6M [00:00<00:01, 16.6M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  46%|█▎ | 16.7M/36.6M [00:00<00:01, 17.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  55%|█▋ | 20.1M/36.6M [00:01<00:00, 16.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  64%|█▉ | 23.5M/36.6M [00:01<00:00, 16.5M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  72%|██▏| 26.4M/36.6M [00:01<00:00, 15.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  77%|██▎| 28.1M/36.6M [00:01<00:00, 16.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  86%|██▌| 31.5M/36.6M [00:01<00:00, 16.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  95%|██▊| 34.8M/36.6M [00:02<00:00, 16.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz: 100%|██▉| 36.5M/36.6M [00:02<00:00, 16.6M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   5%|▏  | 1.75M/36.6M [00:00<00:01, 17.5M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  14%|▍  | 5.12M/36.6M [00:00<00:01, 17.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  19%|▌  | 6.94M/36.6M [00:00<00:01, 17.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  24%|▋  | 8.79M/36.6M [00:00<00:01, 17.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  33%|▉  | 12.2M/36.6M [00:00<00:01, 17.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  39%|█▏ | 14.1M/36.6M [00:00<00:01, 17.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  44%|█▎ | 16.0M/36.6M [00:00<00:01, 17.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  53%|█▌ | 19.5M/36.6M [00:01<00:00, 17.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  58%|█▋ | 21.3M/36.6M [00:01<00:00, 17.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  68%|██ | 24.9M/36.6M [00:01<00:00, 18.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  78%|██▎| 28.5M/36.6M [00:01<00:00, 18.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  88%|██▋| 32.1M/36.6M [00:01<00:00, 17.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  92%|██▊| 33.8M/36.6M [00:01<00:00, 17.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  97%|██▉| 35.7M/36.6M [00:02<00:00, 17.6M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.6M [00:00<?, ? Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.7M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:  10%|▎  | 3.67M/36.7M [00:00<00:01, 18.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  15%|▍  | 5.54M/36.7M [00:00<00:01, 18.5M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  25%|▋  | 9.17M/36.7M [00:00<00:01, 18.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  30%|▉  | 11.0M/36.7M [00:00<00:01, 18.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  35%|█  | 13.0M/36.7M [00:00<00:01, 18.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  46%|█▎ | 16.7M/36.7M [00:00<00:01, 18.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  55%|█▋ | 20.3M/36.7M [00:01<00:00, 18.4M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  61%|█▊ | 22.4M/36.7M [00:01<00:00, 18.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  70%|██ | 25.8M/36.7M [00:01<00:00, 18.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  76%|██▎| 27.9M/36.7M [00:01<00:00, 18.9M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  81%|██▍| 29.9M/36.7M [00:01<00:00, 18.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  91%|██▋| 33.6M/36.7M [00:01<00:00, 18.1M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.7M [00:00<?, ? Bytes/s]
Total:  77%|████████████████████      | 2.26G/2.93G [02:28<00:44, 15.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.7M [00:00<?, ? Bytes/s]
Get sub-02/s .. _bold.nii.gz:   5%|▏  | 1.89M/36.7M [00:00<00:01, 18.8M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  10%|▎  | 3.82M/36.7M [00:00<00:01, 19.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  21%|▌  | 7.65M/36.7M [00:00<00:01, 19.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  26%|▊  | 9.59M/36.7M [00:00<00:01, 19.2M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  36%|█  | 13.4M/36.7M [00:00<00:01, 19.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  47%|█▍ | 17.2M/36.7M [00:00<00:01, 19.0M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  52%|█▌ | 19.1M/36.7M [00:01<00:00, 19.1M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  61%|█▊ | 22.6M/36.7M [00:01<00:00, 18.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  67%|█▉ | 24.5M/36.7M [00:01<00:00, 18.5M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  77%|██▎| 28.3M/36.7M [00:01<00:00, 18.7M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  87%|██▌| 31.8M/36.7M [00:01<00:00, 18.3M Bytes/s]
Get sub-02/s .. _bold.nii.gz:  92%|██▊| 34.0M/36.7M [00:01<00:00, 18.5M Bytes/s]
                                                                                
Get sub-02/s .. _bold.nii.gz:   0%|            | 0.00/36.7M [00:00<?, ? Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:   5%|▏  | 1.84M/35.3M [00:00<00:01, 18.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  11%|▎  | 3.75M/35.3M [00:00<00:01, 18.6M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  16%|▍  | 5.77M/35.3M [00:00<00:01, 19.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  22%|▋  | 7.72M/35.3M [00:00<00:01, 19.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  33%|▉  | 11.6M/35.3M [00:00<00:01, 19.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  43%|█▎ | 15.3M/35.3M [00:00<00:01, 19.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  53%|█▌ | 18.9M/35.3M [00:01<00:00, 18.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  59%|█▊ | 21.0M/35.3M [00:01<00:00, 19.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  71%|██ | 24.9M/35.3M [00:01<00:00, 19.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  81%|██▍| 28.6M/35.3M [00:01<00:00, 18.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  86%|██▌| 30.5M/35.3M [00:01<00:00, 18.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  92%|██▋| 32.3M/35.3M [00:01<00:00, 18.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  97%|██▉| 34.2M/35.3M [00:01<00:00, 18.3M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:   5%|▏  | 1.89M/35.2M [00:00<00:01, 18.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  11%|▎  | 3.82M/35.2M [00:00<00:01, 19.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  21%|▋  | 7.38M/35.2M [00:00<00:01, 18.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  27%|▊  | 9.41M/35.2M [00:00<00:01, 18.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  37%|█  | 13.1M/35.2M [00:00<00:01, 18.7M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  43%|█▎ | 15.1M/35.2M [00:00<00:01, 19.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  54%|█▌ | 18.9M/35.2M [00:01<00:00, 18.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  60%|█▊ | 21.0M/35.2M [00:01<00:00, 19.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  65%|█▉ | 23.0M/35.2M [00:01<00:00, 19.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  76%|██▎| 26.7M/35.2M [00:01<00:00, 18.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  87%|██▌| 30.5M/35.2M [00:01<00:00, 18.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  97%|██▉| 34.2M/35.2M [00:01<00:00, 18.3M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:  11%|▎  | 3.77M/35.3M [00:00<00:01, 18.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  16%|▍  | 5.70M/35.3M [00:00<00:01, 19.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  27%|▊  | 9.43M/35.3M [00:00<00:01, 18.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  32%|▉  | 11.4M/35.3M [00:00<00:01, 18.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  38%|█▏ | 13.3M/35.3M [00:00<00:01, 19.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  43%|█▎ | 15.3M/35.3M [00:00<00:01, 19.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  54%|█▌ | 19.1M/35.3M [00:01<00:00, 19.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  60%|█▊ | 21.2M/35.3M [00:01<00:00, 19.6M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  71%|██ | 24.9M/35.3M [00:01<00:00, 19.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  76%|██▎| 26.9M/35.3M [00:01<00:00, 19.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  82%|██▍| 28.9M/35.3M [00:01<00:00, 19.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  88%|██▋| 30.9M/35.3M [00:01<00:00, 18.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  93%|██▊| 32.8M/35.3M [00:01<00:00, 18.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  99%|██▉| 34.8M/35.3M [00:01<00:00, 18.7M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
Total:  82%|█████████████████████▎    | 2.40G/2.93G [02:37<00:34, 15.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.1M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:  11%|▎  | 3.83M/35.1M [00:00<00:01, 19.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  22%|▋  | 7.62M/35.1M [00:00<00:01, 18.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  28%|▊  | 9.70M/35.1M [00:00<00:01, 19.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  39%|█▏ | 13.7M/35.1M [00:00<00:01, 19.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  45%|█▎ | 15.8M/35.1M [00:00<00:00, 19.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  56%|█▋ | 19.9M/35.1M [00:01<00:00, 20.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  67%|██ | 23.5M/35.1M [00:01<00:00, 19.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  73%|██▏| 25.8M/35.1M [00:01<00:00, 20.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  85%|██▌| 29.7M/35.1M [00:01<00:00, 20.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  96%|██▉| 33.9M/35.1M [00:01<00:00, 20.2M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.1M [00:00<?, ? Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:  12%|▎  | 4.11M/35.3M [00:00<00:01, 20.6M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  18%|▌  | 6.20M/35.3M [00:00<00:01, 20.7M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  24%|▋  | 8.30M/35.3M [00:00<00:01, 20.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  30%|▉  | 10.4M/35.3M [00:00<00:01, 20.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  40%|█▏ | 14.2M/35.3M [00:00<00:01, 20.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  47%|█▍ | 16.7M/35.3M [00:00<00:00, 21.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  59%|█▊ | 20.8M/35.3M [00:01<00:00, 20.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  66%|█▉ | 23.2M/35.3M [00:01<00:00, 21.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  78%|██▎| 27.5M/35.3M [00:01<00:00, 21.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  91%|██▋| 31.9M/35.3M [00:01<00:00, 21.7M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:   6%|▏  | 2.13M/35.2M [00:00<00:01, 21.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  18%|▌  | 6.32M/35.2M [00:00<00:01, 21.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  30%|▉  | 10.5M/35.2M [00:00<00:01, 20.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  42%|█▎ | 14.8M/35.2M [00:00<00:00, 21.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  54%|█▋ | 19.2M/35.2M [00:00<00:00, 21.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  67%|█▉ | 23.5M/35.2M [00:01<00:00, 21.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  79%|██▎| 27.8M/35.2M [00:01<00:00, 21.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  85%|██▌| 30.0M/35.2M [00:01<00:00, 21.7M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  92%|██▊| 32.4M/35.2M [00:01<00:00, 22.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  98%|██▉| 34.7M/35.2M [00:01<00:00, 22.0M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:   7%|▏  | 2.41M/35.2M [00:00<00:01, 24.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  19%|▌  | 6.60M/35.2M [00:00<00:01, 21.7M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  31%|▉  | 10.9M/35.2M [00:00<00:01, 21.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  42%|█▎ | 14.8M/35.2M [00:00<00:00, 20.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  48%|█▍ | 17.0M/35.2M [00:00<00:00, 20.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  55%|█▋ | 19.2M/35.2M [00:00<00:00, 21.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  66%|█▉ | 23.2M/35.2M [00:01<00:00, 20.7M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  78%|██▎| 27.4M/35.2M [00:01<00:00, 20.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  88%|██▋| 30.9M/35.2M [00:01<00:00, 19.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  99%|██▉| 34.9M/35.2M [00:01<00:00, 18.9M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:   5%|▏  | 1.67M/35.3M [00:00<00:03, 8.69M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  13%|▍  | 4.53M/35.3M [00:00<00:01, 17.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  24%|▋  | 8.40M/35.3M [00:00<00:01, 18.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  35%|█  | 12.5M/35.3M [00:00<00:01, 19.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  41%|█▏ | 14.5M/35.3M [00:00<00:01, 19.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  47%|█▍ | 16.7M/35.3M [00:00<00:00, 20.0M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  58%|█▋ | 20.5M/35.3M [00:01<00:00, 17.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  67%|██ | 23.8M/35.3M [00:01<00:00, 17.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  76%|██▎| 26.9M/35.3M [00:01<00:00, 16.7M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  86%|██▌| 30.2M/35.3M [00:01<00:00, 16.6M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  94%|██▊| 33.3M/35.3M [00:01<00:00, 16.1M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.3M [00:00<?, ? Bytes/s]
Total:  88%|██████████████████████▊   | 2.58G/2.93G [02:47<00:23, 15.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
Get sub-07/s .. _bold.nii.gz:   9%|▎  | 3.26M/35.2M [00:00<00:01, 16.3M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  14%|▍  | 4.91M/35.2M [00:00<00:01, 16.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  23%|▋  | 8.09M/35.2M [00:00<00:01, 16.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  28%|▊  | 9.74M/35.2M [00:00<00:01, 16.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  37%|█  | 13.0M/35.2M [00:00<00:01, 16.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  42%|█▏ | 14.7M/35.2M [00:00<00:01, 16.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  47%|█▍ | 16.5M/35.2M [00:01<00:01, 16.8M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  57%|█▋ | 19.9M/35.2M [00:01<00:00, 16.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  62%|█▊ | 21.7M/35.2M [00:01<00:00, 16.9M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  71%|██ | 24.8M/35.2M [00:01<00:00, 16.5M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  76%|██▎| 26.7M/35.2M [00:01<00:00, 16.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  80%|██▍| 28.3M/35.2M [00:01<00:00, 16.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  85%|██▌| 30.0M/35.2M [00:01<00:00, 16.1M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  90%|██▋| 31.6M/35.2M [00:01<00:00, 16.2M Bytes/s]
Get sub-07/s .. _bold.nii.gz:  95%|██▊| 33.3M/35.2M [00:02<00:00, 15.4M Bytes/s]
Get sub-07/s .. _bold.nii.gz: 100%|██▉| 35.1M/35.2M [00:02<00:00, 15.9M Bytes/s]
                                                                                
Get sub-07/s .. _bold.nii.gz:   0%|            | 0.00/35.2M [00:00<?, ? Bytes/s]
Total:  89%|███████████████████████▏  | 2.61G/2.93G [02:50<00:20, 15.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
Get sub-08/s .. _bold.nii.gz:  10%|▎  | 3.51M/35.6M [00:00<00:01, 17.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  15%|▍  | 5.31M/35.6M [00:00<00:01, 17.7M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  20%|▌  | 7.12M/35.6M [00:00<00:01, 17.8M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  25%|▊  | 8.93M/35.6M [00:00<00:01, 17.9M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  35%|█  | 12.4M/35.6M [00:00<00:01, 17.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  40%|█▏ | 14.2M/35.6M [00:00<00:01, 17.7M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  50%|█▍ | 17.7M/35.6M [00:01<00:01, 17.6M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  55%|█▋ | 19.5M/35.6M [00:01<00:00, 17.4M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  60%|█▊ | 21.5M/35.6M [00:01<00:00, 18.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  70%|██ | 25.0M/35.6M [00:01<00:00, 16.9M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  80%|██▍| 28.4M/35.6M [00:01<00:00, 17.0M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  85%|██▌| 30.2M/35.6M [00:01<00:00, 17.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  90%|██▋| 32.0M/35.6M [00:01<00:00, 17.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  95%|██▊| 33.7M/35.6M [00:01<00:00, 17.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz: 100%|██▉| 35.5M/35.6M [00:02<00:00, 17.4M Bytes/s]
                                                                                
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
Total:  90%|███████████████████████▍  | 2.65G/2.93G [02:52<00:18, 15.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
Get sub-08/s .. _bold.nii.gz:   5%|▏  | 1.66M/35.6M [00:00<00:02, 16.6M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  10%|▎  | 3.47M/35.6M [00:00<00:01, 17.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  15%|▍  | 5.42M/35.6M [00:00<00:01, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  25%|▊  | 9.06M/35.6M [00:00<00:01, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  31%|▉  | 10.9M/35.6M [00:00<00:01, 18.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  36%|█  | 12.8M/35.6M [00:00<00:01, 18.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  46%|█▍ | 16.4M/35.6M [00:00<00:01, 18.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  56%|█▋ | 20.1M/35.6M [00:01<00:00, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  66%|█▉ | 23.6M/35.6M [00:01<00:00, 17.9M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  71%|██▏| 25.4M/35.6M [00:01<00:00, 18.0M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  76%|██▎| 27.2M/35.6M [00:01<00:00, 18.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  86%|██▌| 30.7M/35.6M [00:01<00:00, 17.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  91%|██▋| 32.5M/35.6M [00:01<00:00, 17.0M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  96%|██▉| 34.3M/35.6M [00:01<00:00, 17.0M Bytes/s]
                                                                                
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.6M [00:00<?, ? Bytes/s]
                                                                                
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
Get sub-08/s .. _bold.nii.gz:  10%|▎  | 3.68M/35.7M [00:00<00:01, 18.4M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  20%|▌  | 7.24M/35.7M [00:00<00:01, 18.0M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  26%|▊  | 9.20M/35.7M [00:00<00:01, 18.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  36%|█  | 12.9M/35.7M [00:00<00:01, 18.4M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  46%|█▍ | 16.5M/35.7M [00:00<00:01, 18.4M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  57%|█▋ | 20.2M/35.7M [00:01<00:00, 18.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  67%|██ | 23.8M/35.7M [00:01<00:00, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  76%|██▎| 27.3M/35.7M [00:01<00:00, 17.9M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  82%|██▍| 29.2M/35.7M [00:01<00:00, 17.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  87%|██▌| 30.9M/35.7M [00:01<00:00, 17.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  92%|██▊| 32.7M/35.7M [00:01<00:00, 17.4M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  97%|██▉| 34.6M/35.7M [00:01<00:00, 17.6M Bytes/s]
                                                                                
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
Total:  93%|████████████████████████  | 2.72G/2.93G [02:57<00:14, 15.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
Get sub-08/s .. _bold.nii.gz:  10%|▎  | 3.64M/35.7M [00:00<00:01, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  15%|▍  | 5.53M/35.7M [00:00<00:01, 18.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  26%|▊  | 9.15M/35.7M [00:00<00:01, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  31%|▉  | 11.0M/35.7M [00:00<00:01, 18.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  36%|█  | 13.0M/35.7M [00:00<00:01, 18.6M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  47%|█▍ | 16.7M/35.7M [00:00<00:01, 18.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  52%|█▌ | 18.5M/35.7M [00:01<00:00, 18.6M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  62%|█▊ | 22.1M/35.7M [00:01<00:00, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  67%|██ | 24.0M/35.7M [00:01<00:00, 18.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  77%|██▎| 27.5M/35.7M [00:01<00:00, 17.0M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  82%|██▍| 29.4M/35.7M [00:01<00:00, 17.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  87%|██▌| 31.2M/35.7M [00:01<00:00, 17.6M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  98%|██▉| 34.8M/35.7M [00:01<00:00, 17.8M Bytes/s]
                                                                                
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.7M [00:00<?, ? Bytes/s]
                                                                                
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.8M [00:00<?, ? Bytes/s]
Get sub-08/s .. _bold.nii.gz:   5%|▏  | 1.81M/35.8M [00:00<00:01, 18.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  10%|▎  | 3.69M/35.8M [00:00<00:01, 18.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  21%|▋  | 7.53M/35.8M [00:00<00:01, 18.7M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  26%|▊  | 9.40M/35.8M [00:00<00:01, 18.7M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  36%|█  | 13.0M/35.8M [00:00<00:01, 18.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  42%|█▎ | 15.0M/35.8M [00:00<00:01, 18.8M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  52%|█▌ | 18.6M/35.8M [00:01<00:00, 18.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  58%|█▋ | 20.6M/35.8M [00:01<00:00, 18.8M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  68%|██ | 24.3M/35.8M [00:01<00:00, 17.5M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  73%|██▏| 26.1M/35.8M [00:01<00:00, 17.6M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  83%|██▍| 29.8M/35.8M [00:01<00:00, 17.9M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  88%|██▋| 31.6M/35.8M [00:01<00:00, 18.0M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  94%|██▊| 33.5M/35.8M [00:01<00:00, 18.1M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  99%|██▉| 35.4M/35.8M [00:01<00:00, 18.2M Bytes/s]
                                                                                
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.8M [00:00<?, ? Bytes/s]
Total:  95%|████████████████████████▋ | 2.79G/2.93G [03:02<00:09, 15.3M Bytes/s]
Get sub-08/s .. _bold.nii.gz:   0%|            | 0.00/35.8M [00:00<?, ? Bytes/s]
Get sub-08/s .. _bold.nii.gz:  10%|▎  | 3.63M/35.8M [00:00<00:01, 18.4M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  15%|▍  | 5.52M/35.8M [00:00<00:01, 18.6M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  25%|▊  | 9.12M/35.8M [00:00<00:01, 18.2M Bytes/s]
Get sub-08/s .. _bold.nii.gz:  31%|▉  | 11.1M/35.8M [00:00<00:01, 18.6M Bytes/s]
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get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-01_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-02_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-03_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-04_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-05_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-06_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-07_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-08_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-06/ses-mri/func/sub-06_ses-mri_task-facerecognition_run-09_bold.nii.gz (file) [from s3-PUBLIC...]
get(ok): sub-05/ses-mri/func/sub-05_ses-mri_task-facerecognition_run-01_bold.nii.gz (file) [from s3-PUBLIC...]
  [71 similar messages have been suppressed; disable with datalad.ui.suppress-similar-results=off]
get(ok): sub-06/ses-mri/func (directory)
get(ok): sub-05/ses-mri/func (directory)
get(ok): sub-09/ses-mri/func (directory)
get(ok): sub-01/ses-mri/func (directory)
get(ok): sub-04/ses-mri/func (directory)
get(ok): sub-03/ses-mri/func (directory)
get(ok): sub-02/ses-mri/func (directory)
get(ok): sub-07/ses-mri/func (directory)
get(ok): sub-08/ses-mri/func (directory)
action summary:
  get (ok: 90)

# get preprocessed normalized func images of 9 individuals  
PATTERN_PREP = "sub-0*/ses-mri/func/*space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.nii.gz"

!datalad install https://github.com/OpenNeuroDerivatives/ds000117-fmriprep.git
!cd ds000117-fmriprep && datalad get $PATTERN_PREP

%%capture
!pip install nilearn pandas scipy traits==7.1.0

Load SPM and import Python and Nipype modules

import module
await module.purge(force=True)
await module.load('spm12/r7771')
await module.list()

['spm12/r7771']

import pandas as pd
import numpy as np
from nilearn import plotting
import matplotlib.pyplot as plt
import json
import os
from os.path import join as opj
from scipy.io import loadmat

import nipype.algorithms.modelgen as model
from nipype.interfaces import spm
from nipype.interfaces.io import DataSink, DataGrabber
from nipype.interfaces.utility import IdentityInterface, Function
from nipype import Node, Workflow, MapNode
from nipype.algorithms.misc import Gunzip

import nipype
NIPYPE_VERSION = nipype.__version__
print(NIPYPE_VERSION)

1.10.0

from packaging.version import Version

if Version(NIPYPE_VERSION) <= Version("1.8.6"):
    print('Contrasts need to be defined manually and wont be computed automatically when they are defined in Level1Design using the factor_info parameter')
    
# starting in nipype version 1.8.7., when factor_info parameter is used in Level1design T and F contrasts (ess*, con*, spmF* and spmT* images) 
# are created automatically by in EstimateModel by SPM

Analysis

1. First Level Analysis

Prepare Data Input

#base directories
data_base_dir = os.getcwd() 
experiment_dir = opj(data_base_dir, 'spm_analysis/') #where to store the working and datasink directories

#list of subject identifiers and runs
sub_list =  ['01', '02', '03', '04', '05', '06', '07', '08', '09']

#only take run 1 and 2 for computational reasons
run_id = [1,2]

#TR of functional images
with open(opj(data_base_dir,'ds000117/task-facerecognition_bold.json'), 'rt') as fp:
    task_info = json.load(fp)
TR = float(task_info['RepetitionTime'])
print('Repetition Time:', TR)

Repetition Time: 2.0

Start the workflow

wf = Workflow(name='level1_spm', base_dir=experiment_dir)
wf.config["execution"]["crashfile_format"] = "txt"

Input stream

infosource = Node(IdentityInterface(fields=["subject_id"]),
                  name="infosource")
infosource.iterables = [("subject_id", sub_list)]

SPM12 can accept NIfTI files as input, but only if they are not compressed (‘unzipped’). Use Gunzip node to unzip the files, before feeding them it to the model specification node.

gunzip_func = MapNode(Gunzip(), name='gunzip_func', iterfield='in_file')

datagrabber = Node(interface=DataGrabber(
        infields=["subject_id","run_id"], outfields=["func", "events"]
    ), name="datagrabber"
)

# Specify task names and return a sorted filelist to ensure to match files to correct runs
datagrabber.inputs.run_id = run_id
datagrabber.inputs.sort_filelist = True
datagrabber.inputs.template = "*"
datagrabber.inputs.base_directory = data_base_dir

# Define arguments fill the wildcards in the below paths 
datagrabber.inputs.template_args = dict(
    func=[["subject_id","subject_id","run_id"]],
    events=[["subject_id","subject_id", "run_id"]]
)

datagrabber.inputs.field_template = dict(
    func= "ds000117-fmriprep/sub-%s/ses-mri/func/sub-%s_ses-mri_task-facerecognition_run-%d_space-MNI152NLin6Asym_desc-smoothAROMAnonaggr_bold.nii.gz",
    events="ds000117/sub-%s/ses-mri/func/sub-%s_ses-mri_task-facerecognition_run-0%d_events.tsv", 
)

wf.connect([
        (infosource, datagrabber, [("subject_id", "subject_id")])])

wf.connect([(datagrabber, gunzip_func, [('func', 'in_file')])])

First-level GLM

The subsequent task involves obtaining information such as stimuli type, onset, duration, and other regressors for integration into the GLM model. To accomplish this, a helper function needs to be created, which will be referred to as subjectinfo.

A TSV file for each run looks like this:

!cat ds000117/sub-01/ses-mri/func/sub-01_ses-mri_task-facerecognition_run-01_events.tsv

onset	duration	circle_duration	stim_type	trigger	button_pushed	response_time	stim_file
0	.908	.534	FAMOUS	5	4	2.158	func/f013.bmp
3.273	.962	.586	FAMOUS	6	4	1.233	func/f013.bmp
6.647	.825	.546	UNFAMILIAR	13	4	1.183	func/u014.bmp
9.838	.968	.597	UNFAMILIAR	14	4	.930	func/u014.bmp
12.978	.904	.415	UNFAMILIAR	13	7	1.068	func/u016.bmp
16.219	.859	.558	UNFAMILIAR	14	7	1.207	func/u016.bmp
19.443	.804	.585	UNFAMILIAR	13	4	1.286	func/u010.bmp
22.55	.879	.526	UNFAMILIAR	14	4	1.008	func/u010.bmp
25.606	.866	.416	SCRAMBLED	17	7	1.929	func/s002.bmp
28.697	.884	.461	SCRAMBLED	18	4	1.300	func/s002.bmp
31.319	20.000	20.000	n/a	999	20000	20.000	func/i999.bmp
51.898	.974	.543	FAMOUS	5	7	2.477	func/f004.bmp
55.173	.925	.534	SCRAMBLED	17	7	1.372	func/s008.bmp
58.313	.985	.439	UNFAMILIAR	13	4	1.431	func/u012.bmp
61.587	.862	.533	FAMOUS	5	4	1.086	func/f012.bmp
64.677	.869	.461	FAMOUS	6	4	1.018	func/f012.bmp
67.75	.804	.446	SCRAMBLED	17	7	1.267	func/s007.bmp
70.774	.873	.445	SCRAMBLED	17	7	1.211	func/s011.bmp
73.881	.983	.466	FAMOUS	7	7	1.203	func/f004.bmp
77.105	.998	.468	SCRAMBLED	17	7	1.240	func/s015.bmp
80.445	.833	.583	SCRAMBLED	19	7	1.379	func/s008.bmp
83.469	.940	.434	FAMOUS	5	7	1.637	func/f006.bmp
86.676	.813	.488	FAMOUS	6	7	1.209	func/f006.bmp
89.833	.994	.570	UNFAMILIAR	15	4	1.444	func/u012.bmp
93.19	.997	.594	FAMOUS	5	7	1.352	func/f009.bmp
96.448	.869	.491	FAMOUS	5	7	1.012	func/f005.bmp
99.655	.821	.571	SCRAMBLED	19	7	1.483	func/s007.bmp
102.695	.933	.437	FAMOUS	5	7	1.228	func/f002.bmp
105.869	.818	.481	FAMOUS	6	7	1.109	func/f002.bmp
108.942	.925	.487	SCRAMBLED	19	7	1.205	func/s011.bmp
111.615	20.000	20.000	n/a	999	20000	20.000	func/i999.bmp
132.144	.980	.498	FAMOUS	5	7	.898	func/f014.bmp
135.351	.833	.458	SCRAMBLED	19	n/a	0	func/s015.bmp
138.541	.999	.591	SCRAMBLED	17	4	1.940	func/s004.bmp
141.849	.868	.548	FAMOUS	5	4	1.366	func/f001.bmp
144.906	.832	.421	FAMOUS	6	4	1.433	func/f001.bmp
147.946	.897	.445	FAMOUS	7	7	.767	func/f009.bmp
151.186	.971	.575	UNFAMILIAR	13	7	1.604	func/u013.bmp
154.393	.978	.458	FAMOUS	7	7	1.137	func/f005.bmp
157.701	.984	.564	FAMOUS	5	7	1.248	func/f015.bmp
161.008	.900	.556	UNFAMILIAR	13	4	1.239	func/u011.bmp
164.265	.828	.594	UNFAMILIAR	14	4	1.269	func/u011.bmp
167.306	.830	.447	FAMOUS	7	7	1.116	func/f014.bmp
170.312	.992	.415	FAMOUS	5	7	1.806	func/f003.bmp
173.519	.942	.442	FAMOUS	6	7	1.361	func/f003.bmp
176.777	.874	.539	SCRAMBLED	19	4	1.363	func/s004.bmp
179.984	.845	.559	SCRAMBLED	17	4	1.131	func/s012.bmp
183.058	.981	.467	SCRAMBLED	17	7	.872	func/s001.bmp
186.365	.880	.559	SCRAMBLED	18	7	1.000	func/s001.bmp
189.422	.858	.414	UNFAMILIAR	15	4	1.095	func/u013.bmp
192.027	20.000	20.000	n/a	999	20000	20.000	func/i999.bmp
212.623	.873	.554	SCRAMBLED	17	4	1.718	func/s006.bmp
215.83	.863	.558	FAMOUS	7	7	1.115	func/f015.bmp
218.937	.984	.479	SCRAMBLED	17	7	1.449	func/s014.bmp
222.178	.873	.487	SCRAMBLED	18	7	.834	func/s014.bmp
225.452	.912	.536	UNFAMILIAR	13	4	1.099	func/u002.bmp
228.559	.952	.419	UNFAMILIAR	14	4	.915	func/u002.bmp
231.733	.891	.465	UNFAMILIAR	13	4	1.023	func/u004.bmp
234.89	.947	.496	UNFAMILIAR	14	4	.953	func/u004.bmp
238.164	.824	.560	SCRAMBLED	19	7	1.068	func/s012.bmp
241.187	.922	.425	FAMOUS	5	4	1.154	func/f011.bmp
244.478	.941	.593	SCRAMBLED	17	7	1.283	func/s005.bmp
247.668	.857	.482	SCRAMBLED	18	7	1.023	func/s005.bmp
250.825	.813	.524	SCRAMBLED	19	7	1.329	func/s006.bmp
253.849	.921	.450	FAMOUS	5	7	1.221	func/f007.bmp
257.072	.903	.528	SCRAMBLED	17	7	.788	func/s016.bmp
260.163	.969	.419	FAMOUS	5	4	1.369	func/f010.bmp
263.403	.892	.516	FAMOUS	6	4	1.183	func/f010.bmp
266.627	.950	.562	SCRAMBLED	17	7	1.326	func/s009.bmp
269.901	.935	.561	SCRAMBLED	18	7	1.010	func/s009.bmp
273.025	.896	.434	FAMOUS	7	7	1.431	func/f011.bmp
275.664	20.000	20.000	n/a	999	20000	20.000	func/i999.bmp
296.109	.895	.410	SCRAMBLED	17	4	1.541	func/s010.bmp
299.233	.908	.452	SCRAMBLED	18	4	1.010	func/s010.bmp
302.373	.866	.457	UNFAMILIAR	13	4	1.137	func/u008.bmp
305.53	.880	.519	FAMOUS	7	7	1.127	func/f007.bmp
308.588	.821	.417	SCRAMBLED	17	7	1.208	func/s003.bmp
311.594	.873	.403	SCRAMBLED	18	7	.965	func/s003.bmp
314.784	.967	.543	SCRAMBLED	19	7	1.126	func/s016.bmp
318.008	.913	.495	SCRAMBLED	17	4	1.137	func/s013.bmp
321.165	.957	.477	UNFAMILIAR	13	4	1.073	func/u007.bmp
324.423	.826	.534	UNFAMILIAR	14	4	.934	func/u007.bmp
327.479	.998	.457	UNFAMILIAR	13	4	1.293	func/u015.bmp
330.77	.981	.534	UNFAMILIAR	13	4	1.075	func/u009.bmp
334.011	.920	.491	UNFAMILIAR	15	4	1.075	func/u008.bmp
337.134	.967	.442	FAMOUS	5	7	.754	func/f008.bmp
340.425	.946	.552	FAMOUS	6	7	.917	func/f008.bmp
343.632	.870	.489	UNFAMILIAR	13	4	1.286	func/u003.bmp
346.839	.990	.552	UNFAMILIAR	14	4	1.141	func/u003.bmp
350.146	.937	.542	SCRAMBLED	19	4	1.334	func/s013.bmp
352.836	20.000	20.000	n/a	999	20000	20.000	func/i999.bmp
373.298	.816	.420	UNFAMILIAR	13	4	1.415	func/u001.bmp
376.455	.837	.577	UNFAMILIAR	14	4	1.247	func/u001.bmp
379.646	.825	.572	UNFAMILIAR	13	7	1.292	func/u006.bmp
382.786	.995	.540	UNFAMILIAR	15	4	1.003	func/u015.bmp
385.993	.976	.447	UNFAMILIAR	13	7	1.536	func/u005.bmp
389.3	.906	.558	UNFAMILIAR	15	4	1.078	func/u009.bmp
392.508	.957	.526	FAMOUS	5	7	1.223	func/f016.bmp
395.264	.012	0	n/a	999	0	0	func/Circle.bmp

As mentioned in the introduction, these event files will be adapted in the function ‘subjectinfo’ to demonstrate the setup of a 3x2 factorial design analysis. The original stimulus types (stim_types) FAMOUS, NONFAMILIAR, SCRAMBLED will be replaced with F1 (first presentation of an image of a famous face)/ F2 (second presentation of image), U1/U2 and S1/S1 due to the first or second occurance of the respective stimulus file (stim_file). In addition, stimuli of stimulus type n/a are deleted.

# Get the subject information: to create a GLM model, Nipype needs a list of Bunch objects per run (session)

def subjectinfo(events):

    # packages need to be imported within the function for node to work (function is executed in a standalone environment)
    from nipype.interfaces.base import Bunch
    import pandas as pd
    from collections import OrderedDict

    trialinfo = pd.read_table(events)

    # Filter out rows where stim_type does not contain 'FAMOUS', 'UNFAMILIAR', or 'SCRAMBLED' --> n/a
    trialinfo = trialinfo[trialinfo['stim_type'].isin(['FAMOUS', 'UNFAMILIAR', 'SCRAMBLED'])].reset_index(drop=True)
    
    # Create a dictionary to store the count of occurrences for each stim_file
    stim_file_count = {}
    
    
    # Iterate over each row in the dataframe
    for index, row in trialinfo.iterrows():
        # Get the stim_file value for the current row
        stim_file = row['stim_file']
        
        # If the stim_file is not in the stim_file_count dictionary, add it with count 1
        if stim_file not in stim_file_count:
            stim_file_count[stim_file] = 1
        else:
            # Increment the count for the stim_file and update the dictionary
            stim_file_count[stim_file] += 1
        
        # Get the count of occurrences for the current stim_file
        count = stim_file_count[stim_file]
        
        # Determine the new stim_type based on the stim_file and its count
        if 'FAMOUS' in row['stim_type']:
            new_stim_type = f'F{count}'
        elif 'UNFAMILIAR' in row['stim_type']:
            new_stim_type = f'U{count}'
        else:
            # If it's not 'FAMOUS' or 'UNFAMILIAR', it must be 'SCRAMBLED'
            new_stim_type = f'S{count}'
        
        # Update the stim_type in the dataframe
        trialinfo.at[index, 'stim_type'] = new_stim_type

    
    # Define the custom sorting order (instead of an alphabetic ordering F1, F2, S1, S2, U1, U2
    sorting_order = OrderedDict([('F1', 1), ('F2', 2), ('U1', 3), ('U2', 4), ('S1', 5), ('S2', 6)])
    
    conditions = []
    onsets = []
    durations = []
    
    # Group trialinfo by 'stim_type' and iterate over groups
    grouped_trials = trialinfo.groupby('stim_type')
    for group_key in sorting_order.keys():  # Use keys() to iterate over keys
        group_data = grouped_trials.get_group(group_key)
        conditions.append(group_key)
        onsets.append(group_data['onset'].tolist())
        durations.append(group_data['duration'].tolist())

    subject_info = Bunch(conditions=conditions, 
                     onsets=onsets, 
                     durations=durations)
        
    return subject_info
    


getsubjectinfo = MapNode(Function(input_names=['events'],
                                output_names=['subject_info'],
                                function=subjectinfo),
                                name='getsubjectinfo', iterfield=['events'])

wf.connect(datagrabber, 'events', getsubjectinfo, 'events')

modelspec = Node(model.SpecifySPMModel(concatenate_runs=True,
                                input_units = 'secs',
                                output_units = 'secs',
                                time_repetition= TR, 
                                high_pass_filter_cutoff=128), #in secs, slow signal drifts with a period > 128 will be removed
                                name='modelspec')
                              
wf.connect(getsubjectinfo, 'subject_info', modelspec,'subject_info')
wf.connect(gunzip_func, 'out_file', modelspec, 'functional_runs')

Level1Design: canonical HRF

The design matrix will be constructed without including derivatives of the hemodynamic response function (HRF) and therefore assumes a constant delay and dispersion for the hemodynamic response.

Starting in nipype version 1.8.7., when factor_info parameter is used in Level1design, T and F contrasts (ess, con, spmF and spmT images) are created automatically in EstimateModel by SPM. They need to be connected directly to a data output module

The following lines automatically inform SPM to create a default set of contrats for a factorial design.

# Level1Design - Generates an SPM design matrix
level1design = Node(spm.Level1Design(bases={'hrf':{'derivs': [0,0]}}, # no derivatives
                                timing_units='secs',
                                interscan_interval=TR, 
                                microtime_onset=8, #The onset/time-bin in seconds for alignment
                                microtime_resolution=16, #Number of time-bins per scan in secs
                                mask_threshold=0.8,
                                global_intensity_normalization='none',
                                volterra_expansion_order=1, #do not model interactions
                                model_serial_correlations='AR(1)'), # serial correlations --> autoregressive AR(1) model during Classical (ReML) parameter estimation
                                name='level1design')

if Version(NIPYPE_VERSION) > Version("1.8.6"):
# Factors need to match conditions: product of levels (here 6) needs to match number of condition names --> F1, F2, U1, U2, S1, S2
    level1design.inputs.factor_info = [dict(name = 'Face', levels = 3),
                                        dict(name = 'Rep', levels = 2)]
                            
wf.connect(modelspec,'session_info', level1design, 'session_info')

stty: 'standard input': Inappropriate ioctl for device

# EstimateModel - estimate the parameters of the model 
level1estimate = Node(spm.EstimateModel(estimation_method={'Classical':1}), 
                                   name='level1estimate')

wf.connect(level1design, 'spm_mat_file', level1estimate, 'spm_mat_file')

Specify GLM contrast for nipype<=1.8.6

Contrasts need to be set up manually as they are not created automatically in EstimateModel when factor_info parameter is used in Level1Design.

condition_names =   ['F1', 'F2', 'U1', 'U2', 'S1', 'S2'] #The condition names must match the names listed in the subjectinfo function described above.

cond1 = ('Positive effect of condition', 'T', condition_names, [1, 1, 1, 1, 1, 1])

# positive effect face
face1 = ('Positive effect of Face_1', 'T', condition_names, [1, 1, -1, -1, 0, 0])
face2 = ('Positive effect of Face_2', 'T', condition_names, [0, 0, 1, 1, -1, -1])

# rep1 > rep2
rep1 = ('Positive effect of Rep', 'T', condition_names, [1, -1, 1, -1, 1, -1])

# positive interaction face x rep
int1 = ('Positive interaction of Face x Rep1', 'T', condition_names, [1, -1, -1, 1, 0, 0])
int2 = ('Positive interaction of Face x Rep2', 'T', condition_names, [0, 0, 1, -1, -1, 1])

contf1 = ['Average effect condition', 'F', [cond1]]
contf2 = ['Main effect Face', 'F', [face1, face2]]
contf3 = ['Main effect Rep', 'F', [rep1]]
contf4 = ['Interaction: Face x Rep', 'F', [int1, int2]]

contrasts = [contf1, contf2, contf3, contf4, cond1, face1, face2,  rep1,  int1, int2]

# EstimateContrast - explicit contrast estimation with nipype version <= 1.8.6 with the defined contrast list

if Version(NIPYPE_VERSION) <= Version("1.8.6"):
    level1conest = Node(spm.EstimateContrast(), 
                        name='level1conest')
    level1conest.inputs.contrasts = contrasts
    
                                                        
    wf.connect([(level1estimate, level1conest, [('spm_mat_file','spm_mat_file'),
                                                ('beta_images','beta_images'),
                                            ('residual_image','residual_image')])])
else:
    # NEW Nipype: contrasts already created in EstimateModel
    pass

Output stream

# save all results into one
datasink = Node(DataSink(), name='sinker')
datasink.inputs.base_directory=opj(experiment_dir, "level1_spm_results")

wf.connect(infosource, 'subject_id', datasink, 'container')

if Version(NIPYPE_VERSION)<= Version("1.8.6"):
    wf.connect([(level1conest, datasink,    [('spm_mat_file', '1stLevel.@spm_mat'),
                                            ('spmT_images', '1stLevel.@T'),
                                            ('con_images', '1stLevel.@con'),
                                            ('spmF_images', '1stLevel.@F'),
                                            ('ess_images', '1stLevel.@ess')]),
                                            ])
# starting in nipype version 1.8.7., when factor_info parameter is used in Level1Design T and F contrasts (ess*, con*, spmF* and spmT* images) 
# are created automatically by SPM in EstimateModel
else: 
    wf.connect(level1design, 'spm_mat_file', datasink, '1stLevel.@spm_mat')
    wf.connect([(level1estimate, datasink,  [
                                            ('spmT_images', '1stLevel.@T'),
                                            ('con_images', '1stLevel.@con'),
                                            ('spmF_images', '1stLevel.@F'),
                                            ('ess_images', '1stLevel.@ess')]),
                                            ])

subFolders = [('%s/1stLevel' % s, 'sub-%s/' % s) 
               for s in sub_list]

subFolders1 = [('_subject_id_%s'%(s), '')
              for s in sub_list]

subFolders.extend(subFolders1)
datasink.inputs.substitutions = subFolders

# Create 1st-level analysis output graph
wf.write_graph(graph2use='colored', format='png', simple_form=True)

# Visualize the graph
from IPython.display import Image
Image(filename=opj(wf.base_dir, wf.name, 'graph.png'))

260120-17:11:00,987 nipype.workflow INFO:
	 Generated workflow graph: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/graph.png (graph2use=colored, simple_form=True).

wf.run(plugin="MultiProc") #will use all CPUs

260120-17:11:06,791 nipype.workflow INFO:
	 Workflow level1_spm settings: ['check', 'execution', 'logging', 'monitoring']
260120-17:11:06,835 nipype.workflow INFO:
	 Running in parallel.
260120-17:11:06,845 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 9 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:11:07,580 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/datagrabber".
260120-17:11:07,580 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/datagrabber".
260120-17:11:07,589 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/datagrabber".
260120-17:11:07,593 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/datagrabber".
260120-17:11:07,590 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/datagrabber".
260120-17:11:07,592 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/datagrabber".
260120-17:11:07,593 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/datagrabber".
260120-17:11:07,593 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/datagrabber".
260120-17:11:07,608 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,593 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.datagrabber" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/datagrabber".
260120-17:11:07,625 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,625 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,625 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.013454s.
260120-17:11:07,627 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,626 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,633 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,634 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,634 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,648 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.00743s.
260120-17:11:07,648 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.006655s.
260120-17:11:07,649 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.013683s.
260120-17:11:07,649 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.008322s.
260120-17:11:07,661 nipype.workflow INFO:
	 [Node] Executing "datagrabber" <nipype.interfaces.io.DataGrabber>
260120-17:11:07,662 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.001866s.
260120-17:11:07,662 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.001884s.
260120-17:11:07,662 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.002271s.
260120-17:11:07,667 nipype.workflow INFO:
	 [Node] Finished "datagrabber", elapsed time 0.002164s.
260120-17:11:08,853 nipype.workflow INFO:
	 [Job 0] Completed (level1_spm.datagrabber).
260120-17:11:08,857 nipype.workflow INFO:
	 [Job 1] Completed (level1_spm.datagrabber).
260120-17:11:08,858 nipype.workflow INFO:
	 [Job 2] Completed (level1_spm.datagrabber).
260120-17:11:08,859 nipype.workflow INFO:
	 [Job 3] Completed (level1_spm.datagrabber).
260120-17:11:08,860 nipype.workflow INFO:
	 [Job 4] Completed (level1_spm.datagrabber).
260120-17:11:08,861 nipype.workflow INFO:
	 [Job 5] Completed (level1_spm.datagrabber).
260120-17:11:08,862 nipype.workflow INFO:
	 [Job 6] Completed (level1_spm.datagrabber).
260120-17:11:08,863 nipype.workflow INFO:
	 [Job 7] Completed (level1_spm.datagrabber).
260120-17:11:08,864 nipype.workflow INFO:
	 [Job 8] Completed (level1_spm.datagrabber).
260120-17:11:08,866 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 18 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:11:10,852 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 36 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:11:11,14 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,15 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,16 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,15 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,17 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,16 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,17 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,20 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,49 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,48 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,56 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,35 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,35 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,66 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,50 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,42 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,42 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,34 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,67 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,67 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,66 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,68 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,66 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,20 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,42 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,35 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,65 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,69 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,66 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,66 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,69 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,66 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,69 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,69 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:11,20 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,69 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:11,18 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,69 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:11,108 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,93 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,108 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,109 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,124 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,49 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:11,116 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,110 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,101 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,116 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,116 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,129 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,142 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,143 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,142 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,143 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,143 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,143 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,101 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,135 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,116 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,144 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,144 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:11,161 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,197 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,192 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:11,261 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.172949s.
260120-17:11:11,264 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.069493s.
260120-17:11:11,271 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.157013s.
260120-17:11:11,292 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.167421s.
260120-17:11:11,264 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.147364s.
260120-17:11:11,293 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.147664s.
260120-17:11:11,291 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.172071s.
260120-17:11:11,308 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.163338s.
260120-17:11:11,322 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.140532s.
260120-17:11:11,334 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.171405s.
260120-17:11:11,341 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.179897s.
260120-17:11:11,350 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.163382s.
260120-17:11:11,362 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.206274s.
260120-17:11:11,411 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.220396s.
260120-17:11:11,416 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.167757s.
260120-17:11:11,426 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.17153s.
260120-17:11:12,859 nipype.workflow INFO:
	 [Job 65] Completed (_getsubjectinfo0).
260120-17:11:12,875 nipype.workflow INFO:
	 [Job 66] Completed (_getsubjectinfo1).
260120-17:11:12,880 nipype.workflow INFO:
	 [Job 69] Completed (_getsubjectinfo0).
260120-17:11:12,881 nipype.workflow INFO:
	 [Job 70] Completed (_getsubjectinfo1).
260120-17:11:12,882 nipype.workflow INFO:
	 [Job 73] Completed (_getsubjectinfo0).
260120-17:11:12,883 nipype.workflow INFO:
	 [Job 74] Completed (_getsubjectinfo1).
260120-17:11:12,884 nipype.workflow INFO:
	 [Job 77] Completed (_getsubjectinfo0).
260120-17:11:12,885 nipype.workflow INFO:
	 [Job 78] Completed (_getsubjectinfo1).
260120-17:11:12,886 nipype.workflow INFO:
	 [Job 81] Completed (_getsubjectinfo0).
260120-17:11:12,887 nipype.workflow INFO:
	 [Job 82] Completed (_getsubjectinfo1).
260120-17:11:12,888 nipype.workflow INFO:
	 [Job 85] Completed (_getsubjectinfo0).
260120-17:11:12,889 nipype.workflow INFO:
	 [Job 86] Completed (_getsubjectinfo1).
260120-17:11:12,890 nipype.workflow INFO:
	 [Job 89] Completed (_getsubjectinfo0).
260120-17:11:12,895 nipype.workflow INFO:
	 [Job 90] Completed (_getsubjectinfo1).
260120-17:11:12,900 nipype.workflow INFO:
	 [Job 93] Completed (_getsubjectinfo0).
260120-17:11:12,905 nipype.workflow INFO:
	 [Job 94] Completed (_getsubjectinfo1).
260120-17:11:12,916 nipype.workflow INFO:
	 [MultiProc] Running 16 tasks, and 12 jobs ready. Free memory (GB): 216.28/219.48, Free processors: 16/32, Free GPU slot:0/0.
                     Currently running:
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
260120-17:11:13,778 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,772 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,807 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,807 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/gunzip_func/mapflow/_gunzip_func0".
260120-17:11:13,807 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,822 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/gunzip_func/mapflow/_gunzip_func1".
260120-17:11:13,822 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,829 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:13,829 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,841 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,842 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:13,843 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo0" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:13,847 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:13,842 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func0" <nipype.algorithms.misc.Gunzip>
260120-17:11:13,848 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,848 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:13,848 nipype.workflow INFO:
	 [Node] Executing "_getsubjectinfo1" <nipype.interfaces.utility.wrappers.Function>
260120-17:11:13,848 nipype.workflow INFO:
	 [Node] Executing "_gunzip_func1" <nipype.algorithms.misc.Gunzip>
260120-17:11:13,855 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:13,850 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,849 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:13,867 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:13,861 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:13,861 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:13,891 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,898 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,894 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:13,894 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:13,894 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,907 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:13,906 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,912 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,912 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:13,912 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:13,925 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:13,930 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:13,929 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:13,912 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,929 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:13,929 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:13,930 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:13,937 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:13,941 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:13,948 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:13,956 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:13,958 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:13,930 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:13,947 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo0", elapsed time 0.080316s.
260120-17:11:13,962 nipype.workflow INFO:
	 [Node] Finished "_getsubjectinfo1", elapsed time 0.094683s.
260120-17:11:13,972 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:13,978 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:13,976 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:13,981 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:14,1 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:14,10 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:14,21 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:14,31 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:14,37 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:14,860 nipype.workflow INFO:
	 [Job 10] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,864 nipype.workflow INFO:
	 [Job 12] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,865 nipype.workflow INFO:
	 [Job 14] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,866 nipype.workflow INFO:
	 [Job 16] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,872 nipype.workflow INFO:
	 [Job 18] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,873 nipype.workflow INFO:
	 [Job 20] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,878 nipype.workflow INFO:
	 [Job 22] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,884 nipype.workflow INFO:
	 [Job 24] Completed (level1_spm.getsubjectinfo).
260120-17:11:14,885 nipype.workflow INFO:
	 [Job 97] Completed (_getsubjectinfo0).
260120-17:11:14,885 nipype.workflow INFO:
	 [Job 98] Completed (_getsubjectinfo1).
260120-17:11:14,890 nipype.workflow INFO:
	 [MultiProc] Running 18 tasks, and 1 jobs ready. Free memory (GB): 215.88/219.48, Free processors: 14/32, Free GPU slot:0/0.
                     Currently running:
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
260120-17:11:16,472 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/getsubjectinfo/mapflow/_getsubjectinfo0".
260120-17:11:16,525 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo0" - collecting precomputed outputs
260120-17:11:16,528 nipype.workflow INFO:
	 [Node] "_getsubjectinfo0" found cached.
260120-17:11:16,544 nipype.workflow INFO:
	 [Node] Setting-up "_getsubjectinfo1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/getsubjectinfo/mapflow/_getsubjectinfo1".
260120-17:11:16,566 nipype.workflow INFO:
	 [Node] Cached "_getsubjectinfo1" - collecting precomputed outputs
260120-17:11:16,578 nipype.workflow INFO:
	 [Node] "_getsubjectinfo1" found cached.
260120-17:11:16,866 nipype.workflow INFO:
	 [Job 26] Completed (level1_spm.getsubjectinfo).
260120-17:11:16,881 nipype.workflow INFO:
	 [MultiProc] Running 18 tasks, and 0 jobs ready. Free memory (GB): 215.88/219.48, Free processors: 14/32, Free GPU slot:0/0.
                     Currently running:
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
260120-17:11:36,8 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 24.85264s.
260120-17:11:36,897 nipype.workflow INFO:
	 [Job 91] Completed (_gunzip_func0).
260120-17:11:36,905 nipype.workflow INFO:
	 [MultiProc] Running 17 tasks, and 0 jobs ready. Free memory (GB): 216.08/219.48, Free processors: 15/32, Free GPU slot:0/0.
                     Currently running:
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
260120-17:11:59,654 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 48.462505s.
260120-17:11:59,696 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 48.513712s.
260120-17:12:00,518 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 49.408359s.
260120-17:12:00,698 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 49.585901s.
260120-17:12:00,915 nipype.workflow INFO:
	 [Job 67] Completed (_gunzip_func0).
260120-17:12:00,919 nipype.workflow INFO:
	 [Job 68] Completed (_gunzip_func1).
260120-17:12:00,920 nipype.workflow INFO:
	 [Job 71] Completed (_gunzip_func0).
260120-17:12:00,921 nipype.workflow INFO:
	 [Job 72] Completed (_gunzip_func1).
260120-17:12:00,932 nipype.workflow INFO:
	 [MultiProc] Running 13 tasks, and 2 jobs ready. Free memory (GB): 216.88/219.48, Free processors: 19/32, Free GPU slot:0/0.
                     Currently running:
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
                       * _gunzip_func1
                       * _gunzip_func0
260120-17:12:00,918 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 49.713511s.
260120-17:12:01,182 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 50.01816s.
260120-17:12:01,210 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 50.085481s.
260120-17:12:01,505 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:01,534 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:01,533 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 50.351127s.
260120-17:12:01,557 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:01,557 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:01,561 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:01,566 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:01,575 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:01,576 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:01,602 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:01,603 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:01,603 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:01,604 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:01,684 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 50.5353s.
260120-17:12:01,870 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 50.680639s.
260120-17:12:01,905 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 50.814732s.
260120-17:12:02,262 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 48.389936s.
260120-17:12:02,307 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 48.424079s.
260120-17:12:02,341 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 51.11562s.
260120-17:12:02,513 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func0", elapsed time 51.339986s.
260120-17:12:02,683 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 51.540656s.
260120-17:12:02,909 nipype.workflow INFO:
	 [Job 63] Completed (_gunzip_func0).
260120-17:12:02,911 nipype.workflow INFO:
	 [Job 64] Completed (_gunzip_func1).
260120-17:12:02,912 nipype.workflow INFO:
	 [Job 75] Completed (_gunzip_func0).
260120-17:12:02,912 nipype.workflow INFO:
	 [Job 76] Completed (_gunzip_func1).
260120-17:12:02,913 nipype.workflow INFO:
	 [Job 79] Completed (_gunzip_func0).
260120-17:12:02,914 nipype.workflow INFO:
	 [Job 80] Completed (_gunzip_func1).
260120-17:12:02,915 nipype.workflow INFO:
	 [Job 83] Completed (_gunzip_func0).
260120-17:12:02,916 nipype.workflow INFO:
	 [Job 84] Completed (_gunzip_func1).
260120-17:12:02,916 nipype.workflow INFO:
	 [Job 87] Completed (_gunzip_func0).
260120-17:12:02,917 nipype.workflow INFO:
	 [Job 92] Completed (_gunzip_func1).
260120-17:12:02,917 nipype.workflow INFO:
	 [Job 95] Completed (_gunzip_func0).
260120-17:12:02,918 nipype.workflow INFO:
	 [Job 96] Completed (_gunzip_func1).
260120-17:12:02,919 nipype.workflow INFO:
	 [Job 11] Completed (level1_spm.gunzip_func).
260120-17:12:02,920 nipype.workflow INFO:
	 [Job 13] Completed (level1_spm.gunzip_func).
260120-17:12:02,921 nipype.workflow INFO:
	 [MultiProc] Running 1 tasks, and 8 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32, Free GPU slot:0/0.
                     Currently running:
                       * _gunzip_func1
260120-17:12:02,982 nipype.workflow INFO:
	 [Node] Finished "_gunzip_func1", elapsed time 51.851894s.
260120-17:12:03,182 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:03,201 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:03,204 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:03,201 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/modelspec".
260120-17:12:03,206 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:03,205 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:03,204 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/modelspec".
260120-17:12:03,207 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:03,208 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:03,215 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:03,229 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:03,229 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:03,229 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:03,229 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:03,233 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:03,233 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:03,234 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:03,234 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:03,235 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:03,234 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:03,235 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:03,233 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:03,236 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:03,236 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:03,237 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:03,237 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:03,238 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:03,238 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:03,239 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:03,239 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:03,238 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:03,240 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:03,240 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:03,240 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:03,241 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:03,241 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:03,241 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:03,252 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:03,252 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:03,255 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:03,286 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.04517s.
260120-17:12:03,289 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.020469s.
260120-17:12:04,909 nipype.workflow INFO:
	 [Job 88] Completed (_gunzip_func1).
260120-17:12:04,911 nipype.workflow INFO:
	 [Job 9] Completed (level1_spm.gunzip_func).
260120-17:12:04,912 nipype.workflow INFO:
	 [Job 15] Completed (level1_spm.gunzip_func).
260120-17:12:04,913 nipype.workflow INFO:
	 [Job 17] Completed (level1_spm.gunzip_func).
260120-17:12:04,914 nipype.workflow INFO:
	 [Job 19] Completed (level1_spm.gunzip_func).
260120-17:12:04,915 nipype.workflow INFO:
	 [Job 23] Completed (level1_spm.gunzip_func).
260120-17:12:04,916 nipype.workflow INFO:
	 [Job 25] Completed (level1_spm.gunzip_func).
260120-17:12:04,917 nipype.workflow INFO:
	 [Job 28] Completed (level1_spm.modelspec).
260120-17:12:04,918 nipype.workflow INFO:
	 [Job 29] Completed (level1_spm.modelspec).
260120-17:12:04,920 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 9 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:12:05,101 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/modelspec".
260120-17:12:05,101 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/modelspec".
260120-17:12:05,113 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/modelspec".
260120-17:12:05,125 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/modelspec".
260120-17:12:05,131 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/modelspec".
260120-17:12:05,132 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/level1design".
260120-17:12:05,139 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/level1design".
260120-17:12:05,113 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/modelspec".
260120-17:12:05,139 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/gunzip_func/mapflow/_gunzip_func0".
260120-17:12:05,148 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func0" - collecting precomputed outputs
260120-17:12:05,156 nipype.workflow INFO:
	 [Node] "_gunzip_func0" found cached.
260120-17:12:05,173 nipype.workflow INFO:
	 [Node] Setting-up "_gunzip_func1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/gunzip_func/mapflow/_gunzip_func1".
260120-17:12:05,173 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:05,177 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:05,175 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:05,179 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:05,179 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:05,180 nipype.workflow INFO:
	 [Node] Cached "_gunzip_func1" - collecting precomputed outputs
260120-17:12:05,180 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:05,182 nipype.workflow INFO:
	 [Node] "_gunzip_func1" found cached.
260120-17:12:05,190 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.013804s.
260120-17:12:05,201 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.019914s.
260120-17:12:05,207 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.019238s.
260120-17:12:05,207 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.020401s.
260120-17:12:05,208 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.026315s.
260120-17:12:05,209 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.02539s.
260120-17:12:05,215 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:05,217 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:06,915 nipype.workflow INFO:
	 [Job 21] Completed (level1_spm.gunzip_func).
260120-17:12:06,923 nipype.workflow INFO:
	 [Job 27] Completed (level1_spm.modelspec).
260120-17:12:06,925 nipype.workflow INFO:
	 [Job 30] Completed (level1_spm.modelspec).
260120-17:12:06,926 nipype.workflow INFO:
	 [Job 31] Completed (level1_spm.modelspec).
260120-17:12:06,927 nipype.workflow INFO:
	 [Job 32] Completed (level1_spm.modelspec).
260120-17:12:06,928 nipype.workflow INFO:
	 [Job 34] Completed (level1_spm.modelspec).
260120-17:12:06,929 nipype.workflow INFO:
	 [Job 35] Completed (level1_spm.modelspec).
260120-17:12:06,941 nipype.workflow INFO:
	 [MultiProc] Running 2 tasks, and 7 jobs ready. Free memory (GB): 219.08/219.48, Free processors: 30/32, Free GPU slot:0/0.
                     Currently running:
                       * level1_spm.level1design
                       * level1_spm.level1design
260120-17:12:07,165 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.modelspec" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/modelspec".
260120-17:12:07,165 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/level1design".
260120-17:12:07,165 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/level1design".
260120-17:12:07,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/level1design".
260120-17:12:07,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/level1design".
260120-17:12:07,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/level1design".
260120-17:12:07,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/level1design".
260120-17:12:07,180 nipype.workflow INFO:
	 [Node] Executing "modelspec" <nipype.algorithms.modelgen.SpecifySPMModel>
260120-17:12:07,213 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:07,213 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:07,213 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:07,213 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:07,214 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:07,220 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:07,220 nipype.workflow INFO:
	 [Node] Finished "modelspec", elapsed time 0.031969s.
260120-17:12:08,915 nipype.workflow INFO:
	 [Job 33] Completed (level1_spm.modelspec).
260120-17:12:08,934 nipype.workflow INFO:
	 [MultiProc] Running 8 tasks, and 1 jobs ready. Free memory (GB): 217.88/219.48, Free processors: 24/32, Free GPU slot:0/0.
                     Currently running:
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
260120-17:12:09,397 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1design" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/level1design".
260120-17:12:09,473 nipype.workflow INFO:
	 [Node] Executing "level1design" <nipype.interfaces.spm.model.Level1Design>
260120-17:12:10,916 nipype.workflow INFO:
	 [MultiProc] Running 9 tasks, and 0 jobs ready. Free memory (GB): 217.68/219.48, Free processors: 23/32, Free GPU slot:0/0.
                     Currently running:
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
                       * level1_spm.level1design
260120-17:13:30,7 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 82.784824s.
260120-17:13:30,7 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 82.786052s.
260120-17:13:30,12 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 84.785761s.
260120-17:13:30,75 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 82.854316s.
260120-17:13:30,103 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 84.883687s.
260120-17:13:30,112 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 80.623335s.
260120-17:13:30,136 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 82.915011s.
260120-17:13:30,147 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 82.925525s.
260120-17:13:30,516 nipype.workflow INFO:
	 [Node] Finished "level1design", elapsed time 83.294683s.
260120-17:13:30,987 nipype.workflow INFO:
	 [Job 37] Completed (level1_spm.level1design).
260120-17:13:30,988 nipype.workflow INFO:
	 [Job 38] Completed (level1_spm.level1design).
260120-17:13:30,989 nipype.workflow INFO:
	 [Job 36] Completed (level1_spm.level1design).
260120-17:13:30,990 nipype.workflow INFO:
	 [Job 39] Completed (level1_spm.level1design).
260120-17:13:30,991 nipype.workflow INFO:
	 [Job 40] Completed (level1_spm.level1design).
260120-17:13:30,993 nipype.workflow INFO:
	 [Job 41] Completed (level1_spm.level1design).
260120-17:13:30,994 nipype.workflow INFO:
	 [Job 43] Completed (level1_spm.level1design).
260120-17:13:30,995 nipype.workflow INFO:
	 [Job 44] Completed (level1_spm.level1design).
260120-17:13:30,996 nipype.workflow INFO:
	 [Job 42] Completed (level1_spm.level1design).
260120-17:13:30,997 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 9 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:13:31,158 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/level1estimate".
260120-17:13:31,157 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/level1estimate".
260120-17:13:31,165 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/level1estimate".
260120-17:13:31,164 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/level1estimate".
260120-17:13:31,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/level1estimate".
260120-17:13:31,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/level1estimate".
260120-17:13:31,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/level1estimate".
260120-17:13:31,166 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/level1estimate".
260120-17:13:31,167 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.level1estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/level1estimate".
260120-17:13:31,208 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,209 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,214 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,214 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,215 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,214 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,214 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,221 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:31,228 nipype.workflow INFO:
	 [Node] Executing "level1estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:13:32,988 nipype.workflow INFO:
	 [MultiProc] Running 9 tasks, and 0 jobs ready. Free memory (GB): 217.68/219.48, Free processors: 23/32, Free GPU slot:0/0.
                     Currently running:
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
                       * level1_spm.level1estimate
260120-17:16:47,321 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 196.091498s.
260120-17:16:48,274 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.052117s.
260120-17:16:48,283 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.053127s.
260120-17:16:48,306 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.076599s.
260120-17:16:48,306 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.083802s.
260120-17:16:48,307 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.078842s.
260120-17:16:48,308 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.079299s.
260120-17:16:48,315 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.084413s.
260120-17:16:48,342 nipype.workflow INFO:
	 [Node] Finished "level1estimate", elapsed time 197.114185s.
260120-17:16:49,149 nipype.workflow INFO:
	 [Job 45] Completed (level1_spm.level1estimate).
260120-17:16:49,151 nipype.workflow INFO:
	 [Job 46] Completed (level1_spm.level1estimate).
260120-17:16:49,152 nipype.workflow INFO:
	 [Job 47] Completed (level1_spm.level1estimate).
260120-17:16:49,153 nipype.workflow INFO:
	 [Job 48] Completed (level1_spm.level1estimate).
260120-17:16:49,154 nipype.workflow INFO:
	 [Job 49] Completed (level1_spm.level1estimate).
260120-17:16:49,155 nipype.workflow INFO:
	 [Job 50] Completed (level1_spm.level1estimate).
260120-17:16:49,160 nipype.workflow INFO:
	 [Job 51] Completed (level1_spm.level1estimate).
260120-17:16:49,161 nipype.workflow INFO:
	 [Job 52] Completed (level1_spm.level1estimate).
260120-17:16:49,162 nipype.workflow INFO:
	 [Job 53] Completed (level1_spm.level1estimate).
260120-17:16:49,164 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 9 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:16:49,376 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_02/sinker".
260120-17:16:49,382 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_03/sinker".
260120-17:16:49,376 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_01/sinker".
260120-17:16:49,408 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_04/sinker".
260120-17:16:49,411 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_07/sinker".
260120-17:16:49,408 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_05/sinker".
260120-17:16:49,412 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_08/sinker".
260120-17:16:49,416 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_06/sinker".
260120-17:16:49,429 nipype.workflow INFO:
	 [Node] Setting-up "level1_spm.sinker" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm/_subject_id_09/sinker".
260120-17:16:49,494 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,507 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,513 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/03/1stLevel/_subject_id_03/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-03///SPM.mat
260120-17:16:49,514 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,519 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/03/1stLevel/_subject_id_03/spmT_0005.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-03///spmT_0005.nii
260120-17:16:49,525 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/01/1stLevel/_subject_id_01/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-01///SPM.mat
260120-17:16:49,525 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,525 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,525 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/03/1stLevel/_subject_id_03/spmT_0006.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-03///spmT_0006.nii
260120-17:16:49,526 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/07/1stLevel/_subject_id_07/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-07///SPM.mat
260120-17:16:49,526 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,533 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,538 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/01/1stLevel/_subject_id_01/spmT_0005.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-01///spmT_0005.nii
260120-17:16:49,519 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,538 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/03/1stLevel/_subject_id_03/spmT_0007.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-03///spmT_0007.nii
260120-17:16:49,538 nipype.workflow INFO:
	 [Node] Executing "sinker" <nipype.interfaces.io.DataSink>
260120-17:16:49,539 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/08/1stLevel/_subject_id_08/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-08///SPM.mat
260120-17:16:49,539 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/07/1stLevel/_subject_id_07/spmT_0005.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-07///spmT_0005.nii
260120-17:16:49,539 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/01/1stLevel/_subject_id_01/spmT_0006.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-01///spmT_0006.nii
260120-17:16:49,540 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/03/1stLevel/_subject_id_03/spmT_0008.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-03///spmT_0008.nii
260120-17:16:49,541 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/01/1stLevel/_subject_id_01/spmT_0007.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-01///spmT_0007.nii
260120-17:16:49,540 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/07/1stLevel/_subject_id_07/spmT_0006.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-07///spmT_0006.nii
260120-17:16:49,540 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/09/1stLevel/_subject_id_09/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-09///SPM.mat
260120-17:16:49,549 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/08/1stLevel/_subject_id_08/spmT_0005.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-08///spmT_0005.nii
260120-17:16:49,550 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/07/1stLevel/_subject_id_07/spmT_0007.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-07///spmT_0007.nii
260120-17:16:49,550 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/01/1stLevel/_subject_id_01/spmT_0008.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-01///spmT_0008.nii
260120-17:16:49,549 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/05/1stLevel/_subject_id_05/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-05///SPM.mat
260120-17:16:49,549 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/03/1stLevel/_subject_id_03/spmT_0009.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level1_spm_results/sub-03///spmT_0009.nii
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<networkx.classes.digraph.DiGraph at 0x7f37ed820910>

Visualize design matrix and list contrasts

# Load
spm_data = loadmat(opj(experiment_dir, 'level1_spm_results/sub-01/SPM.mat'), 
                    struct_as_record=False, squeeze_me=True)

SPM = spm_data['SPM']

# Exract Data
designMatrix = SPM.xX.X
names = SPM.xX.name

# Get contrast name
if hasattr(SPM, 'xCon'):
    # xCon can be single object or list
    if isinstance(SPM.xCon, np.ndarray):
        names_contrast = [con.name for con in SPM.xCon]
    else:
        names_contrast = [SPM.xCon.name]
else:
    names_contrast = []

# Plot
normed_design = designMatrix / np.abs(designMatrix).max(axis=0)

fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(normed_design, aspect='auto', cmap='gray')
ax.set_ylabel('Volume id')
ax.set_xticks(np.arange(len(names)))
ax.set_xticklabels(names, rotation=90)
plt.tight_layout()
plt.show()

2. Second Level Analysis

For a factorial design with 2 factors there are 4 effects to test for: an overall effect, 2 main effects and one two-way interaction:

• To test (1) the overall effect, use a [1 1 1 1 1 1] contrast for each subject and take the resulting con images of all subjects into a one-sample t-test at the second level. Then specify a [1] F-contrast (at the second level) to test for significantly non-zero BOLD responses related to the paradigm.

• To test for (2) the main effect of Factor Repetition (two levels), use a [1 -1 1 -1 1 -1] contrast for each subject and take the resulting con images into a one-sample t-test at the second level.

• To test for (3) the main effect of Factor Face (three levels), use two contrasts per subject [1 1 -1 -1 0 0] and [0 0 1 1 -1 -1] and take all resulting con images (two per subject) into a two-sample t-test design at the second level. Then, use a [1 0; 0 1] F-contrast to test for this main effect.

• To test for (4) the interaction between Factors Face and Rep, use two contrasts per subject [1 -1 -1 1 0 0] and [0 0 1 -1 -1 1] and take all resulting con images (two per subject) into a two-sample t-test design at the second level. Use then a [1 0; 0 1] F-contrast to test for this interaction effect.

2.1 One Sample T-Test: Overall effect, main effect of repetition

Test for significantly non-zero BOLD responses over all subjects.

• con_0005: Positive effect

• con_0006: Positive Effect F>S

• con_0007: Positive Effect S>U

• con_0008: Positive Effect of rep1>rep2

• con_0009: Positive Interaction Face (F/S) x Rep

• con_0010: Positive Interaction Face (S/U) x Rep

wf_2ndlevel_onesample = Workflow(name='level2_spm_1sample', base_dir=experiment_dir)
wf_2ndlevel_onesample.config["execution"]["crashfile_format"] = "txt"

contrast_id = [5, 6, 7, 8, 9, 10] #contrasts con_0005 to con_0010

l2source = Node(DataGrabber(infields= ['con'], outfields=['contrasts']), name='l2source')

l2source.inputs.sort_filelist = True
l2source.inputs.base_directory = opj(experiment_dir, 'level1_spm_results')
l2source.inputs.template = '*'
l2source.inputs.field_template = dict(
   contrasts = '*/con_%04d.nii'
)

# iterate over all contrast images
l2source.iterables = [('con', contrast_id)]

# OneSampleTTest Design
onesamplettestdes = Node(interface=spm.OneSampleTTestDesign(), name="onesampttestdes")

wf_2ndlevel_onesample.connect([(l2source, onesamplettestdes, [('contrasts', 'in_files')])])

# EstimateModel - estimates the model
l2estimate = Node(spm.EstimateModel(estimation_method={'Classical':1}), name='level2estimate')

# EstimateContast - estimates group contrast
l2conestimate = Node(spm.EstimateContrast(group_contrast=True), name = 'level2conestimate')

con_1= ['Group', 'T', ['mean'], [1]]
#con_2= ['Group', 'F', [con_1]] # if an F contrast is also wanted

l2conestimate.inputs.contrasts = [con_1] # con_2, include in list if wanted

# Threshold - thresholds contrasts
level2thresh = Node(spm.Threshold(contrast_index=1,# which contrast in the SPM.mat to use --> here set for con_1: T stat
                                use_topo_fdr=True, # whether to use FDR over cluster extent probabilities
                                use_fwe_correction=False, # whether to use FWE (Bonferroni) correction for initial threshold 
                                extent_threshold=0, # minimum cluster size in voxels
                                height_threshold=0.005, # value for initial thresholding (defining clusters) - voxelwise
                                height_threshold_type='p-value',
                                extent_fdr_p_threshold=0.05), # p threshold on FDR corrected cluster size probabilities
                                name='level2thresh')

wf_2ndlevel_onesample.connect([(onesamplettestdes, l2estimate, [('spm_mat_file', 'spm_mat_file')]),
                    (l2estimate, l2conestimate, [('spm_mat_file', 'spm_mat_file'),
                                                ('beta_images', 'beta_images'),
                                                ('residual_image', 'residual_image')]),
                    (l2conestimate, level2thresh, [('spm_mat_file', 'spm_mat_file'),
                                                    ('spmT_images', 'stat_image')])

                    ])

datasink_2nd = Node(DataSink(), name='datasink_2nd')
datasink_2nd.inputs.base_directory=opj(experiment_dir, 'level2_spm_results_1sample')

wf_2ndlevel_onesample.connect([(l2conestimate, datasink_2nd, [('spm_mat_file', '2ndLevel.@spm_mat'),
                                                    ('spmT_images', '2ndLevel.@T'),
                                                    ('con_images', '2ndLevel.@con')]),
                    (level2thresh, datasink_2nd, [('thresholded_map',
                                                '2ndLevel.@threshold')])                               
                    ])

#replace _con_ with con
subFolders = [('2ndLevel/', '')] 

subFolders1 = [('_con_', 'con')] 
subFolders.extend(subFolders1)

datasink_2nd.inputs.substitutions = subFolders

from IPython.display import Image
wf_2ndlevel_onesample.write_graph(graph2use='colored', format='png', simple_form=True)

Image(filename=opj(wf_2ndlevel_onesample.base_dir, wf_2ndlevel_onesample.name, 'graph.png'))

260120-17:30:53,999 nipype.workflow INFO:
	 Generated workflow graph: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/graph.png (graph2use=colored, simple_form=True).

wf_2ndlevel_onesample.run(plugin="MultiProc")

260120-17:30:54,30 nipype.workflow INFO:
	 Workflow level2_spm_1sample settings: ['check', 'execution', 'logging', 'monitoring']
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	 Running in parallel.
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260120-17:30:56,790 nipype.workflow INFO:
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260120-17:30:56,806 nipype.workflow INFO:
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	 [MultiProc] Running 6 tasks, and 0 jobs ready. Free memory (GB): 218.28/219.48, Free processors: 26/32, Free GPU slot:0/0.
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260120-17:31:48,291 nipype.workflow INFO:
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260120-17:31:48,322 nipype.workflow INFO:
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	 [MultiProc] Running 6 tasks, and 0 jobs ready. Free memory (GB): 218.28/219.48, Free processors: 26/32, Free GPU slot:0/0.
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	 [MultiProc] Running 6 tasks, and 0 jobs ready. Free memory (GB): 218.28/219.48, Free processors: 26/32, Free GPU slot:0/0.
                     Currently running:
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260120-17:32:58,122 nipype.workflow INFO:
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260120-17:32:58,125 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 6 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:32:58,341 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.level2thresh" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_6/level2thresh".
260120-17:32:58,338 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.level2thresh" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_5/level2thresh".
260120-17:32:58,351 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.level2thresh" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_7/level2thresh".
260120-17:32:58,351 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.level2thresh" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_8/level2thresh".
260120-17:32:58,354 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.level2thresh" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_10/level2thresh".
260120-17:32:58,354 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.level2thresh" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_9/level2thresh".
260120-17:32:58,374 nipype.workflow INFO:
	 [Node] Executing "level2thresh" <nipype.interfaces.spm.model.Threshold>
260120-17:32:58,374 nipype.workflow INFO:
	 [Node] Executing "level2thresh" <nipype.interfaces.spm.model.Threshold>
260120-17:32:58,374 nipype.workflow INFO:
	 [Node] Executing "level2thresh" <nipype.interfaces.spm.model.Threshold>
260120-17:32:58,375 nipype.workflow INFO:
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260120-17:32:58,375 nipype.workflow INFO:
	 [Node] Executing "level2thresh" <nipype.interfaces.spm.model.Threshold>
260120-17:32:58,381 nipype.workflow INFO:
	 [Node] Executing "level2thresh" <nipype.interfaces.spm.model.Threshold>
260120-17:33:00,124 nipype.workflow INFO:
	 [MultiProc] Running 6 tasks, and 0 jobs ready. Free memory (GB): 218.28/219.48, Free processors: 26/32, Free GPU slot:0/0.
                     Currently running:
                       * level2_spm_1sample.level2thresh
                       * level2_spm_1sample.level2thresh
                       * level2_spm_1sample.level2thresh
                       * level2_spm_1sample.level2thresh
                       * level2_spm_1sample.level2thresh
                       * level2_spm_1sample.level2thresh
260120-17:33:17,503 nipype.workflow INFO:
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260120-17:33:18,136 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 6 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:33:18,355 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.datasink_2nd" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_7/datasink_2nd".
260120-17:33:18,355 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.datasink_2nd" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_8/datasink_2nd".
260120-17:33:18,353 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.datasink_2nd" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_6/datasink_2nd".
260120-17:33:18,337 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.datasink_2nd" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_5/datasink_2nd".
260120-17:33:18,363 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.datasink_2nd" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_9/datasink_2nd".
260120-17:33:18,363 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_1sample.datasink_2nd" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_1sample/_con_10/datasink_2nd".
260120-17:33:18,376 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd" <nipype.interfaces.io.DataSink>
260120-17:33:18,377 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd" <nipype.interfaces.io.DataSink>
260120-17:33:18,387 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_7/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con7/SPM.mat
260120-17:33:18,387 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_8/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con8/SPM.mat
260120-17:33:18,389 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd" <nipype.interfaces.io.DataSink>
260120-17:33:18,389 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_8/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con8/spmT_0001.nii
260120-17:33:18,389 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_7/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con7/spmT_0001.nii
260120-17:33:18,389 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd" <nipype.interfaces.io.DataSink>
260120-17:33:18,389 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd" <nipype.interfaces.io.DataSink>
260120-17:33:18,389 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd" <nipype.interfaces.io.DataSink>
260120-17:33:18,390 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_8/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con8/con_0001.nii
260120-17:33:18,390 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_7/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con7/con_0001.nii
260120-17:33:18,393 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_9/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con9/SPM.mat
260120-17:33:18,392 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_6/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con6/SPM.mat
260120-17:33:18,393 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_5/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con5/SPM.mat
260120-17:33:18,393 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_8/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con8/spmT_0001_thr.nii
260120-17:33:18,393 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_10/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con10/SPM.mat
260120-17:33:18,394 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_7/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con7/spmT_0001_thr.nii
260120-17:33:18,395 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_5/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con5/spmT_0001.nii
260120-17:33:18,395 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_9/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con9/spmT_0001.nii
260120-17:33:18,395 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_10/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con10/spmT_0001.nii
260120-17:33:18,396 nipype.workflow INFO:
	 [Node] Finished "datasink_2nd", elapsed time 0.007889s.
260120-17:33:18,396 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_5/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con5/con_0001.nii
260120-17:33:18,395 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_6/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con6/spmT_0001.nii
260120-17:33:18,397 nipype.workflow INFO:
	 [Node] Finished "datasink_2nd", elapsed time 0.009197s.
260120-17:33:18,397 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_10/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con10/con_0001.nii
260120-17:33:18,397 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_9/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con9/con_0001.nii
260120-17:33:18,398 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_6/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con6/con_0001.nii
260120-17:33:18,398 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_5/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con5/spmT_0001_thr.nii
260120-17:33:18,399 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_9/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con9/spmT_0001_thr.nii
260120-17:33:18,400 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_6/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con6/spmT_0001_thr.nii
260120-17:33:18,402 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/2ndLevel/_con_10/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_1sample/con10/spmT_0001_thr.nii
260120-17:33:18,401 nipype.workflow INFO:
	 [Node] Finished "datasink_2nd", elapsed time 0.008206s.
260120-17:33:18,401 nipype.workflow INFO:
	 [Node] Finished "datasink_2nd", elapsed time 0.007552s.
260120-17:33:18,402 nipype.workflow INFO:
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260120-17:33:18,410 nipype.workflow INFO:
	 [Node] Finished "datasink_2nd", elapsed time 0.010894s.
260120-17:33:20,129 nipype.workflow INFO:
	 [Job 30] Completed (level2_spm_1sample.datasink_2nd).
260120-17:33:20,131 nipype.workflow INFO:
	 [Job 31] Completed (level2_spm_1sample.datasink_2nd).
260120-17:33:20,138 nipype.workflow INFO:
	 [Job 32] Completed (level2_spm_1sample.datasink_2nd).
260120-17:33:20,139 nipype.workflow INFO:
	 [Job 33] Completed (level2_spm_1sample.datasink_2nd).
260120-17:33:20,140 nipype.workflow INFO:
	 [Job 34] Completed (level2_spm_1sample.datasink_2nd).
260120-17:33:20,147 nipype.workflow INFO:
	 [Job 35] Completed (level2_spm_1sample.datasink_2nd).
260120-17:33:20,149 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.

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<networkx.classes.digraph.DiGraph at 0x7f37ed887950>

2.1 Two Sample T-Test: Main effect of face, Interaction Face x Repetition

Main effect of face: enter the following 2 contrasts per subject into a two-sample t-test and use 1 0, 0 1 F contrast

• con_0006: Positive Effect F>S

• con_0007: Positive Effect S>U

Interaction Face x Rep: enter the following 2 contrasts per subject into a two-sample t-test and use 1 0, 0 1 F contrast

• con_0009: Positive Interaction Face (F/S) x Rep

• con_0010: Positive Interaction Face (S/U) x Rep

wf_2ndlevel_twosample = Workflow(name='level2_spm_2sample', base_dir=experiment_dir)
wf_2ndlevel_twosample.config["execution"]["crashfile_format"] = "txt"

contrast_id_1 = [6] #con_0006
contrast_id_2 = [7] #con_0007 

l2source2 = Node(DataGrabber(outfields=["group_1", "group_2"]), name='l2source')

l2source2.inputs.sort_filelist = True
l2source2.inputs.contrast_id_1 = contrast_id_1
l2source2.inputs.contrast_id_2 = contrast_id_2
l2source2.inputs.base_directory = opj(experiment_dir, 'level1_spm_results')
l2source2.inputs.template = '*' 

l2source2.inputs.template_args = dict(
    group_1=[["contrast_id_1"]],
    group_2=[["contrast_id_2"]])

l2source2.inputs.field_template = dict(
    group_1 = "*/con_%04d.nii",
    group_2 ="*/con_%04d.nii", 
)

# SecondLevelDesign - TwoSampleTTestDesign bases Factorial Design
twosamplettestdes = Node(interface=spm.TwoSampleTTestDesign(), name="twosampttestdes")
twosamplettestdes.inputs.dependent = False # measurements dependent between levels
twosamplettestdes.inputs.unequal_variance = True # equal or unequal between groups

wf_2ndlevel_twosample.connect([(l2source2, twosamplettestdes, [('group_1', 'group1_files')]),
                             (l2source2, twosamplettestdes, [('group_2', 'group2_files')])])

l2estimate2 = Node(spm.EstimateModel(estimation_method={'Classical':1}), name='level2estimate')

# EstimateContast - estimates group contrast
l2conestimate2 = Node(spm.EstimateContrast(group_contrast=True), name = 'level2conestimate')

con_1 = ('Pos effect level 1','T', ['Group_{1}', 'Group_{2}'],[1, 0])
con_2 = ('Pos effect level 2','T', ['Group_{1}', 'Group_{2}'],[0, 1])

con_3 = ('Main effect', 'F', [con_1, con_2]) # main effect of face

l2conestimate2.inputs.contrasts = [con_1, con_2, con_3] 


# Threshold - thresholds contrasts
level2thresh2 = MapNode(spm.Threshold(contrast_index=3,# which contrast in the SPM.mat to use --> here set for con_3: main effect
                                use_topo_fdr=True, # whether to use FDR over cluster extent probabilities
                                use_fwe_correction=False, # whether to use FWE (Bonferroni) correction for initial threshold 
                                extent_threshold=0, # minimum cluster size in voxels
                                height_threshold=0.005, # value for initial thresholding (defining clusters) - voxelwise
                                height_threshold_type='p-value',
                                extent_fdr_p_threshold=0.05), # P threshold on FDR corrected cluster size probabilities
                                iterfield=['stat_image'],
                                name='level2thresh')

wf_2ndlevel_twosample.connect([(twosamplettestdes, l2estimate2, [('spm_mat_file', 'spm_mat_file')]),
                    (l2estimate2, l2conestimate2, [('spm_mat_file', 'spm_mat_file'),
                                                ('beta_images', 'beta_images'),
                                                ('residual_image', 'residual_image')]),
                    (l2conestimate2, level2thresh2, [('spm_mat_file', 'spm_mat_file'),
                                                    ('spmT_images', 'stat_image')])
                    ])

datasink_2nd_2 = Node(DataSink(), name='datasink_2nd_2')
datasink_2nd_2.inputs.base_directory=opj(experiment_dir, 'level2_spm_results_2sample')

wf_2ndlevel_twosample.connect([(l2conestimate2, datasink_2nd_2, [('spm_mat_file', '2ndLevel.@spm_mat'),
                                                    ('spmT_images', '2ndLevel.@T'),
                                                    ('con_images', '2ndLevel.@con')]),
                    (level2thresh2, datasink_2nd_2, [('thresholded_map',
                                                '2ndLevel.@threshold')])                                                 
                    ])

subFolders = [('2ndLevel/', 'MainEffectFace/')]
subFolders1 = [('_con_', 'con')] 
subFolders2 = [('_level2thresh0', 'thresh_con1')]
subFolders3 = [('_level2thresh1', 'thresh_con2')]
subFolders4 = [('_level2thresh2', 'thresh_con3')]

subFolders.extend(subFolders1)
subFolders.extend(subFolders2)
subFolders.extend(subFolders3)
subFolders.extend(subFolders4)

datasink_2nd_2.inputs.substitutions = subFolders

from IPython.display import Image
wf_2ndlevel_twosample.write_graph(graph2use='colored', format='png', simple_form=True)

Image(filename=opj(wf_2ndlevel_twosample.base_dir, wf_2ndlevel_twosample.name, 'graph.png'))

260120-17:33:23,265 nipype.workflow INFO:
	 Generated workflow graph: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/graph.png (graph2use=colored, simple_form=True).

wf_2ndlevel_twosample.run(plugin="MultiProc")

260120-17:33:23,280 nipype.workflow INFO:
	 Workflow level2_spm_2sample settings: ['check', 'execution', 'logging', 'monitoring']
260120-17:33:23,286 nipype.workflow INFO:
	 Running in parallel.
260120-17:33:23,288 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:33:23,981 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.l2source" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/l2source".
260120-17:33:23,997 nipype.workflow INFO:
	 [Node] Executing "l2source" <nipype.interfaces.io.DataGrabber>
260120-17:33:24,3 nipype.workflow INFO:
	 [Node] Finished "l2source", elapsed time 0.002632s.
260120-17:33:25,299 nipype.workflow INFO:
	 [Job 0] Completed (level2_spm_2sample.l2source).
260120-17:33:25,305 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:33:25,766 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.twosampttestdes" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/twosampttestdes".
260120-17:33:25,785 nipype.workflow INFO:
	 [Node] Executing "twosampttestdes" <nipype.interfaces.spm.model.TwoSampleTTestDesign>
260120-17:33:27,289 nipype.workflow INFO:
	 [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32, Free GPU slot:0/0.
                     Currently running:
                       * level2_spm_2sample.twosampttestdes
260120-17:33:47,49 nipype.workflow INFO:
	 [Node] Finished "twosampttestdes", elapsed time 21.261056s.
260120-17:33:47,295 nipype.workflow INFO:
	 [Job 1] Completed (level2_spm_2sample.twosampttestdes).
260120-17:33:47,297 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:33:47,498 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.level2estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2estimate".
260120-17:33:47,509 nipype.workflow INFO:
	 [Node] Executing "level2estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:33:49,299 nipype.workflow INFO:
	 [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32, Free GPU slot:0/0.
                     Currently running:
                       * level2_spm_2sample.level2estimate
260120-17:34:15,148 nipype.workflow INFO:
	 [Node] Finished "level2estimate", elapsed time 27.637034s.
260120-17:34:15,304 nipype.workflow INFO:
	 [Job 2] Completed (level2_spm_2sample.level2estimate).
260120-17:34:15,306 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:34:15,503 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.level2conestimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2conestimate".
260120-17:34:15,524 nipype.workflow INFO:
	 [Node] Executing "level2conestimate" <nipype.interfaces.spm.model.EstimateContrast>
260120-17:34:17,304 nipype.workflow INFO:
	 [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32, Free GPU slot:0/0.
                     Currently running:
                       * level2_spm_2sample.level2conestimate
260120-17:34:37,289 nipype.workflow INFO:
	 [Node] Finished "level2conestimate", elapsed time 21.761631s.
260120-17:34:37,306 nipype.workflow INFO:
	 [Job 3] Completed (level2_spm_2sample.level2conestimate).
260120-17:34:37,308 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:34:39,307 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:34:39,494 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh0".
260120-17:34:39,495 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh1".
260120-17:34:39,496 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh2" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh2".
260120-17:34:39,517 nipype.workflow INFO:
	 [Node] Executing "_level2thresh1" <nipype.interfaces.spm.model.Threshold>
260120-17:34:39,518 nipype.workflow INFO:
	 [Node] Executing "_level2thresh0" <nipype.interfaces.spm.model.Threshold>
260120-17:34:39,518 nipype.workflow INFO:
	 [Node] Executing "_level2thresh2" <nipype.interfaces.spm.model.Threshold>
260120-17:34:41,312 nipype.workflow INFO:
	 [MultiProc] Running 3 tasks, and 0 jobs ready. Free memory (GB): 218.88/219.48, Free processors: 29/32, Free GPU slot:0/0.
                     Currently running:
                       * _level2thresh2
                       * _level2thresh1
                       * _level2thresh0
260120-17:34:58,136 nipype.workflow INFO:
	 [Node] Finished "_level2thresh1", elapsed time 18.61484s.
260120-17:34:58,136 nipype.workflow INFO:
	 [Node] Finished "_level2thresh0", elapsed time 18.614348s.
260120-17:34:58,141 nipype.workflow INFO:
	 [Node] Finished "_level2thresh2", elapsed time 18.619383s.
260120-17:34:59,317 nipype.workflow INFO:
	 [Job 6] Completed (_level2thresh0).
260120-17:34:59,318 nipype.workflow INFO:
	 [Job 7] Completed (_level2thresh1).
260120-17:34:59,319 nipype.workflow INFO:
	 [Job 8] Completed (_level2thresh2).
260120-17:34:59,320 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:34:59,557 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh0".
260120-17:34:59,563 nipype.workflow INFO:
	 [Node] Cached "_level2thresh0" - collecting precomputed outputs
260120-17:34:59,564 nipype.workflow INFO:
	 [Node] "_level2thresh0" found cached.
260120-17:34:59,567 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh1".
260120-17:34:59,569 nipype.workflow INFO:
	 [Node] Cached "_level2thresh1" - collecting precomputed outputs
260120-17:34:59,570 nipype.workflow INFO:
	 [Node] "_level2thresh1" found cached.
260120-17:34:59,572 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh2" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh2".
260120-17:34:59,574 nipype.workflow INFO:
	 [Node] Cached "_level2thresh2" - collecting precomputed outputs
260120-17:34:59,575 nipype.workflow INFO:
	 [Node] "_level2thresh2" found cached.
260120-17:35:01,323 nipype.workflow INFO:
	 [Job 4] Completed (level2_spm_2sample.level2thresh).
260120-17:35:01,325 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:35:01,517 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.datasink_2nd_2" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/datasink_2nd_2".
260120-17:35:01,530 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd_2" <nipype.interfaces.io.DataSink>
260120-17:35:01,532 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/SPM.mat
260120-17:35:01,534 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/spmT_0001.nii
260120-17:35:01,535 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/spmT_0002.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/spmT_0002.nii
260120-17:35:01,536 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/spmF_0003.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/spmF_0003.nii
260120-17:35:01,537 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/con_0001.nii
260120-17:35:01,538 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/con_0002.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/con_0002.nii
260120-17:35:01,539 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/ess_0003.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/ess_0003.nii
260120-17:35:01,540 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/_level2thresh0/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/thresh_con1/spmT_0001_thr.nii
260120-17:35:01,542 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/_level2thresh1/spmT_0002_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/thresh_con2/spmT_0002_thr.nii
260120-17:35:01,543 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/_level2thresh2/spmF_0003_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/MainEffectFace/thresh_con3/spmF_0003_thr.nii
260120-17:35:01,545 nipype.workflow INFO:
	 [Node] Finished "datasink_2nd_2", elapsed time 0.012369s.
260120-17:35:03,317 nipype.workflow INFO:
	 [Job 5] Completed (level2_spm_2sample.datasink_2nd_2).
260120-17:35:03,320 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.

stty: 'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device

<networkx.classes.digraph.DiGraph at 0x7f3859a41940>

contrast_id_1 = [9] #con_0009
contrast_id_2 = [10] #con_0010 

l2source2.inputs.contrast_id_1 = contrast_id_1
l2source2.inputs.contrast_id_2 = contrast_id_2

subFolders = [('2ndLevel/', 'InteractionFace_Repetition/')]
subFolders.extend(subFolders1)
subFolders.extend(subFolders2)
subFolders.extend(subFolders3)
subFolders.extend(subFolders4)

datasink_2nd_2.inputs.substitutions = subFolders

wf_2ndlevel_twosample.run(plugin="MultiProc")

260120-17:35:05,447 nipype.workflow INFO:
	 Workflow level2_spm_2sample settings: ['check', 'execution', 'logging', 'monitoring']
260120-17:35:05,453 nipype.workflow INFO:
	 Running in parallel.
260120-17:35:05,455 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:35:05,654 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.l2source".
260120-17:35:05,654 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.l2source".
260120-17:35:06,219 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.l2source" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/l2source".
260120-17:35:06,225 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.l2source".
260120-17:35:06,233 nipype.workflow INFO:
	 [Node] Executing "l2source" <nipype.interfaces.io.DataGrabber>
260120-17:35:06,240 nipype.workflow INFO:
	 [Node] Finished "l2source", elapsed time 0.002927s.
260120-17:35:07,458 nipype.workflow INFO:
	 [Job 0] Completed (level2_spm_2sample.l2source).
260120-17:35:07,462 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:35:07,843 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.twosampttestdes".
260120-17:35:07,844 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.twosampttestdes".
260120-17:35:07,849 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.twosampttestdes" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/twosampttestdes".
260120-17:35:07,854 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.twosampttestdes".
260120-17:35:07,861 nipype.workflow INFO:
	 [Node] Executing "twosampttestdes" <nipype.interfaces.spm.model.TwoSampleTTestDesign>
260120-17:35:09,457 nipype.workflow INFO:
	 [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32, Free GPU slot:0/0.
                     Currently running:
                       * level2_spm_2sample.twosampttestdes
260120-17:35:29,941 nipype.workflow INFO:
	 [Node] Finished "twosampttestdes", elapsed time 22.077524s.
260120-17:35:31,464 nipype.workflow INFO:
	 [Job 1] Completed (level2_spm_2sample.twosampttestdes).
260120-17:35:31,466 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:35:31,643 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.level2estimate".
260120-17:35:31,644 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.level2estimate".
260120-17:35:31,649 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.level2estimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2estimate".
260120-17:35:31,653 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.level2estimate".
260120-17:35:31,670 nipype.workflow INFO:
	 [Node] Executing "level2estimate" <nipype.interfaces.spm.model.EstimateModel>
260120-17:35:33,464 nipype.workflow INFO:
	 [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32, Free GPU slot:0/0.
                     Currently running:
                       * level2_spm_2sample.level2estimate
260120-17:35:58,705 nipype.workflow INFO:
	 [Node] Finished "level2estimate", elapsed time 27.032168s.
260120-17:35:59,481 nipype.workflow INFO:
	 [Job 2] Completed (level2_spm_2sample.level2estimate).
260120-17:35:59,483 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:35:59,683 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.level2conestimate".
260120-17:35:59,684 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.level2conestimate".
260120-17:35:59,703 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.level2conestimate" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2conestimate".
260120-17:35:59,707 nipype.workflow INFO:
	 [Node] Outdated cache found for "level2_spm_2sample.level2conestimate".
260120-17:35:59,718 nipype.workflow INFO:
	 [Node] Executing "level2conestimate" <nipype.interfaces.spm.model.EstimateContrast>
260120-17:36:01,482 nipype.workflow INFO:
	 [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 219.28/219.48, Free processors: 31/32, Free GPU slot:0/0.
                     Currently running:
                       * level2_spm_2sample.level2conestimate
260120-17:36:23,833 nipype.workflow INFO:
	 [Node] Finished "level2conestimate", elapsed time 24.112067s.
260120-17:36:25,484 nipype.workflow INFO:
	 [Job 3] Completed (level2_spm_2sample.level2conestimate).
260120-17:36:25,492 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:36:27,484 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:36:27,675 nipype.workflow INFO:
	 [Node] Outdated cache found for "_level2thresh0".
260120-17:36:27,676 nipype.workflow INFO:
	 [Node] Outdated cache found for "_level2thresh0".
260120-17:36:27,678 nipype.workflow INFO:
	 [Node] Outdated cache found for "_level2thresh1".
260120-17:36:27,679 nipype.workflow INFO:
	 [Node] Outdated cache found for "_level2thresh1".
260120-17:36:27,681 nipype.workflow INFO:
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260120-17:36:27,681 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh0".
260120-17:36:27,686 nipype.workflow INFO:
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	 [Node] Setting-up "_level2thresh1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh1".
260120-17:36:27,686 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh2" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh2".
260120-17:36:27,689 nipype.workflow INFO:
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260120-17:36:27,695 nipype.workflow INFO:
	 [Node] Executing "_level2thresh0" <nipype.interfaces.spm.model.Threshold>
260120-17:36:27,698 nipype.workflow INFO:
	 [Node] Executing "_level2thresh1" <nipype.interfaces.spm.model.Threshold>
260120-17:36:27,698 nipype.workflow INFO:
	 [Node] Executing "_level2thresh2" <nipype.interfaces.spm.model.Threshold>
260120-17:36:29,484 nipype.workflow INFO:
	 [MultiProc] Running 3 tasks, and 0 jobs ready. Free memory (GB): 218.88/219.48, Free processors: 29/32, Free GPU slot:0/0.
                     Currently running:
                       * _level2thresh2
                       * _level2thresh1
                       * _level2thresh0
260120-17:36:50,334 nipype.workflow INFO:
	 [Node] Finished "_level2thresh1", elapsed time 22.632532s.
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260120-17:36:51,501 nipype.workflow INFO:
	 [Job 6] Completed (_level2thresh0).
260120-17:36:51,502 nipype.workflow INFO:
	 [Job 7] Completed (_level2thresh1).
260120-17:36:51,503 nipype.workflow INFO:
	 [Job 8] Completed (_level2thresh2).
260120-17:36:51,504 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:36:51,686 nipype.workflow INFO:
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260120-17:36:51,721 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh0" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh0".
260120-17:36:51,724 nipype.workflow INFO:
	 [Node] Cached "_level2thresh0" - collecting precomputed outputs
260120-17:36:51,725 nipype.workflow INFO:
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260120-17:36:51,727 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh1" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh1".
260120-17:36:51,729 nipype.workflow INFO:
	 [Node] Cached "_level2thresh1" - collecting precomputed outputs
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260120-17:36:51,738 nipype.workflow INFO:
	 [Node] Setting-up "_level2thresh2" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/level2thresh/mapflow/_level2thresh2".
260120-17:36:51,746 nipype.workflow INFO:
	 [Node] Cached "_level2thresh2" - collecting precomputed outputs
260120-17:36:51,748 nipype.workflow INFO:
	 [Node] "_level2thresh2" found cached.
260120-17:36:53,501 nipype.workflow INFO:
	 [Job 4] Completed (level2_spm_2sample.level2thresh).
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	 [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.
260120-17:36:53,709 nipype.workflow INFO:
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260120-17:36:53,715 nipype.workflow INFO:
	 [Node] Setting-up "level2_spm_2sample.datasink_2nd_2" in "/home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_2sample/datasink_2nd_2".
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	 [Node] Outdated cache found for "level2_spm_2sample.datasink_2nd_2".
260120-17:36:53,739 nipype.workflow INFO:
	 [Node] Executing "datasink_2nd_2" <nipype.interfaces.io.DataSink>
260120-17:36:53,742 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/SPM.mat -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/SPM.mat
260120-17:36:53,743 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/spmT_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/spmT_0001.nii
260120-17:36:53,745 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/spmT_0002.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/spmT_0002.nii
260120-17:36:53,746 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/spmF_0003.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/spmF_0003.nii
260120-17:36:53,747 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/con_0001.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/con_0001.nii
260120-17:36:53,748 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/con_0002.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/con_0002.nii
260120-17:36:53,749 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/ess_0003.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/ess_0003.nii
260120-17:36:53,750 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/_level2thresh0/spmT_0001_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/thresh_con1/spmT_0001_thr.nii
260120-17:36:53,751 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/_level2thresh1/spmT_0002_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/thresh_con2/spmT_0002_thr.nii
260120-17:36:53,752 nipype.interface INFO:
	 sub: /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/2ndLevel/_level2thresh2/spmF_0003_thr.nii -> /home/jovyan/Git_repositories/neurodeskedu/books/examples/functional_imaging/spm_analysis/level2_spm_results_2sample/InteractionFace_Repetition/thresh_con3/spmF_0003_thr.nii
260120-17:36:53,754 nipype.workflow INFO:
	 [Node] Finished "datasink_2nd_2", elapsed time 0.011803s.
260120-17:36:55,501 nipype.workflow INFO:
	 [Job 5] Completed (level2_spm_2sample.datasink_2nd_2).
260120-17:36:55,503 nipype.workflow INFO:
	 [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 219.48/219.48, Free processors: 32/32, Free GPU slot:0/0.

stty: 'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device
stty: stty: 'standard input': Inappropriate ioctl for device
'standard input': Inappropriate ioctl for device
stty: 'standard input': Inappropriate ioctl for device

<networkx.classes.digraph.DiGraph at 0x7f3859a42650>

Results

The group analysis was only done on N=9 subjects, a voxel-wise threshold of p<0.005 was chosen and a cluster-wise FDR threshold of p<0.05 to correct for multiple comparisons.

Look at the positive effect using the plot_stat_map plotting method of nilearn

plotting.plot_stat_map(opj(experiment_dir, 'level2_spm_results_1sample/con5/spmT_0001_thr.nii'), title='Positive Effect', dim=1, display_mode='y', cut_coords=(-45, -30, -15, 0, 15), threshold=2, vmax=8, cmap='viridis');

/tmp/ipykernel_108607/4078776260.py:1: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  plotting.plot_stat_map(opj(experiment_dir, 'level2_spm_results_1sample/con5/spmT_0001_thr.nii'), title='Positive Effect', dim=1, display_mode='y', cut_coords=(-45, -30, -15, 0, 15), threshold=2, vmax=8, cmap='viridis');

Look at the results using the glass brain plotting method of nilearn

plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con5/spmT_0001_thr.nii'), 
                          colorbar=True, threshold=2, display_mode='lyrz', black_bg=True, vmax=10, title='Positive effect');

plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con6/spmT_0001_thr.nii'), 
                          colorbar=True, threshold=2, display_mode='lyrz', black_bg=True, vmax=10, title='Positive effect Famous>Unfamiliar');

plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con7/spmT_0001_thr.nii'), 
                          colorbar=True, threshold=2, display_mode='lyrz', black_bg=True, vmax=10, title='Positive effect Unfamiliar>Scambled');

plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con8/spmT_0001_thr.nii'), 
                          colorbar=True, threshold=2, display_mode='lyrz', black_bg=True, vmax=10, title='Positive Effect of rep1>rep2');

plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con9/spmT_0001_thr.nii'), 
                          colorbar=True, threshold=2, display_mode='lyrz', black_bg=True, vmax=10, title='Positive Interaction Face (Famous/Unfamiliar) x Rep');

plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con10/spmT_0001_thr.nii'), 
                          colorbar=True, threshold=2, display_mode='lyrz', black_bg=True, vmax=10, title='Positive Interaction Face (Unfamiliar/Scrambled) x Rep');

/tmp/ipykernel_108607/3476432501.py:1: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con5/spmT_0001_thr.nii'),
/tmp/ipykernel_108607/3476432501.py:4: UserWarning: empty mask
  plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con6/spmT_0001_thr.nii'),
/tmp/ipykernel_108607/3476432501.py:7: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con7/spmT_0001_thr.nii'),
/tmp/ipykernel_108607/3476432501.py:10: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con8/spmT_0001_thr.nii'),
/tmp/ipykernel_108607/3476432501.py:13: UserWarning: empty mask
  plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con9/spmT_0001_thr.nii'),
/tmp/ipykernel_108607/3476432501.py:16: UserWarning: empty mask
  plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_1sample/con10/spmT_0001_thr.nii'),

Visualize main effects face and interaction face x repetition

plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_2sample/MainEffectFace/thresh_con3/spmF_0003_thr.nii'), 
                          colorbar=True, display_mode='lyrz', black_bg=True, vmax=10, title='Main effect face');


plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_2sample/InteractionFace_Repetition/thresh_con3/spmF_0003_thr.nii'), 
                          colorbar=True, display_mode='lyrz', black_bg=True, vmax=10, title='Interaction face x repetition');

/tmp/ipykernel_108607/281995149.py:1: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  plotting.plot_glass_brain(opj(experiment_dir, 'level2_spm_results_2sample/MainEffectFace/thresh_con3/spmF_0003_thr.nii'),

Dependencies in Jupyter/Python

• Using the package watermark to document system environment and software versions used in this notebook

%load_ext watermark

%watermark
%watermark --iversions

Last updated: 2026-01-20T17:37:20.910180+00:00

Python implementation: CPython
Python version       : 3.13.9
IPython version      : 9.7.0

Compiler    : GCC 14.3.0
OS          : Linux
Release     : 5.15.0-164-generic
Machine     : x86_64
Processor   : x86_64
CPU cores   : 32
Architecture: 64bit

IPython   : 9.7.0
json      : 2.0.9
matplotlib: 3.10.8
nilearn   : 0.13.0
nipype    : 1.10.0
numpy     : 2.3.5
packaging : 25.0
pandas    : 2.3.3
scipy     : 1.16.3