Hi,
I already posted this issue on Github, but I don't know if it is better to post this here:
I'm running ICA-AROMA on .feat directory with command:
python3 ICA_AROMA.py -feat data.feat -out data.feat/ICA_AROMA/
The pipeline works fine, until at the data denoising. It classifies noise and makes a classified_motion_ICs.txt file, but it says that none of the components were classified as motion...
Here is the log:
------------------------------- RUNNING ICA-AROMA -------------------------------
--------------- 'ICA-based Automatic Removal Of Motion Artifacts' ---------------
Step 1) MELODIC
Step 2) Automatic classification of the components
registering the spatial maps to MNI
extracting the CSF & Edge fraction features
extracting the Maximum RP correlation feature
extracting the High-frequency content feature
classification
Step 3) Data denoising
None of the components was classified as motion, so no denoising is applied (a symbolic link to the input file will be created).
Traceback (most recent call last):
File "/../ICA_AROMA.py", line 215, in
aromafunc.denoising(fslDir, inFile, outDir, melmix, denType, motionICs)
File "/../ICA_AROMA_functions.py", line 558, in denoising
os.symlink(inFile,os.path.join(outDir,'denoised_func_data_nonaggr.nii.gz'))
FileExistsError: [Errno 17] File exists: '/..//filtered_func_data.nii.gz' -> '/../denoised_func_data_nonaggr.nii.gz'
I used the latest version of the uploaded python scripts.
I'm wondering if you have a solution for this problem?
Thanks in advance!
I attached the classified noise files, made by AROMA, as attachment.
Hope to hear from you soon.
Kind regards,
Santoucha
IC Motion/noise maximum RP correlation Edge-fraction High-frequency content CSF-fraction
1 True 0.45 0.80 0.04 0.03
2 True 0.31 0.60 0.25 0.29
3 True 0.87 0.52 0.14 0.00
4 False 0.29 0.29 0.05 0.00
5 True 0.18 0.57 0.24 0.14
6 False 0.36 0.56 0.04 0.00
7 True 0.62 0.84 0.88 0.01
8 True 0.29 0.60 0.30 0.19
9 False 0.48 0.55 0.06 0.01
10 False 0.72 0.25 0.31 0.01
11 True 0.25 0.65 0.25 0.30
12 False 0.48 0.44 0.24 0.07
13 False 0.29 0.58 0.04 0.01
14 False 0.41 0.56 0.04 0.01
15 True 0.27 0.44 0.33 0.17
16 True 0.57 0.67 0.77 0.03
17 True 0.58 0.61 0.87 0.02
18 False 0.38 0.55 0.07 0.00
19 False 0.49 0.40 0.08 0.00
20 False 0.25 0.37 0.19 0.00
21 False 0.30 0.47 0.08 0.00
22 True 0.23 0.63 0.24 0.16
23 True 0.39 0.58 0.31 0.12
24 False 0.21 0.52 0.04 0.01
25 False 0.41 0.44 0.28 0.02
26 True 0.52 0.69 0.56 0.04
27 False 0.41 0.46 0.27 0.07
28 False 0.45 0.49 0.13 0.01
29 False 0.27 0.52 0.11 0.05
30 False 0.70 0.49 0.21 0.00
31 False 0.27 0.49 0.04 0.00
32 False 0.21 0.50 0.10 0.00
33 False 0.30 0.43 0.24 0.03
34 True 0.27 0.57 0.54 0.22
1,2,3,5,7,8,11,15,16,17,22,23,26,34
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