example illustrating data preprocessing of fMRIprep outputs for use i…#64
example illustrating data preprocessing of fMRIprep outputs for use i…#64rami-hamati wants to merge 5 commits intocvnlab:mainfrom
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Remi-Gau
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Remi-Gau
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Quick question:
is this supposed to be
A) an example on a specific frmriprep dataset and its raw counterpart?
B) something more generic that could apply to any BIDS derivatives?
If A then this will work better if the dataset is openly accessible (openneuro?).
If B then the code needs to be made more generic.
Option A sounds way easier unless you feel like embarking on a journey to create a BIDS app.
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| #!/group/tuominen/anaconda3/bin/python3 | |||
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probably need to change this to make the code more portable
| import numpy as np | ||
| import nibabel as nib | ||
| import pandas as pd | ||
| from bids import BIDSLayout |
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I suspect you will need to add a requirements.txt to mention pybids need to be installed
| new_name['suffix'] = 'events_glmsingle' | ||
| pattern = 'sub-{}_ses-{}_task-{}_run-{}_{}.csv' | ||
| # Save the event file in derivatives, exclude TR column | ||
| new_design.to_csv(os.path.join(root, 'derivatives', 'iter4', | ||
| 'sub-'+new_name['subject'], 'ses-'+new_name['session'], 'func', | ||
| pattern.format(new_name['subject'], | ||
| new_name['session'], new_name['task'], new_name['run'], | ||
| new_name['suffix'])), columns=['CSplusshock','CSplus','CSminus'], | ||
| index=False, header=False) |
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You probably :
A) want to use pybids build_path methods (https://bids-standard.github.io/pybids/generated/bids.layout.writing.html#module-bids.layout.writing) that should happily accept the dict of entities you have just created.
B) want to give a BIDS friendly name to your output file: keep events as suffix but add a desc-glmsingle entity
| def smooth_bold(processed, smooth_num, subjects): | ||
| ''' Smooth data prior to first-level analysis using FSL in nipype. | ||
| Output will have suffix _smooth appended.''' | ||
| from nipype.interfaces.fsl import Smooth |
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maybe I missed it but you did not import nipype, right?
and I don't think it is a dependency of glmsingle so this should probably go into the requirements
also not a python expert, but do we have to go through nipype FSL interface to smooth the data? Doesn't that pre supposes that people have FSL installed on their system?
| gs = GLM_single() | ||
| # Name output folders | ||
| metadata = processed.parse_file_entities(bold_runs[0]) | ||
| outdir = os.path.join(os.path.dirname(bold_runs[0]), metadata['task'] + '_concat_glmsingle') |
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same comment as above to have BIDS friendly filenames
2 participants
Grabs data using pybids
Makes glmsingle compatible event files from .tsv event files in BIDS format
Resamples bold timeseries
Smooths bold timeseries
Batches runs by subject and executes