

In [4]:

    
from scitran_client import ScitranClient, query, Projects, Acquisitions, Sessions, Files
client = ScitranClient('scitran')









    



Found an existing token for this instance.
The existing token has been refreshed.

We can assemble queries by building filters like below. We're looking for sessions in the project called vwfa, so we query for sessions and filter by the associated project. After we find the sessions we're interested in, we query for T1-weighted nifti files that are associated with those sessions.



In [7]:

    
sessions = client.search(query(Sessions).filter(Projects.label.match('vwfa')))

files = client.search(query(Files).filter(
    Files.type.match('nifti'),
    Acquisitions.measurement.match('anatomy_t1w'),
    Acquisitions.session.in_(session['_id'] for session in sessions),
))



In [8]:

    
# Let's analyze the first file.
example_file = files[0]

import os
# fsl-bet looks for files in the nifti subdirectory
nifti_dir = os.path.join(client.gear_in_dir, 'nifti')
if not os.path.exists(nifti_dir):
    os.mkdir(nifti_dir)
example_file_path = client.download_all_file_search_results([example_file], dest_dir=nifti_dir)[0]



In [9]:

    
session_id = example_file['_source']['acquisition']['session']

# We let fsl-bet find the input file by having an empty string for a command.
client.run_gear_and_upload_analysis('testing fsl-bet local run', 'scitran/fsl-bet', session_id, '')









    



Running container scitran/fsl-bet on with input /Users/carlos/Downloads/input and output /Users/carlos/Downloads/output
[scitran/fsl-bet]  Initiated
[scitran/fsl-bet]  Running bet2 on 11353_3_1.nii.gz
[scitran/fsl-bet]  Wrote: 11353_3_1_brain-extracted.nii.gz
[scitran/fsl-bet]  generated /flywheel/v0/output/.metadata.json
[scitran/fsl-bet]  Done!
Docker container finished with exit code 0
Uploading results to collection with id 56e9d386ddea7f915e81f704.
Uploaded analysis has ID 57e6cbdf7667550013270805. Server responded with 200.



In [16]:

    
import nibabel as nib
import scipy.misc
img = nib.load(example_file_path)
scipy.misc.toimage(img.get_data()[100, :, :]).save('before.png')

output_file_path = os.path.join(client.gear_out_dir, '11353_3_1_brain-extracted.nii.gz')
img = nib.load(output_file_path)
scipy.misc.toimage(img.get_data()[100, :, :]).save('after.png')