def main():
conf = AnalysisConfiguration()
data_dir = os.environ.get('DATA_DIR') or '/home/user/data'
op = OpenFMRIData(data_dir, conf.study_name)
analyzer = OpenFMRIAnalyzer(op, conf)
all_subject_dirs = op.all_subjects_dirs_with_raw()
for subject in all_subject_dirs:
analyzer.extract_brain(subject)
for subject in all_subject_dirs:
analyzer.anatomical_registration(subject)
for subject in all_subject_dirs:
#for task in conf.mvpa_tasks:
#subject.remove_volumes_from_model(1, "", task, conf.num_of_volumes_to_delete)
analyzer.motion_correction(subject)
analyzer.functional_registration(subject)
if conf.func_seg:
analyzer.functional_segmentation(subject)
else:
analyzer.segmentation(subject)
analyzer.generate_functional_gm_masks(subject)
#analyzer.warp_standard_mask(subject)
for subject in all_subject_dirs:
# DO SL
out_dir = _opj(subject.path(),'results',conf.dir_name())
if not os.path.exists(out_dir):
os.makedirs(out_dir)
run_searchlight(op, subject, conf, out_dir)
# run_searchlight(op.study_dir(), subject.subcode(), mask_name, k, [['G1','G4']], out_dir,flavor)
#Group Level
output_dir = _opj(op.study_dir(), 'results', "{}".format(conf.dir_name()))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
files = glob(_opj(op.study_dir(), "**", 'results', conf.dir_name(), '*acc_mni.nii.gz'))
print files
generate_group_level_map( files, output_dir)
def test():
fmri_data = OpenFMRIData(
'/home/user/data',
'/home/user/data/raw',
'/home/user/data/behavioural',
'LP')
analyzer = OpenFMRIAnalyzer(fmri_data, [1])
print "{:<40s}{}".format("subjects_list", analyzer._subjects_list)
analyzer.analyze(mc_merge=True)
def main():
conf = AnalysisConfiguration()
data_dir = os.environ.get('DATA_DIR') or '/home/daniel/fsl-analysis/data'
op = OpenFMRIData(data_dir, conf.study_name)
analyzer = OpenFMRIAnalyzer(op, conf)
all_subject_dirs = op.all_subjects_dirs_with_raw()
for subject in all_subject_dirs:
analyzer.extract_brain(subject)
for subject in all_subject_dirs:
analyzer.anatomical_registration(subject)
for subject in all_subject_dirs:
#for task in conf.mvpa_tasks:
#subject.remove_volumes_from_model(1, "", task, conf.num_of_volumes_to_delete)
analyzer.motion_correction(subject)
analyzer.functional_registration(subject)
if conf.func_seg:
analyzer.functional_segmentation(subject)
else:
analyzer.segmentation(subject)
analyzer.generate_functional_gm_masks(subject)
#analyzer.warp_standard_mask(subject)
for subject in all_subject_dirs:
# DO SL
out_dir = _opj(subject.path(),'results',conf.dir_name())
if not os.path.exists(out_dir):
os.makedirs(out_dir)
run_searchlight(op, subject, conf, out_dir)
from OpenFMRIAnalyzer import OpenFMRIAnalyzer
from OpenFMRIData import OpenFMRIData
from create_events import create_events
import os
from glob import glob
import pandas as pd
data_dir = os.environ.get('DATA_DIR') or '/home/daniel/fsl-analysis/data'
study_name = os.environ.get('STUDY_NAME') or 'AV'
raw_dir = os.path.join(data_dir, 'raw')
behav_dir = os.path.join(data_dir, 'behavioral')
op = OpenFMRIData(data_dir, raw_dir, study_name)
# subject_names = ['HiAn'] # Specific subject names
subject_names = op.get_subject_names() # All subjects
for name in subject_names:
subject_dir = op.create_subject_dir(name, overwrite=True)
# if we want to create new data for analysis
# subject_dir = op.create_subject_dir(name)
# if we want to load new data for analysis
# subject_dir = op.load_subject_dir(subname=name)
create_events(subject_dir, behav_dir)
analyzer = OpenFMRIAnalyzer(op,[subject_dir])
brain_image = analyzer.extract_brain(subject_dir)
analyzer.estimate_bias_field(subject_dir, brain_image, overwrite=True)
analyzer.anatomical_registration(subject_dir)
analyzer.anatomical_smoothing(subject_dir)
import sys
import os
import re
from PreProcessing import PreProcessing
from OpenFMRIData import OpenFMRIData
from createFirstLevel import createFirstLevel
# data_dir = os.environ.get('DATA_DIR') or '/home/rack-hezi-03/home/roigilro/fMRI/Data/'
data_dir = '/media/roee/fMRI/Data/'
# study_name = os.environ.get('STUDY_NAME') or 'Self-Other'
study_name = 'Self-Other'
raw_dir = os.path.join(data_dir,'raw')
op = OpenFMRIData(data_dir, raw_dir,study_name)
subject_names = op.get_subject_names() # All subjects
# create open fMRI data structure and conver .dcms to .nii
for name in subject_names:
x=2
# subject_dir = op.create_subject_dir(name, overwrite=True)
#TODO: agree on a standard to include open behvadata.txt files in raw directory structure
# copy over the behavdata.txt files to correct place in open-fmri data struc created above:
# you need to do this with a mat script of by hand for each run for eahc subject
# ~/sub001/behav/tak00X_run00X/behavdata.txt
for name in subject_names:
subject = op.load_subject_dir(subname=name)
conds[key] = value
#run_name = "{}_run{:0>3d}".format(task_sequence, run_number+1)
condition_name = "cond{:0>3d}.txt".format(condition_number + 1)
print ">>> condition mapping: {}->{}".format(
key, condition_number)
for onset_dir in onset_dirs:
value.to_csv(os.path.join(subjectdir.model_dir(),
onset_dir, task_sequence,
condition_name),
sep='\t',
index=False,
header=False)
op = OpenFMRIData(data_dir, raw_dir, study_name)
#subject_names = ['MoCa'] # Specific subject names
subject_names = op.get_subject_names() # All subjects
for name in subject_names:
subject_dir = op.load_subject_dir(subname=name,
create_behav_dict={
'func':
create_evs,
'behav':
os.path.join(behavioural_dir,
study_name, name)
})
# if we want to create new data for analysis
# subject_dir = op.create_subject_dir(name)
def test():
fmri_data = OpenFMRIData('/home/user/data', '/home/user/data/raw',
'/home/user/data/behavioural', 'EPITest')
qa = QualityAnalyzer(fmri_data)
qa.analyze_runs(1)
pass
#!/usr/bin/python
import sys
import os
from OpenFMRIData import OpenFMRIData
from OpenFMRIAnalyzer import OpenFMRIAnalyzer
subnames = sys.argv[1:]
data_dir = os.environ.get('DATA_DIR') or '/home/user/data'
study_name = os.environ.get('STUDY_NAME') or 'LP'
op = OpenFMRIData(data_dir, study_name)
subcodes = []
for subname in subnames:
subject_dir = op.subject_dir(subname=subname)
subcodes.append(subject_dir.subcode())
analyzer = OpenFMRIAnalyzer(op,subcodes)
analyzer.analyze(mc_align_task=True, func_seg=True)
