#!/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)
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)
analyzer.slice_time_correction(subject_dir)
analyzer.motion_correction(subject_dir)
analyzer.functional_registration(subject_dir)
analyzer.functional_smoothing(subject_dir)
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)
