snp_eids = []
for ind, snp in enumerate(snps):
print 'snp', snp['rs_id']
snp['chromosome'] = chr_map[snp['chromosome']]
snp = store.create_entity('Snp', **snp)
snp_eids.append(snp.eid)
if sqlgen_store and ind and ind % 100000 == 0:
store.flush()
# Flush/Commit
if sqlgen_store:
store.flush()
# Platform
platform = {'identifier': 'Affymetrix_6.0'}
platform = store.create_entity('GenomicPlatform', **platform)
for snp_eid in snp_eids:
store.relate(platform.eid, 'related_snps', snp_eid)
### Genetics measures #####################################################
gen_measures = import_genomic_measures(genetics_dir, 'Localizer94')
# Flush/Commit
if sqlgen_store:
store.flush()
###########################################################################
### Subjects ##############################################################
###########################################################################
centers, devices, score_defs = {}, {}, {}
for sid in glob.glob('%s/*' % subjects_dir):
print '-------->', sid, os.path.split(sid)[1]
if options.snps:
snps = import_snps(options.chromosomes, options.snps)
snp_eids = []
for ind, snp in enumerate(snps):
print 'snp', snp['rs_id']
snp['chromosome'] = chr_map[snp['chromosome']]
snp = store.create_entity('Snp', **snp)
snp_eids.append(snp.eid)
if ind and ind % 100000 == 0:
store.flush()
store.flush()
# Platform
platform = {'identifier': 'Affymetrix_6.0'}
platform = store.create_entity('GenomicPlatform', **platform).eid
for snp_eid in snp_eids:
store.relate(platform, 'related_snps', snp_eid)
store.flush()
###############################################################################
### STUDY/PI/CENTERS ##########################################################
###############################################################################
study = store.create_entity('Study', name=u'Demo study',
data_filepath=u'/tmp/demo_data',
description=u'Demo study with random data',
keywords=u'demo;random;semantic').eid
pi_1 = store.create_entity('Investigator', identifier=u'pi_1',
firstname=u'Angus', lastname=u'Young', title=u'PhD',
institution=u'Research Center', department=u'Cognitive Sciences').eid
pi_2 = store.create_entity('Investigator', identifier=u'pi_2',
firstname=u'Malcom', lastname=u'Young', title=u'MD',
institution=u'Research Center', department=u'Cognitive Sciences').eid
for study_path in ('Extra-18_volumes', 'MMRR-21_volumes', 'NKI-RS-22_volume',
'NKI-TRT-20_volumes', 'OASIS-TRT-20_volumes'):
full_study_path = osp.join(base_path, study_path)
if not osp.exists(full_study_path):
print '%s does not exist, skip it' % full_study_path
continue
# Create study
count_subject = 0
study = store.create_entity('Study', data_filepath=unicode(full_study_path),
name = unicode(study_path))
for subject_path in glob.iglob(osp.join(full_study_path,'*')):
subject_id = subject_path.split('/')[-1]
print '--->', subject_id
subject = store.create_entity('Subject', identifier=unicode(subject_id),
gender=u'unknown', handedness=u'unknown')
store.relate(subject.eid, 'related_studies', study.eid)
store.relate(subject.eid, 'related_studies', global_study.eid)
assessment = store.create_entity('Assessment',identifier=u'label',
related_study=study.eid,
protocol=u'mindboggle-label')
store.relate(subject.eid, 'concerned_by', assessment.eid)
for image in glob.iglob(osp.join(full_study_path, subject_path, '*.nii.gz')):
image_id = image.split('/')[-1].split('.')[0]
anat = {'identifier': u'%s_%s' % (subject_id, image_id),
'label': u'labels' if 'labels' in image_id else u'anatomy',
'format': u'nii.gz',
'type': u'normalized T1',
'related_study': study.eid,
'filepath': unicode(osp.relpath(image, start=full_study_path)),
'valid': True, 'completed': True, 'description': 'Image from Mindboggle'}
mri_data = store.create_entity('MRIData', sequence=u'T1')
infos = line.strip().split('\t')
if len(infos) != 4:
print '!!!!', infos
demographics[infos[1]] = {'gender': infos[2], 'age': int(infos[3])}
# Subjects
count_subject = 0
for subject_path in glob.iglob(osp.join(study_path,'sub*')):
subject_id = study_id + '_' + subject_path.split('/')[-1]
print '--->', subject_id
age, gender = None, None
if subject_id in demographics:
age = demographics[subject_id].get('age')
gender = demographics[subject_id].get('gender')
subject = store.create_entity('Subject', identifier=unicode(subject_id),
age = age, gender=gender or u'unknown', handedness=u'unknown')
store.relate(subject.eid, 'related_studies', study.eid)
store.relate(subject.eid, 'related_studies', global_study.eid)
# anatomy
assessment = store.create_entity('Assessment',identifier=u'anat', protocol=u'anat',
related_study=study.eid)
store.relate(subject.eid, 'concerned_by', assessment.eid)
for image in glob.iglob(osp.join(study_path, subject_path, 'anatomy/*.nii.gz')):
image_id = image.split('/')[-1].split('.')[0]
anat = {'identifier': u'%s_%s' % (subject_id, image_id),
'label': u'anatomy',
'format': u'nii.gz',
'type': u'normalized T1',
'related_study': study.eid,
'filepath': unicode(osp.relpath(image, start=study_path)),
'valid': True, 'completed': True, 'description': 'Image from OpenFmri'}
mri_data = store.create_entity('MRIData', sequence=u'T1')
