class LocationSchema(mm.Schema):
studies = mm.Nested('StudySchema', many=True, only='pmid')
images = mm.Nested('ImageSchema', many=True, only='id')
class Meta:
fields = ('x', 'y', 'z', 'studies', 'images')
class StudySchema(mm.Schema):
peaks = mm.Nested('PeakSchema', many=True)
analyses = mm.Nested('AnalysisSchema', many=True, only='id')
class Meta:
fields = ('pmid', 'doi', 'title', 'authors', 'journal', 'year',
'peaks', 'analyses')
class AnalysisSchema(mm.Schema):
studies = mm.Nested('StudySchema', many=True, only='pmid')
images = mm.Nested('ImageSchema', many=True, only='id')
class Meta:
fields = ('id', 'name', 'description', 'n_studies', 'n_activations',
'type', 'studies', 'images')
class ImageSchema(mm.Schema):
analysis = mm.Nested('AnalysisSchema', only='id')
location = mm.Nested('LocationSchema', only='id')
file = mm.Function(lambda obj: url_for('images.download', val=obj.id))
class Meta:
fields = ('id', 'label', 'description', 'stat', 'type', 'analysis',
'location', 'file')
class GeneSchema(mm.Schema):
images = mm.Nested('ImageSchema', many=True, only='id')
class Meta:
fields = ('symbol', 'name', 'synonyms', 'locus_type', 'images')
class DecodingSchema(mm.Schema):
def get_values(self, dec):
data = open(join(settings.DECODING_RESULTS_DIR,
dec.uuid + '.txt')).read().splitlines()
data = [x.split('\t') for x in data]
return dict([(f, round(float(v), 3)) for (f, v) in data if v.strip()])
image = mm.Nested('ImageSchema', allow_null=True)
reference = mm.Function(lambda obj: obj.decoding_set.name)
values = mm.Method('get_values')
class Meta:
fields = ('id', 'url', 'neurovault_id', 'comments', 'image',
'reference', 'values')