class MBA(HBA):
ont = OntMeta(
PARC, 'mbaslim', 'Allen Mouse Brain Atlas Ontology', 'MBA 2011 v2',
'This file is automatically generated from the Allen Brain Atlas API.'
+ NOTICE, TODAY)
concept = PScheme(ILXREPLACE(ont.name),
'Allen Mouse Brain Atlas parcellation concept',
'NCBITaxon:10090', ADULT)
atlas = PSArtifact(
ILXREPLACE(ont.name + 'atlas'),
'Allen Mouse Brain Atlas v2',
'2011 v2',
'November 2011',
'http://mouse.brain-map.org/static/atlas',
'http://help.brain-map.org/download/attachments/2818169/AllenReferenceAtlas_v2_2011.pdf?version=1&modificationDate=1319667383440', # yay no doi! wat
tuple(),
tuple())
PREFIX = 'MBA'
PREFIXES = makePrefixes('NIFRID')
PREFIXES[
PREFIX] = 'http://api.brain-map.org/api/v2/data/Structure/' # FIXME hack to allow both HBA and MBA
ROOT = 997
@classmethod
def datamunge(cls, data):
for node in data:
if node['id'] == cls.ROOT:
node['name'] = 'allen mouse brain atlas parcellation root'
node['safe_name'] = 'allen mouse brain atlas parcellation root'
node['acronym'] = 'mbaroot'
break
class CoCoMac(genericPScheme):
ont = OntMeta(
PARC, 'cocomacslim', 'CoCoMac terminology', 'CoCoMac',
('This file is automatically generated from the CoCoMac '
'database on the terms from BrainMaps_BrainSiteAcronyms.' + NOTICE),
TODAY)
concept = PScheme(ILXREPLACE(ont.name),
'CoCoMac terminology parcellation concept',
'NCBITaxon:9544', 'ilx:various')
atlas = PSArtifact(
ILXREPLACE(ont.name + 'atlas'),
'CoCoMac terminology',
None, #'no version info',
None, #'no date',
'http://cocomac.g-node.org',
'scholarly things',
tuple(),
tuple())
PREFIXES = makePrefixes('NIFRID')
PREFIXES[
'cocomac'] = 'http://cocomac.g-node.org/services/custom_sql_query.php?sql=SELECT%20*%20from%20BrainMaps_BrainSiteAcronyms%20where%20ID=' # looking for better options
@classmethod
def datagetter(cls):
url = 'http://cocomac.g-node.org/services/custom_sql_query.php?sql=SELECT * from BrainMaps_BrainSiteAcronyms;&format=json'
table = requests.get(url).json()
fields = table['fields']
data = [fields] + list(table['data'].values())
return data
@classmethod
def dataproc(cls, graph, data):
class cocomac(rowParse):
def ID(self, value):
self.identifier = 'cocomac:' + value # safe because reset every row (ish)
graph.add_class(self.identifier, cls.concept.curie)
def Key(self, value):
pass
def Summary(self, value):
pass
def Acronym(self, value):
graph.add_trip(self.identifier, ACRONYM, value)
def FullName(self, value):
graph.add_trip(self.identifier, rdfs.label,
'(%s) ' % cls.ont.shortname + value)
graph.add_trip(self.identifier, PARCLAB, value)
def LegacyID(self, value):
graph.add_trip(self.identifier, ACRONYM, value)
def BrainInfoID(self, value):
pass
cocomac(data)
def fmri_atlases():
ATLAS_PATH = '/usr/share/fsl/data/atlases/'
ONT_PATH = 'http://ontology.neuinfo.org/NIF/ttl/generated/'
# ingest the structured xml files and get the name of the atlas/parcellation scheme
# ingest the labels
# for each xml files/set of nii files generate a ttl file
shortnames = {
'JHU White-Matter Tractography Atlas': 'JHU WM',
'Oxford-Imanova Striatal Structural Atlas': 'OISS',
'Talairach Daemon Labels': 'Talairach',
'Subthalamic Nucleus Atlas': 'SNA',
'JHU ICBM-DTI-81 White-Matter Labels': 'JHU ICBM WM',
'Juelich Histological Atlas': 'Juelich',
'MNI Structural Atlas': 'MNI Struct',
}
comment = 'This file is automatically generated from the %s file in the FSL atlas collection.' + NOTICE
ontids = []
atlases = []
for xmlfile in glob.glob(ATLAS_PATH + '*.xml'):
tree = etree.parse(xmlfile)
name = tree.xpath('header//name')[0].text
cname = name + ' concept' if name.endswith(
'parcellation') else name + ' parcellation concept'
shortname = tree.xpath('header//shortname')
if shortname:
shortname = shortname[0].text
else:
shortname = shortnames[name]
filename = os.path.splitext(os.path.basename(xmlfile))[0]
classdict = dict(
ont=OntMeta(PARC, filename, name, shortname, comment % xmlfile,
TODAY),
concept=PScheme(ILXREPLACE(name), cname, 'NCBITaxon:9606', ADULT),
atlas=PSArtifact(
ILXREPLACE(name + 'atlas'),
name,
None, #'no version info',
None, #'date unknown',
'http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases',
None, #'http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases', # TODO: there are MANY defining citations for these...
tuple(),
(shortname, ) if shortname else tuple()),
DATA=tree.xpath('data//label'))
tempclass = type('tempclass', (FMRI, ), classdict)
ontid, atlas_ = tempclass()
#print([(l.get('index'),l.text) for l in tree.xpath('data//label')])
ontids.append(ontid)
atlases.append(atlas_)
return [_ for _ in zip(ontids, atlases)]
class HBA(genericPScheme):
ont = OntMeta(
PARC, 'hbaslim', 'Allen Human Brain Atlas Ontology', 'HBA 2013 v2',
'This file is automatically generated from the Allen Brain Atlas API.'
+ NOTICE, TODAY)
concept = PScheme(ILXREPLACE(ont.name),
'Allen Human Brain Atlas parcellation concept',
'NCBITaxon:9606', ADULT)
atlas = PSArtifact(
ILXREPLACE(ont.name + 'atlas'), 'Allen Human Brain Atlas v2',
'2013 v2', 'October 2013', 'http://human.brain-map.org/',
'http://help.brain-map.org/download/attachments/2818165/HBA_Ontology-and-Nomenclature.pdf?version=1&modificationDate=1382051847989',
tuple(), tuple())
PREFIX = 'HBA'
PREFIXES = {
PREFIX:
'http://api.brain-map.org:80/api/v2/data/Structure/', # FIXME hack to allow both HBA and MBA
}
ROOT = 3999
#VALIDATE = True
@classmethod
def datagetter(cls):
url = 'http://api.brain-map.org/api/v2/tree_search/Structure/{root}.json?descendants=true'.format(
root=cls.ROOT)
resp = requests.get(url).json()
return resp['msg']
@classmethod
def dataproc(cls, graph, data):
for node in data:
curie = graph.expand(cls.PREFIX + ':' + str(node['id']))
graph.add_class(curie, cls.concept.curie)
parent = node['parent_structure_id']
graph.add_node(curie, rdflib.RDFS.label,
'(%s) ' % cls.ont.shortname + node['name'])
graph.add_node(curie, PARCLAB, node['name'])
graph.add_node(curie, ACRONYM, node['acronym'])
if node['safe_name'] != node['name']:
graph.add_node(curie, SYNONYM, node['safe_name'])
if parent:
pcurie = graph.expand(cls.PREFIX + ':' + str(parent))
graph.add_hierarchy(pcurie, PARTOF, curie)
@classmethod
def validate(cls, graph):
check_hierarchy(graph, cls.PREFIX + ':' + str(cls.ROOT), PARTOF,
PARCLAB)
class PAX1(genericPScheme):
source = 'resources/paxinos09names.txt'
ont = OntMeta(
PARC, 'paxinos_r_s_6', 'Paxinos Rat Parcellation 6th', 'PAXRSTER6',
'This file is automatically generated from ' + source + '.' + NOTICE,
TODAY)
concept = PScheme(ILXREPLACE(ont.name),
'Paxinos Rat Stereological 6th Ed parcellation concept',
'NCBITaxon:10116', ADULT)
atlas = PSArtifact(
ILXREPLACE(ont.name + 'atlas'),
'The Rat Brain in Stereotaxic Coordinates 6th Edition',
'6th',
'02-11-2006', # d-m-y
None, # the fact this is missing is very big problem :/
('Paxinos, George, Charles RR Watson, and Piers C. Emson.'
' "AChE-stained horizontal sections of the rat brain'
' in stereotaxic coordinates." Journal of neuroscience'
' methods 3, no. 2 (1980): 129-149.'), # FIXME
('Paxinos Rat 6th', ),
tuple())
PREFIXES = {
'': ont.path + ont.filename + '.ttl/', # FIXME
}
@classmethod
def datagetter(cls):
with open(cls.source, 'rt') as f:
lines = [
l.rsplit('#')[0].strip() for l in f.readlines()
if not l.startswith('#')
]
return [l.rsplit(' ', 1) for l in lines]
@classmethod
def dataproc(cls, graph, data):
for i, (label, abrv) in enumerate(data):
id_ = ':' + str(i + 1)
display = '(%s) ' % cls.ont.shortname + label
graph.add_class(id_, cls.concept.curie, label=display)
graph.add_node(id_, PARCLAB, label)
graph.add_node(
id_, ACRONYM,
abrv) # FIXME these are listed as abbreviations in the text
class WHSSD(genericPScheme):
source = 'resources/WHS_SD_rat_atlas_v2.label'
ont = OntMeta(
PARC, 'whs_sd_2', 'Waxholm Space Sprague Dawley Ontology', 'WHS SD v2',
'This file is automatically generated from ' + source + '.' + NOTICE,
TODAY)
concept = PScheme(
ILXREPLACE(ont.name),
'Waxholm Space Sprague Dawley parcellation concept',
'NCBITaxon:10116', # TODO
ADULT) # TODO
atlas = PSArtifact(
ILXREPLACE(ont.name + 'atlas'),
'Sprague Dawley Atlas v2',
'v2',
'02-02-2015', # d-m-y
'https://scalablebrainatlas.incf.org/rat/PLCJB14', # FIXME... this seems... off...
'halp!', # FIXME
('WHS SD', ont.shortname),
tuple())
PREFIXES = {
#'':ont.path + ont.filename + '.ttl/', # FIXME
'WHSSD': interlex_namespace('waxholm/sd/labels')
}
@classmethod
def datagetter(cls):
with open(cls.source, 'rt') as f:
lines = [l.strip() for l in f.readlines() if not l.startswith('#')]
rows = [(l[:3].strip(), l.split('"', 1)[1].strip('"')) for l in lines]
return rows
@classmethod
def dataproc(cls, graph, data):
for index, label in data:
id_ = 'WHSSD:' + str(index)
display = '(%s) ' % cls.ont.shortname + label
graph.add_class(id_, cls.concept.curie, label=display)
graph.add_trip(id_, PARCLAB, label)
def predicate_disambig(graph):
# the useful idiot holds 'appriopriate' predicate/phenotype mappings where there may be some ambiguity (e.g. brain region vs layer) it is OK to hardcode these
# the ui is not an owl:Class
ui = ILXREPLACE('phenotype predicate disambiguation')
def uit(p, o):
out = (ui, graph.expand(p), graph.expand(o))
graph.add_trip(*out)
return out
uit('ilx:hasLayerLocation', 'UBERON:0005390')
uit('ilx:hasLayerLocation', 'UBERON:0005391')
uit('ilx:hasLayerLocation', 'UBERON:0005392')
uit('ilx:hasLayerLocation', 'UBERON:0005393')
uit('ilx:hasLayerLocation', 'UBERON:0005394')
uit('ilx:hasLayerLocation', 'UBERON:0005395')
class ncbi(dictParse):
superclass = ILXREPLACE('ilx_gene_concept')
def __init__(self, thing, graph):
self.g = graph
super().__init__(thing, order=['uid'])
def name(self, value):
self.g.add_node(self.identifier, rdflib.RDFS.label, value)
def description(self, value):
#if value:
self.g.add_node(self.identifier, 'skos:prefLabel', value)
def uid(self, value):
self.identifier = 'NCBIGene:' + str(value)
self.g.add_node(self.identifier, rdflib.RDF.type, rdflib.OWL.Class)
self.g.add_node(self.identifier, rdflib.RDFS.subClassOf,
self.superclass)
def organism(self, value):
self._next_dict(value)
def taxid(self, value):
tax = 'NCBITaxon:' + str(value)
self.g.add_node(self.identifier, 'ilx:definedForTaxon',
tax) # FIXME species or taxon???
def otheraliases(self, value):
if value:
for synonym in value.split(','):
self.g.add_node(self.identifier, 'OBOANN:synonym',
synonym.strip())
def otherdesignations(self, value):
if value:
for synonym in value.split('|'):
self.g.add_node(self.identifier, 'OBOANN:synonym', synonym)
def swanson():
""" not really a parcellation scheme """
source = 'resources/swanson_aligned.txt'
ONT_PATH = GENERATED
filename = 'swanson_hierarchies'
ontid = ONT_PATH + filename + '.ttl'
PREFIXES = makePrefixes('', 'ilx', 'owl', 'skos', 'NIFRID', 'ILXREPLACE')
PREFIXES.update({
#'':ontid + '/', # looking for better options
'SWAN': interlex_namespace('swanson/nt/term'),
'SWAA': interlex_namespace('swanson/nt/appendix'),
})
new_graph = makeGraph(filename, PREFIXES, writeloc=WRITELOC)
new_graph.add_ont(
ontid, 'Swanson brain partomies', 'Swanson 2014 Partonomies',
'This file is automatically generated from ' + source + '.' + NOTICE,
TODAY)
# FIXME citations should really go on the ... anatomy? scheme artifact
definingCitation = 'Swanson, Larry W. Neuroanatomical Terminology: a lexicon of classical origins and historical foundations. Oxford University Press, USA, 2014.'
definingCitationID = 'ISBN:9780195340624'
new_graph.add_trip(ontid, 'NIFRID:definingCitation', definingCitation)
new_graph.add_trip(ontid, 'NIFRID:definingCitationID', definingCitationID)
with open(source, 'rt') as f:
lines = [l.strip() for l in f.readlines()]
# join header on page 794
lines[635] += ' ' + lines.pop(636)
#fix for capitalization since this header is reused
fixed = ' or '.join([
' ('.join([n.capitalize() for n in _.split(' (')])
for _ in lines[635].lower().split(' or ')
]).replace('human', 'HUMAN')
lines[635] = fixed
data = []
for l in lines:
if not l.startswith('#'):
level = l.count('.' * 5)
l = l.strip('.')
if ' (' in l:
if ') or' in l:
n1, l = l.split(') or')
area_name, citationP = n1.strip().split(' (')
citation = citationP.rstrip(')')
d = (level, area_name, citation, 'NEXT SYN')
data.append(d)
#print(tc.red(tc.bold(repr(d))))
area_name, citationP = l.strip().split(' (')
citation = citationP.rstrip(')')
else:
area_name = l
citation = None
d = (level, area_name, citation, None)
#print(d)
data.append(d)
results = async_getter(sgv.findByTerm, [(d[1], ) for d in data])
#results = [None] * len(data)
curies = [[r['curie'] for r in _ if 'UBERON' in r['curie']] if _ else []
for _ in results]
output = [_[0] if _ else None for _ in curies]
header = ['Depth', 'Name', 'Citation', 'NextSyn', 'Uberon']
zoop = [header] + [r for r in zip(*zip(*data), output)] + \
[(0, 'Appendix END None', None, None, None)] # needed to add last appendix
class SP(rowParse):
def __init__(self):
self.nodes = defaultdict(dict)
self._appendix = 0
self.appendicies = {}
self._last_at_level = {}
self.names = defaultdict(set)
self.children = defaultdict(set)
self.parents = defaultdict(set)
self.next_syn = False
super().__init__(zoop)
def Depth(self, value):
if self.next_syn:
self.synonym = self.next_syn
else:
self.synonym = False
self.depth = value
def Name(self, value):
self.name = value
def Citation(self, value):
self.citation = value
def NextSyn(self, value):
if value:
self.next_syn = self._rowind
else:
self.next_syn = False
def Uberon(self, value):
self.uberon = value
def _row_post(self):
# check if we are in the next appendix
# may want to xref ids between appendicies as well...
if self.depth == 0:
if self.name.startswith('Appendix'):
if self._appendix:
self.appendicies[self._appendix]['children'] = dict(
self.children)
self.appendicies[self._appendix]['parents'] = dict(
self.parents)
self._last_at_level = {}
self.children = defaultdict(set)
self.parents = defaultdict(set)
_, num, apname = self.name.split(' ', 2)
if num == 'END':
return
self._appendix = int(num)
self.appendicies[self._appendix] = {
'name':
apname.capitalize(),
'type':
self.citation.capitalize() if self.citation else None
}
return
else:
if ' [' in self.name:
name, taxonB = self.name.split(' [')
self.name = name
self.appendicies[self._appendix][
'taxon'] = taxonB.rstrip(']').capitalize()
else: # top level is animalia
self.appendicies[
self._appendix]['taxon'] = 'ANIMALIA'.capitalize()
self.name = self.name.capitalize()
self.citation = self.citation.capitalize()
# nodes
if self.synonym:
self.nodes[self.synonym]['synonym'] = self.name
self.nodes[self.synonym]['syn-cite'] = self.citation
self.nodes[self.synonym]['syn-uberon'] = self.uberon
return
else:
if self.citation: # Transverse Longitudinal etc all @ lvl4
self.names[self.name + ' ' + self.citation].add(
self._rowind)
else:
self.name += str(self._appendix) + self.nodes[
self._last_at_level[self.depth - 1]]['label']
#print(level, self.name)
# can't return here because they are their own level
# replace with actually doing something...
self.nodes[self._rowind]['label'] = self.name
self.nodes[self._rowind]['citation'] = self.citation
self.nodes[self._rowind]['uberon'] = self.uberon
# edges
self._last_at_level[self.depth] = self._rowind
# TODO will need something to deal with the Lateral/
if self.depth > 0:
try:
parent = self._last_at_level[self.depth - 1]
except:
embed()
self.children[parent].add(self._rowind)
self.parents[self._rowind].add(parent)
def _end(self):
replace = {}
for asdf in [
sorted(n) for k, n in self.names.items() if len(n) > 1
]:
replace_with, to_replace = asdf[0], asdf[1:]
for r in to_replace:
replace[r] = replace_with
for r, rw in replace.items():
#print(self.nodes[rw])
o = self.nodes.pop(r)
#print(o)
for vals in self.appendicies.values():
children = vals['children']
parents = vals['parents']
# need reversed so children are corrected before swap
for r, rw in reversed(sorted(replace.items())):
if r in parents:
child = r
new_child = rw
parent = parents.pop(child)
parents[new_child] = parent
parent = list(parent)[0]
children[parent].remove(child)
children[parent].add(new_child)
if r in children:
parent = r
new_parent = rw
childs = children.pop(parent)
children[new_parent] = childs
for child in childs:
parents[child] = {new_parent}
self.nodes = dict(self.nodes)
sp = SP()
tp = [
_ for _ in sorted([
'{: <50}'.format(n['label']) +
n['uberon'] if n['uberon'] else n['label']
for n in sp.nodes.values()
])
]
#print('\n'.join(tp))
#print(sp.appendicies[1].keys())
#print(sp.nodes[1].keys())
nbase = PREFIXES['SWAN'] + '%s'
json_ = {'nodes': [], 'edges': []}
parent = ILXREPLACE('swansonBrainRegionConcept')
for node, anns in sp.nodes.items():
nid = nbase % node
new_graph.add_class(nid, parent, label=anns['label'])
new_graph.add_trip(nid, 'NIFRID:definingCitation', anns['citation'])
json_['nodes'].append({'lbl': anns['label'], 'id': 'SWA:' + str(node)})
#if anns['uberon']:
#new_graph.add_trip(nid, owl.equivalentClass, anns['uberon']) # issues arrise here...
for appendix, data in sp.appendicies.items():
aid = PREFIXES['SWAA'] + str(appendix)
new_graph.add_class(aid, label=data['name'].capitalize())
new_graph.add_trip(
aid, 'ilx:hasTaxonRank',
data['taxon']) # FIXME appendix is the data artifact...
children = data['children']
ahp = HASPART + str(appendix)
apo = PARTOF + str(appendix)
new_graph.add_op(ahp, transitive=True)
new_graph.add_op(apo, inverse=ahp, transitive=True)
for parent, childs in children.items(
): # FIXME does this give complete coverage?
pid = nbase % parent
for child in childs:
cid = nbase % child
new_graph.add_hierarchy(
cid, ahp, pid) # note hierarhcy inverts direction
new_graph.add_hierarchy(pid, apo, cid)
json_['edges'].append({
'sub': 'SWA:' + str(child),
'pred': apo,
'obj': 'SWA:' + str(parent)
})
new_graph.write()
if False:
Query = namedtuple('Query',
['root', 'relationshipType', 'direction', 'depth'])
mapping = (1, 1, 1, 1, 30, 83, 69, 70, 74, 1) # should generate?
for i, n in enumerate(mapping):
a, b = creatTree(*Query('SWA:' + str(n), 'ilx:partOf' + str(i + 1),
'INCOMING', 10),
json=json_)
print(a)
return ontid, None
class HCP(genericPScheme):
source = 'resources/human_connectome_project_2016.csv'
ont = OntMeta(
PARC, 'hcp_parcellation', ('Human Connectome Project Multi-Modal '
'human cortical parcellation'),
'HCP-MMP1.0',
'This file is automatically generated from ' + source + '.' + NOTICE,
TODAY)
concept = PScheme(ILXREPLACE(ont.name), 'HCP parcellation concept',
'NCBITaxon:9606', ADULT)
atlas = PSArtifact(
ILXREPLACE(ont.name + 'atlas'),
'Human Connectome Project Multi-Modal human cortical parcellation',
'1.0',
'20-07-2016', # d-m-y
'awaiting...',
'doi:10.1038/nature18933',
('Human Connectome Project Multi-Modal Parcellation',
'HCP Multi-Modal Parcellation',
'Human Connectome Project Multi-Modal Parcellation version 1.0'),
('HCP_MMP', ont.shortname))
# see also https://balsa.wustl.edu/study/show/RVVG
PREFIXES = makePrefixes('NIFRID')
PREFIXES['HCPMMP'] = interlex_namespace('hcpmmp/labels')
@classmethod
def datagetter(cls):
with open(cls.source, 'rt') as f:
data = [r for r in csv.reader(f)]
return data
@classmethod
def dataproc(cls, graph, data):
class hcp2016(rowParse):
def Parcellation_Index(self, value):
self.id_ = value
self.id_ = 'HCPMMP:' + value # safe because reset every row (ish)
graph.add_class(self.id_, cls.concept.curie)
def Area_Name(self, value):
value = value.strip()
graph.add_trip(self.id_, ACRONYM, value)
def Area_Description(self, value):
value = value.strip()
graph.add_trip(self.id_, rdfs.label,
'(%s) ' % cls.ont.shortname + value)
graph.add_trip(self.id_, PARCLAB, value)
def Newly_Described(self, value):
if value == 'Yes*' or value == 'Yes':
graph.add_trip(self.id_, 'NIFRID:definingCitation',
'Glasser and Van Essen 2016')
def Results_Sections(self, value):
pass
def Other_Names(self, value):
for name in value.split(','):
name = name.strip()
if name:
if len(name) <= 3:
graph.add_trip(self.id_, ACRONYM, name)
else:
graph.add_trip(self.id_, SYNONYM, name)
def Key_Studies(self, value):
for study in value.split(','):
study = study.strip()
if study:
graph.add_trip(self.id_, 'NIFRID:definingCitation',
study)
hcp2016(data)
# objectProperties
UNTAXON = 'ilx:ancestralInTaxon'
EXTAXON = 'ilx:hasInstanceInTaxon' # FIXME instances?
EXSPECIES = 'ilx:hasInstanceInSpecies'
DEFTAXON = 'ilx:definedForTaxon'
DEFSPECIES = 'ilx:definedForSpecies'
DEVSTAGE = 'ilx:definedForDevelopmentalStage'
PARTOF = 'ilx:partOf'
HASPART = 'ilx:hasPart'
DELINEATEDBY = 'ilx:delineatedBy'
# classes
ADULT = 'BIRNLEX:681'
atname = 'Parcellation scheme artifact'
ATLAS_SUPER = ILXREPLACE(atname) # 'NIFRES:nlx_res_20090402' # alternatives?
psname = 'Brain parcellation scheme concept'
PARC_SUPER = ILXREPLACE(psname)
def interlex_namespace(user):
return 'http://uri.interlex.org/' + user + '/'
def check_hierarchy(graph, root, edge, label_edge=None):
a, b = creatTree(*Query(root, edge, 'INCOMING', 10),
json=graph.make_scigraph_json(edge, label_edge))
print(a)
def add_ops(graph):
def irn(inp):
return ILXREPLACE(__name__ + inp)