def get_term2id(
ontology: Ontology
) -> typing.Tuple[typing.Dict[str, str], typing.List[str]]:
terms = []
term2id = {}
for term in ontology.terms():
if term.name:
terms.append(term.name)
term2id[term.name] = term.id
return term2id, terms
def convert_to_json(input="GFOP.owl", output="GFOP.json"):
# create a root to bundle everything
root: Node = Node("GFOP")
nodes = {}
# read owl file and cache all nodes in a dict{name, node}
obo = Ontology(input)
for term in obo.terms():
id = term.id
name = term.name
# find parents in distance 1 (exclude self)
parent_terms = term.superclasses(with_self=False, distance=1).to_set()
if parent_terms is None or len(parent_terms) == 0:
# create root node
nodes[name] = Node(name, id=id)
else:
# currently only uses one parent
parent = parent_terms.pop()
nodes[name] = Node(name,
id=id,
parent_id=parent.id,
parent_name=parent.name)
# link all nodes to their parents
for key, node in nodes.items():
if key is not root.name:
try:
# find parent in cached nodes and set to node
node.parent = nodes[node.parent_name]
except AttributeError:
# no parent - add to root
node.parent = root
# generate json string
exporter = JsonExporter(indent=2, sort_keys=True)
json = exporter.export(root)
# print json and tree for debugging
print(json)
for pre, _, node in RenderTree(root):
print("%s%s" % (pre, node.name))
# export to json file
print("Writing to {}".format(output))
with open(output, "w") as file:
print(json, file=file)
def ontologyExtractor(ontName):
'''
Extracts raw (property, entity) RDF triples from ontology given entity name
'''
ont = Ontology(ontName)
ontology = []
for term in list(ont.terms()):
subclasses = list(term.subclasses())
ontology.extend([(subclass.name, "subclass_of", term.name)
for subclass in subclasses])
ontology = [
tuple(el.split("\t")) for el in list(
set([
"\t".join(relation) for relation in ontology
if relation[0] != relation[-1]
]))
]
ontology = [
ont for ont in ontology if ont[0] == "Pizza" or ont[-1] == "Pizza"
]
ontology = [("hypernym", el[-1]) if el[0] == "Pizza" else
("hyponym", el[0] + " Pizza") for el in ontology]
return ontology
aro = Ontology.from_obo_library("aro.owl")
#exploring ontology
cf = aro['confers_resistance_to_antibiotic']
t = aro['ARO:1000001']
list(t.objects(cf))
list(t.superclasses())
list(t.subclasses())
#change format to obo
with open('aro.obo', 'wb') as f:
aro.dump(f, format='obo')
#find terms
aro = Ontology("aro.obo")
for term in aro.terms():
if term.is_leaf():
print(term.id)
#load resfinder sequences for matching
import pandas as pd
resfinder_seq = pd.read_csv("resfinder.csv", sep=" ", header=None)
aro2seq = {}
for a in set(resfinder_seq['#Aminoglycoside']):
if a not in aro:
continue
t = aro[a]
cf = aro['name.lower()']
cur = list(t.subclasses(cf))
if cur:
aro2seq[a] = ';'.join([c.name for c in cur])
class ChebiOntologySubsetter:
# Extract a subset of ChEBI that is for just the
# classes that are included in the experiment, for visualising
# the F1 score as a colour on a network plot.
def __init__(self):
self.chebislim = None
self.classes_in = None
def createSubsetFor(self, classes_in):
self.classes_in = set(classes_in)
print(len(classes_in))
with open("classes_in.txt", 'w') as outfile:
for c in self.classes_in:
outfile.writelines(c + "\n")
rw = robot_wrapper.RobotWrapper(
robotcmd='/Users/hastingj/Work/Onto/robot/robot')
#get_ontology_cmd = 'curl -L http://purl.obolibrary.org/obo/chebi.obo > chebi.obo'
#rw.__executeCommand__(get_ontology_cmd)
extract_cmd = [
rw.robotcmd, "extract --method MIREOT ", "--input chebi.obo",
"--lower-terms classes_in.txt", "--intermediates minimal",
"--output chebi-slim.obo"
]
rw.__executeCommand__(" ".join(extract_cmd))
self.chebislim = Ontology("chebi-slim.obo")
# For the ontology subset, print a network image for those classes
# including colours for some numeric value assigned to classes
# colour_nums = dictionary of chebi IDs to some number between 0 and 1
# num_cols = how many colours to generate in the range from colour start to end
def printImageOfSubset(self,
image_name,
colour_nums,
num_cols=100,
colour_start="red",
colour_end="green"):
red = Color(colour_start)
colors = list(red.range_to(Color(colour_end), num_cols))
G = nx.DiGraph()
for term in self.chebislim.terms():
chebi_id = term.id
chebi_name = term.name
parents = set([
t.id for rel in term.relationships
for t in term.relationships[rel] if rel.name == 'is a'
])
definition = term.definition
color = 'grey'
if chebi_id in self.classes_in:
cnum = colour_nums[
chebi_id] #f1val = classifres[chebi_id]['f1-score']
color = colors[int(num_cols * cnum) - 1]
G.add_node(chebi_name, color=color)
for p in parents:
G.add_edge(chebi_name, self.chebislim[p].name)
pdot = nx.drawing.nx_pydot.to_pydot(G)
for i, node in enumerate(pdot.get_nodes()):
node.set_shape('box')
node.set_fontcolor('black')
#node.set_fillcolor(node.color)
node.set_style('rounded, filled')
#node.set_color(node.color)
png_path = image_name #"chebi-slim-vis.png"
pdot.write_png(png_path, prog='/usr/local/bin/dot')
