def handle(self, *args, **options):
from rdflib.graph import Graph
from rdflib import RDFS, RDF, OWL
from rdflib.term import URIRef, BNode, Literal
import yaml
OboDefinition = URIRef('http://purl.obolibrary.org/obo/IAO_0000115')
part_of_some = URIRef('http://purl.obolibrary.org/obo/BFO_0000050_some')
Synonym = URIRef(u'http://www.geneontology.org/formats/oboInOwl#hasSynonym')
g = Graph()
g.parse(args[1], 'xml')
Model = MuscleOwl if args[0] == 'm' else BehaviorOwl
# first pass, add the things
for subject in nonblankthings(g, g.subjects()):
slabel = g.label(subject)
m = get_model_instance(Model, unicode(subject))
m.uri = unicode(subject)
m.label = unicode(slabel)
m.rdfs_is_class = (subject, RDF.type, OWL.Class) in g
m.obo_definition = unicode(g.value(subject, OboDefinition, None))
m.rdfs_comment = unicode(g.value(subject, RDFS.comment, None))
synonyms = [unicode(syn) for syn in g.objects(subject, Synonym)]
if len(synonyms):
m.synonyms_comma_separated = ", ".join(synonyms)
else:
m.synonyms_comma_separated = None
m.save()
# second pass, add the relationships
for subject in nonblankthings(g, g.subjects()):
slabel = g.label(subject)
m = Model.objects.get(uri=unicode(subject))
m.rdfs_subClassOf_ancestors.clear()
# add all super-classes to m.rdfs_subClassOf_ancestors
for obj in nonblankthings(g, g.transitive_objects(subject, RDFS.subClassOf)):
if obj != subject:
a = Model.objects.get(uri=unicode(obj))
m.rdfs_subClassOf_ancestors.add(a)
m.bfo_part_of_some.clear()
# add all things that this thing is part of to m.bfo_part_of_some
for obj in nonblankthings(g, g.objects(subject, part_of_some)):
if obj != subject:
a = Model.objects.get(uri=unicode(obj))
m.bfo_part_of_some.add(a)
# add only direct super-classes to m.rdfs_subClassOf
#for obj in nonblankthings(g, g.objects(subject, RDFS.subClassOf)):
# if obj != subject:
# a = Model.objects.get(uri=unicode(obj))
# m.rdfs_subClassOf.add(a)
m.save()
def ExtractTextFeatures():
id2text = open("../dataset/FGraph_ID_Text.txt", "a", encoding="utf-8")
NodeID = open("../dataset/FGraph_Origin_Node_ID.txt",
"r",
encoding="utf-8")
count = 0
for line in NodeID.readlines():
if count < 36661:
count += 1
continue
if count >= 81000: break
uri, id = line.strip().split("\t\t")
text = ""
if match(uri, rule='IRI_reference') is None: text = uri
elif len(uri.split("#")) > 1:
text = re.sub(r"(\w)([A-Z])", r"\1 \2", (uri.split("#"))[-1])
else:
g = Graph()
try:
g.parse(uri)
text = g.label(URIRef(uri))
except:
print("不能解析！")
if text == "":
uri_s1 = uri.split("resource/")
if len(uri_s1) > 1: text = uri_s1[-1]
else:
uri_s2 = (uri.split("/"))[-1]
if uri_s2 != "": text = uri_s2
else: text = uri
id2text.write(id + "\t\t" + text + "\n")
print(count)
count += 1
id2text.close()
NodeID.close()
def ExtractTextFeatures():
uri2label = GetEList()
id2text = open("../dataset/Graph_ID_Text.txt", "w", encoding="utf-8")
NodeID = open("../dataset/Graph_Origin_Node_ID.txt", "r", encoding="utf-8")
count = 0
for line in NodeID.readlines():
uri, id = line.strip().split("\t\t")
text = ""
if match(uri, rule='IRI_reference') is None: text = uri
elif uri in uri2label.keys(): text = uri2label[uri]
elif len(uri.split("#")) > 1:
text = re.sub(r"(\w)([A-Z])", r"\1 \2", (uri.split("#"))[-1])
else:
g = Graph()
try:
g.parse(uri)
text = g.label(URIRef(uri))
except:
print("不能解析！")
if text == "":
uri_s = uri.split("resource/")
if len(uri_s) > 1: text = uri_s[-1]
else: text = (uri.split("/"))[-1]
id2text.write(id + "\t\t" + text + "\n")
print(count)
count += 1
id2text.close()
NodeID.close()
if covar == NIDM_INDEPENDENT_ERROR:
covar = ""
else:
if covar_spatial == NIDM_SPATIALLY_LOCAL_MODEL:
covar_spatial = "local"
elif covar_spatial == NIDM_SPATIALLY_GLOBAL_MODEL:
covar_spatial = "global"
elif covar_spatial == NIDM_SPATIALLY_REGULARIZED_MODEL:
covar_spatial = "spatially regularized"
else:
raise Exception(
'Unknown spatial variance estimation: ' +
str(covar_spatial))
covar = " and a " + covar_spatial + " " + \
owl_graph.label(covar)
if drift_model:
drift_model = "Drift was fit with a " + \
owl_graph.label(drift_model).lower()
if spm_drift_cutoff:
drift_model = drift_model + \
" (" + spm_drift_cutoff + "s cut-off)."
if fsl_drift_cutoff:
drift_model = drift_model + \
" (" + fsl_drift_cutoff + "s FWHM)."
else:
drift_model = ""
print "-------------------"
print "%s-level analysis was performed with %s (version %s). \
clus_thresh, unused = threshold_txt(owl_graph,
height_thresh_type,
height_value, stat_type)
thresh += " with a cluster defining threshold " + clus_thresh
else:
inference_type = "Voxel-wise"
thresh, multiple_compa = threshold_txt(owl_graph,
height_thresh_type,
height_value, stat_type)
thresh += " and clusters smaller than %d were discarded" \
% int(extent_value)
if homoscedasticity:
variance = 'equal'
else:
variance = 'unequal'
print "-------------------"
print "Group statistic was performed in %s (version %s). \
%s was performed assuming %s variances. %s inference \
was performed %susing a threshold %s. The search volume was %d cm^3 \
(%d voxels)." % (owl_graph.label(software), soft_version,
owl_graph.label(est_method).capitalize(),
variance, inference_type, multiple_compa, thresh,
float(search_vol_units) / 1000, int(search_vol_vox))
print "-------------------"
else:
print "Query returned no results."
