def unique_query():
# parse parameters
status, val = parse_resource_field()
# bad args.
if status == 1:
return val
# good args.
resource, field = val
# special case for oncotree.
if resource == 'clinical' and field == 'ONCOTREE_PRIMARY_DIAGNOSIS_NAME':
# make oncotree.
onco_tree = oncotreenx.build_oncotree(settings.DATA_ONCOTREE_FILE)
# loop over every-node and do text match.
#results = set([onco_tree.node[n]['text'] for n in onco_tree.nodes()])
#results.remove("root")
#results = list(results)
# turn into
results = list()
for n in onco_tree.nodes():
tmp = {
'text': onco_tree.node[n]['text'],
'code': n
}
results.append(tmp)
else:
# search for this field.
db = app.data.driver.db
results = db[resource].distinct(field)
# remove non.
tmp = set(results)
if None in tmp:
tmp.remove(None)
results = list(tmp)
# encode response.
data = json.dumps({'resource': resource, 'field': field, 'values': results})
resp = Response(response=data,
status=200,
mimetype="application/json")
return resp
def autocomplete_query():
db = app.data.driver.db
# parse parameters
status, val = parse_resource_field()
# parse the value.
gene = request.args.get("gene")
# bad args.
if status == 1:
return val
resource, field = val
results = list(
db.genomic.aggregate([
{
"$match": {
"TRUE_HUGO_SYMBOL": gene,
field: {
"$ne": None
}
}
},
{
"$group": {
"_id": "$TRUE_HUGO_SYMBOL",
field: {
"$addToSet": f"${field}"
}
}
},
]))
if len(results) > 0 and field in results[0]:
results = results[0][field]
else:
results = []
# encode response.
data = json.dumps({
'resource': resource,
'field': field,
'values': results
})
resp = Response(response=data, status=200, mimetype="application/json")
return resp
def autocomplete_query():
# parse parameters
status, val = parse_resource_field()
# parse the value.
value = request.args.get("value")
gene = request.args.get("gene")
# bad args.
if status == 1:
return val
# good args.
resource, field = val
# get the type.
if resource == "genomic":
schema = data_model.genomic_schema[field]['type']
else:
schema = data_model.clinical_schema[field]['type']
# special cases.
if resource == 'clinical' and field == 'ONCOTREE_PRIMARY_DIAGNOSIS_NAME':
# make oncotree.
onco_tree = oncotreenx.build_oncotree(settings.DATA_ONCOTREE_FILE)
# loop over every-node and do text match.
hit_set = set()
for n in onco_tree.nodes():
#TODO: Verify this doesn't need a decode
a = onco_tree.node[n]['text'].lower()
b = value.lower()
if a.count(b) > 0:
# get predecessors and ancestors
hit_set.add(n)
hit_set = hit_set.union(set(onco_tree.predecessors(n)))
hit_set = hit_set.union(set(onco_tree.successors(n)))
# remove root.
if 'root' in hit_set:
hit_set.remove('root')
# convert to full text.
results = [onco_tree.node[n]['text'] for n in hit_set]
else:
# only support string and integer.
if schema not in set(['string', 'integer']):
data = json.dumps({'error': 'unsupported field type: %s' % schema})
resp = Response(response=data,
status=400,
mimetype="application/json")
return resp
# handle string.
db = app.data.driver.db
if schema == "string":
# finalize search term.
term = '.*%s.*' % value
# first make query.
if gene is None:
query = db[resource].find({
field: {'$regex': term, '$options': '-i'}
})
else:
query = db[resource].find({"$and": [
{field: {'$regex': term, '$options': '-i'}},
{"$or": [
{'TRUE_HUGO_SYMBOL': gene},
{'CNV_HUGO_SYMBOL': gene},
]}
]})
else:
# finalize the search term.
term = "/^%s.*/.test(this.%s)" % (value, field)
# first make the query
if gene is None:
query = db[resource].find({"$and": [
{'$where': term},
]})
else:
query = db[resource].find({"$and": [
{'$where': term},
{"$or": [
{'TRUE_HUGO_SYMBOL': gene},
{'CNV_HUGO_SYMBOL': gene},
]}
]})
# extract distinct from query
results = query.distinct(field)
# remove non.
tmp = set(results)
if None in tmp:
tmp.remove(None)
results = list(tmp)
# encode response.
data = json.dumps({'resource': resource, 'field': field, 'values': results})
resp = Response(response=data,
status=200,
mimetype="application/json")
return resp