def gen_pdm_from_indfile(ont_url, outfile):
"""Reads an owl file from ont_url; Writes a JSON file (outfile) of
types and annotations on individuals in the file.
JSON structure:
id:
label: string
def: string
types:
- isAnonymous:boolean;
- relId:URI_string;
- objectId:URI_string.
"""
ont = Brain()
ont.learn(ont_url)
axioms = {}
if ont.knowsClass("CARO_0030002"):
axioms.update(gen_pdm(ont, ont.getInstances("CARO_0030002", 0), "CARO_0030002")) # expression_patterns
if ont.knowsClass("FBbt_00005106"):
axioms.update(gen_pdm(ont, ont.getInstances("FBbt_00005106", 0), "FBbt_00005106")) # neurons
if ont.knowsClass("FBbt_00007683"):
axioms.update(gen_pdm(ont, ont.getInstances("FBbt_00007683", 0), "FBbt_00007683")) # clones
jfile = open(outfile, "w")
jfile.write(json.dumps(axioms, sort_keys=True, indent=4))
ont.sleep()
def entity_check(SFID_list, cursor):
class owl_entity(): # v.simple object for storing attributes of class
ont = ''
base = ''
sfid = ''
typ = ''
ont_dict = {} # Dict to make a uniq'd list of ontologies
sfid_oe = {} # Dict of owl_entities - as specified by DB
for SFID in SFID_list:
cursor.execute("SELECT DISTINCT ontology_URI AS ont, owl_type as typ, baseURI as base FROM owl_entity WHERE shortFormID = '%s'" % SFID)
dc = dict_cursor(cursor)
brain = Brain()
sfo = owl_entity()
for d in dc:
ont = d['ont']
if not ont in ont_dict:
brain.learn(ont)
ont_dict[ont] = brain
sfo.ont = d['ont']
sfo.base = d['base']
sfo.typ = d['typ']
sfid_oe[SFID] = sfo
for idt in sfid_oe.items():
SFID = idt[0]
owlEnt = idt[1]
ont = owlEnt.ont
brain = ont_dict[ont]
# if brain.getAnnotation(SFID, 'deprecated'): # Need to cope with cases where it is not deprecated!
# print SFID + ' is obsolete!'
if owlEnt.typ == 'class':
if not brain.knowsClass(SFID):
print 'Unknown Class SFID in ' + owlEnt.ont
elif owlEnt.brain == 'objectProperty':
if not ont.knowsClass(SFID):
print 'Unknown objectProperty SFID in ' + owlEnt.ont
known_term_list = []
for term in tc:
oboid = term["extId"][0]
known_term_list.append(oboid)
if oboid in oboid_domId:
if "domainId" in term["domainData"]:
term["domainData"]["domainId"] = oboid_domId[oboid] # Terns that didn't formerly have domainId will not, at the point
else:
term["domainData"] = {"domainColour": [0,128,128,127],"domainId": oboid_domId[oboid],"domainSelected": "false"} # Setting default colour - will need to fix.
else:
if "domainId" in term["domainData"]:
term["domainData"]["domainId"] = '' # Remove old domain_id, if present.
owl_id = re.sub(':','_',oboid)
name = '';
if fbbt.knowsClass(owl_id):
name = fbbt.getLabel(owl_id)
print "Ignoring %s %s as , according to the mapping file, it is not a painted domain." % (oboid, name)
new_tc.append(term)
# Adding records for any new terms
for oboid, domid in oboid_domId.items():
if oboid not in known_term_list:
new_tc.append({"domainData":{"domainColour": [0,128,128,127],"domainId": domid,"domainSelected": "false"}, "extId": [oboid],"nodeState": {"open": "false", "selected": "false"} }) # Anything generated here will need a new nodeId.
update_names(new_tc, fbbt)
fbbt.sleep()
write_json(new_tc, "../json/treeContent_JFRC_BN_final.jso")
query = "SELECT DISTINCT f.uniquename AS fbid, synonym_sgml AS uc_name, f.is_obsolete as obstat " \
"FROM synonym s " \
"JOIN cvterm typ ON (typ.cvterm_id=s.type_id) " \
"JOIN feature_synonym fs ON (fs.synonym_id=s.synonym_id) " \
"JOIN feature f ON (f.feature_id=fs.feature_id) " \
"WHERE f.uniquename in (%s) and fs.is_current IS TRUE and typ.name = 'symbol'" % (class_list_string)
fb_cursor.execute(query) #
fb_dc = dict_cursor(fb_cursor)
for fb in fb_dc:
# print "\nProcessing:" + fb['uc_name']
if fb['obstat']:
warnings.warn(fb['fbid'] +" " + fb['uc_name'] + " is obsolete ! Not adding to fb_feature.owl.")
else:
if not fb_feature.knowsClass(fb['fbid']): # Only add class if not obsolete.
fb_feature.addClass(fb['fbid'])
tmp = re.sub("<up\>", "[",fb['uc_name'])
uc_name = re.sub("<\/up>", "]", tmp)
fb_feature.label(fb['fbid'], uc_name)
if re.match('FBtp\d+', fb['fbid']):
fb_feature.subClassOf(fb['fbid'], 'SO_0000796')
elif re.match('FBti\d+', fb['fbid']):
fb_feature.subClassOf(fb['fbid'], 'SO_0001218')
elif re.match('FBgn\d+', fb['fbid']):
fb_feature.subClassOf(fb['fbid'], 'SO_0000704')
elif re.match('FBal\d+', fb['fbid']):
fb_feature.subClassOf(fb['fbid'], 'SO_0001023')
else:
warnings.warn("Ignoring this, as doesn't look like an FB feature: %s." % fb['fbid'])
continue
