It's for counting biological_process terms which have is_a and part_of.

go_term_stat.py

"""
It's for counting biological_process terms which have is_a and part_of.

Usage:

    $ wget http://purl.obolibrary.org/obo/go.obo
    $ python3 go_term_stat.py < go.obo

Main data structure

    terms = {
        'GO:12345': {
            'is_a': ['GO:12344', 'GO:12333'],
            'part_of': ['GO:1111', 'GO:2222'],
        }
    }
"""
import sys
import pprint

target_terms = {}

def iter_term_lines(afile):
    while True:
        line = next(afile)
        line = line.strip()
        if not line:
            break
    lines = [next(afile)]
    for line in afile:
        line = line.strip()
        if line.startswith('[Term]'):
            yield lines
            lines = []
        lines.append(line)
    yield lines

total_terms = 0
bp_terms = 0
for lines in iter_term_lines(sys.stdin):
    total_terms += 1
    go_id = lines[1][4:]
    data = {
        'is_a': [],
        'part_of': [],
    }
    namespace = lines[3].split(': ')[1]
    if namespace != 'biological_process':
        continue
    bp_terms += 1
    for line in lines:
        key, *values = line.split(':')
        value = ':'.join(values)
        if key == 'is_a':
            data['is_a'].append(value.split()[0])
        if key == 'relationship' and value.startswith(' part_of'):
            data['part_of'].append(value.split()[1])
        if key == 'name':
            data['name'] = value[1:]
    
    if data['is_a'] and data['part_of']:
        target_terms[go_id] = data

pprint.pprint(target_terms)
print(total_terms, bp_terms, len(target_terms))