def generate_abstracts(self, list_of_pmids):
"""
Generates list of documents using pmids and the restapi interface from tmtools.
Source: "http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/"
:param list_of_pmids: strings
:return nalaf.structures.Dataset: dataset
"""
# if os.path.isfile('cache.json'):
# with open('cache.json') as f:
# tm_var = json.load()
# else:
url_tmvar = 'http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/RESTful/tmTool.cgi/Mutation/{0}/JSON/'
url_converter = 'http://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/'
# load cache.json if exists
if os.path.exists('cache.json'):
with open('cache.json', 'r', encoding='utf-8') as f:
tm_var = json.load(f)
else:
tm_var = {}
for pmid in list_of_pmids:
if pmid not in tm_var: # if pmid was not already downloaded from tmTools
req = requests.get(url_tmvar.format(pmid))
try:
tm_var[pmid] = req.json()
except ValueError:
pass
# cache the tmVar annotations so we don't pull them every time
with open('cache.json', 'w') as file:
json.dump(tm_var, file, indent=4)
# for key in tm_var:
# print(json.dumps(tm_var[key], indent=4))
dataset = Dataset()
for doc_id in list_of_pmids:
if doc_id in tm_var:
doc = Document()
text = tm_var[doc_id]['text']
part = Part(text)
denotations = tm_var[doc_id]['denotations']
annotations = []
for deno in denotations:
ann = Entity(
class_id=self.mut_class_id,
offset=int(deno['span']['begin']),
text=text[deno['span']['begin']:deno['span']['end']])
annotations.append(ann)
# note should the annotations from tmvar go to predicted_annotations or annotations?
part.annotations = annotations
doc.parts['abstract'] = part
dataset.documents[doc_id] = doc
return dataset
def setUpClass(cls):
# create a sample dataset1 (1) to test
cls.dataset1 = Dataset()
doc_1 = Document()
text = '.... aaaa .... bbbb .... cccc .... dddd .... eeee .... ffff .... gggg .... hhhh .... jjjj'
part_1 = Part(text)
cls.dataset1.documents['doc_1'] = doc_1
doc_1.parts['part_1'] = part_1
exact_1 = Entity(STUB_E_ID_1, 5, 'aaaa')
exact_1.subclass = 1
exact_2 = Entity(STUB_E_ID_1, 55, 'ffff')
exact_2.subclass = 2
exact_3 = Entity(STUB_E_ID_1, 75, 'hhhh')
exact_3.subclass = 2
overlap_1_1 = Entity(STUB_E_ID_1, 25, 'cccc')
overlap_1_1.subclass = 1
overlap_1_2 = Entity(STUB_E_ID_1, 26, 'cc')
overlap_1_2.subclass = 1
overlap_2_1 = Entity(STUB_E_ID_1, 32, '.. ddd')
overlap_2_1.subclass = 2
overlap_2_2 = Entity(STUB_E_ID_1, 36, 'ddd ...')
overlap_2_2.subclass = 2
overlap_3_1 = Entity(STUB_E_ID_1, 65, 'gggg')
overlap_3_1.subclass = 1
overlap_3_2 = Entity(STUB_E_ID_1, 62, '.. gggg ..')
overlap_3_2.subclass = 2
missing_1 = Entity('e2', 45, 'eeee')
missing_1.subclass = 1
missing_2 = Entity('e2', 84, 'jjjj')
missing_2.subclass = 1
spurios = Entity('e2', 15, 'bbbb')
spurios.subclass = 1
part_1.annotations = [
exact_1, exact_2, exact_3, overlap_1_1, overlap_2_1, overlap_3_1,
missing_1, missing_2
]
part_1.predicted_annotations = [
exact_1, exact_2, exact_3, overlap_1_2, overlap_2_2, overlap_3_2,
spurios
]