def read_one_from_web():
pmid = "11250746"
d = pp.parse_xml_web(pmid, save_xml=True)
xml = d["xml"]
d.pop("xml", None)
pprint.pprint(d)
print()
os.makedirs("xml", exist_ok=True)
with open("xml/{}.xml".format(pmid), "wb") as f:
f.write(xml)
def download_api(ids, outfile, save_dir):
# if more than 1000 ids, do batch
if len(ids) > 1000:
chunks = np.array_split(ids, len(ids) / 1000)
else:
chunks = [ids]
# run in chunks
print("Downloading from API in {:} chunks".format(len(chunks)))
i = 1
for chunk in chunks:
# list to store data
articles = []
citations = []
for id in tqdm(chunk):
try:
# article
time.sleep(0.25)
articles.append(pp.parse_xml_web(id, save_xml=False))
# citations
time.sleep(0.25)
d_ = pp.parse_outgoing_citation_web(id, id_type='PMID')
if d_:
for bpmid in d_['pmid_cited']:
citations.append({
'apmid': d_['doc_id'],
'bpmid': bpmid
})
except TypeError:
pass
# write to SQL
df_data = pd.DataFrame(articles)
df_data.to_pickle(os.path.join(save_dir, 'df_data_' + str(i) + '.pkl'))
df_ref = pd.DataFrame(citations)
df_ref.to_pickle(os.path.join(save_dir, 'df_ref_' + str(i) + '.pkl'))
# write to log
if len(df_data) != 0:
for id in df_data['pmid'].tolist():
outfile.write(id + '\n')
i += 1
print("All done!")
def make_csv(catfile):
headers = [
'pmid', 'title', 'abstract', 'journal', 'affiliation', 'authors',
'year', 'keywords', 'category'
]
csv_db = PD.DataFrame(columns=headers, index=None)
labelled = PD.read_csv(catfile)
pmids = labelled.loc[:, 'pubmed_id']
pmid_cat = labelled.loc[:, 'paper_category']
for n, c in zip(pmids, pmid_cat):
paper = PP.parse_xml_web(n)
paper_row = [n,paper['title'],paper['abstract'],paper['journal'],paper['affiliation'],\
paper['authors'],paper['year'],paper['keywords'],c]
csv_db.loc[len(csv_db) + 1] = paper_row
outfile_name = catfile[:-4] + '_processed.csv'
csv_db.to_csv(outfile_name, index=None)
return outfile_name
def make_csv_(catfile, outfile_name):
headers = [
'pmid', 'title', 'abstract', 'journal', 'affiliation', 'authors',
'year', 'keywords', 'category'
]
csv_db = PD.DataFrame(columns=headers, index=None)
labelled = catfile
pmids = labelled.loc[:, 'id']
pmid_cat = labelled.loc[:, 'label']
counter = 0
for n, c in zip(pmids, pmid_cat):
paper = PP.parse_xml_web(n)
paper_row = [n,paper['title'],paper['abstract'],paper['journal'],paper['affiliation'],\
paper['authors'],paper['year'],paper['keywords'],c]
csv_db.loc[len(csv_db) + 1] = paper_row
counter += 1
if counter % 10 == 0:
print(counter, end=' ')
csv_db.to_csv(outfile_name, index=None)
return outfile_name
'B-Intervention': 7,
'I-Intervention': 8,
'B-Comparator': 9,
'I-Comparator': 10,
'B-Outcome': 11,
'I-Outcome': 12
}
idx2tag = {idx: tag for tag, idx in tag2idx.items()}
#%% App
st.header('PICO extraction for in vivo abstract')
# pmid = st.text_input('Input one PMID: ', 23326526)
pmid = st.text_input('Input one PMID: ', 27231887)
try:
xml = pubmed_parser.parse_xml_web(pmid)
title = xml['title']
text = xml['abstract']
if text == "":
st.text("No abstract available!")
else:
# st.write(title)
# st.write(text)
## Extract pico text
text = sent_detect(text, sent_pth)
# Extract pico phrases
tup, _, _ = pico_extract(text, pth_path, idx2tag)
res = tup2dict(tup)
st.write("""### Extracted PICO text ###
""")
def pmid2text(pmid):
outputs = pp.parse_xml_web(pmid, save_xml=False)
return outputs["title"], outputs["abstract"]
def get_article_info_dict_from_pubmed(self):
"""
:param store_file_name:
:param pmid_list:
:return: a dictionary whose key is pmid, value is information of referred article,
value is also a dictionary, including keys below:
title : title
abstract : abstract
journal : journal
affiliation : affiliation of first author
authors : string of authors, separated by ;
year : Publication year
keywords : keywords or MESH terms of the article
store path: 'articles_info_' + store_file_name
"""
print('------------------------------------------------------------')
print('Get article info:')
def clean(s):
s.replace("*", " ")
s.replace("¶", " ")
s.replace("†", " ")
s.replace("§", " ")
s.replace("-", " ")
s.replace("<", " ")
s.replace(">", " ")
s.replace("#", " ")
s = s.rstrip().lstrip()
if s == '':
return ''
if s[0] == ',' or s[0] == '.':
s = s[1:]
if s == '':
return ''
# if s[-1] == ')':
# s = s[:-1]
return s
begin_time = time.time()
store_file_name = self.store_file_name + 'articles' + '.npy'
article_info_dict = dict()
if os.path.exists(store_file_name):
# TODO(lyj): 为什么这里不用flat[0]
article_info_dict = np.load(store_file_name,
allow_pickle=True).flat[0]
if self.update == False:
return article_info_dict
delete_list = article_info_dict.keys() - self.pmid_list
add_list = self.pmid_list - article_info_dict.keys()
print("need to delete %d articles" % len(delete_list))
print("need to add %d articles" % len(add_list))
for pmid in delete_list:
del article_info_dict[pmid]
i = 0
for pmid in add_list:
if i % 20 == 0:
np.save(store_file_name, article_info_dict)
print('\n%d new articles are saved' % i)
try:
article_info = pp.parse_xml_web(pmid, save_xml=False)
except KeyboardInterrupt:
# do the saving here
exit(-1) # exit the program with the return code -1
except:
print('\nerror in ', pmid)
else:
temp_list = []
for item in article_info['authors'].split(';'):
item = clean(item)
if item == '':
continue
temp_list.append(item)
article_info['authors'] = temp_list
temp_list = []
for item in article_info['affiliation'].split(';'):
item = clean(item)
if item == '':
continue
temp_list.append(item)
article_info['affiliation'] = temp_list
temp_list = []
for item in article_info['keywords'].split(';'):
# item = item.split(':')
# item = item[1] if len(item) > 1 else item[0]
item = clean(item)
if item == '':
continue
if "," in item:
temp_list += item.rstrip().lstrip().split(',')
else:
temp_list.append(str.lower(item.rstrip().lstrip()))
article_info['keywords'] = temp_list
article_info_dict[pmid] = article_info
i += 1
print(i + 1, end=' ')
np.save(store_file_name, article_info_dict)
print('\n%d new articles are saved' % i)
print('Get article info done, used time:', time.time() - begin_time)
print('------------------------------------------------------------')
return article_info_dict