DeepLearningExamples/TensorFlow/LanguageModeling/BERT/data/PubMedDownloader.py
2019-09-13 19:12:50 +02:00

93 lines
3.4 KiB
Python

# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import bz2
import glob
import gzip
import os
import urllib.request
import shutil
import sys
class PubMedDownloader:
def __init__(self, subset, save_path):
self.subset = subset
# Modifying self.save_path in two steps to handle creation of subdirectories
self.save_path = save_path + '/pubmed' + '/'
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
self.save_path = self.save_path + '/' + subset
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
self.download_urls = {
'baseline' : 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/',
'daily_update' : 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/updatefiles/',
'fulltext' : 'ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/',
'open_access' : 'ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/'
}
def download(self):
print('subset:', self.subset)
url = self.download_urls[self.subset]
self.download_files(url)
self.extract_files()
def download_files(self, url):
url = self.download_urls[self.subset]
output = os.popen('curl ' + url).read()
if self.subset == 'fulltext' or self.subset == 'open_access':
line_split = 'comm_use' if self.subset == 'fulltext' else 'non_comm_use'
for line in output.splitlines():
if line[-10:] == 'xml.tar.gz' and \
line.split(' ')[-1].split('.')[0] == line_split:
file = os.path.join(self.save_path, line.split(' ')[-1])
if not os.path.isfile(file):
print('Downloading', file)
response = urllib.request.urlopen(url + line.split(' ')[-1])
with open(file, "wb") as handle:
handle.write(response.read())
elif self.subset == 'baseline' or self.subset == 'daily_update':
for line in output.splitlines():
if line[-3:] == '.gz':
file = os.path.join(self.save_path, line.split(' ')[-1])
if not os.path.isfile(file):
print('Downloading', file)
response = urllib.request.urlopen(url + line.split(' ')[-1])
with open(file, "wb") as handle:
handle.write(response.read())
else:
assert False, 'Invalid PubMed dataset/subset specified.'
def extract_files(self):
files = glob.glob(self.save_path + '/*.xml.gz')
for file in files:
print('file:', file)
input = gzip.GzipFile(file, mode='rb')
s = input.read()
input.close()
out = open(file[:-3], mode='wb')
out.write(s)
out.close()