# Copyright 2022 Cerebras Systems.
#
# 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.
"""
Wrapper script to download PubMed datasets
Reference: https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT
"""
import argparse
import glob
import gzip
import os
import shutil
import tarfile
import urllib.request
[docs]class Downloader:
[docs] def __init__(self, dataset, save_path):
"""
:param save_path: Location to download and extract the dataset
:param dataset: One of
"pubmed_baseline",
"pubmed_daily_update",
"pubmed_fulltext",
"pubmed_open_access"
Extracts to save_path/extracted
"""
if dataset == "all":
self.datasets = [
"pubmed_baseline",
"pubmed_daily_update",
"pubmed_fulltext",
"pubmed_open_access",
]
else:
self.datasets = [dataset]
self.save_path = save_path
if not os.path.exists(save_path):
os.makedirs(save_path)
self.download_urls = {
'pubmed_baseline': 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/',
'pubmed_daily_update': 'ftp://ftp.ncbi.nlm.nih.gov/pubmed/updatefiles/',
'pubmed_fulltext': 'ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/',
'pubmed_open_access': 'ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/',
}
def download(self):
for dataset_name in self.datasets:
print(
f"**** Dataset: {dataset_name}, Download_path: {self.save_path}"
)
url = self.download_urls[dataset_name]
self.download_files(url, dataset_name)
self.extract_files(dataset_name)
def download_files(self, url, dataset):
output = os.popen('curl ' + url).read()
if dataset == 'pubmed_fulltext' or dataset == 'pubmed_open_access':
line_split = (
'comm_use' if dataset == 'pubmed_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(f"Downloading: {file}")
response = urllib.request.urlopen(
url + line.split(' ')[-1]
)
with open(file, "wb") as handle:
shutil.copyfileobj(
response, handle, length=1024 * 256
)
elif dataset == 'pubmed_baseline' or dataset == 'pubmed_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(f"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/dataset specified.'
def extract_files(self, dataset):
extractdir = os.path.join(self.save_path, 'extracted')
if not os.path.exists(extractdir):
os.makedirs(extractdir)
if dataset == "pubmed_baseline" or dataset == "pubmed_daily_update":
files = glob.glob(self.save_path + '/*.xml.gz')
for file in files:
print(f"Extracting: {file}")
input = gzip.GzipFile(file, mode='rb')
s = input.read()
input.close()
filename = os.path.basename(file)
filename = filename[:-3]
out_file = os.path.join(extractdir, filename)
out = open(out_file, mode='wb')
out.write(s)
out.close()
elif dataset == "pubmed_fulltext" or dataset == "pubmed_openaccess":
files = glob.glob(self.save_path + '/*xml.tar.gz')
for file in files:
print(f"Extracting: {file}")
filename = os.path.basename(file)
filename = filename.split('.tar.gz')[0]
filename = filename.replace(".", "_")
extract_dir = os.path.join(extractdir, filename)
with tarfile.open(file, "r:gz") as tar:
tar.extractall(extract_dir)
[docs]def parse_args():
parser = argparse.ArgumentParser(
description='Downloading files from PubMed'
)
parser.add_argument(
'--dataset',
type=str,
help='Specify the dataset to perform --action on',
required=True,
choices={
'pubmed_baseline',
'pubmed_daily_update',
'pubmed_fulltext',
'pubmed_open_access',
'all',
},
)
parser.add_argument(
'--save_path',
type=str,
help='Path to save the downloaded and extracted raw files',
required=True,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
downloader = Downloader(args.dataset, args.save_path)
downloader.download()