Jak parse všechny PubMed baseline files v parallel pomocí Pythonu
V našem previous post How to parse PubMed baseline data using Python jsme investigate, jak použít pubmed_parser library pro parse PubMed medline data pomocí Pythonu.
V tomto follow-up poskytneme example, jak použít glob pro select všechny PubMed baseline files v directory a použít concurrent.futures s tqdm pro provide convenient yet easy-to-use process parallelism pomocí ProcessPoolExecutor a progress bar UI pro command line.
Nejprve install requirements pomocí
install_pubmed_parser.sh
pip install git+git://github.com/titipata/pubmed_parser.git six numpy tqdmNyní download tento script, ensure some files jako pubmed20n0002.xml.gz nebo pubmed20n0004.xml.gz jsou ve stejném directory a run it:
parse_pubmed_parallel.py
#!/usr/bin/env python3
import pubmed_parser as pp
import glob
import os
from collections import Counter
import concurrent.futures
from tqdm import tqdm
# Source: https://techoverflow.net/2017/05/18/how-to-use-concurrent-futures-map-with-a-tqdm-progress-bar/
def tqdm_parallel_map(executor, fn, *iterables, **kwargs):
"""
Equivalent to executor.map(fn, *iterables),
but displays a tqdm-based progress bar.
Does not support timeout or chunksize as executor.submit is used internally
**kwargs is passed to tqdm.
"""
futures_list = []
for iterable in iterables:
futures_list += [executor.submit(fn, i) for i in iterable]
for f in tqdm(concurrent.futures.as_completed(futures_list), total=len(futures_list), **kwargs):
yield f.result()
def parse_and_process_file(filename):
"""
This function contains our parsing code. Usually, you would only modify this function.
"""
# Don't parse authors and references for this example, since we don't need it
dat = pp.parse_medline_xml(filename, author_list=False, reference_list=False)
# For this example, we'll build a set of count of all MeSH IDs in this file
ctr = Counter()
for entry in dat:
terms = [term.partition(":")[0].strip() for term in entry["mesh_terms"].split(";")]
for term in terms:
ctr[term] += 1
return filename, ctr
if __name__ == "__main__":
# Find all pubmed files in the current directory
all_filenames = glob.glob("pubmed*.xml.gz")
# For some workloads you might want to use a ThreadPoolExecutor,
# but a ProcessPoolExecutor is a good default
executor = concurrent.futures.ProcessPoolExecutor(os.cpu_count())
# Iterate results as they come in (the order is not the same as in the input!)
for filename, ctr in tqdm_parallel_map(executor, parse_and_process_file, all_filenames):
# NOTE: If you print() here, this might interfere with the progress bar,
# but we accept that here since it's just an example
print(filename, ctr)Nyní můžete začít modifying example, most notably parse_and_process_file() function pro do whatever processing intend.
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Bioinformatics, Python
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