mirror of
https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nopaque.git
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131 lines
3.6 KiB
Python
Executable File
131 lines
3.6 KiB
Python
Executable File
#!/usr/bin/env python2.7
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# coding=utf-8
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"""
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nlp
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Usage: For usage instructions run with option --help
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Author: Patrick Jentsch <p.jentsch@uni-bielefeld.de>
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"""
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import argparse
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import multiprocessing
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import os
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import sys
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from pyflow import WorkflowRunner
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def parse_arguments():
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parser = argparse.ArgumentParser(
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"Performs NLP of documents utilizing spaCy. \
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Output is .vrt."
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)
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parser.add_argument("-i",
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dest="inputDir",
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help="Input directory.",
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required=True)
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parser.add_argument("-l",
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dest='lang',
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help="Language for NLP",
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required=True)
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parser.add_argument("-o",
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dest="outputDir",
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help="Output directory.",
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required=True)
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parser.add_argument("--nCores",
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default=multiprocessing.cpu_count(),
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dest="nCores",
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help="Total number of cores available.",
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required=False,
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type=int)
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return parser.parse_args()
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class NLPWorkflow(WorkflowRunner):
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def __init__(self, jobs, lang, nCores):
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self.jobs = jobs
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self.lang = lang
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self.nCores = nCores
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def workflow(self):
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###
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# Task "mkdir_job": create output directories
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# Dependencies: None
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###
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mkdir_jobs = []
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mkdir_job_number = 0
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for job in self.jobs:
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mkdir_job_number += 1
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cmd = 'mkdir -p "%s"' % (
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job["output_dir"]
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)
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mkdir_jobs.append(self.addTask(label="mkdir_job_-_%i" % (mkdir_job_number), command=cmd))
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###
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# Task "spacy_nlp_job": perform NLP
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# Dependencies: mkdir_jobs
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###
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self.waitForTasks()
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nlp_jobs = []
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nlp_job_number = 0
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for job in self.jobs:
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nlp_job_number += 1
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cmd = 'spacy_nlp -i "%s" -o "%s" -l "%s"' % (
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job["path"],
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os.path.join(job["output_dir"], os.path.basename(job["path"]).rsplit(".", 1)[0] + ".vrt"),
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self.lang
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)
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nlp_jobs.append(self.addTask(label="nlp_job_-_%i" % (nlp_job_number), command=cmd, dependencies=mkdir_jobs))
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###
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# Task "zip_job": compress output
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# Dependencies: nlp_jobs
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###
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zip_jobs = []
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zip_job_number = 0
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for job in self.jobs:
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zip_job_number += 1
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cmd = 'zip -jqr %s %s' % (
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job["output_dir"] + "_-_nlp",
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job["output_dir"]
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)
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zip_jobs.append(self.addTask(label="zip_job_-_%i" % (zip_job_number), command=cmd, dependencies=nlp_jobs))
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def analyze_jobs(inputDir, outputDir, level=1):
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jobs = []
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if level > 2:
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return jobs
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for file in os.listdir(inputDir):
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if os.path.isdir(os.path.join(inputDir, file)):
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jobs += analyze_jobs(
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os.path.join(inputDir, file),
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os.path.join(outputDir, file),
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level + 1
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)
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elif file.endswith(".txt"):
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jobs.append({"path": os.path.join(inputDir, file), "output_dir": os.path.join(outputDir, file.rsplit(".", 1)[0])})
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return jobs
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def main():
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args = parse_arguments()
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wflow = NLPWorkflow(
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analyze_jobs(args.inputDir, args.outputDir),
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args.lang,
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args.nCores
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)
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retval = wflow.run(nCores=args.nCores)
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sys.exit(retval)
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if __name__ == "__main__":
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main() |