.gitlab-ci.yml

@@ -1,68 +1,42 @@
-image: docker:19.03.13
+image: docker:latest
 
 services:
-  - docker:19.03.13-dind
+  - docker:dind
 
 stages:
   - build
   - push
 
-variables:
+before_script:
-  DOCKER_TLS_CERTDIR: "/certs"
+  - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
-  INTERMEDIATE_IMAGE_TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME-$CI_COMMIT_SHA
 
-.reg_setup:
+Build:
-  before_script:
-    - apk add --no-cache curl
-    - curl --fail --show-error --location "https://github.com/genuinetools/reg/releases/download/v$REG_VERSION/reg-linux-amd64" --output /usr/local/bin/reg
-    - echo "$REG_SHA256  /usr/local/bin/reg" | sha256sum -c -
-    - chmod a+x /usr/local/bin/reg
-  variables:
-    REG_SHA256: ade837fc5224acd8c34732bf54a94f579b47851cc6a7fd5899a98386b782e228
-    REG_VERSION: 0.16.1
-
-build_image:
   script:
     - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
-    - docker build -t $INTERMEDIATE_IMAGE_TAG .
+    - docker build --pull -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA .
-    - docker push $INTERMEDIATE_IMAGE_TAG
+    - docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
   stage: build
   tags:
-    - docker
+  - docker
 
-push_master:
+Push latest:
-  extends:
-    - .reg_setup
   only:
     - master
   script:
-    - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
+    - docker pull $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
-    - docker pull $INTERMEDIATE_IMAGE_TAG
+    - docker tag $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA $CI_REGISTRY_IMAGE:latest
-    - /usr/local/bin/reg rm -d --auth-url $CI_REGISTRY -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $INTERMEDIATE_IMAGE_TAG
+    - docker push $CI_REGISTRY_IMAGE:latest
-    - docker tag $INTERMEDIATE_IMAGE_TAG $IMAGE_TAG
-    - docker push $IMAGE_TAG
   stage: push
   tags:
-    - docker
+  - docker
-  variables:
-    IMAGE_TAG: $CI_REGISTRY_IMAGE:latest
 
-push_other:
+Push tag:
-  extends:
-    - .reg_setup
-  except:
-    - master
   only:
-    - branches
     - tags
   script:
-    - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
+    - docker pull $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
-    - docker pull $INTERMEDIATE_IMAGE_TAG
+    - docker tag $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME
-    - /usr/local/bin/reg rm -d --auth-url $CI_REGISTRY -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $INTERMEDIATE_IMAGE_TAG
+    - docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME
-    - docker tag $INTERMEDIATE_IMAGE_TAG $IMAGE_TAG
-    - docker push $IMAGE_TAG
   stage: push
   tags:
-    - docker
+  - docker
-  variables:
-    IMAGE_TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME

Dockerfile

@@ -1,67 +1,43 @@
-FROM debian:buster-slim
+FROM debian:9-slim
-
-
-LABEL authors="Patrick Jentsch <p.jentsch@uni-bielefeld.de>, Stephan Porada <porada@posteo.de>"
 
+LABEL maintainer="inf_sfb1288@lists.uni-bielefeld.de"
 
+ENV DEBIAN_FRONTEND=noninteractive
 ENV LANG=C.UTF-8
 
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+    build-essential \
+    ca-certificates \
+    python2.7 \
+    python3.5 \
+    python3-dev \
+    python3-pip \
+    python3-setuptools \
+    wget
 
-RUN apt-get update \
+# Install pyFlow
- && apt-get install --no-install-recommends --yes \
+ENV PYFLOW_VERSION 1.1.20
-      procps \
+RUN wget -nv https://github.com/Illumina/pyflow/releases/download/v"$PYFLOW_VERSION"/pyflow-"$PYFLOW_VERSION".tar.gz && \
-      python3.7 \
+    tar -xzf pyflow-"$PYFLOW_VERSION".tar.gz && \
-      python3-pip \
+    cd pyflow-"$PYFLOW_VERSION" && \
-      wget \
+    python2.7 setup.py build install && \
- && python3 -m pip install \
+    cd .. && \
-      chardet \
+    rm -r pyflow-"$PYFLOW_VERSION" pyflow-"$PYFLOW_VERSION".tar.gz
-      setuptools \
-      wheel
 
-# Install the NLP pipeline and it's dependencies #
+# Install spaCy
-## Install pyFlow ##
+RUN pip3 install wheel && pip3 install -U spacy && \
-ENV PYFLOW_VERSION=1.1.20
+    python3.5 -m spacy download de && \
-RUN wget --no-check-certificate --quiet \
+    python3.5 -m spacy download en && \
-      "https://github.com/Illumina/pyflow/releases/download/v${PYFLOW_VERSION}/pyflow-${PYFLOW_VERSION}.tar.gz" \
+    python3.5 -m spacy download es && \
- && tar -xzf "pyflow-${PYFLOW_VERSION}.tar.gz" \
+    python3.5 -m spacy download fr && \
- && cd "pyflow-${PYFLOW_VERSION}" \
+    python3.5 -m spacy download it && \
- && apt-get install --no-install-recommends --yes \
+    python3.5 -m spacy download pt
-      python2.7 \
- && python2.7 setup.py build install \
- && cd - > /dev/null \
- && rm -r "pyflow-${PYFLOW_VERSION}" "pyflow-${PYFLOW_VERSION}.tar.gz"
 
+COPY nlp /usr/local/bin
+COPY spacy_nlp /usr/local/bin
 
-## Install spaCy ##
+RUN mkdir /input /output && \
-ENV SPACY_VERSION=3.2.1
+    chmod a+rw /input /output
-RUN apt-get install --no-install-recommends --yes \
-      python3.7 \
-      python3-pip \
- && pip3 install \
-      chardet \
-      setuptools \
-      wheel \
- && pip3 install --upgrade pip \
- && pip3 install "spacy==${SPACY_VERSION}"
-
-
-ENV SPACY_MODELS="de_core_news_md,en_core_web_md,it_core_news_md,pl_core_news_md,zh_core_web_md"
-ENV SPACY_MODELS_VERSION=3.2.0
-RUN for spacy_model in $(echo ${SPACY_MODELS} | tr "," "\n"); do python3 -m spacy download "${spacy_model}-${SPACY_MODELS_VERSION}" --direct; done
-
-
-COPY packages .
-RUN cd stand-off-data-py \
- && python3 -m pip install . \
- && cd -
-
-
-## Install Pipeline ##
-COPY nlp spacy-nlp vrt-creator /usr/local/bin/
-
-
-RUN rm -r /var/lib/apt/lists/*
-
 
 ENTRYPOINT ["nlp"]
-CMD ["--help"]

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2021 Bielefeld University - CRC 1288 - INF
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -1,41 +1,74 @@
-# NLP - Natural Language Processing
+# Natural language processing
 
-This software implements a heavily parallelized pipeline for Natural Language Processing of text files. It is used for nopaque's NLP service but you can also use it standalone, for that purpose a convenient wrapper script is provided. The pipeline is designed to run on Linux operating systems, but with some tweaks it should also run on Windows with WSL installed.
+This repository provides all code that is needed to build a container image for natural language processing utilizing [spaCy](https://spacy.io).
 
-## Software used in this pipeline implementation
+## Build image
 
-- Official Debian Docker image (buster-slim): https://hub.docker.com/_/debian
+1. Clone this repository and navigate into it:
-  - Software from Debian Buster's free repositories
-- pyFlow (1.1.20): https://github.com/Illumina/pyflow/releases/tag/v1.1.20
-- spaCy (3.2.1): https://github.com/explosion/spaCy/releases/tag/v3.2.1
-- spaCy medium sized models (3.2.0):
-  - https://github.com/explosion/spacy-models/releases/tag/de_core_news_md-3.2.0
-  - https://github.com/explosion/spacy-models/releases/tag/en_core_web_md-3.2.0
-  - https://github.com/explosion/spacy-models/releases/tag/it_core_news_md-3.2.0
-  - https://github.com/explosion/spacy-models/releases/tag/nl_core_news_md-3.2.0
-  - https://github.com/explosion/spacy-models/releases/tag/pl_core_news_md-3.2.0
-  - https://github.com/explosion/spacy-models/releases/tag/zh_core_web_md-3.2.0
-
-
-## Installation
-
-1. Install Docker and Python 3.
-2. Clone this repository: `git clone https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp.git`
-3. Build the Docker image: `docker build -t gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:v0.1.0 nlp`
-4. Add the wrapper script (`wrapper/nlp` relative to this README file) to your `${PATH}`.
-5. Create working directories for the pipeline: `mkdir -p /<my_data_location>/{input,output}`.
-
-
-## Use the Pipeline
-
-1. Place your plain text files inside `/<my_data_location>/input`. Files should all contain text of the same language.
-2. Clear your `/<my_data_location>/output` directory.
-3. Start the pipeline process. Check the pipeline help (`nlp --help`) for more details.
-```bash
-cd /<my_data_location>
-nlp \
-  --input-dir input \
-  --output-dir output \
-  -m <model_code> <optional_pipeline_arguments>
 ```
-4. Check your results in the `/<my_data_location>/output` directory.
+git clone https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp.git && cd nlp
+```
+
+2. Build image:
+```
+docker build -t sfb1288inf/nlp:latest .
+```
+
+Alternatively build from the GitLab repository without cloning:
+
+1. Build image:
+```
+docker build -t sfb1288inf/nlp:latest https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp.git
+```
+
+## Download prebuilt image
+
+The GitLab registry provides a prebuilt image. It is automatically created, utilizing the conquaire build servers.
+
+1. Download image:
+```
+docker pull gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:latest
+```
+
+## Run
+
+1. Create input and output directories for the NLP software:
+```
+mkdir -p /<mydatalocation>/files_for_nlp /<mydatalocation>/files_from_nlp
+```
+
+2. Place your text files inside the `/<mydatalocation>/files_for_nlp` directory. Files should all contain text of the same language.
+
+3. Start the NLP process.
+```
+docker run \
+    --rm \
+    -it \
+    -u $(id -u $USER):$(id -g $USER) \
+    -v /<mydatalocation>/files_for_nlp:/input \
+    -v /<mydatalocation>/files_from_nlp:/output \
+    sfb1288inf/nlp:latest \
+        -i /input \
+        -l <languagecode> \
+        -o /output
+```
+The arguments below `sfb1288inf/nlp:latest` are described in the [NLP arguments](#nlp-arguments) part.
+
+If you want to use the prebuilt image, replace `sfb1288inf/nlp:latest` with `gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:latest`.
+
+4. Check your results in the `/<mydatalocation>/files_from_nlp` directory.
+
+### NLP arguments
+
+`-i path`
+* Sets the input directory using the specified path.
+* required = True
+
+`-o path`
+* Sets the output directory using the specified path.
+* required = True
+
+`-l languagecode`
+* Tells spaCy which language will be used.
+* options = de (German), el (Greek), en (English), es (Spanish), fr (French), it (Italian), nl (Dutch), pt (Portuguese)
+* required = True

nlp

@@ -1,288 +1,133 @@
 #!/usr/bin/env python2.7
 # coding=utf-8
 
-''' A NLP pipeline for text file processing. '''
+"""
-__version__ = '0.1.0'
+nlp
 
-from argparse import ArgumentParser
+Usage:  For usage instructions run with option --help
-from pyflow import WorkflowRunner
+Author: Patrick Jentsch <p.jentsch@uni-bielefeld.de>
-import json
+"""
+
+
+import argparse
+import multiprocessing
 import os
 import sys
+from pyflow import WorkflowRunner
 
 
-SPACY_MODELS = {
+def parse_arguments():
-    'de': 'de_core_news_md',
+    parser = argparse.ArgumentParser(
-    'en': 'en_core_web_md',
+        description='Performs NLP of documents utilizing spaCy. The results are served as verticalized text files.'
-    'it': 'it_core_news_md',
+    )
-    'nl': 'nl_core_news_md',
-    'pl': 'pl_core_news_md',
-    'zh': 'zh_core_web_md'
-}
 
-
+    parser.add_argument(
-class PipelineJob:
+        '-i',
-    '''
+        dest='input_dir',
-    NLP pipeline job class.
+        required=True
-
+    )
-    Each plain text input file of the pipeline is represented as an NLP
+    parser.add_argument(
-    pipeline job, which holds all necessary information for the pipeline to
+        '-l',
-    process it.
+        choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
-
+        dest='lang',
-    Arguments:
+        required=True
-    file -- Path to the file
+    )
-    output_dir -- Path to a directory, where job results are stored
+    parser.add_argument(
-    '''
+        '-o',
-
+        dest='output_dir',
-    def __init__(self, file, output_dir):
+        required=True
-        self.file = file
+    )
-        self.name = os.path.basename(file)[:-4]
+    parser.add_argument(
-        self.output_dir = output_dir
+        '--nCores',
+        default=min(4, multiprocessing.cpu_count()),
+        dest='n_cores',
+        help='total number of cores available',
+        required=False,
+        type=int
+    )
+    return parser.parse_args()
 
 
 class NLPWorkflow(WorkflowRunner):
-    def __init__(self, job, model, check_encoding=False, id_prefix=''):
+    def __init__(self, args):
-        self.job = job
+        self.jobs = analyze_jobs(args.input_dir, args.output_dir)
-        self.model = model
+        self.lang = args.lang
-        self.check_encoding = check_encoding
+        self.n_cores = args.n_cores
-        self.id_prefix = id_prefix
 
     def workflow(self):
-        '''
+        if len(self.jobs) == 0:
-        ' ##################################################
-        ' # spacy                                          #
-        ' ##################################################
-        '''
-        n_cores = 1
-        mem_mb = min(1024, self.getMemMb())
-        cmd = 'spacy-nlp'
-        cmd += ' --input-file "{}"'.format(self.job.file)
-        cmd += ' --output-file "{}"'.format(
-            os.path.join(self.job.output_dir, '{}.json'.format(self.job.name))
-        )
-        cmd += ' -m "{}"'.format(self.model)
-        if self.check_encoding:
-            cmd += ' --check-encoding'
-        cmd += ' --id-prefix "{}"'.format(self.id_prefix)
-        self.addTask(
-            'spacy',
-            command=cmd,
-            memMb=mem_mb,
-            nCores=n_cores
-        )
-
-
-class CreateVrtWorkflow(WorkflowRunner):
-    def __init__(self, job):
-        self.job = job
-
-    def workflow(self):
-        '''
-        ' ##################################################
-        ' # vrt-creator                                    #
-        ' ##################################################
-        '''
-        n_cores = 1
-        mem_mb = min(256, self.getMemMb())
-        cmd = 'vrt-creator'
-        cmd += ' --stand-off-data-file "{}"'.format(
-            os.path.join(self.job.output_dir, '{}.json'.format(self.job.name))
-        )
-        cmd += ' --text-file "{}"'.format(self.job.file)
-        cmd += ' --output-file "{}"'.format(
-            os.path.join(self.job.output_dir, '{}.vrt'.format(self.job.name))
-        )
-        self.addTask(
-            'vrt_creator',
-            command=cmd,
-            memMb=mem_mb,
-            nCores=n_cores
-        )
-
-
-class MainWorkflow(WorkflowRunner):
-    def __init__(
-        self,
-        input_dir,
-        model,
-        output_dir,
-        check_encoding=False,
-        id_prefix=''
-    ):
-        self.input_dir = input_dir
-        self.model = model
-        self.output_dir = output_dir
-        self.check_encoding = check_encoding
-        self.id_prefix = id_prefix
-        self.jobs = []
-
-    def collect_jobs(self):
-        self.jobs = []
-        for file in os.listdir(self.input_dir):
-            if os.path.isdir(os.path.join(self.input_dir, file)):
-                continue
-            if not file.lower().endswith('.txt'):
-                continue
-            job = PipelineJob(
-                os.path.join(self.input_dir, file),
-                os.path.join(self.output_dir, file)
-            )
-            self.jobs.append(job)
-
-    def workflow(self):
-        if not self.jobs:
             return
 
-        # Create output and temporary directories
-        for job in self.jobs:
-            os.mkdir(job.output_dir)
-
         '''
         ' ##################################################
-        ' # nlp                                            #
+        ' # Create output directories                      #
         ' ##################################################
         '''
-        nlp_tasks = []
+        create_output_directories_jobs = []
-        for i, job in enumerate(self.jobs):
+        for index, job in enumerate(self.jobs):
-            task = self.addWorkflowTask(
+            cmd = 'mkdir -p "%s"' % (job['output_dir'])
-                'nlp_-_{}'.format(i),
+            create_output_directories_jobs.append(
-                NLPWorkflow(
+                self.addTask(
-                    job,
+                    command=cmd,
-                    self.model,
+                    label='create_output_directories_job_-_%i' % (index)
-                    check_encoding=self.check_encoding,
-                    id_prefix=self.id_prefix
                 )
             )
-            nlp_tasks.append(task)
 
         '''
         ' ##################################################
-        ' # create vrt                                     #
+        ' # Natural language processing                    #
         ' ##################################################
         '''
-        create_vrt_tasks = []
+        nlp_jobs = []
-        for i, job in enumerate(self.jobs):
+        nlp_job_n_cores = min(
-            task = self.addWorkflowTask(
+            self.n_cores,
-                'create_vrt_-_{}'.format(i),
+            max(1, int(self.n_cores / len(self.jobs)))
-                CreateVrtWorkflow(job),
+        )
-                dependencies='nlp_-_{}'.format(i)
+        for index, job in enumerate(self.jobs):
+            cmd = 'spacy_nlp -l "%s" "%s" "%s"' % (
+                self.lang,
+                job['path'],
+                os.path.join(job['output_dir'], job['name'] + '.vrt')
+            )
+            nlp_jobs.append(
+                self.addTask(
+                    command=cmd,
+                    dependencies='create_output_directories_job_-_%i' % (index),
+                    label='nlp_job_-_%i' % (index),
+                    nCores=nlp_job_n_cores
+                )
             )
-            create_vrt_tasks.append(task)
 
-        self.waitForTasks()
+
-        outputs = []
+def analyze_jobs(input_dir, output_dir):
-        for job in self.jobs:
+    jobs = []
-            # Track output files
+
-            relative_output_dir = os.path.relpath(
+    for file in os.listdir(input_dir):
-                job.output_dir,
+        if os.path.isdir(os.path.join(input_dir, file)):
-                start=self.output_dir
+            jobs += analyze_jobs(
+                os.path.join(input_dir, file),
+                os.path.join(output_dir, file),
             )
-            outputs.append(
+        elif file.endswith('.txt'):
+            jobs.append(
                 {
-                    'description': 'JSON stand off data',
+                    'filename': file,
-                    'file': os.path.join(
+                    'name': file.rsplit('.', 1)[0],
-                        relative_output_dir,
+                    'output_dir': os.path.join(output_dir, file),
-                        '{}.json'.format(job.name)
+                    'path': os.path.join(input_dir, file)
-                    ),
-                    'mimetype': 'application/json'
                 }
             )
-            outputs.append(
-                {
-                    'description': 'CWB vrt file',
-                    'file': os.path.join(
-                        relative_output_dir,
-                        '{}.vrt'.format(job.name)
-                    ),
-                    'mimetype': 'application/vrt+xml'
-                }
-            )
-        with open(os.path.join(self.output_dir, 'outputs.json'), 'w') as f:
-            json.dump(outputs, f, indent=4)
 
-
+    return jobs
-def parse_args():
-    parser = ArgumentParser(
-        description='NLP pipeline for plain text file processing'
-    )
-    parser.add_argument(
-        '-i', '--input-dir',
-        help='Input directory',
-        required=True
-    )
-    parser.add_argument(
-        '-o', '--output-dir',
-        help='Output directory',
-        required=True
-    )
-    parser.add_argument(
-        '-m', '--model',
-        choices=SPACY_MODELS.keys(),
-        help='The model to be used',
-        required=True
-    )
-    parser.add_argument(
-        '--check-encoding',
-        action='store_true',
-        help='Check encoding of the input file, UTF-8 is used instead'
-    )
-    parser.add_argument(
-        '--id-prefix',
-        default='',
-        help='A prefix for all the ids within the stand off annotations'
-    )
-    parser.add_argument(
-        '--log-dir',
-        help='Logging directory (Default: --output-dir)'
-    )
-    parser.add_argument(
-        '--mem-mb',
-        help='Amount of system memory to be used '
-             '(Default: min(--n-cores * 1024, available system memory))',
-        type=int
-    )
-    parser.add_argument(
-        '--n-cores',
-        default=1,
-        help='Number of CPU threads to be used',
-        type=int
-    )
-    parser.add_argument(
-        '-v', '--version',
-        action='version',
-        help='Returns the current version of the NLP pipeline',
-        version='%(prog)s {}'.format(__version__)
-    )
-    args = parser.parse_args()
-
-    # Set some tricky default values and check for insufficient input
-    if args.log_dir is None:
-        args.log_dir = args.output_dir
-    if args.n_cores < 1:
-        raise Exception('--n-cores must be greater or equal 1')
-    if args.mem_mb is None:
-        max_mem_mb = int(os.popen('free -t -m').readlines()[-1].split()[1:][0])
-        args.mem_mb = min(args.n_cores * 1024, max_mem_mb)
-    if args.mem_mb < 1024:
-        raise Exception('--mem-mb must be greater or equal 1024')
-    return args
 
 
 def main():
-    args = parse_args()
+    args = parse_arguments()
-    main_workflow = MainWorkflow(
+
-        args.input_dir,
+    wflow = NLPWorkflow(args)
-        args.model,
+
-        args.output_dir,
+    retval = wflow.run(dataDirRoot=args.output_dir, nCores=args.n_cores)
-        check_encoding=args.check_encoding,
+
-        id_prefix=args.id_prefix
-    )
-    main_workflow.collect_jobs()
-    retval = main_workflow.run(
-        dataDirRoot=args.log_dir,
-        memMb=args.mem_mb,
-        nCores=args.n_cores
-    )
     sys.exit(retval)
 
 

packages/stand-off-data-py/setup.py

@@ -1,14 +0,0 @@
-import setuptools
-
-setuptools.setup(
-    name='Stand off data',
-    author='Patrick Jentsch',
-    author_email='p.jentsch@uni-bielefeld.de',
-    description='A python library to handle stand off data.',
-    py_modules=['stand_off_data'],
-    classifiers=[
-        'Programming Language :: Python :: 3',
-        'Operating System :: OS Independent',
-    ],
-    python_requires='>=3.5'
-)

packages/stand-off-data-py/stand_off_data.py

@@ -1,282 +0,0 @@
-from xml.sax.saxutils import escape
-
-
-class StandOffData:
-    def __init__(self, attrs={}):
-        self.meta = attrs.get('meta', {})
-        self.lookup = {}
-        for x in attrs.get('tags', []):
-            self.add_tag_definition(x)
-        self.annotations = [
-            TagAnnotation(x, self.lookup)
-            for x in attrs.get('annotations', [])
-        ]
-
-    def add_tag_definition(self, attrs):
-        tag_definition = TagDefinition(attrs)
-        if tag_definition.id in self.lookup:
-            raise Exception(f'Tag id already in use: {self.to_dict()}')
-        self.lookup[tag_definition.id] = tag_definition
-
-    def to_dict(self):
-        return {
-            'meta': self.meta,
-            'lookup': {k: v.to_dict() for k, v in self.lookup.items()},
-            'annotations': [x.to_dict() for x in self.annotations]
-        }
-
-    def to_vrt(self, text):
-        # Devide annotations into CWB's verticalized text format (.vrt) logic
-        p_attrs = []    # positional attributes
-        s_attrs = []    # structural attributes
-        for annotation in self.annotations:
-            if annotation.name == 'token':
-                p_attrs.append(annotation)
-            else:
-                s_attrs.append(annotation)
-        # Sort annotations, necessary for the next checks
-        p_attrs.sort()
-        s_attrs.sort()
-        # Check for p_attr<->p_attr overlap
-        for i, p_attr in enumerate(p_attrs[:-1]):
-            next_p_attr = p_attrs[i + 1]
-            # Check if first_p_attr starts/ends within second_p_attr
-            if ((p_attr.start >= next_p_attr.start) and (p_attr.start < next_p_attr.end)  # noqa
-                or (p_attr.end > next_p_attr.start) and (p_attr.end <= next_p_attr.end)):  # noqa
-                raise Exception(
-                    'Positional attribute overlaps another: '
-                    f'{p_attr.to_dict()}<->{next_p_attr.to_dict()}'
-                )
-        # Check for s_attr<->p_attr overlap
-        for i, s_attr in enumerate(s_attrs):
-            for p_attr in p_attrs:
-                # Check if s_attr starts within p_attr
-                if s_attr.start > p_attr.start and s_attr.start < p_attr.end:
-                    # Change s_attr start to p_attr's start
-                    s_attrs[i].start = p_attr.start
-                # Check if s_attr ends within p_attr
-                if s_attr.end < p_attr.end and s_attr.end > p_attr.start:
-                    # Change s_attr end to p_attr's end
-                    s_attrs[i].end = p_attr.end
-                # Check if s_attr starts/ends before/after p_attr
-                if p_attr.start >= s_attr.end or p_attr.end <= s_attr.start:
-                    # No further Checking needed (because p_attrs are sorted)
-                    break
-        p_attr_buffer = {}
-        for i, p_attr in enumerate(p_attrs):
-            p_attr_buffer[p_attr.start] = i
-        s_attr_start_buffer = {}
-        s_attr_end_buffer = {}
-        for i, s_attr in enumerate(s_attrs):
-            if s_attr.start in s_attr_start_buffer:
-                s_attr_start_buffer[s_attr.start].append(i)
-            else:
-                s_attr_start_buffer[s_attr.start] = [i]
-            if s_attr.end in s_attr_end_buffer:
-                s_attr_end_buffer[s_attr.end].insert(0, i)
-            else:
-                s_attr_end_buffer[s_attr.end] = [i]
-        vrt = ''
-        vrt += '<text>\n'
-        current_position = 0
-        text_len = len(text)
-        # As long as we have something in our buffers we process it
-        while current_position <= text_len:
-            # s_attr endings
-            # for k in {k: v for k, v in s_attr_end_buffer.items() if k <= current_position}:  # noqa
-            if current_position in s_attr_end_buffer:
-                # s_attr_indexes = s_attr_end_buffer.pop(k)
-                s_attr_indexes = s_attr_end_buffer.pop(current_position)
-                for s_attr_index in s_attr_indexes:
-                    s_attr = s_attrs[s_attr_index]
-                    vrt += f'</{escape(s_attr.name)}>\n'
-            # s_attrs starts
-            # for k in {k: v for k, v in s_attr_start_buffer.items() if k <= current_position}:  # noqa
-            if current_position in s_attr_start_buffer:
-                # s_attr_indexes = s_attr_start_buffer.pop(k)
-                s_attr_indexes = s_attr_start_buffer.pop(current_position)
-                for s_attr_index in s_attr_indexes:
-                    s_attr = s_attrs[s_attr_index]
-                    vrt += f'<{escape(s_attr.name)}'
-                    for property in s_attr.properties:
-                        vrt += f' {escape(property.name)}="{escape(str(property.value))}"'  # noqa
-                    vrt += '>\n'
-            # p_attrs
-            if current_position not in p_attr_buffer:
-                current_position += 1
-                continue
-            p_attr_index = p_attr_buffer.pop(current_position)
-            p_attr = p_attrs[p_attr_index]
-            if text[p_attr.start:p_attr.end].isspace():
-                current_position = p_attr.end
-                continue
-            _p_attr = {
-                'lemma': 'None',
-                'pos': 'None',
-                'simple_pos': 'None',
-                'word': 'None'
-            }
-            for property in p_attr.properties:
-                if property.name not in _p_attr:
-                    continue
-                _p_attr[property.name] = escape(str(property.value))
-            _p_attr['word'] = escape(text[p_attr.start:p_attr.end])
-            vrt += '{word}\t{pos}\t{lemma}\t{simple_pos}\n'.format(**_p_attr)
-            current_position = p_attr.end
-        vrt += '</text>\n'
-        return vrt
-
-
-class TagAnnotation:
-    def __init__(self, attrs, lookup):
-        self.lookup = lookup
-        self.tag_id = attrs['tag_id']
-        self.start = attrs['start']
-        self.end = attrs['end']
-        self.properties = [
-            PropertyAnnotation(x, self.lookup[self.tag_id].properties)
-            for x in attrs.get('properties', [])
-        ]
-        ''' Sanity checks '''
-        if self.tag_id not in self.lookup:
-            raise Exception(f'Unknown tag: {self.to_dict()}')
-        if self.end < self.start:
-            raise Exception(f'Annotation end less then start: {self.to_dict()}')  # noqa
-        # property_ids = [x.property_id for x in self.properties]
-        # for required_property_id, required_property in self.lookup[self.tag_id].required_properties.items():  # noqa
-        #     if required_property_id not in property_ids:
-        #         raise Exception(
-        #             f'Missing required property: {required_property.to_dict()}'
-        #         )
-
-    @property
-    def name(self):
-        return self.lookup[self.tag_id].name
-
-    def to_dict(self):
-        return {
-            'tag_id': self.tag_id,
-            'start': self.start,
-            'end': self.end,
-            'properties': [x.to_dict() for x in self.properties]
-        }
-
-    def __lt__(self, other):
-        if self.start == other.start:
-            if self.name == 'token' and other.name != 'token':
-                return False
-            elif self.name != 'token' and other.name == 'token':
-                return True
-            else:
-                return self.end > other.end
-        else:
-            return self.start < other.start
-
-    def __le__(self, other):
-        if self.start == other.start:
-            if self.name == 'token' and other.name != 'token':
-                return False
-            elif self.name != 'token' and other.name == 'token':
-                return True
-            else:
-                return self.end >= other.end
-        else:
-            return self.start <= other.start
-
-    def __eq__(self, other):
-        if self.start == other.start:
-            if self.name == 'token' and other.name != 'token':
-                return False
-            elif self.name != 'token' and other.name == 'token':
-                return False
-            else:
-                return self.end == other.end
-        else:
-            return False
-
-    def __ne__(self, other):
-        return not self == other
-
-    def __gt__(self, other):
-        return not self <= other
-
-    def __ge__(self, other):
-        return not self < other
-
-
-class PropertyAnnotation:
-    def __init__(self, attrs, lookup):
-        self.lookup = lookup
-        self.property_id = attrs['property_id']
-        self.value = attrs['value']
-        # TODO: Process attrs['possibleValues'] as self.labels (no id?)
-        ''' Sanity checks '''
-        if self.property_id not in self.lookup:
-            raise Exception(f'Unknown property: {self.to_dict()}')
-
-    @property
-    def name(self):
-        return self.lookup[self.property_id].name
-
-    def to_dict(self):
-        return {
-            'property_id': self.property_id,
-            'tag_id': self.tag_id,
-            'value': self.value
-        }
-
-
-class TagDefinition:
-    def __init__(self, attrs):
-        self.id = attrs['id']
-        self.name = attrs['name']
-        self.description = attrs.get('description', '')
-        self.properties = {}
-        for x in attrs.get('properties', []):
-            self.add_property_definition(x)
-
-    def add_property_definition(self, attrs):
-        property_definition = PropertyDefinition(attrs)
-        if property_definition.id in self.properties:
-            raise Exception(
-                f'Property id already in use: {property_definition.to_dict()}')
-        self.properties[property_definition.id] = property_definition
-
-    # @property
-    # def required_properties(self):
-    #     return {property.id: property for property in self.properties.values()
-    #             if property.is_required}
-
-    def to_dict(self):
-        return {
-            'id': self.id,
-            'name': self.name,
-            'description': self.description,
-            'properties': {k: v.to_dict() for k, v in self.properties.items()}
-        }
-
-
-class PropertyDefinition:
-    def __init__(self, attrs):
-        self.id = attrs['id']
-        self.name = attrs['name']
-        self.description = attrs.get('description', '')
-        self.flags = attrs.get('flags', [])
-        self.labels = attrs.get('labels', [])
-
-    # @property
-    # def is_required(self):
-    #     return 'required' in self.flags
-
-    @property
-    def has_multiple_values(self):
-        return 'multiple' in self.flags
-
-    def to_dict(self):
-        return {
-            'id': self.id,
-            'name': self.name,
-            'description': self.description,
-            'flags': self.flags,
-            'labels': self.labels
-        }

packages/stand-off-data-py/stand_off_data/__init__.py

@@ -1,2 +0,0 @@
-# flake8: noqa
-from .models import StandOffData

spacy-nlp

@@ -1,353 +0,0 @@
-#!/usr/bin/env python3.7
-# coding=utf-8
-
-from argparse import ArgumentParser
-import chardet
-import hashlib
-import json
-import os
-import spacy
-import textwrap
-import uuid
-
-
-spacy_models = {
-    spacy.info(pipeline)['lang']: pipeline
-    for pipeline in spacy.info()['pipelines']
-}
-
-
-# Parse the given arguments
-parser = ArgumentParser(
-    description='Create annotations for a given plain txt file'
-)
-parser.add_argument(
-    '-i', '--input-file',
-    help='Input file',
-    required=True
-)
-parser.add_argument(
-    '-o', '--output-file',
-    help='Output file',
-    required=True
-)
-parser.add_argument(
-    '-m', '--model',
-    choices=spacy_models.keys(),
-    help='The model to be used',
-    required=True
-)
-parser.add_argument(
-    '-c', '--check-encoding',
-    action='store_true',
-    help='Check encoding of the input file, UTF-8 is used instead'
-)
-parser.add_argument(
-    '--id-prefix',
-    default='',
-    help='A prefix for all the ids within the stand off annotations'
-)
-args = parser.parse_args()
-
-
-def generate_id(name):
-    return f'{args.id_prefix}{uuid.uuid3(uuid.NAMESPACE_DNS, name)}'
-
-
-with open(args.input_file, "rb") as input_file:
-    if args.check_encoding:
-        encoding = chardet.detect(input_file.read())['encoding']
-    else:
-        encoding = 'utf-8'
-    input_file.seek(0)
-    text_md5 = hashlib.md5()
-    for chunk in iter(lambda: input_file.read(128 * text_md5.block_size), b''):
-        text_md5.update(chunk)
-
-# Load the text contents from the input file
-with open(args.input_file, encoding=encoding) as input_file:
-    # spaCy NLP is limited to strings with a maximum of 1 million characters at
-    # once. So we split it into suitable chunks.
-    text_chunks = textwrap.wrap(
-        input_file.read(),
-        1000000,
-        break_long_words=False,
-        break_on_hyphens=False,
-        drop_whitespace=False,
-        expand_tabs=False,
-        replace_whitespace=False
-    )
-
-model_name = spacy_models[args.model]
-nlp = spacy.load(model_name)
-
-meta = {
-    'generator': {
-        'name': 'nopaque spacy NLP',
-        'version': '0.1.0',
-        'arguments': {
-            'check_encoding': args.check_encoding,
-            'model': args.model
-        }
-    },
-    'file': {
-        'encoding': encoding,
-        'md5': text_md5.hexdigest(),
-        'name': os.path.basename(args.input_file)
-    }
-}
-
-tags = []
-token = {
-    'id': generate_id('token'),
-    'name': 'token',
-    'description': 'An individual token — i.e. a word, punctuation symbol, whitespace, etc.',  # noqa
-    'properties': []
-}
-# TODO: Check if all languages support token.sentiment
-token['properties'].append(
-    {
-        'id': generate_id('token.sentiment'),
-        'name': 'sentiment',
-        'description': 'A scalar value indicating the positivity or negativity of the token.'  # noqa
-    }
-)
-if nlp.has_pipe('lemmatizer'):
-    token['properties'].append(
-        {
-            'id': generate_id('token.lemma'),
-            'name': 'lemma',
-            'description': 'The base form of the word'
-        }
-    )
-if nlp.has_pipe('morphologizer') or nlp.has_pipe('tagger'):
-    token['properties'].append(
-        {
-            'id': generate_id('token.simple_pos'),
-            'name': 'simple_pos',
-            'description': 'The simple UPOS part-of-speech tag',
-            'labels': [
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'ADJ',
-                    'description': 'adjective'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'ADP',
-                    'description': 'adposition'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'ADV',
-                    'description': 'adverb'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'AUX',
-                    'description': 'auxiliary verb'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'CONJ',
-                    'description': 'coordinating conjunction'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'DET',
-                    'description': 'determiner'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'INTJ',
-                    'description': 'interjection'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'NOUN',
-                    'description': 'noun'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'NUM',
-                    'description': 'numeral'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'PART',
-                    'description': 'particle'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'PRON',
-                    'description': 'pronoun'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'PROPN',
-                    'description': 'proper noun'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'PUNCT',
-                    'description': 'punctuation'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'SCONJ',
-                    'description': 'subordinating conjunction'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'SYM',
-                    'description': 'symbol'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'VERB',
-                    'description': 'verb'
-                },
-                {
-                    'id': generate_id('token.simple_pos=ADJ'),
-                    'name': 'X',
-                    'description': 'other'
-                }
-            ]
-        }
-    )
-if nlp.has_pipe('tagger'):
-    token['properties'].append(
-        {
-            'id': generate_id('token.pos'),
-            'name': 'pos',
-            'description': 'The detailed part-of-speech tag',
-            'labels': [
-                {
-                    'id': generate_id(f'token.pos={label}'),
-                    'name': label,
-                    'description': spacy.explain(label) or ''
-                } for label in spacy.info(model_name)['labels']['tagger']
-            ]
-        }
-    )
-if nlp.has_pipe('ner') or nlp.has_pipe('entity_ruler'):
-    tags.append(
-        {
-            'id': generate_id('ent'),
-            'name': 'ent',
-            'description': 'Encodes the start and end of a named entity',
-            'properties': [
-                {
-                    'id': generate_id('ent.type'),
-                    'name': 'type',
-                    'description': 'Label indicating the type of the entity',
-                    'labels': [
-                        {
-                            'id': generate_id('ent.type={}'.format(label)),
-                            'name': label,
-                            'description': spacy.explain(label) or ''
-                        } for label in spacy.info(model_name)['labels']['ner']
-                    ]
-                }
-            ]
-        }
-    )
-if nlp.has_pipe('parser') or nlp.has_pipe('senter') or nlp.has_pipe('sentencizer'):  # noqa
-    # TODO: Check if all languages support sent.sentiment
-    tags.append(
-        {
-            'id': generate_id('s'),
-            'name': 's',
-            'description': 'Encodes the start and end of a sentence',
-            'properties': [
-                {
-                    'id': generate_id('s.sentiment'),
-                    'name': 'sentiment',
-                    'description': 'A scalar value indicating the positivity or negativity of the sentence.'  # noqa
-                }
-            ]
-        }
-    )
-tags.append(token)
-
-annotations = []
-
-chunk_offset = 0
-while text_chunks:
-    text_chunk = text_chunks.pop(0)
-    doc = nlp(text_chunk)
-    if hasattr(doc, 'ents'):
-        for ent in doc.ents:
-            annotation = {
-                'start': ent.start_char + chunk_offset,
-                'end': ent.end_char + chunk_offset,
-                'tag_id': generate_id('ent'),
-                'properties': [
-                    {
-                        'property_id': generate_id('ent.type'),
-                        'value': ent.label_
-                    }
-                ]
-            }
-            annotations.append(annotation)
-    if hasattr(doc, 'sents'):
-        for sent in doc.sents:
-            annotation = {
-                'start': sent.start_char + chunk_offset,
-                'end': sent.end_char + chunk_offset,
-                'tag_id': generate_id('s'),
-                'properties': []
-            }
-            if hasattr(sent, 'sentiment'):
-                annotation['properties'].append(
-                    {
-                        'property_id': generate_id('s.sentiment'),
-                        'value': sent.sentiment
-                    }
-                )
-            annotations.append(annotation)
-    for token in doc:
-        annotation = {
-            'start': token.idx + chunk_offset,
-            'end': token.idx + len(token.text) + chunk_offset,
-            'tag_id': generate_id('token'),
-            'properties': []
-        }
-        if hasattr(token, 'lemma_'):
-            annotation['properties'].append(
-                {
-                    'property_id': generate_id('token.lemma'),
-                    'value': token.lemma_
-                }
-            )
-        if hasattr(token, 'pos_'):
-            annotation['properties'].append(
-                {
-                    'property_id': generate_id('token.simple_pos'),
-                    'value': token.pos_
-                }
-            )
-        if hasattr(token, 'sentiment'):
-            annotation['properties'].append(
-                {
-                    'property_id': generate_id('token.sentiment'),
-                    'value': token.sentiment
-                }
-            )
-        if hasattr(token, 'tag_'):
-            annotation['properties'].append(
-                {
-                   'property_id': generate_id('token.pos'),
-                   'value': token.tag_
-                }
-            )
-        annotations.append(annotation)
-    chunk_offset += len(text_chunk)
-    text_chunk = None
-
-with open(args.output_file, 'w') as output_file:
-    json.dump(
-        {'meta': meta, 'tags': tags, 'annotations': annotations},
-        output_file,
-        indent=4
-    )

spacy_nlp

@@ -0,0 +1,71 @@
+#!/usr/bin/env python3.5
+# coding=utf-8
+
+import argparse
+import os
+import spacy
+import textwrap
+
+parser = argparse.ArgumentParser(
+    description='Tag a text file with spaCy and save it as a verticalized text file.'
+)
+parser.add_argument(
+    'i',
+    metavar='txt-sourcefile',
+)
+parser.add_argument(
+    '-l',
+    choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
+    dest='lang',
+    required=True
+)
+parser.add_argument(
+    'o',
+    metavar='vrt-destfile',
+)
+args = parser.parse_args()
+
+SPACY_MODELS = {
+    'de': 'de_core_news_sm', 'el': 'el_core_news_sm', 'en': 'en_core_web_sm',
+    'es': 'es_core_news_sm', 'fr': 'fr_core_news_sm', 'it': 'it_core_news_sm',
+    'nl': 'nl_core_news_sm', 'pt': 'pt_core_news_sm'
+}
+
+# Set the language model for spacy
+nlp = spacy.load(SPACY_MODELS[args.lang])
+
+# Read text from the input file and if neccessary split it into parts with a
+# length of less than 1 million characters.
+with open(args.i) as input_file:
+    text = input_file.read()
+    texts = textwrap.wrap(text, 1000000, break_long_words=False)
+    text = None
+
+# Create and open the output file
+output_file = open(args.o, 'w+')
+
+output_file.write(
+    '<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="%s">\n' % (
+        os.path.basename(args.i).rsplit(".", 1)[0]
+    )
+)
+for text in texts:
+    # Run spacy nlp over the text (partial string if above 1 million chars)
+    doc = nlp(text)
+    for sent in doc.sents:
+        output_file.write('<s>\n')
+        for token in sent:
+            # Skip whitespace tokens like "\n" or "\t"
+            if token.text.isspace():
+                continue
+            # Write all information in .vrt style to the output file
+            # text, lemma, simple_pos, pos, ner
+            output_file.write(
+                token.text + '\t' + token.lemma_ + '\t'
+                + token.pos_ + '\t' + token.tag_ + '\t'
+                + (token.ent_type_ if token.ent_type_ != '' else 'NULL') + '\n'
+            )
+        output_file.write('</s>\n')
+output_file.write('</text>\n</corpus>')
+
+output_file.close()

vrt-creator

@@ -1,45 +0,0 @@
-#!/usr/bin/env python3.7
-# coding=utf-8
-
-from argparse import ArgumentParser
-from stand_off_data import StandOffData
-import hashlib
-import json
-
-
-parser = ArgumentParser(
-    description='Convert plain text and JSON stand off to a CWB vrt file'
-)
-parser.add_argument(
-    '-s', '--stand-off-data-file',
-    help='JSON stand off data input file',
-    required=True
-)
-parser.add_argument(
-    '-t', '--text-file',
-    help='Plain text input file',
-    required=True
-)
-parser.add_argument(
-    '-o', '--output-file',
-    help='Output file',
-    required=True
-)
-args = parser.parse_args()
-
-
-with open(args.stand_off_data_file) as stand_of_data_file:
-    stand_off_data = StandOffData(json.load(stand_of_data_file))
-
-with open(args.text_file, "rb") as text_file:
-    text_md5 = hashlib.md5()
-    for chunk in iter(lambda: text_file.read(128 * text_md5.block_size), b''):
-        text_md5.update(chunk)
-    if text_md5.hexdigest() != stand_off_data.meta['file']['md5']:
-        raise Exception('md5 not equal')
-
-with open(args.text_file, encoding=stand_off_data.meta['file']['encoding']) as text_file:  # noqa
-    text = text_file.read()
-
-with open(args.output_file, 'w') as vrt_file:
-    vrt_file.write(stand_off_data.to_vrt(text))

wrapper/nlp

@@ -1,38 +1,39 @@
 #!/usr/bin/env python3
 # coding=utf-8
 
-from argparse import ArgumentParser
+import argparse
 import os
 import subprocess
-import sys
 
-CONTAINER_IMAGE = 'gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:v0.1.0'
+container_image = 'gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:latest'
-CONTAINER_INPUT_DIR = '/input'
+container_input_dir = '/input'
-CONTAINER_OUTPUT_DIR = '/output'
+container_output_dir = '/output'
-CONTAINER_LOG_DIR = '/logs'
+uid = str(os.getuid())
-UID = str(os.getuid())
+gid = str(os.getgid())
-GID = str(os.getgid())
 
-parser = ArgumentParser(add_help=False)
+parser = argparse.ArgumentParser(add_help=False)
-parser.add_argument('-i', '--input-dir')
+parser.add_argument(
-parser.add_argument('-o', '--output-dir')
+    '-i',
-parser.add_argument('--log-dir')
+    dest='input_dir',
+    required=False
+)
+parser.add_argument(
+    '-o',
+    dest='output_dir',
+    required=False
+)
 args, remaining_args = parser.parse_known_args()
 
-cmd = ['docker', 'run', '--rm', '-it', '-u', f'{UID}:{GID}']
+cmd = ['docker', 'run', '--rm', '-it', '-u', uid + ':' + gid]
 if args.input_dir is not None:
-    mapping = f'{os.path.abspath(args.input_dir)}:{CONTAINER_INPUT_DIR}'
+    host_input_dir = os.path.abspath(args.input_dir)
-    cmd += ['-v', mapping]
+    cmd += ['-v', host_input_dir + ':' + container_input_dir]
-    remaining_args += ['-i', CONTAINER_INPUT_DIR]
+    remaining_args += ['-i', container_input_dir]
 if args.output_dir is not None:
-    mapping = f'{os.path.abspath(args.output_dir)}:{CONTAINER_OUTPUT_DIR}'
+    host_output_dir = os.path.abspath(args.output_dir)
-    cmd += ['-v', mapping]
+    cmd += ['-v', host_output_dir + ':' + container_output_dir]
-    remaining_args += ['-o', CONTAINER_OUTPUT_DIR]
+    remaining_args += ['-o', container_output_dir]
-if args.log_dir is not None:
+cmd.append(container_image)
-    mapping = '{os.path.abspath(args.log_dir)}:{CONTAINER_LOG_DIR}'
-    cmd += ['-v', mapping]
-    remaining_args += ['--log-dir', CONTAINER_LOG_DIR]
-cmd.append(CONTAINER_IMAGE)
 cmd += remaining_args
 
-sys.exit(subprocess.run(cmd).returncode)
+subprocess.run(cmd)