Mark required arguments in scripts as required

.gitlab-ci.yml

@@ -1,42 +1,68 @@
-image: docker:latest
+image: docker:19.03.13
 
 services:
-  - docker:dind
+  - docker:19.03.13-dind
 
 stages:
   - build
   - push
 
-before_script:
+variables:
-  - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
+  DOCKER_TLS_CERTDIR: "/certs"
+  INTERMEDIATE_IMAGE_TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME-$CI_COMMIT_SHA
 
-Build:
+.reg_setup:
+  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 --pull -t $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA .
+    - docker build -t $INTERMEDIATE_IMAGE_TAG .
-    - docker push $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
+    - docker push $INTERMEDIATE_IMAGE_TAG
   stage: build
   tags:
     - docker
 
-Push latest:
+push_master:
+  extends:
+    - .reg_setup
   only:
     - master
   script:
-    - docker pull $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
+    - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
-    - docker tag $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA $CI_REGISTRY_IMAGE:latest
+    - docker pull $INTERMEDIATE_IMAGE_TAG
-    - docker push $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 tag $INTERMEDIATE_IMAGE_TAG $IMAGE_TAG
+    - docker push $IMAGE_TAG
   stage: push
   tags:
     - docker
+  variables:
+    IMAGE_TAG: $CI_REGISTRY_IMAGE:latest
 
-Push tag:
+push_other:
+  extends:
+    - .reg_setup
+  except:
+    - master
   only:
+    - branches
     - tags
   script:
-    - docker pull $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
+    - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
-    - docker tag $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME
+    - docker pull $INTERMEDIATE_IMAGE_TAG
-    - docker push $CI_REGISTRY_IMAGE:$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 tag $INTERMEDIATE_IMAGE_TAG $IMAGE_TAG
+    - docker push $IMAGE_TAG
   stage: push
   tags:
     - docker
+  variables:
+    IMAGE_TAG: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_NAME

Dockerfile

@@ -1,43 +1,67 @@
-FROM debian:9-slim
+FROM debian:buster-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 \
+RUN apt-get update \
-    build-essential \
+ && apt-get install --no-install-recommends --yes \
-    ca-certificates \
+      procps \
-    python2.7 \
+      python3.7 \
-    python3.5 \
-    python3-dev \
       python3-pip \
-    python3-setuptools \
+      wget \
-    wget
+ && python3 -m pip install \
+      chardet \
+      setuptools \
+      wheel
 
-# Install pyFlow
+# Install the NLP pipeline and it's dependencies #
-ENV PYFLOW_VERSION 1.1.20
+## Install pyFlow ##
-RUN wget -nv https://github.com/Illumina/pyflow/releases/download/v"$PYFLOW_VERSION"/pyflow-"$PYFLOW_VERSION".tar.gz && \
+ENV PYFLOW_VERSION=1.1.20
-    tar -xzf pyflow-"$PYFLOW_VERSION".tar.gz && \
+RUN wget --no-check-certificate --quiet \
-    cd pyflow-"$PYFLOW_VERSION" && \
+      "https://github.com/Illumina/pyflow/releases/download/v${PYFLOW_VERSION}/pyflow-${PYFLOW_VERSION}.tar.gz" \
-    python2.7 setup.py build install && \
+ && tar -xzf "pyflow-${PYFLOW_VERSION}.tar.gz" \
-    cd .. && \
+ && cd "pyflow-${PYFLOW_VERSION}" \
-    rm -r pyflow-"$PYFLOW_VERSION" pyflow-"$PYFLOW_VERSION".tar.gz
+ && apt-get install --no-install-recommends --yes \
+      python2.7 \
+ && python2.7 setup.py build install \
+ && cd - > /dev/null \
+ && rm -r "pyflow-${PYFLOW_VERSION}" "pyflow-${PYFLOW_VERSION}.tar.gz"
 
-# Install spaCy
-RUN pip3 install wheel && pip3 install -U spacy && \
-    python3.5 -m spacy download de && \
-    python3.5 -m spacy download en && \
-    python3.5 -m spacy download es && \
-    python3.5 -m spacy download fr && \
-    python3.5 -m spacy download it && \
-    python3.5 -m spacy download pt
 
-COPY nlp /usr/local/bin
+## Install spaCy ##
-COPY spacy_nlp /usr/local/bin
+ENV SPACY_VERSION=3.2.1
+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/*
 
-RUN mkdir /input /output && \
-    chmod a+rw /input /output
 
 ENTRYPOINT ["nlp"]
+CMD ["--help"]

LICENSE

@@ -0,0 +1,21 @@
+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

@@ -1,74 +1,41 @@
-# Natural language processing
+# NLP - Natural Language Processing
 
-This repository provides all code that is needed to build a container image for natural language processing utilizing [spaCy](https://spacy.io).
+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.
 
-## Build image
+## Software used in this pipeline implementation
 
-1. Clone this repository and navigate into it:
+- Official Debian Docker image (buster-slim): https://hub.docker.com/_/debian
+  - 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>
 ```
-git clone https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp.git && cd nlp
+4. Check your results in the `/<my_data_location>/output` directory.
-```
-
-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

@@ -1,133 +1,288 @@
 #!/usr/bin/env python2.7
 # coding=utf-8
 
-"""
+''' A NLP pipeline for text file processing. '''
-nlp
+__version__ = '0.1.0'
 
-Usage:  For usage instructions run with option --help
+from argparse import ArgumentParser
-Author: Patrick Jentsch <p.jentsch@uni-bielefeld.de>
+from pyflow import WorkflowRunner
-"""
+import json
-
-
-import argparse
-import multiprocessing
 import os
 import sys
-from pyflow import WorkflowRunner
 
 
-def parse_arguments():
+SPACY_MODELS = {
-    parser = argparse.ArgumentParser(
+    'de': 'de_core_news_md',
-        description='Performs NLP of documents utilizing spaCy. The results are served as verticalized text files.'
+    'en': 'en_core_web_md',
-    )
+    'it': 'it_core_news_md',
+    'nl': 'nl_core_news_md',
+    'pl': 'pl_core_news_md',
+    'zh': 'zh_core_web_md'
+}
 
-    parser.add_argument(
+
-        '-i',
+class PipelineJob:
-        dest='input_dir',
+    '''
-        required=True
+    NLP pipeline job class.
-    )
+
-    parser.add_argument(
+    Each plain text input file of the pipeline is represented as an NLP
-        '-l',
+    pipeline job, which holds all necessary information for the pipeline to
-        choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
+    process it.
-        dest='lang',
+
-        required=True
+    Arguments:
-    )
+    file -- Path to the file
-    parser.add_argument(
+    output_dir -- Path to a directory, where job results are stored
-        '-o',
+    '''
-        dest='output_dir',
+
-        required=True
+    def __init__(self, file, output_dir):
-    )
+        self.file = file
-    parser.add_argument(
+        self.name = os.path.basename(file)[:-4]
-        '--nCores',
+        self.output_dir = output_dir
-        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, args):
+    def __init__(self, job, model, check_encoding=False, id_prefix=''):
-        self.jobs = analyze_jobs(args.input_dir, args.output_dir)
+        self.job = job
-        self.lang = args.lang
+        self.model = model
-        self.n_cores = args.n_cores
+        self.check_encoding = check_encoding
+        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:
-        ' # Create output directories                      #
+            os.mkdir(job.output_dir)
-        ' ##################################################
-        '''
-        create_output_directories_jobs = []
-        for index, job in enumerate(self.jobs):
-            cmd = 'mkdir -p "%s"' % (job['output_dir'])
-            create_output_directories_jobs.append(
-                self.addTask(
-                    command=cmd,
-                    label='create_output_directories_job_-_%i' % (index)
-                )
-            )
 
         '''
         ' ##################################################
-        ' # Natural language processing                    #
+        ' # nlp                                            #
         ' ##################################################
         '''
-        nlp_jobs = []
+        nlp_tasks = []
-        nlp_job_n_cores = min(
+        for i, job in enumerate(self.jobs):
-            self.n_cores,
+            task = self.addWorkflowTask(
-            max(1, int(self.n_cores / len(self.jobs)))
+                'nlp_-_{}'.format(i),
-        )
+                NLPWorkflow(
-        for index, job in enumerate(self.jobs):
+                    job,
-            cmd = 'spacy_nlp -l "%s" "%s" "%s"' % (
+                    self.model,
-                self.lang,
+                    check_encoding=self.check_encoding,
-                job['path'],
+                    id_prefix=self.id_prefix
-                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
                 )
             )
+            nlp_tasks.append(task)
 
-
+        '''
-def analyze_jobs(input_dir, output_dir):
+        ' ##################################################
-    jobs = []
+        ' # create vrt                                     #
-
+        ' ##################################################
-    for file in os.listdir(input_dir):
+        '''
-        if os.path.isdir(os.path.join(input_dir, file)):
+        create_vrt_tasks = []
-            jobs += analyze_jobs(
+        for i, job in enumerate(self.jobs):
-                os.path.join(input_dir, file),
+            task = self.addWorkflowTask(
-                os.path.join(output_dir, file),
+                'create_vrt_-_{}'.format(i),
+                CreateVrtWorkflow(job),
+                dependencies='nlp_-_{}'.format(i)
             )
-        elif file.endswith('.txt'):
+            create_vrt_tasks.append(task)
-            jobs.append(
+
+        self.waitForTasks()
+        outputs = []
+        for job in self.jobs:
+            # Track output files
+            relative_output_dir = os.path.relpath(
+                job.output_dir,
+                start=self.output_dir
+            )
+            outputs.append(
                 {
-                    'filename': file,
+                    'description': 'JSON stand off data',
-                    'name': file.rsplit('.', 1)[0],
+                    'file': os.path.join(
-                    'output_dir': os.path.join(output_dir, file),
+                        relative_output_dir,
-                    'path': os.path.join(input_dir, file)
+                        '{}.json'.format(job.name)
+                    ),
+                    '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_arguments()
+    args = parse_args()
-
+    main_workflow = MainWorkflow(
-    wflow = NLPWorkflow(args)
+        args.input_dir,
-
+        args.model,
-    retval = wflow.run(dataDirRoot=args.output_dir, nCores=args.n_cores)
+        args.output_dir,
-
+        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

@@ -0,0 +1,14 @@
+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

@@ -0,0 +1,282 @@
+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

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

spacy-nlp

@@ -0,0 +1,353 @@
+#!/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

@@ -1,71 +0,0 @@
-#!/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

@@ -0,0 +1,45 @@
+#!/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

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