Dockerfile

@@ -9,14 +9,7 @@ ENV LANG=C.UTF-8
 
 RUN apt-get update \
  && apt-get install --no-install-recommends --yes \
-      procps \
+     wget
-      python3.7 \
-      python3-pip \
-      wget \
- && python3 -m pip install \
-      chardet \
-      setuptools \
-      wheel
 
 # Install the NLP pipeline and it's dependencies #
 ## Install pyFlow ##
@@ -28,12 +21,12 @@ RUN wget --no-check-certificate --quiet \
  && apt-get install --no-install-recommends --yes \
       python2.7 \
  && python2.7 setup.py build install \
- && cd - > /dev/null \
+ && cd .. \
  && rm -r "pyflow-${PYFLOW_VERSION}" "pyflow-${PYFLOW_VERSION}.tar.gz"
 
 
 ## Install spaCy ##
-ENV SPACY_VERSION=3.2.1
+ENV SPACY_VERSION=3.0.5
 RUN apt-get install --no-install-recommends --yes \
       python3.7 \
       python3-pip \
@@ -45,15 +38,17 @@ RUN apt-get install --no-install-recommends --yes \
  && 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"
+# Only models that include the following components are compatibel: 
-ENV SPACY_MODELS_VERSION=3.2.0
+# lemmatizer, ner, parser, senter, tagger,
+ENV SPACY_MODELS="de_core_news_md,en_core_web_md,it_core_news_md,nl_core_news_md,pl_core_news_md,zh_core_web_md"
+ENV SPACY_MODELS_VERSION=3.0.0
 RUN for spacy_model in $(echo ${SPACY_MODELS} | tr "," "\n"); do python3 -m spacy download "${spacy_model}-${SPACY_MODELS_VERSION}" --direct; done
 
 
-COPY packages .
+## Further dependencies ##
-RUN cd stand-off-data-py \
+RUN apt-get install --no-install-recommends --yes \
- && python3 -m pip install . \
+      procps \
- && cd -
+      zip
 
 
 ## Install Pipeline ##

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 +1,48 @@
 # NLP - 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 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.
 
 ## Software used in this pipeline implementation
-
+- Official Debian Docker image (buster-slim) and programs from its free repositories: https://hub.docker.com/_/debian
-- 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 (3.0.5): https://github.com/tesseract-ocr/tesseract/releases/tag/4.1.1
-- spaCy medium sized models (3.2.0):
+- spaCy medium sized models (3.0.0):
-  - https://github.com/explosion/spacy-models/releases/tag/de_core_news_md-3.2.0
+  - https://github.com/explosion/spacy-models/releases/tag/de_core_news_md-3.0.0
-  - https://github.com/explosion/spacy-models/releases/tag/en_core_web_md-3.2.0
+  - https://github.com/explosion/spacy-models/releases/tag/en_core_web_md-3.0.0
-  - https://github.com/explosion/spacy-models/releases/tag/it_core_news_md-3.2.0
+  - https://github.com/explosion/spacy-models/releases/tag/it_core_news_md-3.0.0
-  - https://github.com/explosion/spacy-models/releases/tag/nl_core_news_md-3.2.0
+  - https://github.com/explosion/spacy-models/releases/tag/nl_core_news_md-3.0.0
-  - https://github.com/explosion/spacy-models/releases/tag/pl_core_news_md-3.2.0
+  - https://github.com/explosion/spacy-models/releases/tag/pl_core_news_md-3.0.0
-  - https://github.com/explosion/spacy-models/releases/tag/zh_core_web_md-3.2.0
+  - https://github.com/explosion/spacy-models/releases/tag/zh_core_web_md-3.0.0
 
 
-## Installation
+## Use this image
 
-1. Install Docker and Python 3.
+1. Create input and output directories for the pipeline.
-2. Clone this repository: `git clone https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp.git`
+``` bash
-3. Build the Docker image: `docker build -t gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:v0.1.0 nlp`
+mkdir -p /<my_data_location>/input /<my_data_location>/output
-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>
 ```
+
+2. Place your text files inside `/<my_data_location>/input`. Files should all contain text of the same language.
+
+3. Start the pipeline process. Check the pipeline help (`nlp --help`) for more details.
+```
+# Option one: Use the wrapper script
+## Install the wrapper script (only on first run). Get it from https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp/-/raw/1.0.0/wrapper/nlp, make it executeable and add it to your ${PATH}
+cd /<my_data_location>
+nlp -i input -l <language_code> -o output <optional_pipeline_arguments>
+
+# Option two: Classic Docker style
+docker run \
+    --rm \
+    -it \
+    -u $(id -u $USER):$(id -g $USER) \
+    -v /<my_data_location>/input:/input \
+    -v /<my_data_location>/output:/output \
+    gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:1.0.0 \
+        -i /input \
+        -l <language_code>
+        -o /output \
+        <optional_pipeline_arguments>
+```
+
 4. Check your results in the `/<my_data_location>/output` directory.

nlp

@@ -1,141 +1,68 @@
 #!/usr/bin/env python2.7
 # coding=utf-8
 
-''' A NLP pipeline for text file processing. '''
+"""A NLP pipeline for text file processing."""
-__version__ = '0.1.0'
+
+__author__ = 'Patrick Jentsch <p.jentsch@uni-bielefeld.de>,' \
+             'Stephan Porada <porada@posteo.de>'
+__version__ = '1.0.0'
 
 from argparse import ArgumentParser
 from pyflow import WorkflowRunner
-import json
+import multiprocessing
 import os
 import sys
 
 
-SPACY_MODELS = {
+SPACY_MODELS = {'de': 'de_core_news_md',
-    'de': 'de_core_news_md',
+                'en': 'en_core_web_md',
-    'en': 'en_core_web_md',
+                'it': 'it_core_news_md',
-    'it': 'it_core_news_md',
+                'nl': 'nl_core_news_md',
-    'nl': 'nl_core_news_md',
+                'pl': 'pl_core_news_md',
-    'pl': 'pl_core_news_md',
+                'zh': 'zh_core_web_md'}
-    'zh': 'zh_core_web_md'
-}
 
 
-class PipelineJob:
+class NLPPipelineJob:
-    '''
+    """An NLP pipeline job class
-    NLP pipeline job class.
 
-    Each plain text input file of the pipeline is represented as an NLP
+    Each input file of the pipeline is represented as an NLP pipeline job,
-    pipeline job, which holds all necessary information for the pipeline to
+    which holds all necessary information for the pipeline to process it.
-    process it.
 
     Arguments:
     file -- Path to the file
-    output_dir -- Path to a directory, where job results are stored
+    output_dir -- Path to a directory, where job results a stored
-    '''
+    """
 
     def __init__(self, file, output_dir):
         self.file = file
-        self.name = os.path.basename(file)[:-4]
+        self.name = os.path.basename(file).rsplit('.', 1)[0]
         self.output_dir = output_dir
 
 
-class NLPWorkflow(WorkflowRunner):
+class NLPPipeline(WorkflowRunner):
-    def __init__(self, job, model, check_encoding=False, id_prefix=''):
+    def __init__(self, input_dir, output_dir, check_encoding, lang, zip):
-        self.job = job
-        self.model = model
-        self.check_encoding = check_encoding
-        self.id_prefix = id_prefix
-
-    def workflow(self):
-        '''
-        ' ##################################################
-        ' # 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.lang = lang
-        self.jobs = []
+        self.zip = zip
-
+        self.jobs = collect_jobs(self.input_dir, self.output_dir)
-    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)
+        ' # setup output directory                         #
+        ' ##################################################
+        '''
+        setup_output_directory_tasks = []
+        for i, job in enumerate(self.jobs):
+            cmd = 'mkdir -p "{}"'.format(job.output_dir)
+            lbl = 'setup_output_directory_-_{}'.format(i)
+            task = self.addTask(command=cmd, label=lbl)
+            setup_output_directory_tasks.append(task)
 
         '''
         ' ##################################################
@@ -143,116 +70,104 @@ class MainWorkflow(WorkflowRunner):
         ' ##################################################
         '''
         nlp_tasks = []
+        n_cores = max(1, int(self.getNCores() / len(self.jobs)))
+        mem_mb = min(n_cores * 2048, int(self.getMemMb() / len(self.jobs)))
         for i, job in enumerate(self.jobs):
-            task = self.addWorkflowTask(
+            output_file = os.path.join(job.output_dir, '{}.nopaque-stand-off.json'.format(job.name))  # noqa
-                'nlp_-_{}'.format(i),
+            cmd = 'spacy-nlp'
-                NLPWorkflow(
+            cmd += ' -l "{}"'.format(self.lang)
-                    job,
+            cmd += ' --check-encoding' if self.check_encoding else ''
-                    self.model,
+            cmd += ' "{}"'.format(job.file)
-                    check_encoding=self.check_encoding,
+            cmd += ' "{}"'.format(output_file)
-                    id_prefix=self.id_prefix
+            deps = 'setup_output_directory_-_{}'.format(i)
-                )
+            lbl = 'nlp_-_{}'.format(i)
-            )
+            task = self.addTask(command=cmd, dependencies=deps, label=lbl,
+                                memMb=mem_mb, nCores=n_cores)
             nlp_tasks.append(task)
 
         '''
         ' ##################################################
-        ' # create vrt                                     #
+        ' # vrt creation                                   #
         ' ##################################################
         '''
-        create_vrt_tasks = []
+        vrt_creation_tasks = []
         for i, job in enumerate(self.jobs):
-            task = self.addWorkflowTask(
+            output_file = os.path.join(job.output_dir, '{}.vrt'.format(job.name))  # noqa
-                'create_vrt_-_{}'.format(i),
+            nlp_file = os.path.join(job.output_dir, '{}.nopaque-stand-off.json'.format(job.name))  # noqa
-                CreateVrtWorkflow(job),
+            cmd = 'vrt-creator'
-                dependencies='nlp_-_{}'.format(i)
+            cmd += ' "{}"'.format(job.file)
-            )
+            cmd += ' "{}"'.format(nlp_file)
-            create_vrt_tasks.append(task)
+            cmd += ' "{}"'.format(output_file)
+            deps = 'nlp_-_{}'.format(i)
+            lbl = 'vrt_creation_-_{}'.format(i)
+            task = self.addTask(command=cmd, dependencies=deps, label=lbl)
+            vrt_creation_tasks.append(task)
 
-        self.waitForTasks()
+        '''
-        outputs = []
+        ' ##################################################
-        for job in self.jobs:
+        ' # zip creation                                   #
-            # Track output files
+        ' ##################################################
-            relative_output_dir = os.path.relpath(
+        '''
-                job.output_dir,
+        zip_creation_tasks = []
-                start=self.output_dir
+        if self.zip is not None:
-            )
+            cmd = 'cd "{}"'.format(self.output_dir)
-            outputs.append(
+            cmd += ' && '
-                {
+            cmd += 'zip'
-                    'description': 'JSON stand off data',
+            cmd += ' -r'
-                    'file': os.path.join(
+            cmd += ' "{}.zip" .'.format(self.zip)
-                        relative_output_dir,
+            cmd += ' -x "pyflow.data*"'
-                        '{}.json'.format(job.name)
+            cmd += ' -i "*.vrt" "*.json"'
-                    ),
+            cmd += ' && '
-                    'mimetype': 'application/json'
+            cmd += 'cd -'
-                }
+            deps = vrt_creation_tasks
-            )
+            lbl = 'zip_creation'
-            outputs.append(
+            task = self.addTask(command=cmd, dependencies=deps, label=lbl)
-                {
+            zip_creation_tasks.append(task)
-                    'description': 'CWB vrt file',
+
-                    'file': os.path.join(
+
-                        relative_output_dir,
+def collect_jobs(input_dir, output_dir):
-                        '{}.vrt'.format(job.name)
+    jobs = []
-                    ),
+    for file in os.listdir(input_dir):
-                    'mimetype': 'application/vrt+xml'
+        if os.path.isdir(os.path.join(input_dir, file)):
-                }
+            continue
-            )
+        if file.lower().endswith('.txt'):
-        with open(os.path.join(self.output_dir, 'outputs.json'), 'w') as f:
+            job = NLPPipelineJob(os.path.join(input_dir, file),
-            json.dump(outputs, f, indent=4)
+                                 os.path.join(output_dir, file))
+            jobs.append(job)
+    return jobs
 
 
 def parse_args():
-    parser = ArgumentParser(
+    parser = ArgumentParser(description='NLP pipeline for TXT file processing',
-        description='NLP pipeline for plain text file processing'
+                            prog='NLP pipeline')
-    )
+    parser.add_argument('-i', '--input-dir',
-    parser.add_argument(
+                        help='Input directory',
-        '-i', '--input-dir',
+                        required=True)
-        help='Input directory',
+    parser.add_argument('-o', '--output-dir',
-        required=True
+                        help='Output directory',
-    )
+                        required=True)
-    parser.add_argument(
+    parser.add_argument('-l', '--language',
-        '-o', '--output-dir',
+                        choices=SPACY_MODELS.keys(),
-        help='Output directory',
+                        help='Language of the input (2-character ISO 639-1 language codes)',  # noqa
-        required=True
+                        required=True)
-    )
+    parser.add_argument('--check-encoding',
-    parser.add_argument(
+                        action='store_true',
-        '-m', '--model',
+                        help='Check encoding of the input file, UTF-8 is used instead')  # noqa
-        choices=SPACY_MODELS.keys(),
+    parser.add_argument('--log-dir',
-        help='The model to be used',
+                        help='Logging directory')
-        required=True
+    parser.add_argument('--mem-mb',
-    )
+                        help='Amount of system memory to be used (Default: min(--n-cores * 2048, available system memory))',  # noqa
-    parser.add_argument(
+                        type=int)
-        '--check-encoding',
+    parser.add_argument('--n-cores',
-        action='store_true',
+                        default=min(4, multiprocessing.cpu_count()),
-        help='Check encoding of the input file, UTF-8 is used instead'
+                        help='Number of CPU threads to be used (Default: min(4, number of CPUs))',  # noqa
-    )
+                        type=int)
-    parser.add_argument(
+    parser.add_argument('--zip',
-        '--id-prefix',
+                        help='Create one zip file per filetype')
-        default='',
+    parser.add_argument('-v', '--version',
-        help='A prefix for all the ids within the stand off annotations'
+                        action='version',
-    )
+                        help='Returns the current version of the NLP pipeline',
-    parser.add_argument(
+                        version='%(prog)s {}'.format(__version__))
-        '--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
@@ -262,27 +177,20 @@ def parse_args():
         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)
+        args.mem_mb = min(args.n_cores * 2048, max_mem_mb)
-    if args.mem_mb < 1024:
+    if args.mem_mb < 2048:
-        raise Exception('--mem-mb must be greater or equal 1024')
+        raise Exception('--mem-mb must be greater or equal 2048')
+    if args.zip is not None and args.zip.lower().endswith('.zip'):
+        # Remove .zip file extension if provided
+        args.zip = args.zip[:-4]
+        args.zip = args.zip if args.zip else 'output'
     return args
 
 
 def main():
     args = parse_args()
-    main_workflow = MainWorkflow(
+    nlp_pipeline = NLPPipeline(args.input_dir, args.output_dir, args.check_encoding, args.language, args.zip)  # noqa
-        args.input_dir,
+    retval = nlp_pipeline.run(dataDirRoot=args.log_dir, memMb=args.mem_mb, nCores=args.n_cores)  # noqa
-        args.model,
-        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

@@ -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

@@ -8,67 +8,41 @@ import json
 import os
 import spacy
 import textwrap
-import uuid
 
 
-spacy_models = {
+spacy_models = {spacy.info(pipeline)['lang']: pipeline
-    spacy.info(pipeline)['lang']: pipeline
+                for pipeline in spacy.info()['pipelines']}
-    for pipeline in spacy.info()['pipelines']
-}
 
 
 # Parse the given arguments
-parser = ArgumentParser(
+parser = ArgumentParser(description='Create annotations for a given txt file')
-    description='Create annotations for a given plain txt file'
+parser.add_argument('input', help='Path to txt input file')
-)
+parser.add_argument('output', help='Path to JSON output file')
-parser.add_argument(
+parser.add_argument('-l', '--language',
-    '-i', '--input-file',
+                    choices=spacy_models.keys(),
-    help='Input file'
+                    help='Language of the input (2-character ISO 639-1 language codes)',  # noqa
-)
+                    required=True)
-parser.add_argument(
+parser.add_argument('-c', '--check-encoding',
-    '-o', '--output-file',
+                    action='store_true',
-    help='Output file',
+                    help='Check encoding of the input file, UTF-8 is used instead')  # noqa
-    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()
 
-
+with open(args.input, "rb") as text_file:
-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']
+        encoding = chardet.detect(text_file.read())['encoding']
     else:
         encoding = 'utf-8'
-    input_file.seek(0)
+    text_file.seek(0)
     text_md5 = hashlib.md5()
-    for chunk in iter(lambda: input_file.read(128 * text_md5.block_size), b''):
+    for chunk in iter(lambda: text_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:
+with open(args.input, encoding=encoding) as text_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(),
+        text_file.read(),
         1000000,
         break_long_words=False,
         break_on_hyphens=False,
@@ -77,197 +51,84 @@ with open(args.input_file, encoding=encoding) as input_file:
         replace_whitespace=False
     )
 
-model_name = spacy_models[args.model]
+model = spacy_models[args.language]
-nlp = spacy.load(model_name)
+nlp = spacy.load(model)
 
 meta = {
     'generator': {
-        'name': 'nopaque spacy NLP',
+        'name': 'nopaque NLP service',
-        'version': '0.1.0',
+        'version': '1.0.0',
         'arguments': {
             'check_encoding': args.check_encoding,
-            'model': args.model
+            'language': args.language
         }
     },
     'file': {
         'encoding': encoding,
         'md5': text_md5.hexdigest(),
-        'name': os.path.basename(args.input_file)
+        'name': os.path.basename(args.input)
     }
 }
 
-tags = []
+
-token = {
+tags = {
-    'id': generate_id('token'),
+    'token': {
-    'name': 'token',
+        'description': '',
-    'description': 'An individual token — i.e. a word, punctuation symbol, whitespace, etc.',  # noqa
+        'properties': {
-    'properties': []
+            'lemma': {
-}
+                'description': 'The base form of the word',
-# TODO: Check if all languages support token.sentiment
+                'flags': ['required'],
-token['properties'].append(
+                'tagset': None
-    {
+            },
-        'id': generate_id('token.sentiment'),
+            'pos': {
-        'name': 'sentiment',
+                'description': 'The detailed part-of-speech tag',
-        'description': 'A scalar value indicating the positivity or negativity of the token.'  # noqa
+                'flags': ['required'],
+                'tagset': {label: spacy.explain(label) for label in spacy.info(model)['labels']['tagger']}  # noqa
+            },
+            'simple_pos': {
+                'description': 'The simple UPOS part-of-speech tag',
+                'flags': ['required'],
+                'tagset': {
+                    'ADJ': 'adjective',
+                    'ADP': 'adposition',
+                    'ADV': 'adverb',
+                    'AUX': 'auxiliary verb',
+                    'CONJ': 'coordinating conjunction',
+                    'DET': 'determiner',
+                    'INTJ': 'interjection',
+                    'NOUN': 'noun',
+                    'NUM': 'numeral',
+                    'PART': 'particle',
+                    'PRON': 'pronoun',
+                    'PROPN': 'proper noun',
+                    'PUNCT': 'punctuation',
+                    'SCONJ': 'subordinating conjunction',
+                    'SYM': 'symbol',
+                    'VERB': 'verb',
+                    'X': 'other'
+                }
+            },
+            'ner': {
+                'description': 'Label indicating the type of the entity',
+                'tagset': {label: spacy.explain(label) for label in spacy.info(model)['labels']['ner']}  # noqa
+            }
+        }
+    },
+    's': {
+        'description': 'Encodes the start and end of a sentence',
+        'properties': None
+    },
+    'ent': {
+        'description': 'Encodes the start and end of a named entity',
+        'properties': {
+            'type': {
+                'description': 'Label indicating the type of the entity',
+                'flags': ['required'],
+                'tagset': {label: spacy.explain(label) for label in spacy.info(model)['labels']['ner']}  # 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 = []
 
@@ -275,78 +136,37 @@ 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 = {
+        if token.is_space:
-            'start': token.idx + chunk_offset,
+            continue
-            'end': token.idx + len(token.text) + chunk_offset,
+        if token.is_sent_start:
-            'tag_id': generate_id('token'),
+            annotation = {'start': token.sent.start_char + chunk_offset,
-            'properties': []
+                          'end': token.sent.end_char + chunk_offset,
-        }
+                          'tag': 's'}
-        if hasattr(token, 'lemma_'):
+            annotations.append(annotation)
-            annotation['properties'].append(
+        # Check if the token is the start of an entity
-                {
+        if token.ent_iob == 3:
-                    'property_id': generate_id('token.lemma'),
+            for ent_candidate in token.sent.ents:
-                    'value': token.lemma_
+                if ent_candidate.start_char == token.idx:
-                }
+                    ent = ent_candidate
-            )
+                    annotation = {'start': ent.start_char + chunk_offset,
-        if hasattr(token, 'pos_'):
+                                  'end': ent.end_char + chunk_offset,
-            annotation['properties'].append(
+                                  'tag': 'ent',
-                {
+                                  'properties': {'type': token.ent_type_}}
-                    'property_id': generate_id('token.simple_pos'),
+                    annotations.append(annotation)
-                    'value': token.pos_
+                    break
-                }
+        annotation = {'start': token.idx + chunk_offset,
-            )
+                      'end': token.idx + len(token.text) + chunk_offset,
-        if hasattr(token, 'sentiment'):
+                      'tag': 'token',
-            annotation['properties'].append(
+                      'properties': {'pos': token.tag_,
-                {
+                                     'lemma': token.lemma_,
-                    'property_id': generate_id('token.sentiment'),
+                                     'simple_pos': token.pos_}}
-                    'value': token.sentiment
+        if token.ent_type_:
-                }
+            annotation['properties']['ner'] = token.ent_type_
-            )
-        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:
+with open(args.output, 'w') as output_file:
-    json.dump(
+    json.dump({'meta': meta, 'tags': tags, 'annotations': annotations},
-        {'meta': meta, 'tags': tags, 'annotations': annotations},
+              output_file, indent=4)
-        output_file,
-        indent=4
-    )

vrt-creator

@@ -2,40 +2,129 @@
 # coding=utf-8
 
 from argparse import ArgumentParser
-from stand_off_data import StandOffData
+from xml.sax.saxutils import escape
 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'
-)
-parser.add_argument(
-    '-t', '--text-file',
-    help='Plain text input file'
-)
-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:
+# Two global ressources - Not very elegant but it works for now
-    stand_off_data = StandOffData(json.load(stand_of_data_file))
+stand_off_data = None
+text = None
 
-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
+def meta_to_string():
-    text = text_file.read()
+    string = ''
+    string += '<generator software="{} ({})" arguments="check_encoding: {}; language: {}"/>\n'.format(  # noqa
+        stand_off_data['meta']['generator']['name'],
+        stand_off_data['meta']['generator']['version'],
+        stand_off_data['meta']['generator']['arguments']['check_encoding'],
+        stand_off_data['meta']['generator']['arguments']['language']
+    )
+    string += '<file encoding="{}" name="{}" md5="{}"/>\n'.format(
+        stand_off_data['meta']['file']['encoding'],
+        stand_off_data['meta']['file']['name'],
+        stand_off_data['meta']['file']['md5']
+    )
+    return string
 
-with open(args.output_file, 'w') as vrt_file:
+
-    vrt_file.write(stand_off_data.to_vrt(text))
+def tags_to_string():
+    return ''
+
+
+def annotations_to_string(end=float('inf')):
+    string = ''
+    while stand_off_data['annotations']:
+        if stand_off_data['annotations'][0]['start'] >= end:
+            break
+        annotation = stand_off_data['annotations'].pop(0)
+        #######################################################################
+        # Check for malformed annotations                                     #
+        #######################################################################
+        if 'tag' not in annotation:
+            raise Exception('Annotation tag is missing')
+
+        if annotation['tag'] not in stand_off_data['tags']:
+            raise Exception('Unknown annotation tag: ' + annotation['tag'])
+
+        tag_model = stand_off_data['tags'][annotation['tag']]
+        if 'properties' in tag_model:
+            properties_model = tag_model['properties']
+            if properties_model is not None:
+                required_properties = filter(lambda x: 'flags' in x and 'required' in x['flags'], properties_model)  # noqa
+                if required_properties and annotation['properties'] is None:
+                    raise Exception('There are required properties but the "Properties" attribute is missing')  # noqa
+                for property in required_properties:
+                    if property not in annotation['properties']:
+                        raise Exception('Required property is missing: ' + property)  # noqa
+        #######################################################################
+        # Process tokens ~ cwb's positional attributes                        #
+        #######################################################################
+        if annotation['tag'] == 'token':
+            string += '{}\t{}\t{}\t{}\t{}\n'.format(
+                escape(text[annotation['start']:annotation['end']]),
+                escape(annotation['properties']['pos']),
+                escape(annotation['properties']['lemma']),
+                escape(annotation['properties']['simple_pos']),
+                escape(annotation['properties']['ner'] if 'ner' in annotation['properties'] else 'None')  # noqa
+            )
+        #######################################################################
+        # Process other tags ~ cwb's structural attributes                    #
+        #######################################################################
+        else:
+            properties = ''
+            if 'properties' in annotation and annotation['properties'] is not None:  # noqa
+                for property, value in annotation['properties'].items():
+                    if not value:
+                        continue
+                    if properties_model and property in properties_model:
+                        if 'flags' in properties_model and 'multiple' in properties_model['flags']:  # noqa
+                            properties += ' {}="|{}|"'.format(property, '|'.join(value))  # noqa
+                        else:
+                            properties += ' {}="{}"'.format(property, value)
+            string += '<' + annotation['tag'] + properties + '>\n'
+            string += annotations_to_string(end=min(annotation['end'], end))
+            string += '</' + annotation['tag'] + '>\n'
+    return string
+
+
+def main():
+    global stand_off_data
+    global text
+
+    # Parse the given arguments
+    parser = ArgumentParser(description='Create a vrt from JSON and txt')
+    parser.add_argument('text', help='Path to txt file')
+    parser.add_argument('stand_off_data', help='Path to JSON file')
+    parser.add_argument('output', help='Path to vrt output file')
+    args = parser.parse_args()
+
+    with open(args.stand_off_data) as stand_of_data_file:
+        stand_off_data = json.load(stand_of_data_file)
+
+    with open(args.text, "rb") as text_file:
+        text_md5 = hashlib.md5()
+        for chunk in iter(lambda: text_file.read(128 * text_md5.block_size), b''):  # noqa
+            text_md5.update(chunk)
+        if text_md5.hexdigest() != stand_off_data['meta']['file']['md5']:
+            raise Exception('md5 not equal')
+
+    with open(args.text, encoding=stand_off_data['meta']['file']['encoding']) as text_file:  # noqa
+        text = text_file.read()
+
+    vrt = ''
+    vrt += '<?xml version="1.0" encoding="UTF-8" standalone="yes"?>\n'
+    vrt += '<corpus>\n'
+    vrt += '<text>\n'
+    vrt += meta_to_string()
+    vrt += tags_to_string()
+    vrt += annotations_to_string()
+    vrt += '</text>\n'
+    vrt += '</corpus>'
+
+    with open(args.output, 'w') as vrt_file:
+        vrt_file.write(vrt)
+
+
+if __name__ == '__main__':
+    main()

wrapper/nlp

@@ -6,7 +6,7 @@ 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:1.0.0b'
 CONTAINER_INPUT_DIR = '/input'
 CONTAINER_OUTPUT_DIR = '/output'
 CONTAINER_LOG_DIR = '/logs'
@@ -19,17 +19,17 @@ parser.add_argument('-o', '--output-dir')
 parser.add_argument('--log-dir')
 args, remaining_args = parser.parse_known_args()
 
-cmd = ['docker', 'run', '--rm', '-it', '-u', f'{UID}:{GID}']
+cmd = ['docker', 'run', '--rm', '-it', '-u', '{}:{}'.format(UID, GID)]
 if args.input_dir is not None:
-    mapping = f'{os.path.abspath(args.input_dir)}:{CONTAINER_INPUT_DIR}'
+    mapping = os.path.abspath(args.input_dir) + ':' + CONTAINER_INPUT_DIR
     cmd += ['-v', mapping]
     remaining_args += ['-i', CONTAINER_INPUT_DIR]
 if args.output_dir is not None:
-    mapping = f'{os.path.abspath(args.output_dir)}:{CONTAINER_OUTPUT_DIR}'
+    mapping = os.path.abspath(args.output_dir) + ':' + CONTAINER_OUTPUT_DIR
     cmd += ['-v', mapping]
     remaining_args += ['-o', CONTAINER_OUTPUT_DIR]
 if args.log_dir is not None:
-    mapping = '{os.path.abspath(args.log_dir)}:{CONTAINER_LOG_DIR}'
+    mapping = os.path.abspath(args.log_dir) + ':' + CONTAINER_LOG_DIR
     cmd += ['-v', mapping]
     remaining_args += ['--log-dir', CONTAINER_LOG_DIR]
 cmd.append(CONTAINER_IMAGE)