# Extending xtas¶

This is a short guide to extending xtas to suit specific needs and (optionally) contributing code back. It describes how to write now tasks and tie them in with the package.

If you have a custom task that you want xtas to perform, then you can add it as follows. Suppose you want to perform sentiment analysis on French text using the Pattern toolkit. First, install Pattern:

pip install pattern


Now define an xtas task that uses Pattern to process a French text. Put the following a file, say, pattern_tasks.py:

import pattern.fr
from xtas.core import app

def fr_sentiment(text):
"""Perform sentiment analysis on French text.

Returns the text (for reference), a subjectivity score,
and a positive/negative score.
"""
return (text,) + pattern.fr.sentiment(text)


Make sure this file can be imported:

>>> from pattern_tasks import fr_sentiment


If the above gave an error, adjust your PYTHONPATH (in the shell):

export PYTHONPATH=.:$PYTHONPATH  Now adjust the xtas configuration. Run python -m xtas.make_config to get a configuration file xtas_config.py in the current directory. At the bottom of the file is an empty list called EXTRA_MODULES. Put your module in it: EXTRA_MODULES = [ 'pattern_tasks', ]  Now restart the worker. It should report pattern_tasks.fr_sentiment as its first task, followed by all the built-in tasks. You can now run your task function asynchronously, e.g. in a Python shell: >>> from pattern_tasks import fr_sentiment >>> result = fr_sentiment.apply_async(["Bon!"]) >>> result.get() ['Bon!', 0.875, 0.7]  If you also restart the webserver, you should see the new task in the list of single-document tasks: $ curl -s http://localhost:5000/tasks | python -m json.tool | grep pattern


To use the custom task from the REST API, e.g. with /run_es, give its fully qualified name (pattern_tasks.fr_sentiment). Only built-in tasks have their name abbreviated to not include the module name.

Note

The webserver will currently assume your task is a single-document one, rather than a batch task. This is a known defect.

## Contributing code¶

If you have code that is reusable for others, and you want and are legally capable of distributing it, then we’re happy to consider it for inclusion in xtas. Make sure you have copyright to the code you write or your employer gives you permission to contribute under the terms of the Apache License (LICENSE.txt in the main source directory).

Fork the main repository on GitHub, then install this instead of the released version. First make a new virtualenv:

virtualenv --system-site-packages /some/where/xtas-work
. /some/where/xtas-work/bin/activate


Then, in the xtas source directory, issue:

pip install .


When you make changes, issue this command to update the virtualenv:

pip install --upgrade --no-deps .


To contribute code back, commit your changes to a separate branch. Push this branch to GitHub and do a pull request. Your code will be reviewed before pulling.

In the case of new tasks, put them in either xtas/tasks/single.py or xtas/tasks/cluster.py, depending on the type of task. Follow the conventions laid out in the docstrings of the modules. All xtas code should conform to PEP 8, the style guide for the Python standard library. Use the pep8 and pyflakes tools to check code for compliance. Also, be sure to use names starting with an underscore for private helper functions.

## Writing documentation¶

Make sure to document your tasks. Documentation is primarily written in the form of docstrings, and we tend to follow the NumPy docstring conventions.

To tie docstrings into the HTML documentation, edit the api.rst file in the directory doc. To generate HTML, make sure you have Sphinx, numpydoc and Celery 3.1.10 or later:

pip install -U sphinx numpydoc celery sphinx_bootstrap_theme


then type make html inside the doc directory. HTML will be generated in doc/_build/html.