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Data format for storing structured data in spreadsheet tables

Project description

Metatab
=======

.. image:: https://travis-ci.org/Metatab/metatab.svg?branch=master
:target: https://travis-ci.org/Metatab/metatab

Parse and manipulate structured data and metadata in a tabular format.

`Metatab <http://metatab.org>`_ is a data format that allows structured metadata -- the sort you'd normally store in JSON, YAML or XML -- to be stored and edited in tabular forms like CSV or Excel. Metatab files look exactly like you'd expect, so they
are very easy for non-technical users to read and edit, using tools they already have. Metatab is an excellent format
for creating, storing and transmitting metadata. For more information about metatab, visit http://metatab.org.

This repository has a Python module and executable. For a Javascript version, see the `metatab-js <https://github.com/CivicKnowledge/metatab-js>`_ repository.

What is Metatab For?
--------------------

Metatab is a tabular format that allows storing metadata for demographics, health and research datasets in a tabular format. The tabular format is much easier for data creators to write and for data consumers to read, and it allows a complete data packages to be stored in a single Excel file.


Install
-------



Install the package from PiPy with:

.. code-block:: bash

$ pip install metatab

Or, install the master branch from github with:

.. code-block:: bash

$ pip install https://github.com/CivicKnowledge/metatab.git

Then test parsing using a remote file with:

.. code-block:: bash

$ metatab -j https://raw.githubusercontent.com/CivicKnowledge/metatab/master/test-data/example1.csv

Run ``metatab -h`` to get other program options.

The ``test-data`` directory has test files that also serve as examples to parse. You can either clone the repo and parse them from the files, or from the Github page for the file, click on the ``raw`` button to get raw view of the flie, then copy the URL.


Running tests
+++++++++++++

Run ``python setup.py tests`` to run normal development tests. You can also run ``tox``, which will
try to run the tests with python 3.4, 3.5 and 3.6, ignoring non-existent interpreters.


Development Testing with Docker
+++++++++++++++++++++++++++++++

Testing during development for other versions of Python is a bit of a pain, since you have
to install the alternate version, and Tox will run all of the tests, not just the one you want.

One way to deal with this is to install Docker locally, then run the docker test container
on the source directory. This is done automatically from the Makefile in metatab/test, just run:

.. code-block:: bash
$ cd metatab/test
$ make build # to create the container image
$ make test
# or just ..
$ make

You can also run the container shell, and run tests from the command line.

.. code-block:: bash

$ cd metatab/test
$ make build # to create the container image
$ make shell # to run bash the container

You now have a docker container where the /code directory is the metatab source dir.

Now, run tox to build the tox virtual environments, then enter the specific version you want to
run tests for and activate the virtual environment.

.. code-block:: bash

# tox
# cd .tox/py34
# source bin/activate # Activate the python 3.4 virtual env
# cd ../../
# python setup.py test # Cause test deps to get installed
#
# python -munittest metatab.test.test_parser.TestParser.test_parse_everython # Run one test

Note that your development environment is mounted into the Docker container, so you can edit local
files and test the changes in Docker.






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