DataSounds¶

Get sounds from temporal series, or another sequecial data. Visit us at www.datasounds.org.

Dependencies¶

Numpy is a necessary packages to use DataSounds.

Numpy can be installed using pip. If you use virtualenvwrapper, this could be done inside your virtual environment. Normally, Numpy is installed as a dependency of DataSounds and should work if it was successfully compiled.

Simple usage¶

Simple Python script to use DataSounds module to sonify dataset.

from DataSounds.sounds import get_music, w2Midi
import numpy as np

data = np.random.rand(24)
music = get_music(data)
w2Midi('my_musica_data', music)

w2Midi writes a .midi file inside the current directory. In this case my_music_data.midi will be saved on disk.

Play MIDI files inside Python is a little bit tricky, and it is possible using the compilation of Pygame library.

To not bring this dependency inside DataSounds, we try to use the system player. For Linux you could install timidity (apt-get install timidity) or use totem with fluid-soundfont-gm. For OS X users, we try to use the simple open or timidity, and for windows users you should install timidity.

DataSounds comes with default pre settings of get_music function, which may be changed accordingly the user prefferences. The help function displays:

Setting parameters¶

Parameters can be changed if you want to use DataSounds to differentiate two or more series. This usage requires the difference of instruments and also the number of octaves of each series. For example:

This example shows how to use different instruments for each series (e.g. Acoustic Grand Piano for ts_a and Rock Organ for ts_b.

The usage of octaves can better perform distinction between values. We can correlate this parameter as colorbars, if you have a colorbar with 7 colors to represent more than 7 distinct values, your display may be masking some results. Same logical procedure can be adopted here.

List of MIDI instruments¶

Numbers at left side of instrument name are already written considering Python listing index (e.g. first number is == 0).

Types of Contributions¶

Get Started!¶

Ready to contribute? Here’s how to set up DataSounds for local development.

1. Fork the DataSounds repo on GitHub.

2. Clone your fork locally:

   $ git clone git@github.com:your_name_here/DataSounds.git
   

3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

   $ mkvirtualenv devDS
   $ cd DataSounds/
   $ python setup.py develop
   

4. Create a branch for local development:

   $ git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

5. When you’re done making changes, check that your changes pass through tests, including testing other Python versions with tox:

   $ python setup.py test
   $ tox
   

   To get the necessary packeges just install it as follows inside yours virtualenv:

   $ pip install -r dev-requirements.txt
   

6. Commit your changes and push your branch to GitHub:

   $ git add .
   $ git commit -m "Your detailed description of your changes."
   $ git push origin name-of-your-bugfix-or-feature
   

7. Submit a pull request through the GitHub website.

Pull Request Guidelines¶

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include tests.
2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
3. The pull request should work for Python 2.6, 2.7, and 3.3, and for PyPy. Check https://travis-ci.org/DataSounds/DataSounds/pull_requests and make sure that the tests pass for all supported Python versions.

Tips¶

To run a subset of tests:

$ python -m unittest tests.test_sounds.py

DataSounds development Lead¶

• Arnaldo Russo <arnaldo@datasounds.org>
• Luiz Irber <luiz@datasounds.org>

Contributors¶

None yet. Why not be the first?

1.2.0 (2013-12-22)¶

1.1.0 (2013-06-08)¶

0.1.0 (2013-03-15)¶

• First release on PyPI.