:maxdepth: 2
.. automodule:: timeside.decoder.core
+
+
+File Decoder
+===========
+
+.. autoclass:: FileDecoder
:members:
+ :undoc-members:
+ :show-inheritance:
-
+Array Decoder
+=============
+
+.. autoclass:: ArrayDecoder
+ :members:
+ :undoc-members:
+ :show-inheritance:
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
-extensions = ['sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.coverage']
+extensions = ['sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'numpydoc']
doctest_path = os.path.abspath('../../')
autodoc_default_flags = 'show-inheritance'
+autoclass_content = 'both'
#autosummary_generate = True
numpydoc_show_class_members = False
==============================================
.. image:: https://secure.travis-ci.org/yomguy/TimeSide.png?branch=master
- :target: http://travis-ci.org/yomguy/TimeSide/
+ :target: https://travis-ci.org/yomguy/TimeSide/
TimeSide is a set of python components enabling audio analysis, imaging, transcoding and streaming. Its high-level API has been designed to enable complex processing on big media data corpus. Its simple plugin architecture can be adapted to various usecases.
* `CNRS <http://www.cnrs.fr>`_ (National Center of Science Research, France)
* `Huma-Num <http://www.huma-num.fr/>`_ (big data equipment for digital humanities, ex TGE Adonis)
* `CREM <http://www.crem-cnrs.fr>`_ (french National Center of Ethomusicology Research, France)
- * `Université Pierre et Marie Curie <http://www-upmc.fr>`_ (UPMC Paris, France)
+ * `Université Pierre et Marie Curie <http://www.upmc.fr>`_ (UPMC Paris, France)
* `ANR <http://www.agence-nationale-recherche.fr/>`_ (CONTINT 2012 project : DIADEMS)
* `MNHN <http://www.mnhn.fr>`_ : Museum National d'Histoire Naturelle (Paris, France)
DataObject(value=array([], dtype=float64), time=array([], dtype=float64))
Specification of data_mode
-=========================
+==========================
Two different data_mode can be specified :
- 'value' : Data are returned as numpy Array of arbitrary type