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tffpy
=====
tffpy: a Python package for time-frequency fading using Gabor multipliers
See paper
*Time-frequency fading algorithms based on Gabor multipliers*
by A. Marina Krémé, Valentin Emiya, Caroline Chaux and Bruno Torré́sani, 2020.
:py:mod:tffpy: is a Python package for time-frequency fading using Gabor
multipliers based on the work in paper *Time-frequency fading algorithms
based on Gabor multipliers* by A. Marina Krémé, Valentin Emiya, Caroline
Chaux and Bruno Torré́sani, 2020.
Install
-------
......@@ -14,6 +13,14 @@ Install the current release with ``pip``::
pip install tffpy
Download the data from `this link <https://gitlab.lis-lab
.fr/skmad-suite/tff2020/-/tree/master/data>`.
Then run function :py:fun:tffpy.utils.generate_config(): in order to create
a configuration file and modify it to specify the path to your data folder.
The location of the configuration file is given by function
:py:fun:tffpy.utils.get_config_file():.
For additional details, see doc/install.rst.
Usage
......@@ -51,7 +58,7 @@ Contributors
------------
* `Valentin Emiya <mailto:valentin.emiya@lis-lab.fr>`_
* `Ama Marina Kreme <mailto:ama-marina.kreme@lis-lab.fr`_
* `Ama Marina Krémé <mailto:ama-marina.kreme@lis-lab.fr>`_
License
-------
......
If you only want to get the documentation, note that a pre-built
version for the latest release is available
[online](http://templates.pages.lis-lab.fr/tffpy/).
[online](http://skmad-suite.pages.lis-lab.fr/tffpy/).
Sphinx is used to generate the API and reference documentation.
......
Installation
############
``tffpy`` requires the following packages:
``tffpy`` requires the following packages, which will be automatically
installed with ``tffpy`` using ``pip``:
* `python >= 3.5 <https://wiki.python.org/moin/BeginnersGuide/Download>`_
* `python >= 3.6 <https://wiki.python.org/moin/BeginnersGuide/Download>`_
* `numpy >= 1.13 <http://www.numpy.org>`_
* `scipy <https://www.scipy.org/>`_
* `matplotlib <https://matplotlib.org/>`_
* `pandas <https://pandas.pydata.org/>`_
* `xarray <https://xarray.pydata.org/>`_
* `ltfatpy <http://dev.pages.lis-lab.fr/ltfatpy/>`_
* `skpomade <http://valentin.emiya.pages.lis-lab.fr/skpomade/>`_
* `yafe <http://skmad-suite.pages.lis-lab.fr/yafe/>`_
* `madarrays <https://gitlab.lis-lab.fr/skmad-suite/madarrays>`_
Make sure your Python environment is properly configured. It is recommended to
install ``tffpy`` in a virtual environment.
......@@ -30,13 +39,23 @@ into your user directory using the ``--user`` flag::
pip install --user tffpy
Alternatively, you can manually download ``tffpy`` from its `GitLab project
<https://gitlab.lis-lab.fr/templates/python-package-template>`_ or `PyPI
<https://gitlab.lis-lab.fr/skmad-suite/tff2020>`_ or `PyPI
<https://pypi.python.org/pypi/tffpy>`_. To install one of these versions,
unpack it and run the following from the top-level source directory using the
Terminal::
pip install .
Dataset installation
--------------------
Download the data from `this link <https://gitlab.lis-lab
.fr/skmad-suite/tff2020/-/tree/master/data>`_.
Then run function :py:func:`tffpy.utils.generate_config` in order to create
a configuration file and modify it to specify the path to your data folder.
The location of the configuration file is given by function
:py:func:`tffpy.utils.get_config_file`.
Development version
-------------------
......@@ -50,7 +69,7 @@ standard version of ``tffpy`` using ``pip``::
Clone the Git repository::
git clone git@gitlab.lis-lab.fr:templates/python-package-template.git
git clone git@gitlab.lis-lab.fr:skmad-suite/tff2020.git
cd tffpy
You may also need to install required packages::
......
......@@ -8,7 +8,7 @@ import sys
NAME = 'tffpy'
DESCRIPTION = 'Time-frequency fading using Gabor multipliers'
LICENSE = 'GNU General Public License v3 (GPLv3)'
URL = 'https://gitlab.lis-lab.fr/templates/{}'.format(NAME)
URL = 'https://gitlab.lis-lab.fr/skmad-suite/{}'.format(NAME)
AUTHOR = 'Valentin Emiya, Ama Marina Kreme'
AUTHOR_EMAIL = ('valentin.emiya@lis-lab.fr, '
'ama-marina.kreme@lis-lab.fr')
......
......@@ -82,9 +82,11 @@ def generate_config():
config.add_section('DATA')
config.set('DATA', '# path to data')
config.set('DATA', 'data_path', '/to/be/completed/tffpy/data')
with open(get_config_file(), 'w') as config_file:
config_file = get_config_file()
with open(config_file, 'w') as config_file:
config.write(config_file)
print('Configuration file created: {}. Please update it with your data '
'path.'.foramt(config_file))
def get_data_path():
......
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