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import nengolib
===============
Additional extensions and tools for modelling dynamical systems in
`Nengo <https://github.com/nengo/nengo>`__.
`Documentation <https://arvoelke.github.io/nengolib-docs/>`__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This project's documentation is hosted on GitHub.IO:
https://arvoelke.github.io/nengolib-docs/.
Development
~~~~~~~~~~~
To install the development version of nengolib::
git clone https://github.com/arvoelke/nengolib
cd nengolib
python setup.py develop
Notebooks can be run manually in ``docs/notebooks`` by running::
pip install jupyter
jupyter notebook
***************
Release History
***************
0.5.2 (September 10, 2019)
==========================
**Fixed**
- Solved an issue where scipy.misc imports were relocated.
(`#182 <https://github.com/arvoelke/nengolib/pull/182>`_)
0.5.1 (April 17, 2019)
======================
Tested against Nengo versions 2.2.0-2.8.0. Requires ``nengo<3.0``.
**Fixed**
- A variety of miscellaneous fixes were made to the documentation.
The ``nengolib.networks.RollingWindow`` documentation references the
shifted Legendre polynomial equations for ``legendre == True``.
(`#176 <https://github.com/arvoelke/nengolib/pull/176>`_)
0.5.0 (March 9, 2019)
=====================
Tested against Nengo versions 2.2.0-2.8.0.
We now require ``numpy>=1.13.0``, ``scipy>=0.19.0``, and ``nengo>=2.2.0``.
**Added**
- Added the ``nengolib.RLS()`` recursive least-squares (RLS)
learning rule. This can be substituted for ``nengo.PES()``.
See ``notebooks/examples/full_force_learning.ipynb`` for an
example that uses this to implement spiking FORCE in Nengo.
(`#133 <https://github.com/arvoelke/nengolib/pull/133>`_)
- Added the ``nengolib.stats.Rd()`` method for quasi-random sampling of
arbitrarily high-dimensional vectors. It is now the default method for
scattered sampling of encoders and evaluation points.
The method can be manually switched back to ``nengolib.stats.Sobol()``.
(`#153 <https://github.com/arvoelke/nengolib/pull/153>`_)
- Added the ``nengolib.neuron.init_lif(sim, ens)`` helper function
for initializing the neural state of a ``LIF`` ensemble, from within
a simulator block, to represent ``0`` uniformly at the start.
(`#156 <https://github.com/arvoelke/nengolib/pull/156>`_)
- Added ``nengolib.synapses.LegendreDelay`` as an alternative to
``nengolib.synapses.PadeDelay`` -- it has an equivalent transfer function
but a state-space realization corresponding to the shifted
Legendre basis.
The network ``nengolib.networks.RollingWindow`` support ``legendre=True``
to make this system the default realization.
(`#161 <https://github.com/arvoelke/nengolib/pull/161>`_)
**Fixed**
- Release no longer requires ``pytest``.
(`#156 <https://github.com/arvoelke/nengolib/pull/156>`_)
0.4.2 (May 18, 2018)
====================
Tested against Nengo versions 2.1.0-2.7.0.
**Added**
- Solving for connection weights by accounting for the neural
dynamics. To use, pass in ``nengolib.Temporal()`` to
``nengo.Connection`` for the ``solver`` parameter.
Requires ``nengo>=2.5.0``.
(`#137 <https://github.com/arvoelke/nengolib/pull/137>`_)
0.4.1 (December 5, 2017)
========================
Tested against Nengo versions 2.1.0-2.6.0.
**Fixed**
- Compatible with newest SciPy release (1.0.0).
(`#130 <https://github.com/arvoelke/nengolib/pull/130>`_)
0.4.0b (June 7, 2017)
=====================
Initial beta release of nengolib.
Tested against Nengo versions 2.1.0-2.4.0.
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