semvec/pypi-packages huggingface dataset:
django-smartmenus 0.1.0
A Django app that provides template tags to easily incorporate smartmenus.
License: MIT

=================
django-smartmenus
=================

A Django app that provides template tags to easily incorporate smartmenus (http://www.smartmenus.org/).

Installation
============

To get started using ``django-smartmenus``:

- install it with ``pip``::

    $ pip install django-smartmenus

- add the app to ``INSTALLED_APPS``. If you are using Django CMS, make sure it's before ``menus``::

    INSTALLED_APPS = (
        ...
        'django_smartmenus',
        'menus', #Only if you are using Django CMS
        ...
    )

Usage
=====

The best way to see how it works is exploring the example project. But for the impatient, all you need to do is:

- load smartmenus tags in the templates::

	{% load smartmenus_tags %}


- Call smartmenus template tags where css and js files should show::

	{% smartmenus_css theme %}
	<script src="path/to/jquery"></script>
	{% smartmenus_js %}

  ``theme`` is a string based parameter. Out of box, ``theme`` can be one of 'sm-blue', 'sm-clean', 'sm-mint' and 'sm-simple'. If you create a custom theme of your own, please put the theme file under static folder following smartmenus theme folder structure convention, ie. smartmenus/css/``theme``/``theme``.css.


- Render menu html in the templates. Please refer to smartmenus tutorial to see what the html should be.


This app also supports Django CMS:

- load smartmenus and menu tags in the templates::

	{% load menu_tags smartmenus_tags %}


- Call Django CMS specific template tags anywhere in the templates::

	{% addtoblock "js" %}<script src="path/to/jquery"></script>{% endaddtoblock %}
	{% smartmenu_bootstrap_cms theme %}

  ``theme``'s usage is the same as above.


- Render Django CMS menu::

	<ul class="sm theme-class-name">
	    {% show_menu 0 100 100 100 %}
	</ul>

  ``theme-class-name`` is a string based parameter. Please refer to smartmenus doc regarding possible values. Usually it's the same as theme name unless it's a custom theme with different naming conventions.
fclear 0.1.0
Clear compiled files.
License: MIT

fclear
======

Clear compiled files.

.. _Python: https://www.python.org/


Installation
------------

.. sourcecode:: bash

   ~ $ python setup.py install

or can use pip

.. sourcecode:: bash

   ~ $ pip install fclear


Quick start
-----------

.. sourcecode:: bash

   ~ $ fclear python              # Remove pyc, __pycache__
   ~ $ fclear py                  # Remove pyc, __pycache__
   ~ $ fclaer vim                 # Remove swp
   ~ $ fclear vi                  # Remove swp
   ~ $ fclear java                # Remove class
py-lookingglass 1.01
Python looking glass WSGI implementation
License: MIT

py-lookingglass
===============

Python looking glass wsgi implementation

Requires:
---------

Python 2.6+

Standard python modules:

- telnetlib
- os
- socket
- cgi
- wsgi\_ref
- traceback
- random
- argparse
- json

External:

- paramiko

Setup:
------

To run out of the box just clone the repo or install using ``pip install py-lookingglass`` or ``easy_install py-lookingglass``, then:

``# python -m lg -h``

Or you can use it as WSGI callback

``lg.lookingglass(name="Looking Glass", cmds={'profile':{'command_name','actual command'}}, hosts=[('password','ip',int(port),int(connection_type),'name','profile')])``

Any additional hacks can be applied before init of class

Check ``help(lg.lookingglass)`` for more info

Should also install executable py-lookingglass
pickups 0.8.0
Pickups delivers all neccessary tools to run Atlassian Bamboo with RasberryPi based extreme feedback devices
License: MIT
Keywords: pickups

=======
Pickups
=======

.. image:: https://badge.fury.io/py/pickups.png
    :target: http://badge.fury.io/py/pickups

.. image:: http://img.shields.io/pypi/dm/pickups.png
    :target: https://pypi.python.org/pypi/pickups

.. image:: https://travis-ci.org/maiksensi/pickups.png?branch=master
    :target: https://travis-ci.org/maiksensi/pickups

.. image:: https://codecov.io/github/maiksensi/pickups/coverage.svg?branch=master
    :target: https://codecov.io/github/maiksensi/pickups?branch=master

.. image:: https://readthedocs.org/projects/pickups/badge/?version=latest
        :target: https://readthedocs.org/projects/pickups/?badge=latest
        :alt: Documentation Status


Python pickups contains everything to setup extreme feedback devices with Atlassian Bamboo,
a RasberryPi and an USB power-plug.


Documentation
-------------

The full documentation is at http://pickups.rtfd.org.


Features
--------

* Predefined structure to integrate extreme feedback devices into builds
* Easy to extend with more devices and servers
* Helpers to handle signals in a custom way
* Well tested, and easy to rebuild with tox


.. :changelog:

History
-------

0.8.0 (2015-09-02)
++++++++++++++++++

* various cleanups of project structure

0.7.0 (2015-02-10)
++++++++++++++++++

* documentation for tests https://github.com/maiksensi/pickups/issues/5

0.6.0 (2015-01-23)
++++++++++++++++++

* integrated a preconfigured logger module https://github.com/maiksensi/pickups/issues/6
* read package contents from _package.py

0.5.2 (2015-01-23)
++++++++++++++++++

* hotfix issue https://github.com/maiksensi/pickups/issues/3 again

0.5.1 (2015-01-14)
++++++++++++++++++

* hotfix issue https://github.com/maiksensi/pickups/issues/3

0.5.0 (2015-01-08)
++++++++++++++++++

* added proper badges

0.3.0 (2015-01-07)
++++++++++++++++++

* First release on PyPi

0.2.0 (2014-12-30)
++++++++++++++++++

* Added tests

0.1.0 (2014-12-17)
++++++++++++++++++

* Added core functionality
* Added helpers
djauto 1.2.3
Django project generator
License: MIT
Keywords: django,python,django-packages,packages

Django-project-generator
========================
Create Django projects the easy way. From installing django to creating app for project, One command, that's all!

Install
-------

    pip install djauto

How to use it?
--------------

  djauto PROJECT_NAME APP_NAME

What does above command do?
---------------------------
1. Downloads latest version of [Django](https://www.djangoproject.com/) using pip
2. Creates a django project named "PROJECT_NAME" in current directory.
3. Creates and add the app named "APP_NAME" in INSTALLED_APPS in `settings.py`.
4. Asks if you want to create github repository. If yes, then
	1. Asks for github username.
	2. Creates repo named PROJECT_NAME.
	3. Asks for short description.
	4. Asks for password.
	5. Adds all file and push it on github.
5. We're done!
neural-python 0.0.7
NeuralPy is the Artificial Neural Network library implemented in Python.
License: MIT
Keywords: neural networks

NeuralPy
========

NeuralPy is a Python library for Artificial Neural Networks.
You can run and test different Neural Network algorithms.

|Travis|_

.. |Travis| image:: https://api.travis-ci.org/itdxer/neuralpy.png?branch=master
.. _Travis: https://travis-ci.org/itdxer/neuralpy

Installation
------------

.. code-block:: bash

    $ pip install neural-python

Links
-----

* `Documentation <http://neuralpy.com>`_
* `Issues <https://github.com/itdxer/neuralpy/issues>`_
* `Tutorials <http://neuralpy.com/archive.html>`_
* `Available algorithms <http://neuralpy.com/docs/algorithms.html>`_

Dependence
----------

* Python 2.7, 3.3, 3.4
* NumPy >= 1.9.0
* SciPy >= 0.14.0
* Matplotlib >= 1.4.0

Next steps
----------

* Bug fixing and version stabilization
* Speeding up algorithms
* Adding more algorithms

Library support
---------------

* Radial Basis Functions Networks (RBFN)
* Backpropagation and different optimization for it
* Neural Network Ensembles
* Associative and Autoasociative Memory
* Competitive Networks
* Step update algorithms for backpropagation
* Weight control algorithms for backpropagation
* Basic Linear Networks

Algorithms
----------

* Backpropagation

  * Classic Gradient Descent
  * Mini-batch Gradient Descent
  * Conjugate Gradient

    * Fletcher-Reeves
    * Polak-Ribiere
    * Hestenes-Stiefel
    * Conjugate Descent
    * Liu-Storey
    * Dai-Yuan

  * quasi-Newton

    * BFGS
    * DFP
    * PSB
    * SR1

  * Levenberg-Marquardt
  * Hessian diagonal
  * Momentum
  * RPROP
  * iRPROP+
  * QuickProp

* Weight update rules

  * Weight Decay
  * Weight Elimination

* Learning rate update rules

  * Adaptive Learning Rate
  * Error difference Update
  * Linear search by Golden Search or Brent
  * Wolfe line search
  * Search than converge
  * Simple Step Minimization

* Ensembles

  * Mixture of Experts
  * Dynamically Averaged Network (DAN)

* Radial Basis Functions Networks (RBFN)

  * Generalized Regression Neural Network (GRNN)
  * Probabilistic Neural Network (PNN)
  * Radial basis function K-means

* Autoasociative Memory

  * Discrete BAM Network
  * CMAC Network
  * Discrete Hopfield Network

* Competitive Networks

  * Adaptive Resonance Theory (ART1) Network
  * Self-Organizing Feature Map (SOFM or SOM)

* Linear networks

  * Perceptron
  * LMS Network
  * Modified Relaxation Network

* Associative

  * OJA
  * Kohonen
  * Instar
  * Hebb

Tests
-----

.. code-block:: bash

    $ pip install tox
    $ tox