semvec/pypi-packages huggingface dataset:
django-bootstrap-select 0.1.3
A Django wrapper for the Bootstrap Select library
License: MIT
Keywords: django-bootstrap-select

=============================
Django Bootstrap Select
=============================

.. image:: https://badge.fury.io/py/django-bootstrap-select.svg
    :target: https://badge.fury.io/py/django-bootstrap-select

.. image:: https://travis-ci.org/voxy/django-bootstrap-select.svg?branch=master
    :target: https://travis-ci.org/voxy/django-bootstrap-select

.. image:: https://codecov.io/gh/voxy/django-bootstrap-select/branch/master/graph/badge.svg
    :target: https://codecov.io/gh/voxy/django-bootstrap-select

A Django wrapper for the Bootstrap Select library

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

The full documentation is at https://django-bootstrap-select.readthedocs.io.

Quickstart
----------

Need to render an image in a select box?

Turn this:

.. image:: docs/img/select_box.png

Into this:

.. image:: docs/img/bootstrap_select_box.png

Install Django Bootstrap Select::

    pip install django-bootstrap-select

Add it to your `INSTALLED_APPS`:

.. code-block:: python

    INSTALLED_APPS = (
        ...
        'bootstrap_select.apps.BootstrapSelectConfig',
        ...
    )

Using it in a form:

.. code-block:: python

    def get_choices():
        choices = (('face.jpg', escape('<img src="path-to-face.jpg"/>'),)
        return choices

    class IconForm(forms.Form):
        icon = forms.URLField(widget=BootstrapSelect(choices=get_choices()))

Features
--------

* Renders labels from form choices as `custom-content`_.
* Renders tokens from form choices for `live-search`_.

.. _`custom-content`: https://silviomoreto.github.io/bootstrap-select/examples/#custom-content
.. _`live-search`: https://silviomoreto.github.io/bootstrap-select/examples/#live-search

Running Tests
-------------

Does the code actually work?

::

    source <YOURVIRTUALENV>/bin/activate
    (myenv) $ pip install tox
    (myenv) $ tox

Credits
-------

Tools used in rendering this package:

*  Cookiecutter_
*  `cookiecutter-djangopackage`_

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`cookiecutter-djangopackage`: https://github.com/pydanny/cookiecutter-djangopackage




History
-------

0.1.0 (2017-08-20)
++++++++++++++++++

* First release on PyPI.
animats 0.0.18
Ecosystem with animats for development of Artificial General Intelligence
License: MIT
Keywords: animat ai agi artificial intelligence

Animats
=======

Reference code for:

A General Model for Learning and Decision-Making in Artificial Animals by
Claes Strannegård, Nils Svangård, Jonas Colmsjö, David Lindström, Joscha Bach and Bas Steunebrink

Submitted to IJCAI-17 AGA workshop, Melbourne, Australia

This `dev` branch is work in progress and will contain a completely refactored
version of the original code.


Setup
=====

At least Python 3.5 is needed since `async` is used in `wsserver.py`. I'm using Python 3.6 here.

* First init `virtualenv` for Python3: `virtualenv -p python3.6 venv3` (`virutalenv` needs to be installed)
* Activate `virtualenv`: `source venv3/bin/activate`
* Install the necessary Python packages: `pip install -r requirements.txt`. Add `--no-compile` when running on ubuntu.


Run the program
==============

Examples using the ecosystem classes are available in the
repo [examples](https://github.com/animatai/examples)

A little setup is needed first:

* Activate `virtualenv`: `source venv3/bin/activate`
* Create `config.py`. Start with copying `config.py.template` and try some of
the examples from the repo mentioned above.

Start a web server and a browser:

* Run the server: `python wsserver.py`
* Run `index.html` in a browser and follow the instructions.


Development
===========

Use [Google Style Guide](https://google.github.io/styleguide/pyguide.html)
and make sure that the unit tests are maintained.

Build (lint and run unit tests) with: `./build.sh`
Building will also Create a source distribution in the `dist` folder.

Upload the build to the public package repo for installation with `pip`:
`twine upload dist/animats-X.Y.Z.tar.gz`


Credits
=======

Using some classes from the [AIMA book](https://github.com/aimacode/aima-python)