semvec/pypi-packages huggingface dataset:
fitr 0.0.1
Fit reinforcement learning models to behavioural data
License: MIT
Keywords: reinforcement learning,computational psychiatry,python,model fitting

.. -*- mode: rst -*-

fitr |Build|_ |Documentation|_ |Codecov|_ |Health|_ |DOI|_
==========================================================

.. |Build| image:: https://travis-ci.org/ComputationalPsychiatry/fitr.svg?branch=master
.. _Build: https://travis-ci.org/ComputationalPsychiatry/fitr

.. |Documentation| image:: https://readthedocs.com/projects/computationalpsychiatry-fitr/badge/?version=latest
.. _Documentation: https://computationalpsychiatry-fitr.readthedocs-hosted.com/en/latest/?badge=latest

.. |Codecov| image:: https://codecov.io/gh/ComputationalPsychiatry/fitr/branch/master/graphs/badge.svg
.. _Codecov: https://codecov.io/gh/ComputationalPsychiatry/fitr/branch/master

.. |Health| image:: https://landscape.io/github/ComputationalPsychiatry/fitr/master/landscape.svg?style=flat
.. _Health: https://landscape.io/github/ComputationalPsychiatry/fitr/master

.. |DOI| image:: https://zenodo.org/badge/82499710.svg
.. _DOI: https://zenodo.org/badge/latestdoi/82499710

Python implementation of package to fit reinforcement learning models to
behavioural data

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

The current PyPI release of Fitr can be installed as follows::

    pip install fitr

If you want the latest version on the GitHub master branch, install as follows::

    pip install git+https://github.com/ComputationalPsychiatry/fitr.git

Tutorials
---------

Tutorials (Jupyter Notebooks) can be found in the examples folder. They include

1. `Introductory tutorial (EM and Bayesian Model Selection) <https://github.com/ComputationalPsychiatry/fitr/blob/master/examples/intro-tutorial.ipynb>`_
2. `Fitting a Model with MCMC <https://github.com/ComputationalPsychiatry/fitr/blob/master/examples/Fitting%20a%20Model%20with%20MCMC.ipynb>`_
3. `Use MCMC with your own Stan Code <https://github.com/ComputationalPsychiatry/fitr/blob/master/examples/Use%20MCMC%20with%20your%20own%20Stan%20Code.ipynb>`_
4. `Using Multiple Model-Fitting Routines for Same Model <https://github.com/ComputationalPsychiatry/fitr/blob/master/examples/Using%20Multiple%20Methods%20to%20fit%20Models.ipynb>`_

How to Cite
-----------

If you use Fitr in your work, we would very much appreciate the citation, which can be done as follows:

- Abraham Nunes, Alexander Rudiuk, & Thomas Trappenberg. (2017). Fitr: A Toolbox for Computational Psychiatry Research. Zenodo. http://doi.org/10.5281/zenodo.439989
doxybuild 0.1.1-alpha
Scripts for building documents in Doxy
License: MIT
Keywords: ci,doxy,tex,latex,document

Doxy Build
==========

This repo contains build scripts for the Doxy system. At present, only LaTeX
is supported, via latexmk.

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

To install, run::

  pip install doxybuild

Maintainers
-----------

* `Alex Thorne <mailto:alex@thorne.cc>`_
amaboko 0.0.3
get book information by ISBN in some regions
License: MIT
Keywords: amazon

# amaboko - Amazon Book Information API

just wrapping [Amazon Simple Product API](https://github.com/yoavaviram/python-amazon-simple-product-api)

### Dependencies
* Python 2.7.x
* Amazon Product Advertising account (for APIAccessKey, APISecretKey)
* AWS account (for AmazonAssociateTag)

```
$ pip install -r requirements.txt
```

### Description

Choose 2 regions for endpoints, primary and secondary. If specified book information was not found in primary region, instead of that, this module will automatically get the information from secondary region. Of course, you can't necessarily get the information you want.

### Preparation for test

set environment variable **APIAccessKey, APISecretKey, AssocTag**.

```
$ AMAZON_ACCESS_KEY = "APIAccessKey"
$ AMAZON_SECRET_KEY = "APISecretKey"
$ AMAZON_ASSOC_TAG = "AssocTag"
```

or create `test_settings.py`

```
$ vim test_settings.py
```

```
AMAZON_ACCESS_KEY = "APIAccessKey"
AMAZON_SECRET_KEY = "APISecretKey"
AMAZON_ASSOC_TAG = "AssocTag"
```

If you didn't do above both of the above settings, you should pass those variables when you instantiate AmazonBook class. In the case of you imported this module, you have got to pass variables to AmazonBook constructor.

### Usage

You can set primary and secondary regions from `["US", "FR", "CN", "UK", "IN", "CA", "DE", "JP", "IT", "ES"]` in instantiation. (default regions are "JP" and "US")

```
>>> from amabako import AmazonBook, is_isbn_validate
>>> a = AmazonBook(access_key, secret_key, assoc_tag)

>>> isbn = "9784048816592"
>>> print is_isbn_validate(isbn)
False

>>> isbn = "9781433551666"
>>> print is_isbn_validate(isbn)
True

>>> book = a.lookup(isbn, IdType="ISBN", SearchIndex="Books")

>>> print book.title
Holy Bible: English Standard Version, Value Compact Bible, Trutone Turquoise, Emblem Design

>>> print book.large_image_url
http://ecx.images-amazon.com/images/I/412mbT1AvIL.jpg

>>> print book.get_attribute('Publisher')
Crossway Books

>>> print book.price_and_currency
('1531', 'JPY')
```
wagtail-asciinema 0.0.2
Implements Wagtail CMS asciinema integration
License: MIT

Wagtail Asciinema
=================

Implements
`asciinema-player <https://github.com/asciinema/asciinema-player>`__
embedding block.

Requirements (tested)
---------------------

-  Python 3.5+
-  Wagtail 1.9+
-  Django 1.10

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

Install the library with pip:

.. code:: bash

    $ pip install wagtail-asciinema

Add ``wagtail_asciinema`` to your INSTALLED\_APPS setting like this:

.. code:: python

    INSTALLED_APPS = [
        ...
        'wagtail_asciinema',
    ]

Download ``asciinema-player`` sources from it's `release
page <https://github.com/asciinema/asciinema-player/releases>`__ and
then place it in your STATIC directory.

Add ``AsciinemaBlock`` to your StreamField:

.. code:: python

    from wagtail_asciinema.blocks import AsciinemaBlock
    asciinema = AsciinemaBlock(classname='full')

You can add a method to determine if you need to include asciinema code
into your static/js blocks:

.. code:: python

    from wagtail.wagtailcore.models import Page
    class ArticlePage(Page):
        @property
        def has_asciinema(self):
            for stream_child in self.content:
                if stream_child.block.name == 'asciinema':
                    return True
            return False

And then add asciinema on your page on demand:

.. code:: djangotemplate

    {% block extra_css %}
      {% if self.has_asciinema %}
        <link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}frontend/asciinema/v2.4.1/asciinema-player.css">
      {% endif %}
    {% endblock %}
    {% block extra_js %}
      {% if self.has_asciinema %}
        <script src="{{ STATIC_URL }}frontend/asciinema/v2.4.1/asciinema-player.js"></script>
      {% endif %}
    {% endblock %}
npy4chan 0.1.2.1
A trivial, minimalistic 4chan API wrapper.
License: MIT
Keywords: 4chan imageboard API

npy4chan
========

Very simple, minimalistic wrapper for 4chan's API.

To create an instance of the wrapper::

    api = npy4chan.load()

Attributes and methods::

    api.boards # dictionary of board identifiers and title (i.e; {"b": "Random"})
    api.getPosts(board, page=1) # Get list of thread on page <page> of <board>
    api.getThread(board,threadno) # Get posts from <board>'s thread <threadno>
    api.getArchived(board) # get archived threadno's from <board>