semvec/pypi-packages huggingface dataset:
buttercms-django 0.3.7
Company blogs as a service. Built for developers.
License: MIT
Keywords: django blog service

Butter CMS for Django
=========================

https://www.buttercms.com

**Why Butter?**

Butter makes setting up a company blog on Django insanely easy. It's built for Django developers to save us from hosting, DB setup, themeing, maintaining yet another Wordpress install. It's designed to be quickly integrated to an existing Django project.

Butter provides a marketing friendly blogging UI, hosted on buttercms.com, and exposes blog content created via an API.

This package provides thin wrapper that interacts with the Butter API and a quick start blog application.


Setup
-----
Requires Python 2.7.9 or newer. If you're on an older version of 2.7 please take a few seconds to upgrade: https://www.python.org/downloads/

.. code:: bash

    $ pip install buttercms-django


.. code:: python

    # In settings.py
    # Add buttercms to INSTALLED_APPS
    INSTALLED_APPS = (
        ...
        'buttercms-django',
    )

Grab your API token from https://buttercms.com/profile/

.. code:: python

    # In settings.py
    # Add your BUTTER_CMS_TOKEN
    BUTTER_CMS_TOKEN = '<your_api_token>'

Define your blog path

.. code:: python

    # In urls.py
    # Add your new blog path
    urlpatterns = patterns('',
        ...
        url(r'^blog/', include('buttercms-django.urls')),
    )

Now generate your blog templates into one of your existing Django apps with our handy butter_templates command

.. code:: bash

    $ python manage.py butter_templates <app_name>

Open up `<app_name>/templates/blog/blog_base.html` and update the `{% block content %}` to match your existing site's main body content block. Also, if your root parent file is not called `base.html`, update `blog/blog_base.html` to extend whichever template makes sense for your app.

Now check it out:  http://localhost:8000/blog

Log into https://buttercms.com/ to start blogging!

Sitemap + Feeds
=====================
Your new blog comes built-in with a sitemap.xml, RSS + Atom feeds for maximum SEO benefit. You can use these to syndicate your content to social networks, improve search engine indexation, and more.

Sitemap: http://localhost:8000/blog/sitemap.xml

RSS: http://localhost:8000/blog/rss/  (named route blog_rss)

Atom: http://localhost:8000/blog/atom/ (named route blog_atom)


Customizing your blog
=====================

Customize the blog template
---------------------------
We've provided you with a pre-scaffolded blog. Simply crack open any of the files in the newly created /blog/ directory you created in the step above and modify those Django template as you would any other. Customizing your blog is now the same flow as the rest of your site! No longer do you have to hack Wordpress PHP scripts.
AnechoDB-Access 1.0
A library to comunicate with the database Belen to search, download and compute beam stored in it
License: MIT

===============
AnechoDB_Access
===============

It is a library used to connect to a specific database, download data stored in it and make some simple calculation. The data are beam patterns obtained from measurements in two anechoic chambers saved on the database as HDF5 files.

The package is divided in two distinct modules called **connection** and **computation** that have different tasks.

*************
connection.py
*************
This module is a class with useful function to establish a connection with the chosen database.
The database is structered in a way that each *beam* identifier (the data user is looking for) is linked to a *measurements* page with information about the measure and the links to  *projects* and *instruments* pages.
With **connection** is possible to find the desired beam identifier through a search in one of those pages and finally download the data that is converted from a .h5 file to a Python dict variable preserving the same structure

**************
computation.py
**************
This module has some function used to perform simple (but useful) calculation from the data previously obtained from **connection**. Till now this module has only two functions, one to compute mean and variance of the data and the other to perform normalization and centering of beam patterns, but more will be added in the future.

Example of usage
----------------
After being installed here is a classic way to use this package.

.. code-block:: python

    >>> c = share_belen.connection.Connection(Host)
    >>> i_m = c.search_meas_by_instruments('Instrument To Search')
    >>> #More than one measurement can be linked at the same instrument
    >>> i_b = c.search_beam_by_meas(i_1[0])
    >>> #More than one beam can be linked at the same measurement
    >>> b = c.get_beam_in_dict_by_id(i_b[0])
    >>> b_c = share_belen.computation.make_beam_meanvar(b)
    >>> b_c_2 = share_belen.computation.center_norm_beam(b_c)

Requirements
------------

* `Python <http://www.python.org>`_ (tested with version >=3.3)
* `h5py <http://www.h5py.org/>`_