semvec/pypi-packages huggingface dataset:
django_cms_auth_apphook 1.0.0
App hook to make integration with the standard Django Auth Library easier. Also includes templates for all auth views.
License: MIT

Django CMS Auth Apphook
=======================

App hook to make integration with the standard Django Auth Library (`django.contrib.auth`) easier. Also includes templates for all auth views.

Installation
------------

To install, you will need to clone the git, then run the setup.py script:

    git clone https://github.com/flungo/django_cms_auth_apphook.git
    cd django_cms_auth_apphook
    python setup.py install
  
Configuration
-------------

Add `auth_apphook` to your list of `INSTALLED_APPS`. It's position doesn't matter unless you have `django.contrib.admin` installed (which to be honest, you probably do) and you want to use the included templates. `django.contrib.admin` contains several templates for `django.contrib.auth` so in order to use the included templates (see bellow) you should make sure it is listed before `django.contrib.admin`.

    INSTALLED_APPS = (
        # ...
        'auth_apphook',
        # ...
        'django.contrib.auth',
        # ...
    )

Templates
---------------------

Included with this app are the core templates required by the front end to generate the appropriate pages. I have made them all extend "base.html" so they will by default have your theme but should you need to customise these, you can copy the `registration` directory to your templates directory and customise till your heart's content.
sparkprob 0.1
Sparklines for probability distributions
License: MIT
Keywords: sparklines probability distribution print bar graph

Sprarkprob - Sparklines for Probability Distributions
=====================================================

This package helps you to print out probability distribution on the
commandline.

Getting Started
---------------

::

    pip install sparkprob

Then in python:

::

    >>> from sparkprob import sparkprob
    >>> print sparkprob([0.2, 0.1, 0.4, 0.15, 0.15])
    ▁ ▁ ▂ ▁ ▁
    >>> print sparkprob([0.05, 0.2, 0.55, 0.1, 0.1])
      ▁ ▃ ▁ ▁
    >>> print sparkprob([0.05, 0.2, 0.7, 0.0, 0.0])
      ▁ ▅
    >>> print sparkprob([0.05, 0.0, 0.95, 0.0, 0.0])
        ▇