semvec/pypi-packages huggingface dataset:
aldryn-installer 0.1.1
Command to easily bootstrap django CMS projects
License: BSD
Keywords: aldryn-installer

===============================
Aldryn Installer
===============================

.. image:: https://badge.fury.io/py/aldryn-installer.png
    :target: http://badge.fury.io/py/aldryn-installer
    
.. image:: https://travis-ci.org/nephila/aldryn-installer.png?branch=master
        :target: https://travis-ci.org/nephila/aldryn-installer

.. image:: https://pypip.in/d/aldryn-installer/badge.png
        :target: https://crate.io/packages/aldryn-installer?version=latest

.. image:: https://coveralls.io/repos/nephila/aldryn-installer/badge.png?branch=master
        :target: https://coveralls.io/r/nephila/aldryn-installer?branch=master

Command to easily bootstrap django CMS projects

* Free software: BSD license

Features
--------

``aldryn-installer`` is a console wizard to help bootstrapping a django CMS
project.

Refer to `django CMS Tutorial <http://slid.es/chive/djangocms/fullscreen>`_ on
how to properly setup your first django CMS project.

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

#. Create an empty virtualenv::

    virtualenv /virtualenv/path/my_project

#. Install `aldryn-installer`::

    pip install aldryn-installer

   or::

    pip install -e git+https://github.com/nephila/aldryn-installer#egg=aldryn-installer

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

See http://aldryn-installer.readthedocs.org

Caveats
-------

While this wizard try to handle most of the things for you, it doesn't check for
all the proper native (non python) libraries to be installed.
Before running this, please check you have the proper header and libraries
installed and available for packages to be installed.

Libraries you would want to check:

* libjpeg (for JPEG support in ``Pillow``)
* zlib (for PNG support in ``Pillow``)
* postgresql (for ``psycopg``)
* libmysqlclient (for ``Mysql-Python``)




History
-------

0.1.0 (2013-10-19)
++++++++++++++++++

* First public release.

0.1.1 (2013-10-20)
++++++++++++++++++

* Improved documentation on how to fix installation in case of missing libraries.
msgpack-pypy 0.0.2
A msgpack extension with special optimizations for PyPy
License: BSD
Keywords: msgpack PyPy

msgpack-pypy: a msgpack extension with special optimizations for PyPy
=====================================================================

PyPy has a special optimization to store lists of ``ints`` and ``floats`` in a
compact way, as they were C arrays of ``longs`` and ``doubles``.

msgpack-pypy is an extension for msgpack which exploits this optimization to
provide a very fast packing/unpacking of those. Essentially, it serializes a
Python list of ints/floats by copying the whole array at once.

Note that such lists are serialized using the "ext types" mechanism provided
by msgpack 2.0, which means that if you pack an object like ``[1, 2, 3]`` with
msgpack-pypy, you **cannot** unpack it using the plain msgpack-python module,
because the ext types are not recognized. You need msgpack-pypy to unpack it.

Despite the name, msgpack-pypy **does** work on CPython, although it does not
bring any speed benefit there. It is useful if you have a system in which a
PyPy process packs and object and a CPython process needs to unpack it.


Performance
-----------

For best performance, you should use a nightly PyPy built after Oct 21, 2013,
as it contains a special optimization to read/write all the bytes representing
the Python list at once.

Here are the results got by running this benchmark:
https://bitbucket.org/antocuni/msgpack-pypy/src/default/benchmark.py

::

    list of ints
      CPython/msgpack:   dump: 0.2287 ms     load: 0.2075 ms
      PyPy/msgpack:      dump: 0.2019 ms     load: 0.2447 ms
      PyPy/msgpack_pypy: dump: 0.1321 ms     load: 0.0391 ms

    list of floats
      CPython/msgpack:   dump: 0.2295 ms     load: 0.2096 ms
      PyPy/msgpack:      dump: 1.1049 ms     load: 1.9803 ms
      PyPy/msgpack_pypy: dump: 0.1373 ms     load: 0.0392 ms

As you can see, dumping is 1.73x faster for ints and 8x faster for floats. The
speedup for loading is even more impressive, as it is 6.2x faster for ints and
50x (fifty!) faster for floats.

If you use an older version of PyPy (such as the last stable release, 2.1),
you still get a small speedup compared to the plain msgpack, but not as
impressive. Dumping is 1.66x **slower** for ints and 1.67x **faster** for
floats. Loading is 4x faster for ints and 16x faster for floats.
dqn 0.2.0
The dqn create ReST files for sphinx from source code
License: BSD

The dqn create ReST files for sphinx from source code.

Install
===========

Need doxygen.
http://www.stack.nl/~dimitri/doxygen/download.html

And

::

    pip install dqn


How to use
============

The SRC_DIR is source code directory root.


Create rst files from source code.
::

    $ dqn $SRC_DIR
    $ make
probe 0.0.2
Probe lets you ship personal metrics (feelings, energy level, productivity) to external services for analysis.
License: BSD
Keywords: probe

===============================
Probe
===============================

.. image:: https://badge.fury.io/py/probe.png
    :target: http://badge.fury.io/py/probe
    
.. .. image:: https://travis-ci.org/BrianHicks/probe.png?branch=master
..         :target: https://travis-ci.org/BrianHicks/probe

.. image:: https://pypip.in/d/probe/badge.png
        :target: https://crate.io/packages/probe?version=latest


Probe lets you ship personal metrics (feelings, energy level, productivity) to
external services for analysis.

* Free software: BSD license
* Documentation: http://probe.rtfd.org.

Features
--------

* Keep track of numbers, ratings, hours, and yes/no questions.
* Ship personal metrics to multiple services.
* Schedule how often you want to collect data, it'll keep track.

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

Run ``probe init`` after installing. Then look at the "configuration example"
below. To anwer questions, run ``probe answer``.

Configuration Example
---------------------

::

    questions:
      - sleep.hours:
        text: How long did you sleep last night? (in hours)
        unit: hours
        interval: every day

      - energy:
        text: what is your energy level right now?
        unit: rating
        lower: 0
        upper: 10

    outputs:
      - stdout:
        level: debug

      - tempodb:
        key: ...
        secret: ...


Save this as ``~/.probe_config`` and run ``probe``, and you'll get some nice output
asking you questions at appropriate times. Run it as many times as you like,
the questions will only be asked once per appropriate period.

Available Units:

* Hours (key: ``hours``) takes upper and lower limit (lower defaults to 0)

* Rating (key: ``rating``) takes upper and lower limit. (default to 10 and 0,
  respectively)

* Number (key: ``number``) keep track of arbitrary floating-point numbers.

* Yes/No (key: ``yesno``) answers a yes/no question. (accepts yes/no, 0/1, and
  true/false or shortened versions like t/f.)

Available Outputs:

* StdOut (key: ``stdout``) accepts a "level" argument - currently info or
  debug.

* TempoDB (key: ``tempodb``) accepts any arguments passed to ``tempodb.Client``
  but you'll probably only need to know ``key`` and ``secret``. Get these for a
  database in your account.
  
  You'll need to install with the "tempodb" extra (``pip install
  probe[tempodb]``) for this to work. You could also just install the tempodb
  client, but installing with the extra ensures you'll get future dependencies.




History
-------

0.0.1 (2013-10-20)
++++++++++++++++++

* First release on PyPI.