semvec/pypi-packages huggingface dataset:
Collectors 1.0
Monitor your (SimPy) simulation models or other objects and collect data from them.
License: BSD

Collectors
==========

*Collectors* was initially developed to monitor 
`SimPy <http://simpy.sourceforge.net/>`_ simulation models but it can also be used to monitor any Python objects and collect data from them.

Our main development goals were:

* Ease of use (simple API, little typing)
* Efficiency:

  * No impact on simulation speed if you don’t use it.
  * As little impact as possible if you use it.

* Flexibility and easy extensibility
* Separation of data collection and data analysis

Simple usage example
--------------------

    >>> class Spam(object):
    ...     a = 1
    ...     b = 2
    ...
    >>> spam = Spam()
    >>>
    >>> # Create and configure the collector
    >>> col = Collector(
    ...     ('a', lambda: spam.a),
    ...     ('b', lambda: self.b)
    ... )
    >>>
    >>> # Collect all monitored variables (spam.a and spam.b)
    >>> col()
    >>> spam.a, spam.b = 3, 4
    >>> col()
    >>>
    >>> # Get the collector’s data
    >>> col
    ([1, 3], [2, 4])
    >>> # You can also access it's elements by their name ...
    >>> col.a
    [1, 3]
    >>> # ... or by their index
    >>> col[1]
    [2, 4]


Requirements
------------

*Collectors* has only been tested with *Python 2.6* but older versions should
also work. *Python 3.0* might also work; if not, we’ll put it on our schedule.


Installation
------------

The easiest way to install *Collectors* is via 
`PIP <http://pip.openplans.org/>`_ or 
`distribute <http://packages.python.org/distribute/>`_::

    pip install Collectors
    
or ::

    easy_install Collectors

If you downloaded the archive, execute::

    python setup.py install
    
And finally, if you checked out the repository and always want to use the newest 
version, type::

    pip install -e path/to/Collectors
    
or ::

    pip install -e https+hg://bitbucket.org/sscherfke/collectors/
    

Usage
-----

The Documentation can be found in the *docs/* directory or
`online <http://stefan.sofa-rockers.org/docs/Collectors/>`_.
django-flickrsets 0.1.6
Reusable Django application to synchronize Flickr sets.
License: BSD
Keywords: django,flickr,sets,flickrsets,photo

=================
Django Flickrsets
=================

**Reusable Django application to synchronize Flickr sets**.

**The application is currently not ready for production use. If you still want 
to deploy it on a production server, do it at your own risk.**

**Please, be cool with Flickr API.**
 
For instructions on how to install and use this application, and on what it 
provides, take a look in the ``docs/`` directory.

To generate the documentation, you should have `Sphinx`_ installed on your
system and run the following commands::

    git clone git://github.com/gillesfabio/django-flickrsets.git
    cd django-flickrsets/docs
    make html

Open ``django-flickrsets/docs/_build/html/index.html`` in your browser.

You can also `browse`_ this documentation online.

**Screenshot**:

.. image:: http://dl.dropbox.com/u/176040/django-flickrsets-example.png

.. _Sphinx: http://sphinx.pocoo.org/
.. _browse: http://gillesfabio.github.com/django-flickrsets/
multichain_mcmc 0.3
Multichain MCMC framework and algorithms
License: BSD

A simple framework based on PyMC for multichain MCMC algorithms. 
                    
                    Contains working implementations of:
                        * DREAM/DREAM_ZS sampler : multichain_mcmc.dream.DreamSampler
                        * Adaptive Metropolis Adjusted Langevin Algorithm (AMALA) sampler : multichain_mcmc.amala.AmalaSampler
                        
                    See the sampler classes for details. AMALA sampler requires PyMC branch with gradient information support to function.
                        http://github.com/pymc-devs/pymc/tree/gradientBranch