semvec/pypi-packages huggingface dataset:
tartpy 0.2.1
Tiny Actor Run-Time in Python.
License: MIT

=======
tart.py
=======

Python implementation of the `Actor Model`_ inspired from `tart.js`_.

Overview
========

The initial ideas for ``tartpy`` are copied from `@dalnefre`_ and
`@tristanls`_.  At the beginning, the goal was to replicate `tart.js`_
in Python.  Lately, it has diverged slightly, to experiment with the
new Python asynchrony model introduced in version 3.4.

``tartpy`` aims to be an actor library with the following features:

- it implements the pure actor model (rather than the Erlang model),

- it can be used in practical situations to express lock-free and
  concurrent algorithms,

- it abstracts the network transport to allow arbitrary protocols,

- it implements a `capability based approach`_ to isolate actors,
  using membranes (again, the ideas are due to `@dalnefre`_ and
  `@tristanls`_).  The semantics of membranes may differ between
  ``tartpy`` and ``tartjs``.

Installing
==========

Install with::

    $ pip install git+https://github.com/waltermoreira/tartpy

or clone this repository and install with ``python3 setup.py install``::

    $ git clone https://github.com/waltermoreira/tartpy
    $ cd tartpy
    $ python3 setup.py install

Test
====

Run tests with::

    $ python3 setup.py test


Examples
========

Run example with:

.. code-block:: bash

   python3 tartpy/example.py

There is also an `IPython notebook`_ showing the basic properties of actors.

The project `actor_model`_ contains slides and IPython notebooks from
a talk presented at TACC_ in April 17, 2014.


Erlang Challenge
================

Create a ring of ``M`` actors, sending ``N`` messages around the ring:

.. code-block:: bash

   python3 tartpy/erlang_challenge.py M N

Benchmarks
----------

For ``M = 100000`` and ``N = 10``::

    Starting 100000 actor ring
    Construction time: 1.5192079544067383 seconds
    Loop times:
      0.7743091583251953 seconds
      0.7793149948120117 seconds
      0.7702958583831787 seconds
      0.7602570056915283 seconds
      0.7704610824584961 seconds
      0.779731035232544 seconds
      0.7654228210449219 seconds
      0.7625432014465332 seconds
      0.7740719318389893 seconds
      0.7699680328369141 seconds
    Average: 0.7706375122070312 seconds

.. _Actor Model: http://en.wikipedia.org/wiki/Actor_model
.. _tart.js: https://github.com/organix/tartjs
.. _@dalnefre: https://github.com/dalnefre
.. _@tristanls: https://github.com/tristanls
.. _capability based approach: http://en.wikipedia.org/wiki/Capability-based_security
.. _IPython notebook: http://nbviewer.ipython.org/github/waltermoreira/tartpy/blob/master/demo/tartpy_demo.ipynb
.. _actor_model: https://github.com/waltermoreira/actor_model
.. _TACC: https://www.tacc.utexas.edu/
densityestimation 0.1.2
Density estimation functions.
License: MIT

==================
Density Estimation
==================

This package provides functions for three specific types of density estimators.  
You might find it most useful for tasks involving plotting kernel density estimates, 
histograms and nearest-neighbor density estimates.

Typical intallation often looks like this::

    !pip install densityestimation


Dependencies
============

* Numpy

* Matplotlib


Examples
========

| import numpy as np
| sample = np.random.normal(0,1,1000)
| kde(sample) 
|
| import numpy as np
| sample = np.random.normal(0,1,1000)
| hist(sample)
|
| import numpy as np
| sample = np.random.normal(0,1,1000)
| nnde(sample)


Contributors
=============

We would like to thank our professor, Brian Chapman, for guiding us
through our first ever endeavors involving Python.  It is because of
you, this class and this project that density estimation in Python
will never be the same, or at least in our eyes.