semvec/pypi-packages huggingface dataset:
pyrsync2 0.2.6
A Python 3 module which implements rsync binary diff
    algorithm.
License: MIT

===============================
#pyrsync2
===============================
This is a fork of the `pyrsync` package with some changes such as:

    - The functions `blockchecksums` and `rsyncdelta` are now generators
      which make it easier to send through the network
    - Some unittests are added
    - Implementation changes (uses bytearray instead of a deque, faster hash lookup etc.)
    - The block size is no longer in the delta itself, it needs to be passed
      to the patchstream function
    - Does not use try/except for programming logic

It's a Python 3 only package!

pyrsync2 is a Python module which implements the [rsync algorithm] [1],
written in pure Python. It *is not* a wrapper for rsync, but a set of
functions which apply full rsync functionality through Python.

The majority of the code for this module is taken from [Eric Pruitt's
post at ActiveState] [2]. It was orginally licensed under an [MIT license]
[3], and this license has been kept wthout modifications.

## Installation
--------------------------------
You need to have PIP:

    $ sudo pip install pyrsync2

## Use
--------------------------------
An example use case for this module:

    # On the system containing the file that needs to be patched
    >>> import pyrsync2
    >>> unpatched = open("unpatched.file", "rb")
    >>> hashes = pyrsync2.blockchecksums(unpatched)

    # On the remote system after having received hashes
    >>> import pyrsync2
    >>> patchedfile = open("patched.file", "rb")
    >>> delta = pyrsync2.rsyncdelta(patchedfile, hashes)

    # System with the unpatched file after receiving delta
    >>> unpatched.seek(0)
    >>> save_to = open("locally-patched.file", "wb")
    >>> pyrsync2.patchstream(unpatched, save_to, delta)

[1]: http://samba.anu.edu.au/rsync/ "Andrew Tridgell and Paul Mackerras. The rsync algorithm. Technical Report TR-CS-96-05, Canberra 0200 ACT, Australia, 1996."
[2]: https://code.activestate.com/recipes/577518-rsync-algorithm/ "Rsync Algorithm (Python Recipe)"
[3]: http://www.opensource.org/licenses/mit-license.php "OSI MIT License"
pyrallel 0.2.1
Experimental tools for parallel machine learning
License: MIT

# Pyrallel - Parallel Data Analytics in Python

**Overview**: experimental project to investigate distributed computation
patterns for machine learning and other semi-interactive data analytics
tasks.

**Scope**:

- focus on small to medium dataset that fits in memory on a small
  (10+ nodes) to medium cluster (100+ nodes).

- focus on small to medium data (with data locality when possible).

- focus on CPU bound tasks (e.g. training Random Forests) while trying to
  limit disk / network access to a minimum.

- do not focus on HA / Fault Tolerance (yet).

- do not try to invent new set of high level programming abstractions
  (yet): use a low level programming model (IPython.parallel) to finely
  control the cluster elements and messages transfered and help identify
  what are the practical underlying constraints in distributed machine
  learning setting.


**Disclaimer**: the public API of this library will probably not be
stable soon as the current goal of this project is to experiment.


## Dependencies

The usual suspects: Python 2.7, NumPy, SciPy.

Fetch the development version (master branch) from:

- https://github.com/ipython/ipython

- https://github.com/scikit-learn/scikit-learn

StarCluster `develop` branch and its `IPCluster` plugin is also required
to easily startup a bunch of nodes with IPython.parallel setup.

## Patterns currently under investigation

- Asynchronous & randomized hyper-parameters search (a.k.a. Randomized Grid
  Search) for machine learning models

- Share numerical arrays efficiently over the nodes and make them
  available to concurrently running Python processes without making
  copies in memory using memory-mapped files.

- Distributed Random Forests fitting.

- Ensembling heterogeneous library models.

- Parallel implementation of online averaged models using a MPI AllReduce, for
  instance using MiniBatchKMeans on partitioned data.


See the content of the `examples/` folder for more details.


## License

Simplified BSD.


## History

This project started at the [PyCon 2012 PyData
sprint](http://wiki.ipython.org/PyCon12Sprint)
as a set of proof of concept [IPython.parallel
scripts](https://github.com/ogrisel/pycon-pydata-sprint).
asqc 0.1.7
A SPARQL Query Client
License: MIT

ASQC provides a simple command-line SPARQL query client.

The intent is that this client can be used in Unix-style pipeline operations to perform sequences of query operations that pass information as RDF (from CONSTRUCT queries) or variable bindings (from SELECT queries).


== Installation ==

Assumes Python 2.7 installed; not yet tested with other versions.  Also not yet tested on systems other than Linux or MacOS.

Installation is from the Python Package Index (PyPI - http://pypi.python.org/pypi):

    pip install asqc

See the project page at github for more details.


== Documentation ==

Right now, there's not much.  After installation, type:

    asq --help

for a usage summary, or see the example queries.


== Examples ==

The package includes a small number of sample queries.  See the project page at github for more details.