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python-gpod-0.8.3-8.lbn19.1.x86_64
A python module to access iPod content. This module provides bindings to the
libgpod library.
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python-greenlet-0.4.0-1.fc19.armv6hl
The greenlet package is a spin-off of Stackless, a version of CPython
that supports micro-threads called "tasklets". Tasklets run
pseudo-concurrently (typically in a single or a few OS-level threads)
and are synchronized with data exchanges on "channels".
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python-gunicorn-19.4.5-1.lbn19.noarch
Gunicorn ("Green Unicorn") is a Python WSGI HTTP server for UNIX. It uses the
pre-fork worker model, ported from Ruby's Unicorn project. It supports WSGI,
Django, and Paster applications.
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python-httplib2-0.9.2-2.lbn19.noarch
A comprehensive HTTP client library that supports many features left out of
other HTTP libraries.
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python-idna-2.0-1.lbn19.noarch
A library to support the Internationalised Domain Names in Applications (IDNA)
protocol as specified in RFC 5891 <http://tools.ietf.org/html/rfc5891>. This
version of the protocol is often referred to as "IDNA2008" and can produce
different results from the earlier standard from 2003.
The library is also intended to act as a suitable drop-in replacement for the
"encodings.idna" module that comes with the Python standard library but
currently only supports the older 2003 specification.
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python-imaging-sane-2.4.0-2.lbn19.armv6hl
This package contains the sane module for Python which provides access to
various raster scanning devices such as flatbed scanners and digital cameras.
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python-imaging-sane-2.4.0-2.lbn19.x86_64
This package contains the sane module for Python which provides access to
various raster scanning devices such as flatbed scanners and digital cameras.
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python-imgcreate-19.6-1.fc19.armv6hl
Python modules that can be used for building images for things
like live image or appliances.
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python-ipykernel-4.2.1-1.lbn19.noarch
This package provides the IPython kernel for Jupyter.
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python-ipyparallel-4.1.0-1.lbn19.noarch
IPyParallel is IPython’s sophisticated and powerful architecture for parallel and distributed computing. This architecture abstracts out parallelism in a very general way, which enables IPython to support many different styles of parallelism including:
Single program, multiple data (SPMD) parallelism.
Multiple program, multiple data (MPMD) parallelism.
Message passing using MPI.
Task farming.
Data parallel.
Combinations of these approaches.
Custom user defined approaches.
Most importantly, IPython enables all types of parallel applications to be developed, executed, debugged and monitored interactively. Hence, the I in IPython. The following are some example usage cases for IPython:
Quickly parallelize algorithms that are embarrassingly parallel using a number of simple approaches. Many simple things can be parallelized interactively in one or two lines of code.
Steer traditional MPI applications on a supercomputer from an IPython session on your laptop.
Analyze and visualize large datasets (that could be remote and/or distributed) interactively using IPython and tools like matplotlib/TVTK.
Develop, test and debug new parallel algorithms (that may use MPI) interactively.
Tie together multiple MPI jobs running on different systems into one giant distributed and parallel system.
Start a parallel job on your cluster and then have a remote collaborator connect to it and pull back data into their local IPython session for plotting and analysis.
Run a set of tasks on a set of CPUs using dynamic load balancing.
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