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python-ipdb-0.7-1.lbn13.noarch
ipdb exports functions to access the IPython debugger, which features tab completion, syntax highlighting, better tracebacks, better introspection with the same interface as the pdb module.
Example usage:
import ipdb
ipdb.set_trace()
ipdb.pm()
ipdb.run('x[0] = 3')
result = ipdb.runcall(function, arg0, arg1, kwarg='foo')
result = ipdb.runeval('f(1,2) - 3')
The post-mortem function, ipdb.pm(), is equivalent to the magic function %debug.
If you install ipdb with a tool which supports setuptools entry points, an ipdb script is made for you. You can use it to debug your scripts like
$ bin/ipdb mymodule.py
With Python 2.7 only, you can also use
$ python -m ipdb mymodule.py
You can also enclose code with the with statement to launch ipdb if an exception is raised:
from ipdb import launch_ipdb_on_exception
with launch_ipdb_on_exception():
[...]
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BastionLinux 13
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python-ipdb-0.8-4.fc19.noarch
IPython features (tab completion, syntax highlighting, better tracebacks,
better introspection) right in pdb.
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BastionLinux 19
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python-IPy-0.70-1.fc13.noarch
IPy is a Python module for handling IPv4 and IPv6 Addresses and Networks
in a fashion similar to perl's Net::IP and friends. The IP class allows
a comfortable parsing and handling for most notations in use for IPv4
and IPv6 Addresses and Networks.
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Cloud Computing
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BastionLinux 13
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python-IPy-0.70-1.fc13.noarch
IPy is a Python module for handling IPv4 and IPv6 Addresses and Networks
in a fashion similar to perl's Net::IP and friends. The IP class allows
a comfortable parsing and handling for most notations in use for IPv4
and IPv6 Addresses and Networks.
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LBN
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Plone and Zope
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BastionLinux 13
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python-IPy-0.70-1.fc13.noarch
IPy is a Python module for handling IPv4 and IPv6 Addresses and Networks
in a fashion similar to perl's Net::IP and friends. The IP class allows
a comfortable parsing and handling for most notations in use for IPv4
and IPv6 Addresses and Networks.
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LBN
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Core Linux
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BastionLinux 13
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python-IPy-0.83-1.lbn19.noarch
IPy is a Python module for handling IPv4 and IPv6 Addresses and Networks
in a fashion similar to perl's Net::IP and friends. The IP class allows
a comfortable parsing and handling for most notations in use for IPv4
and IPv6 Addresses and Networks.
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LBN
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Core Linux
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BastionLinux 19
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python-IPy-1.00-1.lbn25.noarch
IPy is a Python module for handling IPv4 and IPv6 Addresses and Networks
in a fashion similar to perl's Net::IP and friends. The IP class allows
a comfortable parsing and handling for most notations in use for IPv4
and IPv6 Addresses and Networks.
Located in
LBN
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…
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Cloud Computing
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BastionLinux 25
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python-IPy-python3-0.83-1.lbn19.noarch
IPy is a Python3 module for handling IPv4 and IPv6 Addresses and Networks
in a fashion similar to perl's Net::IP and friends. The IP class allows
a comfortable parsing and handling for most notations in use for IPv4
and IPv6 Addresses and Networks.
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Core Linux
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BastionLinux 19
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python-ipykernel-4.2.1-1.lbn19.noarch
This package provides the IPython kernel for Jupyter.
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BastionLinux 19
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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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