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资源说明：A simple python module for running tasks in parallel (SMP or local network connected computers)
runlib
======
Some python utility tools for massively parallel processing and
data handling.
1. Installation
--------------
This module can be installed with pip from the git repository:
pip install --no-deps git+http://git@github.com/fsteinmetz/runlib.git
2. condor.py
------------
A simple python interface to massively parallel computing frameworks.
Initially developped for [HTCondor](http://research.cs.wisc.edu/htcondor/) ; it
has been extended to support Sun Grid Engine (qsub).
_Example:_
from condor import CondorPool # or QsubPool
def f(x):
return x**2
if __name__ == '__main__':
p = CondorPool()
results = p.map(f, range(5))
3. tmpfiles.py
--------------
Management of temporary files: inputs to a processing (TmpManager().input),
outputs of a processing (TmpManager().output), pure temporary files
(TmpManager().file) and temporary directories (TmpManager().directory).
Includes several features: cleanup after use, automatic uncompress of input
files (gz, bz, tar, zip), check disck space, unique paths, etc.
_Example:_
with TmpManager('/tmp/') as tm: # instantiate the tmp manager on directory '/tmp/'
# decompress a file to tmp directory and return the name
# of the decompressed file
input1 = tm.input('/data/file.gz')
# if the input is an archive, returns a list of all the files in
# the archive
file_list = tm.input('/data/file.tar.gz')
# returns a temporary file that will be cleaned up
tmp = tm.file('filename.txt')
# returns a temporary directory
dir = tm.directory()
# returns a filename in tmp directory
# this file will be created afterwards, and moved to destination
# upon commit()
out = tm.output('/data/result.dat')
# move all output files to their destination
# (otherwise they are cleared)
tm.commit()
# NOTE: all temporary files are cleared up when leaving the 'with' context
# even in case of error in the python code.
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