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Robustness fixes in test suite machinery....
Robustness fixes in test suite machinery. Added a module-level INSTALLED flag, which can be set to false if the test suite is being run in-place (without ipython having been installed at all). This is because how we call and import things must be done differently depending on whether the code is installed or is being run in-place. The only ones that can know this reliably are the entry-point scripts, so those are responsible for setting this flag. Also made the code that validates ipython in subprocesses report errors better, by checking stderr for errors before validating stdout output, as anything on stderr will be likely informative of the real problem.

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map.py
121 lines | 3.7 KiB | text/x-python | PythonLexer
# encoding: utf-8
"""Classes used in scattering and gathering sequences.
Scattering consists of partitioning a sequence and sending the various
pieces to individual nodes in a cluster.
"""
__docformat__ = "restructuredtext en"
#-------------------------------------------------------------------------------
# Copyright (C) 2008 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# Imports
#-------------------------------------------------------------------------------
import types
from IPython.utils.genutils import flatten as genutil_flatten
#-------------------------------------------------------------------------------
# Figure out which array packages are present and their array types
#-------------------------------------------------------------------------------
arrayModules = []
try:
import Numeric
except ImportError:
pass
else:
arrayModules.append({'module':Numeric, 'type':Numeric.arraytype})
try:
import numpy
except ImportError:
pass
else:
arrayModules.append({'module':numpy, 'type':numpy.ndarray})
try:
import numarray
except ImportError:
pass
else:
arrayModules.append({'module':numarray,
'type':numarray.numarraycore.NumArray})
class Map:
"""A class for partitioning a sequence using a map."""
def getPartition(self, seq, p, q):
"""Returns the pth partition of q partitions of seq."""
# Test for error conditions here
if p<0 or p>=q:
print "No partition exists."
return
remainder = len(seq)%q
basesize = len(seq)/q
hi = []
lo = []
for n in range(q):
if n < remainder:
lo.append(n * (basesize + 1))
hi.append(lo[-1] + basesize + 1)
else:
lo.append(n*basesize + remainder)
hi.append(lo[-1] + basesize)
result = seq[lo[p]:hi[p]]
return result
def joinPartitions(self, listOfPartitions):
return self.concatenate(listOfPartitions)
def concatenate(self, listOfPartitions):
testObject = listOfPartitions[0]
# First see if we have a known array type
for m in arrayModules:
#print m
if isinstance(testObject, m['type']):
return m['module'].concatenate(listOfPartitions)
# Next try for Python sequence types
if isinstance(testObject, (types.ListType, types.TupleType)):
return genutil_flatten(listOfPartitions)
# If we have scalars, just return listOfPartitions
return listOfPartitions
class RoundRobinMap(Map):
"""Partitions a sequence in a roun robin fashion.
This currently does not work!
"""
def getPartition(self, seq, p, q):
return seq[p:len(seq):q]
#result = []
#for i in range(p,len(seq),q):
# result.append(seq[i])
#return result
def joinPartitions(self, listOfPartitions):
#lengths = [len(x) for x in listOfPartitions]
#maxPartitionLength = len(listOfPartitions[0])
#numberOfPartitions = len(listOfPartitions)
#concat = self.concatenate(listOfPartitions)
#totalLength = len(concat)
#result = []
#for i in range(maxPartitionLength):
# result.append(concat[i:totalLength:maxPartitionLength])
return self.concatenate(listOfPartitions)
dists = {'b':Map}