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trivial: M-x whitespace-cleanup

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This commit is contained in:
Zooko O'Whielacronx 2010-09-03 07:47:12 -07:00
parent cb83f2e41c
commit e6a380241a
1 changed files with 9 additions and 9 deletions
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18
src/allmydata/util/happinessutil.py
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@ -29,7 +29,7 @@ def failure_message(peer_count, k, happy, effective_happy):
"file." %
(peer_count, k, happy, k))
# Otherwise, if there is an x-happy subset of peers where
# x >= needed_shares, but x < servers_of_happiness, then
# x >= needed_shares, but x < servers_of_happiness, then
# we use this message.
else:
msg = ("shares could be placed on only %d server(s) "
@ -129,13 +129,13 @@ def servers_of_happiness(sharemap):
sharemap = shares_by_server(sharemap)
graph = flow_network_for(sharemap)
# This is an implementation of the Ford-Fulkerson method for finding
# a maximum flow in a flow network applied to a bipartite graph.
# Specifically, it is the Edmonds-Karp algorithm, since it uses a
# a maximum flow in a flow network applied to a bipartite graph.
# Specifically, it is the Edmonds-Karp algorithm, since it uses a
# BFS to find the shortest augmenting path at each iteration, if one
# exists.
#
# The implementation here is an adapation of an algorithm described in
# "Introduction to Algorithms", Cormen et al, 2nd ed., pp 658-662.
# exists.
#
# The implementation here is an adapation of an algorithm described in
# "Introduction to Algorithms", Cormen et al, 2nd ed., pp 658-662.
dim = len(graph)
flow_function = [[0 for sh in xrange(dim)] for s in xrange(dim)]
residual_graph, residual_function = residual_network(graph, flow_function)
@ -188,7 +188,7 @@ def flow_network_for(sharemap):
num_servers = len(sharemap)
graph = [] # index -> [index], an adjacency list
# Add an entry at the top (index 0) that has an edge to every server
# in sharemap
# in sharemap
graph.append(sharemap.keys())
# For each server, add an entry that has an edge to every share that it
# contains (or will contain).
@ -238,7 +238,7 @@ def residual_network(graph, f):
for v in graph[i]:
if f[i][v] == 1:
# We add an edge (v, i) with cf[v,i] = 1. This means
# that we can remove 1 unit of flow from the edge (i, v)
# that we can remove 1 unit of flow from the edge (i, v)
new_graph[v].append(i)
cf[v][i] = 1
cf[i][v] = -1