From e6a380241ac7c388ad4a8d1a395ec12e531b7f4c Mon Sep 17 00:00:00 2001 From: Zooko O'Whielacronx Date: Fri, 3 Sep 2010 07:47:12 -0700 Subject: [PATCH] trivial: M-x whitespace-cleanup --- src/allmydata/util/happinessutil.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/src/allmydata/util/happinessutil.py b/src/allmydata/util/happinessutil.py index 6be8cee8..8029addd 100644 --- a/src/allmydata/util/happinessutil.py +++ b/src/allmydata/util/happinessutil.py @@ -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 -- 2.45.2