def run_multi_round(puzzle_path):
# Look for multiround links and read those in
# Assuming that the puzzle will only be part of a single multiround
found_multi_round = False
multi_round_paths = []
links = []
with open('../json/links.json') as f:
json_data = json.load(f)
links = json_data['links']
for link in links:
if puzzle_path in link['paths']:
found_multi_round = True
multi_round_paths = link['paths']
# Read the build option list
split_path = puzzle_path.split('/')
dir_path = ''
for path_part in split_path[:-1]:
dir_path += path_part+'/'
options_path = dir_path+'build_options.json'
build_options = []
with open(options_path) as f:
build_options = json.load(f)
# Produce network JSON files for each param combination for multiround
if found_multi_round:
multi_round_designs = []
for path in multi_round_paths:
with open(path) as f:
json_data = json.load(f)
multi_round_designs.append(json_data['designs'])
phylo_obj = build_phylogeny(multi_round_designs)
for build in build_options['options']:
direction_type = build['direction']
cluster_type = build['clustering']
score_type = build['scoring']
threshold = build['threshold']
maxCluster = build['maxCluster']
print 'Build options: Direction='+direction_type+' Cluster='+cluster_type+' Score='+score_type+' Threshold='+str(threshold)+' MaxCluster='+str(maxCluster)
# Assigning proper scoring function
scoreFunc = lambda a,b: 50
if score_type == 'Levenshtein':
scoreFunc = scoreLevenshtein
elif score_type == 'MotifBased':
scoreFunc = scoreByMotif
elif score_type == 'Naive':
scoreFunc = scoreNaive
else:
print 'Not found score_type option'
exit()
# Running proper clustering method and direction
if cluster_type == 'MaxForwards':
phylo_obj.buildMaxForwards(threshold,scoreFunc)
elif cluster_type == 'MaxBackwards':
phylo_obj.buildMaxBackwards(threshold,scoreFunc)
elif direction_type == 'Forwards':
if cluster_type == 'Threshold':
phylo_obj.buildForwardsThreshold(threshold,scoreFunc)
elif cluster_type == 'Hierarchical':
phylo_obj.buildForwardsHierarchical(threshold,maxCluster,scoreFunc)
elif direction_type == 'Backwards':
if cluster_type == 'Threshold':
phylo_obj.buildBackwardsThreshold(threshold,scoreFunc)
elif cluster_type == 'Hierarchical':
phylo_obj.buildBackwardsHierarchical(threshold,maxCluster,scoreFunc)
elif direction_type == 'NoDirection':
if cluster_type == 'Threshold':
phylo_obj.buildNoDirectionThreshold(threshold,scoreFunc)
elif cluster_type == 'Hierarchical':
phylo_obj.buildNoDirectionHierarchical(maxCluster,scoreFunc)
out_name = 'Direction:'+direction_type+'_Clustering:'+cluster_type+'_Scoring:'+score_type+'_Threshold:'+str(threshold)+'_MaxClusters:'+str(maxCluster)+'.json'
phylo_obj.findAverageChildren()
phylo_obj.buildEdges()
phylo_obj.saveNetworkToJSON('../json/Rounds/Multirounds/test/'+out_name)
phylo_obj.clearNodeConnections()
def run_single_round(puzzle_path):
# Read just this puzzle round into json
single_round_designs = []
with open(puzzle_path) as f:
json_data = json.load(f)
single_round_designs = json_data['designs']
# Read the build option list
split_path = puzzle_path.split('/')
dir_path = ''
for path_part in split_path[:-1]:
dir_path += path_part+'/'
options_path = dir_path+'build_options.json'
build_options = []
with open(options_path) as f:
build_options = json.load(f)
network_file_paths = []
phylo_obj = build_phylogeny([single_round_designs])
# Produce network JSON files for each parameter combination
for build in build_options['options']:
direction_type = build['direction']
cluster_type = build['clustering']
score_type = build['scoring']
threshold = build['threshold']
maxCluster = build['maxCluster']
# 'Build options single: Direction='+direction_type+' Cluster='+cluster_type+' Score='+score_type+' Threshold='+str(threshold)
# Assigning proper scoring function
scoreFunc = lambda a,b: 50
if score_type == 'Levenshtein':
scoreFunc = scoreLevenshtein
elif score_type == 'MotifBased':
scoreFunc = scoreByMotif
elif score_type == 'Naive':
scoreFunc = scoreNaive
else:
print 'Not found score_type option'
# Running proper clustering method and direction
if cluster_type == 'MaxForwards':
# Skipping Building MaxForwards Since Only 1 Round
continue
elif cluster_type == 'MaxBackwards':
# Skipping Building MaxBackwards Since Only 1 Round
continue
elif direction_type == 'Forwards':
if cluster_type == 'Threshold':
# Skipping Building Forwards Threshold Since Only 1 Round
continue
elif cluster_type == 'Hierarchical':
# Skipping Building Forwards Hierarchical Since Only 1 Round
continue
elif direction_type == 'Backwards':
if cluster_type == 'Threshold':
# Skipping Building Backwards Threshold Since Only 1 Round
continue
elif cluster_type == 'Hierarchical':
# Skipping Building Backwards Hierarchical Since Only 1 Round
continue
elif direction_type == 'NoDirection':
if cluster_type == 'Threshold':
# Building NoDirection Threshold'
phylo_obj.buildNoDirectionThreshold(threshold,scoreFunc)
elif cluster_type == 'Hierarchical':
# Building NoDirection Hierarchical
phylo_obj.buildNoDirectionHierarchical(maxCluster,scoreFunc)
out_name = 'Direction:'+direction_type+'_Clustering:'+cluster_type+'_Scoring:'+score_type+'_Threshold:'+str(threshold)+'_MaxClusters:'+str(maxCluster)+'.json'
phylo_obj.findAverageChildren()
phylo_obj.buildEdges()
phylo_obj.saveNetworkToJSON(dir_path+'networks/'+out_name)
phylo_obj.clearNodeConnections()
network_file_paths.append(dir_path+'networks/'+out_name)
return network_file_paths
