def makeCloud(tips, RF_norm_cloud, name, cloud_size, starting_trees):
# Make cluster of trees around each starting tree
# Get starting trees parsed
t1 = starting_trees[0]
t2 = starting_trees[1]
# Calculate number of NNI moves based on desired normalized RF distance.
RF_max = 2 * (tips - 2)
NNI_moves_cloud = int((RF_max * RF_norm_cloud) / 2)
if NNI_moves_cloud == 0:
NNI_moves_cloud = 1
# Make a cloud for each starting tree
c_size = int(cloud_size) - 1
print("Making clouds...")
# Make clouds
cluster1 = readTree.NNI_mult_trees(in_tree=t1,
num_out_trees=c_size,
num_nni_moves=NNI_moves_cloud,
out='list')
cluster2 = readTree.NNI_mult_trees(in_tree=t2,
num_out_trees=c_size,
num_nni_moves=NNI_moves_cloud,
out='list')
tree_list = [cluster1, cluster2]
# Make a nexus file with starting trees and cloud trees
readTree.list_to_out(tree_list, '%s_cloud.tree' % name)
# Make a cloud for each starting tree
c_size = int(cloud_size) - 1
print("Making clouds...")
# Make clouds
cluster1 = readTree.NNI_mult_trees(in_tree=t1,
num_out_trees=c_size,
num_nni_moves=NNI_moves_cloud,
out='list')
cluster2 = readTree.NNI_mult_trees(in_tree=t2,
num_out_trees=c_size,
num_nni_moves=NNI_moves_cloud,
out='list')
# Make a nexus file with starting trees and cloud trees
readTree.list_to_out(cluster1, cluster2, '%s_cloud.tree' % name)
############################################################
# Print info to log file
############################################################
print("Calculating stats on trees...")
# Calculate emperical distance between two start trees
RF_emp = readTree.rf_unweighted(t1, t2, normalized='T')[1]
# Calculate average density of each cloud
RF_cloud1 = readTree.cluster_density_avg(in_file='%s_cloud.tree' % name,
NNI_trees=int(cloud_size) - 1,
starting_tree_number=0)
RF_cloud2 = readTree.cluster_density_avg(in_file='%s_cloud.tree' % name,
NNI_trees=int(cloud_size) - 1,
starting_tree_number=cloud_size)
# write to log file
