def makeStarting(tips,RF_norm_start,name,RF_norm_cloud,cloud_size):
#Choose starting trees
print(name)
print("Making starting trees...")
# Create random tree. Average branch length of 1. No variation in branch length. Don't print out tree structure
t1 = readTree.rand_tree(tips=tips,brl_avg=1,brl_std=None,verbose='F')
# Calculate number of NNI moves based on desired normalized RF distance.
RF_max = 2*(tips-2)
NNI_moves_start = int((RF_max * RF_norm_start)/2)
if NNI_moves_start == 0:
NNI_moves_start = 1
NNI_moves_cloud = int((RF_max * RF_norm_cloud)/2)
# Create second starting tree
t2 = readTree.NNI_mult_moves(in_tree=t1,num_moves=NNI_moves_start,node_choice='random',no_dup_start_tree='F', req_min_RF=RF_norm_start)
# Write out tree files
readTree.write_single_tree(t1,'%s_starting_tree_1.tree' % name)
readTree.write_single_tree(t2,'%s_starting_tree_2.tree' % name)
# Write out log file
# Calculate emperical distance between two start trees as a gut check
RF_emp = readTree.rf_unweighted(t1,t2,normalized='T')[1]
# write to log file
with open('%s.log' % name, "w") as log_file:
line1 = "File name: "+str(name)
line1b = "Tips: "+str(tips)+", Trees per cloud: "+str(cloud_size)
line2 = "Starting trees - RF_input: "+str(RF_norm_start)+", RF_calc: "+str(RF_emp)+", NNI_moves: "+str(NNI_moves_start)
line3 = "Cloud of trees - RF_input: "+str(RF_norm_cloud)+", NNI_moves: "+str(NNI_moves_cloud)
log_file.write("%s\n%s\n%s" % (line1, line2, line3))
# Pass tree files to next function
return t1,t2
def makeStarting(tips,RF_norm_start,name,RF_norm_cloud,cloud_size):
#Choose starting trees
print(name)
print("Making starting trees...")
# Create random tree. Average branch length of 1. No variation in branch length. Don't print out tree structure
t1 = readTree.rand_tree(tips=tips,brl_avg=1,brl_std=None,verbose='F')
# Calculate number of NNI moves based on desired normalized RF distance.
RF_max = 2*(tips-2)
NNI_moves_start = int((RF_max * RF_norm_start)/2)
if NNI_moves_start == 0:
NNI_moves_start = 1
# Calc cloud for log file
NNI_moves_cloud = int((RF_max * RF_norm_cloud)/2)
# Create second starting tree
t2 = readTree.NNI_mult_moves(in_tree=t1,num_moves=NNI_moves_start,node_choice='random',no_dup_start_tree='F', req_min_RF=RF_norm_start)
readTree.rf_unweighted(t1,t2,"T")
t3 = readTree.NNI_mult_moves(in_tree=t2,num_moves=NNI_moves_start,node_choice='random',no_dup_start_tree='F', req_min_RF=RF_norm_start)
d12=readTree.rf_unweighted(t1,t2,"T")[1]
d23=readTree.rf_unweighted(t2,t3,"T")[1]
d13=readTree.rf_unweighted(t1,t3,"T")[1]
print("first t2 to t3 RF"+str(d23))
#t1 and t2 are guarenteed to be RF start apart, same with t2 and t3. So we only need to check t1 and t3.
while d23 != RF_norm_start:
print("redoing third start tree")
t3 = readTree.NNI_mult_moves(in_tree=t1,num_moves=NNI_moves_start,node_choice='random',no_dup_start_tree='F', req_min_RF=RF_norm_start)
d23=readTree.rf_unweighted(t2,t3,"T")[1]
print(d23)
# do it all again with another starting tree
t4 = readTree.NNI_mult_moves(in_tree=t1,num_moves=NNI_moves_start,node_choice='random',no_dup_start_tree='F', req_min_RF=RF_norm_start)
# now we need to check d24 and d34
d14=readTree.rf_unweighted(t1,t4,"T")[1]
d24=readTree.rf_unweighted(t2,t4,"T")[1]
d34=readTree.rf_unweighted(t3,t4,"T")[1]
while d24 != RF_norm_start or d34 != RF_norm_start:
print("redoing fourth start tree")
t4 = readTree.NNI_mult_moves(in_tree=t1,num_moves=NNI_moves_start,node_choice='random',no_dup_start_tree='F', req_min_RF=RF_norm_start)
d24=readTree.rf_unweighted(t2,t4,"T")[1]
d34=readTree.rf_unweighted(t3,t4,"T")[1]
print(str(d24)+", "+str(d34))
# Write out tree files
readTree.write_single_tree(t1,'%s_starting_tree_1.tree' % name)
readTree.write_single_tree(t2,'%s_starting_tree_2.tree' % name)
readTree.write_single_tree(t3,'%s_starting_tree_3.tree' % name)
readTree.write_single_tree(t4,'%s_starting_tree_4.tree' % name)
# Write out log file
rfs=str(d12)+", "+str(d13)+", "+str(d23)+", "+str(d14)+", "+str(d24)+", "+str(d34)
with open('%s.log' % name, "w") as log_file:
line1 = "File name: "+str(name)
line1b = "Tips: "+str(tips)+", Trees per cloud: "+str(cloud_size)
line2 = "Starting trees - RF_input: "+str(RF_norm_start)+", RF_calc: "+str(rfs)+", NNI_moves: "+str(NNI_moves_start)
line3 = "Cloud of trees - RF_input: "+str(RF_norm_cloud)+", NNI_moves: "+str(NNI_moves_cloud)
log_file.write("%s\n%s\n%s" % (line1, line2, line3))
# Pass tree files to next function
return t1,t2,t3,t4
def makeStarting(tips, RF_norm_start_far, RF_norm_start, name, RF_norm_cloud,
cloud_size):
#Choose starting trees
print(name)
print("Making starting trees...")
# Round the things
RF_norm_start_far = round(RF_norm_start_far, 2)
RF_norm_start = round(RF_norm_start, 2)
# Calculate number of NNI moves based on desired normalized RF distance.
RF_max = 2 * (tips - 2)
NNI_moves_start = int((RF_max * RF_norm_start) / 2)
if NNI_moves_start == 0:
NNI_moves_start = 1
NNI_moves_start_far = int((RF_max * RF_norm_start_far) / 2)
print("NNI: " + str(NNI_moves_start) + "NNI far: " +
str(NNI_moves_start_far))
# Calc cloud for log file
NNI_moves_cloud = int((RF_max * RF_norm_cloud) / 2)
# Create random tree. Average branch length of 1. No variation in branch length. Don't print out tree structure
t1 = readTree.rand_tree(tips=tips, brl_avg=1, brl_std=None, verbose='F')
# Create second starting tree
t2 = readTree.NNI_mult_moves(in_tree=t1,
num_moves=NNI_moves_start,
node_choice='random',
no_dup_start_tree='F',
req_min_RF=RF_norm_start)
# Make third tree that is farther away
t3 = readTree.NNI_mult_moves(in_tree=t1,
num_moves=NNI_moves_start_far,
node_choice='random',
no_dup_start_tree='F',
req_min_RF=RF_norm_start_far)
d12 = readTree.rf_unweighted(t1, t2, "T")[1]
d23 = readTree.rf_unweighted(t2, t3, "T")[1]
d13 = readTree.rf_unweighted(t1, t3, "T")[1]
print("first t1 to t2 RF - " + str(round(d12, 2)))
print("first t1 to t3 RF - " + str(round(d13, 2)))
print("first t2 to t3 RF - " + str(round(d23, 2)))
#t1 and t2 are guarenteed to be RF start apart, same with t2 and t3. So we only need to check t1 and t3.
while round(d23, 2) != RF_norm_start_far:
print("redoing third start tree - " + name)
print(round(d23, 2), RF_norm_start_far)
t3 = readTree.NNI_mult_moves(in_tree=t1,
num_moves=NNI_moves_start_far,
node_choice='random',
no_dup_start_tree='F',
req_min_RF=RF_norm_start_far)
d23 = readTree.rf_unweighted(t2, t3, "T")[1]
print("t1 to t2 RF - " + str(round(d12, 2)))
print("t1 to t3 RF - " + str(round(d13, 2)))
print("t2 to t3 RF - " + str(round(d23, 2)))
# Write out tree files
readTree.write_single_tree(t1, '%s_starting_tree_1.tree' % name)
readTree.write_single_tree(t2, '%s_starting_tree_2.tree' % name)
readTree.write_single_tree(t3, '%s_starting_tree_3.tree' % name)
# Write out log file
rfs = str(d12) + ", " + str(d13) + ", " + str(d23)
with open('%s.log' % name, "w") as log_file:
line1 = "File name: " + str(name)
line1b = "Tips: " + str(tips) + ", Trees per cloud: " + str(cloud_size)
line2 = "Starting trees - RF_input: " + str(
RF_norm_start) + "RF_input_far: " + str(
RF_norm_start_far) + ", RF_calc: " + str(
rfs) + ", NNI_moves: " + str(
NNI_moves_start) + ", NNI_moves_far: " + str(
NNI_moves_start_far)
line3 = "Cloud of trees - RF_input: " + str(
RF_norm_cloud) + ", NNI_moves: " + str(NNI_moves_cloud)
log_file.write("%s\n%s\n%s" % (line1, line2, line3))
# Pass tree files to next function
return t1, t2, t3
t1 = readTree.rand_tree(tips=tips, brl_avg=1, brl_std=None, verbose='F')
# Calculate number of NNI moves based on desired normalized RF distance.
RF_max = 2 * (tips - 2)
NNI_moves_start = int((RF_max * RF_norm_start) / 2)
if NNI_moves_start == 0:
NNI_moves_start = 1
# Create second starting tree
t2 = readTree.NNI_mult_moves(in_tree=t1,
num_moves=NNI_moves_start,
node_choice='random',
no_dup_start_tree='F',
req_min_RF=RF_norm_start)
# Write out tree files
readTree.write_single_tree(t1, '%s_starting_tree_1.tree' % name)
readTree.write_single_tree(t2, '%s_starting_tree_2.tree' % name)
############################################################
# Make cluster of trees around each starting tree
############################################################
# Calculate number of NNI moves based on desired normalized RF distance.
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
