def delimit(self, args):
self.partitions = []
self.settings = []
cnt = 1
if len(self.trees) == 1:
tree = self.trees[0]
me = None
if args.spe_rate <= 0:
me = exponential_mixture(tree=tree,
max_iters=args.max_iter,
min_br=args.min_brl)
else:
me = exponential_mixture(tree=tree,
max_iters=args.max_iter,
min_br=args.min_brl,
sp_rate=args.spe_rate,
fix_sp_rate=True)
if args.whiten:
me.whitening_search(reroot=self.reroot,
strategy=args.sstrategy)
else:
me.search(reroot=self.reroot, strategy=args.sstrategy)
if args.sprint:
me.count_species(pv=args.pvalue)
me.print_species()
else:
print("Number of species: " +
repr(me.count_species(pv=args.pvalue)))
to, par = me.output_species(taxa_order=self.taxa_order)
self.partitions.append(par)
self.settings.append(me.max_setting)
else:
for tree in self.trees:
print("Running PTP on tree " + repr(cnt) + " ........")
cnt = cnt + 1
me = exponential_mixture(tree=tree,
max_iters=args.max_iter,
min_br=args.min_brl)
me.search(reroot=self.reroot, strategy=args.sstrategy)
me.count_species(pv=args.pvalue, print_log=False)
to, par = me.output_species(taxa_order=self.taxa_order)
self.partitions.append(par)
self.settings.append(me.max_setting)
print("")
return self.partitions, self.settings
def delimit_EPA(self, sstrategy, reroot, max_iters, min_brl, sp_rate, pvalue=0.001):
tree = self.trees[0]
me = exponential_mixture(tree=tree, max_iters=max_iters, min_br=min_brl, sp_rate=sp_rate, fix_sp_rate=True)
me.search(reroot=reroot, strategy=sstrategy)
num_spe = me.count_species(print_log=False, pv=pvalue)
taxa_order, partition = me.output_species(taxa_order=self.taxa_order)
return partition2names(taxa_order, partition)
def __init__(self, tree, start_config = None, reroot = False, startmethod = "H0", min_br = 0.0001, seed = 1234, thinning = 100, sampling = 10000, burning = 0.1, taxa_order = []): if start_config == None: me = exponential_mixture(tree= tree) me.search(strategy = startmethod, reroot = reroot) me.count_species(print_log = False, pv = 0.0) self.tree = me.tree self.current_setting = me.max_setting else: self.current_setting = start_config self.tree = Tree(tree, format = 1) self.burning = burning self.last_setting = self.current_setting self.current_logl = self.current_setting.get_log_l() self.last_logl = self.last_setting.get_log_l() self.min_br = min_br self.rand_nr = random.Random() self.rand_nr.seed(seed) self.thinning = thinning self.sampling = sampling if taxa_order == []: self.taxaorder = self.tree.get_leaf_names() else: self.taxaorder = taxa_order self.numtaxa = len(self.taxaorder) self.partitions = [] self.llhs = [] self.nsplit = 0 self.nmerge = 0 """remember the ML partition""" self.maxllh = self.current_logl to, spe = self.current_setting.output_species(taxa_order = self.taxaorder) self.maxpar = spe self.max_setting = self.current_setting """record all delimitation settings for plotting, this could consume a lot of MEM""" self.settings = []
def __init__(self, tree, start_config = None, reroot = False, startmethod = "H0", min_br = 0.0001, seed = 1234, thinning = 100, sampling = 10000, burning = 0.1, taxa_order = []): if start_config == None: me = exponential_mixture(tree= tree) me.search(strategy = startmethod, reroot = reroot) me.count_species(print_log = False, pv = 0.0) self.tree = me.tree self.current_setting = me.max_setting else: self.current_setting = start_config self.tree = Tree(tree, format = 1) self.burning = burning self.last_setting = self.current_setting self.current_logl = self.current_setting.get_log_l() self.last_logl = self.last_setting.get_log_l() self.min_br = min_br self.rand_nr = random.Random() self.rand_nr.seed(seed) self.thinning = thinning self.sampling = sampling if taxa_order == []: self.taxaorder = self.tree.get_leaf_names() else: self.taxaorder = taxa_order self.numtaxa = len(self.taxaorder) self.partitions = [] self.llhs = [] self.nsplit = 0 self.nmerge = 0 """remember the ML partition""" self.maxllh = self.current_logl to, spe = self.current_setting.output_species(taxa_order = self.taxaorder) self.maxpar = spe self.max_setting = self.current_setting """record all delimitation settings for plotting, this could consume a lot of MEM""" self.settings = []
def delimit(self, args):
self.partitions = []
self.settings = []
cnt = 1
if len(self.trees) == 1:
tree = self.trees[0]
me = None
if args.spe_rate <= 0:
me = exponential_mixture(tree=tree, max_iters=args.max_iter, min_br=args.min_brl)
else:
me = exponential_mixture(
tree=tree, max_iters=args.max_iter, min_br=args.min_brl, sp_rate=args.spe_rate, fix_sp_rate=True
)
if args.whiten:
me.whitening_search(reroot=self.reroot, strategy=args.sstrategy)
else:
me.search(reroot=self.reroot, strategy=args.sstrategy)
# print to screen
# me.count_species(pv = args.pvalue)
# me.print_species()
print("Number of species: " + repr(me.count_species(pv=args.pvalue)))
to, par = me.output_species(taxa_order=self.taxa_order)
self.partitions.append(par)
self.settings.append(me.max_setting)
else:
for tree in self.trees:
print("Running PTP on tree " + repr(cnt) + " ........")
cnt = cnt + 1
me = exponential_mixture(tree=tree, max_iters=args.max_iter, min_br=args.min_brl)
me.search(reroot=self.reroot, strategy=args.sstrategy)
me.count_species(pv=args.pvalue, print_log=False)
to, par = me.output_species(taxa_order=self.taxa_order)
self.partitions.append(par)
self.settings.append(me.max_setting)
print("")
return self.partitions, self.settings
def delimit_EPA(self,
sstrategy,
reroot,
max_iters,
min_brl,
sp_rate,
pvalue=0.001):
tree = self.trees[0]
me = exponential_mixture(tree=tree,
max_iters=max_iters,
min_br=min_brl,
sp_rate=sp_rate,
fix_sp_rate=True)
me.search(reroot=reroot, strategy=sstrategy)
num_spe = me.count_species(print_log=False, pv=pvalue)
taxa_order, partition = me.output_species(taxa_order=self.taxa_order)
return partition2names(taxa_order, partition)