Python Tree.get_descendants方法代码示例

# 需要导入模块: from ete3 import Tree [as 别名]
# 或者: from ete3.Tree import get_descendants [as 别名]
class exponential_mixture:
    """ML search PTP, to use: __init__(), search() and count_species()"""
    def __init__(self, tree, sp_rate = 0, fix_sp_rate = False, max_iters = 20000, min_br = 0.0001):
        self.min_brl = min_br
        self.tree = Tree(tree, format = 1)
        self.tree.resolve_polytomy(recursive=True)
        self.tree.dist = 0.0
        self.fix_spe_rate = fix_sp_rate
        self.fix_spe = sp_rate
        self.max_logl = float("-inf") 
        self.max_setting = None
        self.null_logl = 0.0
        self.null_model()
        self.species_list = None
        self.counter = 0
        self.setting_set = set([])
        self.max_num_search = max_iters


    def null_model(self):
        coa_br = []
        all_nodes = self.tree.get_descendants()
        for node in all_nodes:
            if node.dist > self.min_brl:
                coa_br.append(node.dist)
        e1 = exp_distribution(coa_br)
        self.null_logl = e1.sum_log_l()
        return e1.rate


    def __compare_node(self, node):
        return node.dist


    def re_rooting(self):
        node_list = self.tree.get_descendants()
        node_list.sort(key=self.__compare_node)
        node_list.reverse()
        rootnode = node_list[0]
        self.tree.set_outgroup(rootnode)
        self.tree.dist = 0.0


    def comp_num_comb(self):
        for node in self.tree.traverse(strategy='postorder'):
            if node.is_leaf():
                node.add_feature("cnt", 1.0)
            else:
                acum = 1.0
                for child in node.get_children():
                    acum = acum * child.cnt
                acum = acum + 1.0
                node.add_feature("cnt", acum)
        return self.tree.cnt


    def next(self, sp_setting):
        self.setting_set.add(frozenset(sp_setting.spe_nodes))
        logl = sp_setting.get_log_l()
        if logl > self.max_logl:
            self.max_logl = logl
            self.max_setting = sp_setting
        for node in sp_setting.active_nodes:
            if node.is_leaf():
                pass
            else:
                childs = node.get_children()
                sp_nodes = []
                for child in childs:
                    sp_nodes.append(child)
                for nod in sp_setting.spe_nodes:
                    sp_nodes.append(nod)
                new_sp_setting = species_setting(spe_nodes = sp_nodes, root = sp_setting.root, sp_rate = sp_setting.spe_rate, fix_sp_rate = sp_setting.fix_spe_rate, minbr = self.min_brl)
                if frozenset(sp_nodes) in self.setting_set:
                    pass
                else:
                    self.next(new_sp_setting)


    def H0(self, reroot = True):
        self.H1(reroot)
        self.H2(reroot = False)
        self.H3(reroot = False)


    def H1(self, reroot = True):
        if reroot:
            self.re_rooting()
            
        #self.init_tree()
        sorted_node_list = self.tree.get_descendants()
        sorted_node_list.sort(key=self.__compare_node)
        sorted_node_list.reverse()
        
        first_node_list = []
        first_node_list.append(self.tree)
        first_childs = self.tree.get_children()
        for child in first_childs:
            first_node_list.append(child)
        first_setting = species_setting(spe_nodes = first_node_list, root = self.tree, sp_rate = self.fix_spe, fix_sp_rate = self.fix_spe_rate, minbr = self.min_brl)
#.........这里部分代码省略.........

# 需要导入模块: from ete3 import Tree [as 别名]
# 或者: from ete3.Tree import get_descendants [as 别名]
from ete3 import Tree
tree = Tree( '(A:1,(B:1,(C:1,D:1):0.5):0.5);' )
# Prints the name of every leaf under the tree root
print "Leaf names:"
for leaf in tree.get_leaves():
    print leaf.name
# Label nodes as terminal or internal. If internal, saves also the
# number of leaves that it contains.
print "Labeled tree:"
for node in tree.get_descendants():
    if node.is_leaf():
        node.add_features(ntype="terminal")
    else:
        node.add_features(ntype="internal", size=len(node))
# Gets the extended newick of the tree including new node features
print tree.write(features=[])

# 需要导入模块: from ete3 import Tree [as 别名]
# 或者: from ete3.Tree import get_descendants [as 别名]
print t
#          /-A
#---------|
#         |          /-B
#          \--------|
#                   |          /-C
#                    \--------|
#                              \-D
# Counts leaves within the tree
nleaves = 0
for leaf in t.get_leaves():
    nleaves += 1
print "This tree has", nleaves, "terminal nodes"
# But, like this is much simpler :)
nleaves = len(t)
print "This tree has", nleaves, "terminal nodes [proper way: len(tree) ]"
# Counts leaves within the tree
ninternal = 0
for node in t.get_descendants():
    if not node.is_leaf():
        ninternal +=1
print "This tree has", ninternal,  "internal nodes"
# Counts nodes with whose distance is higher than 0.3
nnodes = 0
for node in t.get_descendants():
    if node.dist >  0.3:
        nnodes +=1
# or, translated into a better pythonic
nnodes = len([n for n in t.get_descendants() if n.dist>0.3])
print "This tree has", nnodes,  "nodes with a branch length > 0.3"