本文整理汇总了Python中ete3.Tree.resolve_polytomy方法的典型用法代码示例。如果您正苦于以下问题：Python Tree.resolve_polytomy方法的具体用法？Python Tree.resolve_polytomy怎么用？Python Tree.resolve_polytomy使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类ete3.Tree的用法示例。
在下文中一共展示了Tree.resolve_polytomy方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: ReadTreeFromFile
# 需要导入模块: from ete3 import Tree [as 别名]
# 或者: from ete3.Tree import resolve_polytomy [as 别名]
def ReadTreeFromFile(filepath):
"""
Uses ete3 to read a newick tree file, and converts this to a Scoary-readable nested list
"""
try:
myTree = Tree(filepath)
except NewickError as e:
sys.exit("Corrupted or non-existing custom tree file? %s" % e)
myTree.resolve_polytomy(recursive=True)
myTreeList, members = RecTree2List(myTree,Members=None)
return myTreeList, members示例2: __init__
# 需要导入模块: from ete3 import Tree [as 别名]
# 或者: from ete3.Tree import resolve_polytomy [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.........