本文整理汇总了Python中skbio.tree.TreeNode.append方法的典型用法代码示例。如果您正苦于以下问题：Python TreeNode.append方法的具体用法？Python TreeNode.append怎么用？Python TreeNode.append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skbio.tree.TreeNode的用法示例。
在下文中一共展示了TreeNode.append方法的1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: unifrac
# 需要导入模块: from skbio.tree import TreeNode [as 别名]
# 或者: from skbio.tree.TreeNode import append [as 别名]
def unifrac(self, weighted=True, rank="auto"):
"""A beta diversity metric that takes into account the relative relatedness of community
members. Weighted UniFrac looks at abundances, unweighted UniFrac looks at presence.
Parameters
----------
weighted : `bool`
Calculate the weighted (True) or unweighted (False) distance metric.
rank : {'auto', 'kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species'}, optional
Analysis will be restricted to abundances of taxa at the specified level.
Returns
-------
skbio.stats.distance.DistanceMatrix, a distance matrix.
"""
# needs read counts, not relative abundances
if self._guess_normalized():
raise OneCodexException("UniFrac requires unnormalized read counts.")
df = self.to_df(rank=rank, normalize=False)
counts = []
for c_id in df.index:
counts.append(df.loc[c_id].tolist())
tax_ids = df.keys().tolist()
tree = self.tree_build()
tree = self.tree_prune_rank(tree, rank=df.ocx_rank)
# there's a bug (?) in skbio where it expects the root to only have
# one child, so we do a little faking here
from skbio.tree import TreeNode
new_tree = TreeNode(name="fake root")
new_tree.rank = "no rank"
new_tree.append(tree)
# then finally run the calculation and return
if weighted:
return skbio.diversity.beta_diversity(
"weighted_unifrac", counts, df.index.tolist(), tree=new_tree, otu_ids=tax_ids
)
else:
return skbio.diversity.beta_diversity(
"unweighted_unifrac", counts, df.index.tolist(), tree=new_tree, otu_ids=tax_ids
)