本文整理汇总了Python中skbio.tree.TreeNode.rank方法的典型用法代码示例。如果您正苦于以下问题：Python TreeNode.rank方法的具体用法？Python TreeNode.rank怎么用？Python TreeNode.rank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.rank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: unifrac
# 需要导入模块: from skbio.tree import TreeNode [as 别名]
# 或者: from skbio.tree.TreeNode import rank [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
)示例2: tree_build
# 需要导入模块: from skbio.tree import TreeNode [as 别名]
# 或者: from skbio.tree.TreeNode import rank [as 别名]
def tree_build(self):
"""Build a tree from the taxonomy data present in this `ClassificationsDataFrame` or
`SampleCollection`.
Returns
-------
`skbio.tree.TreeNode`, the root node of a tree that contains all the taxa in the current
analysis and their parents leading back to the root node.
"""
from skbio.tree import TreeNode
# build all the nodes
nodes = {}
for tax_id in self.taxonomy.index:
node = TreeNode(name=tax_id, length=1)
node.tax_name = self.taxonomy["name"][tax_id]
node.rank = self.taxonomy["rank"][tax_id]
node.parent_tax_id = self.taxonomy["parent_tax_id"][tax_id]
nodes[tax_id] = node
# generate all the links
for tax_id in self.taxonomy.index:
try:
parent = nodes[nodes[tax_id].parent_tax_id]
except KeyError:
if tax_id != "1":
warnings.warn(
"tax_id={} has parent_tax_id={} which is not in tree"
"".format(tax_id, nodes[tax_id].parent_tax_id)
)
continue
parent.append(nodes[tax_id])
return nodes["1"]