phylogeny.models package

Submodules

phylogeny.models.cfn module

Module implementing the Cavender-Farris-Neymann stochastic tree model.

The model is based on the description by the book:

Computational Phylogenetics.

An introduction to designing methods for phylogeny estimation. – by Tandy Warnow

The CFN model in words from the book:

“The Cavender-Farris-Neyman (CFN) model describes how a trait (which

can either be present or absent) evolves down a tree (Cavender, 1978; Farris, 1973; Neyman, 1971).

…a CFN model has a rooted binary tree T (i.e., a tree in which every node is either a leaf or has two children) with numerical parameters that describe the evolutionary process of a trait. Under the CFN model, the probability of absence (0) or presence (1) is the same at the root, but the state can change on the edges (also called branches) of the tree. Thus, we associate a parameter p(e) to every edge e in the tree, where p(e) denotes the probability that the endpoints of the edge e have different states. In other words, p(e) is the probability of changing state (from 1 to 0, or vice-versa) … we require 0 < p(e) < 0.5.

Under the CFN model, a trait (which is also called a “character”) evolves down the tree under this random process, and hence attains a state at every node in the tree, and in particular at the leaves of the tree. You could write a computer program for a CFN model tree that would generate 0s and 1s at the leaves of the tree; thus, CFN is a generative model.

Each time you ran the program you would get another pattern of 0s and 1s at the leaves of the tree. Thus, if you repeated the process 10 times, each time independently generating a new trait down the tree, you would produce sequences of length 10 at the leaves of the tree.”

– from the book.

class phylogeny.models.cfn.CFN_Tree(*args, leaves=None, **kwargs)

Bases: phylogeny.core.tree.Tree

A Cavender-Farris-Neymann stochastic tree model.

Usage:

# Create a new empty tree.
>>> cfn = CFN_Tree()

# Branch randomly until you have 5 leaves.
>>> cfn.populate(5)
>>> print(cfn)

     Node: 0, node probability: 0
     Node: 1, node probability: 0.2192846999188683
     Node: 2, node probability: 0.06144844447962278
     Node: 3, node probability: 0.14342505932071808
     Node: 4, node probability: 0.1370117188846906
     Node: 5, node probability: 0.44060196062669255
     Node: 6, node probability: 0.009555798385131098
     Node: 7, node probability: 0.48946332859444935
     Node: 8, node probability: 0.39505550345399304

           /-3
        /1|
       |  |   /-7
       |   |
     -0|      \-8
       |
       |   /-5
        |
           \-6

# Evolve 5 traits through the tree
>>> sequences = cfn.evolve_traits([1,1,1,1,1])
>>> sequences

     { 3: [0, 0, 0, 1, 1], 
       7: [1, 0, 1, 0, 1], 
       8: [1, 0, 0, 1, 1], 
       5: [1, 1, 1, 1, 1], 
       6: [1, 1, 1, 1, 1] }

static cfn_metric(probability)

Return the length of an edge with the given probability.

evolve_traits(traits)

Evolve the binary traits through the tree.

populate(n)

Populate the tree with nodes and change probabilities.

trait_traverse(path_from_root, init)

phylogeny.models.cfn.random_test(probability)

Return True with a given probability, otherwise return False.

phylogeny.models.cfn.swap(binary)

Module contents