Tree, Node, and Trees classes

Trees in p4 are described by a list of nodes, one of which is the root. All trees have a root, but this has nothing to do with the number of children that the root has.


In phylogenetics when we talk about rooted trees we usually mean that we think that one node, the root, is the ancestor of all the other nodes; however the root in p4 carries no such implication. The root in p4 is just a node that is a starting place for the rest of the nodes.

Can the root ever be a leaf? — Not usually, but it could be.

Nodes are the vertexes of the tree, where the branches meet. (My dictionary tells me that both ‘vertices’ and ‘vertexes’ are correct plurals of vertex.) P4 describes relationships among nodes with 3 pointers- we have a pointer to the parent node, to a child node (which I imagine to be always on the left, so it is the leftChild), and to a sibling node (which I imagine to always be on the right). Any of these pointers might be None; for example the root node parent is None, the leftChild of any tip node is None, and the sibling of the rightmost child of a node is None

#    leftChild
#             \\
#              \\
#               Node --- sibling
#               |
#               |
#               parent

All the nodes except the root in a p4 Tree also have NodeBranch attributes, which embody the branches leading to the node from the parent. It has information about for example the length of the branch, and so for a node n we have

The usual way to get around trees is by recursive traversal, and p4 provides a few avenues for that, both as Node methods and as Tree methods. See Knuth’s The Art of Computer Programming for a good explanation of preorder and postorder tree traversal. (Using a previous version of p4 that used recursion to read trees, I was given a large tree to read, and was surprised to find that it caused p4 to bump into the recursion limit of Python. That limit can be re-set, but instead I re-wrote tree reading in p4 in a stack-based approach, rather than a recursion-based approach.)

Sometimes it is natural to deal with trees by the bunch, and so we have the Trees class. It is this class that is able to write Nexus tree files that use translations, and it is this class that interfaces with consel to compare several trees.

This week, p4 will read trees in Nexus format, or in Phylip or raw Newick format. The Nexus format is described in the 1997 paper in Systematic Biology (MadSwofMad97). I am not sure where the Phylip/Newick format is described. Older Phylip tree files began with a number, indicating the number of trees in the file, but it appears that newer Phylip tree files do not have or need that number. When did it change?

P4 reads both Nexus and Phylip trees with Nexus-like rules, so if you have punctuation or spaces in your taxon names then you need to put the name in single quotes. P4 fails to read multi-word Phylip taxon names, so for example p4 will gag on the tree:

((A, B), (C, Homo sapiens));

because one of the taxa has a space in it. That sort of taxon name is perfectly acceptable if

  1. you put it inside single quotes, or

  2. you True the flag var.newick_allowSpacesInNames

The tree files output by Tree-Puzzle have modifications to the Phylip/Newick format, where [comments bounded by square brackets] precede the tree description. P4 can handle those.

Branch lengths go after colons in the tree description, as usual. You can label internal nodes, including the root, immediately after unparens, as in the following:

p4> read("((A, B)the_AB_clade:0.2, (C, 'Homo sapiens')98:0.3)theRoot;")
p4> t=var.trees[0]
p4> t.draw()
        |        +------3:B
        |          +------5:C
                   +------6:Homo sapiens


P4 separates Node objects (vertices) and NodeBranch objects (edges).

It is the branch that holds the length of the branch. Branches and branch lengths can be accessed by:

After doing a consensus tree, it is the branch that holds the support values. That makes the branch supports immune to re-rooting. But it also means that in order to see them and save them in newick or Nexus format you will need to convert them to node.names. This has the advantage that you can format the conversion to your liking — you can make it percent, or as a a value from zero to 1.0 with however many significant figures you wish:

tp = TreePartitions('myTrees.nex')
t = tp.consensus()

# list the support values
for n in t.iterInternalsNoRoot():
    print "node %3i branch support is %f" % (n.nodeNum,

# perhaps collapse nodes where the support is too small for you
toCollapse = [n for n in t.iterInternalsNoRoot() if < 0.7]
for n in toCollapse:

# optionally re-root it

# Make the internal node names the percent support values
for n in t.iterInternalsNoRoot(): = '%.0f' % (100. *

# Drawings get a bit messy with both node nums and support

The Nexus/Newick format can hold node names and branch lengths, but it is awkward and non-standard to make it hold more information (eg split support, branch colour, model usage information, rooting information, and so on). Perhaps an XML-based format would be useful here, but these are early days for XML in phylogenetics, and XML files can get very big very quickly. Update: NeXML looks interesting (files are still big, tho).

As an alternative, you can store information-rich trees by pickling them, a standard Python thing to do to archive objects to files. Trees, nodes, and models may have pointers to c-structures; these should not be archived, nor should you archive data objects along with the trees. To facilitate pickling trees you can use the Tree method tPickle(), which strips out the c-pointers and data before pickling the tree. Trees so pickled (with the p4_tPickle suffix) are autorecognized by p4 with the read() function or at the command line.

Tree pictures and drawings

You can make a text drawing of trees to the screen with the p4.tree.Tree.draw() method. It provides some control over the presentation, for example the width, whether node numbers are displayed, and so on.

You can make a basic encapsulated postscript drawing of a tree with the p4.tree.Tree.eps() method, or an svg drawing with the p4.tree.Tree.svg() method. While they are nice vector graphics, these diagrams are fairly basic, and if you want a tree drawing program with more ability then you might consider using the Gram package (which uses p4, but Gram is not included in p4). Gram is very flexible, and uses LaTeX for typesetting.

There is a GUI tree viewer in p4, using the Tree method, usable with interactive p4.

Big trees, that are really too big to print, are a special problem for both paper and screen. If you have a tree with 1000 taxa, and each taxon is only 1 mm high (too small to read) then the drawing will be 1 m on the page or the screen. If you make the text big enough to read, say 1cm, then it will be 10 m high! One solution, that seems to work for trees up to about 5K or so taxa, uses the Tree method p4.tree.Tree.btv() (Big Tree Viewer). This requires a python with Tkinter installed. This viewer is in 2 parts, where in the right panel you can see the whole tree in outline with a viewport, and in the right panel you get to see what is in that viewport.

See Drawing trees

Topology distance

You can compare tree topologies such that the root of the tree and any rotations of clades do not matter. For example, these 2 trees have identical topologies (by this definition), even though they do not look like each other:

|        +--------3:B
|        +--------4:C
|        +--------6:D

|                  +--------5:C
|        +---------4
+--------3         +--------6:B

With the p4.tree.Tree.topologyDistance() method you can compare topologies without taking branch length differences into account, or you can use metrics that do take branch lengths into account. The default metric is the symmetric difference, aka the unweighted Robinson Foulds distance, which ignores branch lengths. The p4.tree.Tree.topologyDistance() method also provides the weighted Robinson-Foulds distance, and the branch length distance, which take branch lengths into account. These are described in Felsenstein’s book. To do several trees at once, you can use the p4.trees.Trees.topologyDistanceMatrix() method.

Patristic distances

This is just the length along the tree path between all pairs of nodes. The method p4.tree.Tree.patristicDistanceMatrix() returns a p4.distancematrix.DistanceMatrix object, which you probably want to write to a file. For example, you might say:

t = var.trees[0]
dm = t.patristicDistanceMatrix()