Quickstart

Get started with PhyloZoo in minutes! This guide walks you through installation, creating networks, and working with related data types, at every step referring to the corresponding section in the manual.

Getting Started

Install PhyloZoo using pip. For most users (including visualization), use:

pip install phylozoo[viz]

For a minimal install without plotting, use pip install phylozoo. See the Installation Guide for full details.

Networks

Let’s create a simple phylogenetic network:

from phylozoo import DirectedPhyNetwork

# Create a simple tree network
network = DirectedPhyNetwork(
    edges=[("root", "A"), ("root", "B"), ("root", "C")],
    nodes=[
        ("A", {"label": "A"}),
        ("B", {"label": "B"}),
        ("C", {"label": "C"})
    ]
)

print(f"Network has {network.number_of_nodes()} nodes")
print(f"Leaves: {network.leaves}")
print(f"Is tree: {network.is_tree()}")

This creates a simple tree with three leaves.

Now let’s create a network with a hybrid node. Hybrid edges use a gamma value (must sum to 1.0 for edges into the same hybrid):

from phylozoo.core.network.dnetwork.classifications import level

# Create a network with hybridization
# Edge attributes (e.g. gamma) are passed as dicts with "u" and "v" keys
hybrid_net = DirectedPhyNetwork(
    edges=[
        ("root", "u1"), ("root", "u2"),          # Root to tree nodes
        {"u": "u1", "v": "h", "gamma": 0.6},     # Hybrid edge
        {"u": "u2", "v": "h", "gamma": 0.4},     # Hybrid edge (must sum to 1.0)
        ("h", "leaf_a"),                          # Hybrid to leaf
        ("u1", "leaf_b"),                         # Extra leaf on u1
        ("u2", "leaf_c"),                         # Extra leaf on u2
    ],
    nodes=[
        ("leaf_a", {"label": "A"}),
        ("leaf_b", {"label": "B"}),
        ("leaf_c", {"label": "C"}),
    ]
)

print(f"Network level: {level(hybrid_net)}")

PhyloZoo also supports semi-directed networks, which allow undirected tree edges for modeling root uncertainty. See Semi-Directed Networks for details.

Load and Save

Networks can be saved and loaded in standard formats:

# Save network to file (eNewick format)
network.save("my_network.enewick")

# Load network from file
loaded_net = DirectedPhyNetwork.load("my_network.enewick")

Supported formats include eNewick, NEXUS, and DOT. See the I/O overview for all formats.

Visualization

Plot networks with the built-in visualization module:

import matplotlib.pyplot as plt
from phylozoo.viz import plot

# Plot network (opens interactive window)
plot(network, show=True)

# Save to file via matplotlib
fig, ax = plt.subplots()
plot(network, ax=ax)
fig.savefig("network.png")

For layout options and styling, see Visualization.

Related Data Types

PhyloZoo provides integrated support for related data types used in phylogenetic analysis.

Distance Matrices

Create distance matrices from NumPy arrays or compute them from sequences:

import numpy as np
from phylozoo.core.distance import DistanceMatrix

# Create from array
matrix = np.array([[0, 1, 2], [1, 0, 1], [2, 1, 0]])
dm = DistanceMatrix(matrix, labels=["A", "B", "C"])

Sequences

Create multiple sequence alignments (MSAs) and compute Hamming distances:

from phylozoo import MSA
from phylozoo.core.sequence import hamming_distances

sequences = {"taxon1": "ACGTACGT", "taxon2": "TGCAACGT", "taxon3": "ACGTTTAA"}
msa = MSA(sequences)
dm = hamming_distances(msa)

See Distance Matrices and Sequences for more.

Splits and Quartets

For split-based and quartet-based analyses:

from phylozoo.core.split import Split, SplitSystem
from phylozoo.core.quartet import Quartet, QuartetProfile

# Create a split (bipartition of taxa)
split = Split({"A", "B"}, {"C", "D"})
system = SplitSystem([split])

# Create a quartet
quartet = Quartet(split)
profile = QuartetProfile([quartet])

Splits represent bipartitions of taxa; quartets are four-taxon unrooted trees. See Splits and Quartets for details.

Quick Links

• Installation Guide — Detailed installation and requirements

• Introduction — PhyloZoo’s design and features

• Networks — Directed and semi-directed networks

• Distance Matrices — Pairwise distances

• Sequences — Multiple sequence alignments

• I/O — File formats and operations

• Visualization — Plotting and styling

Next Steps

Ready to dive deeper? Check out:

1. The Manual for comprehensive guides on all modules

2. The API Reference for complete function signatures and examples