bijux-phylogenetics 0.1.1

Comprehensive phylogenetics runtime for tree analysis, native inference, comparative models, parsimony, and evidence-linked reporting.

bijux-phylogenetics

Canonical runtime package for the bijux-phylogenetics repository.

bijux-phylogenetics is the full public Python and CLI surface for tree validation, alignment handling, native likelihood and inference, comparative analysis, ancestral reconstruction, explicit parsimony, and evidence-linked report generation.

Choose this package when you want the full runtime contract rather than the shorter compatibility alias.

The full handbook includes diagrams, architecture maps, and study navigation on the documentation site. This PyPI page stays text-first so it renders cleanly in the package index.

Install

bijux-phylogenetics supports Python 3.11 and newer.

python3.11 -m pip install bijux-phylogenetics
bijux-phylogenetics --help

The installed runtime also ships packaged example inputs:

from pathlib import Path

from bijux_phylogenetics.core import copy_example_inputs

copy_example_inputs(Path("artifacts/example-inputs"))

What This Package Covers

Capability family	Examples
Trees	validation, rootedness review, support normalization, clade extraction, MRCA lookup, comparison, rendering, tree-set inspection
Alignments	FASTA validation, trimming, coding checks, translation, partition-aware input handling
Native likelihood	nucleotide, protein, codon, and discrete Mk finite-state likelihood foundations
Native inference	native maximum-likelihood tree inference results and native Bayesian public inference entry points
Comparative analysis	PGLS, signal, Brownian and OU modeling, discrete-state evolution, comparative diagnostics
Ancestral analysis	continuous and discrete reconstruction, uncertainty ledgers, transition review, report artifacts
Parsimony	Fitch, Wagner, Sankoff, Dollo, Camin-Sokal, ACCTRAN, DELTRAN, bootstrap, jackknife, NNI, SPR, ratchet
Reports and artifacts	reviewer-facing TSV, JSON, HTML, manifest, benchmark, and evidence-linked bundle surfaces

Why This Package Is More Than A Thin Wrapper

• it owns the PhyloTree runtime and the topology, support, and tree-distance semantics that sit underneath multiple workflows
• it exposes typed Python workflow results with stable artifact writers instead of only printing shell output
• it includes native maximum-likelihood tree inference results and native Bayesian public inference entry points as documented public surfaces
• it keeps wrapper-backed orchestration honest by separating native ownership from external-engine execution

Trust And Evidence

The runtime surface is broader than the closed evidence surface, and the evidence surface is narrower than the full runtime surface. That distinction is deliberate:

• a documented runtime surface is usable
• a native surface is locally implemented rather than only wrapped
• an evidence-book study explains how much trust a scientific claim currently carries

Use the package for execution. Use the docs and evidence-book when you need quality boundaries, parity review, or study-specific provenance.

Python Workflow Surface

The stable notebook-and-pipeline surface lives under bijux_phylogenetics.api.

It exposes typed workflow results for:

• FASTA validation
• multiple-sequence alignment
• alignment trimming
• full FASTA-to-tree execution
• maximum-likelihood tree inference
• branch-support estimation
• topology comparison
• PGLS comparative modeling
• discrete ancestral reconstruction
• reviewer-facing report generation
• config-driven workflow execution

from pathlib import Path

from bijux_phylogenetics.api import (
    render_report_workflow,
    run_comparative_model_workflow,
    run_sequence_to_tree_workflow,
)

workflow = run_sequence_to_tree_workflow(
    Path("dataset/sequences.fasta"),
    out_dir=Path("artifacts/sequence-to-tree"),
    sequence_type="dna",
)

comparative = run_comparative_model_workflow(
    Path("dataset/tree.nwk"),
    Path("dataset/traits.tsv"),
    response="response",
    predictors=["predictor_one"],
    lambda_value=1.0,
)

report = render_report_workflow(
    tree_path=workflow.output_paths["tree"],
    alignment_path=workflow.output_paths["trimmed_alignment"],
    traits_path=Path("dataset/traits.tsv"),
    metadata_path=Path("dataset/metadata.tsv"),
    out_path=Path("artifacts/sequence-to-tree/report.html"),
)

workflow.write_json(Path("artifacts/sequence-to-tree/workflow.json"))
workflow.write_tsv(Path("artifacts/sequence-to-tree/workflow.tsv"))
comparative.write_tsv(Path("artifacts/comparative-model.tsv"))

Choose This Package When

• you want the canonical package name for notebooks, papers, or downstream integration
• you want both CLI workflows and typed Python workflow results
• you want the full public runtime contract rather than the shorter alias surface

Read Next

• Runtime guide
• Surface selection guide
• Artifact consumption guide
• Native Inference And Benchmark Surfaces
• Native maximum-likelihood workflows
• Native Bayesian workflows
• Native benchmark review
• Evidence-book guide