hstrat

hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations - Published in JOSS (2022)

https://github.com/mmore500/hstrat

Science Score: 93.0%

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Published in Journal of Open Source Software

Scientific Fields

Mathematics Computer Science - 84% confidence

Last synced: 6 months ago · JSON representation

Repository

hstrat enables phylogenetic inference on distributed digital evolution populations

Basic Info

Host: GitHub
Owner: mmore500
License: other
Language: Python
Default Branch: master
Size: 131 MB

Statistics

Stars: 4
Watchers: 2
Forks: 2
Open Issues: 35
Releases: 3

Created almost 4 years ago · Last pushed 8 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Authors

README.md

hstrat wordmark

hstrat enables phylogenetic inference on distributed digital evolution populations

Free software: MIT license
Documentation: https://hstrat.readthedocs.io
Repository: https://github.com/mmore500/hstrat

Install

python3 -m pip install hstrat

A containerized release of hstrat is available via ghcr.io

bash singularity exec docker://ghcr.io/mmore500/hstrat:v1.20.13 python3 -m hstrat --help

Features

hstrat serves to enable robust, efficient extraction of evolutionary history from evolutionary simulations where centralized, direct phylogenetic tracking is not feasible. Namely, in large-scale, decentralized parallel/distributed evolutionary simulations, where agents' evolutionary lineages migrate among many cooperating processors over the course of simulation.

hstrat can

accurately estimate time since MRCA among two or several digital agents, even for uneven branch lengths
reconstruct phylogenetic trees for entire populations of evolving digital agents
serialize genome annotations to/from text and binary formats
provide low-footprint genome annotations (e.g., reasonably as low as 64 bits each)
be directly configured to satisfy memory use limits and/or inference accuracy requirements

hstrat operates just as well in single-processor simulation, but direct phylogenetic tracking using a tool like phylotrackpy should usually be preferred in such cases due to its capability for perfect record-keeping given centralized global simulation observability.

Example Usage

This code briefly demonstrates,

initialization of a population of HereditaryStratigraphicColumn of objects,
generation-to-generation transmission of HereditaryStratigraphicColumn objects with simple synchronous turnover, and then
reconstruction of phylogenetic history from the final population of HereditaryStratigraphicColumn objects.

```python3 from random import choice as rchoice import alifedataphyloinformaticsconvert as apc from hstrat import hstrat; print(f"{hstrat.version=}") # when last ran? from hstrat.auxiliarylib import seedrandom; seedrandom(1) # reproducibility

initialize a small population of hstrat instrumentation

(in full simulations, each column would be attached to an individual genome)

population = [hstrat.HereditaryStratigraphicColumn() for __ in range(5)]

evolve population for 40 generations under drift

for generation in range(40): population = [rchoice(population).CloneDescendant() for _ in population]

reconstruct estimate of phylogenetic history

alifestddf = hstrat.buildtree(population, versionpin=hstrat.version) treeascii = apc.RosettaTree(alifestddf).asdendropy.asasciiplot(width=20) print(tree_ascii) ```

hstrat.__version__='1.8.8' /--- 1 /---+ /--+ \--- 3 | | /---+ \------- 2 | | +--+ \---------- 0 | \-------------- 4

In actual usage, each hstrat column would be bundled with underlying genetic material of interest in the simulation --- entire genomes or, in systems with sexual recombination, individual genes. The hstrat columns are designed to operate as a neutral genetic annotation, enhancing observability of the simulation but not affecting its outcome.

How it Works

In order to enable phylogenetic inference over fully-distributed evolutionary simulation, hereditary stratigraphy adopts a paradigm akin to phylogenetic work in natural history/biology. In these fields, phylogenetic history is inferred through comparisons among genetic material of extant organisms, with --- in broad terms --- phylogenetic relatedness established through the extent of genetic similarity between organisms. Phylogenetic tracking through hstrat, similarly, is achieved through analysis of similarity/dissimilarity among genetic material sampled over populations of interest.

Rather than random mutation as with natural genetic material, however, genetic material used by hstrat is structured through hereditary stratigraphy. This methodology, described fully in our documentation, provides strong guarantees on phylogenetic inferential power, minimizes memory footprint, and allows efficient reconstruction procedures.

See here for more detail on underlying hereditary stratigraphy methodology.

Getting Started

Refer to our documentation for a quickstart guide and an annotated end-to-end usage example.

The examples/ folder provides extensive usage examples, including

incorporation of hstrat annotations into a custom genome class,
automatic stratum retention policy parameterization,
pairwise and population-level phylogenetic inference, and
phylogenetic tree reconstruction.

Interested users can find an explanation of how hereditary stratigraphy methodology implemented by hstrat works "under the hood," information on project-specific hstrat configuration, and full API listing for the hstrat package in the documentation.

Citing

If hstrat software or hereditary stratigraphy methodology contributes to a scholarly work, please cite it according to references provided here. We would love to list your project using hstrat in our documentation, see more here.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

hcat

Owner

Name: Matthew Andres Moreno
Login: mmore500
Kind: user
Location: East Lansing, MI
Company: @devosoft

Website: mmore500.github.io
Twitter: MorenoMathewA
Repositories: 43
Profile: https://github.com/mmore500

doctoral student, Computer Science and Engineering at Michigan State University

JOSS Publication

hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations

Published

December 16, 2022

DOI

10.21105/joss.04866

Volume 7, Issue 80, Page 4866

Authors

Matthew Andres Moreno

Michigan State University

Emily Dolson

Michigan State University

Charles Ofria

Michigan State University

Editor

Øystein Sørensen

GitHub Events

Total

Create event: 134
Release event: 1
Issues event: 43
Delete event: 88
Issue comment event: 111
Push event: 1,039
Pull request review event: 166
Pull request review comment event: 190
Pull request event: 148
Fork event: 1

Last Year

Create event: 134
Release event: 1
Issues event: 43
Delete event: 88
Issue comment event: 111
Push event: 1,039
Pull request review event: 166
Pull request review comment event: 190
Pull request event: 148
Fork event: 1

Committers

Last synced: 7 months ago

All Time

Total Commits: 3,168
Total Committers: 8
Avg Commits per committer: 396.0
Development Distribution Score (DDS): 0.139

Past Year

Commits: 1,316
Committers: 4
Avg Commits per committer: 329.0
Development Distribution Score (DDS): 0.237

Top Committers

Name	Email	Commits
Matthew Andres Moreno	m**g@g**m	2,729
vivaansinghvi07	s**n@g**m	169
vivaansinghvi07	v**8@g**m	142
Santiago Rodriguez Papa	r****0	113
Connor Yang	c**5@g**m	9
Juan Julián Merelo Guervós	j**o@g**m	4
joeymsu	j**4@g**m	1
Emily Dolson	e**n@g**m	1

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Last synced: 6 months ago

All Time

Total issues: 86
Total pull requests: 147
Average time to close issues: 2 months
Average time to close pull requests: 6 days
Total issue authors: 6
Total pull request authors: 5
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Average comments per pull request: 1.13
Merged pull requests: 133
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 17
Pull requests: 112
Average time to close issues: 26 days
Average time to close pull requests: 3 days
Issue authors: 2
Pull request authors: 2
Average comments per issue: 0.35
Average comments per pull request: 1.1
Merged pull requests: 99
Bot issues: 0
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mmore500 (69)
osorensen (11)
GeekLogan (4)
JJ (3)
kgd-al (2)
vivaansinghvi07 (2)
joeymsu (1)

Pull Request Authors

mmore500 (161)
vivaansinghvi07 (24)
JJ (4)
rodsan0 (2)
emilydolson (1)

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enhancement (1)

Packages

Total packages: 1
Total downloads:
- pypi 6,419 last-month

Total dependent packages: 1
Total dependent repositories: 1
Total versions: 84
Total maintainers: 1

pypi.org: hstrat

hstrat enables phylogenetic inference on distributed digital evolution populations

Documentation: https://hstrat.readthedocs.io/
License: MIT license
Latest release: 1.20.13
published 9 months ago

Versions: 84
Dependent Packages: 1
Dependent Repositories: 1
Downloads: 6,419 Last month

Rankings

Dependent packages count: 10.1%

Downloads: 10.8%

Average: 14.2%

Dependent repos count: 21.6%

Maintainers (1)

mmore500

Last synced: 6 months ago

Dependencies

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webencodings ==0.5.1 development
wheel ==0.33.6 development
zipp ==3.7.0 development

hstrat

Science Score: 93.0%

Scientific Fields

Repository

Basic Info

Statistics

Metadata Files

README.md

Install

Features

Example Usage

initialize a small population of hstrat instrumentation

(in full simulations, each column would be attached to an individual genome)

evolve population for 40 generations under drift

reconstruct estimate of phylogenetic history

How it Works

Getting Started

Citing

Credits

hcat

Owner

JOSS Publication

hstrat: a Python Package for phylogenetic inference on distributed digital evolution populations

Authors

Editor

Tags

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

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All Time

Past Year

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Packages

pypi.org: hstrat

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Maintainers (1)

Dependencies