| Name | hstrat JSON |
| Version |
1.18.0
JSON |
| download |
| home_page | https://github.com/mmore500/hstrat |
| Summary | hstrat enables phylogenetic inference on distributed digital evolution populations |
| upload_time | 2025-02-02 03:36:55 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT license |
| keywords |
hstrat
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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| coveralls test coverage |
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|
[
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[
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[
](https://hstrat.readthedocs.io/en/latest/?badge=latest)
[
](https://github.com/mmore500/hstrat)
[
](https://zenodo.org/badge/latestdoi/464531144)
[](https://doi.org/10.21105/joss.04866)
_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:v1.18.0 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](https://github.com/emilydolson/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,
1. initialization of a population of `HereditaryStratigraphicColumn` of objects,
2. generation-to-generation transmission of `HereditaryStratigraphicColumn` objects with simple synchronous turnover, and then
3. reconstruction of phylogenetic history from the final population of `HereditaryStratigraphicColumn` objects.
```python3
from random import choice as rchoice
import alifedata_phyloinformatics_convert as apc
from hstrat import hstrat; print(f"{hstrat.__version__=}") # when last ran?
from hstrat._auxiliary_lib import seed_random; seed_random(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
alifestd_df = hstrat.build_tree(population, version_pin=hstrat.__version__)
tree_ascii = apc.RosettaTree(alifestd_df).as_dendropy.as_ascii_plot(width=20)
print(tree_ascii)
```
```
hstrat.__version__='1.8.8'
/--- 1
/---+
/--+ \--- 3
| |
/---+ \------- 2
| |
+--+ \---------- 0
|
\-------------- 4
```
In [actual usage](https://hstrat.readthedocs.io/en/latest/demo-ping.html), 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](https://hstrat.readthedocs.io/en/latest/mechanism.html) for more detail on underlying hereditary stratigraphy methodology.
## Getting Started
Refer to our documentation for a [quickstart guide](https://hstrat.readthedocs.io/en/latest/quickstart.html) and an [annotated end-to-end usage example](https://hstrat.readthedocs.io/en/latest/demo-ping.html).
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](https://hstrat.readthedocs.io/).
## Citing
If _hstrat_ software or hereditary stratigraphy methodology contributes to a scholarly work, please cite it according to references provided [here](https://hstrat.readthedocs.io/en/latest/citing.html).
We would love to list your project using _hstrat_ in our documentation, see more [here](https://hstrat.readthedocs.io/en/latest/projects.html).
## Credits
This package was created with Cookiecutter and the `audreyr/cookiecutter-pypackage` project template.
## hcat
Raw data
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print(f\"{hstrat.__version__=}\") # when last ran?\nfrom hstrat._auxiliary_lib import seed_random; seed_random(1) # reproducibility\n\n# initialize a small population of hstrat instrumentation\n# (in full simulations, each column would be attached to an individual genome)\npopulation = [hstrat.HereditaryStratigraphicColumn() for __ in range(5)]\n\n# evolve population for 40 generations under drift\nfor _generation in range(40):\n population = [rchoice(population).CloneDescendant() for __ in population]\n\n# reconstruct estimate of phylogenetic history\nalifestd_df = hstrat.build_tree(population, version_pin=hstrat.__version__)\ntree_ascii = apc.RosettaTree(alifestd_df).as_dendropy.as_ascii_plot(width=20)\nprint(tree_ascii)\n```\n\n```\nhstrat.__version__='1.8.8'\n /--- 1\n /---+\n /--+ \\--- 3\n | |\n /---+ \\------- 2\n | |\n+--+ \\---------- 0\n |\n \\-------------- 4\n```\n\nIn [actual usage](https://hstrat.readthedocs.io/en/latest/demo-ping.html), 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.\nThe _hstrat_ columns are designed to operate as a neutral genetic annotation, enhancing observability of the simulation but not affecting its outcome.\n\n## How it Works\n\nIn order to enable phylogenetic inference over fully-distributed evolutionary simulation, hereditary stratigraphy adopts a paradigm akin to phylogenetic work in natural history/biology.\nIn 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.\nPhylogenetic tracking through _hstrat_, similarly, is achieved through analysis of similarity/dissimilarity among genetic material sampled over populations of interest.\n\nRather than random mutation as with natural genetic material, however, genetic material used by _hstrat_ is structured through _hereditary stratigraphy_.\nThis methodology, described fully in our documentation, provides strong guarantees on phylogenetic inferential power, minimizes memory footprint, and allows efficient reconstruction procedures.\n\nSee [here](https://hstrat.readthedocs.io/en/latest/mechanism.html) for more detail on underlying hereditary stratigraphy methodology.\n\n## Getting Started\n\nRefer to our documentation for a [quickstart guide](https://hstrat.readthedocs.io/en/latest/quickstart.html) and an [annotated end-to-end usage example](https://hstrat.readthedocs.io/en/latest/demo-ping.html).\n\nThe `examples/` folder provides extensive usage examples, including\n\n- incorporation of hstrat annotations into a custom genome class,\n- automatic stratum retention policy parameterization,\n- pairwise and population-level phylogenetic inference, and\n- phylogenetic tree reconstruction.\n\nInterested 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](https://hstrat.readthedocs.io/).\n\n## Citing\n\nIf _hstrat_ software or hereditary stratigraphy methodology contributes to a scholarly work, please cite it according to references provided [here](https://hstrat.readthedocs.io/en/latest/citing.html).\nWe would love to list your project using _hstrat_ in our documentation, see more [here](https://hstrat.readthedocs.io/en/latest/projects.html).\n\n## Credits\n\nThis package was created with Cookiecutter and the `audreyr/cookiecutter-pypackage` project template.\n\n## hcat\n\n\n",
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