| Name | hstrat JSON |
| Version |
1.11.7
JSON |
| download |
| home_page | None |
| Summary | hstrat enables phylogenetic inference on distributed digital evolution populations |
| upload_time | 2024-03-21 22:28:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT license |
| keywords |
hstrat
|
| VCS |
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| requirements |
No requirements were recorded.
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[
](https://pypi.python.org/pypi/hstrat)
[
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[
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[
](https://github.com/mmore500/hstrat/actions)
[
](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`
## 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
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"description": "\n\n[\n\n](https://pypi.python.org/pypi/hstrat)\n[\n\n](https://codecov.io/gh/mmore500/hstrat)\n[\n\n](https://www.codacy.com/gh/mmore500/hstrat/dashboard)\n[\n\n](https://github.com/mmore500/hstrat/actions)\n[\n\n](https://hstrat.readthedocs.io/en/latest/?badge=latest)\n[\n](https://github.com/mmore500/hstrat)\n[\n\n](https://zenodo.org/badge/latestdoi/464531144)\n[](https://doi.org/10.21105/joss.04866)\n\n_hstrat_ enables phylogenetic inference on distributed digital evolution populations\n\n- Free software: MIT license\n- Documentation: <https://hstrat.readthedocs.io>\n- Repository: <https://github.com/mmore500/hstrat>\n\n## Install\n\n`python3 -m pip install hstrat`\n\n## Features\n\n_hstrat_ serves to enable **robust, efficient extraction of evolutionary history** from evolutionary simulations where centralized, direct phylogenetic tracking is not feasible.\nNamely, in large-scale, **decentralized parallel/distributed evolutionary simulations**, where agents' evolutionary lineages migrate among many cooperating processors over the course of simulation.\n\n_hstrat_ can\n\n- accurately estimate **time since MRCA** among two or several digital agents, even for uneven branch lengths\n- **reconstruct phylogenetic trees** for entire populations of evolving digital agents\n- **serialize genome annotations** to/from text and binary formats\n- provide **low-footprint** genome annotations (e.g., reasonably as low as **64 bits** each)\n- be directly configured to satisfy **memory use limits** and/or **inference accuracy requirements**\n\n_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._\n\n## Example Usage\n\nThis code briefly demonstrates,\n\n1. initialization of a population of `HereditaryStratigraphicColumn` of objects,\n2. generation-to-generation transmission of `HereditaryStratigraphicColumn` objects with simple synchronous turnover, and then\n3. reconstruction of phylogenetic history from the final population of `HereditaryStratigraphicColumn` objects.\n\n```python3\nfrom random import choice as rchoice\nimport alifedata_phyloinformatics_convert as apc\nfrom hstrat import hstrat; 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