semvec/pypi-packages huggingface dataset:
pylabeledrf 0.3.2
A package to compute the Robinson Fould distance extended to labeled topologies.

# pylabeledrf

pylabeledrf is a package to compute an extension of the Robinson Foulds tree topological distance between labeled topologies, where inner nodes are labeled with speciation or duplication events.

From version 0.2 onwards, pylabeledrf includes two functions:

 - computeLRF(), which computes exactly and efficiently the Labeled Robinson Foulds (LRF).
 - computeELRF(), which is a heuristic to compute the edge-based Labeled Robinson Foulds (ELRF). 

## Citation
If you use our package in your work, please consider citing:

 - For LRF: Samuel Briand, Christophe Dessimoz, Nadia El-Mabrouk, Yannis Nevers, *A linear time solution to the Labeled Robinson-Foulds distance problem*, Systematic Biology 71(6):1391–1403 2022
 - For ELRF: Samuel Briand, Christophe Dessimoz, Nadia El-Mabrouk, Manuel Lafond, Gabriela Lobinska, *Extending the Robinson-Foulds distance to reconciled trees*, APBC 2020 and BMC Genomics, 21(S10) 2020


## Installation

The package requires Python 3 (>=3.6). The easiest way to install is using 
`pip`, to install the <a href="https://pypi.org/project/pylabeledrf/">package from 
PyPI</a>.

```
pip install pylabeledrf
```

## Documentation

Documentation is available <a href="http://dessimozlab.github.io/pylabeledrf/build/html/">here</a>.

## Example

```python
from pylabeledrf.computeLRF import *
import dendropy
taxa = dendropy.TaxonNamespace()

# retrieve the test TP53 reconciled tree (from Ensembl compara 96)
p53 = dendropy.Tree.get_from_url(
    'https://raw.githubusercontent.com/DessimozLab/pylabeledrf/master/test/p53.nhx', 
    'newick', taxon_namespace=taxa)
t1 = parseEnsemblLabels(p53)

# introduce 5 random edits and compute the distance
t2 = mutateLabeledTree(t1, 5)
computeLRF(t1,t2)

# randomise the labels and compute the distance
t3 = randomLabels(t1)
computeLRF(t1,t3)
```


## License

Copyright 2019-2020 Christophe Dessimoz

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
py-jwt-cpp 0.1.0
A Python wrapper around jwt-cpp
Requires Python: >=3.8

# py_jwt_cpp

A Python wrapper around [jwt-cpp](https://github.com/Thalhammer/jwt-cpp).


## Installation

`pip install py_jwt_cpp`

## Usage

```python
import py_jwt_cpp

jwt = py_jwt_cpp.encode(data, private_key)
```

where:

- `data` is a `dict` with string key and string value.
- `RS256` algorithm will be used.


## Development

- To install deployment dependencies, run `poetry install`.
- To test the change locally, run `poetry run python setup.py build_ext --inplace`

## Local Build

```
poetry install
poetry build
```

## CI Build

1. tag the ref with a name staring with `cibuildwheel`
2. Push the tag onto Github. It will trigger `cibuildwheel` workflow building wheels for:

- OS
    - `latest-ubuntu`
    - `macos-13`
    - `macos-latest`
- Python:
    - `ubuntu`: `>= 3.8`
    - `macos`: `>= 3.9`

## Release

1. Tag your branch with a name staring with `cibuildwheel`.
2. Push it onto Github to trigger the wheel build.
3. Build sdist by `poetry build`.
4. Download the wheels from Github and put them into the `dist` folder.
5. Commit the change, tag a version such as `0.1.0`, and push the tag back to Github.

## TODO

- [ ] `decode` function.
- [ ] allowing `headers`
- [ ] allowing algorithms other than `RS256`


## LICENSE

MIT