semvec/pypi-packages huggingface dataset:
partition-sknetwork 0.0.3
Ensemble Clustering for Graphs (ECG) and Graph aware measurse (GAM) for sknetwork.
Requires Python: >=3.10

# Graph Partition and Measures

Python code implementing 11 graph-aware measures (gam) for comparing graph partitions as well as a stable ensemble-based graph partition algorithm (ecg). This verion works with the sknetwork package. Versions for networkx and igraph are also available: partition-networkx, partition-igraph.

## Graph aware measures (gam)

The measures are respectively:
* 'rand': the RAND index
* 'jaccard': the Jaccard index
* 'mn': pairwise similarity normalized with the mean function
* 'gmn': pairwise similarity normalized with the geometric mean function
* 'min': pairwise similarity normalized with the minimum function
* 'max': pairwise similarity normalized with the maximum function

Each measure can be adjusted (recommended) or not, except for 'jaccard'.
Details can be found in: 

Valérie Poulin and François Théberge, "Comparing Graph Clusterings: Set Partition Measures vs. Graph-aware Measures",
    IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 6 (2021) https://doi.org/10.1109/TPAMI.2020.3009862

## Ensemble clustering for graphs (ecg)

This is a good, stable graph partitioning algorithm. Details for ecg can be found in: 

Valérie Poulin and François Théberge, "Ensemble clustering for graphs: comparisons and applications", Appl Netw Sci 4, 51 (2019). 
    https://doi.org/10.1007/s41109-019-0162-z

# Example

We need to import the supplied Python file partition_sknetwork.

```pyhon
import sknetwork as sn
import partition_sknetwork as ps
```

Next, let's build a graph with communities.

```python
block_sizes = [100 for _ in range(10)]
g = sn.data.models.block_model(block_sizes, 0.1, 0.025, seed=42)

# Store the ground truth communities
labels = np.array([i for i,block_size in enumerate(block_sizes) for _ in range(block_size)])
```

Run Louvain and ecg:

```python
louvain = sn.clustering.Louvain(shuffle_nodes=True, random_state=42).fit_predict(g)
ecg = ps.ECG(random_state=42).fit_predict(g)
```

Finally, we show a few examples of measures we can compute with gam:

```python
print('Adjusted Graph-Aware Rand Index for Louvain:',ps.gam(g, labels, louvain))
print('Adjusted Graph-Aware Rand Index for ECG:',ps.gam(g, labels, ecg))
print('Jaccard Graph-Aware for Louvain:',ps.gam(g, labels, louvain, method="jaccard", adjusted=False))
print('Jaccard Graph-Aware for ECG:',ps.gam(g, labels, ecg, method="jaccard", adjusted=False))
```
py-commons-ip 0.3.2
A wrapper library to use the commons-ip library in Python
Requires Python: >=3.10

# Python Commons IP

## Introduction
This repository provides a Python interface to the Commons IP validator.

## Usage
```python
import py_commons_ip
succes, report = py_commons_ip.validate("/path/to/unzipped/zip")
```

## Building

Fetch the commons-ip submodule.

```sh
git submodule update --init --remote
```

Use the Makefile to build the JAR file. This requires a Java runtime and Maven.

```sh
make
```

The JAR file will be copied to `py_commons_ip/resources`.

## Release

Bump the version in `pyproject.toml` and add a matching tag `vX.Y.Z` to the commit to start the CI/CD.
sipin-eark-models 0.0.12
Models for the meemoo SIP
Requires Python: >=3.10

# sipin E-ARK models
Pydantic models for the E-ARK CSIP. Currently only includes PREMIS.

## Usage

Create a PREMIS pydantic model with

```py
from eark_models.premis.v3_0 import Premis

premis = Premis.from_xml("premis.xml")
```
holocontour 0.1.6
A minimalist toolkit for holographic plankton image segmentation
Requires Python: >=3.8

<div style="display: flex; align-items: center; gap: 20px;">
  <img src="docs/source/_static/logo.png" alt="HoloContour Logo" width="100"/>
</div>




## HoloContour

Holocontour is a small but capable toolkit that turns raw in‑line digital holograms into clean binary masks of planktonic particles. It couples intensity‑driven region growing with a contour‑refinement step, and wraps everything in a reproducible, YAML‑configurable pipeline built on top of [MorphoCut](https://github.com/morphocut/morphocut). Outputs ship in an archive ready for direct upload to [EcoTaxa](https://ecotaxa.obs-vlfr.fr/).

---

## 🚀 Features

- Contour-based object detection with:
  - Intensity filtering
  - Histogram matching (optional)
  - Region growing from intensity minima
- Optional plot saving of initial vs. refined masks
- Compatible with `EcoTaxaWriter`
- YAML-based configuration
- Safe fallback for empty masks

---

## 🖼 Example

The figure below shows an example of the pipeline's segmentation output:
  
<p align="left">
  <img src="docs/source/_static/sample.jpg" alt="Segmentation Result" width="400"/>
</p>


- **Red dashed**: Initial region estimate
- **Blue solid**: Refined segmentation contour

---
## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---
spsdk-pqc 0.6.8
Post-Quantum Crypto support for SPSDK
Requires Python: >=3.9
Keywords: NXP, SPSDK, PQC, Dilithium, ML-DSA


Post-Quantum Crypto plugin for SPSDK
====================================


* Free software: BSD-3-Clause


Features
--------

* Supported Algorithms:
    - Dilithium 2, 3, 5
    - ML-DSA 44, 65, 87
* Supported operation: Generate key pair, Sign data, Verify signature


Installation
------------

* Activate virtual env, where you have SPSDK
    - if SPSDK is not installed, the latest one will be installed automatically from [PyPI](https://pypi.org/project/spsdk/)
* `pip install spsdk-pqc`

Usage
-----

* Use tools such as nxpcrypto, nxpimage as you usually do.
* Plugin also comes with a cli app `pqctool`

Credits
-------

This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [SPSDK Signature Provider project template](https://github.com/nxp-mcuxpresso/spsdk/blob/master/examples/plugins/templates/cookiecutter-spsdk-sp-plugin.zip).