semvec/pypi-packages huggingface dataset:
validarnif 1.0.0
Módulo Python para la validación de identificadores fiscales españoles: NIF, NIE y CIF.
Requires Python: >=3.10

# validarnif

Módulo Python para la validación de identificadores fiscales españoles: NIF (Número de Identificación Fiscal), 
NIE (Número de Identidad de Extranjero) y CIF (Código de Identificación Fiscal).

## Características

Proporciona funciones de validación conforme a la normativa vigente en 2024, incluyendo el CIF como concepto 
independiente a pesar de que ya no tiene entidad legal propia al haberse integrado en la denominación 
general de NIF.

Por omisión, valida el identificador tal cual aparece en la cadena de texto. Opcionalmente, se puede aplicar un 
preprocesamiento para estandarizar el formato antes de validar: elimina espacios y caracteres extraños, completa 
el número con ceros por la izquierda e iguala mayúsculas/minúsculas.

## Instalación

```bash
# Clonar el repositorio
$ git clone https://github.com/sustoja/validarnif.git

# Acceder al directorio
$ cd validarnif
```

## Uso

Importa el módulo y utiliza las funciones para validar NIF, NIE o CIF:

```python
from src import validar_dni, validar_nie, validar_cif, validar_nif_nie_cif

# Validar un DNI
print(validar_dni("77697094N"))

# Validar un NIE
print(validar_nie("X0631255C"))

# Validar un CIF
print(validar_cif("H27513647"))

# Validar cualquier identificador con preprocesamiento
print(validar_nif_nie_cif("x-631255-c", preprocesar=True))  
```

## Funciones principales

- `validar_dni(dni: str) -> bool`: Valida un DNI.
- `validar_nie(nie: str) -> bool`: Valida un NIE.
- `validar_cif(cif: str) -> bool`: Valida un CIF.
- `validar_nif_nie_cif(identificador: str, preprocesar: bool = True) -> bool`: Estandariza el formato del 
- identificador antes de validar.

## Referencias

- [Ministerio del Interior: Cálculo del dígito de control del NIF/NIE](https://www.interior.gob.es/opencms/es/servicios-al-ciudadano/tramites-y-gestiones/dni/calculo-del-digito-de-control-del-nif-nie/)
- [Wikipedia: Número de Identificación Fiscal](https://es.wikipedia.org/wiki/N%C3%BAmero_de_identificaci%C3%B3n_fiscal)
- [Wikipedia: Código de Identificación Fiscal](https://es.wikipedia.org/wiki/C%C3%B3digo_de_identificaci%C3%B3n_fiscal)
- [GitHub: NIF, DNI, NIE, CIF validation](https://github.com/josegoval/nif-dni-nie-cif-validation/tree/master)
- [fjrodriguezg: Validación de CIF en Java](https://fjrodriguezg.wordpress.com/2015/08/14/validando-cif-en-java-definitivo/)

## Contribuciones

Se agradecen las contribuciones mediante fork del repositorio y solicitudes de pull request.

## Licencia

Este proyecto utiliza la **Licencia MIT**.
ludicolo 0.0.1
Lotad helps you identify schema changes, data differences, and structural modifications between database versions.
Requires Python: >=3.12

# lotad

A Python library for tracking and analyzing data drift between DuckDB databases. 
Lotad helps you identify schema changes, data differences, and structural modifications between database versions.

## Features

- Compare table schemas and data between two DuckDB databases
- Track missing tables, columns, and type mismatches 
- Analyze row-level data differences using consistent hashing
- Generate detailed comparison reports
- Configurable tracking with support for excluding specific tables and columns

## Installation

```bash
pip install lotad
```

## Quick Start

```python
from lotad import DatabaseComparator

# Initialize comparator with database paths
comparator = DatabaseComparator('path/to/db1.db', 'path/to/db2.db')

# Run full comparison
results = comparator.compare_all()

# Generate comparison report
comparator.generate_comparison_report(results, 'comparison_report.txt')
```

## Basic Usage

Compare specific tables while ignoring others:

```python
# Compare only specified tables
results = comparator.compare_all(tables=['users', 'orders'])

# Exclude specific tables from comparison
results = comparator.compare_all(ignore_tables=['logs', 'temp_data'])
```

Access detailed comparison results:

```python
# Get schema differences
schema_diff = comparator.compare_table_schemas('users')
print(f"Missing columns in DB2: {schema_diff['missing_in_db2']}")
print(f"Type mismatches: {schema_diff['type_mismatches']}")

# Compare table data
data_diff = comparator.compare_table_data('orders')
print(f"Row count difference: {data_diff['row_count_diff']}")

## License

This project is licensed under the MIT License.

## Why Lotad?

Named after the water lily Pokémon, Lotad helps you stay afloat in a sea of database changes by tracking data drift between your DuckDB instances.
```
agraph-utils 0.5
A set of utilities for working with AllegroGraph

# agraph_utils

## Introduction

`agraph_utils` is a python package that supplies a set of helpful functions
to use along with the `agraph-python` package.

## Installation

```bash
pip install agraph-utils
```

## Usage

### BufferTriples

the BufferTriples class allows the user to quickly add large sets
of triples. You can either add triples individually with `add_triple` 
or you can add a list to the buffer as `add_triples`.

### GeoJSON

This class allows you to easily convert various coordinates of
points/lines/multipolygons into RDF Literals that can be used
for GeoSPARQL.

### Vector Search

This class allows you to easily use the base magic sparql predicates
used in AllegroGraph for vector search. This assumes the user has
a vector store with stored vectors.
PyDepManger 0.0.7
A small example package
Requires Python: >=3.8
Keywords: f5432

# PyHLicorn
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/PyDepManger)
![PyPI - Version](https://img.shields.io/pypi/v/PyDepManger)
![GitHub repo size](https://img.shields.io/github/repo-size/AbuAwadM/PyDepManger)


A Python package that re-implements an existing deprecated R package.

- **[Documentation](https://www.google.co.uk/)**
- **[Source Code](https://www.google.co.uk/)**
- **[Research Paper](https://www.google.co.uk/)**
- **[Issues](https://www.google.co.uk/)**

The PyHLicorn package aims to infer a large-scale transcription co-regulatory network from transcriptomic data and integrate external data on gene regulation to infer and analyze transcriptional programs. The unique aspect of the network inference algorithm proposed in the package is its ability to learn co-regulation networks where gene regulation is modeled by transcription factors acting cooperatively to synergistically regulate target genes.

## About the Project
The package was utilized in a study of Bladder Cancer to identify the driver transcriptional programs from a set of 183 samples. Throughout this vignette, a smaller version of the transcriptomic dataset is used to illustrate the package's usage.

## Installation

To install the package, use pip:
```sh
pip install PyHLicorn
```

To clone the repository, use GitHub:
```sh
git clone https://www.google.co.uk/
```

## Usage
Refer to the [documentation](https://www.google.co.uk/) for detailed usage instructions.

Here is an example of how to use the PyHLicorn package in your Python code:

### Import the Library
```python
import pandas as pd
from PyHLicorn import HLicorn
```

### Import the Data
```python
numerical_expression = pd.read_csv(file_path, index_col=0)
discrete_expression = pd.read_csv(file_path, index_col=0)
tf_list = pd.read_csv(file_path)
```

### Create the Gene Regulatory Network
```python
GRN = HLicorn(numerical_expression, tf_list, discrete_expression)
```
## Authors

- **John Doe** - *Initial work* - [JohnDoe]()
- **Jane Smith** - *Contributor* - [JaneSmith]()

## Maintainers

- **Alice Johnson** - [AliceJohnson]()
- **Bob Brown** - [BobBrown]()

## Credits

- Special thanks to the [XYZ Lab](https://www.xyzlab.com) for their support and resources.
- Thanks to all contributors who have helped improve this project.

## Citation

If you use this package in your research, please cite the following paper:

```
@article{Doe2023,
        title={PyHLicorn: A Python package for transcription co-regulatory network inference},
        author={Doe, John and Smith, Jane},
        journal={Journal of Computational Biology},
        volume={30},
        number={4},
        pages={123-134},
        year={2023},
        publisher={Bioinformatics Press}
}
```
cosmax 0.0.7
Fast and differentiable implementations of operations needed for inference and analysis in cosmology. Powered by JAX
Requires Python: >=3.8

# Cosmax

Fast and differentiable tools for analysis and inference on structured and unstructured data in cosmology. Powered by JAX. 

Install with `pip install cosmax`.

## What can I do with this package?

### Classical Use Cases

Generate the [Matter Power Spectrum](examples/power_spectrum.ipynb) from a 3D densitiy field:

<img src="data/power.png" alt="drawing" width="400"/>

Generate the [ICs for a given matter power spectrum](examples/generate_ic.ipynb):

<img src="data/ic.png" alt="drawing" width="400"/>

Cloud in a cell mass assignment scheme to [convert unstructured particles to a 3D density field](examples/cic.ipynb):

<img src="data/cic.png" alt="drawing" width="400"/>

### Optimization

With gradient optimization over the cic mass assignment scheme, we can find the particle positions that best [fit an observed density field](examples/fit.ipynb):

<img src="data/fit.png" alt="drawing" width="400"/>

We can also use the power spectrum as a loss function to find a [conditional IC density field ](examples/conditional_ic.ipynb):

<img src="data/cond_ic.png" alt="drawing" width="400"/>

## What are the limitations?

- The package is currently in development and the API is not stable.
- The package generally ONLY works with 3D square boxes and periodic boundary conditions.
- I am not a physicist, so the package may contain errors and inaccuracies.

## Development

To develop, clone the repository and install the package in editable mode:

```
pip install -e .
```

To release as pip package, tests, docs and builds are handled automatically by github actions as defined in
.github/workflows. To make a new release:

```
git tag v*.*.*
git push origin v*.*.*
```
and change the version number in pyproject.toml.

### Test

```
pytest
```

### Build 

```
python -m build
```

### Local Docs

With the pip package sphinx installed, run

```
sphinx-apidoc -o docs/source cosmax/
sphinx-build -b html docs/source docs/_build
```

to view locally

```
cd docs/_build
python -m http.server
```

## Acknowledgements

- [PowerBox](https://powerbox.readthedocs.io/en/latest/) was used as a reference implementation the matter power spectrum.

- [Felix Köhler](https://github.com/Ceyron/machine-learning-and-simulation/)s implementation of Fourier Neural Operator was used as a guide to adapt the fno to jax and equinox.

- The original [NeuralOperator](https://github.com/neuraloperator/neuraloperator) served as a reference for the fno implementation.
soed 1.0.10
Self Organizing Error Driven Artificial Neural Network
Keywords: machine learning AI Artificial Neural Network

# SOED
Self Organizing Error Driven Artificial Neural Network



# Publications
- Self-organizing and error driven (SOED) artificial neural network for smarter classifications https://academic.oup.com/jcde/article/4/4/282/5729001
- An optimum ANN-based breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals https://www.sciencedirect.com/science/article/abs/pii/S1568494618304484
- From in-situ monitoring toward high-throughput process control: cost-driven decision-making framework for laser-based additive manufacturing https://www.sciencedirect.com/science/article/abs/pii/S0278612518302929
- Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry https://link.springer.com/article/10.1007/s00521-020-04850-6
- Supervised or unsupervised learning? Investigating the role of pattern recognition assumptions in the success of binary predictive prescriptions https://www.sciencedirect.com/science/article/abs/pii/S0925231220319639

## How to work
```
pip install soed
```

```python

from soed import SOEDClassifier

soed = SOEDClassifier(mlp_max_iter=10000,som_x=10, som_y=10,som_input_len=X_train.shape[1])

```


## How to run project
```
virtualenv soed_dev
source soed_dev/bin/activate
pip install -r requirements.txt
```

```
jupyter notebook \
    --notebook-dir="./notebooks" \
    --ip=0.0.0.0 --port=3225
```

## At the time new py package installed
```
pip install <package>

pip freeze > requirements.txt
```



## for installation in local
```
pip install -e .
```


## build project
```
python setup.py sdist bdist_wheel
```

## Push project
```
twine upload dist/*
```

## Release pypi module
```
pip install twine
```
### Ensure you have pypi credential in
```
# nano ~/.pypirc
[pypi]
  username = __token__
  password = <Token>
```
```bash
./releaseNewVersionAndAddTag.sh
```