semvec/pypi-packages huggingface dataset:
lib-mx-colab 0.2.6
IA Sistema de Interes
Requires Python: >=3.6

**Descripcin:**  
Este cuaderno fue creado por **IA (Sistema de Interes)** como parte de un sistema de inteligencia artificial destinado a la integracin con Google Colab y otros entornos. El cdigo est diseado para facilitar el uso de IA en proyectos colaborativos.

## Autor

- **Nombre del autor**: IA (Sistema de Interes)
- **Canal de Youtube**: https://www.youtube.com/@IA.Sistema.de.Interes

Este proyecto fue desarrollado con el objetivo de proporcionar y hacer simple y funcional para facilitar tareas relacionadas con IA en plataformas como Google Colab.

## Indicaciones

Si deseas utilizar este cuaderno, puedes hacerlo libremente. Solo debes dar crdito al autor y no apropiarte de su trabajo.
sigkan 0.2.0
Path Signature-Weighted Kolmogorov-Arnold Networks for Time Series
Requires Python: <3.13,>=3.9

# SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series

![SIGKAN representation](image/SigKAN.drawio.png)

This folder includes the original code implemented for the [paper of the same name](https://arxiv.org/abs/2406.17890).

A pypi package is available at [pypi](https://pypi.org/project/sigkan/)

The SigKAN is a novel layer that combines the power of path signature and Kolmogorov-Arnold Networks.

The idea behing is to use a learnable path signature that is transformed in weights to the KAN layer.

The Signature is passed through a GRKAN (Gated Residual KAN unit) that is a modified GRN where some Dense layers are replaced by KAN layers.

New in version 0.2.0:

The signature is now computed using [keras_sig](https://github.com/remigenet/keras_sig) instead of [iisignature_tensorflow_2](https://github.com/remigenet/iisignature-tensorflow-2/) that is a keras implementation of the signature based on [signatory](https://github.com/patrick-kidger/signatory) and [signax](https://github.com/anh-tong/signax/). Keras_sig being in pure keras3 the package is compatible and tested with all keras backend (tensorflow2, jax and torch). However, we recommend strongly to use jax as backend as it is the most efficient for this task. 

The computation of the signature also profits from an optimization for GPU computation inside keras_sig. 

The KAN part implementation has been inspired from [efficient_kan](https://github.com/Blealtan/efficient-kan) and works similarly to it, thus not exactly like the [original implementation](https://github.com/KindXiaoming/pykan).

The SigKAN is a keras layers and can be used as any other keras layer, for example:

```python
import tensorflow as tf
from sigkan import SigKAN
model = Sequential([
    Input(shape=X_train.shape[1:]),
    SigKAN(100, 2, dropout = 0.), # 100 units, signature of order 2, takes an input shape (batch, sequence, features) and returns a tensor of shape (batch, sequence, 100)
    Flatten(),
    Dense(100, 'relu'),
    Dense(units=n_ahead, activation='linear')
])
```

A more complete example is provided in a notebook in the example folder.

The code is provided as is and is not specially maintained.

Please cite our work if you use this repo:

```
@article{inzirillo2024sigkan,
  title={SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series},
  author={Inzirillo, Hugo and Genet, Remi},
  journal={arXiv preprint arXiv:2406.17890},
  year={2024}
}
```

Shield: [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]

This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
myDebuG 0.1.0
A simple debugging tool for Python

# DebugMode

## Overview
DebugMode is a class that provides functionality for debugging, logging, and printing messages with different colors.

## Methods

### `__init__(self, debugMode=True, logFile=None)`
Initializes the DebugMode instance.
- **Args:**
  - `debugMode` (bool): A flag to enable or disable debug mode. Default is True.
  - `logFile` (str): The path to a log file. If provided, logs will be written to this file. Default is None.

### `setDebugMode(self, debugMode)`
Sets the debug mode.
- **Args:**
  - `debugMode` (bool): A flag to enable or disable debug mode.

### `getDebugMode(self)`
Returns the current debug mode.
- **Returns:**
  - `bool`: The current debug mode.

### `debugPrint(self, message=None, fonction=None)`
Prints a debug message or executes a function if provided.
- **Args:**
  - `message` (str): The message to print. Default is None.
  - `fonction` (callable): A function to execute. Default is None.

### `warningPrint(self, message)`
Prints a warning message.
- **Args:**
  - `message` (str): The warning message to print.

### `logPrint(self, message)`
Logs a message to the log file if it is open.
- **Args:**
  - `message` (str): The message to log.
- **Raises:**
  - `ValueError`: If the log file is not open.
scikit-rt 0.8.2
Toolkit for analysis of radiotherapy data
Requires Python: >=3.8

# Scikit-rt

Scikit-rt provides tools for loading, plotting, and analysing
radiotherapy data in [DICOM](https://www.dicomstandard.org/) and
[NIfTI](https://nifti.nimh.nih.gov/) formats.  It includes
image registration via [elastix](https://elastix.lumc.nl/)
and [NiftyReg](http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftyReg),
single- and multi-atlas segmentation, and region-of-interest (ROI)
comparisons.

Scikit-rt documentation is at:  
[https://scikit-rt.github.io/scikit-rt/](https://scikit-rt.github.io/scikit-rt/)

If you encounter problems with scikit-rt, please submit a help request
or bug report via the project issues tracker:  
[https://github.com/scikit-rt/scikit-rt/issues](https://github.com/scikit-rt/scikit-rt/issues)

This work was supported by Cancer Research UK RadNet Cambridge [C17918/A28870].
parendum-astra-api 0.1.3
A library to interact with the Astra Portal API of Parendum.

# Parendum Astra API Library

**Parendum OÃœ**  
Creation Date: September 2023  
Copyright Â© 2023 Parendum OÃœ. All rights reserved.

## Description

The Parendum Astra API library provides a Python client for interacting with the Parendum Astra API. This library facilitates secure communication with the API, including HMAC signature generation, request header creation, and encrypted response decryption.

## Features

- HMAC signature generation for secure API requests.
- Encrypted response decryption using AES.
- Simple methods for fetching reports and company lists.

## Installation

To use this library in your project, simply install it with the next command.

```bash
pip install git+https://gitlab.com/parendumteam/parendum-astra-api/
```

## Usage

Initialize the API client with your API key and secret:

```python
from parendum_astra_api import ParendumAstraAPI

client = ParendumAstraAPI(api_key="YOUR_API_KEY", api_secret="YOUR_API_SECRET")
```

Retrieve reports:

```python
reports = client.get_reports(summary=True)
print(reports)
```

Retrieve a list of companies:

```python
companies = client.get_companies()
print(companies)
```

## Contact

For any inquiries, feedback, or issues, please contact:

- Email: info@parendum.com
- Website: https://parendum.com


## License

Unauthorized use, reproduction, modification, or distribution without the express consent of Parendum OÃœ is strictly prohibited.