semvec/pypi-packages huggingface dataset:
insert-license-header 1.3.0
Tool to insert license headers at the beginning of text-based files.
Requires Python: >=3.8

# Tool: insert-license-header
This python script automatically inserts your license header at the beginning of specified source code files.

Forked from [Lucas-C/pre-commit-hooks](https://github.com/Lucas-C/pre-commit-hooks) and modified to realize the following behaviour:

> :warning: The behaviour of `--dynamic-years` changed in version `1.1.0`.
>
Add argument `--dynamic-years` which determines the start year of the copyright time range automatically - based on when
the file was first tracked with Git. If a start year is already present, it is not touched.
If a file is not tracked by Git, the current year is used as start year.
The end year is automatically set to the date of the last commit that affected the file.
If an end year is already present that is in the future, don't touch it. It is, however,
incremented if it lies in the past. If the file is not tracked by Git, use the current year.

Include a `{year_start}` and `{year_end}` in your license header to use this feature.

Add argument `--license-base64` to include a license not via a file but through
a `base64` encoded string that is passed as a value for this argument.
Obtain your license `base64` encoded string with `cat LICENSE.txt | base64`.
Including a license via `--license-base64 {base64string}` overrides the
`--license-filepath` option.

> :warning: This is not a pre-commit hook anymore. Instead, this repository contains just the base script to insert licenses in text-based files. To check out the resulting pre-commit hook, visit: https://github.com/Quantco/pre-commit-insert-license
pynattas 0.0.0
Pynattas, a powerful open-source Python package that provides a comprehensive set of tools for model building and deployment
Requires Python: >=3.8, <4

# PyNA-tta-S

![PyNA-tta-S](SRC/BG.png?raw=true)

## Overview
PyNA-tta-S is the pi├▒ata package designed for onboard deployment optimization on edge devices. It leverages advanced metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO), to efficiently determine the most effective model architecture.

## Key Features
- Metaheuristic Algorithms: Incorporates GA, PSO, and GWO for robust optimization.
- Model Architecture Selection: Automates the selection of optimal architectures for specific onboard applications.
- Edge Device Compatibility: Tailored for efficient deployment on various edge devices.
- Performance Metrics: Evaluates architectures based on predefined or custom metrics relevant to edge computing.
## Customization: 
Allows users to define custom constraints and requirements for model architecture.
User-Friendly Interface: Easy-to-use API, facilitating integration with existing projects.
## Use Cases
- IoT Applications: Optimizing models for IoT devices with limited computing resources.
- Remote Sensing: Enhancing the efficiency of models deployed in remote sensing edge devices.
- Autonomous Vehicles: Streamlining models for real-time processing in autonomous vehicles.