semvec/pypi-packages huggingface dataset:
photonx 0.0.4
Collection of scripts useful for photonic design.
Requires Python: >=3.8

# photonx
Collection of scripts useful for photonic design. I will be adding functions as they become useful and document them when possible, but the main goal is for personal use.

# contributing
If you want to build off of these functions and add more, feel free to create a pull request! I don't currently have time to formalize this into a library, and it will likely be very sparse for the time being, but if you are so inclined, you are welcome to help. Just remember to keep every function as simple as possible and well-commented.
pytest-max-warnings 0.1.0
A Pytest plugin to exit non-zero exit code when the configured maximum warnings has been exceeded.
Requires Python: >=3.9

# pytest-max-warnings

A Pytest plugin to exit non-zero exit code
when the configured maximum warnings has been exceeded.

## Installation

You can install "pytest-max-warnings" via [pip](https://pypi.org/project/pytest-max-warnings/):

```bash
pip install pytest-max-warnings
```

or any other package manager you prefer.

## Usage

You can set the maximum number of warnings allowed by using the `--max-warnings` option:

```bash
pytest --max-warnings 10
```

If the number of warnings exceeds the maximum, the test run will exit with a non-zero exit code.

## License

Distributed under the terms of the MIT license, "pytest-max-warnings" is free and open source software.
See [LICENSE](./LICENSE) for more details.

## Authors

- [@miketheman](https://github.com/miketheman)
drivecatplus 0.2.0
NREL's Drive Cycle Analysis Tool Plus
Requires Python: >=3.8
Keywords: drivecat, drivecycle, drivecycle-metrics, drivestats, speed-profile, time-speed-trace

# DriveCAT+ - Drive Cycle Analysis Tool Plus

DriveCAT+ is a drivecycle analysis python package that takes in vehicle time, speed, and elevation profiles, processes them, and outputs several useful statistical metrics that help quantify a drivecycle.

## Overview
DriveCAT+ contains a class `Cycle` used for taking a drivecycle as an input. The `DriveStats` class initializes various categories of drivestats like `DistanceStats`, `TotalSpeedStats`, `TimeStats`, etc as subclasses and facilitates the calculation of relevant statical metrics within their constructor methods.

## Installation

First, clone the repository from GitHub:

    git clone https://github.com/NREL/drivecatplus.git drivecatplus

DriveCAT+ depends on python versions >=3.8 and <=3.11. One way to satisfy this is to use conda:

    conda create -n dcp python=3.10
    conda activate dcp

After creating the environment, navigate to the parent directory containing the DriveCAT+ repository e.g. `cd github/drivecatplus/` and run:

    pip install -e .

from within the dcp python 3.10 environment you created.

This will install DriveCAT+ with minimal dependencies such that the code files can be edited. Developers will find the `-e` option handy since DriveCAT+ will be installed in place from the installation location, and any updates will be propagated each time DriveCAT+ is freshly imported.

## Demo

This repo is provided with a sample [drivecycle input file](https://github.com/NREL/drivecatplus/blob/4b69af23fbf9099293ebd1e37b9785eab1d9e460/src/resources/demo_cycle_without_elevation.csv) to test out in a basic demo. From the parent directory:

```bash
cd src/demos/
python demo_drivestats_basic.py
```

or, use VS Code's Interactive Window to run the demo code blocks.

## Run DriveCAT+ for multiple drivecycles

The run_dcp.py module can be used to analyze multiple drivecycles and export the results as a single CSV. --input-path accepts path of a single CSV file input or the path to a directory. When a directory path is provided, the module searches all subdirectories for .CSV files and runs DriveCAT+ for each of them.

```bash
cd src/drivecatplus/
python run_dcp.py --input-path=/path/to/file-or-folder
```
mdfa 0.1.0
MDFA: A Multi-Dimensional Feature Attention model
Requires Python: >=3.6

# MDFA: Multi-Dimensional Feature Attention

MDFA is a multi-dimensional feature attention module designed for deep learning applications. This package provides the MDFA class that can be integrated into various neural network models.

## Installation

You can install this package directly from GitHub:

```bash
pip install git+https://github.com/yourusername/your_project.git