semvec/pypi-packages huggingface dataset:
costools 1.2.7
Tools for COS (Cosmic Origins Spectrograph)

# COSTools

[![build](https://github.com/spacetelescope/costools/actions/workflows/publish-to-pypi.yml/badge.svg?branch=master)](https://github.com/spacetelescope/costools/actions/workflows/publish-to-pypi.yml)
[![tests](https://github.com/spacetelescope/costools/actions/workflows/tests.yml/badge.svg?branch=master)](https://github.com/spacetelescope/costools/actions/workflows/tests.yml)
[![Codecov](https://codecov.io/gh/spacetelescope/costools/branch/master/graph/badge.svg)](https://codecov.io/gh/spacetelescope/costools)
[![ReadtheDocs](https://readthedocs.org/projects/costools/badge/?version=latest)(http://costools.readthedocs.io/en/latest/)
[![Powered by Astropy](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)(http://www.astropy.org)

## About

COSTools provides utility functions for analyzing and doing custom reductions
for hst/cos datasets.

You can read the full specification at [the online documentation](http://costools.readthedocs.io/).

## Contributing

Please open a new [issue](https://github.com/spacetelescope/costools/issues)
or new [pull request](https://github.com/spacetelescope/costools/pulls)
for bugs, feedback, or new features you would like to see.

If there is an issue you would like to work on, please leave a comment and
we will be happy to assist. New contributions and contributors are very welcome!
atsuko 0.1.0
A tool for easily create command-line interfaces.
Requires Python: >=3.9

# atsuko

A tool for easily create command-line interfaces in Python.

## Installation

Install `atsuko` using `pip`:

```bash
pip install atsuko
```

## Usage

Here's a simple example of how to use `atsuko` to create a command-line interface:

```python
import sys
from atsuko import CLI

# Create a new CLI application
app = CLI(
    name="my-app",
    description="A simple example application.",
    version="0.1.0"
)

@app.command
def greet(name: str):
    """Greets the given name."""
    app.log(f"Hello, {name}!")

if __name__ == "__main__":
    app.run(sys.argv[1:])
```

You can then run the application from the command line:

```bash
python my_app.py greet World
# Output: Hello, World!
```

## Contributing

Contributions are welcome! Please feel free to submit a pull request.

## License

This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.
rastertoolkit 0.4.6
Raster and shape tools
Requires Python: >=3.10
Keywords: modeling, IDM

# RasterToolkit

RasterToolkit is a Python package for processing rasters with minimal dependencies. For example, with rastertoolkit you can extract populations corresponding to an administrative shapefile from a raster file.

## Setup

Install from github:

    python -m pip install .

## Getting Started

A typical `raster_clip` API usage scenario:
```
    from rastertoolkit import raster_clip

    # Clipping raster with shapes  
    pop_dict = raster_clip(raster_file, shape_file)  
```
See the complete code in the WorldPop example (examples/worldpop)

A typical `shape_subdivide` API usage scenario:
```
    from rastertoolkit import shape_subdivide

    # Create shape subdivision layer
    subdiv_stem = shape_subdivide(shape_stem=shape_file)
```
See the complete code in the Shape Subdivision example (examples/shape_subdivide)


## Running Tests

Install additional packages (like pytest)::
```
    python -m pip install .[test]
```
Run `pytest` command::
```
    # Run unit tests (recommended during development)
    python -m pytest -m unit -v

    # Run test for a specific module, for example
    python -m pytest tests/test_shape.py -v     # run shape unit tests
    python -m pytest tests/test_download.py -v  # run GDx download tests

    # All tests (before a commit or merging a PR)
    python -m pytest -v
```
beast-backbones 1.1.3
Behavioral analysis via self-supervised pretraining of transformers
Requires Python: >=3.10
Keywords: machine learning, deep learning, computer_vision

# beast

![GitHub](https://img.shields.io/github/license/paninski-lab/beast)
![PyPI](https://img.shields.io/pypi/v/beast-backbones)

**Be**havioral **a**nalysis via **s**elf-supervised pretraining of **t**ransformers

`beast` is a package for pretraining vision transformers on unlabeled data to provide backbones 
for downstream tasks like pose estimation, action segmentation, and neural encoding.

See the preprint [here](https://arxiv.org/pdf/2507.09513). 

## Installation

### Step 1: Install ffmpeg
First, check to see if you have ffmpeg installed by typing the following in the terminal:

```commandline
ffmpeg -version
```

If not, install:

```commandline
sudo apt install ffmpeg
```

### Step 2: Create a conda environment

First, [install anaconda](https://docs.anaconda.com/free/anaconda/install/index.html).

Next, create and activate a conda environment:

```commandline
conda create --yes --name beast python=3.10
conda activate beast
```

### Step 3: Download and install
Move to your home directory (or wherever you would like to download the code) and install via Github clone or through PyPI.

For Github cloning:

```commandline
git clone https://github.com/paninski-lab/beast
cd beast
pip install -e .
```

For installation through PyPI:

```commandline
pip install beast-backbones
```

## Usage

`beast` comes with a simple command line interface. To get more information, run
```commandline
beast -h
```

### Extract frames

Extract frames from a directory of videos to train `beast` with.

```commandline
beast extract --input <video_dir> --output <output_dir> [options]
```

Type "beast extract -h" in the terminal for details on the options.

### Train a model

You will need to specify a config path; see the `configs` directory for examples.

```commandline
beast train --config <config_path> [options]
```

Type "beast train -h" in the terminal for details on the options.

### Run inference

Inference on a single video or a directory of videos: 

```commandline
beast predict --model <model_dir> --input <video_path> [options]
```

Inference on (possibly nested) directories of images: 

```commandline
beast predict --model <model_dir> --input <video_path> [options]
```

Type "beast predict -h" in the terminal for details on the options.
iflow-mcp_openhutb-carla-mcp 0.1.0
Carla MCP server for vehicle simulation with mock mode
Requires Python: >=3.10

# HutbMCP - Unreal 模型上下文协议集成

## 概述

HutbMCP 是一个 Python 软件包，提供将 Hutb 与模型上下文协议 (Model Context Protocol, MCP) 集成的接口。它允许用户与 Hutb 的仿真环境交互，并使用 MCP 管理模型上下文。该软件包旨在通过在 Hutb 和 MCP 之间提供无缝连接来促进机器人应用程序的开发。


## 特性
- [x] 与 Hutb 仿真环境集成
- [x] 支持模型上下文协议 (MCP)
- [x] 用于管理模型上下文的易于使用的 API
- [ ] 支持各种 Hutb 传感器和参与者
- [ ] 运行模拟和收集数据的能力


## 安装

要安装 HutbMCP，您可以使用 uv。请确保您已安装 Python 3.10 或更高版本。

```bash
pip install D:/hutb/PythonAPI/carla/dist/hutb-2.9.16-cp310-cp310-win_amd64.whl
# 启动 WindowsNoEditor/CarlaUE4.exe
python main.py
```

#### 警告 ⚠️：该项目处于初始阶段，希望贡献者帮助开发。

## 参考

* [MuJoCo-Unreal-Engine-Plugin](https://github.com/oneclicklabs/MuJoCo-Unreal-Engine-Plugin) ，其他 [使用 ROS 与 WebSockets 连接实现机器人可视化的 Unreal 插件](https://github.com/HoangGiang93/URoboViz)
ezmsg-event 0.8.0
Namespace package for ezmsg to work with signal events like neural spikes and heartbeats
Requires Python: >=3.10

# ezmsg-event

ezmsg namespace package for working with signal events like neural spikes and heartbeats

## Overview

``ezmsg-event`` ezmsg namespace package for working with signal events like neural spikes and heartbeats.

Key features:

* **Event detection** - Detect and track signal events in neural data
* **Spike handling** - Process neural spike events
* **Heartbeat tracking** - Monitor physiological heartbeat events
* **Event timestamps** - Precise timing for event occurrences

## Installation

```bash
pip install ezmsg-event
```

## Dependencies

- `ezmsg`
- `numpy`
- `ezmsg.baseproc`
- `ezmsg.sigproc`

## Usage

TODO: Add usage examples

## Development

We use [`uv`](https://docs.astral.sh/uv/getting-started/installation/) for development.

1. Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) if not already installed.
2. Fork this repository and clone your fork locally.
3. Open a terminal and `cd` to the cloned folder.
4. Run `uv sync` to create a `.venv` and install dependencies.
5. (Optional) Install pre-commit hooks: `uv run pre-commit install`
6. After making changes, run the test suite: `uv run pytest tests`

## License

MIT License - see [LICENSE](LICENSE) for details.
acti-data-analyzer 1.0.2
A library for processing and analyzing actigraphic data.
Requires Python: >=3.11
Keywords: actigraphy, sleep, circadian rhythms

# ADA – Actigraphic Data Analyzer
ADA stands for Actigraphic Data Analyzer, coincidentally being also the name of the first programmer in the world.
It is an open source Python module for processing, analyzing and visualizing actigraphic data, with the main focus on bedtime sleep/wake classification and circadian rhythms analysis.
ADA is usable in form of a python package, and in form of standalone application with graphical interface (GUI).

## Installation
Python 3.11 or higher is required. To install the Python package use pip:

```shell
pip install acti-data-analyzer
```

<!-- The GUI for Windows 10 and Windows 11 is availabe as a standalone executable. -->

GUI version of ADA can installed and run on any system as a Python script, best within a venv. To do use following commands:

```shell
pip install 'acti-data-analyzer[gui]'
python -m ada_gui
```

## Main features

* Reading and writing data from:
  * GENEActiv .bin and .csv files
  * Actigraph Corp .gt3x and .csv files
  * The MESA dataset.
* Converting data to native .ada format (much more efficient in terms of disk space and reading speed).
* Multiple algorithms allowing collapsing into epochs:
  * MIMS
  * ActivityIndex
  * ENMO
  * Resampling
* Sleep/wake scoring using multiple algorithms:
  * Cole-Kripke
  * Webster
  * Scripps Clinic
  * UCSD
  * Sazonov
  * Unified Filter, with possibility of custom filter construction.
* Estimating multiple sleep metrics:
  * Sleep efficiency
  * Sleep fragmentation index
  * Sleep onset latency
  * Wake after sleep onset
  * And more.
* Assessing circadian rhythms:
  * Single and multi-component cosinor in linear and nonlinear variants
  * Sigmoidally-transformed cosinor
  * Spectrum estimator using autoregressive model
  * Detrended Fluctuation Analysis
  * Nonparametric measures: Interdaily Stability, Intradaily Variability, M10, L5.
* Easy to read and analyze summaries of sleep/wake and circadian assessments for multiple subjects (in a form of comma-separated CSV).
* Automatic and customizable synchronization of actigraphic data with PSG staging.
* Plotting of all analysis steps.

## Documentation

Full documentation together with tutorial providing basic insight into functionalities of ADA as a Python package can be found under the [link](https://fuw_software.gitlab.io/aktygrafy/).

## Acknowledgments

`pygt3x` source code included in ADA is developed under the GPL-3.0 license by the Actigraph team. The original repository can be found under the [link](https://github.com/actigraph/pygt3x).
utic-public-types 3.1.0
Public/Open types shared among different projects in the Unstructured.io ecosystem.
Requires Python: >=3.10

# Introduction

A collection of Types and Schemas for use in 
various projects in the Unstructured ecosystem.

The project is intended to be very lightweight, and to play the role of the abstraction
in "Concretions can depend on abstractions, but not vice-versa".

In other words, this project should exclusively define types and remain lightweight enough
to encourage other projects to take it on as a dependency. Ideally, the project would itself
have no dependencies. For rapid development in early phases, we take a dependency on Pydantic
for easier modeling.

Right now, it is mostly utilized for defining unstructured's plugin architecture to
enable developers to define their own plugins.

**Note:** This package is automatically published to [PyPI](https://pypi.org/project/utic-public-types/) on every merge to main.

## Requirements

- Python 3.12
- `uv`: `pip install uv`


## Quick start

- `make install`
- `make test`
dalexor 0.1.2
Dalexor MI: Global MCP Brain & Sovereign CLI. Zero-knowledge code intelligence.
Requires Python: >=3.9

# Dalexor CLI (Sovereign Agent) 🧠

Install the local agent to monitor your project and sync with the Global Brain. Dalexor provides zero-knowledge code intelligence and synaptic mapping for professional developers.

## 🚀 Installation

```bash
pip install dalexor
```

## 🛠️ Setup

```bash
dalexor init
```

## 📖 Available Commands

| Command | Description |
| :--- | :--- |
| **`dalexor init`** | Configure your API Key and select your project universe. |
| **`dx watch`** | Real-time monitoring of file changes with immediate neural sync. |
| **`dx sync`** | Deep analysis of all project files and global synaptic mapping. |
| **`dx chat`** | Interactive CLI chat with your project's neural memory. |
| **`dx tail`** | Live intelligence stream of incoming project signals (**Shannon Stream**). |
| **`dx mcp`** | Start the Model Context Protocol server for IDE integration (SSE). |

---

## ⚠️ PROPRIETARY SOFTWARE
This software is **Proprietary**. All rights are reserved by Dalexor MI. 
Unauthorized copying, modification, or redistribution of this package is strictly prohibited.

Visit [dalexor.com](https://dalexor.com) for more details.
cuda-pathfinder 1.3.4
Pathfinder for CUDA components
Requires Python: >=3.10

.. SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
.. SPDX-License-Identifier: Apache-2.0

*******************************************************
cuda-pathfinder: Utilities for locating CUDA components
*******************************************************

.. image:: https://img.shields.io/badge/NVIDIA-black?logo=nvidia
   :target: https://www.nvidia.com/
   :alt: NVIDIA

`cuda.pathfinder <https://nvidia.github.io/cuda-python/cuda-pathfinder/>`_
aims to be a one-stop solution for locating CUDA components. Currently
it supports locating and loading dynamic libraries (``.so``, ``.dll``), and
locating CTK header directories. Support for other artifacts is in progress.

* `Documentation <https://nvidia.github.io/cuda-python/cuda-pathfinder/>`_
* `Releases <https://nvidia.github.io/cuda-python/cuda-pathfinder/latest/release.html>`_
* `Repository <https://github.com/NVIDIA/cuda-python/tree/main/cuda_pathfinder/>`_
* `Issue tracker <https://github.com/NVIDIA/cuda-python/issues/>`_ (select component ``cuda.pathfinder``)

``cuda.pathfinder`` is under active development. Feedback and suggestions are welcome.


Installation
============

.. code-block:: bash

   pip install cuda-pathfinder

``cuda-pathfinder`` is `CUDA Toolkit (CTK) <https://developer.nvidia.com/cuda-toolkit>`_
version-agnostic. It follows the general CUDA Toolkit support policy: the
two most recent major versions are supported simultaneously.