semvec/pypi-packages huggingface dataset:
streamax 0.3.1
A JAX-accelerated stream generator (StreaMAX)
Requires Python: >=3.7

![Banner](./im.png)
*Image credit: [Sarah Kane](https://www.ast.cam.ac.uk/people/sarah.kane)*

# A JAX-Accelerated Stellar Stream Generator

StreaMAX is a lightweight, high-performance simulator for stellar streams built with JAX.  
It’s designed to make modeling and inference on stellar streams fast enough for Bayesian analysis and modern data-intensive workflows.

---

## 🚀 Features

- Particle-spray modeling of stellar streams  
- Fast 2D track extraction  
- JAX-accelerated integration for GPU/TPU compatibility  
- Automatic differentiation, can easily compute gradients of the models directly via JAX  
- Modular architecture

---

## 🧩 Models

StreaMAX supports:
- Rapid generation of stellar streams using the particle-spray method  
- Efficient extraction of stream tracks for analysis and inference  

---

## 🧪 Quick Start

See the `quick_start.ipynb` notebook for an example on how to:

- Define potential and stream parameters  
- Generate a stream using the particle-spray method  
- Extract and visualize the resulting track  

---

## ⚙️ Installation

### From PyPI

```bash
pip install StreaMAX
```

### From GitHub (development version)

```bash
git clone https://github.com/David-Chemaly/StreaMAX.git
cd StreaMAX
pip install -e .
```

### Manual dependency installation

```bash
pip install -r requirements.txt
```

---

## 📚 Citation & License

This project is released under the MIT License.  
If you use StreaMAX in your research, please cite and reference the following paper:

[Chemaly, D. et al. 2025, Hierarchical bayesian inference: constraining population distribution of dark matter halo shapes via stellar streams](https://ui.adsabs.harvard.edu/abs/2026arXiv260115373C/abstract)

---

## 🪐 Contributing

Pull requests and feature suggestions are welcome.  
If you encounter bugs or wish to contribute new potentials or integrators, please open an issue on GitHub.

---

**Fast. Differentiable. Modular. JAX-powered.**
dagshub-annotation-converter 0.1.16
Annotation converter between different formats
Requires Python: >=3.8

# DagsHub Annotation Converter

This package is intended to be a multi-type importer/exporter/converter
between different annotation formats.

This package is currently in development and has not that many features implemented.
The API is not stable and is subject to change.

The package consists of the Intermediary Representation (IR) annotation format in Python Objects,
and importers/exporters for different annotation formats.

## Installation

```bash
pip install dagshub-annotation-converter
```

## Importers (Image):
- [YOLO BBox, Segmentation, Poses](dagshub_annotation_converter/converters/yolo.py#L81)
- [Label Studio](dagshub_annotation_converter/formats/label_studio/task.py#L72) (Only task schema implemented, importing from a project is left up to user):
```python
from dagshub_annotation_converter.formats.label_studio.task import LabelStudioTask
task_obj = LabelStudioTask.from_json("path/to/label_studio_task.json")

annotations = task_obj.to_ir_annotations()
```
- [CVAT Image](dagshub_annotation_converter/converters/cvat.py#L46)

## Exporters (Image):
- [YOLO BBox, Segmentation, Poses](dagshub_annotation_converter/converters/yolo.py#L126)
- [Label Studio](dagshub_annotation_converter/formats/label_studio/task.py#L225) (Again, only task schema, uploading the task to the project is left to the user)

## Experimental Features:

The package contains some experimental features that can be toggled on by setting a corresponding environment variable:

- `DAGSHUB_ANNOTATION_EXPERIMENTAL_CVAT_POSE_GROUPING_BY_GROUP_ID_ENABLED` - When importing CVAT annotations, 
you can put a single bbox and either a points or skeletons annotation in a group, and then they will be grouped together
into a pose annotation. When exporting that to YOLO, the bbox and the points will be exported as a single annotation.
 
You can read more about the features that exist, their limitations and potential side effects
in the [features.py](dagshub_annotation_converter/features.py) file
scopeql 0.3.1
A command-line interface for ScopeDB
Requires Python: >=3.10

# ScopeQL: ScopeDB Command Line Interface

[![Apache 2.0 licensed][license-badge]][license-url]
[![Build Status][actions-badge]][actions-url]

[license-badge]: https://img.shields.io/crates/l/scopeql
[license-url]: LICENSE
[actions-badge]: https://github.com/scopedb/scopeql/workflows/CI/badge.svg
[actions-url]:https://github.com/scopedb/scopeql/actions?query=workflow%3ACI

## Overview

`scopeql` provides a command line interface and interactive shell for ScopeDB.

## License

This project is licensed under [Apache License, Version 2.0](LICENSE).
torch-model-inspector 0.1.2
Lightweight utilities for inspecting PyTorch model shapes.
Requires Python: >=3.8
Keywords: pytorch, model, debug, shapes

# torchinspect

Lightweight utilities for inspecting PyTorch model shapes and common mismatch errors.

## Install

From PyPI (recommended):

```bash
pip install torch_model_inspector
```

Local editable install (for development):

```bash
pip install -e .
```

## Usage

```python
import torch
import torch.nn as nn
from torchinspect import analyze, count_parameters, print_report

class BadModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(3, 8, kernel_size=3, stride=1, padding=1)
        self.fc = nn.Linear(16, 10)  # intentionally wrong

    def forward(self, x):
        x = self.conv(x)
        return self.fc(x)

m = BadModel()
x = torch.randn(2, 3, 32, 32)

res = analyze(m, x)
print_report(res, last_n=20)

total_params = count_parameters(m)
print("trainable params:", total_params)
```

## What’s included

- `analyze(model, *example_inputs)`: records per-layer input/output shapes
- `print_report(result)`: prints a readable summary
- `suggest_fixes(records, error)`: helpful hints for common shape issues
- `count_parameters(model, trainable_only=True)`: total parameter count

## Import name

The PyPI package is `torch_model_inspector`, but you import it as:

```python
import torchinspect
```

## License

MIT
metacar 0.4.0
智能网联平台API
Requires Python: >=3.10

# MetaCar - 智能网联平台API

MetaCar 是一个用于智能网联汽车仿真平台的 Python API，提供了与仿真环境通信、获取场景数据以及发送车辆控制命令的功能。

## 功能特性

- 与仿真环境的 TCP 通信
- 获取场景静态数据（道路、路径等）
- 实时获取车辆状态和传感器数据
- 发送车辆控制命令（油门、刹车、转向等）
- 摄像头视频流处理
- 矢量计算和几何工具
- 丰富的数据模型

## 安装

```bash
pip install metacar
```

## 基本使用

```python
from metacar import SceneAPI, VehicleControl, GearMode

# 创建 API 实例并连接
api = SceneAPI()
api.connect()

# 获取场景静态数据
static_data = api.get_scene_static_data()

# 主循环
for sim_car_msg, frames in api.main_loop():
    # 创建控制命令
    vc = VehicleControl()
    vc.throttle = 0.5  # 设置油门 (0-1)
    vc.steering = 0.0  # 设置方向盘 (-1 左, 0 中, 1 右)
    
    # 发送控制命令
    api.set_vehicle_control(vc)
```

## 示例代码

在 `examples` 目录中包含了多个示例：

- `main.py` - 基础的键盘控制示例
- `gui.py` - 带图形界面的状态显示示例

## 文档

完整的文档可以在以下方式获取：

1. [在线文档](https://autodrive-api-python.readthedocs.io/zh-cn/latest/) - 查看最新的 API 文档
2. 本地构建：
   ```bash
   cd docs
   pip install -r requirements.txt
   make html
   ```

## 系统要求

- Python 3.10 或更高版本

## 许可证

本项目使用 MIT 许可证。详见 [LICENSE](LICENSE) 文件。
matita 1.0.3
Python wrapper for the VBA Object library
Requires Python: >=3.13

# *Matita* - Full Microsoft Office automation in Python ✏️

*Matita* is a Python wrapper for the [VBA Object library](https://learn.microsoft.com/en-us/office/vba/api/overview/).
You get the full power of VBA with the convenience of Python.
See the [documentation](https://matita.readthedocs.io/en/latest/documentation) for more details.

## Hello world with *matita*

### Excel 📊

```python
from matita.office import excel as xl

def hello_world():
    xl_app = xl.Application()
    xl_app.visible = True

    wkb = xl_app.workbooks.add()
    wks = wkb.worksheets(1)
    c = wks.cells(1,1)
    c.value = "Hello world!"
```

### PowerPoint 🖼️

```python
from matita.office import powerpoint as pp

def hello_world():
    pp_app = pp.Application()
    pp_app.visible = True
    prs = pp_app.presentations.add()
    sld = prs.slides.add(1, pp.ppLayoutText)
    shp = sld.shapes.addshape(pp.msoShapeRectangle, 100, 100, 200, 100)
    shp.text_frame.text_range.text = "Hello world!"
```

### Word 📄

```python
from matita.office import word as wd

def hello_world():
    wd_app = wd.Application()
    wd_app.visible = True
    doc = wd_app.documents.add()
    par = doc.content.paragraphs.add()
    par.range.text = "Hello world!"
```

### Outlook 📧

```python
from matita.office import outlook as ol

def hello_world():
    ol_app = ol.Application()
    mail = ol.MailItem(ol_app.create_item(ol.olMailItem))
    mail.body = "Hello world!"
    mail.display()
```

## Installation

```powershell
python -m pip install matita
```

## License

Copyright 2026 Luca Franceschini (lucaf.eu)

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

This program is based on the [Office VBA Reference](https://learn.microsoft.com/en-us/office/vba/api/overview) by Microsoft Corporation, [licensed](https://github.com/MicrosoftDocs/VBA-Docs/blob/main/LICENSE) under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).