semvec/pypi-packages huggingface dataset:
styxdefs 0.6.0
Styx definitions and minimal runtime
Requires Python: >=3.10

# Styx type definitions and minimal runtime

[![Build](https://github.com/styx-api/styxdefs/actions/workflows/test.yaml/badge.svg?branch=main)](https://github.com/styx-api/styxdefs/actions/workflows/test.yaml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/styx-api/styxdefs/branch/main/graph/badge.svg?token=22HWWFWPW5)](https://codecov.io/gh/styx-api/styxdefs)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
![stability-stable](https://img.shields.io/badge/stability-stable-green.svg)
[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/styx-api/styxdefs/blob/main/LICENSE)
[![pages](https://img.shields.io/badge/api-docs-blue)](https://styx-api.github.io/styxdefs)

Type definitions and minimal runtime for [Styx](https://github.com/styx-api/styx) generated wrappers and
Styx Runners. This package defines the common API for all Styx generated code.

## Installation

```bash
pip install styxdefs
```
apifairy 1.5.1
A minimalistic API framework built on top of Flask, Marshmallow and friends.
Requires Python: >=3.9

# APIFairy

[![Build status](https://github.com/miguelgrinberg/apifairy/workflows/build/badge.svg)](https://github.com/miguelgrinberg/apifairy/actions) [![codecov](https://codecov.io/gh/miguelgrinberg/apifairy/branch/main/graph/badge.svg)](https://codecov.io/gh/miguelgrinberg/APIFairy)

APIFairy is a minimalistic API framework built on top of Flask, and with the
support of Marshmallow schemas. Using a familiar decorator syntax you can
generate a live documentation site directly from your source code.

Check out [Microblog-API](https://github.com/miguelgrinberg/microblog-api) to
see APIFairy in action in a non-trivial project.

![APIFairy example](docs/_static/apispec-example.png)

Resources
---------

- [Documentation](http://apifairy.readthedocs.io/en/latest/)
- [PyPI](https://pypi.python.org/pypi/APIFairy)
- [Change Log](https://github.com/miguelgrinberg/APIFairy/blob/main/CHANGES.md)
simulateur-trafic-ahmed-hamed-2025 1.0.1
A traffic simulation application
Requires Python: <3.12,>=3.10


 

# Simulateur de Trafic (Traffic Simulator)

**Simulateur de Trafic** is a Python package for simulating vehicle traffic on customizable road networks.
It models vehicle motion, road connections, and collects useful traffic statistics for analysis and optimization.

 

## Features

* Vehicle movement simulation on a configurable road network
* Roads with speed limits and lengths
* Traffic statistics (speed, flow, time)
* Results exportable to CSV or JSON
* Fully customizable through a JSON configuration file
 
 

## Installation

### From PyPI

```bash
pip install simulateur-trafic-ahmed-hamed-2025
```

### For development (editable mode)

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

---

## Usage

### Default execution

```bash
simulateur-trafic
```

Runs the simulation with:

* `n_tours=5`
* `delta_t=60` (seconds)
* `config=config_reseau.json`

---

### Custom parameters

You can specify the number of simulation turns, time step, and configuration file path:

```bash
simulateur-trafic --n_tours 10 --delta_t 30 --config config_reseau.json
```

Or run it directly from Python:

```bash
python -m simulateur_trafic.main --n_tours 10 --delta_t 30 --config config_reseau.json
```

---

## License

This project is licensed under the **MIT License**.
See the `LICENSE` file for details.

---

## Author

**Ahmed Hamed**
📧 [ahmad.hamed.work@gmail.com](mailto:ahmad.hamed.work@gmail.com)

---
glow-cli 0.3.7
Smart CLI tool for shell template
Keywords: smart cli, shell template

# glow
The glow command line interface.

[![PyPI version](https://img.shields.io/pypi/v/glow-cli)](https://pypi.org/project/glow-cli/)
[![📦 pypi](https://github.com/loopsocial/glow/actions/workflows/publish.yml/badge.svg)](https://github.com/loopsocial/glow/actions/workflows/publish.yml) 

```
   ________    ____ _       __
  / ____/ /   / __ \ |     / /
 / / __/ /   / / / / | /| / /
/ /_/ / /___/ /_/ /| |/ |/ /
\____/_____/\____/ |__/|__/
____________________________
__________________________________
________________________________________
```

## 📦 Installation
```shell
pip install glow-cli
```

You can set up configuration in `~/.glow/commands/<task>.yml`

## 🚀 Usage

```shell
g list
```

## 🦋 Templating
Say you set up a config at "./commands/pod-shell.yml"
```yaml
description: |
  Entering the shell of the 1st pod that matches the keyword
command: |
  kubectl exec -it $(kubectl get pods | grep { kw } | head -n 1 | awk '{print $1}') -- /bin/bash
inputs:
  kw:
    description: keyword to match
    type: str
```

you can run the command with:
```shell
g ./commands/pod-shell.yml --kw "app1"
```

Or you can store the configuration by
```shell
g install ./commands/pod-shell.yml
```

Then you can run the command with:
```shell
g pod-shell --kw "app1"
```

## Setup Configs and Secrets
### Setup(CRUD) secrets
```shell
# to make secret token accessible to g command line
g secrets add SOME_TOKEN

g secrets list
g secrets remove SOME_TOKEN
```
### Setup(CRUD) configs
```shell
g config add SOME_CONFIG "us-east-1"
g config list
g config remove SOME_CONFIG
```

## 🦙 LLM in CLI 💻
> Why memorize commands when you can just ask?


Then you can try:
```shell
g llm "redub a.mp4 file with b.mp3, save to c.mp4 with fps 12"
```

Or you can ask other things in general:
```shell
g llm "explain btree to me"
```

Or with certain flavor to the robot role
```shell
g llm "explain btree to me" "you are a poet return everything in homer style"
```

### ✨ combine with other commands
Here's some good use cases to combine with other commands
```shell
g llm "shell into one of the pods: $(kubectl get pods) with keyword app1"
```

```shell
g llm "please summarize the commit message for $(git diff)"
```

```shell
g llm "can you explain the structure of this folder: $(ls -l)"
```

### Choose other model
Use the following to choose other model
```
g configs add GLOW_LLM
```

## Glow hub
Setup a hub on github to manage your glow commands. Say if the hub is at `https://github.com/repo_owner/hub`

Example structure of a hub:

```
./
├── README.md
├── glow
│   ├── README.md
│   ├── script-1.yml
│   ├── build-image.yml
│   ├── deploy-abc.yml
```

### Setup
```shell
g secrets add GH_PATH
g configs add GLOW_HUB_OWNER repo_owner
g configs add GLOW_HUB_REPO hub
```

### Install script
```shell
g glowhub script-1.yml
```
sodas-legacy 1.1.1
Legacy Python SDK for the SODAS Framework
Requires Python: >=3.7

# sodas-legacy

SODAS 프레임워크를 위한 공식 Python SDK입니다.
이 SDK를 통해 데이터셋의 메타데이터를 효율적으로 관리하고 표준화된 방식으로 처리할 수 있습니다.
DCAT(Data Catalog Vocabulary) 표준을 기반으로 하여 데이터셋의 메타데이터를 체계적으로 관리할 수 있습니다.

## 설치 방법

```bash
pip install sodas-legacy
```

## 주요 기능

- 데이터셋 메타데이터 생성 및 관리 (DCAT 기반)
- 다국어 메타데이터 지원 (제목, 설명, 키워드 등)
- 프로파일 기반의 메타데이터 스키마 관리
- 리소스 설명자를 통한 데이터 리소스 관리
- 버전 관리 및 이력 추적

## 핵심 컴포넌트

### API 초기화

SDK를 사용하기 전에 반드시 API URL을 초기화해야 합니다:

```python
from sodas_sdk import configure_api_url

DATAHUB_API_URL = "http://sodas-profile.example.com"
GOVERNANCE_API_URL = "http://api.example.com"

configure_api_url(DATAHUB_API_URL, GOVERNANCE_API_URL)
```

### Dataset

데이터셋의 메타데이터를 관리하는 핵심 클래스입니다.

```python
from sodas_sdk import Dataset,Distribution

dataset = Dataset()
# 기본 메타데이터 설정
dataset.set_title("데이터셋 제목")
dataset.set_description("데이터셋 설명")

# 다국어 지원
dataset.set_title("English Title", "en")
dataset.set_description("English Description", "en")

# 기타 메타데이터
dataset.type = "http://purl.org/dc/dcmitype/Dataset"
dataset.access_rights = "http://purl.org/eprint/accessRights/OpenAccess"
dataset.lincense = "http://creativecommons.org/licenses/by/4.0/"\

# 디스트리뷰션
distribution = dataset.create_distribution()
distribution.set_title("디스트리뷰션 제목")
distribution.set_description("디스트리뷰션 설명")
# 파일을 업로드하는 경우
if file exists:
    distribution.set_uploading_data("상대경로")
# download_url이 존재하는 경우
if downlod url exists:
    distribution.download_url = "https://DOWNLOAD_URL"

#만약 버킷 정보를 입력하고 싶으면 다음과 같이 합니다.
Distribution.configure_bucket_name("bucket")
# dataset에서 db_record api를 호출하면 distribution은 자동호출됩니다.
await dataset.create_db_record()
# dataset과 distribution은 백엔드와 통신 후 정보를 업데이트합니다.
# 파일 설정시 파일을 업로드하고 download_url이 생깁니다.
downnload_url = distribution.download_url
```

### DatasetSeries

데이터셋들의 집합을 관리합니다.
데이터셋은 단 하나의 데이터셋 시리즈에만 들어갈 수 있으며(1:N),
현재는 데이터셋 시리즈는 데이터셋 시리즈에 들어가지 못합니다.

```python
from sodas_sdk import DatasetSeries
# 처음 데이터 활용
creating_series = DatasetSeries()

dataset1 = await Dataset.get_db_record(dataset1_ID)
dataset2 = await Dataset.get_db_record(dataset2_ID)

creating_series.append_series_member(dataset1)
creating_series.append_series_member(dataset2)

await creating_series.create_db_record()

# 이후 만들어진 데이터셋 시리즈 활용. create_db_record 이후 똑같은 프로세스로 활용 가능합니다.
updating_series = await DatasetSeries.get_db_record(existing_series.id)
appending_dataset = await Dataset.get_db_record(exsiting_dataset.id)
updating_series.append_series_member(appending_dataset)

await updating_series.update_db_record()
```

### Profile

```python
from sodas_sdk import Profile,Dataset
# 처음 데이터 활용
existing_dataset = await Dataset.get_db_record(existing_dataset_id)

dcatProfile = await Profile.get_db_record(exsiting_dataset.profile_iri)
dataProfile = await Profile.get_db_record(existing_dataset.conforms_to)

mapping_value = dataProfile.get_template_descriptor_value_of_role(ResourceDescriptorRole.MAPPING)
schema_value = dataProfile.get_template_descriptor_value_of_role(ResourceDescriptorRole.SCHEMA)
```

### ResourceDescriptor

데이터 리소스의 구조와 특성을 기술하는 클래스입니다.