semvec/pypi-packages huggingface dataset:
kitech-repository-dev 0.1.9
KITECH Manufacturing Data Repository CLI and Library - Development Version
Requires Python: <3.14,>=3.10
Keywords: cli, data, file-manager, kitech, manufacturing, repository, tui

# KITECH Manufacturing Data Repository CLI

KITECH 제조 데이터 리포지토리를 위한 Python CLI 도구입니다.

## 기능

- 🖥️ **대화형 파일 관리자 (TUI)** - 듀얼 패널 UI로 직관적인 파일 관리
- 🔐 API Token 기반 인증
- ⬇️ 파일/폴더 다운로드 (MD5 무결성 검증)
- ⬆️ 파일/폴더 업로드 (MD5 무결성 검증)
- 📊 실시간 진행률 표시

## 요구 사항

- Python 3.10 ~ 3.13
- pip

## 설치

```bash
pip install kitech-repository-dev
```

## 사용법

```bash
# 파일 관리자 실행
kitech-dev manager start

# 키보드 단축키
# Tab       - 패널 간 이동
# ↑/↓       - 파일 선택
# Enter     - 폴더 열기/파일 선택
# F3        - 다운로드
# F5        - 업로드
# F10/ESC   - 종료
```

## 환경 설정

API 서버 주소를 설정해야 합니다:

```bash
# 환경 변수로 설정
export KITECH_API_BASE_URL=https://your-api-server.com

# 또는 .env 파일에 작성
echo "KITECH_API_BASE_URL=https://your-api-server.com" > .env
```

**주의**: `/v1`은 자동으로 추가되므로 입력하지 마세요.

---

## 개발자를 위한 Library API

Python 프로그램에서 직접 사용할 수 있습니다:

```python
from kitech_repository import KitechClient

client = KitechClient(token="kt_xxx")
repos = client.list_repositories()
client.download_file(repository_id=123, path="/data/file.csv")
client.upload_file(repository_id=123, file_path="local.csv")
```

## 라이센스

TBD
sed-extractor 1.3.0b15
SED Extractor is a tool from the CADE project. It allows to extract a Spectral Energy Distribution from Healpix, WCS or mixture of Healpix and WCS files, in a specific region of the sky.
Requires Python: >=3.10

# SED Extractor

SED Extractor is a tool from the [CADE](https://cade.irap.omp.eu) project.

It allows to extract a **Spectral Energy Distribution** from Healpix, WCS or mixture of Healpix and WCS files, in a specific
region of the sky. This tool guarantees the photometric accuracy, by using the [drizzlib library](https://cade.irap.omp.eu/dokuwiki/doku.php?id=drizzlib).

The current library is available in Python3.

If all files are in the Healpix format, mixing Allsky and Partial Healpix, use the 'mask' keyword if possible (with the
file having the less observed pixels) in order to restrict the area to find the common pixels and to significantly
reduce time computing.

If you use the SED Extractor for your research, please include the following acknowledgement in your publications:

“We acknowledge the use of the SED Extractor software provided by the Centre d'Analyse de Données Etendues (CADE), a
service of IRAP-UPS/CNRS (http://cade.irap.omp.eu, Paradis et al., 2012, A&A, 543, 103).”
amazon-scraper-vivektyagi 0.1.12
A Python module to scrape product details from Amazon India using Selenium.
Requires Python: >=3.7
Keywords: amazon scraper selenium india

#amazon_scraper

A simple and powerful multi-country Amazon product scraper built using Python and Selenium.
It fetches product details like Title, Price, Bought, Rating, Reviews, Image URL, Brand, Category Ranks, and checks for Product Description and From the Manufacturer sections using ASINs.

##🚀 Features

🌍 Scrape from any Amazon marketplace by passing country_code (e.g., in, us, ae, uk)
🔍 Fetch detailed product info:
Title
Price
Bought
Rating
Reviews
Image URL
Brand
Parent Category Rank
Child Category Rank
Product Description Available
From the Manufacturer Available

📦 Supports single or multiple ASINs
🧼 Automatically handles browser setup and teardown
💡 Lightweight and easy to use

##📦 Installation
###From PyPI:
pip install amazon-scraper-vivektyagi

💻 Usage
Scrape a single product
from amazon_scraper_vivektyagi import get_amazon_product_details

data = get_amazon_product_details("B0C1234567", country_code="in")
print(data)

Scrape multiple products

from amazon_scraper_vivektyagi import get_multiple_product_details
asins = ["B0C1234567", "B0D7654321"]
results = get_multiple_product_details(asins, country_code="ae")

for product in results:
    print(product)

✅ Output Format
{
    'ASIN': 'B0C1234567',
    'Title': 'Sample Product Title',
    'Price': '₹1,299.00',
    'Bought':'200',
    'Rating': '4.3 out of 5 stars',
    'Reviews': '345',
    'Image': 'https://m.media-amazon.com/images/I/xxxxx.jpg',
    'Brand': 'Sample Brand',
    'Parent_Category_Rank': '#12 in Electronics',
    'Child_Category_Rank': '#1 in Headphones',
    'Product_Description': 'Yes',
    'From_Manufacturer': 'No'
}

📄 Requirements
Python 3.6 or higher
Google Chrome installed
ChromeDriver (auto-installed via webdriver-manager)

Install dependencies:
pip install selenium beautifulsoup4 webdriver-manager


👤 Author
Vivek Tyagi

📄 License
This project is licensed under the MIT License.

⚠️ Disclaimer
This tool is for educational purposes only.
Scraping Amazon may violate their Terms of Service. Use responsibly.
cka-pytorch 1.1.3
A PyTorch implementation of Centered Kernel Alignment (CKA) with GPU support.
Requires Python: >=3.11
Keywords: pytorch, cka, centered kernel alignment

# Centered Kernel Alignment (CKA) - PyTorch Implementation

A PyTorch implementation of Centered Kernel Alignment (CKA) with GPU support for fast and efficient computation.

> [!WARNING]
> This project is for educational and academic purposes (and for fun 🤷🏻).

## Features

- **GPU Accelerated:** Leverages the power of GPUs for significantly faster CKA calculations compared to NumPy-based implementations.
- **On-the-Fly Calculation:** Computes CKA on-the-fly using mini-batches, avoiding the need to cache large intermediate feature representations.
- **Easy to Use:** Simple and intuitive API for calculating the CKA matrix between two models.
- **Flexible:** Can be used with any PyTorch models and dataloaders.

## Installation
```bash
pip install cka-pytorch
```

## Usage

```python
import torch

from torchvision.models import resnet18
from torch.utils.data import DataLoader

from cka_pytorch.cka import CKACalculator


# 1. Define your models and dataloader
model1 = resnet18(pretrained=True).cuda()
model2 = resnet18(pretrained=True).cuda() # Or a different model

# Create a dummy dataloader for demonstration
dummy_data = torch.randn(100, 3, 224, 224)
dummy_labels = torch.randint(0, 10, (100,))
dummy_dataset = torch.utils.data.TensorDataset(dummy_data, dummy_labels)
dataloader = DataLoader(dummy_dataset, batch_size=32)

# 2. Initialize the CKACalculator
# By default, we will calculate CKA across all layers of the two models
calculator = CKACalculator(
    model1=model1,
    model2=model2,
    model1_name="ResNet18",
    model2_name="ResNet18",
    batched_feature_size=256,
    verbose=True,
)

# 3. Calculate the CKA matrix
cka_matrix = calculator.calculate_cka_matrix(dataloader)

# 4. Plot the CKA Matrix as heatmap
calculator.plot_cka_matrix(title="CKA between ResNet18 and ResNet18")
```

## Contributing

- If you find this repository helpful, please give it a :star:.
- If you encounter any bugs or have suggestions for improvements, feel free to open an issue.
- This implementation has been primarily tested with ResNet architectures.

## Acknowledgement
This project is based on:
- [CKA.pytorch](https://github.com/numpee/CKA.pytorch)
- [centered-kernel-alignment](https://github.com/RistoAle97/centered-kernel-alignment)