semvec/pypi-packages huggingface dataset:
s2dl 0.4.1
S2DL is a Python library for downloading Sentinel-2 L1C and L2A satellite imagery data.
Requires Python: >=3.9
Keywords: sentinel-2, remote-sensing, satellite-imagery, download

# S2DL - Sentinel 2 Downloader

S2DL is a Python library for downloading Sentinel-2 L1C and L2A satellite imagery data.
## Features

- Download Sentinel-2 product data using product IDs

## Installation

You can install the S2DL library via pip:

```bash
pip install s2dl
```

## Usage

Here's a simple usage example:

```python
from s2dl import fetch_single_sentinel_product
from pathlib import Path

product_id = "S2A_MSIL2A_20220111T021351_N0301_R060_T50HLK_20220111T041611"
target_directory = Path("./data")
target_directory.mkdir(exist_ok=True)

# Fetch and save a single Sentinel-2 product's data
scene_dir = fetch_single_sentinel_product(product_id, target_directory)
```

For downloading multiple products:

```python
from s2dl import fetch_multiple_sentinel_products
from pathlib import Path

product_ids = [
    "S2A_MSIL2A_20220111T021351_N0301_R060_T50HLK_20220111T041611",
    "S2A_MSIL1C_20230712T032521_N0509_R018_T52WFD_20230712T051642",
]
target_directory = Path("./data")
target_directory.mkdir(exist_ok=True)

# Fetch and save multiple Sentinel-2 products' data
scene_dirs = fetch_multiple_sentinel_products(product_ids, target_directory)
```

## Contributions

We welcome contributions from the community. Please submit a pull request with your improvements or bug fixes.

## License

S2DL is licensed under the MIT License.
code-tokenizer 1.0.3
A professional CLI tool for counting AI model tokens in code projects
Requires Python: >=3.10
Keywords: ai, claude, cli, code-analysis, gpt, llm, openai, tokens

# Code Tokenizer

**Language:** [English](README.md) | [中文](docs/README_CN.md)

![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)
![Python Version](https://img.shields.io/badge/python-3.10%2B-blue.svg)
![PyPI Version](https://img.shields.io/pypi/v/code-tokenizer.svg)
![Build Status](https://img.shields.io/badge/build-passing-brightgreen.svg)
![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)

A simple command-line tool to quickly calculate AI model Token usage for entire projects, helping you determine if your project is suitable for direct AI analysis.

Modern LLM models (like GPT-4 Turbo, Claude-4) have context lengths of 200k+, which can load entire project codebases at once. If your project's total code Token count is less than 200k, you can submit the entire project to LLM models for analysis at once, rather than having the model read files one by one. This tool provides a one-click feature to package all code into a single file, making this process easy.

## 🎯 Features

- **Token Statistics** - Accurately calculate Token counts for your entire project's code across different AI models
- **Context Analysis** - Display the percentage of each AI model's context window used by your project to determine if it exceeds limits
- **One-Click Packaging** - Merge all code files into a single file for easy one-time submission to AI models
- **Smart Filtering** - Automatically exclude irrelevant files (node_modules, .git, etc.) and keep only core code
- **GitIgnore Integration** - Automatically read and apply .gitignore rules to filter out ignored files and folders

## 📦 Installation

```bash
pip install code-tokenizer
```

## 🚀 Usage

```bash
# Count Tokens for current project (automatically applies .gitignore rules)
code-tokenizer

# Count Tokens for specified project
code-tokenizer /path/to/project

# Count and package all code into a single file
code-tokenizer --package my_project.txt

# Show only the top 5 largest files
code-tokenizer --max-show 5

# Disable automatic .gitignore rule integration
code-tokenizer --no-gitignore
```

## 📊 Example Output

![Code Tokenizer Output](docs/images/screenshot.png)

## 🔧 Supported File Types

Go, Python, JavaScript, TypeScript, Java, C/C++, Swift, Kotlin, PHP, Ruby, Vue, HTML, CSS, YAML, JSON, XML, SQL, Shell scripts, Markdown, and more

## ⚠️ Disclaimer

This project is developed based on [OpenAI tiktoken](https://github.com/openai/tiktoken). Token count results are for reference only and may vary due to tokenizer differences across AI models.

**Privacy Protection:** This project runs locally only and does not upload any code information to external servers, protecting your code privacy and security.

## 📄 License

MIT License
fa2svg 0.1.10
Convert Font Awesome HTML tags into inline SVG
Requires Python: >=3.6

# fa2svg

A small Python package that converts Font Awesome `<i>`/`<span>` tags into email‑friendly inline images. You can embed either raw SVG (`to_inline_svg`) or high‑density PNG (`to_inline_png_img`), preserving font‑size, color, aspect ratio, and baseline alignment. Includes a simple test script at `tests/run_test.py` to verify basic functionality.

---

## Installation

```bash
pip install fa2svg
```

Or for local development:

```bash
git clone https://github.com/meena-erian/fa2svg.git
cd fa2svg
pip install -e .
```

---

## Upload to PyPI

Ensure your `.pypirc` is configured, then:

```bash
# from project root
env/bin/python -m build
twine upload --config-file "./.pypirc" dist/*
```

---

## Usage

```python
from fa2svg.converter import to_inline_svg, to_inline_png_img

html = '''
  <p>
    Coffee time:
    <i class="fas fa-mug-saucer" style="font-size:64px;color:#c60"></i>
    and stars:
    <span class="far fa-star" style="font-size:48px;color:gold"></span>
  </p>
'''

# Embed as SVG data URI:
converted_svg = to_inline_svg(html)

# Embed as higher-resolution PNG data URI:
converted_png = to_inline_png_img(html)

print(converted_svg)
print(converted_png)
```

---

## API

* **`to_inline_svg(html: str) -> str`**
  
  Fetches the correct FA SVG, inlines your `font-size` and `color`, and outputs an `<img>` tag with a base64‑encoded SVG data URI, plus `width`, `height`, and `vertical-align` CSS.

* **`to_inline_png_img(html: str) -> str`**
  
  Same as above but renders a PNG at higher pixel density (default 2×) for crisper images in clients with varying DPI.

---

## Testing

A basic test harness is provided at `tests/run_test.py`:

```bash
python -m tests.run_test
```

---

## Contributing

1. Fork the repo
2. Create a feature branch
3. Commit & push your changes
4. Open a Pull Request

Issues and PRs are very welcome!
ros-kilted-ament-cmake-gen-version-h 2.7.3
Meta-package for ament_cmake_gen_version_h (ros-ament-cmake-gen-version-h) for ROS 2 kilted.

Meta-package for ament_cmake_gen_version_h (ros-ament-cmake-gen-version-h) for ROS 2 kilted. Built using https://github.com/ycheng517/ros-python-wheels
ros-kilted-ament-cmake-flake8 0.19.2
Meta-package for ament_cmake_flake8 (ros-ament-cmake-flake8) for ROS 2 kilted.

Meta-package for ament_cmake_flake8 (ros-ament-cmake-flake8) for ROS 2 kilted. Built using https://github.com/ycheng517/ros-python-wheels
ros-kilted-ament-cmake-cppcheck 0.19.2
Meta-package for ament_cmake_cppcheck (ros-ament-cmake-cppcheck) for ROS 2 kilted.

Meta-package for ament_cmake_cppcheck (ros-ament-cmake-cppcheck) for ROS 2 kilted. Built using https://github.com/ycheng517/ros-python-wheels
ros-kilted-ament-cmake-export-libraries 2.7.3
Meta-package for ament_cmake_export_libraries (ros-ament-cmake-export-libraries) for ROS 2 kilted.

Meta-package for ament_cmake_export_libraries (ros-ament-cmake-export-libraries) for ROS 2 kilted. Built using https://github.com/ycheng517/ros-python-wheels
ros-kilted-rosidl-dynamic-typesupport 0.3.1
Meta-package for rosidl_dynamic_typesupport (ros-rosidl-dynamic-typesupport) for ROS 2 kilted.

Meta-package for rosidl_dynamic_typesupport (ros-rosidl-dynamic-typesupport) for ROS 2 kilted. Built using https://github.com/ycheng517/ros-python-wheels
ros-kilted-ament-cmake-vendor-package 2.7.3
Meta-package for ament_cmake_vendor_package (ros-ament-cmake-vendor-package) for ROS 2 kilted.

Meta-package for ament_cmake_vendor_package (ros-ament-cmake-vendor-package) for ROS 2 kilted. Built using https://github.com/ycheng517/ros-python-wheels
ros-kilted-ament-cmake-pep257 0.19.2
Meta-package for ament_cmake_pep257 (ros-ament-cmake-pep257) for ROS 2 kilted.

Meta-package for ament_cmake_pep257 (ros-ament-cmake-pep257) for ROS 2 kilted. Built using https://github.com/ycheng517/ros-python-wheels
finsignals 0.1.0
Lightweight library for financial signal generation and analysis
Requires Python: >=3.8

# FinSignals

Lightweight Python package for generating financial signals and indicators.

## Installation
```bash
pip install finsignals
```

## Example
```python
import pandas as pd
from finsignals import indicators, preprocessing

data = pd.Series([100, 101, 99, 102, 105])
returns = preprocessing.compute_returns(data)
mid, up, low = indicators.bollinger_bands(data)
```