semvec/pypi-packages huggingface dataset:
pandukabhaya 1.0.1
An ASCII to Unicode text converter for 'FM Abhaya' Sinhala font, with extensibility for other fonts via JSON mappings.
Requires Python: >=3.6

# Pandukabhaya

Pandukabhaya is an ASCII to Unicode text converter. Currently, it only supports the 'FM Abhaya' ASCII Sinhala font, but other font mappings will be added in the future. Pandukabhaya is written as a generic text conversion tool that uses JSON mappings to transform text. Therefore, it can be used to convert any text given the mapping.

## Name
The most famous Sinhala ASCII font is 'FM Abhaya' named after King Abhaya (474 BCE to 454 BCE). The tool is named after his nephew, who was named after King Abhaya and King Panduvasdeva. Pandukabhaya accended to the throne replacing Abhaya (technically Tissa succeeded to the throne right after Abhaya. But we chose the most notable successor 😀)

## Features

- A iteratively generated mapping file for 'FM Abhaya' font.
- Loads mappings from JSON files for flexibility.
- Command-line interface for quick and easy usage.

## Installation
Package is pending release in Pypi. For now clone the repository and install from the clone.
```bash
pip install pandukabhaya
```

## Usage

### As a CLI Tool
```bash
pandukabhaya <mapping_name> -t <text>
```
```bash
pandukabhaya <mapping_name> -t <text> -o <output_file>
```
```bash
pandukabhaya <mapping_name> -i <input_file> -o <output_file>
```

### As a Python Module
```python
from pandukabhaya import Converter

converter = Converter("fm_abhaya")
output = converter.convert("rkaosl")
print(output)
```
### Run tests
```bash
python -m unittest tests.test_converter
```

## Folder Structure and Explanations
```
PANDUKABHAYA/
├── pandukabhaya/
│   ├── cli.py
│   ├── converter.py
│   └── mappings/
│       └── fm_abhaya.json
├── scripts/
│   ├── generation.ipynb
│   └── prep.ipynb
└── tests/
    ├── test_cases.json
    └── test_converter.py
```

* `pandukabhaya/`: Core package directory containing the simple code modules (cli.py, converter.py)
    * `mappings/`: Contains mapping files.
* `scripts/`: Contains Jupyter notebooks and mapping files for data analysis, preparation, and generation processes.
    * prep.ipynb - Cleans and corrects the UCSC mapping file.
    * generation.ipynb - Using improved UCSC mappings as a guide, generates mappings iteratively
* `tests/`: Contains unit test scripts and test data to ensure code quality and functionality.
quantecon_wasm 0.7.2.post1
Import the main names to top level.
Requires Python: >=3.7
Keywords: quantitative, economics

# QuantEcon.py-wasm

QuantEcon's pure python package for WASM based environments. This library is a mirror of [QuantEcon.py](https://github.com/QuantEcon/QuantEcon.py).
We highly recommend to use the original `quantecon` for better performance.

Anything that works with `quantecon_wasm.*` is expected to work with `quantecon.*` with equal or higher performance.

## Installation

You can install `quantecon_wasm` by opening a terminal prompt and typing

```
pip install quantecon_wasm
```
samv2-webcam 0.0.3
CPU compatible fork of the official SAMv2 implementation with Webcam support
Requires Python: >=3.8
Keywords: deep-learning, pytorch

<a href="https://colab.research.google.com/github/SauravMaheshkar/samv2/blob/main/examples/notebooks/samv2_prompted_segmentation_with_wandb_tables.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
[![Build and Tests](https://github.com/SauravMaheshkar/samv2/actions/workflows/ci.yml/badge.svg)](https://github.com/SauravMaheshkar/samv2/actions/workflows/ci.yml) [![](https://img.shields.io/badge/Weights_&_Biases-FFCC33?&logo=WeightsAndBiases&logoColor=black)](https://wandb.ai/sauravmaheshkar/samv2)

CPU **compatible** fork of the official SAMv2 implementation with webcam support.

## Features 🚀

* CPU compatible
* ships with config files
* Run image and video, and webcam inference on CPUs
* [Example notebooks](../examples/notebooks/) showcasing inference using weights and biases.

## Installation

You can download it from [pypi](https://pypi.org/) using `pip` as follows:

```bash
pip install samv2-webcam
```

or from the repository:

```bash
pip install git+https://github.com/sonnyky/samv2.git
```

## Usage

After downloading the official weights, you can use the `load_model()` helper method to instantiate a model.

```python
from sam2 import load_model

model = load_model(
    variant="tiny",
    ckpt_path="artifacts/sam2_hiera_tiny.pt",
    device="cpu"
)
```

* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SauravMaheshkar/samv2/blob/main/examples/notebooks/samv2_prompted_segmentation_with_wandb_tables.ipynb) Example Notebook to run prompted segmentation on images logging predictions as W&B Tables.
* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SauravMaheshkar/samv2/blob/main/examples/notebooks/samv2_automatic_segmentation_with_wandb_tables.ipynb) Example Notebook to run automatic segmentation on images logging predictions as W&B Tables.

## Citation

```bibtex
@article{ravi2024sam2,
  title={SAM 2: Segment Anything in Images and Videos},
  author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
  journal={arXiv preprint},
  year={2024}
}
```