semvec/pypi-packages huggingface dataset:
lacosmic 1.4.0
Laplacian Cosmic Ray Identification
Requires Python: >=3.11
Keywords: astronomy, astrophysics, images, cosmic rays, cosmic-ray removal, filtering

========
lacosmic
========

|PyPI Version| |CI Status| |Codecov Status| |Stable RTD Status| |Zenodo| |Astropy|

lacosmic is a Python package to remove cosmic rays from an
astronomical image using the `L.A.Cosmic algorithm
<http://www.astro.yale.edu/dokkum/lacosmic/>`_.  The algorithm is
based on Laplacian edge detection and is described in `van Dokkum
2001 (PASP 113, 1420)
<https://ui.adsabs.harvard.edu/abs/2001PASP..113.1420V/abstract>`_.


.. |PyPI Version| image::  https://img.shields.io/pypi/v/lacosmic.svg?logo=pypi&logoColor=white&label=PyPI
    :target: https://pypi.org/project/lacosmic/
    :alt: PyPI Latest Release

.. |Astropy| image:: https://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat
    :target: https://www.astropy.org/
    :alt: Powered by Astropy

.. |CI Status| image:: https://github.com/larrybradley/lacosmic/workflows/CI%20Tests/badge.svg#
    :target: https://github.com/larrybradley/lacosmic/actions
    :alt: CI Status

.. |Codecov Status| image:: https://img.shields.io/codecov/c/github/larrybradley/lacosmic?logo=codecov
    :target: https://codecov.io/gh/larrybradley/lacosmic
    :alt: Codecov Status

.. |Stable RTD Status| image:: https://img.shields.io/readthedocs/lacosmic/latest.svg?logo=read%20the%20docs&logoColor=white&label=Docs&version=stable
    :target: https://lacosmic.readthedocs.io/en/stable/
    :alt: Stable Documentation Status

.. |Zenodo| image:: https://zenodo.org/badge/28157875.svg
    :target: https://zenodo.org/badge/latestdoi/28157875
    :alt: Zenodo Latest DOI
dismoji 0.3.1
A Python library for converting Discord emoji names to their Unicode equivalents.
Requires Python: <3.15,>=3.10
Keywords: bot, discord, emoji, emojis


![PyPI - Version](https://img.shields.io/pypi/v/dismoji)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dismoji)
![PyPI - Types](https://img.shields.io/pypi/types/dismoji)
![PyPI - License](https://img.shields.io/pypi/l/dismoji)
![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/Paillat-dev/dismoji/CI.yaml)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/Paillat-dev/dismoji/main.svg)](https://results.pre-commit.ci/latest/github/Paillat-dev/dismoji/main)


---

Dismoji is a lightweight Python library that provides a simple way to convert Discord
emoji names to their Unicode equivalents and vice versa. With just two function calls,
you can transform text containing Discord-style emoji codes (like `:smile:`) into text
with actual Unicode emoji characters (like "😄") and back again.

This library uses
[Paillat-dev/discord-emojis](https://github.com/Paillat-dev/discord-emojis) as the
source for Discord emoji names and aliases.


```python
import dismoji

# Convert Discord emoji names to Unicode emojis
text = "Hello, :wave: I'm excited! :partying_face:"
converted_text = dismoji.emojize(text)
print(converted_text)  # Output: "Hello, 👋 I'm excited! 🥳"

# Convert Unicode emojis back to Discord emoji names
emoji_text = "Hello, 👋 I'm excited! 🥳"
named_text = dismoji.demojize(emoji_text)
print(named_text)  # Output: "Hello, :wave: I'm excited! :partying_face:"
```

## Features

- **Simple API**: Just two functions to remember - `dismoji.emojize()` and
  `dismoji.demojize()`
- **Discord Compatible**: Supports Discord's emoji naming conventions
- **Comprehensive**: Includes all standard emojis available on Discord
- **Type Safe**: Fully type-annotated for better IDE integration
- **Zero Dependencies**: Lightweight with no external dependencies
- **Fast**: Optimized for quick emoji replacement
- **Bidirectional**: Convert between emoji names and characters in both directions

## Getting Help

If you encounter issues or have questions about dismoji:

- **GitHub Issues**:
  [Submit a bug report or feature request](https://github.com/Paillat-dev/dismoji/issues)
- **Discord Support**: Join the [Pycord Official Server](https://discord.gg/pycord) and
  mention `@paillat`

## Development

### Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run linter, formatter and type checker: `ruff check .`, `ruff format .`,
   `basedpyright .`
5. Submit a pull request

**Development Tools**:

- **uv**: For dependency management
- **Ruff**: For linting and formatting
- **HashiCorp Copywrite**: For managing license headers
- **basedpyright**: For type checking

## Acknowledgements

- [`emoji`](https://pypi.org/project/emoji/) as inspiration for the API design

## License

MIT License - Copyright (c) 2025 Paillat-dev

---

Made with ❤ by Paillat-dev
topsis-hardik-102303494 0.1.1
TOPSIS implementation for decision making
Requires Python: >=3.8

# topsis-hardik-102303494

## Project Description

**topsis-hardik-102303494** is a Python library for solving **Multiple Criteria Decision Making (MCDM)** problems using the  
**Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**.

This project has been developed as part of **Project-1 (UCS654)**  
and demonstrates how TOPSIS can be implemented as a **command-line Python package** and distributed via **PyPI**.

---

## Student Details

- **Name:** Hardik  
- **Roll Number:** 102303494  
- **Course:** UCS654  
- **Project:** Project-1 (TOPSIS)

---

## Installation
Install the package using `pip`:
```bash
pip install topsis-hardik-102303494
```

# TOPSIS CLI Usage

## Command Format
```bash
topsis <input_file.csv> <weights> <impacts> <output_file.csv>
```

## Parameters
- **input_file.csv**: CSV file with dataset (first column: names, remaining: numeric criteria)
- **weights**: Comma-separated numeric weights (use quotes if needed)
- **impacts**: Comma-separated impacts (`+` for benefit, `-` for cost)
- **output_file.csv**: CSV file for results

## Example

### Input (sample.csv)

| Model | Storage (GB) | Camera (MP) | Price ($) | Looks |
|-------|--------------|-------------|-----------|-------|
| M1    | 16           | 12          | 250       | 5     |
| M2    | 16           | 8           | 200       | 3     |
| M3    | 32           | 16          | 300       | 4     |
| M4    | 32           | 8           | 275       | 4     |
| M5    | 16           | 16          | 225       | 2     |

### Command
```bash
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output.csv
```

### Output

| Model | Topsis Score | Rank |
|-------|--------------|------|
| M1    | 0.534277     | 3    |
| M2    | 0.308368     | 5    |
| M3    | 0.691632     | 1    |
| M4    | 0.534737     | 2    |
| M5    | 0.401046     | 4    |

*Rank 1 = Best Alternative*
toktab 0.1.0
CLI tool for TokTab LLM pricing data
Requires Python: >=3.10
Keywords: ai, cli, llm, pricing, toktab

# TokTab CLI

> LLM pricing data at your fingertips

A command-line interface for accessing [TokTab](https://toktab.com/), a free API providing pricing data for 2000+ LLM models. Powered by [LiteLLM](https://github.com/BerriAI/litellm) and updated nightly.

## Installation

```bash
# Using uvx (recommended)
uvx toktab gpt-4o

# Or install with pip
pip install toktab
```

## Usage

### Get pricing info for a specific model

```bash
toktab gpt-4o
toktab claude-3-opus
toktab gemini-1-5-flash
```

Output:
```
╭──────────────────────────────────────────────────────────────────────────────╮
│ gpt-4o (openai)                                                               │
╰──────────────────────────────────────────────────────────────────────────────╯

Pricing
 Type    Cost / 1M tokens 
 Input              $2.50 
 Output            $10.00 

Context Window
 Limit       Tokens 
 Max input      128K 
 Max output      16K 

Capabilities
✓ Vision · ✓ Functions · ✓ Tool choice · ✓ System msgs
```

### Search for models

```bash
toktab search claude
toktab search "gemini 3"
toktab search provider:anthropic
```

### JSON output

All commands support `--json` for machine-readable output:

```bash
toktab --json gpt-4o
toktab search --json claude
```

### Options

```
Options:
  --json     Output raw JSON
  --version  Show version
  --help     Show this message and exit.
```

## Model Slugs

Model identifiers are derived from LiteLLM model names with special characters replaced by hyphens.  
For example:
- `gemini/gemini-pro` → `gemini-gemini-pro`
- `anthropic/claude-3-opus` → `anthropic-claude-3-opus`

Use the search command to find the exact slug for a model.

## Features

- 🚀 **Fast**: Lightweight CLI with minimal dependencies
- 📊 **Rich output**: Beautiful tables with cost color-coding (green=cheap, yellow=medium, red=expensive)
- 🔍 **Fuzzy search**: Find models by name or provider
- 💰 **Cost per million tokens**: Easy-to-read pricing format
- 🎨 **JSON output**: Perfect for scripting and automation
- 🆓 **Free**: No API key required

## Development

```bash
# Clone the repo
gh repo clone tomdyson/toktab-cli
cd toktab-cli

# Install with dev dependencies
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"

# Run tests
pytest

# Test locally
toktab gpt-4o
```

## License

MIT License - see [LICENSE](LICENSE) for details.

## Credits

- Data sourced from [LiteLLM](https://github.com/BerriAI/litellm)
- API provided by [TokTab](https://toktab.com/)