semvec/pypi-packages huggingface dataset:
batgrl 0.51.3
badass terminal graphics library
Requires Python: >=3.12

# batgrl - badass terminal graphics library

Create games:

![Tetris](https://raw.githubusercontent.com/salt-die/batgrl/main/preview_images/game.png)

Simulations:

![Simulation](https://raw.githubusercontent.com/salt-die/batgrl/main/preview_images/simulation.png)

Or entire feature-filled applications:

![Application](https://raw.githubusercontent.com/salt-die/batgrl/main/preview_images/application.png)

[See it in action](https://youtu.be/q1K6_P1COTI)

[Read the docs](https://salt-die.github.io/batgrl/index.html)
nanopy-scan 3.0.3
Full-featured blockchain explorer for NanoPy Network with SQLite indexing
Requires Python: >=3.8
Keywords: blockchain explorer nanopy ethereum sqlite indexer

# NanoPy Scan

Blockchain explorer for NanoPy with SQLite indexing and WebSocket sync.

## Install

```bash
pip install nanopy-scan
nanopy-scan
```

## Networks

```bash
# NanoPy L1 Mainnet (default)
nanopy-scan --network mainnet

# NanoPy L1 Testnet
nanopy-scan --network testnet

# NanoPy Turbo L2
nanopy-scan --network turbo

# NanoPy Turbo L2 Testnet
nanopy-scan --network turbo-testnet
```

## Features

- Real-time sync via WebSocket (`eth_subscribe newHeads`)
- HTTP polling fallback
- SQLite indexing
- Block/Transaction/Address explorer
- Validator tracking
- Multi-network support (L1 + L2)

## API

| Endpoint | Description |
|----------|-------------|
| `/api/status` | Network status |
| `/api/blocks` | List blocks |
| `/api/block/:id` | Block details |
| `/api/transactions` | List transactions |
| `/api/tx/:hash` | Transaction details |
| `/api/address/:addr` | Address info |
| `/api/validators` | Validators |
menu-kit 0.0.1
A modular, menu-agnostic launcher for Linux
Requires Python: >=3.11
Keywords: dmenu, fuzzel, launcher, linux, menu, rofi

# menu-kit

A modular, menu-agnostic launcher for Linux with plugin-first architecture.

## Overview

menu-kit is a launcher/menu system that works with multiple menu backends (rofi, fuzzel, dmenu, fzf) while providing a unified plugin system. Everything is a plugin - file browsing, app launching, settings, search.

**Status:** Early development - architecture defined, implementation starting.

## Features (Planned)

- **Menu-agnostic** - Works with rofi, fuzzel, dmenu, wofi, bemenu, fzf
- **Plugin-first** - Core features implemented as plugins
- **Smart indexing** - .gitignore aware file scanning
- **SQLite backend** - Fast queries, frequency tracking, incremental updates
- **TOML config** - Human-readable configuration
- **Systemd integration** - Background index rebuilding

## Architecture

```
┌─────────────────────────────────────┐
│         Core (Python 3.11+)         │
│  - Config (TOML)                    │
│  - Database (SQLite)                │
│  - Plugin loader                    │
└──────────────┬──────────────────────┘
               │
┌──────────────▼──────────────────────┐
│      Menu Abstraction Layer         │
│   (stdin/stdout dmenu protocol)     │
└──────────────┬──────────────────────┘
               │
     ┌─────────┼─────────┐
     ▼         ▼         ▼
   rofi     fuzzel     dmenu
```

## Built-in Plugins

| Plugin | Description |
|--------|-------------|
| `apps` | Application launcher (.desktop files) |
| `files` | File browser with contextual navigation |
| `settings` | In-menu configuration |
| `calculator` | Evaluate expressions |
| `websearch` | Configurable search engines |

## Requirements

- Python 3.11+
- One of: rofi, fuzzel, dmenu, fzf

## Configuration

Config lives at `~/.config/menu-kit/config.toml`:

```toml
[menu]
backend = "auto"  # auto-detect, or: rofi, fuzzel, dmenu, fzf
prompt = ">"
lines = 15

[indexing]
watch_folders = ["~/"]
ignore_patterns = [".git", "node_modules", "__pycache__"]
respect_gitignore = true

[plugins]
enabled = ["files", "apps", "settings", "calculator"]
```

## Background

menu-kit is the spiritual successor to [dmenu-extended](https://github.com/markhedleyjones/dmenu-extended), redesigned with:
- Menu-agnostic architecture (not tied to dmenu)
- Plugin-first design (everything is a plugin)
- Modern Python practices (type hints, dataclasses, SQLite)
- Better separation of concerns

## Licence

MIT
regional-observer-workbook 0.3.0
Library for working with scans of SPC/FFA regional observer workbooks
Requires Python: >=3.12

# Regional Observer Workbook

## Background
This Python library encapsulates the logic for extracting content from SPC/FFA regional observer workbooks.
It maintains original images and bounding boxes for forms.  It also includes logic to align scans to an original using
OpenCV's homography capabilities.  **NOTE:**  The current version of the library only works for scanned documents.  Lens distortion from camera images prevents successful feature matching.

In addition to the library, there is a simple CLI utility that can perform two tasks.  The first task is to extract each unique bounding box to an image.
The second task is to draw the bounding boxes on an aligned scan.  The capabilities are useful as examples of how to use the library as well being visual debugging aids.


## Work-in-Progress

The following items are still work in progress:

* Additional form types and bounding boxes for recent purse seine observer workbooks
* Enabling camera images
* Evaluating the `SIFT` and `AKAZE` feature matching algorithms
* Creating convenience methods for working with row/column bounding box definitions
cerebras-sdk 2.9.0
Cerebras Wafer Scale Cluster SDK

# cerebras_sdk

[![PyPI Version](https://img.shields.io/pypi/v/cerebras_sdk)](https://pypi.org/project/cerebras_sdk/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cerebras_sdk)](https://pypi.org/project/cerebras_sdk/)
[![Discord](https://img.shields.io/discord/1085960591052644463)](https://discord.gg/ZqvYS2e2rY)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/cerebras_sdk)](https://pypi.org/project/cerebras_sdk/#files)

The cerebras_sdk package is a component of Cerebras's Wafer-Scale cluster software stack, offering a framework for kernel development.

* **Website**: https://cerebras.net/
* **Documentation**: https://sdk.cerebras.net/
* **Support**: support@cerebras.net
* **Discord**: https://discord.gg/ZqvYS2e2rY

## Installation

```bash
pip install cerebras_sdk
```
cerebras-pytorch 2.9.0
PyTorch support for Cerebras Wafer Scale Cluster and ML research

# cerebras_pytorch

[![PyPI Version](https://img.shields.io/pypi/v/cerebras_pytorch)](https://pypi.org/project/cerebras_pytorch/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cerebras_pytorch)](https://pypi.org/project/cerebras_pytorch/)
[![Discord](https://img.shields.io/discord/1085960591052644463)](https://discord.gg/ZqvYS2e2rY)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/cerebras_pytorch)](https://pypi.org/project/cerebras_pytorch/#files)

The cerebras_pytorch package is a component of Cerebras's Wafer-Scale cluster software stack, offering a framework for training and evaluating neural networks with near-perfect linear scaling across millions of cores without the inconvenience of distributed computing. Additionally, it provides general ML helpers for running on CPU/GPU, empowering [Cerebras' modelzoo](https://github.com/Cerebras/modelzoo).

* **Website**: https://cerebras.net/
* **Documentation**: https://docs.cerebras.net/
* **Support**: support@cerebras.net
* **Discord**: https://discord.gg/ZqvYS2e2rY

## Installation

```bash
pip install cerebras_pytorch
```
cerebras-appliance 2.9.0
Base client for Cerebras Wafer Scale Cluster

# cerebras_appliance

[![PyPI Version](https://img.shields.io/pypi/v/cerebras_apliance)](https://pypi.org/project/cerebras_apliance/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/cerebras_apliance)](https://pypi.org/project/cerebras_apliance/)
[![Discord](https://img.shields.io/discord/1085960591052644463)](https://discord.gg/ZqvYS2e2rY)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/cerebras_apliance)](https://pypi.org/project/cerebras_apliance/#files)

cerebras_appliance package is a set of framework-agnostic utilities required by Cerebras's Wafer-Scale cluster software stack which offers a framework to train and evaluate a neural network with near-perfect linear scaling across millions of cores without the inconvenience of distributed computing.

* **Website**: https://cerebras.net/
* **Documentation**: https://docs.cerebras.net/
* **Support**: support@cerebras.net
* **Discord**: https://discord.gg/ZqvYS2e2rY

## Installation

```bash
pip install cerebras_appliance
```
complicator 0.3.0
Estimate thermodynamic properties of aqueous metal complexes with monovalent ligands.
Requires Python: >=3.10

# The Complicator

Code Authors: Grayson Boyer, Everett Shock
Data Compilers: GEOPIG Lab, Apar Prasad

This is a Python package for estimating standard state thermodynamic properties and Helgeson-Kirkham-Flowers (HKF) equation of state parameters for aqueous metal complexes with monovalent ligands using published methods from [Sverjensky et al. (1997)](https://doi.org/10.1016/s0016-7037(97)00009-4) and [Shock et al. (1997)](https://doi.org/10.1016/S0016-7037(96)00339-0)

## Installation

This package can be installed through PyPI with:
`pip install Complicator`

## Usage

The best way to learn about the Complicator is through the WORM Portal ([worm-portal.asu.edu](worm-portal.asu.edu)). Check out Jupyter notebook demo 4-2-1 in the WORM Library that is available when you log in.

What follows is a brief example of how the Complicator can be used.

Supply a dataframe containing names of metals, ligands, stability constants for the first through fourth association at 25 °C, and optionally, standard state entropies of association for the first through fourth association complex. An example is assigned to the variable `df_input` in the example below.

```
import pandas as pd
from Complicator import complicate

df_input = pd.DataFrame({
        "Metal":["Ag+", "Al+3"],
        "Ligand":["OH-", "OH-"],
        "BETA_1":[2, 9.03], # stability constant for the first association complex
        "BETA_2":[3.97, 17.6], # second stability constant
        "BETA_3":[float('NaN'), 26.4], # third stability constant
        "BETA_4":[float('NaN'), 33.8], # fourth stability constant
        "Sass_1":[float('NaN'), 36.5], # entropy of association for the first complex (cal/mol/K)
        })

df_out, _, _, _ = complicate(df_in=df_input)

df_out
```

The output is a dataframe containing estimated thermodynamic properties, parameters for the revised Helgeson Kirkham Flowers (HKF) equation of state, and more. [The format of the output is explained in more detail here](https://worm-portal.asu.edu/docs/database/). This Water-Organic-Rock-Microbe (WORM) database format is designed to be compatible with the free and open source online geochemical modeling platform [WORM Portal](https://worm-portal.asu.edu/). Estimated thermodynamic properties of complexes from the Complicator can be used in conjunction with the rest of the WORM database to:
- calculate properties of reactions and create activity or predominance diagrams.
- expand the number of aqueous complexes available in geochemical speciation (equilibration) and mass transfer calculations.