semvec/pypi-packages huggingface dataset:
weba 0.2.32
html framework
Requires Python: <=3.13,>=3.11
Keywords: weba, htmx, python

# weba

[![Release](https://img.shields.io/github/v/release/cj/weba)](https://img.shields.io/github/v/release/cj/weba)
[![Build status](https://img.shields.io/github/actions/workflow/status/cj/weba/main.yml?branch=main)](https://github.com/cj/weba/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/cj/weba/branch/main/graph/badge.svg)](https://codecov.io/gh/cj/weba)
[![Commit activity](https://img.shields.io/github/commit-activity/m/cj/weba)](https://img.shields.io/github/commit-activity/m/cj/weba)
[![License](https://img.shields.io/github/license/cj/weba)](https://img.shields.io/github/license/cj/weba)

Weba is a Python library for building web user interfaces using a declarative,
component-based approach. It provides a clean API for creating HTML elements and
components with proper context management and type safety. The library extends
BeautifulSoup's functionality by adding a custom Tag class that supports modern web
development patterns, including HTMX integration, class list manipulation, and
comment-based selectors. It allows developers to create reusable UI components with
proper isolation, supports both synchronous and asynchronous contexts, and handles
attribute management (including JSON serialization) elegantly. The library emphasizes
type safety and follows clean code practices, making it particularly suitable for
building modern web applications in Python with a focus on maintainability and developer
experience.

## Configuration

The library supports the following environment variables for configuration:

- `WEBA_LRU_CACHE_SIZE`: Controls caching of parsed HTML templates and file contents:
  - When set: Enables LRU caching with the specified maximum size
  - When not set: Disables caching, templates and files are re-read on every access

Example:

```bash
export WEBA_LRU_CACHE_SIZE=256  # Enable caching with max 256 entries
unset WEBA_LRU_CACHE_SIZE      # Disable caching completely
```

- **Github repository**: <https://github.com/cj/weba/>
- **Documentation** <https://weba.cj.io/>
us-sewersheds 0.1.2
Process and analyze US sewershed data from CWNS
Requires Python: >=3.11
Keywords: sewersheds, wastewater, CWNS, data

# US Sewersheds

Process and analyze US sewershed data from the Clean Watersheds Needs Survey (CWNS).

## Overview

This repository includes code to visualize sewershed interconnections in the US based on the 2022 Clean Watershed Needs Survey. The us_sewersheds folder includes two scripts:

1. **merge_cwns_data.py**
   - Merges multiple sources for population served into the primary facilities list.
   - Functions:
     - `main(state=None)`: Main processing function that can process all states or a single state.
     - `merge_population_data(facilities_df, ww_df, sso_df)`: Merges population data from multiple sources.
     - `build_sewershed_map(facilities_df)`: Creates network connections between treatment facilities.
   - Required inputs:
     - data/2022CWNS_NATIONAL_APR2024: Clean Watersheds Needs Survey 2022 dataset
       - FACILITIES.csv: Main facilities data
       - FACILITY_PERMIT.csv: Facility permit information
       - AREAS_COUNTY.csv: County area information
       - FACILITY_TYPES.csv: Facility type information
       - FLOW.csv: Flow data
       - POPULATION_WASTEWATER.csv: Wastewater population data
       - POPULATION_WASTEWATER_CONFIRMED.csv: Confirmed wastewater population data
       - POPULATION_DECENTRALIZED.csv: Decentralized population data
       - DISCHARGES.csv: Discharge information
2. **sewersheds_app.py**
   - Deploys Streamlit application to visualize different sewersheds in the US, by state and county

## Installation

### From PyPI
```bash
pip install us-sewersheds
```

### From Source
jetplot 0.6.6
jetplot is collection of miscellaneous utils for Matplotlib.
Requires Python: >=3.10

# Jetplot

[![Tests](https://github.com/nirum/jetplot/actions/workflows/ci.yml/badge.svg)](https://github.com/nirum/jetplot/actions/workflows/ci.yml)
[![Typecheck](https://github.com/nirum/jetplot/actions/workflows/typecheck.yml/badge.svg)](https://github.com/nirum/jetplot/actions/workflows/typecheck.yml)
![PyPI - Version](https://img.shields.io/pypi/v/jetplot)

## About

`jetplot` is collection of miscellaneous utils for Matplotlib.


## Installation

```bash
pip install jetplot
```

## Documentation

Documentation is built with [MkDocs](https://www.mkdocs.org/). To view the
pages locally run:

```bash
mkdocs serve
```
The documentation sources live in the [`docs/`](docs/) folder.

## Changelog

| Version | Release Date | Description                                                                                                                                                                                                     |
| ------: | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 0.6.0   | Jul 21 2024  | Migrated from setup.py to pyproject.toml.                                                                                                                                                     |
| 0.5.3   | Aug 30 2022  | Stops jetplot from updating Matplotlib rcParams on import.                                                                                                                                                     |
| 0.5.0   | Jul 15 2022  | Updates default color palettes, adds new Palette class, adds ridgeline plot.                                                                                                                                                     |
| 0.4.0   | Oct 20 2021  | Name change! Package renamed to `jetplot`.                                                                                                                                                       |
| 0.3.0   | Oct 13 2021  | Drops animation module and the `moviepy` dependency                                                                                                                                                             |
| 0.0.0   | Jan 19 2015  | Initial commit                                                                                                                                                                                                  |

## License

MIT. See [`LICENSE.md`](./LICENSE.md)
aiattribution 0.1.0
A Python utility for creating and interpreting AI Attribution statements
Requires Python: >=3.10

# AI Attribution

A Python utility for creating and interpreting AI Attribution statements, providing a standardized way to
communicate the level and nature of AI involvement in creative and professional work.

## About

This tool is based on the research published by IBM Research at
[https://aiattribution.github.io/](https://aiattribution.github.io/). AI Attribution statements provide transparency
about how AI tools were used in creating content, making it easier for readers, reviewers, and collaborators to
understand the role of AI in the creative process.

## What is an AI Attribution Statement?

AI Attribution statements consist of several components that describe:

- **Proportion of AI use**: How much of the work was AI-generated vs. human-created
- **AI contributions**: What types of contributions AI made (stylistic edits, content edits, new content)
- **Initiative**: Whether AI assistance was prompted by humans or proactively offered by AI
- **Human review**: Whether AI-generated content was reviewed and approved
- **Model information**: Which AI model or application was used

### Example

```
AIA PAI Ce Hin R Gemini 2.5 Pro v1.0
```

This translates to:

> This work was primarily AI-generated. AI was used to make content edits, such as changes to scope, information,
> and ideas. AI was prompted for its contributions, or AI assistance was enabled. AI-generated content was reviewed
> and approved. The following model(s) or application(s) were used: Gemini 2.5 Pro.

## Installation

```bash
uv tool install aiattribution
```

## Usage

```bash
# Create an AI Attribution statement
uv run aia create
```
```bash
# Understand an existing AI Attribution statement
uv run aia interpret "AIA PAI Ce Hin R Gemini 2.5 Pro v1.0"
```