semvec/pypi-packages huggingface dataset:
hwc-ndarray-letterbox 0.1.0a0
Letterbox an HWC ndarray image to fit the target width and height while updating the homogeneous transformation matrix.
Requires Python: >=2

# `hwc-ndarray-letterbox`

Letterbox an HWC ndarray image to fit the target width and height while updating the homogeneous transformation matrix.

Commonly used in computer vision pipelines (e.g., YOLO object detection), where you need to reverse-map coordinates such
as bounding boxes from the preprocessed image back to the original image space.

## Usage

```python
import cv2
import numpy as np
from hwc_ndarray_letterbox import hwc_ndarray_letterbox

# Load an image (HWC ndarray)
image = cv2.imread('input.jpg')
current_homogeneous_transformation_matrix = np.eye(3)

# Desired output size
target_width = 640
target_height = 640

# Perform letterbox resize with matrix tracking
(
    letterboxed_image,
    homogeneous_transformation_matrix,
) = hwc_ndarray_letterbox(
    image,
    current_homogeneous_transformation_matrix,
    target_width,
    target_height,
)

print('Homogeneous transformation matrix:', homogeneous_transformation_matrix)

# To map points from letterboxed image back to original:
# inverse_homogeneous_transformation_matrix = np.linalg.inv(homogeneous_transformation_matrix)
# original_point = inverse_homogeneous_transformation_matrix @ np.ndarray([x, y, 1])
```

## Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

## License

This project is licensed under the [MIT License](LICENSE).
typofix 0.2.0
A cross-platform CLI tool to fix typos using LLM.
Requires Python: >=3.9

# typofix

A cross-platform CLI tool to fix typos and improve text with an LLM that supports both Chinese and English.

- Default behavior: `typofix <TEXT>` runs typo-fixing (no subcommand needed).
- Extra modes: `--suggest` (suggest improvements + reasons), `--rewrite` (rewrite alternatives).
- Clipboard: in default fix mode, the final text is copied to clipboard.
- Supports OpenAI-compatible providers.

## Requirements

- Python: >= 3.9

## Installation

### Local (recommended for development)

```bash
pip install -e .
```

### Regular install

```bash
pip install typofix
```

## Quickstart

1) Configure API key and (optionally) model:

```bash
typofix config --api-key YOUR_KEY
# optional:
typofix config --model MODEL_NAME
```

2) Fix a sentence:

```bash
typofix "Helo world"
```

## Usage

### Default (fix)

Fix grammar/typos with minimal changes.

```bash
typofix "这是一段可能由语病的测试"
```

The output is also copied to clipboard in fix mode.

### Suggest improvements (with explanations)

Gives prioritized suggestions and brief reasons.

```bash
typofix --suggest "This sentence have a problem."
```

### Rewrite

Provides 2–3 rewritten alternatives.

```bash
typofix --rewrite "帮我把这句话写得更自然一点"
```

### From stdin (pipes)

```bash
echo "Helo world" | typofix
```

### Help

`typofix --help` shows the default command help. Configuration is available as a command:

```bash
typofix config --help
```

## Configuration

Configuration is stored under your home directory (JSON). You can always re-run the config command to update values.

### Set / view configuration

```bash
typofix config
```

### Set model

```bash
typofix config --model MODEL_NAME
```

### List available models

Lists models available to your API key (and the configured base URL).

```bash
typofix config --list
```

### Use an OpenAI-compatible provider (base URL)

This tool supports OpenAI-compatible APIs by configuring `--base-url`.

Example (DashScope compatible mode):

```bash
typofix config --base-url https://dashscope.aliyuncs.com/compatible-mode/v1
# then set provider key + model

typofix config --api-key YOUR_PROVIDER_KEY

typofix config --model qwen-plus
```

Switch back to OpenAI:

```bash
typofix config --base-url https://api.openai.com/v1
```

## Troubleshooting

- "API key not configured": run `typofix config --api-key YOUR_KEY`.
- "No such command '<text>'": your installed entry point may be outdated. Reinstall with `pip install -e .` from the repo root.
- Model list fails: verify `--base-url` and `--api-key` are correct for your provider.
fuzzless 0.1.0
A simple Hello World Textual TUI application
Requires Python: >=3.13

# fuzzless

A simple Hello World Textual TUI application built with Python and uv.

## Installation

This project uses [uv](https://github.com/astral-sh/uv) for dependency management.

```bash
# Clone the repository
git clone <repository-url>
cd fuzzless

# Install dependencies (uv will create a virtual environment automatically)
uv sync
```

## Running the Application

There are several ways to run the application:

### Method 1: Using uv run (recommended)
```bash
uv run fuzzless
```

### Method 2: Direct script execution
```bash
uv run python -m fuzzless.app
```

### Method 3: After activating the virtual environment
```bash
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
fuzzless
```

## Usage

Once running:
- Press `q` to quit the application

## Development

This project uses:
- [uv](https://github.com/astral-sh/uv) - Fast Python package installer and resolver
- [Textual](https://github.com/Textualize/textual) - Modern TUI framework for Python

## License

See LICENSE file for details.
popgen3 3.0.4
A population synthesis and sample weighting tool for transportation planning.
Requires Python: >=3.8

# PopGen3

## Introduction

**PopGen** is a state-of-the-art open-source synthetic population generator for advanced travel demand modeling. Developed under the guidance of Professor Ram M. Pendyala at Arizona State University, PopGen utilizes a heuristic algorithm (IPU) to control and match both household-level and person-level attribute distributions.

## Key Features

- Advanced control of household and person variables across multiple geographic resolutions (region and geo).
- Command-line interface for simplified usage and enhanced computational efficiency.
- Fully compatible with Python 3, with updated dependencies.

## Run PopGen3 Online
- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1j1Stb8IA8OfaoPRh232kId8hqi3dUtur?usp=sharing)
- [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/chnfanyu/PopGen3/HEAD)

## Contents

### Data Inputs

#### Survey sample data:
  `household_sample.csv`
  `person_sample.csv`

#### Population marginal data:

- **geo-level marginals:**  
     `person_marginals.csv`  
     `household_marginals.csv`  

- **region-level marginals:**  
  `region_person_marginals.csv`  
  `region_household_marginals.csv`  

#### Multi-Geographic Resolution Mapping Data:

  `region_geo_mapping.csv`  
  `geo_sample_mapping.csv`

Example of Multi-Geographic Resolution Levels:
- <small><i>Region Level:</i> Set as census county subdivision</small>
- <small><i>Geo Level:</i> Set as census tracts</small>
- <small><i>Sample Geo Level:</i> Set as Public Use Microdata Areas (PUMAs)</small>


### Configuration File 

The `configuration.yaml` file contains several key sections for the PopGen setup. 

#### Designed Input Keys

| **Project Wide Setting** | **Input Data Files** | **Scenario Settings** |
| --- | --- | --- |
| - synthesize<br>- name<br>- location | - entities<br>- column_names<br>- location | - description<br>- apply_region_controls<br>- control_variables<br>- parameters<br>- geos_to_synthesize<br>- outputs |

### Key Outputs

#### Sample Weighting Results
- `weights.csv`
#### Synthetic Population Generated
- `housing_synthetic.csv`
- `person_synthetic.csv`