semvec/pypi-packages huggingface dataset:
cycleosm 0.0.2.4
A small package to download structured bike infrastructure data from Open Street Map (OSM).
Requires Python: >=3.7

## CycleOSM

[![PyPI version](https://img.shields.io/pypi/v/cycleosm.svg)](https://pypi.org/project/cycleosm/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

The purpose of CycleOSM is to download structured bike infrastructure data from Open Street Map (OSM).

Now on [PyPi](https://pypi.org/project/cycleosm/)

This package is easy to use. You can get started by installing the package: 
```
pip install cycleosm
```

Then import the BikeOSM module.  
```
from cycleosm.bikeosm import BikeOSM
```

Next define the output directory and the .pbf url
```
output_path = r'/Users/danielpatterson/Documents/GitHub/ExtractOSM/src/output'
urls = {'District of Columbia': 'http://download.geofabrik.de/north-america/us/district-of-columbia-latest.osm.pbf'}
```

Finally, extract the pbf
```
BikeOSM(urls, output_path).handle_pbfs()
```

![Denver Bike Facs](https://user-images.githubusercontent.com/22425199/218263077-a6554521-5697-40fa-824e-1051c4b46009.png)

![image](https://user-images.githubusercontent.com/22425199/218263087-fe33097f-ae0b-4449-9c7d-3e9585d0d560.png)
whisper-run 1.3.0
Whisper with speaker diarization
Requires Python: >=3.8

# Whisper-Run

Whisper-Run is a pip CLI tool for processing audio files using Whisper models with speaker diarization capabilities. The tool allows you to process audio files, select models for audio processing, and save the results in JSON format.

It uses the OpenAI-Whisper model implementation from [OpenAI Whisper](https://github.com/openai/whisper), based on the ctranslate2 library from [faster-whisper](https://github.com/SYSTRAN/faster-whisper), and [pyannote's speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1). Check their documentation if needed.

## Installation

To install Whisper-Run, run the following command:

```bash
pip install whisper-run
```

## Usage

You can call Whisper-Run from the command line using the following syntax:

```bash
whisper-run --file_path=<file_path>
```

### Example

To process an audio file using the CPU and a specific file path:

```bash
whisper-run --device=cpu --file_path=your_file_path
```

When you run the command, you'll be prompted to select a model for audio processing:

```
[?] Select a model for audio processing:
 > distil-large-v3
   distil-large-v2
   large-v3
   large-v2
   large
   medium
   small
   base
   tiny
```

### Flags

- `--device`: Specify the device to use for processing (e.g., `cpu` or `cuda`).
- `--file_path`: Specify the path to the audio file you want to process.
- `--hf_auth_token`: Optional. Pass the Hugging Face Auth Token or set the `HF_AUTH_TOKEN` environment variable.

### Programmatic Usage

You can also use Whisper-Run programmatically in your Python scripts. Below is a basic usage example demonstrating how to use the Whisper-Run library:

#### Example Script

```python
from whisper_run import AudioProcessor

def main():
    processor = AudioProcessor(file_path="your_file_path",
                               device="cpu",
                               model_name="large-v3"
                               )
    processor.process()

if __name__ == "__main__":
    main()
```

### Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

### License

This project is licensed under the Apache 2.0 License.
cspyexamclient 0.1.10
An internal tool for exam submission

# Coderschool Exam Client

![Version](https://img.shields.io/badge/Latest%20Version-v0.0.3-%2300b4d8.svg?&style=for-the-badge&logo=git&logoColor=white)
![Python](https://img.shields.io/badge/Python-%230096c7.svg?&style=for-the-badge&logo=python&logoColor=white)
![Google Colab](https://img.shields.io/badge/Google%20Colab-4285F4?style=for-the-badge&logo=googledrive&logoColor=white)

Update (29 Sep 2024): Publish package to PyPi

## About the project
**cspyexamclient** is a Python client for interacting with online exam system. It allows students to register, answer questions, and submit their answers. The client uses `ipywidgets` for creating interactive widgets in ipython notebooks before uploading students answers to the exam server.

## Features

- **Register Student**: Allows students to register using their email.
- **Generate Questions**: Dynamically displays questions and answer options.
- **Display Submission Summary**: Displays a summary of all questions and corresponding answers.
- **Submit Answers**: Submits student answers to the server.

### Submit - Resubmit - Reset

<img width="500" src="assets/test_submission.gif">

### Exam Summary

<img width="500" src="assets/exam_summary.png">

## Exam notebook
| Module       | Description       | Link                                                                                  |
|--------------|-------------------|---------------------------------------------------------------------------------------|
| M1.1         | Basic SQL         | https://colab.research.google.com/drive/1E3sppN-vjschXS46WTeqZnQSmRBcuggN?usp=sharing |
| M1.2         | Advanced SQL      | https://colab.research.google.com/drive/1i-aGTjpXL-En8kVFB0Cl8elHn9Z866yv?usp=sharing |
| M2.1         | Python 101        | https://colab.research.google.com/drive/1zX-ZgjEot_uGHh8x2nCl7hGWUklLuYeL?usp=sharing |
| M3.1         | Pandas 101        | https://colab.research.google.com/drive/143rJnko61nMUGGwMccgxaBJRA-CmJEqT?usp=sharing |
| M4.1         | Data Visualization| Exam is coding and feedback thus no questions generation needed                       |
agl-frame-extractor 0.2.1
Extracts frames from .mov files
Requires Python: <4.0,>=3.11

# agl-frame-extractor
## Description

`agl_frame_extractor` is a Python package designed to extract individual frames and metadata from .MOV video files. This package is highly useful for researchers and clinicians who require frame-by-frame analysis of video data. With applications ranging from medical research to training simulations, the package aims to improve standards and classifications in gastrointestinal endoscopy by enhancing objectivity and reproducibility.

## Features

- Extracts individual frames from .MOV files and saves them as PNG images.
- Gathers video metadata including total number of frames, frames per second, and video duration.
- Offers optional multithreading support for faster frame extraction.
- Generates a log file to record the extraction process.

## Installation

To install this package, clone the repository and run the following command in the repository root:

```bash
pip install -e .
```

## Usage

### Basic Usage

```python
from video_frame_extractor.extractor import VideoFrameExtractor

input_folder = "input_videos"
output_folder = "output_frames_metadata"

extractor = VideoFrameExtractor(input_folder, output_folder)
extractor.extract_frames_and_metadata()

# If you want to extract png files instead of jpgs:
input_folder = "input_videos"
output_folder = "output_frames_metadata"

extractor = VideoFrameExtractor(input_folder, output_folder, image_format='png')
extractor.extract_frames_and_metadata()
```

### Multithreaded Usage

To enable multithreading for faster frame extraction:

```python
from video_frame_extractor.extractor import VideoFrameExtractor

input_folder = "input_videos"
output_folder = "output_frames_metadata"

extractor = VideoFrameExtractor(input_folder, output_folder, use_multithreading=True)
extractor.extract_frames_and_metadata()
```

## Dependencies

- OpenCV
- tqdm

## Logging

The package generates a log file `video_frame_extraction.log` in the directory where it is executed. This log file contains detailed information about the extraction process.

## Contributing

We welcome contributions to improve this package. Please follow the standard GitHub pull request process. Make sure to document your code thoroughly, keeping in mind that the package targets an academic audience focused on research.