semvec/pypi-packages huggingface dataset:
highlight-video-maker 0.2.3
A utility to take several video inputs, take the loudest points, and create a compilation of them with smooth transitions
Requires Python: >=3.12

# Highlight Video Maker

This script automatically creates a highlight reel from multiple video inputs by identifying and combining the loudest moments.

## How It Works

1. **Finds a Representative Video**

   - Chooses the shortest video as the "representative" video.

2. **Detects Loudest Moments**

   - Analyzes the representative video to find the 10 loudest segments.

3. **Applies to All Videos**

   - Uses the timestamps from the representative video to divide all others into clips.
     - Note: This will make all videos the same length, removing the excess content from the end.

4. **Combines Clips**

   - Merges the loudest segments using:
     - The representative video’s timestamps
     - A randomly selected perspective clip from the other videos

5. **Final Outputs**
   - A combonation of all vthe loudest clips, perspective randomly chosen
   - Same as above, but with a mobile-friendly vertical aspect ratio with blurred letterboxing
   - Both clips will be watermarked by an image input, with the image being slightly transparent
     on the standard video output and opaque on the mobile one

## CLI Options

All options except `--log-level` are required.

```
$ vr-video-maker --help
Usage: vr-video-maker [OPTIONS] COMMAND [ARGS]...

Options:
  --log-level Sets the logging verbosity. Choose betweenDEBUG, INFO (default), WARNING, ERROR, or CRITICAL.Can be uppercase or lowercase.
  --help                          Show this message and exit.

Commands:
  run  Main function that orchestrates the video processing pipeline.



$ vr-video-maker run --help
Usage: vr-video-maker run [OPTIONS]

  Main function that orchestrates the video processing pipeline.

Options:
  --input-dir The input directory to get the source videos from.
  --watermark-image The path of the watermark image to overlay over the final output. If not specified, no watermark will be applied. It will not be scaled, so it should be sized appropriately relative to the input.
  --horiz-output-file The path to output the final video to. It should not exist and must either be an absolute path or start with "./".
  --vert-output-file The path to output the final video to. It should not exist and must either be an absolute path or start with "./".
  --help                          Show this message and exit.
```

## Requirements

- **Operating System:** Linux (tested on Arch Linux, will not work on Windows-like systems)
- **Dependencies:** [FFmpeg](https://ffmpeg.org/) must be installed on the host system

## Temporary Storage

- The script uses `/tmp/video-maker-cache` for temporary files.
- This folder can be safely deleted after the script finishes running.
crudzilla 0.1.0
Generate Pydantic models and FastCRUD routers from database schema
Requires Python: >=3.10
Keywords: code-generation, crud, database, fastapi, postgresql, pydantic, sqlalchemy

# crudzilla

Generate Pydantic models and FastCRUD routers from your PostgreSQL database schema.

## Installation

```bash
pip install crudzilla
# or
uv add crudzilla
```

## Quick Start

```bash
export DATABASE_URL="postgresql://user:password@localhost:5432/mydb"

crudzilla                                    # Pydantic models only
crudzilla --generate-routers                 # Include FastCRUD routers
crudzilla --generate-routers --include-sqlalchemy  # Full generation
```

## Options

| Option | Description |
|--------|-------------|
| `-o, --output-dir` | Output directory for models (default: `generated_models`) |
| `--routers-dir` | Output directory for routers (default: `src/app/routers/generated`) |
| `--env-file` | Path to `.env` file containing `DATABASE_URL` |
| `--schema` | Database schema (default: `public`) |
| `--exclude-tables` | Tables to exclude (supports wildcards: `audit_*`, `*_log`) |
| `--include-sqlalchemy` | Generate SQLAlchemy models |
| `--generate-routers` | Generate FastCRUD routers |
| `--async-mode` | Generate async CRUD operations |
| `--dry-run` | Preview without writing files |
| `--backup` | Backup existing files before overwriting |
| `-v, --verbose` | Verbose output |

## Output Structure

```
generated_models/
├── fastapi/
│   └── schema_public_latest.py    # Pydantic schemas
└── sqlalchemy_models.py           # SQLAlchemy models

src/app/routers/generated/
└── generated.py                   # FastCRUD routers
```

## Usage in FastAPI

```python
from fastapi import FastAPI
from src.app.routers.generated.generated import register_generated_routers

app = FastAPI()
register_generated_routers(app, prefix="/api/v1/db")
```

## License

MIT
terralab-cli 3.0.0
Command line interface for interacting with the Terra Scientific Pipelines Service, or Teaspoons.
Requires Python: <4.0,>=3.12

# Terralab CLI

[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=DataBiosphere_terra-scientific-pipelines-service-cli&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=DataBiosphere_terra-scientific-pipelines-service-cli)

## Overview
The terralab CLI provides a command-line interface with which to interact with the [Broad DSP Scientific Pipelines Service](https://github.com/DataBiosphere/terra-scientific-pipelines-service/blob/main/README.md).

Currently, terralab supports running [Array Imputation](https://allofus-anvil-imputation.terra.bio/). For more information about Array Imputation, see our [documentation](https://broadscientificservices.zendesk.com/hc/en-us/articles/39901941351323).

Find instructions on using this CLI [here](https://broadscientificservices.zendesk.com/hc/en-us/articles/39901313672859).

Note that in order to use terralab and the Scientific Pipelines Service, you must have a [Terra](https://services.terra.bio/) account.

## Prerequisites
Your system must run Python 3.12+.

## Installation
You can install the terralab CLI [from PyPi](https://pypi.org/project/terralab-cli/) using your favorite package management tool. 

For example, run
```bash
pip install terralab-cli
```

## Using the CLI
Once installed, to run the CLI, run the following command:
```bash
terralab COMMAND [ARGS]
```

For example, to list the pipelines you can run using Terralab, run the following command:
```bash
terralab pipelines list
```

## For Developers
See [CONTRIBUTING.md](CONTRIBUTING.md) for details on local development setup.

## Changelog
See [CHANGELOG.md](https://github.com/DataBiosphere/terra-scientific-pipelines-service-cli/blob/main/CHANGELOG.md)