semvec/pypi-packages huggingface dataset:
glocalflex 0.0.1
trading API for the flexible energy trading platform GLocalFlex
Requires Python: >=3.8,<4.0

# glocalflex

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

trading API for the flexible energy trading platform GLocalFlex

- **Github repository**: <https://github.com/smstep/glocalflex/>
- **Documentation** <https://smstep.github.io/glocalflex/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

``` bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:smstep/glocalflex.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with 

```bash
make install
```

You are now ready to start development on your project! The CI/CD
pipeline will be triggered when you open a pull request, merge to main,
or when you create a new release.

To finalize the set-up for publishing to PyPi or Artifactory, see
[here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see
[here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

## Releasing a new version

- Create an API Token on [Pypi](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting 
[this page](https://github.com/smstep/glocalflex/settings/secrets/actions/new).
- Create a [new release](https://github.com/smstep/glocalflex/releases/new) on Github. 
Create a new tag in the form ``*.*.*``.

For more details, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
signature-video-summary 0.1.0
A video summarization package using the signature transform.
Requires Python: >=3.6

# Signature Video Summary
Signature Video Summary is a Python package that generates video summaries using the signature transform. It extracts keyframes from a video and creates a summary based on the minimum standard deviation of the root mean square error (RMSE) between the frames' signature transform.

## Installation
To install the package, first ensure you have Python 3.6 or higher installed on your system. Then, run the following command:


pip install .[ffmpeg]

Note that this package requires the ffmpeg binary to be installed on your system. You can find installation instructions for various platforms in the official FFmpeg documentation.

## Usage
You can use the Signature Video Summary package in your Python scripts or from the command line.


from signature_video_summary import summarize_video

input_video = "path/to/your/video.mp4"
summary_length = 27
summary_n = 10
fps = 1

summarize_video(input_video, summary_length, summary_n, fps)


## Command line
python -m signature_video_summary path/to/your/video.mp4 -l 27 -n 10 -f 1

Arguments
input_video: Path to the input video file (.mp4).
-l or --summary_length: Length of the summary (default: 27).
-n or --summary_n: Number of summary iterations (default: 10).
-f or --fps: Frames per second to sample from the input video (default: 1).

## License
This project is licensed under the MIT License. See the LICENSE file for details.
flowui-project 0.0.22
FlowUI project
Requires Python: >=3.7
Keywords: airflow,UI

[<img src="https://img.shields.io/pypi/v/flowui-project?color=%231BA331&label=PyPI&logo=python&logoColor=%23F7F991%20">](https://pypi.org/project/flowui-project/)
[<img src="https://img.shields.io/docker/v/taufferconsulting/flowui-backend?label=Backend&logo=docker&style=flat">](https://hub.docker.com/r/taufferconsulting/flowui-backend)
[<img src="https://img.shields.io/docker/v/taufferconsulting/flowui-frontend?label=Frontend&logo=docker&style=flat">](https://hub.docker.com/r/taufferconsulting/flowui-frontend)
[<img src="https://img.shields.io/readthedocs/flowui?color=%23799194&label=Docs&logo=Read%20the%20Docs&logoColor=white">](link)




# FlowUI Project
FlowUI is an open source workflow management platform, containing:

- an intuitive Graphical User Interface that facilitates creating, editing and supervising any type of Workflows (e.g. data processing, machine learning, etc...)
- a REST API that controls a running Apache Airflow instance
- a standard way of writing Operators which follows good practices for data typing, documentation and distribution

<br>

# FlowUI Infrastructure

Per Platform:
- Frontend service
- Backend service
- Database
- Airflow services
- Github repository for GitSync of Workflows

<br>


## Shared storage structure:
Shared workflow data could be stored in a remote source (e.g. S3 bucket) or locally (for dev and tests only).

```
/shared_storage
..../{dag-id}
......../{run-id}
............/{task-id}
................/results
..................../log.txt
..................../result.npy
..................../result.html
................/report
................/xcom_out
..................../xcom_out.json


```

<br>

## Operators
Each Operator will have:
- A `operator.py` file with the source code to be executed, as the `operator_function()`
- A `models.py` file containing the Pydantic models that define the input, output and secrets for the Operator
- A `metadata.json` file containing the Operators metadata, including frontend node style

Each dependency group from an Operators repository will build an independent Docker image. This dependency group image has the following basic file struture within `/home`:
```
# This path holds the source code from the Operators repository, it comes built in the Image
/operators_repository
..../config.toml
..../operators
......../{OPERATOR-NAME}
............/metadata.json    # OPTIONAL
............/model.py         # REQUIRED
............/operator.py      # REQUIRED
..../.flowui
......../dependencies_map.json
......../compiled_metadata.json
..../dependencies
......../requirements.txt     # If dependency group was defined with a requirements.txt file
```