semvec/pypi-packages huggingface dataset:
dag-generator 1.0.1
DPaaS Airflow DAG (Dynamic Acyclic Graph) and DQ (Data Quality) generator
License: MIT
Keywords: airflow dag data-quality

# dag-dq-generator
DPaaS Airflow DAG (Dynamic Acyclic Graph) and DQ (Data Quality) generator.

*dag-dq-generator* is a DPaaS [Apache Airflow](https://github.com/apache/incubator-airflow) Airflow DAG (Dynamic Acyclic Graph) and DQ (Data Quality) generator  from YAML configuration files.
- [Usage](#usage)
- [YAML Definition](#yaml-definition)
- [Benefits](#benefits)
- [Contributing](#contributing)

## Usage
### Setup
*dag-dq-generator* requires Python 3.6.0+. To set up your environment, you can run `sh build.sh` which installs the required Python packages and run the generator program. Otherwise, you can run `pip install -r requirements.txt` to install the required Python packages and run `python dag_generator.py` with the following parameters:
* `--config-path` defines the path to the configurations folder. Defaults to `./configs/`
* `--dag-storage-path` defines the path to the folder where generated DAGs will be stored. Defaults to `./dags/`
* `--dq-storage-path` defines the path to the folder where DQ SQL files will be stored. Defaults to `./sql/`

## YAML Definition

## Benefits

* Construct DAGs without knowing Python
* Construct DAGs without learning Airflow primitives
* Avoid duplicative code

## Contributing

Contributions are welcome! Just submit a Pull Request or Github Issue. Feel free to join the discussions on the `#dag-dq-generator` Slack channel.
datasets2 0.1.1
Add-ons to the huggingface `datasets`
License: MIT
Requires Python: >=3.6

`datasets2` provides add-ons to the huggingface `datasets`.

Example usage:

```
# datasets is just the huggingface datasets
# load_dataset and save_to_disk adds parquet support to datasets.load_dataset and datasets.Dataset.save_to_disk
from datasets2 import datasets, load_dataset, save_to_disk

my_dataset = prepare_my_huggingface_dataset()
output_dir = "my_dataset_dir"
save_to_disk(my_dataset, output_dir, parquet=True)

load_dataset(output_dir)  # automatically infers if the dataset uses parquet format.

```
python-cfdp 2.0.0
CCSDS File Delivery Protocol
License: MIT

# Python CFDP

The CCSDS File Delivery Protocol (CFDP) was developed by the
[Consultative Committee for Space Data Systems (CCSDS)](https://public.ccsds.org).
The CFDP protocol provides reliable transfer of files from one endpoint to
another and has been designed to work well over space links that suffer
from outtakes and long delays. The basic operation of CFDP is to transfer a
file from a sender to a receiver (both referred to as CFDP entities). The
sender and receiver must be configured and running at the same time to perform
a file transfer. It can be used to perform  space to ground, ground to space,
space to space, and ground to ground file transfers. For example it can be used
to transfer science data from satellite over mission control to the science
center in an automated fashion with minimal to no human intervention.

This Python module is an implementation of the CCSDS File Delivery Protocol.
It supports all features as outlined in the latest version of the [CFDP Blue Book](docs/727x0b5.pdf). These are:

- Class 1 (unacknowledged) file transfer
- Class 2 (acknowledged) file transfer
- Filestore requests
- Proxy operations
- Directory listing request
- Native filestore implementation
- UDP as default transport layer (optional ZMQ)

## Installation

Clone the repository and then install via pip:

```
$ pip install cfdp
```

> Depending on the transport layer to use with CFDP, one needs to install additional dependencies.

## Documentation

Find the detailed documentation [here](docs/README.md).

## Tests

The protocol is tested for cross-support as outlined in CCSDS CFDP Yellow Book.
See [tests/README.md](tests/README.md) for details.

## Contribute

- Issue Tracker: https://gitlab.com/librecube/lib/python-cfdp/-/issues
- Source Code: https://gitlab.com/librecube/lib/python-cfdp

To learn more on how to successfully contribute please read the contributing
information in the [LibreCube guidelines](https://gitlab.com/librecube/org/guidelines).

## Support

If you are having issues, please let us know. Reach us at
[Matrix](https://app.element.io/#/room/#librecube.org:matrix.org)
or via [Email](mailto:info@librecube.org).

## License

The project is licensed under the MIT license. See the [LICENSE](./LICENSE.txt) file for details.
dag_dq_generator 1.0.1
DPaaS Airflow DAG (Dynamic Acyclic Graph) and DQ (Data Quality) generator
License: MIT
Keywords: airflow dag data-quality

# dag-dq-generator
DPaaS Airflow DAG (Dynamic Acyclic Graph) and DQ (Data Quality) generator.

*dag-dq-generator* is a DPaaS [Apache Airflow](https://github.com/apache/incubator-airflow) Airflow DAG (Dynamic Acyclic Graph) and DQ (Data Quality) generator  from YAML configuration files.
- [Usage](#usage)
- [YAML Definition](#yaml-definition)
- [Benefits](#benefits)
- [Contributing](#contributing)

## Usage
### Setup
*dag-dq-generator* requires Python 3.6.0+. To set up your environment, you can run `sh build.sh` which installs the required Python packages and run the generator program. Otherwise, you can run `pip install -r requirements.txt` to install the required Python packages and run `python dag_generator.py` with the following parameters:
* `--config-path` defines the path to the configurations folder. Defaults to `./configs/`
* `--dag-storage-path` defines the path to the folder where generated DAGs will be stored. Defaults to `./dags/`
* `--dq-storage-path` defines the path to the folder where DQ SQL files will be stored. Defaults to `./sql/`

## YAML Definition

## Benefits

* Construct DAGs without knowing Python
* Construct DAGs without learning Airflow primitives
* Avoid duplicative code

## Contributing

Contributions are welcome! Just submit a Pull Request or Github Issue. Feel free to join the discussions on the `#dag-dq-generator` Slack channel.
rootbox 0.0.12
Portable reproducible environments for Linux applications
License: MIT

# Rootbox

[![image](https://img.shields.io/pypi/v/rootbox.svg)](https://pypi.python.org/pypi/rootbox)
[![image](https://img.shields.io/pypi/pyversions/rootbox.svg)](https://pypi.python.org/pypi/rootbox)
![License](https://img.shields.io/github/license/joaompinto/rootbox?style=flat-square)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)
[![Actions status](https://github.com/joaompinto/rootbox/actions/workflows/ci.yaml/badge.svg
)](https://github.com/joaompinto/rootbox/actions)

An extremely simple and fast container engine for Linux and WSL, written in Python.

- 🌎 Installable/usable by any user, does not require root privileges
- ⚡️ Faster due to the use of in-memory filesystems and minimal set of features
- 🐍 Simple code base written in Python, simple to understand and extend
- 🛠️ Development library for embedding container support in other applications
- 🤝 Python 3.11 compatibility
- 📦 Uses regular remote or local tarball for images, no need for a registry
- 🐧 [LXC Containers Images](https://images.linuxcontainers.org/) support
- 📖 [Documentation](https://rootbox.pages.dev/)