semvec/pypi-packages huggingface dataset:
f1laps-telemetry-2020 0.1.5
This package lets you sync F1 game data into F1Laps automatically. It receives telemetry packets via the UDP protocol in your local network.
Requires Python: >=3.6

# F1Laps Telemetry Sync
![unittests](https://github.com/f1laps/f1laps-telemetry/actions/workflows/python-test.yml/badge.svg)

This package lets you sync F1 game data into [F1Laps](https://www.f1laps.com) automatically. It receives telemetry packets via the UDP protocol in your local network.

## Requirements

* Python 3
* Package requirements (see requirements.txt)

## Installation & Usage

There are two use cases for this package:

**1. Run it directly locally to upload your F1 telemetry data into F1Laps. See the full installation and usage guide below.**

Please check out our comprehensive documentation here:

**[F1Laps Telemetry Sync User Documentation](https://www.notion.so/F1Laps-Telemetry-Documentation-55ad605471624066aa67bdd45543eaf7)**

**2. Use it as a package in your own project. You can import this package via pip:**

`python3 -m pip install f1laps-telemetry-2020`


## Configuration

Set your F1Laps API key in the config.py. You can get your API key from [https://www.f1laps.com/api/telemetry_apps](https://www.f1laps.com/api/telemetry_apps).
```python
F1LAPS_API_KEY = "YOUR_API_KEY"
```

Once you start the script, it will show the IP and port it's receiving packages on, like so:
```bash
2021-02-19 19:34:42   Set your F1 game telemetry IP to:   192.168.4.20
2021-02-19 19:34:42   Set your F1 game telemetry port to: 20777
```

In your F1 game settings (Settings > Telemetry), change the IP and port accordingly.

## Testing
You can run all unit tests with this command:
```bash
python3 -m unittest discover
```

## Credits & Contributions
This repository was heavily inspired and contributed to by:
* [Redditor _jsplit](https://www.reddit.com/user/_jsplit)
* [gparent's f1-2020-telemetry package](https://gitlab.com/gparent/f1-2020-telemetry/)
trainingopz 0.1.3.43
DO NOT USE - This is a sample program
Requires Python: >=3.6

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# Trainingopz

This library is a pre-alpha right now

The intent of this package is to work as a back-end build and deploy engine to take - given a configuration and an infrastructure, 
on-demand scientific computing workflows.


## Application Configuration

```yaml
versionon: 1

docker_configuration:
    docker_registry:  REPO_URL
workflow_validation:
type:  YAMLValidator
  conf:
    project_path: /

pipeline_composition:
  type: YAMLPipelineComposer
  conf:
    project_path: /

package:
  type: DockerPackager
  conf:
    project_path: /

build_deployment_artifact:
  type: BuildAirflowArtifact
  conf: null

deploy:
  type: AirflowDeployer
  conf: null

```