semvec/pypi-packages huggingface dataset:
chatgpt-dnac-analyzer 0.1.2
A chatGPT powered Cisco DNAC analyzer
Requires Python: >=3.8,<4.0

# chatGPT_Meraki_Event_Analyzer
A chatGPT / Meraki SDK Python powered event analyzer

## Current API Coverage

Network Events

## Current chatGPT Prompts

### Network Events 

```json

{"role": "system", "content": "You are a network troubleshooting tool"},
{"role": "user", "content": f"Please describe the following ncondition { event }"},

```

```json

{"role": "system", "content": "You are a chatbot"},
{"role": "user", "content": f"Please describe the following network condition { event } and explain it like I'm 5"},

```

```json

{"role": "system", "content": "You are a network troubleshooting tool"},
{"role": "user", "content": f"Is the network event { event } a problem with the network?"}

```

```json

{"role": "system", "content": "You are a network troubleshooting tool"},
{"role": "user", "content": f"How do I troubleshoot a { event } problem with Meraki?"},

```
## Installation

```console
$ python3 -m venv meraki
$ source meraki/bin/activate
(meraki) $ pip install chatgpt_meraki_analyzer
```

## Usage - Environment Variables - IMPORTANT

Please export / setup this environment variable prior to running vivlio

```console
(meraki) $ export MERAKI_DASHBOARD_API_KEY=<Meraki API Token>

```

Also please create a .env file with the following 

* Required
OPENAI_KEY=""

Available from platform.openai.com

** Optional for WebEx Messages

WEBEX_TOKEN=""
WEBEX_ROOMID=""

## Usage - In-line

```console
(meraki) $ chatgpt_meraki_analyzer
```

## Always On Sandbox

This code works with the always on sandbox! 

https://devnetsandbox.cisco.com/RM/Diagram/Index/a9487767-deef-4855-b3e3-880e7f39eadc?diagramType=Topology

```console
export MERAKI_DASHBOARD_API_KEY=fd6dd87d96915f21bc0e0b3d96a866ff0e53e381


```

## Contact

Please contact John Capobianco if you need any assistance
ZnVis 0.1.0
Visualization of particle trajectories.
Requires Python: >=3.8

![PyTest](https://github.com/zincware/ZnVis/actions/workflows/pytest.yaml/badge.svg)
[![code-style](https://img.shields.io/badge/code%20style-black-black)](https://github.com/psf/black/)
[![zincware](https://img.shields.io/badge/Powered%20by-zincware-darkcyan)](https://github.com/zincware)


# ZnVis

ZnVis is a visualisation engine for particle simulation data.
Simply define the particles in the simulation with details including their positions,
colour, direction, and shape, and the visualization engine will display the system
using the Open3D engine.
ZnVis works both from a Python script and in Jupyter!

ZnVis can currently perform the following tasks:

* Visualize simulations and trajectories
* Create spherical and cylindrical mesh's for visualization
* Handle custom mesh objects
* Export png stills from the visualizer
* Export scences as `.obj` files.

## Installation

ZnVis is a purely Python package hosted on PyPi.
It can therefore be installed using pip with:

```sh
pip install znvis
```

If you prefer to access the source code, run the following from a terminal:

```sh
   git clone https://github.com/zincware/ZnVis.git
   cd ZnVis
   pip install .
```

Once complete, you will be able to start using the visualizer by importing it as:

```python
import znvis
```

## How does it work?

ZnVis is essentially a convenience wrapper of the 
[Open3D](https://github.com/isl-org/Open3D) project with a focus on mesh visualization.
The idea came out of wanting a simple way of visualizing particle trajectories from 
numpy arrays directly from a simulation script.

Below we show an example script from a reinforcement learning experiment built using
*Shameless plug alert* [SwarmRL](https://github.com/SwarmRL/SwarmRL)

```python
import numpy as np
import h5py as hf

import znvis as vis

# Load data from the database
with hf.File("training/trajectory.hdf5", 'r') as db:
    positions = db["colloids"]["Unwrapped_Positions"][:]

# Split data for convenience
colloid_positions = positions[:, 0:10, :]
rod_positions = positions[:, 10:, :]

# Create free colloid mesh
colloid_mesh = vis.Sphere(radius=2.14, colour=np.array([30, 144, 255]) / 255, resolution=10)
colloid_particle = vis.Particle(name="Colloid", mesh=mesh, position=colloid_positions)

# Create rod colloid mesh
rod_mesh = vis.Sphere(radius=2.14, colour=np.array([255, 140, 0]) / 255, resolution=10)
rod_particle = vis.Particle(name="Rod", mesh=mesh, position=rod_positions)

# Run the visualizer
visualizer = vis.Visualizer(particles=[colloid_particle, rod_particle], frame_rate=80)
visualizer.run_visualization()
```

Just like that, a visualization window (shown below) will pop up from which you can play 
the trajectory and watch your RL agents rotate a rod.

![Visualizer Example](./readme_image.png)