> For the complete documentation index, see [llms.txt](https://cellular-automata.gitbook.io/cellular-automata-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cellular-automata.gitbook.io/cellular-automata-documentation/chapter-4-implementation-guide/quickstart.md).

# Tools and Platforms

<figure><img src="/files/t1QXjFwZoUG90QJOODKf" alt=""><figcaption></figcaption></figure>

* **Frameworks**:
  * Python libraries like `matplotlib` and `numpy`.
  * AI libraries like OpenAI API.
* **Interactive Platforms**:
  * Integrate with web tools (e.g., React, D3.js).

#### Sample Code

* Basic Cellular Automata simulation:

```
import numpy as np
import matplotlib.pyplot as plt

# Initialize grid
grid = np.zeros((100, 100))
grid[50, 50] = 1  # Seed

# Define rules
def update(grid):
    new_grid = np.zeros(grid.shape)
    for x in range(1, grid.shape[0]-1):
        for y in range(1, grid.shape[1]-1):
            neighbors = grid[x-1:x+2, y-1:y+2].sum() - grid[x, y]
            if grid[x, y] == 1 and (neighbors == 2 or neighbors == 3):
                new_grid[x, y] = 1
            elif grid[x, y] == 0 and neighbors == 3:
                new_grid[x, y] = 1
    return new_grid

# Run simulation
for _ in range(100):
    grid = update(grid)
    plt.imshow(grid, cmap='binary')
    plt.pause(0.1)
```

#### Integrating AI

* Use AI to parse natural language prompts into CA rules:

```
from openai import ChatCompletion

prompt = "Create a rule where cells grow like a forest fire."
response = ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}]
)
rules = response["choices"][0]["message"]["content"]
```

***

###


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