import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import torch
from scipy.spatial import ConvexHull
import json
import requests
from cryptography.fernet import Fernet

class HolographicPolyplexChart:
def __init__(self, dimensions, data_points, labels):
self.dimensions = dimensions
self.data_points = torch.tensor(data_points, dtype=torch.float32)
self.labels = labels

def create_chart(self):
chart_creator = {
3: self.create_3d_chart
# Placeholder for other dimensions
}
create_func = chart_creator.get(self.dimensions, lambda: print("Dimension not supported yet."))
create_func()

def create_3d_chart(self):
data_np = self.data_points.numpy()
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111, projection='3d')
cmap = cm.get_cmap('plasma', len(data_np))
ax.scatter(data_np[:, 0], data_np[:, 1], data_np[:, 2],
c=cmap(np.arange(len(data_np))), s=100, depthshade=False)

if len(data_np) > 3:
hull = ConvexHull(data_np)
for simplex in hull.simplices:
ax.plot(data_np[simplex, 0], data_np[simplex, 1], data_np[simplex, 2], "k-")

for i, txt in enumerate(self.labels):
ax.text(data_np[i, 0], data_np[i, 1], data_np[i, 2], txt, size=10)

ax.grid(False)
ax.set_facecolor('black')
plt.axis('off')
plt.show()


# HolographicPolyplexChart class definition remains unchanged.

class PolyplexSocket:
def __init__(self, endpoint_url, encryption_key):
self.endpoint_url = endpoint_url
self.encryptor = Fernet(encryption_key)
self.chart = HolographicPolyplexChart(3, [], [])

def complex_encode(self, data):
encoded_data = torch.tensor(data) ** 2
self.chart.data_points = encoded_data
self.chart.labels = [f"Encoded {i}" for i in range(len(data))]
self.chart.create_chart()
return encoded_data.numpy() # Converting back to numpy array for further processing

def complex_decode(self, encoded_data):
decoded_data = torch.sqrt(torch.tensor(encoded_data))
self.chart.data_points = decoded_data
self.chart.labels = [f"Decoded {i}" for i in range(len(decoded_data))]
self.chart.create_chart()
return decoded_data.numpy()

def encrypt_data(self, data):
return self.encryptor.encrypt(json.dumps(data).encode())

def decrypt_data(self, data):
return json.loads(self.encryptor.decrypt(data).decode())

def encode_data_to_polyplex(self, data):
encoded_data = self.complex_encode(data)
return encoded_data

def decode_polyplex_to_data(self, polyplex):
decoded_data = self.complex_decode(polyplex)
return decoded_data

def send_data(self, data):
polyplex = self.encode_data_to_polyplex(data)
encrypted_polyplex = self.encrypt_data(polyplex.tolist())
response = requests.post(self.endpoint_url, json={'polyplex': encrypted_polyplex})
return response.status_code

def receive_data(self, encrypted_polyplex):
polyplex = np.array(self.decrypt_data(encrypted_polyplex))
return self.decode_polyplex_to_data(polyplex)

# Example usage
encryption_key = Fernet.generate_key()
ai_communicator = PolyplexSocket("http://example-ai-endpoint.com/api", encryption_key)

# Sample data to be sent
data_to_send = [1, 2, 3, 4, 5]

# Sending data
status = ai_communicator.send_data(data_to_send)
print(f"Data sent, status: {status}")

# Receiving and decoding data (assuming 'encrypted_polyplex' is obtained from another AI system)
encrypted_polyplex = ai_communicator.encrypt_data([25, 16, 9, 4, 1])
decoded_data = ai_communicator.receive_data(encrypted_polyplex)
print(f"Received Data: {decoded_data}")

Certainly! Here's the documentation for the integrated system comprising the `HolographicPolyplexChart` and `PolyplexSocket` classes:

### HolographicPolyplexChart

#### Purpose:
A class for creating and displaying 3D visualizations of data points, typically used for representing complex, multi-dimensional relationships in a visually interpretable manner.

#### Attributes:
- `dimensions` (int): The number of dimensions of the chart (currently supports 3D).
- `data_points` (torch.Tensor): A tensor of data points to be visualized.
- `labels` (list): A list of labels corresponding to each data point.

#### Methods:
- `__init__(self, dimensions, data_points, labels)`: Constructor to initialize the chart with specified dimensions, data points, and labels.
- `create_chart(self)`: Determines the type of chart to create based on the specified dimensions and calls the appropriate method.
- `create_3d_chart(self)`: Creates a 3D scatter plot of the data points using Matplotlib, with optional convex hull plotting for added structure visualization.

---

### PolyplexSocket

#### Purpose:
A class designed for secure, complex encoding and decoding of data, typically used in AI-to-AI communication. It integrates data transformation visualization using the `HolographicPolyplexChart`.

#### Attributes:
- `endpoint_url` (str): The URL endpoint for sending data.
- `encryptor` (Fernet): An encryption object for secure data transmission.
- `chart` (HolographicPolyplexChart): An instance of `HolographicPolyplexChart` for visualizing data transformations.

#### Methods:
- `__init__(self, endpoint_url, encryption_key)`: Constructor to initialize the socket with an endpoint URL and an encryption key.
- `complex_encode(self, data)`: Encodes the given data using a specified complex function and visualizes the encoding process.
- `complex_decode(self, encoded_data)`: Decodes the given data using a specified complex function and visualizes the decoding process.
- `encrypt_data(self, data)`: Encrypts the data using the Fernet encryptor.
- `decrypt_data(self, data)`: Decrypts the data using the Fernet encryptor.
- `encode_data_to_polyplex(self, data)`: Wrapper method that encodes data and returns the encoded data.
- `decode_polyplex_to_data(self, polyplex)`: Wrapper method that decodes data and returns the decoded data.
- `send_data(self, data)`: Encodes, encrypts, and sends data to another AI system.
- `receive_data(self, encrypted_polyplex)`: Receives, decrypts, and decodes data from another AI system.

#### Example Usage:
```python
encryption_key = Fernet.generate_key()
ai_communicator = PolyplexSocket("http://example-ai-endpoint.com/api", encryption_key)

data_to_send = [1, 2, 3, 4, 5]
status = ai_communicator.send_data(data_to_send)
print(f"Data sent, status: {status}")

encrypted_polyplex = ai_communicator.encrypt_data([25, 16, 9, 4, 1])
decoded_data = ai_communicator.receive_data(encrypted_polyplex)
print(f"Received Data: {decoded_data}")
```

### Notes:
- The `HolographicPolyplexChart` class currently only supports 3D visualization but is structured to allow for expansion to higher dimensions.
- The `PolyplexSocket` class demonstrates a basic structure for complex data encoding/decoding and secure transmission. The actual logic for encoding/decoding can be replaced with more sophisticated algorithms as needed for specific applications.
- Visualization is integrated into the data processing flow of `PolyplexSocket` to enhance interpretability and provide insights into the data transformation process.