The Bitmatrix Toolkit is designed to optimize computational resources, enhance data processing efficiency, and provide a flexible framework for a wide range of applications. This documentation includes complete code implementations, parameter details, return value specifications, error handling guidelines, dependencies, and integration examples.
# Clone the repository
git clone https://github.com/DigitalEuan/bitmatrix-toolkit.git (not functional now)
# Navigate to the directory
cd bitmatrix-toolkit
# Install dependencies
pip install -r requirements.txt
# Install the package
pip install -e .
# Import the Bitmatrix toolkit
import bitmatrix as bm
# Initialize a 3D bitfield
bitfield = bm.init_3d(x=100, y=100, z=100)
# Encode data into the bitfield
bm.encode_bit(bitfield, data)
# Process data using Oen
agent = bm.spawn_agent(id=1)
result = agent.process(bitfield)
The Bitfield is a multidimensional data structure that forms the foundation of Bitmatrix. It enables efficient storage and processing of complex data by representing information in spatial, temporal, and contextual dimensions.
import numpy as np
import mmap
import os
# Define custom dtype for bit properties
bit_properties_dtype = np.dtype([
('value', np.bool_), # 1 bit: The fundamental binary state
('spacing', np.float32), # 4 bytes: Spatial distance between bits
('shape', np.uint8), # 1 byte: Geometric form (cube, sphere, etc.)
('color', np.uint8, 3), # 3 bytes: RGB color
('perspective', np.float16), # 2 bytes: Angular orientation
('frequency', np.float32), # 4 bytes: Temporal rate
('phase', np.float16) # 2 bytes: Wave offset
])
def init_3d(x, y, z, dtype=None):
"""
Initialize a 3D bitfield with the specified dimensions.
Args:
x (int): Width of the bitfield
y (int): Height of the bitfield
z (int): Depth of the bitfield
dtype (numpy.dtype, optional): Data type for the bitfield
Returns:
numpy.ndarray: The initialized 3D bitfield
"""
if dtype is None:
dtype = bit_properties_dtype
# Create the bitfield with the specified dimensions
bitfield = np.zeros((x, y, z), dtype=dtype)
# Initialize default values
bitfield['value'] = False
bitfield['spacing'] = 1.0
bitfield['shape'] = 0 # 0 = cube
bitfield['color'] = [0, 0, 0] # Black
bitfield['perspective'] = 0.0
bitfield['frequency'] = 0.0
bitfield['phase'] = 0.0
return bitfield
def init_4d(x, y, z, t, dtype=None):
"""
Initialize a 4D bitfield with the specified dimensions.
Args:
x (int): Width of the bitfield
y (int): Height of the bitfield
z (int): Depth of the bitfield
t (int): Time dimension of the bitfield
dtype (numpy.dtype, optional): Data type for the bitfield
Returns:
numpy.ndarray: The initialized 4D bitfield
"""
if dtype is None:
dtype = bit_properties_dtype
# Create the bitfield with the specified dimensions
bitfield = np.zeros((x, y, z, t), dtype=dtype)
# Initialize default values
bitfield['value'] = False
bitfield['spacing'] = 1.0
bitfield['shape'] = 0 # 0 = cube
bitfield['color'] = [0, 0, 0] # Black
bitfield['perspective'] = 0.0
bitfield['frequency'] = 0.0
bitfield['phase'] = 0.0
return bitfield
def init_5d(x, y, z, t, c, dtype=None):
"""
Initialize a 5D bitfield with the specified dimensions.
Args:
x (int): Width of the bitfield
y (int): Height of the bitfield
z (int): Depth of the bitfield
t (int): Time dimension of the bitfield
c (int): Context dimension of the bitfield (reality context)
dtype (numpy.dtype, optional): Data type for the bitfield
Returns:
numpy.ndarray: The initialized 5D bitfield
"""
if dtype is None:
dtype = bit_properties_dtype
# Create the bitfield with the specified dimensions
bitfield = np.zeros((x, y, z, t, c), dtype=dtype)
# Initialize default values
bitfield['value'] = False
bitfield['spacing'] = 1.0
bitfield['shape'] = 0 # 0 = cube
bitfield['color'] = [0, 0, 0] # Black
bitfield['perspective'] = 0.0
bitfield['frequency'] = 0.0
bitfield['phase'] = 0.0
return bitfield
def encode_bit(bitfield, data, encoding_method='auto'):
"""
Encode data into the bitfield using the specified encoding method.
Args:
bitfield (numpy.ndarray): The bitfield to encode data into
data (array-like): The data to encode
encoding_method (str, optional): The encoding method to use
Options: 'auto', 'spatial', 'temporal', 'contextual'
Returns:
numpy.ndarray: The bitfield with encoded data
"""
# Make a copy of the bitfield to avoid modifying the original
encoded_bitfield = bitfield.copy()
# Determine the encoding method if 'auto' is specified
if encoding_method == 'auto':
# Determine the best encoding method based on data characteristics
if len(data.shape) == 1: # 1D data (e.g., audio)
encoding_method = 'temporal'
elif len(data.shape) == 2: # 2D data (e.g., image)
encoding_method = 'spatial'
else: # Higher-dimensional data
encoding_method = 'contextual'
# Encode the data using the specified method
if encoding_method == 'spatial':
# Spatial encoding (e.g., for images)
# Map data values to spatial positions in the bitfield
data_normalized = (data - np.min(data)) / (np.max(data) - np.min(data))
# Reshape data if necessary
data_reshaped = np.resize(data_normalized, encoded_bitfield.shape[:3])
# Encode data into the bitfield
for x in range(min(encoded_bitfield.shape[0], data_reshaped.shape[0])):
for y in range(min(encoded_bitfield.shape[1], data_reshaped.shape[1])):
for z in range(min(encoded_bitfield.shape[2], data_reshaped.shape[2])):
# Set the value based on the data
encoded_bitfield[x, y, z]['value'] = data_reshaped[x, y, z] > 0.5
# Set other properties based on the data
encoded_bitfield[x, y, z]['spacing'] = 1.0 + data_reshaped[x, y, z]
encoded_bitfield[x, y, z]['color'] = [
int(255 * data_reshaped[x, y, z]),
int(255 * (1 - data_reshaped[x, y, z])),
int(255 * abs(0.5 - data_reshaped[x, y, z]) * 2)
]
elif encoding_method == 'temporal':
# Temporal encoding (e.g., for audio)
# Map data values to temporal positions in the bitfield
data_normalized = (data - np.min(data)) / (np.max(data) - np.min(data))
# Calculate the number of time steps
t_steps = encoded_bitfield.shape[3] if len(encoded_bitfield.shape) > 3 else 1
# Resample data to match the number of time steps
data_resampled = np.interp(
np.linspace(0, len(data_normalized) - 1, t_steps),
np.arange(len(data_normalized)),
data_normalized
)
# Encode data into the bitfield
for t in range(t_steps):
# Set the value based on the data
encoded_bitfield[:, :, :, t]['value'] = data_resampled[t] > 0.5
# Set frequency and phase based on the data
encoded_bitfield[:, :, :, t]['frequency'] = data_resampled[t] * 1000 # Scale to Hz
encoded_bitfield[:, :, :, t]['phase'] = data_resampled[t] * 2 * np.pi
elif encoding_method == 'contextual':
# Contextual encoding (e.g., for complex data with context)
# This is a more advanced encoding method for 5D bitfields
if len(encoded_bitfield.shape) < 5:
raise ValueError("Contextual encoding requires a 5D bitfield")
# Normalize and reshape data
data_normalized = (data - np.min(data)) / (np.max(data) - np.min(data))
data_reshaped = np.resize(data_normalized, encoded_bitfield.shape[:3])
# Calculate context values
c_steps = encoded_bitfield.shape[4]
# Encode data into the bitfield with context
for c in range(c_steps):
context_factor = c / (c_steps - 1) # 0 to 1
for x in range(encoded_bitfield.shape[0]):
for y in range(encoded_bitfield.shape[1]):
for z in range(encoded_bitfield.shape[2]):
# Apply context-based transformation
value = data_reshaped[x, y, z] * (1 + context_factor)
# Set the value based on the transformed data
encoded_bitfield[x, y, z, :, c]['value'] = value > 0.5
# Set other properties based on the context
encoded_bitfield[x, y, z, :, c]['spacing'] = 1.0 + context_factor
encoded_bitfield[x, y, z, :, c]['shape'] = int(c % 5) # Vary shape by context
return encoded_bitfield
def decode_bit(bitfield, decoding_method='auto'):
"""
Decode data from the bitfield using the specified decoding method.
Args:
bitfield (numpy.ndarray): The bitfield to decode data from
decoding_method (str, optional): The decoding method to use
Options: 'auto', 'spatial', 'temporal', 'contextual'
Returns:
array-like: The decoded data
"""
# Determine the decoding method if 'auto' is specified
if decoding_method == 'auto':
# Determine the best decoding method based on bitfield dimensions
if len(bitfield.shape) == 3: # 3D bitfield
decoding_method = 'spatial'
elif len(bitfield.shape) == 4: # 4D bitfield
decoding_method = 'temporal'
elif len(bitfield.shape) == 5: # 5D bitfield
decoding_method = 'contextual'
else:
raise ValueError("Unsupported bitfield dimensions")
# Decode the data using the specified method
if decoding_method == 'spatial':
# Spatial decoding (e.g., for images)
# Extract data from spatial positions in the bitfield
decoded_data = np.zeros(bitfield.shape[:3])
for x in range(bitfield.shape[0]):
for y in range(bitfield.shape[1]):
for z in range(bitfield.shape[2]):
# Combine value and other properties to reconstruct the data
value_component = float(bitfield[x, y, z]['value'])
spacing_component = (bitfield[x, y, z]['spacing'] - 1.0) / 2.0
color_component = np.mean(bitfield[x, y, z]['color']) / 255.0
# Weighted combination of components
decoded_data[x, y, z] = 0.5 * value_component + 0.3 * spacing_component + 0.2 * color_component
elif decoding_method == 'temporal':
# Temporal decoding (e.g., for audio)
# Extract data from temporal positions in the bitfield
t_steps = bitfield.shape[3]
decoded_data = np.zeros(t_steps)
for t in range(t_steps):
# Average the values across spatial dimensions
value_component = np.mean(bitfield[:, :, :, t]['value'].astype(float))
# Combine with frequency and phase information
frequency_component = np.mean(bitfield[:, :, :, t]['frequency']) / 1000.0 # Scale from Hz
phase_component = np.mean(bitfield[:, :, :, t]['phase']) / (2 * np.pi)
# Weighted combination of components
decoded_data[t] = 0.4 * value_component + 0.4 * frequency_component + 0.2 * phase_component
elif decoding_method == 'contextual':
# Contextual decoding (e.g., for complex data with context)
# This is a more advanced decoding method for 5D bitfields
if len(bitfield.shape) < 5:
raise ValueError("Contextual decoding requires a 5D bitfield")
# Extract data with context consideration
c_steps = bitfield.shape[4]
decoded_data = np.zeros(bitfield.shape[:3])
for c in range(c_steps):
context_factor = c / (c_steps - 1) # 0 to 1
context_weight = np.sin(context_factor * np.pi) # Weight context importance
for x in range(bitfield.shape[0]):
for y in range(bitfield.shape[1]):
for z in range(bitfield.shape[2]):
# Extract value with context consideration
value_component = np.mean(bitfield[x, y, z, :, c]['value'].astype(float))
# Apply context-based transformation
value = value_component / (1 + context_factor)
# Add to decoded data with context weighting
decoded_data[x, y, z] += value * context_weight
# Normalize by total context weights
total_context_weight = sum(np.sin(c / (c_steps - 1) * np.pi) for c in range(c_steps))
decoded_data /= total_context_weight
return decoded_data
def mmap_bitfield(file_path, shape, access_mode='r+'):
"""
Create a memory-mapped bitfield for handling large datasets.
Args:
file_path (str): Path to the file to memory-map
shape (tuple): Shape of the bitfield (e.g., (x, y, z, t))
access_mode (str, optional): File access mode. Defaults to 'r+' (read/write).
Returns:
numpy.memmap: The memory-mapped bitfield
"""
# Calculate the size of the bitfield
dtype = bit_properties_dtype
itemsize = dtype.itemsize
total_size = np.prod(shape) * itemsize
# Create the file if it doesn't exist
if not os.path.exists(file_path) or os.path.getsize(file_path) != total_size:
with open(file_path, 'wb') as f:
f.seek(total_size - 1)
f.write(b'\0')
# Create the memory-mapped array
mmap_bitfield = np.memmap(file_path, dtype=dtype, mode=access_mode, shape=shape)
return mmap_bitfield
import bitmatrix as bm
import numpy as np
# Initialize a 4D bitfield
bitfield = bm.init_4d(x=100, y=100, z=50, t=10)
# Create sample data (e.g., audio waveform)
sample_rate = 44100
duration = 1.0 # seconds
t = np.linspace(0, duration, int(sample_rate * duration))
audio_data = np.sin(2 * np.pi * 440 * t) # 440 Hz sine wave
# Encode the audio data into the bitfield
encoded_bitfield = bm.encode_bit(bitfield, audio_data)
# Decode the data from the bitfield
decoded_data = bm.decode_bit(encoded_bitfield)
# For large datasets, use memory-mapped bitfields
large_bitfield = bm.mmap_bitfield(
file_path="large_dataset.bin",
shape=(1000, 1000, 500, 100)
)
# Access a chunk of the memory-mapped bitfield
chunk = large_bitfield[0:100, 0:100, 0:50, 0:10]
The Oen Collective is a decentralized processing system that dynamically optimizes computational processes. It consists of multiple agent threads that work together to manage different computational domains and make decisions based on a reputation-weighted voting system.
import numpy as np
import threading
import time
import heapq
from collections import defaultdict
class OenAgent:
"""
Represents an agent in the Oen Collective, responsible for a specific computational domain.
"""
def __init__(self, id, domain=None):
"""
Initialize an Oen agent.
Args:
id (int): Unique identifier for the agent
domain (str, optional): Computational domain for the agent
"""
self.id = id
self.domain = domain if domain else self._assign_domain(id)
self.reputation = 50 # Start with neutral reputation (0-100)
self.tasks_completed = 0
self.running = False
self.thread = None
def _assign_domain(self, id):
"""
Assign a domain based on the agent ID.
Args:
id (int): Agent ID
Returns:
str: Assigned domain
"""
domains = ['storage', 'rendering', 'resilience', 'network', 'processing',
'optimization', 'analysis', 'coordination']
return domains[id % len(domains)]
def start(self):
"""
Start the agent in a separate thread.
Returns:
OenAgent: The agent instance
"""
if not self.running:
self.running = True
self.thread = threading.Thread(target=self._run)
self.thread.daemon = True
self.thread.start()
return self
def stop(self):
"""
Stop the agent.
Returns:
OenAgent: The agent instance
"""
self.running = False
if self.thread and self.thread.is_alive():
self.thread.join(timeout=1.0)
return self
def _run(self):
"""
Main agent loop.
"""
while self.running:
# Agent processing logic
time.sleep(0.1) # Prevent CPU hogging
def process(self, data):
"""
Process data using the agent's domain expertise.
Args:
data: The data to process
Returns:
The processed data
"""
# Process data based on the agent's domain
if self.domain == 'storage':
return self._process_storage(data)
elif self.domain == 'rendering':
return self._process_rendering(data)
elif self.domain == 'resilience':
return self._process_resilience(data)
elif self.domain == 'network':
return self._process_network(data)
elif self.domain == 'processing':
return self._process_computation(data)
elif self.domain == 'optimization':
return self._process_optimization(data)
elif self.domain == 'analysis':
return self._process_analysis(data)
elif self.domain == 'coordination':
return self._process_coordination(data)
else:
return data # Default: return data unchanged
def _process_storage(self, data):
"""Process data for storage optimization."""
# Simulate compression
if isinstance(data, np.ndarray):
# Apply a simple compression algorithm (for demonstration)
shape = data.shape
flattened = data.flatten()
# Keep only values above mean as a simple "compression"
mean_val = np.mean(flattened)
compressed = flattened[flattened > mean_val]
# Store the compression metadata
metadata = {
'original_shape': shape,
'compression_threshold': mean_val,
'compression_ratio': len(compressed) / len(flattened)
}
# Update reputation based on compression ratio
self._update_reputation(50 * (1 - metadata['compression_ratio']))
return {'data': compressed, 'metadata': metadata}
return data
def _process_rendering(self, data):
"""Process data for rendering optimization."""
# Simulate rendering optimization
if isinstance(data, np.ndarray):
# Apply a simple rendering optimization (for demonstration)
# Convert to lower precision to simulate optimization
optimized = data.astype(np.float16)
# Calculate optimization metrics
memory_saved = (data.nbytes - optimized.nbytes) / data.nbytes
# Update reputation based on memory saved
self._update_reputation(50 * memory_saved)
return optimized
return data
def _process_resilience(self, data):
"""Process data for error resilience."""
# Simulate error correction
if isinstance(data, np.ndarray):
# Apply a simple error correction (for demonstration)
# Add redundancy by duplicating every 10th value
shape = data.shape
flattened = data.flatten()
# Introduce a few random errors
error_indices = np.random.choice(len(flattened), size=int(len(flattened) * 0.001), replace=False)
flattened_with_errors = flattened.copy()
flattened_with_errors[error_indices] = np.random.rand(len(error_indices))
# "Correct" the errors by comparing with original
# In a real implementation, this would use error correction codes
corrected = flattened_with_errors.copy()
corrected[error_indices] = flattened[error_indices]
# Calculate correction metrics
errors_corrected = len(error_indices)
correction_rate = errors_corrected / len(flattened)
# Update reputation based on correction rate
self._update_reputation(50 * correction_rate)
# Reshape to original dimensions
return corrected.reshape(shape)
return data
def _process_network(self, data):
"""Process data for network optimization."""
# Simulate network optimization
return data # Simplified implementation
def _process_computation(self, data):
"""Process data for computational optimization."""
# Simulate computational optimization
return data # Simplified implementation
def _process_optimization(self, data):
"""Process data for general optimization."""
# Simulate general optimization
return data # Simplified implementation
def _process_analysis(self, data):
"""Process data for analysis."""
# Simulate data analysis
return data # Simplified implementation
def _process_coordination(self, data):
"""Process data for coordination between agents."""
# Simulate coordination
return data # Simplified implementation
def _update_reputation(self, change):
"""
Update the agent's reputation based on task performance.
Args:
change (float): The amount to change the reputation by
"""
self.reputation += change
self.reputation = max(0, min(90, self.reputation)) # Cap at 0-90
self.tasks_completed += 1
# Reset reputation every 100 tasks
if self.tasks_completed >= 100:
self.reputation = 50
self.tasks_completed = 0
def spawn_agent(id, domain=None):
"""
Create a new Oen agent with the specified ID and domain.
Args:
id (int): Unique identifier for the agent
domain (str, optional): Computational domain for the agent
Returns:
OenAgent: The created agent
"""
return OenAgent(id, domain)
def vote_method(method_score):
"""
Cast a vote for a processing method based on its score.
Args:
method_score (dict): Dictionary mapping method names to their scores
Returns:
str: The selected method name
"""
if not method_score:
return None
# Select the method with the highest score
return max(method_score.items(), key=lambda x: x[1])[0]
def tally_scores(methods):
"""
Tally the scores for different methods and select the best one.
Args:
methods (list): List of method names to evaluate
Returns:
str: The selected method name
"""
if not methods:
return None
# Create a dictionary to store method scores
method_scores = {}
# Evaluate each method (simplified implementation)
for method in methods:
# In a real implementation, this would evaluate the method's performance
# For demonstration, assign random scores
method_scores[method] = np.random.randint(50, 100)
# Select the method with the highest score
return vote_method(method_scores)
def assign_zone(vote):
"""
Assign a zone based on the voting outcome.
Args:
vote (str): The selected method name
Returns:
int: The assigned zone ID
"""
# In a real implementation, this would assign a zone based on the method
# For demonstration, return a random zone ID
return np.random.randint(0, 100)
def astar_path(zone_grid):
"""
Find the optimal path through zones using A* algorithm.
Args:
zone_grid (numpy.ndarray): Grid representing zones and their connections
Returns:
list: The optimal path through zones
"""
if not isinstance(zone_grid, np.ndarray):
raise ValueError("zone_grid must be a numpy array")
# Define the start and goal positions
start = (0, 0)
goal = (zone_grid.shape[0] - 1, zone_grid.shape[1] - 1)
# Define the heuristic function (Manhattan distance)
def heuristic(a, b):
return abs(a[0] - b[0]) + abs(a[1] - b[1])
# Define the neighbors function
def neighbors(position):
x, y = position
candidates = [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]
return [p for p in candidates if 0 <= p[0] < zone_grid.shape[0] and
0 <= p[1] < zone_grid.shape[1] and
zone_grid[p] == 0]
# Initialize the open and closed sets
open_set = []
heapq.heappush(open_set, (0, start))
came_from = {}
g_score = defaultdict(lambda: float('inf'))
g_score[start] = 0
f_score = defaultdict(lambda: float('inf'))
f_score[start] = heuristic(start, goal)
# A* algorithm
while open_set:
_, current = heapq.heappop(open_set)
if current == goal:
# Reconstruct the path
path = []
while current in came_from:
path.append(current)
current = came_from[current]
path.append(start)
path.reverse()
return path
for neighbor in neighbors(current):
tentative_g_score = g_score[current] + 1
if tentative_g_score < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = tentative_g_score
f_score[neighbor] = tentative_g_score + heuristic(neighbor, goal)
heapq.heappush(open_set, (f_score[neighbor], neighbor))
# No path found
return []
import bitmatrix as bm
import numpy as np
import threading
import time
# Create Oen agents for different domains
storage_agent = bm.spawn_agent(id=1, domain='storage')
rendering_agent = bm.spawn_agent(id=2, domain='rendering')
resilience_agent = bm.spawn_agent(id=3, domain='resilience')
# Create a zone grid for task assignment
zone_grid = np.zeros((10, 10), dtype=np.int32)
zone_grid[2:5, 2:5] = 1 # Mark some zones as occupied
# Find optimal path through zones
path = bm.astar_path(zone_grid)
print(f"Optimal path: {path}")
# Evaluate different compression methods
method_scores = {
'huffman': 85,
'lzw': 90,
'rle': 70
}
# Vote for the best method
selected_method = bm.vote_method(method_scores)
print(f"Selected method: {selected_method}")
# Assign a zone based on the vote
zone_id = bm.assign_zone(selected_method)
print(f"Assigned zone: {zone_id}")
# Process data using the Oen Collective
def process_data(data, agent_id):
agent = bm.spawn_agent(id=agent_id)
result = agent.process(data)
return result
# Create sample data
data = np.random.rand(100, 100)
# Process data using multiple agents in parallel
threads = []
results = [None] * 3
for i in range(3):
thread = threading.Thread(
target=lambda i=i: results.__setitem__(i, process_data(data, i+1))
)
threads.append(thread)
thread.start()
# Wait for all threads to complete
for thread in threads:
thread.join()
# Combine results from all agents
final_result = np.mean(results, axis=0)
print(f"Final result shape: {final_result.shape}")
Recursive Layering is a fundamental architectural principle in Bitmatrix2, involving interconnected layers that dynamically communicate and reshape themselves to optimize resource allocation and enhance efficiency.
import numpy as np
class Layer:
"""
Represents a layer in the recursive layering architecture.
"""
def __init__(self, name, function, next_layer=None):
"""
Initialize a layer.
Args:
name (str): Name of the layer
function (callable): Function to apply to data
next_layer (Layer, optional): Next layer in the chain
"""
self.name = name
self.function = function
self.next_layer = next_layer
self.feedback = None
def process(self, data):
"""
Process data through this layer and subsequent layers.
Args:
data: The data to process
Returns:
The processed data
"""
# Apply this layer's function to the data
processed_data = self.function(data)
# Pass the processed data to the next layer if it exists
if self.next_layer:
result = self.next_layer.process(processed_data)
# Store feedback from subsequent layers
self.feedback = result
# Reshape based on feedback (simplified implementation)
if isinstance(result, dict) and 'feedback' in result:
# Apply feedback to reshape the layer
self._reshape(result['feedback'])
return result
else:
return processed_data
def _reshape(self, feedback):
"""
Reshape the layer based on feedback.
Args:
feedback: Feedback data for reshaping
"""
# In a real implementation, this would modify the layer's behavior
# For demonstration, just print the feedback
print(f"Layer {self.name} reshaping based on feedback: {feedback}")
def create_recursive_layers(layer_configs):
"""
Create a recursive layering structure from configuration.
Args:
layer_configs (list): List of layer configurations
Each config should be a dict with 'name' and 'function' keys
Returns:
Layer: The first layer in the chain
"""
if not layer_configs:
return None
# Create layers in reverse order
prev_layer = None
for config in reversed(layer_configs):
layer = Layer(config['name'], config['function'], prev_layer)
prev_layer = layer
return prev_layer
def layer_feed(layer, next_layer):
"""
Connect a layer to the next layer in the chain.
Args:
layer (Layer): The layer to connect from
next_layer (Layer): The layer to connect to
Returns:
Layer: The updated layer
"""
layer.next_layer = next_layer
return layer
import bitmatrix as bm
import numpy as np
# Define layer functions
def base_layer_function(data):
"""Compress data for storage efficiency."""
print("Base layer: Compressing data")
# Simulate compression
if isinstance(data, np.ndarray):
compressed = data.copy()
# Apply simple compression (for demonstration)
compressed[compressed < np.mean(compressed)] = 0
return compressed
return data
def growth_layer_function(data):
"""Enhance processing speed."""
print("Growth layer: Enhancing processing speed")
# Simulate speed enhancement
if isinstance(data, np.ndarray):
# Apply simple enhancement (for demonstration)
enhanced = data.copy()
# Simplify data to enhance processing speed
enhanced = enhanced.astype(np.float16)
return enhanced
return data
def regulation_layer_function(data):
"""Ensure data resilience."""
print("Regulation layer: Ensuring data resilience")
# Simulate error correction
if isinstance(data, np.ndarray):
# Apply simple error correction (for demonstration)
corrected = data.copy()
# Fix any NaN or Inf values
corrected[~np.isfinite(corrected)] = 0
return {
'data': corrected,
'feedback': {'error_rate': 0.0005}
}
return data
# Create layer configurations
layer_configs = [
{'name': 'base', 'function': base_layer_function},
{'name': 'growth', 'function': growth_layer_function},
{'name': 'regulation', 'function': regulation_layer_function}
]
# Create recursive layers
layers = bm.create_recursive_layers(layer_configs)
# Process data through the layers
data = np.random.rand(100, 100)
result = layers.process(data)
# Access the processed data
if isinstance(result, dict) and 'data' in result:
processed_data = result['data']
else:
processed_data = result
print(f"Processed data shape: {processed_data.shape}")
Kinetic Transform Arithmetic (formerly KTA Math) is a set of mathematical functions designed to enhance processing speed within Bitmatrix2. It integrates with every layer of the system to accelerate computations.
import numpy as np
# Golden ratio constant
PHI = (1 + np.sqrt(5)) / 2 # Approximately 1.618033988749895
def scarab_cycle(n):
"""
Apply the ScarabCycle formula to stabilize chaotic data.
Formula: SC(n) = n + (Φ * n) / (n + 1)
Args:
n (float or numpy.ndarray): Input value(s)
Returns:
float or numpy.ndarray: Transformed value(s)
"""
return n + (PHI * n) / (n + 1)
def kta_transform(data, iterations=1):
"""
Apply Kinetic Transform Arithmetic to accelerate computations.
Args:
data (numpy.ndarray): Input data
iterations (int, optional): Number of iterations to apply
Returns:
numpy.ndarray: Transformed data
"""
result = data.copy()
for _ in range(iterations):
# Apply ScarabCycle formula
result = scarab_cycle(result)
# Apply additional KTA transformations
result = np.sin(result) * np.cos(result) + result
# Normalize to prevent overflow
if np.max(np.abs(result)) > 0:
result = result / np.max(np.abs(result))
return result
def kta_compress(data, threshold=0.1):
"""
Compress data using Kinetic Transform Arithmetic.
Args:
data (numpy.ndarray): Input data
threshold (float, optional): Compression threshold
Returns:
dict: Compressed data and metadata
"""
# Apply KTA transform
transformed = kta_transform(data)
# Compress by removing values below threshold
mask = np.abs(transformed) >= threshold
compressed = transformed[mask]
# Store compression metadata
metadata = {
'original_shape': data.shape,
'mask': mask,
'threshold': threshold,
'compression_ratio': len(compressed) / np.prod(data.shape)
}
return {
'data': compressed,
'metadata': metadata
}
def kta_decompress(compressed_data):
"""
Decompress data that was compressed using KTA compression.
Args:
compressed_data (dict): Compressed data and metadata
Returns:
numpy.ndarray: Decompressed data
"""
data = compressed_data['data']
metadata = compressed_data['metadata']
# Recreate the original array
decompressed = np.zeros(np.prod(metadata['original_shape']))
# Place the compressed values back in their original positions
decompressed[metadata['mask'].flatten()] = data
# Reshape to the original dimensions
decompressed = decompressed.reshape(metadata['original_shape'])
# Apply inverse KTA transform (simplified approximation)
result = np.arcsin(decompressed) / PHI
return result
import bitmatrix as bm
import numpy as np
import matplotlib.pyplot as plt
# Create sample data
x = np.linspace(0, 10, 1000)
y = np.sin(x) + np.random.normal(0, 0.1, len(x))
# Apply ScarabCycle formula
stabilized = bm.scarab_cycle(y)
# Apply KTA transform
transformed = bm.kta_transform(y, iterations=3)
# Compress using KTA
compressed = bm.kta_compress(y, threshold=0.2)
print(f"Compression ratio: {compressed['metadata']['compression_ratio']:.2f}")
# Decompress
decompressed = bm.kta_decompress(compressed)
# Plot the results
plt.figure(figsize=(12, 8))
plt.subplot(4, 1, 1)
plt.title('Original Data')
plt.plot(x, y)
plt.subplot(4, 1, 2)
plt.title('Stabilized with ScarabCycle')
plt.plot(x, stabilized)
plt.subplot(4, 1, 3)
plt.title('KTA Transformed')
plt.plot(x, transformed)
plt.subplot(4, 1, 4)
plt.title('Decompressed after KTA Compression')
plt.plot(x, decompressed)
plt.tight_layout()
plt.savefig('kta_example.png')
plt.close()
print("KTA example complete. Results saved to 'kta_example.png'")
Multiple compression and error correction codes are used in combination to optimize efficiency and ensure data resilience.
import numpy as np
from collections import Counter, defaultdict
import heapq
def huffman_tree(data):
"""
Build a Huffman tree for text data compression.
Args:
data (str or bytes): Input data to compress
Returns:
dict: Huffman coding table mapping characters to binary codes
"""
# Count frequency of each character
freq = Counter(data)
# Create a priority queue (min heap)
heap = [[weight, [char, ""]] for char, weight in freq.items()]
heapq.heapify(heap)
# Build the Huffman tree
while len(heap) > 1:
lo = heapq.heappop(heap)
hi = heapq.heappop(heap)
for pair in lo[1:]:
pair[1] = '0' + pair[1]
for pair in hi[1:]:
pair[1] = '1' + pair[1]
heapq.heappush(heap, [lo[0] + hi[0]] + lo[1:] + hi[1:])
# Extract the codes
huffman_codes = {char: code for char, code in sorted(heap[0][1:])}
return huffman_codes
def huffman_compress(data):
"""
Compress data using Huffman coding.
Args:
data (str or bytes): Input data to compress
Returns:
dict: Compressed data and metadata
"""
# Build the Huffman tree
codes = huffman_tree(data)
# Encode the data
encoded = ''.join(codes[char] for char in data)
# Convert binary string to bytes
# Pad to ensure length is a multiple of 8
padding = 8 - (len(encoded) % 8) if len(encoded) % 8 else 0
encoded += '0' * padding
# Convert to bytes
encoded_bytes = bytearray()
for i in range(0, len(encoded), 8):
encoded_bytes.append(int(encoded[i:i+8], 2))
# Calculate compression metrics
original_size = len(data)
compressed_size = len(encoded_bytes)
compression_ratio = compressed_size / original_size
return {
'data': encoded_bytes,
'codes': codes,
'padding': padding,
'original_size': original_size,
'compressed_size': compressed_size,
'compression_ratio': compression_ratio
}
def huffman_decompress(compressed):
"""
Decompress data that was compressed using Huffman coding.
Args:
compressed (dict): Compressed data and metadata
Returns:
str or bytes: Decompressed data
"""
# Extract components
encoded_bytes = compressed['data']
codes = compressed['codes']
padding = compressed['padding']
# Convert bytes to binary string
encoded = ''.join(format(byte, '08b') for byte in encoded_bytes)
# Remove padding
encoded = encoded[:-padding] if padding else encoded
# Create a reverse mapping of codes
reverse_codes = {code: char for char, code in codes.items()}
# Decode the data
decoded = []
code = ""
for bit in encoded:
code += bit
if code in reverse_codes:
decoded.append(reverse_codes[code])
code = ""
# Convert back to original format
if isinstance(list(codes.keys())[0], int):
# If original was bytes, convert back to bytes
return bytes(decoded)
else:
# If original was string, join characters
return ''.join(decoded)
def run_length(sparse):
"""
Compress sparse data using Run-Length Encoding.
Args:
sparse (numpy.ndarray): Input sparse data to compress
Returns:
dict: Compressed data and metadata
"""
if not isinstance(sparse, np.ndarray):
raise TypeError("Input must be a numpy array")
# Flatten the array
flattened = sparse.flatten()
# Encode using run-length encoding
encoded = []
count = 1
current = flattened[0]
for i in range(1, len(flattened)):
if flattened[i] == current:
count += 1
else:
encoded.append((current, count))
current = flattened[i]
count = 1
# Add the last run
encoded.append((current, count))
# Calculate compression metrics
original_size = len(flattened)
compressed_size = len(encoded) * 2 # Each entry is a pair (value, count)
compression_ratio = compressed_size / original_size
return {
'data': encoded,
'shape': sparse.shape,
'original_size': original_size,
'compressed_size': compressed_size,
'compression_ratio': compression_ratio
}
def run_length_decompress(compressed):
"""
Decompress data that was compressed using Run-Length Encoding.
Args:
compressed (dict): Compressed data and metadata
Returns:
numpy.ndarray: Decompressed data
"""
# Extract components
encoded = compressed['data']
shape = compressed['shape']
# Decode the data
decoded = []
for value, count in encoded:
decoded.extend([value] * count)
# Reshape to original dimensions
return np.array(decoded).reshape(shape)
def lzw_dict(multimedia):
"""
Compress multimedia data using LZW compression.
Args:
multimedia (bytes or numpy.ndarray): Input multimedia data to compress
Returns:
dict: Compressed data and metadata
"""
# Convert numpy array to bytes if necessary
if isinstance(multimedia, np.ndarray):
data = multimedia.tobytes()
else:
data = multimedia
# Initialize dictionary with single-byte entries
dictionary = {bytes([i]): i for i in range(256)}
next_code = 256
# Compress the data
compressed = []
current = bytes([data[0]])
for byte in data[1:]:
next_byte = bytes([byte])
if current + next_byte in dictionary:
current = current + next_byte
else:
compressed.append(dictionary[current])
dictionary[current + next_byte] = next_code
next_code += 1
current = next_byte
# Add the last code
compressed.append(dictionary[current])
# Calculate compression metrics
original_size = len(data)
compressed_size = len(compressed)
compression_ratio = compressed_size / original_size
return {
'data': compressed,
'original_size': original_size,
'compressed_size': compressed_size,
'compression_ratio': compression_ratio,
'is_numpy': isinstance(multimedia, np.ndarray),
'shape': multimedia.shape if isinstance(multimedia, np.ndarray) else None,
'dtype': str(multimedia.dtype) if isinstance(multimedia, np.ndarray) else None
}
def lzw_decompress(compressed):
"""
Decompress data that was compressed using LZW compression.
Args:
compressed (dict): Compressed data and metadata
Returns:
bytes or numpy.ndarray: Decompressed data
"""
# Extract components
data = compressed['data']
is_numpy = compressed['is_numpy']
# Initialize dictionary with single-byte entries
dictionary = {i: bytes([i]) for i in range(256)}
next_code = 256
# Decompress the data
decompressed = [data[0]]
current = dictionary[data[0]]
for code in data[1:]:
if code in dictionary:
entry = dictionary[code]
elif code == next_code:
entry = current + current[0:1]
else:
raise ValueError("Invalid compressed data")
decompressed.append(entry)
dictionary[next_code] = current + entry[0:1]
next_code += 1
current = entry
# Combine the decompressed bytes
result = b''.join(decompressed)
# Convert back to numpy array if original was numpy
if is_numpy:
shape = compressed['shape']
dtype = np.dtype(compressed['dtype'])
return np.frombuffer(result, dtype=dtype).reshape(shape)
else:
return result
def rs_correct(burst):
"""
Apply Reed-Solomon error correction to fix burst errors.
Args:
burst (bytes or numpy.ndarray): Input data with potential burst errors
Returns:
bytes or numpy.ndarray: Error-corrected data
"""
# This is a simplified implementation of Reed-Solomon error correction
# In a real implementation, this would use a proper Reed-Solomon library
# For demonstration, we'll just simulate error correction
if isinstance(burst, np.ndarray):
# Create a copy to avoid modifying the original
corrected = burst.copy()
# Simulate error correction by fixing any NaN or Inf values
if np.issubdtype(corrected.dtype, np.floating):
corrected[~np.isfinite(corrected)] = 0
return corrected
else:
# For bytes, just return the original (in a real implementation, this would correct errors)
return burst
def hamming_fix(single):
"""
Apply Hamming code error correction to fix single-bit errors.
Args:
single (bytes or numpy.ndarray): Input data with potential single-bit errors
Returns:
bytes or numpy.ndarray: Error-corrected data
"""
# This is a simplified implementation of Hamming code error correction
# In a real implementation, this would use a proper Hamming code library
# For demonstration, we'll just simulate error correction
if isinstance(single, np.ndarray):
# Create a copy to avoid modifying the original
corrected = single.copy()
# Simulate error correction by fixing any extreme outliers
if np.issubdtype(corrected.dtype, np.number):
mean = np.mean(corrected)
std = np.std(corrected)
outliers = np.abs(corrected - mean) > 3 * std
corrected[outliers] = mean
return corrected
else:
# For bytes, just return the original (in a real implementation, this would correct errors)
return single
import bitmatrix as bm
import numpy as np
import matplotlib.pyplot as plt
# Create sample text data
text_data = "This is a sample text for Huffman compression. " * 10
# Compress using Huffman coding
huffman_compressed = bm.huffman_compress(text_data)
print(f"Huffman compression ratio: {huffman_compressed['compression_ratio']:.2f}")
# Decompress
huffman_decompressed = bm.huffman_decompress(huffman_compressed)
print(f"Huffman decompression successful: {huffman_decompressed == text_data}")
# Create sample sparse data
sparse_data = np.zeros((100, 100))
sparse_data[10:20, 10:20] = 1
sparse_data[50:60, 50:60] = 2
# Compress using Run-Length Encoding
rle_compressed = bm.run_length(sparse_data)
print(f"RLE compression ratio: {rle_compressed['compression_ratio']:.2f}")
# Decompress
rle_decompressed = bm.run_length_decompress(rle_compressed)
print(f"RLE decompression successful: {np.array_equal(rle_decompressed, sparse_data)}")
# Create sample multimedia data
multimedia_data = np.random.randint(0, 256, (50, 50, 3), dtype=np.uint8)
# Compress using LZW
lzw_compressed = bm.lzw_dict(multimedia_data)
print(f"LZW compression ratio: {lzw_compressed['compression_ratio']:.2f}")
# Decompress
lzw_decompressed = bm.lzw_decompress(lzw_compressed)
print(f"LZW decompression successful: {np.array_equal(lzw_decompressed, multimedia_data)}")
# Apply error correction
# Create data with simulated errors
data_with_errors = multimedia_data.copy().astype(float)
data_with_errors[10:15, 10:15, 0] = np.nan # Simulate burst errors
data_with_errors[30, 30, 1] = np.inf # Simulate single-bit error
# Apply Reed-Solomon correction for burst errors
rs_corrected = bm.rs_correct(data_with_errors)
print(f"Reed-Solomon correction applied, fixed NaN values: {not np.any(np.isnan(rs_corrected))}")
# Apply Hamming correction for single-bit errors
hamming_corrected = bm.hamming_fix(rs_corrected)
print(f"Hamming correction applied, fixed Inf values: {not np.any(np.isinf(hamming_corrected))}")
# Plot the results
plt.figure(figsize=(15, 10))
plt.subplot(2, 2, 1)
plt.title('Original Sparse Data')
plt.imshow(sparse_data, cmap='viridis')
plt.subplot(2, 2, 2)
plt.title('RLE Decompressed Data')
plt.imshow(rle_decompressed, cmap='viridis')
plt.subplot(2, 2, 3)
plt.title('Original Multimedia Data')
plt.imshow(multimedia_data)
plt.subplot(2, 2, 4)
plt.title('Error Corrected Data')
plt.imshow(hamming_corrected.astype(np.uint8))
plt.tight_layout()
plt.savefig('compression_example.png')
plt.close()
print("Compression example complete. Results saved to 'compression_example.png'")
Memory-mapped bitfields enable scaling to handle very large datasets using limited RAM.
import numpy as np
import mmap
import os
import tempfile
def create_mmap_file(size, file_path=None):
"""
Create a file for memory mapping.
Args:
size (int): Size of the file in bytes
file_path (str, optional): Path to the file. If None, a temporary file is created.
Returns:
str: Path to the created file
"""
if file_path is None:
# Create a temporary file
fd, file_path = tempfile.mkstemp(suffix='.bin')
os.close(fd)
# Create the file with the specified size
with open(file_path, 'wb') as f:
f.seek(size - 1)
f.write(b'\0')
return file_path
def mmap_open(file_path, shape, dtype=np.float32, access_mode='r+'):
"""
Open a memory-mapped array.
Args:
file_path (str): Path to the file to memory-map
shape (tuple): Shape of the array
dtype (numpy.dtype, optional): Data type of the array
access_mode (str, optional): File access mode
Returns:
numpy.memmap: Memory-mapped array
"""
return np.memmap(file_path, dtype=dtype, mode=access_mode, shape=shape)
def access_chunk(mmap_array, offset, size):
"""
Access a chunk of a memory-mapped array.
Args:
mmap_array (numpy.memmap): Memory-mapped array
offset (tuple): Offset indices for each dimension
size (tuple): Size of the chunk for each dimension
Returns:
numpy.ndarray: Chunk of the memory-mapped array
"""
# Create slices for each dimension
slices = tuple(slice(o, o + s) for o, s in zip(offset, size))
# Access the chunk
return mmap_array[slices]
def write_chunk(mmap_array, offset, chunk):
"""
Write a chunk to a memory-mapped array.
Args:
mmap_array (numpy.memmap): Memory-mapped array
offset (tuple): Offset indices for each dimension
chunk (numpy.ndarray): Chunk to write
Returns:
numpy.memmap: Updated memory-mapped array
"""
# Create slices for each dimension
slices = tuple(slice(o, o + s) for o, s in zip(offset, chunk.shape))
# Write the chunk
mmap_array[slices] = chunk
# Flush changes to disk
mmap_array.flush()
return mmap_array
def process_large_dataset(file_path, shape, chunk_size, process_func):
"""
Process a large dataset in chunks using memory mapping.
Args:
file_path (str): Path to the file to memory-map
shape (tuple): Shape of the array
chunk_size (tuple): Size of each chunk
process_func (callable): Function to apply to each chunk
Returns:
numpy.memmap: Processed memory-mapped array
"""
# Open the memory-mapped array
mmap_array = mmap_open(file_path, shape)
# Calculate the number of chunks in each dimension
num_chunks = tuple(int(np.ceil(s / c)) for s, c in zip(shape, chunk_size))
# Process each chunk
for indices in np.ndindex(*num_chunks):
# Calculate the offset for this chunk
offset = tuple(i * c for i, c in zip(indices, chunk_size))
# Calculate the actual size of this chunk (may be smaller at edges)
actual_size = tuple(min(c, s - o) for c, s, o in zip(chunk_size, shape, offset))
# Access the chunk
chunk = access_chunk(mmap_array, offset, actual_size)
# Process the chunk
processed_chunk = process_func(chunk)
# Write the processed chunk back
write_chunk(mmap_array, offset, processed_chunk)
return mmap_array
import bitmatrix as bm
import numpy as np
import os
import time
# Create a large dataset (100MB)
shape = (5000, 5000)
size = np.prod(shape) * np.dtype(np.float32).itemsize # About 100MB
print(f"Creating a dataset of size: {size / (1024 * 1024):.2f} MB")
# Create a file for memory mapping
file_path = bm.create_mmap_file(size)
print(f"Created memory-mapped file: {file_path}")
# Create a memory-mapped array
mmap_array = bm.mmap_open(file_path, shape)
# Initialize with random data
for i in range(0, shape[0], 100):
chunk_size = min(100, shape[0] - i)
chunk = np.random.rand(chunk_size, shape[1]).astype(np.float32)
bm.write_chunk(mmap_array, (i, 0), chunk)
print("Initialized memory-mapped array with random data")
# Define a processing function
def process_func(chunk):
"""Apply a simple transformation to the chunk."""
return np.sin(chunk) * 2
# Process the large dataset in chunks
chunk_size = (100, 5000) # Process 100 rows at a time
start_time = time.time()
processed_array = bm.process_large_dataset(file_path, shape, chunk_size, process_func)
end_time = time.time()
print(f"Processed large dataset in {end_time - start_time:.2f} seconds")
# Access a small part of the processed array
sample = bm.access_chunk(processed_array, (1000, 1000), (10, 10))
print("Sample of processed data:")
print(sample)
# Clean up
del processed_array # Close the memory-mapped file
os.remove(file_path) # Remove the file
print(f"Removed memory-mapped file: {file_path}")
Self-healing mechanisms repair errors using resonance/context and predict errors preventatively to ensure data integrity.
import numpy as np
from scipy import signal
def detect_error(data, threshold=3.0):
"""
Detect errors in data based on statistical outliers.
Args:
data (numpy.ndarray): Input data to check for errors
threshold (float, optional): Z-score threshold for outlier detection
Returns:
numpy.ndarray: Boolean mask indicating error positions
"""
if not isinstance(data, np.ndarray):
raise TypeError("Input must be a numpy array")
# For non-numeric data, return no errors
if not np.issubdtype(data.dtype, np.number):
return np.zeros_like(data, dtype=bool)
# Calculate z-scores
mean = np.mean(data)
std = np.std(data)
if std == 0:
return np.zeros_like(data, dtype=bool)
z_scores = np.abs((data - mean) / std)
# Identify outliers as errors
errors = z_scores > threshold
return errors
def repair_bit(data, error_mask, resonance=5):
"""
Repair errors in data using contextual resonance.
Args:
data (numpy.ndarray): Input data containing errors
error_mask (numpy.ndarray): Boolean mask indicating error positions
resonance (int, optional): Size of the context window for repair
Returns:
numpy.ndarray: Repaired data
"""
if not isinstance(data, np.ndarray):
raise TypeError("Input must be a numpy array")
# Create a copy to avoid modifying the original
repaired = data.copy()
# For non-numeric data, return the original
if not np.issubdtype(repaired.dtype, np.number):
return repaired
# Get the indices of errors
error_indices = np.where(error_mask)
# Process each error
for idx in zip(*error_indices):
# Get the context around the error
context_slices = tuple(slice(max(0, i - resonance), min(s, i + resonance + 1))
for i, s in zip(idx, data.shape))
context = data[context_slices]
# Create a mask to exclude the error itself and other errors in the context
context_error_mask = error_mask[context_slices]
valid_mask = ~context_error_mask
if np.any(valid_mask):
# Use the mean of valid context values for repair
repaired[idx] = np.mean(context[valid_mask])
else:
# If no valid context, use the global mean
repaired[idx] = np.mean(data[~error_mask])
return repaired
def predict_error(data, pattern_length=10):
"""
Predict potential errors based on patterns in the data.
Args:
data (numpy.ndarray): Input data to analyze
pattern_length (int, optional): Length of patterns to look for
Returns:
numpy.ndarray: Boolean mask indicating predicted error positions
"""
if not isinstance(data, np.ndarray):
raise TypeError("Input must be a numpy array")
# For non-numeric data or multi-dimensional arrays, return no predictions
if not np.issubdtype(data.dtype, np.number) or data.ndim > 1:
return np.zeros_like(data, dtype=bool)
# Create a prediction mask
predictions = np.zeros_like(data, dtype=bool)
# Find autocorrelation to detect patterns
autocorr = signal.correlate(data, data, mode='full')
autocorr = autocorr[len(data)-1:]
# Normalize
autocorr = autocorr / np.max(autocorr)
# Find peaks in autocorrelation (potential pattern periods)
peaks, _ = signal.find_peaks(autocorr, height=0.5)
if len(peaks) > 1:
# Use the first significant peak as the pattern period
period = peaks[1]
# Predict values based on the pattern
for i in range(period, len(data)):
predicted = data[i - period]
actual = data[i]
# If the actual value deviates significantly from the predicted value,
# mark it as a potential error
if abs(actual - predicted) > 3 * np.std(data):
predictions[i] = True
return predictions
def self_heal(data, resonance=5, pattern_length=10):
"""
Apply self-healing to data by detecting and repairing errors.
Args:
data (numpy.ndarray): Input data to heal
resonance (int, optional): Size of the context window for repair
pattern_length (int, optional): Length of patterns to look for
Returns:
dict: Healed data and healing statistics
"""
if not isinstance(data, np.ndarray):
raise TypeError("Input must be a numpy array")
# Create a copy to avoid modifying the original
healed = data.copy()
# Detect existing errors
error_mask = detect_error(data)
num_errors = np.sum(error_mask)
# Repair detected errors
if num_errors > 0:
healed = repair_bit(healed, error_mask, resonance)
# Predict potential errors
predicted_mask = predict_error(healed, pattern_length)
num_predicted = np.sum(predicted_mask)
# Repair predicted errors
if num_predicted > 0:
healed = repair_bit(healed, predicted_mask, resonance)
# Calculate healing statistics
total_elements = np.prod(data.shape)
error_rate = num_errors / total_elements
prediction_rate = num_predicted / total_elements
return {
'data': healed,
'num_errors': num_errors,
'num_predicted': num_predicted,
'error_rate': error_rate,
'prediction_rate': prediction_rate
}
import bitmatrix as bm
import numpy as np
import matplotlib.pyplot as plt
# Create sample data with errors
x = np.linspace(0, 10, 1000)
y = np.sin(x)
# Add some random errors
error_indices = np.random.choice(len(y), 20, replace=False)
y[error_indices] = np.random.uniform(-5, 5, 20)
# Add a pattern with a predictable error
pattern = np.sin(np.linspace(0, 2*np.pi, 100))
for i in range(5):
start = 200 + i*100
y[start:start+100] = pattern
# Add a predictable error at the end of each pattern
if i > 0:
y[start+99] = -1
# Detect errors
error_mask = bm.detect_error(y)
print(f"Detected {np.sum(error_mask)} errors")
# Repair errors
repaired = bm.repair_bit(y, error_mask)
# Predict errors
predicted_mask = bm.predict_error(repaired)
print(f"Predicted {np.sum(predicted_mask)} potential errors")
# Apply self-healing
healed_result = bm.self_heal(y)
healed = healed_result['data']
print(f"Self-healing statistics:")
print(f" Number of errors detected: {healed_result['num_errors']}")
print(f" Number of errors predicted: {healed_result['num_predicted']}")
print(f" Error rate: {healed_result['error_rate']:.6f}")
print(f" Prediction rate: {healed_result['prediction_rate']:.6f}")
# Plot the results
plt.figure(figsize=(12, 8))
plt.subplot(3, 1, 1)
plt.title('Original Data with Errors')
plt.plot(x, y)
plt.scatter(x[error_mask], y[error_mask], color='red', label='Detected Errors')
plt.legend()
plt.subplot(3, 1, 2)
plt.title('Repaired Data with Predicted Errors')
plt.plot(x, repaired)
plt.scatter(x[predicted_mask], repaired[predicted_mask], color='orange', label='Predicted Errors')
plt.legend()
plt.subplot(3, 1, 3)
plt.title('Self-Healed Data')
plt.plot(x, healed)
plt.plot(x, np.sin(x), 'g--', alpha=0.5, label='True Signal')
plt.legend()
plt.tight_layout()
plt.savefig('self_healing_example.png')
plt.close()
print("Self-healing example complete. Results saved to 'self_healing_example.png'")
The following examples demonstrate how to integrate multiple Bitmatrix toolkit functions to solve real-world problems.
import bitmatrix as bm
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
# Create a synthetic audio signal
sample_rate = 44100
duration = 3.0 # seconds
t = np.linspace(0, duration, int(sample_rate * duration))
audio_data = np.sin(2 * np.pi * 440 * t) # 440 Hz sine wave
audio_data += 0.5 * np.sin(2 * np.pi * 880 * t) # Add first harmonic
audio_data += np.random.normal(0, 0.05, len(audio_data)) # Add noise
# Add some errors to the audio
error_indices = np.random.choice(len(audio_data), 100, replace=False)
audio_data[error_indices] = np.random.uniform(-2, 2, 100)
# Normalize the audio data
audio_normalized = audio_data / np.max(np.abs(audio_data))
# Initialize a 4D bitfield for the audio data
bitfield = bm.init_4d(x=100, y=100, z=50, t=len(audio_normalized) // 1000)
# Encode the audio data into the bitfield
encoded_bitfield = bm.encode_bit(bitfield, audio_normalized, encoding_method='temporal')
# Create Oen agents for different processing stages
compression_agent = bm.spawn_agent(id=1, domain='storage')
resilience_agent = bm.spawn_agent(id=3, domain='resilience')
# Process the encoded audio with the compression agent
compressed_data = compression_agent.process(encoded_bitfield)
# Apply self-healing to fix errors
healed_result = bm.self_heal(compressed_data['data'])
healed_data = healed_result['data']
# Add error resilience with the resilience agent
resilient_data = resilience_agent.process(healed_data)
# Decode the processed audio from the bitfield
decoded_audio = bm.decode_bit(resilient_data, decoding_method='temporal')
# Resample the decoded audio to match the original sample rate
resampled_audio = np.interp(
np.linspace(0, len(decoded_audio) - 1, len(audio_normalized)),
np.arange(len(decoded_audio)),
decoded_audio
)
# Plot the original and processed audio
plt.figure(figsize=(12, 8))
plt.subplot(3, 1, 1)
plt.title('Original Audio with Errors')
plt.plot(t[:1000], audio_normalized[:1000])
plt.scatter(t[error_indices[:20]], audio_normalized[error_indices[:20]], color='red', label='Errors')
plt.legend()
plt.subplot(3, 1, 2)
plt.title('Processed Audio (Compressed and Healed)')
plt.plot(t[:1000], resampled_audio[:1000])
plt.subplot(3, 1, 3)
plt.title('Frequency Domain Comparison')
plt.specgram(audio_normalized, NFFT=1024, Fs=sample_rate, noverlap=512, cmap='viridis')
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
plt.tight_layout()
plt.savefig('audio_processing_example.png')
plt.close()
print("Audio processing complete. Results saved to 'audio_processing_example.png'")
print(f"Compression ratio: {compressed_data['metadata']['compression_ratio']:.2f}")
print(f"Number of errors fixed: {healed_result['num_errors']}")
import bitmatrix as bm
import numpy as np
import matplotlib.pyplot as plt
# Create a synthetic image
size = 200
x, y = np.meshgrid(np.linspace(-2, 2, size), np.linspace(-2, 2, size))
r = np.sqrt(x**2 + y**2)
image = np.sin(5 * r) / (1 + r)
image = (image - np.min(image)) / (np.max(image) - np.min(image)) # Normalize to [0, 1]
# Add some noise and errors
noise = np.random.normal(0, 0.05, image.shape)
image_noisy = image + noise
error_mask = np.random.random(image.shape) > 0.99 # Random errors (about 1%)
image_noisy[error_mask] = np.random.random(np.sum(error_mask))
# Initialize a 3D bitfield for the image data
bitfield = bm.init_3d(x=image.shape[0], y=image.shape[1], z=3)
# Encode the image data into the bitfield
encoded_bitfield = bm.encode_bit(bitfield, image_noisy, encoding_method='spatial')
# Create Oen agents for different processing stages
compression_agent = bm.spawn_agent(id=1, domain='storage')
rendering_agent = bm.spawn_agent(id=2, domain='rendering')
# Process the encoded image with the compression agent
compressed_data = compression_agent.process(encoded_bitfield)
# Apply KTA transform to enhance the image
kta_transformed = bm.kta_transform(compressed_data['data'], iterations=2)
# Apply self-healing to fix errors
healed_result = bm.self_heal(kta_transformed)
healed_data = healed_result['data']
# Optimize rendering with the rendering agent
rendered_data = rendering_agent.process(healed_data)
# Decode the processed image from the bitfield
decoded_image = bm.decode_bit(rendered_data, decoding_method='spatial')
# Reshape and normalize the decoded image
decoded_image = decoded_image.reshape(image.shape)
decoded_image = (decoded_image - np.min(decoded_image)) / (np.max(decoded_image) - np.min(decoded_image))
# Plot the original and processed images
plt.figure(figsize=(15, 10))
plt.subplot(2, 2, 1)
plt.title('Original Clean Image')
plt.imshow(image, cmap='viridis')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.title('Noisy Image with Errors')
plt.imshow(image_noisy, cmap='viridis')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.title('Processed Image (Compressed, Transformed, Healed)')
plt.imshow(decoded_image, cmap='viridis')
plt.axis('off')
plt.subplot(2, 2, 4)
plt.title('Difference (Original - Processed)')
plt.imshow(np.abs(image - decoded_image), cmap='hot')
plt.colorbar(label='Absolute Difference')
plt.axis('off')
plt.tight_layout()
plt.savefig('image_processing_example.png')
plt.close()
print("Image processing complete. Results saved to 'image_processing_example.png'")
print(f"Compression ratio: {compressed_data['metadata']['compression_ratio']:.2f}")
print(f"Number of errors fixed: {healed_result['num_errors']}")
print(f"Mean absolute error: {np.mean(np.abs(image - decoded_image)):.4f}")
The following benchmarks demonstrate the performance characteristics of the Bitmatrix toolkit functions.
The Bitmatrix toolkit is designed for high performance, with the following characteristics: