import numpy as np

# Neural signal processing and amplification for nanorobots

class NanoRobot:

def __init__(self, id, amplification_threshold=1.2):

self.id = id # Unique identifier for the nanorobot

self.amplification_threshold = amplification_threshold # Minimum signal strength to trigger amplification

self.repair_mode = False # Indicator if repair mode is active

# Capture and analyze the neural signal

def capture_signal(self, signal):

processed_signal = self.filter_noise(signal)

if processed_signal ＜ self.amplification_threshold:

print(f"Robot {self.id}: Weak signal detected, bypassing amplification.")

return processed_signal

return self.amplify_signal(processed_signal)

# Filter noise from the signal using basic thresholding

def filter_noise(self, signal):

noise_reduction_factor = np.random.uniform(0.95, 1.05) # Simulate noise filtering

filtered_signal = signal * noise_reduction_factor

print(f"Robot {self.id}: Signal filtered to {filtered_signal}")

return filtered_signal

# Amplify the signal if it's above a certain threshold

def amplify_signal(self, signal):

amplification_factor = np.random.uniform(1.5, 2.0) # Random amplification within range

amplified_signal = signal * amplification_factor

print(f"Robot {self.id}: Amplified signal to {amplified_signal}")

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return amplified_signal

# Check for cell damage and initiate repair if needed