e 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}\")
return amplified_signal
# check for cell damage and initiate repair if needed
def check_and_repair(self, signal):
if signal ＜ 0:
self.repair_mode = true
print(f\"Robot {self.id}: damaged tissue detected, initiating repair.\")
return self.perform_repair(signal)
return signal
# perform cell repair process
def perform_repair(self, signal):
print(f\"Robot {self.id}: Repairing damaged cells...\")