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Earthquake

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

Simulate seismic data with random noise

def generate_seismic_data(): return np.random.normal(0, 1, 1000)

Update the plot with new seismic data

def update_plot(frame_num, line, ax): line.set_ydata(generate_seismic_data()) ax.relim() ax.autoscale_view() return line,

Set up the initial plot

fig, ax = plt.subplots() line, = ax.plot(generate_seismic_data()) ax.set_ylim(-5, 5)

Animate the plot with new data every 100ms

ani = animation.FuncAnimation(fig, update_plot, fargs=(line, ax), interval=100)

plt.show()

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Simulating Digital Focal Plane Array (DFPA) behavior

class DFPA: def init(self, pixel_count): self.pixel_count = pixel_count self.pixel_data = [0] * pixel_count # Initialize pixel data (16-bit)

def capture_image(self, raw_image):
    # Simulate analog-to-digital conversion for each pixel
    for i in range(self.pixel_count):
        self.pixel_data[i] = self.convert_to_digital(raw_image[i])

def process_image(self):
    # Simulate on-chip processing (e.g., noise reduction, feature extraction)
    processed_image = self.apply_noise_reduction(self.pixel_data)
    processed_image = self.extract_features(processed_image)
    # Your custom processing logic here

    return processed_image

def convert_to_digital(self, analog_value):
    # Simulate ADC conversion (16-bit)
    return int(analog_value * 65535)  # Assuming input range [0, 1]

def apply_noise_reduction(self, image_data):
    # Simulate noise reduction algorithm
    # Your noise reduction logic here
    return image_data

def extract_features(self, image_data):
    # Simulate feature extraction (e.g., edge detection, object recognition)
    # Your feature extraction logic here
    return image_data

Example usage

if name == "main": pixel_count = 1024 # Adjust based on your sensor raw_image_data = [0.75, 0.82, 0.68, ...] # Simulated analog pixel values

dfpa_sensor = DFPA(pixel_count)
dfpa_sensor.capture_image(raw_image_data)
processed_image = dfpa_sensor.process_image()

print("Processed image:", processed_image)