ESP32 Oscilloscope

A simple ESP32-based oscilloscope that reads analog voltage using the ESP32’s ADC and visualizes it in real time on a laptop using Python and Matplotlib.

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Features

• Real-time ADC voltage sampling
• Serial communication over USB
• Live oscilloscope-style waveform display
• Supports input range of 0–3.3V

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Hardware Requirements

• ESP32 development board
• USB cable
• Analog signal source (sensor / potentiometer)

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Software Requirements

ESP32

• MicroPython firmware

Laptop

• Python 3.x
• Required libraries:

  pip install pyserial matplotlib
  

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Project Structure

ESP32-Oscilloscope/ ├── main.py # ESP32 ADC reading & serial output ├── run_on_laptop.py # Live waveform plotting └── README.md

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Working Principle

• The ESP32 uses its internal 12-bit ADC to sample an analog voltage signal from GPIO 34.
• The raw ADC value (0–4095) is converted into a voltage (0–3.3V).
• These voltage readings are continuously transmitted to a laptop over serial communication.
• A Python script on the laptop reads the serial data and plots voltage vs time in real time, functioning as a basic oscilloscope.

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How to Run

1. Flash MicroPython firmware onto the ESP32.
2. Upload the main.py file to the ESP32 using Thonny or any MicroPython-compatible tool.
3. Connect the ESP32 to the laptop via USB.
4. Edit the serial port name in run_on_laptop.py (e.g., COM7 or /dev/ttyUSB0).
5. Run the plotting script:

   python run_on_laptop.py

6. Apply an analog signal (0–3.3V) to GPIO 34 to observe the waveform.

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Limitations

• Low sampling rate due to serial communication and Python-based plotting.
• Absence of trigger functionality for stable waveform capture.
• No frequency-domain analysis (FFT).
• Not suitable for high-frequency signal measurement.
• Input voltage limited to the ESP32 ADC range (~0–3.3V).
• Accuracy affected by ADC noise and limited resolution.

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Future Scope

• Improve sampling rate using buffering and optimized data transfer.
• Implement trigger modes for consistent waveform display.
• Add FFT for frequency-domain signal analysis.
• Develop a GUI or web-based interface for better user interaction.
• Enable waveform data logging and export functionality.
• Support external ADCs for higher resolution and accuracy.