Python for Embedded Systems

8 min readFeb 23, 2023

Embedded systems have become an integral part of our daily lives. They are present in everything from smartphones and home appliances to cars and medical devices. These systems require low-level programming languages such as C or assembly to achieve the required performance, low memory usage, and real-time response. However, the flexibility and ease of use of Python make it an attractive option for embedded systems development, despite its reputation for high memory usage and slower performance.

In this blog post, we will explore how Python can be used for embedded systems, what challenges need to be addressed, and what benefits Python offers compared to other languages.

MicroPython: Bringing Python to Embedded Systems

MicroPython is a variant of the Python programming language designed to run on microcontrollers and other embedded systems with limited resources. It provides a subset of Python’s functionality and syntax, optimized for memory-constrained environments.

One of the main advantages of using MicroPython is that it allows developers to use a familiar and high-level programming language to develop embedded systems. Python’s syntax and structure are designed to be easy to read and write, making it an attractive option for rapid prototyping and experimentation.

In addition, MicroPython provides access to a large number of libraries and frameworks that can simplify development and reduce development time. These libraries range from simple I/O and control functions to more complex libraries for machine learning and artificial intelligence.

Using Python for Embedded Systems

Python can be used for a wide range of embedded systems applications, including sensor data acquisition and processing, control systems, and machine learning. Let’s take a look at a few examples:

Raspberry Pi

The Raspberry Pi is a popular single-board computer that has become an essential tool for makers, hobbyists, and professionals alike. It is a versatile and powerful platform that can be used for a wide range of applications, including robotics, home automation, and media centers.

Python is the primary programming language used for the Raspberry Pi, thanks to its ease of use and the availability of numerous libraries and frameworks that support the platform. The Raspberry Pi Foundation provides an official distribution of Linux called Raspbian, which comes with Python pre-installed.

Arduino

The Arduino is a popular microcontroller platform that has become the go-to choice for many hobbyists and makers. It is a low-cost, open-source platform that is designed to be easy to use, even for beginners.

Although the Arduino platform is primarily programmed using C or C++, it is possible to use Python with the help of the PySerial library. This library provides a simple interface for serial communication with the Arduino, allowing developers to control and monitor the device from their Python code.

BeagleBone

The BeagleBone is a powerful single-board computer that is designed for embedded systems and Internet of Things (IoT) applications. It features a powerful ARM processor, built-in Ethernet, and support for numerous expansion boards.

Python is the primary programming language used for the BeagleBone, thanks to its ease of use and the availability of numerous libraries and frameworks that support the platform. The BeagleBone Black is the most popular variant of the platform, and it comes with the Debian Linux distribution pre-installed, which includes Python.

ESP8266

The ESP8266 is a low-cost, low-power, Wi-Fi enabled microcontroller that has become popular for IoT applications. It is an excellent platform for low-power sensor applications and remote monitoring.

Python can be used for the ESP8266 using the MicroPython firmware. This firmware provides a subset of Python’s functionality, optimized for the ESP8266’s limited resources. It includes support for Wi-Fi and MQTT protocols, making it an attractive option for IoT applications.

Challenges of Using Python

While Python provides a lot of benefits for embedded systems development, there are also some challenges that need to be addressed. One of the main challenges is Python’s high memory usage, which can be a problem for embedded systems with limited resources.

To address this challenge, developers can use techniques such as memory optimization and garbage collection to reduce the memory footprint of their Python code. There are also tools such as memory profilers that can help identify memory leaks and other memory-related issues.

Another challenge is Python’s slower performance compared to low-level languages such as C or assembly. While this may not be a significant issue for some applications, it can be a problem for real-time systems or applications that require high performance.

To address this challenge, developers can use techniques such as code profiling and optimization to improve the performance of their Python code. They can also use libraries and frameworks that are optimized for embedded systems, such as NumPy and SciPy, to achieve better performance.

Benefits of Using Python

Despite the challenges, Python offers several benefits for embedded systems development. These include:

Ease of Use

Python is designed to be easy to read and write, making it an attractive option for rapid prototyping and experimentation. It also has a large and active community that provides support, resources, and libraries that can simplify development and reduce development time.

Flexibility

Python’s syntax and structure are flexible, allowing developers to write code that is easy to maintain and modify. This flexibility also allows Python to be used in a wide range of applications, from simple control systems to complex machine learning algorithms.

Rapid Development

Python’s ease of use and flexibility can lead to faster development times and lower development costs. This can be especially important for small projects or prototypes, where speed and flexibility are critical.

Compatibility

Python is a widely used language that is supported by a large number of platforms and architectures. This compatibility makes it easier to port Python code between different platforms, reducing development time and costs.

Real-time use case

Real-time systems require precise timing and predictable behavior, which can be challenging to achieve with Python’s slower performance and garbage collection. However, there are still some use cases where Python can be used in real-time embedded systems, especially when combined with optimized libraries and frameworks.

One unique use case where Python has been used in real-time systems is in robotics and automation. In these applications, Python can be used for high-level control and decision-making, while low-level control and communication with hardware can be handled by optimized libraries and frameworks.

One such library is PyVISA, which provides a Python interface for controlling and communicating with instruments and devices using various protocols such as GPIB, USB, and Ethernet. PyVISA is built on top of optimized C libraries, making it suitable for real-time applications.

Here is an example of using PyVISA to communicate with a Keithley 2400 source meter:

import pyvisa

# Open connection to Keithley 2400
rm = pyvisa.ResourceManager()
keithley = rm.open_resource('GPIB0::24::INSTR')

# Set output voltage to 1V and measure current
keithley.write(':SOUR:VOLT:LEV 1')
current = keithley.query(':MEAS:CURR?')

# Close connection
keithley.close()

In this example, we first create a PyVISA resource manager and open a connection to the Keithley 2400 source meter using GPIB protocol. We then set the output voltage to 1V and measure the current using a query command. Finally, we close the connection to the device.

While Python may not be suitable for low-level control and real-time tasks in robotics and automation, it can still be useful for high-level control and decision-making. By using optimized libraries and frameworks such as PyVISA, developers can leverage the benefits of Python while still achieving real-time performance and precision.

Another unique use case for Python in embedded systems is in data acquisition and analysis. Python’s ease of use and flexibility make it ideal for quickly prototyping and testing data acquisition systems, while libraries such as NumPy and SciPy provide optimized data analysis and signal processing capabilities.

Here is an example of using Python and NumPy to perform real-time signal processing on data from an accelerometer:

import pyvisa

# Open connection to Keithley 2400
rm = pyvisa.ResourceManager()
keithley = rm.open_resource('GPIB0::24::INSTR')

# Set output voltage to 1V and measure current
keithley.write(':SOUR:VOLT:LEV 1')
current = keithley.query(':MEAS:CURR?')

# Close connection
keithley.close()

Here is an example of using Python and NumPy to perform real-time signal processing on data from an accelerometer:

import numpy as np
import board
import busio
import adafruit_lis3dh
import time
from scipy import signal

# Initialize accelerometer
i2c = busio.I2C(board.SCL, board.SDA)
accelerometer = adafruit_lis3dh.LIS3DH_I2C(i2c)

def highpass_filter(data, cutoff_freq):
    # Compute filter coefficients
    fs = 1000 # Sampling frequency
    nyquist_freq = 0.5 * fs
    normalized_cutoff = cutoff_freq / nyquist_freq
    b, a = signal.butter(1, normalized_cutoff, btype='highpass', analog=False)

    # Apply filter to data
    filtered_data = signal.filtfilt(b, a, data, axis=0)

    return filtered_data

# Collect and process data in real-time
while True:
    # Collect acceleration data from accelerometer
    acceleration = accelerometer.acceleration

    # Apply high-pass filter to remove gravity component
    gravity = np.array([0, 0, 9.81])
    acceleration = acceleration - gravity
    filtered_acceleration = highpass_filter(acceleration, 0.1)

    # Perform FFT on filtered acceleration data
    fft_data = np.fft.fft(filtered_acceleration)
    freq = np.fft.fftfreq(len(filtered_acceleration))

    # Find frequency and amplitude of highest peak in FFT data
    max_index = np.argmax(np.abs(fft_data))
    max_freq = freq[max_index]
    max_amp = np.abs(fft_data[max_index])

    # Print results
    print("Frequency: ", max_freq)
    print("Amplitude: ", max_amp)

    # Wait for next sample
    time.sleep(0.1)

In this example, we first initialize an accelerometer using the Adafruit LIS3DH library and collect acceleration data in real-time. We then apply a high-pass filter to remove the gravity component and perform a fast Fourier transform (FFT) on the filtered acceleration data. Finally, we find the frequency and amplitude of the highest peak in the FFT data and print the results.

The `highpass_filter` function uses the `signal` module from the `scipy` library to design and apply a high-pass filter to the data.

While Python may not be the fastest language for real-time signal processing, its ease of use and flexibility make it ideal for quickly prototyping and testing data acquisition and analysis systems. The optimized signal processing capabilities provided by libraries such as NumPy and SciPy also make Python a viable option for real-time signal processing in certain applications.

Conclusion

Python may not be the first language that comes to mind when thinking about embedded systems development, but it is a powerful and flexible language that can be used in a wide range of applications. With the help of MicroPython and other tools and techniques, developers can overcome the challenges of using Python in embedded systems and take advantage of its benefits. By leveraging Python’s ease of use, flexibility, and compatibility, developers can create embedded systems that are efficient, reliable, and easy to maintain.

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