Moku API

# Moku Examples

A comprehensive collection of examples demonstrating how to use Liquid Instruments Moku devices (opens new window) across multiple programming languages and environments.

# Overview

This repository contains example code for working with Moku devices, including:

  • Python API examples - Scripts and Jupyter notebooks for Python-based instrument control
  • MATLAB API examples - MATLAB scripts for instrument automation
  • Neural Network examples - Deep learning applications using the Moku Neural Network instrument
  • Multi-language examples - Demonstrations in other programming languages

# Prerequisites

# Hardware

  • A Moku device
  • Network connection to your Moku device

# Software

  • For Python examples: Python 3.9 or higher
  • For MATLAB examples: MATLAB R2016b or later

# Important: Configuration Required

Before running any example, you must edit the file to configure your device connection:

  • Update the IP address (e.g., '192.168.1.100') or device name to match your Moku device
  • Some examples may require additional configuration (sample rates, channels, etc.)

Look for lines like:

MOKU_IP = '192.168.1.100'  # Update with your device IP
# or
i = Oscilloscope('192.168.###.###', force_connect=True)
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No example will work "out of the box" without this configuration step.

# Quick Start

# Python Examples

  1. Install the Moku Python API:

    pip install moku
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  2. Run a basic example:

    python python-api/oscilloscope_plotting.py
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  3. Try a Jupyter notebook:

    jupyter notebook python-api/hello_moku.ipynb
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# MATLAB Examples

  1. Install the Moku MATLAB API from the Liquid Instruments website (opens new window)

  2. Run an example:

    run('matlab-api/oscilloscope_basic.m')
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# Neural Network Examples

  1. Install neural network dependencies:

    pip install 'moku[neuralnetwork]'
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  2. Run a neural network example:

    jupyter notebook neural-network/Simple_sine.ipynb
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# Repository Structure

moku-examples/
├── python-api/          # Python scripts and Jupyter notebooks
│   ├── oscilloscope_*.py
│   ├── datalogger_*.py
│   ├── waveformgenerator_*.py
│   └── ...
├── matlab-api/          # MATLAB scripts (.m files)
│   ├── oscilloscope_*.m
│   ├── laser_lock_box_*.m
│   └── ...
├── neural-network/      # Neural network instrument examples
│   ├── Classification.ipynb
│   ├── Autoencoder.ipynb
│   └── ...
├── other-language-api/  # Examples in other languages
└── mc/                 # Multi-instrument Moku Compile examples
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# Example Categories

# Python API

The python-api/ directory contains examples for:

  • Oscilloscope: Data acquisition, plotting, streaming, deep memory mode
  • Waveform Generator: Signal generation, modulation, triggering
  • Data Logger: High-speed data logging and streaming
  • Spectrum Analyzer: Frequency domain analysis
  • Lock-in Amplifier: Phase-sensitive detection
  • Phasemeter: Phase and frequency measurements
  • PID Controller: Feedback control systems
  • Logic Analyzer: Digital signal analysis and protocol decoding
  • FIR Filter: Digital signal filtering
  • Laser Lock Box: Laser stabilization
  • Multi-Instrument Mode (MIM): Using multiple instruments simultaneously

# Neural Network

The neural-network/ directory contains examples for:

  • Signal Classification: Detecting anomalies in time-series data
  • Autoencoders: Signal compression and reconstruction
  • Regression: Predicting signal characteristics
  • Control Systems: Neural network-based PID control
  • Signal Processing: FFT, filtering, and signal identification

# MATLAB API

The matlab-api/ directory mirrors the Python examples with MATLAB implementations.

# Finding the Right Example

# By Instrument Type

  • Oscilloscope: *oscilloscope* files
  • Waveform Generator: *waveformgenerator* or *wavegen* files
  • Data Logger: *datalogger* files
  • Spectrum Analyzer: *spectrumanalyzer* files
  • And more...

# By Feature

  • Plotting: *plotting* files - Show how to visualize data
  • Streaming: *streaming* files - Real-time data streaming
  • Basic: *basic* files - Simple getting-started examples
  • MIM: mim_* files - Multi-instrument mode examples

# Documentation

# Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines on:

  • Setting up your development environment
  • Running tests and code quality checks
  • Submitting pull requests

# Support

# License

This project is licensed under the MIT License - see the LICENSE file for details.

# Acknowledgments

Examples and code maintained by Liquid Instruments (opens new window).