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

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

• A Moku device
• Network connection to your Moku device

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

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)

No example will work "out of the box" without this configuration step.

1. Install the Moku Python API:

   pip install moku

2. Run a basic example:

   python python-api/oscilloscope_plotting.py

3. Try a Jupyter notebook:

   jupyter notebook python-api/hello_moku.ipynb

1. Install the Moku MATLAB API from the Liquid Instruments website

2. Run an example:

   run('matlab-api/oscilloscope_basic.m')

1. Install neural network dependencies:

   pip install 'moku[neuralnetwork]'

2. Run a neural network example:

   jupyter notebook neural-network/Simple_sine.ipynb

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

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

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

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

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

• 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

• Moku Python API Documentation
• Moku MATLAB API Documentation
• Moku User Manuals
• Neural Network Instrument Guide

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

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

• Issues: Report bugs or request examples via GitHub Issues
• Email: support@liquidinstruments.com
• Forum: Liquid Instruments Community Forum

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

Examples and code maintained by Liquid Instruments.