Q: If I want to use an iPad to run my Moku:Lab, what model / operating system is required?
A: The minimum requirements are an iPad Air or iPad mini 2 or newer running iOS 8 or later. Choose your model from www.apple.com/ipad.
Q: I don't have an iPad. Can I still use Moku:Lab?
A: Yes! We have a Python library which you can use to interact with Moku:Lab from any computer running Mac OS, Windows or Linux. We are also working on MATLAB and LabVIEW interfaces.
For information on the Python interface, see our pymoku page.
Q: All of my lab computers run Windows. Will I need to change them all to Mac OS?
A: No, Moku:Lab works perfectly well in a Windows lab environment. You can transfer data to your Windows computer via email or Dropbox using the the in-app sharing button. Or use our Python library to integrate Moku:Lab into your Windows-based experiment control system.
Q: The Moku:Lab has an FPGA inside it. Can I get access to the FPGA to execute my own code?
A: Sorry, not yet. We hope to bring you this capability in the future. We won't do anything to stop you running your own code but we are unable to support it at this time. If this feature is important to you please let us know, but for now Moku:Lab is designed to work out of the box in the same way as conventional test and measurement instruments like oscilloscopes and waveform generators.
Q: What is the difference between "Normal" and "Precision" acquisition modes?
A: Moku:Lab records samples from the analog inputs at a rate of 500 MS/s. When looking at long time spans, this sampling rate must be reduced to show a trace on the screen. In "Normal" acquisition mode, the input is simply downsampled; that is, only every Mth sample is taken. This can cause aliasing of high frequency signals: for example, a high frequency sine wave may appear as a lower frequency sine wave when the oscilloscope timebase is zoomed out. In "Precision" mode, the input is decimated (lowpass filtered before downsampling). This reduces aliasing and increases the resolution of the trace. Note that in this mode, high frequency signals can be filtered out, so the oscilloscope trace may appear to be zero even if a high frequency signal is present at the input.
Q: How do I convert .li binary files to .csv?
A: The LI File Converter can be used to convert binary data from a .li file into plain text data in CSV (comma-separated values) format. Download it here: https://github.com/liquidinstruments/lireader.
Q: How can I import a CSV file from Moku:Datalogger into MATLAB?
A: If you only need the data itself, simply type `load yourfile.csv` at the MATLAB command prompt. CSV files generated by Moku:DataLlgger also contain a text header with information about when the data was recorded, the instrument settings, and what each column in the data represents. If you want to import this metadata as well, use the command `moku = importdata('yourfile.csv')`.
Q: How long will the Moku:Datalogger record for?
A: The Moku:Datalogger is limited only by storage. The Moku:Lab has 500MB of internal storage and can also log to an SD card of any size. Note though that SD Cards are limited to a maximum file size of 4GB, so this is the largest amount of data you can record at a time.
Recording a single channel at minimum rate to a binary file, this gives you approximately 4 months of recording to internal storage, up to 2.5 years to an SD card.