Log and download .li file

First, start a logging session and download your file

# moku example: Basic Datalogger
# (c) 2025 Liquid Instruments Pty. Ltd.
import os
import time
from moku.instruments import Datalogger

# Connect to your Moku
i = Datalogger('192.168.###.###', force_connect=False)
try:
    # Set-up the Datalogger
    i.set_samplerate(100)
    i.set_acquisition_mode(mode='Precision')
    i.generate_waveform(channel=1, type='Sine', amplitude=1, frequency=10e3)

    # Start a logging session
    logFile = i.start_logging(duration=10)

    # Track progress percentage of the data logging session
    complete = False
    while complete is False:
        time.sleep(0.5)
        progress = i.logging_progress()
        complete = progress['complete']
        if 'time_remaining' in progress:
            print(f"Remaining time {progress['time_remaining']} seconds")

    # Download the .li log from Moku
    i.download(logFile['location'], logFile['file_name'],
               os.path.join(os.getcwd(), logFile['file_name']))
    print(f"Downloaded log file to local directory. {logFile['file_name']}")

except Exception as e:
    print(f'Exception occurred: {e}')
finally:
    # Close the connection to the Moku device
    i.relinquish_ownership()

This will output a trace similar to this:

{'sample_rate': 100.0}
{'mode': 'Precision'}
{'amplitude': 1.0, 'frequency': 10000.0, 'offset': 0.0, 'phase': 0.0, 'type': 'Sine'}
...
Remaining time 0 seconds
Downloaded log file to local directory. MokuDataLoggerData_20250116_102402.li

Mokucli method

Ensure you have mokucli installed, you can download it from the Utilities page.

From your command line, you can convert to your desired file type; csv, npy, mat, or hdf5. We'll be converting to csv for this example. Change directories to where the file is located or use the full file path:

C:\Users\user\Downloads>mokucli convert MokuDataLoggerData_20250116_102402.li --format=csv
[===========================================================================]
Done.

LI File Converter method

Alternatively, you can convert the file using the LI File Converter GUI, this can be accessed from the Moku: App or downloaded from the Utilities page

You can choose the file type, then drag and drop the file to the converter, or open the file from the converter using “File | Open file” or "Ctrl/Cmd + O"

Analyze data

You can then use the converted file to analyze your data.

import os
import csv
import numpy as np

file_name = "MokuDataLoggerData_20250116_102402.csv"
channels = 2
print(file_name)

assert os.path.isfile(file_name), "Convert failed, no file received"
data = {'time':[], 'ch1':[]}
with open(file_name, 'r') as f:
    load = csv.reader(f)
    file = np.array([[float(row[0])] + [float(row[i]) for i in range(1, channels+1)] for row in load if row[0][0] != '%'])
    f.close()
data['time'] = file[:,0]
for i in range(1, channels+1):
    data[f'ch{i}'] = file[:,i]

keys = data.keys()
for key in keys:
    print(f"'{key}': {str(data[key][:3])[:-1]} ... {data[key][-1]}]")

This will return something similar to

MokuDataLoggerData_20250116_102402.csv
'time': [0.   0.01 0.02 ... 9.99]
'ch1': [0.00063297 0.00077466 0.00071688 ... 0.00067479823461]
'ch2': [0.01406568 0.01389566 0.014152   ... 0.014421103911]

Other file type conversions and reading methods for Python

Here is a cheat-sheet style summary of the file type conversions and how to read them in Python, adjust the file_name and channels parameters to match your experiment.

.mat

C:\Users\LItes\Downloads>mokucli convert MokuDataLoggerData_20250116_102402.li --format=mat
[===========================================================================]
Done.

import os
from scipy.io import loadmat

file_name = "MokuDataLoggerData_20250116_102402.mat"
channels = 2
print(file_name)

assert os.path.isfile(file_name), "Convert failed, no file received"
data = {'time':[], 'ch1':[]}
file = loadmat(file_name)
data['time'] = file['moku']['data'][0, 0][:,0]
for i in range(1, channels+1):
    data[f'ch{i}'] = file['moku']['data'][0, 0][:,i]

keys = data.keys()
for key in keys:
    print(f"'{key}': {str(data[key][:3])[:-1]} ... {data[key][-1]}]")

.npy

C:\Users\LItes\Downloads>mokucli convert MokuDataLoggerData_20250116_102402.li --format=npy
[===========================================================================]
Done.

import os
import numpy as np

file_name = "MokuDataLoggerData_20250116_102402.npy"
channels = 2
print(file_name)

assert os.path.isfile(file_name), "Convert failed, no file received"
data = {'time':[], 'ch1':[]}
load = np.load(file_name)
file = np.array([[load[i][0]] + [load[i][j] for j in range(1, channels+1)] for i in range(len(load))])
data['time'] = file[:,0]
for i in range(1, channels+1):
    data[f'ch{i}'] = file[:,i]

keys = data.keys()
for key in keys:
    print(f"'{key}': {str(data[key][:3])[:-1]} ... {data[key][-1]}]")

.hdf5

C:\Users\LItes\Downloads>mokucli convert MokuDataLoggerData_20250116_102402.li --format=hdf5
[===========================================================================]
Done.

import os
import numpy as np
import h5py

file_name = "MokuDataLoggerData_20250116_102402.hdf5"
channels = 2
print(file_name)

assert os.path.isfile(file_name), "Convert failed, no file received"
data = {'time':[], 'ch1':[]}
load = h5py.File(file_name, 'r')
file = np.array(list(load['moku']),dtype=np.dtype('float'+',float'*channels))
file.dtype.names=['time'] + [f'ch{i}' for i in range(1, channels+1)]
data['time'] = file['time']
for i in range(1, channels+1):
    data[f'ch{i}'] = file[f'ch{i}']

keys = data.keys()
for key in keys:
    print(f"'{key}': {str(data[key][:3])[:-1]} ... {data[key][-1]}]")