Streaming options

For more detailed information on the API's please visit our Datalogger | API Reference 

 streaming        stream_to_file       Data Streaming Examples for Python

stream_to_file method

This is useful for when you want to log data but you don't want to convert it externally using mokucli, see API download log file and convert | Python for this use case. This converts the file as it is being downloaded and will be ready to analyze once the streaming session is complete.

# moku example: Datalogger Stream to File
# (c) 2025 Liquid Instruments Pty. Ltd.
import os
import csv
import time
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
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 streaming session and stream to a file, include the file type in the
    # file name; csv, hdf5, npy, or mat
    i.start_streaming(duration=10)
    now = datetime.now().strftime("%Y%m%d_%H%M%S")
    file_name = f'MokuDataStreamingData_{now}.csv'
    i.stream_to_file(file_name)

    print(f"Streaming to file: {file_name}")
    print(i.get_stream_status()['status'])
    # Track progress percentage of the data streaming session
    status = 'RUNNING'
    while status == 'RUNNING':
        time.sleep(0.5)
        progress = i.get_stream_status()
        status = progress['status']

    # It may take a few moments for the file to appear in the system as accessible
    time.sleep(5)
    channels = 2
    print(file_name)
except Exception as e:
    i.relinquish_ownership()
    raise e
else:
    # Close the connection to the Moku device
    i.relinquish_ownership()

# Check for the file, then open the file and create data dictionary
assert os.path.isfile(file_name), "Streaming 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]

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

# Plot the streamed data
fig = plt.figure(figsize=(10, 3))
for i in range(1, channels+1):
    plt.plot(data['time'], data[f'ch{i}'], label=f'Channel {i}')
plt.xlim([data['time'][0], data['time'][-1]])
plt.title("Streamed to File Data")
plt.grid(True)
plt.xlabel("Time (s)")
plt.ylabel("Voltage (V)")
plt.legend(loc=0)
plt.show()

Example output:

Streaming to file: MokuDataStreamingData_20250116_165246.csv
RUNNING
MokuDataStreamingData_20250116_165246.csv
'time': [0.   0.01 0.02 ... 9.99]
'ch1': [0.00117748 0.00105914 0.00114431 ... 0.001142182695]
'ch2': [0.01485419 0.01477556 0.01476971 ... 0.01479722506]

For streaming to other file types:

file_name = f'MokuDataStreamingData_{now}.npy'
i.stream_to_file(file_name)

file_name = f'MokuDataStreamingData_{now}.hdf5'
i.stream_to_file(file_name)

file_name = f'MokuDataStreamingData_{now}.mat'
i.stream_to_file(file_name)

To analyze the data from these file types, refer to this article:

Other file type conversions and reading methods for Python

streaming method

This method is useful for when you want to analyze the data in real-time rather than waiting for the streaming session to complete.

# moku example: Datalogger Streaming
# (c) 2025 Liquid Instruments Pty. Ltd.
import os
import time
from moku.instruments import Datalogger
import matplotlib.pyplot as plt

# 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)

    # stream the data for 10 seconds..
    i.start_streaming(duration=10)

    # Set up the plotting parameters
    plt.ion()
    plt.show()
    plt.grid(visible=True)
    plt.ylim([-1, 1])
    plt.title("Streamed Data")
    plt.xlabel("Time (s)")
    plt.ylabel("Voltage (V)")
    line1, = plt.plot([])
    line2, = plt.plot([])
    plt.legend(['Channel 1', 'Channel 2'], loc=1)

    # Configure labels for axes
    ax = plt.gca()
    # This loops continuously updates the plot with new data
    while True:
        # get the chunk of streamed data
        data = i.get_stream_data()
        if data:
            plt.xlim([data['time'][0], data['time'][-1]])
            # Update the plot
            line1.set_ydata(data['ch1'])
            line1.set_xdata(data['time'])
            line2.set_ydata(data['ch2'])
            line2.set_xdata(data['time'])
            plt.pause(0.001)

except Exception as e:
    i.stop_streaming()
    print(e)
finally:
    i.relinquish_ownership()