Example Python script to implement the Digital Filter Box (plotting)

For more Python examples, please refer to this link.

#
# Moku example: Plotting Digital Filter Box
#
# This example demonstrates how you can configure the Digital
# Filter Box instrument to filter and display two signals. Filter
# 1 takes its input from Input1 and applies a lowpass Butterworth
# filter. Filter 2 takes its input 2 and then implements a low sampled
# Butterworth lowpass to show the difference from sampling rates.
#
# (c) 2023 Liquid Instruments Pty. Ltd.
#

import matplotlib.pyplot as plt
from moku import MokuException
from moku.instruments import DigitalFilterBox

# Launch Digital Filter Box and connect to your device via IP
i = DigitalFilterBox('192.168.###.###', force_connect=True)

try:
    # SetChannel 1 and 2 to DC coupled, 1 Mohm impedance, and
    # default input range (400 mVpp range on Moku:Pro, 1 Vpp
    # range on Moku:Lab, 10 Vpp range on Moku:Go)
    i.set_frontend(1, coupling='DC', impedance='1MOhm',
                   attenuation='0dB')
    i.set_frontend(2, coupling='DC', impedance='1MOhm',
                   attenuation='0dB')

    # Channel 1 signal: Input 1
    # Channel 2 signal: Input 1 + Input 2
    i.set_control_matrix(1, input_gain1=1, input_gain2=0)
    i.set_control_matrix(2, input_gain1=1, input_gain2=1)

    # Configure Digital Filter on Channel 1 and Channel 2
    # Channel 1 and 2 are 8th-order ChebyshevI filters
    # Channel 1 is the high sampling rate and Channel 2 is the
    # low sampling rate.
    i.set_filter(1, "39.06MHz", shape="Lowpass", type="ChebyshevI", low_corner=1e3, order=8)
    i.set_filter(2, "4.883MHz", shape="Lowpass", type="ChebyshevI", low_corner=1e3, order=8)

    # Monitor ProbeA: Filter Channel1 output
    # Monitor ProbeB: Filter Channel2 output
    i.set_monitor(1, "Output1")
    i.set_monitor(2, "Output2")

    # Enable Digital Filter Box output ports
    i.enable_output(1, signal=True, output=True)
    i.enable_output(2, signal=True, output=True)

    # View +/- 1 microsecond with the trigger point centered
    i.set_timebase(-1e-3, 1e-3)

    # Get initial data frame to set up plotting parameters.
    data = i.get_data()

    # Set up the plotting parameters
    plt.ion()
    plt.show()
    plt.grid(True, which='both', axis='both')
    plt.ylim([-1, 1])
    plt.xlim([data['time'][0], data['time'][-1]])

    line1, = plt.plot([])
    line2, = plt.plot([])

    # Configure labels for axes
    ax = plt.gca()

    # This loops continuously updates the plot with new data
    while True:
        # Get new data
        data = i.get_data()

        # Update the plot
        line1.set_ydata(data['ch1'])
        line2.set_ydata(data['ch2'])
        line1.set_xdata(data['time'])
        line2.set_xdata(data['time'])
        plt.pause(0.001)
            
except MokuException as e:
    print("Couldn't configure Moku. Please check your IP address and that you've updated the script parameters (such as sampling rate) to match your device.")
    raise e
finally:
    # Releasing ownership of the Moku allows other users to connect
    # to it without forcing a takeover.
    i.relinquish_ownership()