Example Python script to implement the FIR Filter Builder (plotting)

For more Python examples, please refer to this link.

#
# Moku example: FIR Filter Builder Plotting Example
# 
# This script demonstrates how to generate an FIR filter kernel with specified
# parameters using the scipy library, and how to configure settings of the FIR
# instrument.
# 
# 
# (c) 2024 Liquid Instruments
#

from moku.instruments import FIRFilterBox
from scipy.fft import fft
from numpy import arange
import math
import matplotlib.pyplot as plt

# Specify nyquist and cutoff (-3dB) frequencies
nyq_rate = 125e6 / 2**10 / 2.0
cutoff_hz = 1e3

# Calculate FIR kernel using 1000 taps and a chebyshev window with -60dB
# stop-band attenuation
taps = [cutoff_hz / nyq_rate] * 1000

# Launch FIR Filter and connect to your device through IP
i = FIRFilterBox('192.168.xxx.xxx', force_connect=True)

try:
    # Configure the Moku frontend settings
    i.set_frontend(1, impedance='50Ohm', attenuation='0dB', coupling='DC')
    i.set_frontend(2, impedance='50Ohm', attenuation='0dB', coupling='DC')

    # Both filter channels are configured with the same FIR kernel. A
    # decimation factor of 10 is used to achieve the desired nyquist rate and
    # FIR kernel length of 1000.
    i.set_custom_kernel_coefficients(1, sample_rate='2.441MHz', coefficients=taps)
    i.set_custom_kernel_coefficients(2, sample_rate='2.441MHz', coefficients=taps)

    # Channel 1 has unity input/output gain and acts solely on ADC1.
    # Channel 2 has an input gain of 0.5, output gain of 2.0, input offset of
    # -0.1V and acts on signal 0.5 * ADC1 + 0.5 * ADC2.
    i.set_input_gain(1, gain=1.0)
    i.set_output_gain(1, gain=1.0)
    i.set_input_gain(2, gain=0.5)
    i.set_output_gain(2, gain=1.0)
    i.set_input_offset(2, offset=-0.1)
    i.set_control_matrix(1, 1.0, 0.0)
    i.set_control_matrix(2, 0.5, 0.5)

    # Set which signals to view on each monitor channel, and the timebase on
    # which to view them.
    i.set_timebase(-5e-3, 5e-3)
    i.set_monitor(1, 'Input1')
    i.set_monitor(2, 'Output1')

    # Calculate and plot the quantized FIR kernel and transfer function for
    # reference.
    taps_quantized = [round(taps[x] * 2.0 ** 24 - 1) / (2 ** 24 - 1) for x in range(0, len(taps))]
    fft_taps = fft(taps_quantized)
    fft_mag = [abs(fft_taps[x]) for x in range(0, len(fft_taps[0:499]))]
    epsilon = 1e-14
    fft_db = [20 * math.log10(max(fft_mag[x], epsilon)) for x in range(0, len(fft_mag))]
    fft_f = arange(0,2.441e6 - 2.441e6/499, 2.441e6/499)

    plt.subplot(221)
    plt.plot(taps)
    plt.title('Filter Kernel')
    plt.ylabel('Normalised Value')
    plt.grid(True, which='major')
    plt.xlabel('Kernel Tap Number')
    plt.subplot(222)
    plt.plot(fft_f, fft_db)
    plt.title('Filter Transfer Function')
    plt.ylabel('Magnitude (dB)')
    plt.xlabel('Frequency (Hz)')
    plt.grid(True, which='major')

    # Set up the live FIR Filter Box monitor signal plot
    plt.subplot(212)
    plt.title("Monitor Signals")
    plt.suptitle("FIR Filter Box", fontsize=16)
    plt.grid(True, which='both', axis='both')
    data = i.get_data() # Get data to determine the signal timebase
    dt = data['time']
    plt.xlim([dt[0], dt[-1]])
    plt.ylim([-1.0, 1.0]) # View up to +-1V

    line1, = plt.plot([], label='Channel A')
    line2, = plt.plot([], label='Channel B')
    plt.legend(["Input1", "Output1"])
    plt.ylabel('Voltage (Volts)')
    plt.xlabel('Time (seconds)')

    # Continually update the monitor signal data being displayed
    while True:
        data = i.get_data()
        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 Exception as e:
    print(f'Exception Occured: {e}')
finally:
    i.relinquish_ownership()