Example Python script to implement the Frequency Response Analyzer (plotting)

# pymoku example: Plotting Frequency Response Analyzer
#
# This example demonstrates how you can generate output sweeps using the
# Frequency Response Analyzer instrument, and view transfer function data in
# real-time.
#
# (c) 2019 Liquid Instruments Pty. Ltd.
#

from pymoku import Moku
from pymoku.instruments import FrequencyResponseAnalyzer

import matplotlib.pyplot as plt

# Connect to your Moku by its device name
# Alternatively, use Moku.get_by_serial('#####') or Moku('192.168.###.###')
m = Moku.get_by_name('Moku')

# Define output sweep parameters here for readability
f_start = 10 # Hz
f_end = 100e6 # Hz
sweep_length = 512
log_scale = True
single_sweep = False
amp_ch1 = 0.5 # Vpp
amp_ch2 = 0.5 # Vpp
averaging_time = 1e-6 # sec
settling_time = 1e-6 # sec
averaging_cycles = 1
settling_cycles = 1

try:
 # See whether there's already a Frequency Response Analyzer running. If
 # there is, take control of it; if not, deploy a new one.
 i = m.deploy_or_connect(FrequencyResponseAnalyzer)

 # Many PCs struggle to plot magnitude and phase for both channels at the
 # default 10fps, turn it down so it remains smooth, albeit slow. Turn the
 # output to 'sweep' mode so we can see the in-progress sweep (set to
 # 'full_frame' or leave blank if if you only want to get completed traces,
 # e.g. for analysis rather than viewing)
 i.set_framerate(5)
 i.set_xmode('sweep')

 # Set the output sweep amplitudes
 i.set_output(1, amp_ch1)
 i.set_output(2, amp_ch2)

 # Set the sweep configuration
 i.set_sweep(f_start, f_end, sweep_length, log_scale, averaging_time,
 settling_time, averaging_cycles, settling_cycles)

 # Start the output sweep in loop mode
 i.start_sweep(single=single_sweep)

 # Set up the amplitude plot
 plt.subplot(211)
 if log_scale:
 # Plot log x-axis if frequency sweep scale is logarithmic
 line1, = plt.semilogx([])
 line2, = plt.semilogx([])
 else:
 line1, = plt.plot([])
 line2, = plt.plot([])
 ax_1 = plt.gca()
 ax_1.set_xlabel('Frequency (Hz)')
 ax_1.set_ylabel('Magnitude (dB)')

 # Set up the phase plot
 plt.subplot(212)
 if log_scale:
 line3, = plt.semilogx([])
 line4, = plt.semilogx([])
 else:
 line3, = plt.plot([])
 line4, = plt.plot([])
 ax_2 = plt.gca()
 ax_2.set_xlabel('Frequency (Hz)')
 ax_2.set_ylabel('Phase (Cycles)')

 plt.ion()
 plt.show()
 plt.grid(b=True)

 # Retrieves and plot new data
 while True:
 frame = i.get_realtime_data(timeout=5)

 # Set the frame data for each channel plot
 plt.subplot(211)
 line1.set_ydata(frame.ch1.magnitude_dB)
 line2.set_ydata(frame.ch2.magnitude_dB)
 line1.set_xdata(frame.frequency)
 line2.set_xdata(frame.frequency)

 # Phase
 plt.subplot(212)
 line3.set_ydata(frame.ch1.phase)
 line4.set_ydata(frame.ch2.phase)
 line3.set_xdata(frame.frequency)
 line4.set_xdata(frame.frequency)

 # Ensure the frequency axis is a tight fit
 ax_1.set_xlim(min(frame.frequency), max(frame.frequency))
 ax_2.set_xlim(min(frame.frequency), max(frame.frequency))
 ax_1.relim()
 ax_1.autoscale_view()
 ax_2.relim()
 ax_2.autoscale_view()

 # Redraw the lines
 plt.draw()

 plt.pause(0.001)
finally:
 m.close()