Using Python to generate arbitrary waveforms while observing the output signal

Python implementation of Arbitrary Waveform Generator and oscilloscope

Moku:Lab's Arbitrary Waveform Generator (AWG) can be deployed within Python to drive output signals.  At the same time,  the Python AWG can be used as an oscilloscope to view the output signal.  In order to do so,  you would need to loop back output 1 to input 1.

This is implemented in the Python below :

# pymoku example: Plotting Oscilloscope with Artibrary Waveform Generator
# This example demonstrates how you can configure the ArbWaveGen instrument,
# and view triggered time-voltage data frames in real-time.
# (c) 2019 Liquid Instruments Pty. Ltd.
from pymoku import *
from pymoku.instruments import Oscilloscope,ArbitraryWaveGen

import numpy as np

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter

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

current_sine = np.zeros(1000)

g = 0
for x in current_sine:
current_sine [g] = np.sin((float(g)*2*np.pi/len(current_sine)))
g +=1

i.write_lut(2, current_sine)

# Generate an output sinewave on Channel 2, 500mVpp, 1MHz, 0V offset
i.gen_waveform(2, period = 1.0/1e6, amplitude = 0.5, interpolation = True)

# Trigger on input Channel 2, rising edge, 0V with 0.1V hysteresis)
i._set_trigger(2, 'rising', 0, None, None, 0.1, False, 'auto')
# View +-5usec, i.e. trigger in the centre
i.set_timebase(-5e-6, 5e-6)

#i.gen_sinewave(2, 0.5, 1e6, 0.0)

# Set the data source of Channel 2 to the generated output sinewave
i._set_source(2, 1)


# Get initial data frame to set up plotting parameters. This can be done once
# if we know that the axes aren't going to change (otherwise we'd do
# this in the loop)
data = i.get_realtime_data()

# Set up the plotting parameters
plt.xlim([data.time[0], data.time[-1]])

line1, = plt.plot([])

# Configure labels for axes
ax = plt.gca()
ax.fmt_xdata = data.get_xcoord_fmt
ax.fmt_ydata = data.get_ycoord_fmt

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

# Update the plot

# Close the connection to the Moku device
# This ensures network resources and released correctly