Using Python to generate arbitrary waveforms while observing the output signal
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Moku:Go
Moku:Go Arbitrary Waveform Generator Moku:Go Data Logger Moku:Go Frequency Response Analyzer Moku:Go Logic Analyzer & Pattern Generator Moku:Go Oscilloscope & Voltmeter Moku:Go PID Controller Moku:Go Spectrum Analyzer Moku:Go Waveform Generator Moku:Go Power Supplies Moku:Go Digital Filter Box Moku:Go FIR Filter Builder Moku:Go Lock-in Amplifier
- Moku:Lab
- Moku:Pro
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) try: g = 0 for x in current_sine: current_sine [g] = np.sin((float(g)*2*np.pi/len(current_sine))) g +=1 print(current_sine) 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) i.commit() # 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() print(data.ch2) # Set up the plotting parameters plt.ion() plt.show() plt.grid(b=True) plt.ylim([-1,1]) plt.xlim([data.time[0], data.time[-1]]) line1, = plt.plot([]) # Configure labels for axes ax = plt.gca() ax.xaxis.set_major_formatter(FuncFormatter(data.get_xaxis_fmt)) ax.yaxis.set_major_formatter(FuncFormatter(data.get_yaxis_fmt)) 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 line1.set_ydata(data.ch2) line1.set_xdata(data.time) plt.pause(0.001) finally: # Close the connection to the Moku device # This ensures network resources and released correctly m.close()