pymso5000-tspspi 0.0.10

Rigol MSO5000 control library (unofficial)

Rigol MSO5xxx oscilloscope Python library (unofficial)

A simple Python library and utility to control and query data from Rigol MSO5xxx oscilloscopes (not supporting all features of the oscilloscope, work in progress). This library implements the Oscilloscope class from the pylabdevs package which exposes the public interface.

Patches for raw mode sample query by MasterJubei

Installing

There is a PyPi package that can be installed using

pip install pymso5000-tspspi

Simple example to fetch waveforms:

from pymso5000.mso5000 import MSO5000

with MSO5000(address = "10.0.0.123") as mso:
   print(f"Identify: {mso.identify()}")

   mso.set_channel_enable(0, True)
   mso.set_channel_enable(1, True)

   data = mso.query_waveform((0, 1))
   print(data)

   import matplotlib.pyplot as plt
   plt.plot(data['x'], data['y0'], label = "Ch1")
   plt.plot(data['x'], data['y1'], label = "Ch2")
   plt.show()

Note that numpy usage is optional for this implementation. One can enable numpy support using useNumpy = True in the constructor.

Querying additional statistics

This module allows - via the pylabdevs base class to query additional statistics:

• mean Calculates the mean values and standard deviations
  • A single value for each channels mean at ["means"]["yN_avg"] and a single value for each standard deviation at ["means"]["yN_std"] where N is the channel number
• fft runs Fourier transform on all queried traces
  • The result is stored in ["fft"]["yN"] (complex values) and in ["fft"]["yN_real"] for the real valued Fourier transform. Again N is the channel number
• ifft runs inverse Fourier transform on all queried traces
  • Works as fft but runs the inverse Fourier transform and stores its result in ifft instead of fft
• correlate calculates the correlation between all queried waveform pairs.
  • The result of the correlations are stored in ["correlation"]["yNyM"] for the correlation between channels M and N
• autocorrelate performs calculation of the autocorrelation of each queried channel.
  • The result of the autocorrelation is stored in ["autocorrelation"]["yN"] for channel N

To request calculation of statistics pass the string for the desired statistic or a list of statistics to the stats parameter of query_waveform:

with MSO5000(address = "10.0.0.123") as mso:
	data = mso.query_waveform((1,2), stats = [ 'mean', 'fft' ])

Supported methods

More documentation in progress ...

• identify()
• Connection management (when not using with context management):
  • connect()
  • disconnect()
• set_channel_enable(channel, enabled)
• is_channel_enabled(channel)
• set_sweep_mode(mode)
• get_sweep_mode()
• set_trigger_mode(mode)
• get_trigger_mode()
• force_trigger()
• set_timebase_mode(mode)
• get_timebase_mode()
• set_run_mode(mode)
• get_run_mode()
• set_timebase_scale(secondsPerDivision)
• get_timebase_scale()
• set_channel_coupling(channel, couplingMode)
• get_channel_coupling(channel)
• set_channel_probe_ratio(channel, ratio)
• get_channel_probe_ratio(channel)
• set_channel_scale(channel, scale)
• get_channel_scale(channel)
• query_waveform(channel, stats = None)
• off()

CLI fetching utility

This package comes with a mso5000fetch command line utility. This utility allows one to simply fetch one or more traces and store them either inside an npz or a matplotlib plot. In addition it can run all of the pylabdevs statistics functions (currently no plot, only stored in the npz) and execute manually assisted differential scans.

Help for this utility is available via mso5000fetch --help