pymso5000-tspspi 0.0.10
Rigol MSO5000 control library (unofficial)
Requires Python: >=3.6

# Rigol MSO5xxx oscilloscope Python library (unofficial)

![A Rigol MSO5074 in a physics laboratory](https://raw.githubusercontent.com/tspspi/pymso5000/master/doc/msophoto.png)

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](https://github.com/tspspi/pylabdevs/blob/master/src/labdevices/oscilloscope.py) class from
the [pylabdevs](https://github.com/tspspi/pylabdevs) package which
exposes the public interface.

Patches for raw mode sample query by [MasterJubei](https://github.com/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```