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DavidLutton / LabToolkit

Python module for instrument control and automation.
MIT License
21 stars 4 forks source link
gpib laboratory-automation python rf

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Labtoolkit

Python package for instrument control, data acquisition and automation.

Demo enumeration

import labtoolkit
import pyvisa
from pyvisa_py.tcpip import TCPIPInstrVxi11 as TCPVXI11  # TCPVXI11.list_resources()

rm = pyvisa.ResourceManager()
resources = rm.list_resources('(GPIB[0129]::?*::INSTR)|(USB?*)')
ignores = ['GPIB2::22::INSTR',]
resources = [resource for resource in resources if resource not in ignores]

resources = resources + TCPVXI11.list_resources()
ignores = ['TCPIP::192.168.100.5::INSTR',]
resources = [resource for resource in resources if resource not in ignores]

lab = labtoolkit.Enumerate(resourcemanager=rm, resources=resources)

sa = lab.enumeration.iloc[0].inst

sa.frequency_center = 1e9
sa.frequency_span = 10e6
sa.sweep_points = 8192
sa.OPC
df = sa.trace
# returns a DataFrame of the trace data
df.plot(grid=True, figsize=(8, 6))
df.attrs
# df.attrs are used to store metadata (sweep_time, resolution_bandwidth, etc)

Demo manual driver selection

# Communicate with Hardware with PyVISA
import pyvisa
import labtoolkit.SpectrumAnalyser.AgilentE44nn

rm = pyvisa.ResourceManager()
sa = labtookit.SpectrumAnalyser.AgilentE44nn.AgilentE44nn(rm.open_resource('GPIB0::18::INSTR'))

sa.frequency_center = 1e9
sa.frequency_span = 10e6
sa.sweep_points = 8192
sa.OPC
df = sa.trace
# returns a DataFrame of the trace data
df.plot(grid=True, figsize=(8, 6))
df.attrs
# df.attrs are used to store metadata (sweep_time, resolution_bandwidth, etc)

SpectrumAnalyser example

Badges

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Authors

Acknowledgements

Makes use of

Notes

Lots of the libaries that exist around PyVISA start their own ResourceManager.

Some intentionally operate differently when using a different interface (GPIB, LAN, USB, serial)

Absolutely no changes needed to run on Linux or Windows (I don't have a Mac to test with)

To rescan the avalable instruments if needed (between tests)

To assign drivers that simplify setting and retreaving data from instruments. Which provide a fairly common interface to instruments of the same kind (spectrum analyser, oscilloscope, VNA)

Do most of the instrument response conversion out of view (see query_bool or query_float)

Make sure I could explain how this driver layer behaves to test enginners or auditors

I know I am at least number 15 in this situation xkcd: Standards

I will be processing reasonably sized arrays (8k min or 40k to 100k on spectrum analyser) more from an oscilloscope so Numpy and Pandas are essential

Units

Wherever practicible units returned are in the basic unit

Wherever practicible inputs units are in the basic unit

For example 5e-12 rather than 5 ps

Use ... for formatting when passing to users as needed

GUI

I have used JupyterLab for myself

And for a couple of single flow applications I have used Voilà to turn Jupyter notebooks into standalone web applications

As it stands I'm planning on using Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies

Existing xkcd: Standards