python-spectrometer

This package implements data acquisition, processing, and visualization for estimating power spectral densities using Welch's method. It provides the Spectrometer class that serves as a central interface which acquires and manges the data. Several processing steps can be applied to the raw timeseries data, for instance to convert from a voltage signal to an acceleration given a known calibration from a signal conditioning unit.

To demonstrate the basic features, here is some example code using the Keysight DMM qcodes driver for data acquisition. For a more detailed walkthrough, see the notebook script in doc/walkthroughs.

from python_spectrometer import Spectrometer, daq
from qcodes.instrument_drivers.Keysight.Keysight_34465A_submodules import Keysight_34465A
dmm = Keysight_34465A('dmm', 'some_tcpip_address')

# Pre-defined functions that set up and execute a measurement using a DMM
spect = Spectrometer(daq.qcodes.Keysight344xxA(dmm),
                     procfn=lambda V: V*1000,
                     processed_unit='mV')
settings = {'f_min': 0.1, 'f_max': 1000, 'phase_of_the': 'moon'}  # any other settings or metadata
spect.take('a comment', n_avg=5, **settings)
spect.hide(0)
spect.show('a comment')  # same as spect.show(0)
# Save and recall functionality
spect.serialize_to_disk('./foo')
spect_loaded = Spectrometer.recall_from_disk('./foo')  # read-only because no DAQ given
spect_loaded.show_keys()
# (0, 'a comment')

You can also play around with simulated noise (requires qopt):

from python_spectrometer import Spectrometer, daq
spect = Spectrometer(daq.simulator.QoptColoredNoise(lambda f, A, **_: A/f))
spect.take('foobar', n_avg=10, n_seg=5, A=42)

Leveraging qutil.plotting.live_view, the package also allows continuous acqusition and plotting of data:

spect = Spectrometer(daq, plot_timetrace=True)
freq_live_view, time_live_view = spect.live_view(fs=100e3)

This opens two figures which continuously update as new data is acquired in a background thread.

Installing

If you just want to use it you can install the latest "released" version via

python -m pip install python-spectrometer[complete]

However, this package profits from everybody's work and the releases are infrequent. Please make a development install and contribute your changes. You can do this via

python -m pip install -e git+https://git.rwth-aachen.de/qutech/python-spectrometer.git#egg=python-spectrometer[complete]

This will download the source code (i.e. clone the git repository) into a subdirectory of the ./src argument and link the files into your environment instead of copying them. If you are on Windows you can use SourceTree which is a nice GUI for git. You can specify the source code directory with the --src argument (which needs to be BEFORE -e):

python -m pip install --src some_directory/my_python_source -e git+https://git.rwth-aachen.de/qutech/python-spectrometer.git#egg=python-spectrometer[complete]

If you have already downloaded/cloned the package yourself you can use python -m pip install -e .[complete].

Please file an issue if any of these instructions does not work.

Documentation

Some of the development of this package took place during a course taught at the II. Institute of Physics at RWTH Aachen University in the winter semester 2022/23. Targeting applied research topics too specific for lectures but too general for lab courses, several modules intended for self-learning were developed, one of which focuses on "characterizing and avoiding noise and interference in instrumentation". The material can be found here: - Part 1, - Part 2.

For a walkthrough of the main features and interaction with the tool, see the doc/walkthroughs directory. The python_spectrometer package has an auto-generated documentation that can be found at the Gitlab Pages.

To build the documentation locally, navigate to doc/ and run

make html

or

sphinx-build -b html source build

Make sure the dependencies are installed via

python -m pip install -e .[doc]

in the top-level directory.

To check if everything works for a clean install (requires hatch to be installed), run

python -m hatch run doc:build

Tests

There are some basic tests in tests/ as well as a couple of doctests.

You can run the tests either via

python -m pytest --doctest-modules

or to check if everything works for a clean install (requires hatch to be installed)

python -m hatch run tests:run

Docker

1. Make sure docker is installed and running:

   1. pamac install docker docker-buildx

   2. (sudo) docker buildx install

   3. (sudo) systemctl status docker

      Example output:

       ● docker.service - Docker Application Container Engine
             Loaded: loaded (/usr/lib/systemd/system/docker.service; disabled; preset: disabled)
             Active: active (running) since Tue 2025-02-11 20:09:55 CET; 1 week 1 day ago
       Invocation: 609d5a409daf4e99b7b3b8da9305776d
       TriggeredBy: ● docker.socket
             Docs: https://docs.docker.com
          Main PID: 54128 (dockerd)
             Tasks: 22
             Memory: 38.8M (peak: 1.2G, swap: 21.3M, swap peak: 23.9M)
                CPU: 1min 2.133s
             CGroup: /system.slice/docker.service
                   └─54128 /usr/bin/dockerd -H fd:// --containerd=/run/containerd/containerd.sock

2. Build the docker image:

   (sudo) docker build -t pyspeck-dev .

3. Run the image…

   • … either running the tests and exiting: sh (sudo) docker run --rm pyspeck-dev
   • … or entering an interactive console: sh (sudo) docker run --rm -it pyspeck-dev /bin/bash

Releases

Releases on Gitlab, PyPI, and Zenodo are automatically created and pushed whenever a commit is tagged matching CalVer in the form vYYYY.MM.MICRO or vYYYY.0M.MICRO.