scraps: SuperConducting Resonator Analysis and Plotting Software

Scraps is a package designed to help you analyze lots of data from superconducting resonators. The basic idea is that you hook up your VNA (or mixer) and measure S21 at several different temperatures and driving powers. Maybe you have a PID and some decent automation software that takes several hundred traces, and now you don’t know what to do with all that data. Enter scraps. Scraps will organize it all and run fits on it, and make pretty pictures you can show your advisor or funding committee or spouse (when asked what you do all day upon returning home) or even put into a journal article.

Currently scraps is designed to handle resonator S21 data at varying temperatures and input powers. There are plans to extend scraps to handle varying magnetic field also, as well as noise in addition to S21. If you would like to be a part of that, get involved by posting a message at the github repo.

Scraps is licensed under the MIT license, so feel free to copy it, play with it, modify it, etc. under those terms (which are pretty loose!).

Development of scraps happens over at github so that is a great place to post bug reports, requests, kudos, etc.

Contents:

• Installation
  • Required packages
  • Downloading and installing
  • Development
• Example 1: Introduction
  • Load up the scraps modules
  • Load a file and process the data
  • Make a Resonator object
  • Make a pretty plot
  • Find the maximum liklhood estimate of the fit params using emcee
  • Make a sweet giant triangle confusogram of your emcee results.
• Example 2: Lots of Data
  • Load up the scraps modules
  • Make a list of Resonator objects
  • Fit I and Q data to a model
  • Make a pretty plot
  • Use the ResonatorSweep object to look at fit parameters
• Figure generation for a manuscript (using NbN CPW resonator data)
  • Import some stuff
  • Load the data using the makeResList tool from scraps
  • If you have cached data, skip a few cells down to “Load up previously cached data”
  • Cache fit results for future analysis
  • Load up previously cached data
• Now we can make a plot of the traces.
• Use the MCMC sampler to calculate covariances for one of the fits
  • Use pygtc to plot the parameter covariances
• S21 fit results vs temperature and power
  • Now we can look at the fit parameters from the previous step vs Temperature or Power
• Fitting secondary fit parameters to a model
  • Generate model parameters
  • Run the fit with the toy model
  • Plot the results as a surface
  • Use MCMC to look at the fit parameter covariances
  • Use pygtc to plot the parameter covariances
  • Joint fit of \(f_0\) and \(Q_\mathrm{i}\).
• API
  • Resonator class
  • ResonatorSweep class
  • Plotting tools
  • plotResSweepParamsVsX()
  • Fit models