scresonators-fit 0.6.1

Python library for measuring and fitting superconducting resonator data

scresonators

Welcome to the scresonators repository of the Boulder Cryogenic Quantum Testbed! This is a library for measuring the loss in superconducting resonators.

Importing the Library

1. Install the library and its relevant dependencies with pip install scresonators-fit
2. Lead your python code with import fit_resonator.resonator as resonator

Contributing/Modifying

1. clone the repository into a folder of your choice with git clone https://github.com/Boulder-Cryogenic-Quantum-Testbed/scresonators.git
2. Install the dependencies, we strongly recommend using virtual environments for managing your dependences. To install dependencies run: pip install -r requirements.txt
3. If you are running on Windows, install Microsoft Visual Studio before using the library

Using the library

Fitting resonator data will revolve around the resonator class.

Here's an example using some of the data hosted on this repository. Hosted datasets from groups around the world can be found here.

import numpy as np
import fit_resonator.resonator as scres

# The object all following code will be called from
my_resonator = scres.Resonator()

# Load the raw data
url = 'https://raw.githubusercontent.com/Boulder-Cryogenic-Quantum-Testbed/scresonators/master/cryores/test_data/AWR/AWR_Data.csv'
raw = np.loadtxt(url, delimiter=',')
# Can also use our file input system of my_resonator.from_file(url)

# Test with file load into class
my_resonator.from_columns(raw)

# Assign your desired fit method variables
fit_type = 'DCM'
MC_iteration = 10
MC_rounds = 1e3
MC_fix = ['w1']
manual_init = None

# Pass these to your resonator object
my_resonator.fit_method(fit_type, MC_iteration, MC_rounds=MC_rounds, MC_fix=MC_fix, manual_init=manual_init,
                 MC_step_const=0.3)

# Fit!
my_resonator.fit()

Ane in depth description is given in the fit_resonators folder.

Code Organization

For fitting code collaboration, all code should live in the fit_resonator namespace. This ensures easy integration with other Python packages, and avoids name collisions; everything is referred to as e.g. fit_resonator.experiments rather than just experiments.