README.md

Quantum_fitter

The purpose of this package is to provide a uniform, easy-to-use fitting protocol that everyone can share and extend, in the cQED-at-QDev group. It's highly flexible, and pretty easy to use. To use the Qfit package, you initialize an instance with your x-data, y-data, and the models you want to fit to as well as the initial guesses for parameters:

qfit = quantum_fitter.Qfit(x, y, ['GaussianModel', 'LinearModel'], params_init); 

Where params_init can be a dict of the type {'amplitude': initial_guess} (or, in the case of a user-defined model, it can be a list, where the ordering reflects the order in which the user-defined model accepts parameters (see example below)), with initial values of the free parameters in the model. It can be an empty list, to let the fitter try to find initial values itself. An example of params_init for the Gaussian + Linear--model, initialized above:

params_init = {'intercept': 0,
              'slope': 0,
              'amplitude': 5,
              'center': 5,
              'sigma': 1}

You can use your own model functions or build-in models in lmfit. The class instance accepts both a string (e.g. GaussianModel) or a list of strings (if you want to combine multiple models, i.e. a GaussianModel with a LinearModel on top). See more about builtin lmfit build-in models here

If you want to change any parameters' properties, use qfit.set_params() to alter.

# set_params(name: str, value: float=None, vary:bool=True, minimum=None, maximum=None, expression=None, brute_step=None)
qfit.set_params('amplitude', 5, maximum = 10)

If need to smooth the data beforehand, use qfit.wash() to filter the spikes.

qfit.wash(method='savgol', window_length=5, polyorder=1)

Then, use do_fit() to fit through lmfit:

qfit.do_fit()

If need plot, use pretty_print():

qfit.pretty_print()

To print the resulting plot to a pdf, use pdf_print:

qfit.pdf_print('qfit.pdf')

To get the fit parameters' results, we can use qfit.fit_params, qfit.err_params. The method qfit.fit_values returns the y-data from the fit.

f_p = qfit.fit_params() # return dictionary with all the fitting parameters 
f_e = qfit.err_params('amplitude') # return float of amplitude's fitting stderr
y_fit = qfit.fit_values()

Example

An example of using 'qfit' can be found here: Example

Appendix A: The build-in function list

Peak-like models, for more models, tap here.

To obtain the parameters in the models, use qf.params('GaussianModel') to get.

GaussianModel

LorentzianModel

SplitLorentzianModel

VoigtModel

PseudoVoigtModel

MoffatModel

Pearson7Model

StudentsTModel

BreitWignerModel

LognormalModel

DampedOscillatorModel

DampedHarmonicOscillatorModel

ExponentialGaussianModel

SkewedGaussianModel

SkewedVoigtModel

ThermalDistributionModel

DoniachModel

Installation

On a good day, installation is as easy as

$ git clone https://github.com/qdev-dk/quantum_fitter.git
$ cd quantum_fitter
$ pip install .

Running the tests

If you have gotten 'quantum_fitter' from source, you may run the tests locally.

Install quantum_fitter along with its test dependencies into your virtual environment by executing the following in the root folder

$ pip install .
$ pip install -r test_requirements.txt

Then run pytest in the tests folder.

Building the documentation

If you have gotten quantum_fitter from source, you may build the docs locally.

Install quantum_fitter along with its documentation dependencies into your virtual environment by executing the following in the root folder

$ pip install .
$ pip install -r docs_requirements.txt

You also need to install pandoc. If you are using conda, that can be achieved by

$ conda install pandoc

else, see here for pandoc's installation instructions.

Then run make html in the docs folder. The next time you build the documentation, remember to run make clean before you run make html.