Models for fitting various physical models against data.

Wraps lmfit, which is a wrapper for scipy.optimize.

Models that exist:

Superconducting transition (resistance vs. temperature)
Complex IQ transmission resonance (S21 vs. frequency)
NS and NSN proximity effect models for calulating Tc vs film thickness

Each model must contain at least one method that implements a model and returns a residual. Ideally, each model will also implement a parameter guessing method that parses the data, makes some reasonable guesses and returns an lmfit.Paramters object. Signatures of functions must be as follows:

model_function(params, xvals, yvals=None, **kwargs)
    #code goes here
    return residual

params_function(*args, **kwargs)
    params = lmfit.Parameters()
    #code goes here to modify params
    return params

It is highly encouraged to have your model function also have an option to return a model calculated at the xvals if no data is passed, and it is good practice to weight the residual with some uncertainty so that chi-squared values make sense.

It is also highly encouraged to include docstrings in both functions to describe the options.

Running an actual fit is as simple as calling the do_lmfit function from fit_tools.py and passing it a model function, a parameters object, the data, and any extra keywords that the model supports.

FaustinCarter/fit_tools

Models for fitting various physical models against data.