foofit

X-ray reflectivity (XRR) fitting library using the Parratt recursion formalism and the refraction-corrected master formula.

Installation

pip install -e .

Usage

from foofit import *
from lmfit import Parameters

params = Parameters()
params.add('numbLayers', value=1, vary=False)
params.add('wavelength', value=1.0, vary=False)
params.add('I0', value=1, vary=False)
params.add('bkg', value=0, vary=False)
params.add('pre_rho', value=0, vary=False)
params.add('pre_beta', value=0, vary=False)
params.add('layer0_dd', value=220, min=150, max=300, vary=True)
params.add('layer0_rho', value=0.25, min=0.1, max=0.4, vary=True)
params.add('layer0_sig', value=5, min=0.5, max=15, vary=True)
params.add('layer0_beta', value=0, vary=False)
params.add('sub_rho', value=0.71, vary=False)
params.add('sub_sig', value=4, min=0.5, max=10, vary=True)
params.add('sub_beta', value=0, vary=False)

# Single fit
performFit('data.xrr', params, fitFunc=xrr_parratt_fit,
           method='powell', qmin=0.0, qmax=0.4, plot=2,
           outputName='my_sample', weight=2)

# Monte Carlo error analysis (parallelized)
performFit_mc('data.xrr', params, fitFunc=xrr_parratt_fit,
              method='powell', qmin=0.0, qmax=0.4,
              outputName='my_sample', weight=2, NN=250)

Functions

Function	Description
xrr_parratt_calc	Parratt recursion reflectivity calculation
xrr_master_refractionCorrected_calc	Refraction-corrected master formula calculation
xrr_parratt_fit	Parratt fit residual (for lmfit Minimizer)
xrr_master_refractionCorrected_fit	Master formula fit residual
xrr_eDens	Electron density profile
xrr_eDens_zeroRoughness	Electron density profile (sharp interfaces)
xrr_beta	Absorption profile
performFit	Single fit with plotting and output
performFit_mc	Monte Carlo error analysis (parallelized)
loadParams	Load saved fit parameters
analyze_mc	Corner plot of MC parameter distributions