Opiniated set of helper functions to handle measurements from SCM's self-built static light scattering (SLS) setup and fit models to it.
Author: Roy Hoitink L.D.Hoitink@uu.nl
Installation of this package can be done via pip. To install the newest version of this package, run the following command:
pip install git+https://github.com/UU-SCMB/slstools --upgradeThis ensures that the package and its dependencies are installed.
The documentation can be found at uu-scmb.github.io/slstools.
Several helper classes are available for loading experiments (Experiment class), creating models (Model class) and fitting a model to an experiment (Fit class).
Below an example is given on how to fit a model to an experiment.
# import the helpers and libaries
from slstools import Experiment, Model, Fit
import matplotlib.pyplot as plt
# load the experiment that is saved in the file 'Sample01.sls'
experiment = Experiment("Sample01.sls", K_unit="nm")
# correct the experimental data for scattered reflection, optional
experiment.correct_for_reflection()
# Create an initial model of 1000nm (diameter) spheres with a polydispersity of 5%
# Refractive index medium: 1.333 (water) and particle: 1.4345 (n-hexadecane)
# Make sure the given diameter rougly matches the expected diameter, as this will improve the fitting (and its speed)
model = Model(d=1000, pd=5, n_p=1.4345, n_m=1.333)
# Load both model and experiment into the Fit class
# Fitting will occur for scattering angles between 45 and 110 degrees
fit = Fit(experiment, model, fit_theta_bounds=(45.0, 110.0),model_kwargs=dict(K_unit=experiment.K_unit))
# Obtain the optimal model after fitting
optimal_model = fit.fit()
# plot the experimental data and the fit
# scale the optimal_model with the found prefactor such that the lines overlap
plt.plot(optimal_model.K, fit.parameters["prefactor"]*optimal_model.intensity, label=f"Fit: d={optimal_model.diameter:.0f}nm ({optimal_model.polydispersity:.0f}%)")
# plot experimental data as comparison
plt.plot(experiment.K, experiment.intensity, 'k.', alpha=.3, label="Experimental data")
# setting labels, scales and legend
plt.xlabel(r"K (%s$^{-1}$)" % optimal_model.K_unit)
plt.ylabel("Normalised intensity (a.u.)")
plt.yscale("log")
plt.legend()
plt.show()