Spectrum Fitting#

Theoretical fluctuation spectra for granules.

Given an experimental spectrum find the best fit values of surface tension and bending rigidity.

class flickerprint.fluctuation.spectra.FittingBendingRigidityOnlyLeastSquares(fourier_terms: DataFrame, frame_info)#

use lease square method, with scan initial guess, to fit spectra and get errors

get_best_fit(x0=None)#

Return a results tuple with the best fit parameters.

In this simple implementation we use a L-BFGS minimiser however, this tends to perform badly when there is a neglible surface tension contribution.

class flickerprint.fluctuation.spectra.FittingLBFGS(fourier_terms: DataFrame, frame_info)#: Fitting for the granules using an additional step to calculate the error bars.

class flickerprint.fluctuation.spectra.FittingLeastSquares(fourier_terms: DataFrame, frame_info)#

use lease square method, with scan initial guess, to fit spectra and get errors

get_best_fit(x0=None)#

Return a results tuple with the best fit parameters.

In this simple implementation we use a L-BFGS minimiser however, this tends to perform badly when there is a neglible surface tension contribution.

class flickerprint.fluctuation.spectra.FittingSurfaceTensionOnlyLeastSquares(fourier_terms: DataFrame, frame_info)#

get_best_fit(x0=None)#

Return a results tuple with the best fit parameters.

In this simple implementation we use a L-BFGS minimiser however, this tends to perform badly when there is a neglible surface tension contribution.

class flickerprint.fluctuation.spectra.Result(sigma_bar: float, kappa_scale: float, surface_tension_defined: float)#: Storage for the fitting results.

flickerprint.fluctuation.spectra.numerator(l, q)#: Calculate the numerator terms for spectrum, while caching the results.