Direct FRET fitting¶
See also Fit framework
This module contains functions for direct fitting of burst populations (FRET peaks) without passing through a FRET histogram.
This module provides a standard interface for different fitting algorithms.

fretbursts.fret_fit.
fit_E_E_size
(nd, na, weights=None, gamma=1.0, gamma_correct=False)¶ Fit the E with leastsquare minimization of errors on burst E values.

fretbursts.fret_fit.
fit_E_binom
(nd, na, noprint=False, method=’c’, **kwargs)¶ Fit the E with MLE using binomial distribution. method (‘a’,’b’, or ‘c’) choose how to handle negative (nd,na) values.

fretbursts.fret_fit.
fit_E_cdf
(nd, na, gamma=1.0, **kwargs)¶ Fit E using the CDF curvefit (see gaussian_fit_cdf). No weights are possible with this method.

fretbursts.fret_fit.
fit_E_hist
(nd, na, gamma=1.0, **kwargs)¶ Fit E using the histogram curvefit (see gaussian_fit_hist). You can specify
weights
that will be passed to thehistogram
function.

fretbursts.fret_fit.
fit_E_m
(nd, na, weights=None, gamma=1.0, gamma_correct=False)¶ Fit the E with a weighted mean of burst E values.

fretbursts.fret_fit.
fit_E_poisson_na
(nd, na, bg_a, **kwargs)¶ Fit the E using MLE with na extracted from a Poisson.

fretbursts.fret_fit.
fit_E_poisson_nd
(nd, na, bg_d, **kwargs)¶ Fit the E using MLE with nd extracted from a Poisson.

fretbursts.fret_fit.
fit_E_poisson_nt
(nd, na, bg_a, **kwargs)¶ Fit the E using MLE with na extracted from a Poisson.

fretbursts.fret_fit.
fit_E_slope
(nd, na, weights=None, gamma=1.0)¶ Fit E with a leastsquares fitting of slope on (nd,na) plane.

fretbursts.fret_fit.
get_dist_euclid
(nd, na, E_fit=None, slope=None)¶ Returns the euclidean distance of (nd,na) from a fit line. The fit line is specified by
slope
or byE_fit
. Intercept is always 0.

fretbursts.fret_fit.
get_weights
(nd, na, weights, naa=0, gamma=1.0, widths=None)¶ Return burst weights computed according to different criteria.
The burst size is computed as
nd*gamma + na + naa
.Parameters:  nd, na, naa (1D arrays) – photon counts in each burst.
 gamma (float) – gamma factor used for corrected burst size.
 width (None array) – array of burst durations used when weights=’brightness’
 weights (string or None) – type of weights, possible weights are: ‘size’ burst size, ‘size_min’ burst size  min(burst size), ‘size2’ (burst size)^2, ‘sqrt’ sqrt(burst size), ‘inv_size’ 1/(burst size), ‘inv_sqrt’ 1/sqrt(burst size), ‘cum_size’ CDF_of_burst_sizes(burst size), ‘cum_size2’ CDF_of_burst_sizes(burst size)^2, ‘brightness’ the burst size divided by the burst width. If None returns uniform weights.
 widths (1D array) – bursts duration in seconds, needed only when weights = ‘brightness’.
Returns: 1D array of weights, one element per burst.

fretbursts.fret_fit.
log_likelihood_binom
(E, nd, na)¶ Likelihood function for (nd,na) to be from a binom with p=E (no BG).

fretbursts.fret_fit.
log_likelihood_poisson_na
(E, nd, na, bg_a)¶ Likelihood function for na extracted from Poisson. nd, na BG corrected.

fretbursts.fret_fit.
log_likelihood_poisson_nd
(E, nd, na, bg_d)¶ Likelihood function for nd extracted from Poisson. nd, na BG corrected.

fretbursts.fret_fit.
log_likelihood_poisson_nt
(E, nd, na, bg_a)¶ Likelihood function for na extracted from Poisson. nd, na BG corrected.

fretbursts.fret_fit.
sim_nd_na
(E, N=1000, size_mean=100)¶ Simulate an exponentialsize burst distribution with binomial (nd,na)