deletions | additions       diff --git a/Burst_Weights_Theory.tex b/Burst_Weights_Theory.tex  index 5fa14ae..da6fd01 100644  --- a/Burst_Weights_Theory.tex  +++ b/Burst_Weights_Theory.tex 
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the accompanying Jupyter notebook (\href{}{link}).  \subsubsection{Comparison FRET histograms}  The second simulation, is much more realistic and captures effect of weighting  the complexity FRET histogram is here illustrated with a simulation  of a mixture of two static FRET populations and then with  experimentalsmFRET  data. Starting We performed a realistic simulation of a static mixture of two FRET populations  starting  from 3-D Brownian motion diffusion of $N$ particles excited by a numerically computed PSF, we simulated timestamps  of a smFRET experiments with two static FRET populations. numerically-computed (non-Gaussian PSF).  Input parameters includes the diffusion coefficient, the particle brightness, the two FRET efficiencies,  as well as detectors DCR. The simulation is performed with the open source software   PyBroMo~\cite{Ingargiola_2016} which creates smFRET data files (i.e. timestamps and detectors  arrays)  in Photon-HDF5 format~\cite{Ingargiola2016}. After a The simulated data file is processed with FRETBursts performing  burst search, and only a minimal burst size selection of with threshold of 10 photons,  we obtained the photons.  The resulting  weighted and unweighted FRET histograms are  reported in figure~\ref{fig:YYY}. We notice as the use of the weights results in better definition of FRET peaks.  As a final comparison, we report the weighted and unweighted FRET histogram of   an experimental FRET population from measurement of a di-labeled dsDNA sample.  Figure X show a comparison of a FRET histogram obtained from the same burst  with and without weights. The burst selection is obtained applying a burst size  of threshold of 10 counts (after background correction), in order to filter the extreme low-end of the burst size distribution.  The use of size-weighted FRET histograms is a simple way to obtain a representation of FRET   distribution that maintains high power of resolving FRET  peaks while including the full burst population and thus reducing statistical noise.  As a final remark, note that when increasing the size-threshold for burst selection