Deer lib gen.m

Generates a library of distance distributions and corresponding DEER traces for use in neural network training. Full details are given in our paper on the subject.

Syntax

    [time_grid,dist_grid,dist_distr_lib,...
     deer_ffact_lib,background_lib,deer_trace_lib,...
     deer_clean_lib,exchange_lib,parameters]=deer_lib_gen(file_name,parameters)

Arguments

Required fields of the parameters.* structure:

   min_dist          - lower limit of distance distributions, 
                       Angstrom

   max_dist          - upper limit of distance distributions, 
                       Angstrom

   max_time          - DEER trace duration, seconds

   max_exch          - maximum exchange coupling, MHz
                       (NMR convention)

   min_exch          - minimum exchange coupling, MHz
                       (NMR convention)

   ntraces           - number of traces you wish to generate

   ndistmax          - maximum number of skewed gaussians 
                       in the distance distribution

   npoints           - number of digitisation points in the
                       DEER trace and the distance distribu-
                       tion

   noise_lvl         - RMS noise level as a fraction of the
                       modulation depth

   min_fwhm          - minimum FWHM for a gaussian in the dis-
                       tance distribution, fraction of distance

   max_fwhm          - maximum FWHM for a gaussian in the dis-
                       tance distribution, fraction of distance
                       range

   max_mdep          - minimum DEER modulation depth

   min_mdep          - maximum DEER modulation depth

   max_brate         - maximum background signal decay
                       rate, s^-1

   min_brate         - minimum background signal decay 
                       rate, s^-1

   min_bdim          - minimum background dimensionality

   max_bdim          - maximum background dimensionality

Outputs

   time_grid         - time grid (seconds) as a row vector

   dist_grid         - distance grid (Angsrom) as a row vector

   dist_distr_lib    - all distance distributions as a horizonal 
                       stack of row vectors

   deer_ffact_lib    - all DEER form factors as a horizonal 
                       stack of row vectors

   background_lib    - all background signals as a horizonal 
                       stack of row vectors, shifted and scaled
                       to match DEER traces

   deer_trace_lib    - all complete DEER traces as a horizonal 
                       stack of row vectors

   deer_clean_lib    - DEER traces as they would come out, but
                       without the noise
 
   exchange_lib      - exchange interaction (MHz), a row vector
                       conataining the value for each example

   parameters        - parameters array as received

If a file name is provided, these variables are written into that file.

Examples

The example below loads the parameters from one of the example files and generates a library of 1000 DEER traces.

	% Load the training set parameters
	netset_params; 

	% Specify number of traces to produce
	parameters.ntraces=1000;

	% Set the training database name
	file_name='dlg_example_set.mat';

	% Generate the training library
	[time_grid,dist_grid,dist_distr_lib,...
        deer_ffact_lib,background_lib,deer_trace_lib,...
        deer_clean_lib,exchange_lib,parameters]=deer_lib_gen(file_name,parameters)

One of the resulting DEER traces is shown below.

• 

  Distance distribution.

• 

  DEER trace and its components.

Notes

Multiple caveats exist in the training process. Please read our paper carefully before training your own networks.

See also

Neural network module

DEER/PELDOR experiments

Version 2.5, authors: Ilya Kuprov, Steve Worswick, Jake Keeley, Gunnar Jeschke