Deer lib gen.m

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

Syntax

    library=deer_lib_gen(file_name,parameters)

Arguments

Required fields of the parameters.* structure:

   max_time          - DEER trace duration, seconds
 
   max_exch          - maximum exchange coupling, fraction of
                       the maximum frequency representable on
                       the current time discretisation grid

   max_exch          - minimum exchange coupling, fraction of
                       the maximum frequency representable on
                       the current time discretisation grid

   ntraces           - number of traces you wish to generate
 
   ndistmax          - maximum number of skewed gaussians 
                       in the distance distribution

   np_time           - number of digitisation points in the
                       DEER trace

   np_dist           - number of digitisation points in the
                       distance distribution

   npt_acq           - number of digitization points actually
                       acquired in a sparsely sampled dataset;
                       points are distributed randomly with
                       uniform sampling probability

   noise_lvl         - maximum RMS noise level as a fraction
                       of the modulation depth (min is zero)

   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

   expt              - background model, 'deer' or 'ridme'

   max_brate         - maximum background signal decay
                       rate, s^-1 (DEER backgrounds only)

   min_brate         - minimum background signal decay 
                       rate, s^-1 (DEER backgrounds only)

   min_bdim          - minimum background dimensionality
                       (DEER backgrounds only)

   max_bdim          - maximum background dimensionality
                       (DEER backgrounds only)

   max_tshift        - maximum number of time discretisation
                       points to shift the trace by, either
                       forward or backward

Outputs

The function returns library.* structure with the following fields:

   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 horizon-
                       tal stack of column vectors

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

   deer_noisy_lib    - all complete DEER/RIDME traces as a 
                       horizonal stack of column vectors

   deer_clean_lib    - DEER traces as they would come out, but
                       without the noise; horizonal stack of 
                       column vectors
 
   exchange_lib      - exchange interaction (MHz), a row vector
                       containing 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