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.
Contents
Syntax
[time_grid,dist_grid,dist_distr_lib,...
deer_ffact_lib,background_lib,deer_trace_lib,...
noise_line_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 skewed gaussian in
the distance distribution, fraction of
distance
max_fwhm - maximum FWHM for a skewed gaussian in
the distance distribution, fraction of
distance
min_skew - minimum shape parameter for a skewed
gaussian in the distance distribution
max_skew - maximum shape parameter for a skewed
gaussian in the distance distribution
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
noise_line_lib - all noise tracks as a horizonal
stack of row vectors
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
run('net_set_any_peaks/netset_params.m');
% 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,...
noise_line_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
- As the dipolar modulation frequency is a cubic function of the inter-spin distance, a scaling relationship exists betweent the distance range and the duration of the DEER signal.
\[\frac{t_A}{r_A^3}=\frac{t_B}{r_B^3} \]
- An important factor when generating data for training is the dynamic range - the ratio between the longest and shortest distances represented in the training set.
- The training set DEER traces should be sufficiently discretised (parameters.npoints) to reproduce all frequencies present.
See also
netset_curate.m, train_one_net.m
Version 2.2, authors: Ilya Kuprov, Steve Worswick, Gunnar Jeschke
