Deer lib gen.m - Revision history

Kuprov: Update function See also links and function index membership

2026-06-06T18:35:48Z

Update function See also links and function index membership

Kuprov: Rename Arguments section heading to Parameters

2026-06-05T17:48:15Z

Rename Arguments section heading to Parameters

Kuprov: Normalise preformatted blank lines in function documentation blocks

2026-06-05T16:40:49Z

Normalise preformatted blank lines in function documentation blocks

Kuprov: Normalise sequential blank lines

2026-04-25T11:24:44Z

Normalise sequential blank lines

Kuprov: /* Examples */

2023-07-29T08:28:45Z

‎Examples

Kuprov: /* Examples */

2023-07-29T08:27:57Z

‎Examples

Kuprov: /* Examples */

2023-07-29T08:27:35Z

‎Examples

Kuprov: /* Notes */

2023-07-29T08:27:25Z

‎Notes

Kuprov at 08:27, 29 July 2023

2023-07-29T08:27:10Z

Kuprov at 08:22, 29 July 2023

2023-07-29T08:22:39Z

@@ Line 129: / Line 129: @@
 ==See also==
-[[Neural network module]]
+[[deernet.m]], [[dist_range.m]], [[elexsys2deernet.m]], [[process_using.m]], [[train_one_net.m]], [[Neural network module]], [[Built-in_experiments]]
 ''Version 2.8, authors: [[Ilya Kuprov]], [[Steve Worswick]], [[Jake Keeley]], [[Gunnar Jeschke]]''

@@ Line 10: / Line 10: @@
       library=deer_lib_gen(file_name,parameters)
-==Arguments==
+==Parameters==
 Required fields of the parameters.* structure:

@@ Line 14: / Line 14: @@
      max_time          - DEER trace duration, seconds
-−
      max_exch          - maximum exchange coupling, fraction of
                          the maximum frequency representable on
@@ Line 24: / Line 24: @@
      ntraces           - number of traces you wish to generate
-−
      ndistmax          - maximum number of skewed gaussians
                          in the distance distribution
@@ Line 91: / Line 91: @@
                          without the noise; horizonal stack of
                          column vectors
-−
      exchange_lib      - exchange interaction (MHz), a row vector
                          containing the value for each example

← Older revision		Revision as of 11:24, 25 April 2026
Line 132:		Line 132:

	[[Built-in_experiments#DEER.2FPELDOR_experiments\|DEER/PELDOR experiments]]		[[Built-in_experiments#DEER.2FPELDOR_experiments\|DEER/PELDOR experiments]]
−

	''Version 2.8, authors: [[Ilya Kuprov]], [[Steve Worswick]], [[Jake Keeley]], [[Gunnar Jeschke]]''		''Version 2.8, authors: [[Ilya Kuprov]], [[Steve Worswick]], [[Jake Keeley]], [[Gunnar Jeschke]]''

@@ Line 102: / Line 102: @@
 The example below generates a library of 1000 DEER traces.
- 	% Load default parameters
+        % Load default parameters
- 	parameters=library_dd;
+        parameters=library_dd;
- 	% Number of traces
+        % Number of traces
- 	parameters.ntraces=1000;
+        parameters.ntraces=1000;
- 	% Time and distance point counts
+        % Time and distance point counts
          parameters.np_time=512;
          parameters.np_dist=512;
@@ Line 115: / Line 115: @@
          parameters.expt='deer';
- 	% Generate the training library
+        % Generate the training library
- 	library=deer_lib_gen([],parameters);
+        library=deer_lib_gen([],parameters);
 One of the resulting DEER traces is shown below.

@@ Line 126: / Line 126: @@
 ==Notes==
-Multiple caveats exist in the training process. Please read our [https://dx.doi.org/10.1126/sciadv.aat5218 paper] carefully before training your own networks.
+Multiple caveats exist in the training process. Please read our papers carefully before training your own networks.
 ==See also==

@@ Line 1: / Line 1: @@
 {{DISPLAYTITLE:deer_lib_gen.m}} __NOTOC__
-Generates a library of distance distributions and corresponding DEER or RIDME traces for use in neural network training. Full details are given in our [https://dx.doi.org/10.1126/sciadv.aat5218 paper] on the subject.
+Generates a library of distance distributions and corresponding DEER or RIDME traces for use in neural network training. Full details are given in our papers on the subject:
 ==Syntax==
@@ Line 96: / Line 100: @@
 ==Examples==
-The example below loads the parameters from one of the example files and generates a library of 1000 DEER traces.
+The example below generates a library of 1000 DEER traces.
-  	% Load the training set parameters
+  	% Load default parameters
-  	netset_params;
+  	parameters=library_dd;
-  	% Specify number of traces to produce
+  	% Number of traces
   	parameters.ntraces=1000;
-  	% Set the training database name
+  	% Time and distance point counts
-  	file_name='dlg_example_set.mat';
+  	parameters.np_time=512;
   	% Generate the training library
-  	[time_grid,dist_grid,dist_distr_lib,...
+  	library=deer_lib_gen([],parameters);
 One of the resulting DEER traces is shown below.
@@ Line 128: / Line 134: @@
-''Version 2.5, authors: [[Ilya Kuprov]], [[Steve Worswick]], [[Jake Keeley]], [[Gunnar Jeschke]]''
+''Version 2.8, authors: [[Ilya Kuprov]], [[Steve Worswick]], [[Jake Keeley]], [[Gunnar Jeschke]]''

@@ Line 1: / Line 1: @@
 {{DISPLAYTITLE:deer_lib_gen.m}} __NOTOC__
-Generates a library of distance distributions and corresponding DEER traces for use in neural network training. Full details are given in our [https://dx.doi.org/10.1126/sciadv.aat5218 paper] on the subject.
+Generates a library of distance distributions and corresponding DEER or RIDME traces for use in neural network training. Full details are given in our [https://dx.doi.org/10.1126/sciadv.aat5218 paper] on the subject.
 ==Syntax==
-      [time_grid,dist_grid,dist_distr_lib,...
+      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, MHz
+     max_exch          - minimum exchange coupling, fraction of
-                         (NMR convention)
+                         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
-     npoints           - number of digitisation points in the
+     np_time           - number of digitisation points in the
-                         DEER trace and the distance distribu-
+                         DEER trace
-     noise_lvl         - RMS noise level as a fraction of the
+    np_dist           - number of digitisation points in the
-                        modulation depth
+                        distance distribution
      min_fwhm          - minimum FWHM for a gaussian in the dis-
@@ Line 47: / Line 48: @@
      min_mdep          - maximum DEER modulation depth
      max_brate         - maximum background signal decay
-                         rate, s^-1
+                         rate, s^-1 (DEER backgrounds only)
      min_brate         - minimum background signal decay
-                         rate, s^-1
+                         rate, s^-1 (DEER backgrounds only)
      min_bdim          - minimum background dimensionality
      max_bdim          - maximum background dimensionality
 ==Outputs==
      time_grid         - time grid (seconds) as a row vector
@@ Line 64: / Line 74: @@
      dist_grid         - distance grid (Angsrom) as a row vector
-     dist_distr_lib    - all distance distributions as a horizonal
+     dist_distr_lib    - all distance distributions as a horizon-
-                        stack of row vectors
+                         tal stack of column vectors
      background_lib    - all background signals as a horizonal
-                         stack of row vectors, shifted and scaled
+                         stack of column vectors, shifted and
-                         to match DEER traces
+                         scaled to match DEER/RIDME traces
-     deer_trace_lib    - all complete DEER traces as a horizonal
+     deer_noisy_lib    - all complete DEER/RIDME traces as a
-                         stack of row vectors
+                         horizonal stack of column vectors
      deer_clean_lib    - DEER traces as they would come out, but
-                         without the noise
+                         without the noise; horizonal stack of
      exchange_lib      - exchange interaction (MHz), a row vector
-                         conataining the value for each example
+                         containing the value for each example
      parameters        - parameters array as received