# deernet.m

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Uses pre-trained neural networks to extract distance distributions from DEER data. See our paper (https://dx.doi.org/10.1126/sciadv.aat5218) for further details.

## Syntax

   dataset=deernet(deer_trace,time_axis)


## Arguments

   deer_trace - experimental DEER trace, a real column vec-
tor, must be phased into pure absorption and
cropped on the left to make the first point
correspond to the echo modulation maximum

time_axis  - experimental time axis in seconds, a column
vector; must start at zero and have uniform
time step


## Outputs

The function displays a figure if no outputs are requested, or returns a data structure with the following fields.

   dataset.input.

time_axis  - time axis, as supplied by the user

deer_trace - DEER trace, as supplied by the user

dataset.deernet.

library    - the parameters of the library with which
current DEERNet had been trained

resamp_axis  - time axis after the data was resampled
to match DEERNet inputdimension

resamp_trace  - DEER trace after the data was resampl-
ed to match DEERNet input dimension

deernet_rmin  - left edge of the distance grid as com-
puted from the user-supplied time trace
duration and DEERNEt input point count

deernet_rmax  - right edge of the distance grid as com-
puted from the user-supplied time trace
duration and DEERNEt input point count

input_rmin  - left edge of the distance grid as compu-
ted from the user-supplied time trace du-
duration and point count

input_rmax  - right edge of the distance grid as compu-
ted from the user-supplied time trace du-
duration and point count

dataset.output.

background_mean - arithmetic mean of the background sig-
nal over the netset

background_lb - 95% confidence interval lower bound for
the background signal obtained from net-
set statistics

background_ub - 95% confidence interval upper bound for
the background signal obtained from net-
set statistics

dist_distr_mean - arithmetic mean of the distance dist-
ributions over the netset

dist_distr_lb - 95% confidence interval lower bound for
the distance distribution obtained from
netset statistics

dist_distr_ub - 95% confidence interval upper bound for
the distance distribution obtained from
netset statistics

dist_axis - the distance axis matching the distance dis-
ribution and satisfying the constraints im-
posed by both the timing of the input signal
or the limits of DEERNet, whichever is more
stringent

mdepth - modulation depth estimate


## Examples

First, the experimental data is loaded:

    % Load experimental data
[deer_trace,time_axis]=elexsys2deernet('data_deeran/CT_DEER_mix_28_36/CT_DEER_mix_28_36');


The data is then sent to DEERNet:

    % Run deernet analysis
deernet(deer_trace,time_axis);


With no output arguments requested in the call to DEERNet, the following plot is produced.

Further examples are available under examples/deernet/

• example_set_paper.m - The six examples used in the paper on DEERNet.
• example_set_deeran.m - Selection of the examples included with the DeerAnalysis package.

## Notes

1. Do not supply excessively long baseline tails in the input data - unlike Tikhonov tools, DEERNet does not need those, it works better if you cut off baseline tails.
2. The input DEER trace must be cropped on the left to make the first point of the trace correspond to the maximum.

This is done automatically in elexsys2deernet.m, and this is the only preprocessing required by DEERNet - it can handle background and noise.

## See also

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