Uses pre-trained neural networks to extract background functions from DEER data. See our paper (https://dx.doi.org/10.1126/sciadv.aat5218) for further details.
deer_trace - experimental DEER trace, a column vector time_axis - experimental time axis, a column vector in microseconds, must start at zero and have a uniform time step net_dir - directory containing the network ensemble as *.m files generated by train_one_net.m and the corresponding netset_params.m and good_nets.mat files.
time_axis - a column vector (microseconds) bcgs - background signals, a horizontal array of column vectors. The number of column vectors is equal to the number of networks in the ensemble.
If no output parameters are specified, the function produces a plot.
The output of one of the example calculations supplied with Spinach (examples/deernet) is shown below.
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.