function [newshift] = convertShiftsBack(cshift, shiftso)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%NoRMCorre outputs the shifts as a struct. This is hard to visualize, so I
%transform the shifts to a vector and sometimes calculate them as a vector.
%This function takes a vector and turns it back into the corresponding
%NoRMCorre shifts
% Inputs:
%     - cshift: struct containing vectors of the x, y, x upsampled, and y upsampled
%     movements
%     - shiftso: The original struct generated by NoRMCorre
% Output:
%     - filtIm: the filtered image

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ogsize = size(shiftso(1).shifts(:, :, 1, 1));
for fn = 1:size(cshift.x, 1)
    newshift(fn).shifts(:, :, 1, 1) = reshape(cshift.y(fn, :), ogsize);
    newshift(fn).shifts(:, :, 1, 2) = reshape(cshift.x(fn, :), ogsize);
    newshift(fn).shifts_up(:, :, 1, 1) = reshape(cshift.yu(fn, :), ogsize);
    newshift(fn).shifts_up(:, :, 1, 2) = reshape(cshift.xu(fn, :), ogsize);
    newshift(fn).diff = shiftso(fn).diff;
end
end