%Worked example script to correct for sample misalignment and symmetrise
%data to improve the statistics
%Christian Balz 25/07/2022
%In order to symmetrise data we first need to correct for any existing
%sample misalingment. As the symmetrisation routines will asume the sample
%to be perfectly alignetd they will create false results if it is not done
%in this order. We will start with a raw sqw file and apply the sample
%alignment corrections after fitting a set of Bragg peaks. Then we
%will take this corrected sqw and see what the symmetrisation routines can
%do for us.
%Set the data folder
folder = '/mnt/ceph/auxiliary/pace/docs/04_06_align_and_symmetrise/';
%Load the example raw sqw file
sqw_file = [folder 'T15K_B3T_5p50meV.sqw'];
%Create a series of 3 perpendicular slices to see the misalignment
proj = line_proj([1,1,0], [1,-1,2]);
slice1=cut(sqw_file, proj, 0.02, 0.02, [-0.2, 0.2], [-0.25, 0.25]);
plot(compact(slice1));
lz 0 10;
grid on;
keep_figure;
slice2=cut(sqw_file, proj, 0.01, [-0.2, 0.2], 0.01, [-0.25, 0.25]);
plot(compact(slice2));
lz 0 10;
grid on;
keep_figure;
slice3=cut(sqw_file, proj, [-0.2, 0.2], 0.01, 0.01, [-0.25, 0.25]);
plot(compact(slice3));
lz 0 10;
grid on;
keep_figure;
%We can see that we have covered 11 Bragg peaks in the horizontal
%scattering plane. By comparing the Bragg peak positions with the grid
%positions we can see the misalignment of the sample. This is especially
%visible in slice2 and slice3.
%Now we have to work out what the nominal Bragg peak positions are.
%Calculating it from the x- and y-axes labels of slice1 we get
bragg_peaks=[-3,-3,0; -2,-2,0; -1,-1,0; -3,-2,-1; -2,-1,-1;...
-3,-1,-2; -2,0,-2; -1,1,-2; -2,1,-3; -1,2,-3; 0,3,-3];
%Now we have to get the actual Bragg peak positions by fitting Gaussians
%through our elastic scattering data. The function bragg_positions takes
%one radial and two transverse cuts though every nominal Bragg peak
%position. We have to define the cut length, the step size along the cut,
%the perpendicular integration ranges, and energy integration range.
[rlu0, width, wcut, wpeak]=bragg_positions(sqw_file, bragg_peaks, 0.4, 0.01, ...
0.2, 0.4, 0.01, 0.2, 0.25, 'gauss', 'bin_ab');
%Use the command below to plot the fits and see how good they are. Scroll
%through the fits by pressing Return
bragg_positions_view(wcut, wpeak)
%After you are satisfied with your fit you can use the fitted Bragg peak
%positions stored in rlu0 to refine the lattice parameters and create the
%rotation matrix rlu_corr that relates the notational rlu to the true rlu.
alatt = [12.302, 12.302, 12.302]; % original lattice parameters
angdeg = [90, 90, 90]; % cubic symmetry
[rlu_corr, alatt_corr, angdeg_corr] = refine_crystal(rlu0, alatt, angdeg, ...
bragg_peaks, 'fix_angdeg', 'fix_alatt_ratio');
%Now that we have found the misalignment described by the rotation matrix
%rlu_corr we can calculate the goniometer offsets that we can use in
%gen_sqw to correct for the sample misalignment.
u = [1, 1, 0]; %original u
v = [1, -1, 2];%original v
alatt = [12.302, 12.302, 12.302]; % original lattice parameters
angdeg = [90, 90, 90];%cubic symmetry
%original goniometer values
omega=0;
dpsi=0;
gl=0;
gs=0;
[alatt_corr, angdeg_corr, dpsi, gl, gs] = crystal_pars_correct...
(u, v, alatt, angdeg, omega, dpsi, gl, gs, rlu_corr);
%Like this we get the corrected lattice parameters and the values of the
%goniometers dpsi, gl, and gs. These can be used for any subsequent
%generation of sqw files to correct for the misalignment.
[alatt_corr, dpsi, gl, gs]
%Lastly if we do not want to generate a new sqw file at this point we can
%copy and correct the existing sqw file by
sqw_file_corr = [folder 'T15K_B3T_5p50meV_corr.sqw'];
copyfile(sqw_file, sqw_file_corr)
change_crystal(sqw_file_corr, rlu_corr);
%Let us check slice2 and slice3 from above if the misalignment is now
%corrected:
sqw_file_corr = [folder 'T15K_B3T_5p50meV_corr.sqw'];
slice2_corr=cut(sqw_file_corr, proj, 0.01, [-0.2, 0.2], 0.01, [-0.25, 0.25]);
plot(compact(slice2_corr));
lz 0 10;
grid on;
keep_figure;
slice3_corr=cut(sqw_file_corr, proj, [-0.2, 0.2], 0.01, 0.01, [-0.25, 0.25]);
plot(compact(slice3_corr));
lz 0 10;
grid on;
keep_figure;
%Now that we have corrected the alignment we can apply symmetry operations
%to improve statistics by combining equivalent areas of reciprocal space.
%In this example we know that we have a 2-fold rotation axis along [1,1,0]
%and equivalent directions. One of the directions overed in our 140 degree
%rotation is [-1,0,-1]. We can include the following symmetrisation command
%in "gen_sqw" after the "transform_sqw" keyword:
%(x)(symmetrise_sqw(x,[-1,0,-1],[-1,1,1],[0,0,0]))
%A sqw file with the symmetry operation applied is located here:
sqw_file_corr_sym = [folder 'T15K_B3T_5p50meV_corr_sym.sqw'];
%To see what the operation has done to our dataset one can plot the
%horizontal scattering plane after symmetrisation:
slice1_sym=cut(sqw_file_corr_sym, proj, 0.02, 0.02, [-0.2, 0.2], [-0.25, 0.25]);
plot(compact(slice1_sym));
lz 0 10;
grid on;
keep_figure;
%Looking at inealstic data we often suffer from weak statistics. Here you
%can see that symmetrisation has improved statistics for a particular slice
%through the sqw:
%before symmetrisation:
proj2 = line_proj([-2, -1, -1], [0, -1, 1]);
slice4_corr=cut(sqw_file_corr, proj2, 0.03, [-0.1, 0.1], [-0.1, 0.1], 0.04);
plot(compact(slice4_corr));
lz 0 0.25;
keep_figure;
%after symmetrisation
slice4_corr_sym=cut(sqw_file_corr_sym, proj2, 0.03, [-0.1, 0.1], [-0.1, 0.1], 0.04);
plot(compact(slice4_corr_sym));
lz 0 0.25;
keep_figure;
%Note: One has to be careful with symmetrisations. In this example we have
%applied a mirror plane symmetry for a 2-fold rotation axis. This is
%technically not correct. However we were only looking at a slice centered
%in a narrow region around the horizontal scattering plane where the error
%due to our symetry operation is minimal.