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241 lines
12 KiB
Matlab
241 lines
12 KiB
Matlab
%ML Maximum Likelihood refinement
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%
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% Publications most relevant to the Maximum Likelihood refinement
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% + M. Guizar-Sicairos and J. R. Fienup, "Phase retrieval with transverse
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% translation diversity: a nonlinear optimization approach," Opt. Express 16, 7264-7278 (2008)
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% + P. Thibault and M. Guizar-Sicairos, "Maximum-likelihood refinement for
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% coherent diffractive imaging," New J. Phys. 14, 063004 (2012).
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% Academic License Agreement
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%
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% Source Code
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%
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% Introduction
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% • This license agreement sets forth the terms and conditions under which the PAUL SCHERRER INSTITUT (PSI), CH-5232 Villigen-PSI, Switzerland (hereafter "LICENSOR")
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% will grant you (hereafter "LICENSEE") a royalty-free, non-exclusive license for academic, non-commercial purposes only (hereafter "LICENSE") to use the cSAXS
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% ptychography MATLAB package computer software program and associated documentation furnished hereunder (hereafter "PROGRAM").
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%
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% Terms and Conditions of the LICENSE
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% 1. LICENSOR grants to LICENSEE a royalty-free, non-exclusive license to use the PROGRAM for academic, non-commercial purposes, upon the terms and conditions
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% hereinafter set out and until termination of this license as set forth below.
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% 2. LICENSEE acknowledges that the PROGRAM is a research tool still in the development stage. The PROGRAM is provided without any related services, improvements
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% or warranties from LICENSOR and that the LICENSE is entered into in order to enable others to utilize the PROGRAM in their academic activities. It is the
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% LICENSEE’s responsibility to ensure its proper use and the correctness of the results.”
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% 3. THE PROGRAM IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR
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% A PARTICULAR PURPOSE AND NONINFRINGEMENT OF ANY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS. IN NO EVENT SHALL THE LICENSOR, THE AUTHORS OR THE COPYRIGHT
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% HOLDERS BE LIABLE FOR ANY CLAIM, DIRECT, INDIRECT OR CONSEQUENTIAL DAMAGES OR OTHER LIABILITY ARISING FROM, OUT OF OR IN CONNECTION WITH THE PROGRAM OR THE USE
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% OF THE PROGRAM OR OTHER DEALINGS IN THE PROGRAM.
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% 4. LICENSEE agrees that it will use the PROGRAM and any modifications, improvements, or derivatives of PROGRAM that LICENSEE may create (collectively,
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% "IMPROVEMENTS") solely for academic, non-commercial purposes and that any copy of PROGRAM or derivatives thereof shall be distributed only under the same
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% license as PROGRAM. The terms "academic, non-commercial", as used in this Agreement, mean academic or other scholarly research which (a) is not undertaken for
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% profit, or (b) is not intended to produce works, services, or data for commercial use, or (c) is neither conducted, nor funded, by a person or an entity engaged
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% in the commercial use, application or exploitation of works similar to the PROGRAM.
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% 5. LICENSEE agrees that it shall make the following acknowledgement in any publication resulting from the use of the PROGRAM or any translation of the code into
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% another computing language:
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% "Data processing was carried out using the cSAXS ptychography MATLAB package developed by the Science IT and the coherent X-ray scattering (CXS) groups, Paul
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% Scherrer Institut, Switzerland."
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%
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% Additionally, any publication using the package, or any translation of the code into another computing language should cite for difference map:
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% P. Thibault, M. Dierolf, A. Menzel, O. Bunk, C. David, F. Pfeiffer, High-resolution scanning X-ray diffraction microscopy, Science 321, 379–382 (2008).
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% (doi: 10.1126/science.1158573),
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% for maximum likelihood:
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% P. Thibault and M. Guizar-Sicairos, Maximum-likelihood refinement for coherent diffractive imaging, New J. Phys. 14, 063004 (2012).
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% (doi: 10.1088/1367-2630/14/6/063004),
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% for mixed coherent modes:
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% P. Thibault and A. Menzel, Reconstructing state mixtures from diffraction measurements, Nature 494, 68–71 (2013). (doi: 10.1038/nature11806),
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% and/or for multislice:
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% E. H. R. Tsai, I. Usov, A. Diaz, A. Menzel, and M. Guizar-Sicairos, X-ray ptychography with extended depth of field, Opt. Express 24, 29089–29108 (2016).
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% (doi: 10.1364/OE.24.029089).
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% 6. Except for the above-mentioned acknowledgment, LICENSEE shall not use the PROGRAM title or the names or logos of LICENSOR, nor any adaptation thereof, nor the
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% names of any of its employees or laboratories, in any advertising, promotional or sales material without prior written consent obtained from LICENSOR in each case.
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% 7. Ownership of all rights, including copyright in the PROGRAM and in any material associated therewith, shall at all times remain with LICENSOR, and LICENSEE
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% agrees to preserve same. LICENSEE agrees not to use any portion of the PROGRAM or of any IMPROVEMENTS in any machine-readable form outside the PROGRAM, nor to
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% make any copies except for its internal use, without prior written consent of LICENSOR. LICENSEE agrees to place the following copyright notice on any such copies:
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% © All rights reserved. PAUL SCHERRER INSTITUT, Switzerland, Laboratory for Macromolecules and Bioimaging, 2017.
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% 8. The LICENSE shall not be construed to confer any rights upon LICENSEE by implication or otherwise except as specifically set forth herein.
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% 9. DISCLAIMER: LICENSEE shall be aware that Phase Focus Limited of Sheffield, UK has an international portfolio of patents and pending applications which relate
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% to ptychography and that the PROGRAM may be capable of being used in circumstances which may fall within the claims of one or more of the Phase Focus patents,
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% in particular of patent with international application number PCT/GB2005/001464. The LICENSOR explicitly declares not to indemnify the users of the software
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% in case Phase Focus or any other third party will open a legal action against the LICENSEE due to the use of the program.
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% 10. This Agreement shall be governed by the material laws of Switzerland and any dispute arising out of this Agreement or use of the PROGRAM shall be brought before
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% the courts of Zürich, Switzerland.
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function [ p, fdb ] = ML( p )
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import utils.*
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global opt_time
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fdb.status = core.engine_status;
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core.errorplot;
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opt_time = 0;
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verbose(3, 'Starting non-linear optimization')
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if p.probe_mask_bool
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if p.probe_mask_use_auto
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verbose(3, 'Using a probe mask from probe autocorrelation.');
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to_threshold = -real(auto);
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else
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verbose(3, 'Using a circular probe mask.');
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[x,y] = meshgrid(-p.asize(2)/2:floor((p.asize(2)-1)/2),-p.asize(1)/2:floor((p.asize(1)-1)/2));
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to_threshold = (x.^2 + y.^2);
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clear x y
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end
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to_threshold_flat = reshape(to_threshold, [prod(p.asize) 1]);
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[~, ind] = sort(to_threshold_flat);
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probe_mask_flat = zeros([prod(p.asize) 1]);
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probe_mask_flat(ind(1:ceil(p.probe_mask_area * prod(p.asize)))) = 1;
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p.probe_mask = reshape(probe_mask_flat, p.asize);
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clear to_threshold to_threshold_flat dummy ind probe_mask_flat
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else
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p.probe_mask = ones(p.asize);
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end
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% Taking care to pass some needed functions in p
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fnorm = sqrt(prod(p.asize));
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% Arranging optimization vector
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xopt = []; % Optimization vector
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if p.opt_flags(1) == 1
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for obnum = 1:p.numobjs
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xopt = [xopt; real(p.object{obnum}(:)); imag(p.object{obnum}(:))];
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end
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end
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if p.opt_flags(2) == 1,
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xopt = [xopt; real(p.probes(:)); imag(p.probes(:))];
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end
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xopt = single(xopt); % assumed by the MEX scripts
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p.fmag = single(p.fmag);
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% if flags(3) == 1,
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% xopt = [xopt;x;y];
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% else
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% fixed.x = x;
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% fixed.y = y;
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% end
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if isempty(xopt),
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error('At least one element of flags must be 1'),
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end
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%%% Optimization error metric
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if isfield(p,'opt_errmetric'),
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switch lower(p.opt_errmetric)
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case 'l1'
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verbose(2, 'Using ML-L1 error metric'),
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case 'l2'
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verbose(2,'Using ML-L2 error metric'),
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case 'poisson'
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verbose(2,'Using ML-Poisson'),
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otherwise
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error([p.opt_errmetric ' is not defined'])
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return;
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end
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else
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p.opt_errmetric = 'poisson';
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verbose(2, 'Using default Poisson error metric')
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end
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%%% Set specific variables needed for different metrics %%%
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switch lower(p.opt_errmetric)
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case 'poisson'
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fmag2 = p.fmag.^2;
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fmag2renorm = fmag2/p.renorm^2;
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initialerror = p.renorm^2*sum( p.fmask(:).*( (fmag2renorm(:)+0.5).*log(fmag2renorm(:)+1) ...
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- fmag2renorm(:) ...
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- 1 + 0.5*log(2*pi) + 1./(12*(fmag2renorm(:)+1)) ...
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- 1./(360*(fmag2renorm(:)+1).^3) + 1./(1260*(fmag2renorm(:)+1).^5) )) ... %% Approximation to log(n!) http://www.johndcook.com/blog/2010/08/16/how-to-compute-log-factorial/
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+ sum( fmag2(:)*log(renorm^2) );
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clear fmag2renorm
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case 'l1'
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initialerror = 0;
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fmag2 = 0;
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case 'l2'
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initialerror = 0;
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fmag2 = p.fmag.^2;
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otherwise
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error(['Error metric ' p.opt_errmetric 'is not defined'])
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end
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%%% Regularization
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Npix = 0;
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if p. reg_mu > 0
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for obnum = 1:p.numobjs
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Npix = Npix + p.object_size(obnum,1)*p.object_size(obnum,2);
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end
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Nm = prod(p.asize)*size(p.fmag,3);
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K = 8*Npix^2/(Nm*p.Nphot);
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creg = p.renorm^2*p.reg_mu/K;
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else
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creg = 0;
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end
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%%% Sieves preconditioning
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if any(p.smooth_gradient) ~= 0
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if length(p.smooth_gradient) <= 1
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% Hanning regularization
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auxi = fract_hanning_pad(512,512,0);
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auxi = fftshift(ifft2(auxi));
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smooth_gradient = real(auxi(256:258,256:258)); % Regularization kernel ( = 0 to omit)
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end
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else
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smooth_gradient = 0;
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end
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Main optimization loop %%%
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opt_time = tic;
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[tmp,p] = engines.ML.cgmin1('engines.ML.gradient_ptycho',xopt,p.opt_iter,p.opt_ftol,p.opt_xtol,p,fmag2,initialerror,fnorm,...
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creg, smooth_gradient); % ob and probes are passed to use as fixed variables when p.opt_flags is zero
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opt_time = toc(opt_time);
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Arrange solution vector %%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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if p.opt_flags(1) == 1,
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for obnum = 1:p.numobjs
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objectelements = [p.object_size(obnum,1) p.object_size(obnum,2) p.object_modes];
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p.object{obnum} = reshape(tmp(1:prod(objectelements)), objectelements) + ...
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1i*reshape(tmp(prod(objectelements)+1:2*prod(objectelements)), objectelements);
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tmp = tmp(2*prod(objectelements)+1:end);
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end
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end
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if p.opt_flags(2) == 1,
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probeelements = [p.asize p.numprobs p.probe_modes];
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p.probes = reshape(tmp(1:prod(probeelements)),probeelements) + ...
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1i*reshape(tmp(prod(probeelements)+1:2*prod(probeelements)),probeelements);
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tmp = tmp(2*prod(probeelements)+1:end);
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end
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% if flags(3) == 1,
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% x = tmp(1:params.n);
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% y = tmp(params.n+1:2*params.n);
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% end
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if ~isempty(tmp)
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warning('Temporary vector is not empty, optimized values not assigned');
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end
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verbose(3, 'Finished');
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verbose(3, 'Time elapsed in optimization refinement: %f seconds', opt_time);
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%%%%%%%%%%%%%%%%%
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%%% Last plot %%%
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%%%%%%%%%%%%%%%%%
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if p.use_display||p.save.store_images
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p.plot.extratitlestring = sprintf(' (%dx%d) - ML', p.asize(2), p.asize(1));
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core.analysis.plot_results(p, 'use_display', p.use_display);
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end
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core.errorplot;
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end
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%% end optimization refinement %%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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