{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":1305,"title":"Creation of 2D Sinc Surface","description":"This Challenge is to efficiently create the Sombrero function of various sizes, resolutions, and frequencies.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/sinc64_3_1.png\u003e\u003e\r\n\r\n*Figure Example:* Freq=1; XY_max_value=3; Num Rows / Cols 64\r\n\r\nCreate the 2-D array m(row,col)=sin(pi*R*freq)/(pi*R*freq) where R is the distance from the center of the array. The array is [nrc,nrc] with X(1,1)=Y(1,1)= - xymax and X(nrc,nrc)=Y(nrc,nrc)=xymax.\r\n\r\n*Input:* [xymax,nrc,freq]\r\n\r\n*Output:* [m] an array of size(nrc,nrc) representing the sin(x)/x function\r\n\r\n*Hints:*\r\n\r\nMatlab provides excellent functions and array operators to readily create vectors and grids. [ linspace, meshgrid ]  \r\n\r\n*Future:*\r\n\r\nPolar grid creation to produce Zernike surfaces","description_html":"\u003cp\u003eThis Challenge is to efficiently create the Sombrero function of various sizes, resolutions, and frequencies.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/sinc64_3_1.png\"\u003e\u003cp\u003e\u003cb\u003eFigure Example:\u003c/b\u003e Freq=1; XY_max_value=3; Num Rows / Cols 64\u003c/p\u003e\u003cp\u003eCreate the 2-D array m(row,col)=sin(pi*R*freq)/(pi*R*freq) where R is the distance from the center of the array. The array is [nrc,nrc] with X(1,1)=Y(1,1)= - xymax and X(nrc,nrc)=Y(nrc,nrc)=xymax.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [xymax,nrc,freq]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e [m] an array of size(nrc,nrc) representing the sin(x)/x function\u003c/p\u003e\u003cp\u003e\u003cb\u003eHints:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMatlab provides excellent functions and array operators to readily create vectors and grids. [ linspace, meshgrid ]\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture:\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePolar grid creation to produce Zernike surfaces\u003c/p\u003e","function_template":"function m=sinx_div_x(xymax,nrc,freq)\r\n m=zeros(nrc);","test_suite":"%%\r\nnrc=65;\r\nxymax=3;\r\nfreq=1;\r\n\r\nm=sinx_div_x(xymax,nrc,freq);\r\n\r\n%figure(3);imagesc(m)\r\n%figure(4);surf(m)\r\n\r\n xv=repmat(-xymax:2*xymax/(nrc-1):xymax,nrc,1);\r\n yv=xv';\r\n mexp=zeros(nrc);\r\n for r=1:nrc\r\n  for c=1:nrc\r\n  Rv=sqrt(xv(r,c)^2+yv(r,c)^2);\r\n  if Rv\u003eeps\r\n   mexp(r,c)=sin(Rv*pi*freq)/(Rv*pi*freq);\r\n  else\r\n   mexp(r,c)=1;\r\n  end\r\n  end % c\r\n end %r\r\n\r\n%figure(1);imagesc(mexp)\r\n%figure(2);surf(mexp)\r\n\r\nassert(~any(any(isnan(m))))\r\nassert(max(max(abs(m-mexp)))\u003c.01)\r\n\r\n%%\r\nnrc=127;\r\nxymax=3;\r\nfreq=4;\r\n\r\nm=sinx_div_x(xymax,nrc,freq);\r\n\r\n%figure(3);imagesc(m)\r\n%figure(4);surf(m)\r\n\r\n xv=repmat(-xymax:2*xymax/(nrc-1):xymax,nrc,1);\r\n yv=xv';\r\n mexp=zeros(nrc);\r\n for r=1:nrc\r\n  for c=1:nrc\r\n  Rv=sqrt(xv(r,c)^2+yv(r,c)^2);\r\n  if Rv\u003eeps\r\n   mexp(r,c)=sin(Rv*pi*freq)/(Rv*pi*freq);\r\n  else\r\n   mexp(r,c)=1;\r\n  end\r\n  end % c\r\n end %r\r\n\r\n%figure(1);imagesc(mexp)\r\n%figure(2);surf(mexp)\r\n\r\nassert(~any(any(isnan(m))))\r\nassert(max(max(abs(m-mexp)))\u003c.01)\r\n\r\n%%\r\nnrc=96;\r\nxymax=16;\r\nfreq=0.5;\r\n\r\nm=sinx_div_x(xymax,nrc,freq);\r\n\r\n%figure(3);imagesc(m)\r\n%figure(4);surf(m)\r\n\r\n xv=repmat(-xymax:2*xymax/(nrc-1):xymax,nrc,1);\r\n yv=xv';\r\n mexp=zeros(nrc);\r\n for r=1:nrc\r\n  for c=1:nrc\r\n  Rv=sqrt(xv(r,c)^2+yv(r,c)^2);\r\n  if Rv\u003eeps\r\n   mexp(r,c)=sin(Rv*pi*freq)/(Rv*pi*freq);\r\n  else\r\n   mexp(r,c)=1;\r\n  end\r\n  end % c\r\n end %r\r\n\r\n%figure(1);imagesc(mexp)\r\n%figure(2);surf(mexp)\r\n\r\nassert(~any(any(isnan(m))))\r\nassert(max(max(abs(m-mexp)))\u003c.01)\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":24,"test_suite_updated_at":"2013-03-01T06:34:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-28T03:54:48.000Z","updated_at":"2025-06-24T13:11:54.000Z","published_at":"2013-02-28T05:36:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to efficiently create the Sombrero function of various sizes, resolutions, and frequencies.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFigure Example:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Freq=1; XY_max_value=3; Num Rows / Cols 64\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate the 2-D array m(row,col)=sin(pi*R*freq)/(pi*R*freq) where R is the distance from the center of the array. 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\"}]}"},{"id":1284,"title":"Animated GIF Creator","description":"This Challenge is to execute the Function Template which has a fully functional Animated GIF creator of a shape related to a Zernike polynomial, as seen below.\r\n\r\nBased on the Elastic Collision 1 - D I asked my Team how to create a GIF using Matlab.  The response was a splendid implementation. There are multiple links inside mathworks on Animated GIF creation.\r\n\u003chttp://www.mathworks.com/support/solutions/en/data/1-48KECO/ Mathworks Create a GIF\u003e,  \r\n\u003chttp://www.mathworks.com/matlabcentral/fileexchange/21944-animated-gif/content/Animated_GIF/html/AnimatedGif.html Mathworks File Exchange GIF\u003e\r\n\r\nThe Function Template example is courtesy of Team. I added a few comments.\r\nThis implementation uses refreshdata and imwrite.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Zernike_n80_32_256.gif?attredirects=0\u003e\u003e\r\n\r\n*Other methods of GIF creation are welcome. Place a comment with your favorite GIF method and I will try to add example GIF outputs. (Max size 256x256)*\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eThis Challenge is to execute the Function Template which has a fully functional Animated GIF creator of a shape related to a Zernike polynomial, as seen below.\u003c/p\u003e\u003cp\u003eBased on the Elastic Collision 1 - D I asked my Team how to create a GIF using Matlab.  The response was a splendid implementation. There are multiple links inside mathworks on Animated GIF creation. \u003ca href = \"http://www.mathworks.com/support/solutions/en/data/1-48KECO/\"\u003eMathworks Create a GIF\u003c/a\u003e,   \u003ca href = \"http://www.mathworks.com/matlabcentral/fileexchange/21944-animated-gif/content/Animated_GIF/html/AnimatedGif.html\"\u003eMathworks File Exchange GIF\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe Function Template example is courtesy of Team. I added a few comments.\r\nThis implementation uses refreshdata and imwrite.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/Zernike_n80_32_256.gif?attredirects=0\"\u003e\u003cp\u003e\u003cb\u003eOther methods of GIF creation are welcome. Place a comment with your favorite GIF method and I will try to add example GIF outputs. (Max size 256x256)\u003c/b\u003e\u003c/p\u003e","function_template":"function z=makeGIF()\r\n% Courtesy of Team 02/19/2013\r\nclear all\r\nclose all\r\nclc\r\n\r\nz=0; % Not part of GIF creation \r\n\r\n\r\n%% Variables\r\n%\r\nfile_name = 'test.gif';\r\n% linear samples\r\nsamples = 512;\r\n% image steps\r\nsteps = 20;\r\n% plot scales\r\ndlim = [-1 1];\r\n% gif - frame rate in seconds... supposedly\r\ndtime = 0.01;\r\n\r\n\r\n%% Work\r\n% setup\r\nx = linspace(-1,1,samples);\r\n[X Y] = meshgrid(x,x);\r\nR = sqrt(X.^2 + Y.^2);\r\nT = atan2(Y,X);\r\nmask = double(R\u003c=1);\r\nmask = mask ./ mask;\r\nksteps = linspace(dlim(1),dlim(2),steps);\r\ndk = ksteps(2)-ksteps(1);\r\n\r\n% Setup figure\r\nW = R.^2 .* sin(2*T) .* mask;\r\n% plot setup\r\nh = surf(x,x,W,'ZDataSource','W');\r\n% workaround for stupid win7 aero...\r\nset(gcf,'Renderer','zbuffer')\r\n% white background\r\nset(gcf,'Color',[1 1 1]);\r\n% etc...\r\nshading flat\r\naxis off\r\nview([-10 70]) % semi-flat view\r\nview([0 90]) % Overhead flat view\r\nview([-70 14]) % raz  [az el] from 3D rotation view\r\nxlim(dlim);ylim(dlim);zlim(dlim);caxis(dlim)\r\n\r\n% Initialize first image frame; Simple method\r\nW = -1 * R.^2 .* sin(2*T).*mask;\r\nrefreshdata(h,'caller')\r\ndrawnow\r\nframe = getframe(1);\r\nim = frame2im(frame);\r\n[imind,cm] = rgb2ind(im,256);\r\nimwrite(imind,cm,file_name,'gif', 'Loopcount',inf,'DelayTime',dtime);\r\n\r\n% pumped up loops\r\n% Step through surface function \r\nfor k = ksteps(2:end)\r\n    W = k * R.^2 .* sin(2*T).*mask;\r\n    refreshdata(h,'caller')\r\n    drawnow\r\n    frame = getframe(1);\r\n    im = frame2im(frame);\r\n    [imind,cm] = rgb2ind(im,256);\r\n    imwrite(imind,cm,file_name,'gif','WriteMode','append','DelayTime',dtime);\r\nend\r\n% back dat surface up\r\n% Draw surface in reverse to frame#2 to form smooth GIF cycle\r\nfor k = ksteps(end-1):-dk:ksteps(2)\r\n    W = k * R.^2 .* sin(2*T).*mask;\r\n    refreshdata(h,'caller')\r\n    drawnow\r\n    frame = getframe(1);\r\n    im = frame2im(frame);\r\n    [imind,cm] = rgb2ind(im,256);\r\n    imwrite(imind,cm,file_name,'gif','WriteMode','append','DelayTime',dtime);\r\nend\r\n\r\n\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',0);\r\n\r\nz=makeGIF();","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":26,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-20T20:54:26.000Z","updated_at":"2026-01-01T13:50:15.000Z","published_at":"2013-02-21T01:33:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to execute the Function Template which has a fully functional Animated GIF creator of a shape related to a Zernike polynomial, as seen below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased on the Elastic Collision 1 - D I asked my Team how to create a GIF using Matlab. The response was a splendid implementation. There are multiple links inside mathworks on Animated GIF creation.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/support/solutions/en/data/1-48KECO/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMathworks Create a GIF\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/fileexchange/21944-animated-gif/content/Animated_GIF/html/AnimatedGif.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMathworks File Exchange GIF\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Function Template example is courtesy of Team. I added a few comments. This implementation uses refreshdata and imwrite.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOther methods of GIF creation are welcome. Place a comment with your favorite GIF method and I will try to add example GIF outputs. 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\"}]}"},{"id":690,"title":"Remove the two elements next to NaN value","description":"The aim is to *remove the two elements next to NaN values* inside a vector.\r\n\r\nFor example:\r\n\r\n x = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8]\r\n\r\nThe output y will be:\r\n\r\n y = [6 10 5 8 9 7 3 21 43 7 8]\r\n\r\nNan values and the 2 next elements after the NaN (23-9  and 4-6) have been removed.\r\n\r\nThere will be always NaN values in the input vector followed by at least 2 integers.","description_html":"\u003cp\u003eThe aim is to \u003cb\u003eremove the two elements next to NaN values\u003c/b\u003e inside a vector.\u003c/p\u003e\u003cp\u003eFor example:\u003c/p\u003e\u003cpre\u003e x = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8]\u003c/pre\u003e\u003cp\u003eThe output y will be:\u003c/p\u003e\u003cpre\u003e y = [6 10 5 8 9 7 3 21 43 7 8]\u003c/pre\u003e\u003cp\u003eNan values and the 2 next elements after the NaN (23-9  and 4-6) have been removed.\u003c/p\u003e\u003cp\u003eThere will be always NaN values in the input vector followed by at least 2 integers.\u003c/p\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8];\r\ny_correct = [6 10 5 8 9 7 3 21 43 7 8];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = [25 NaN 1 3];\r\ny_correct = 25;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = [ NaN 15 15 17 ]\r\ny_correct = 17;\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":9,"comments_count":7,"created_by":639,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":703,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":12,"created_at":"2012-05-15T09:33:34.000Z","updated_at":"2026-02-10T12:07:16.000Z","published_at":"2012-05-15T09:33:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe aim is to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eremove the two elements next to NaN values\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e inside a vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output y will be:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ y = [6 10 5 8 9 7 3 21 43 7 8]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNan values and the 2 next elements after the NaN (23-9 and 4-6) have been removed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere will be always NaN values in the input vector followed by at least 2 integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":909,"title":"Image Processing 003: Interferometer Data Interpolation","description":"This Challenge is to correct a 2-D Interferometer data set for drop-outs.\r\n\r\nInterferometer data is notorious for drop-outs which result in values of zero.\r\n\r\nExamples of Pre and Post WFE drop-out correction:\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_2D_Drop_250.jpg\u003e\u003e\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_2D_Fix_250.jpg\u003e\u003e\r\n\r\n*surf layed flat*\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_3D_Drop_250.jpg\u003e\u003e\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_3D_Fix_250.jpg\u003e\u003e\r\n\r\n*3-D surf*\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Challenge_WF_500.jpg\u003e\u003e\r\n\r\n*Challenge WFE :Pre Drop-Out insertion*\r\n\r\n\r\n*Input:* \r\n\r\n2-D square array representing surface z-values(NaNs in WFE array)\r\n\r\nCorrupted data points(Drop-Outs) are represented by having the value 0.000\r\n\r\n*Output:*\r\n\r\nsame size 2-D square array as input with Drop-Outs corrected, approximately. \r\n\r\n*Hint:* \r\n\r\nTriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.  \r\n\r\n*Follow-Up:* Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF","description_html":"\u003cp\u003eThis Challenge is to correct a 2-D Interferometer data set for drop-outs.\u003c/p\u003e\u003cp\u003eInterferometer data is notorious for drop-outs which result in values of zero.\u003c/p\u003e\u003cp\u003eExamples of Pre and Post WFE drop-out correction:\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Drop_250.jpg\"\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Fix_250.jpg\"\u003e\u003cp\u003e\u003cb\u003esurf layed flat\u003c/b\u003e\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Drop_250.jpg\"\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Fix_250.jpg\"\u003e\u003cp\u003e\u003cb\u003e3-D surf\u003c/b\u003e\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Challenge_WF_500.jpg\"\u003e\u003cp\u003e\u003cb\u003eChallenge WFE :Pre Drop-Out insertion\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e2-D square array representing surface z-values(NaNs in WFE array)\u003c/p\u003e\u003cp\u003eCorrupted data points(Drop-Outs) are represented by having the value 0.000\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e\u003c/p\u003e\u003cp\u003esame size 2-D square array as input with Drop-Outs corrected, approximately.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHint:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFollow-Up:\u003c/b\u003e Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF\u003c/p\u003e","function_template":"function out = Fix_interferometer(z)\r\n% z is square with a linear square grid\r\n% w=size(z,1) % for square data set\r\n% [xmesh ymesh]=meshgrid(1:w);\r\n% xyz(:,1),xyz(:,2) are subsets of xmesh,ymesh\r\n% size of xyz is [z_nonzero_values,3], [xmesh_subset ymesh_subset z~=0] \r\n% FZ=TriScatteredInterp(xyz(:,1),xyz(:,2),xyz(:,3),'mode');%linear natural\r\n% out=FZ(xmesh,ymesh);\r\n\r\n  out = z;\r\nend","test_suite":"w=100;\r\n[xm ym]=meshgrid(1:w);\r\n% simple ellipsoid\r\nz(xm(:)+(ym(:)-1)*w)=sqrt(1-(xm(:)/142).^2-(ym(:)/142).^2);\r\nz=reshape(z,w,w);\r\nz_orig=z; % used for performance check\r\n%figure(42);surf(z,'LineStyle','none')\r\n\r\n% or ellipsoid by for loops\r\n%for i=1:w\r\n% for j=1:w\r\n%  z(i,j)=sqrt(1-(i/142)^2-(j/142)^2);\r\n% end\r\n%end\r\n\r\nz(50,50)=0;\r\n%figure(43);surf(z,'LineStyle','none')\r\n\r\ntic\r\nout = Fix_interferometer(z);\r\ntoc\r\ndelta=abs(out-z_orig);\r\n\r\nfprintf('Max delta=%e\\n',max(delta(:)))\r\n\r\nassert(max(delta(:))\u003c1e-4,sprintf('Max delta=%f\\n',max(delta(:))))\r\n%%\r\n% Load a real Interferometry Profile\r\n%   Previously TriScatteredInterp corrected\r\n% Randomly induce Drop-Outs\r\n\r\ntic\r\nurlwrite('http://tinyurl.com/matlab-interferometer','Inter_zN.mat')\r\nload Inter_zN.mat\r\ntoc\r\n\r\nzN_orig=zN;\r\n%figure(44);surf(zN_orig,'LineStyle','none')\r\n\r\nzN_idx=find(~isnan(zN));\r\n\r\nfor i=1:20\r\n zN(zN_idx(randi(size(zN_idx,1))))=0;\r\nend\r\n%figure(45);surf(zN,'LineStyle','none')\r\n\r\ntic\r\nout = Fix_interferometer(zN);\r\ntoc\r\ndelta=abs(out-zN_orig);\r\n\r\nfprintf('Max delta=%e\\n',max(delta(:)))\r\n\r\n% Threshold based on a handful of test cases\r\nassert(max(delta(:))\u003c5e-7,sprintf('Max delta=%f\\n',max(delta(:))))\r\n\r\n\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":4,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":0,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-08-13T02:35:40.000Z","updated_at":"2026-03-29T19:25:41.000Z","published_at":"2012-10-13T21:05:14.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId3\",\"target\":\"/media/image3.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId4\",\"target\":\"/media/image4.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId5\",\"target\":\"/media/image5.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to correct a 2-D Interferometer data set for drop-outs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInterferometer data is notorious for drop-outs which result in values of zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples of Pre and Post WFE drop-out correction:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esurf layed flat\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3-D surf\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eChallenge WFE :Pre Drop-Out insertion\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2-D square array representing surface z-values(NaNs in WFE array)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCorrupted data points(Drop-Outs) are represented by having the value 0.000\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003esame size 2-D square array as input with Drop-Outs corrected, approximately.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHint:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFollow-Up:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" 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\"},{\"partUri\":\"/media/image2.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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\"},{\"partUri\":\"/media/image3.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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\"},{\"partUri\":\"/media/image4.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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Non-Co-Planar Points in an N-Cube","description":"This Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\r\n\r\n  N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\n  N=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\r\nOutput is a Qx3 matrix of the non-co-planar points.\r\n\r\nReference: The \u003chttp://68.173.157.131/Contest/Tetrahedra March 2016 Al Zimmermann Non-Coplanar contest\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\r\n\r\nTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003chttp://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar Cody Co-Planar Check\u003e may improve speed. ","description_html":"\u003cp\u003eThis Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eN=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\u003c/pre\u003e\u003cp\u003eOutput is a Qx3 matrix of the non-co-planar points.\u003c/p\u003e\u003cp\u003eReference: The \u003ca href = \"http://68.173.157.131/Contest/Tetrahedra\"\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/a\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\u003c/p\u003e\u003cp\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar\"\u003eCody Co-Planar Check\u003c/a\u003e may improve speed.\u003c/p\u003e","function_template":"function m=MaxNonCoplanarPts(N,Q);\r\n% Place Q or more points in an 0:N-1 #D grid such that each plane created uses only 3 points from the set provided\r\n% N is Cube size\r\n% Q is expected number of points in solution\r\n% Hint: Point [0,0,0] can be assumed to be in the solution for N=2 and N=3\r\n% N=3 is the 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n  m=[];\r\nend","test_suite":"%%\r\nN=2;\r\nQ=5;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\r\n%%\r\nN=3;\r\nQ=8;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-03-07T00:22:02.000Z","updated_at":"2016-03-07T01:01:33.000Z","published_at":"2016-03-07T01:01:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar. Given the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput is a Qx3 matrix of the non-co-planar points.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReference: The\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://68.173.157.131/Contest/Tetrahedra\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody Co-Planar Check\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may improve speed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":1305,"title":"Creation of 2D Sinc Surface","description":"This Challenge is to efficiently create the Sombrero function of various sizes, resolutions, and frequencies.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/sinc64_3_1.png\u003e\u003e\r\n\r\n*Figure Example:* Freq=1; XY_max_value=3; Num Rows / Cols 64\r\n\r\nCreate the 2-D array m(row,col)=sin(pi*R*freq)/(pi*R*freq) where R is the distance from the center of the array. The array is [nrc,nrc] with X(1,1)=Y(1,1)= - xymax and X(nrc,nrc)=Y(nrc,nrc)=xymax.\r\n\r\n*Input:* [xymax,nrc,freq]\r\n\r\n*Output:* [m] an array of size(nrc,nrc) representing the sin(x)/x function\r\n\r\n*Hints:*\r\n\r\nMatlab provides excellent functions and array operators to readily create vectors and grids. [ linspace, meshgrid ]  \r\n\r\n*Future:*\r\n\r\nPolar grid creation to produce Zernike surfaces","description_html":"\u003cp\u003eThis Challenge is to efficiently create the Sombrero function of various sizes, resolutions, and frequencies.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/sinc64_3_1.png\"\u003e\u003cp\u003e\u003cb\u003eFigure Example:\u003c/b\u003e Freq=1; XY_max_value=3; Num Rows / Cols 64\u003c/p\u003e\u003cp\u003eCreate the 2-D array m(row,col)=sin(pi*R*freq)/(pi*R*freq) where R is the distance from the center of the array. The array is [nrc,nrc] with X(1,1)=Y(1,1)= - xymax and X(nrc,nrc)=Y(nrc,nrc)=xymax.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [xymax,nrc,freq]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e [m] an array of size(nrc,nrc) representing the sin(x)/x function\u003c/p\u003e\u003cp\u003e\u003cb\u003eHints:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMatlab provides excellent functions and array operators to readily create vectors and grids. [ linspace, meshgrid ]\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture:\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePolar grid creation to produce Zernike surfaces\u003c/p\u003e","function_template":"function m=sinx_div_x(xymax,nrc,freq)\r\n m=zeros(nrc);","test_suite":"%%\r\nnrc=65;\r\nxymax=3;\r\nfreq=1;\r\n\r\nm=sinx_div_x(xymax,nrc,freq);\r\n\r\n%figure(3);imagesc(m)\r\n%figure(4);surf(m)\r\n\r\n xv=repmat(-xymax:2*xymax/(nrc-1):xymax,nrc,1);\r\n yv=xv';\r\n mexp=zeros(nrc);\r\n for r=1:nrc\r\n  for c=1:nrc\r\n  Rv=sqrt(xv(r,c)^2+yv(r,c)^2);\r\n  if Rv\u003eeps\r\n   mexp(r,c)=sin(Rv*pi*freq)/(Rv*pi*freq);\r\n  else\r\n   mexp(r,c)=1;\r\n  end\r\n  end % c\r\n end %r\r\n\r\n%figure(1);imagesc(mexp)\r\n%figure(2);surf(mexp)\r\n\r\nassert(~any(any(isnan(m))))\r\nassert(max(max(abs(m-mexp)))\u003c.01)\r\n\r\n%%\r\nnrc=127;\r\nxymax=3;\r\nfreq=4;\r\n\r\nm=sinx_div_x(xymax,nrc,freq);\r\n\r\n%figure(3);imagesc(m)\r\n%figure(4);surf(m)\r\n\r\n xv=repmat(-xymax:2*xymax/(nrc-1):xymax,nrc,1);\r\n yv=xv';\r\n mexp=zeros(nrc);\r\n for r=1:nrc\r\n  for c=1:nrc\r\n  Rv=sqrt(xv(r,c)^2+yv(r,c)^2);\r\n  if Rv\u003eeps\r\n   mexp(r,c)=sin(Rv*pi*freq)/(Rv*pi*freq);\r\n  else\r\n   mexp(r,c)=1;\r\n  end\r\n  end % c\r\n end %r\r\n\r\n%figure(1);imagesc(mexp)\r\n%figure(2);surf(mexp)\r\n\r\nassert(~any(any(isnan(m))))\r\nassert(max(max(abs(m-mexp)))\u003c.01)\r\n\r\n%%\r\nnrc=96;\r\nxymax=16;\r\nfreq=0.5;\r\n\r\nm=sinx_div_x(xymax,nrc,freq);\r\n\r\n%figure(3);imagesc(m)\r\n%figure(4);surf(m)\r\n\r\n xv=repmat(-xymax:2*xymax/(nrc-1):xymax,nrc,1);\r\n yv=xv';\r\n mexp=zeros(nrc);\r\n for r=1:nrc\r\n  for c=1:nrc\r\n  Rv=sqrt(xv(r,c)^2+yv(r,c)^2);\r\n  if Rv\u003eeps\r\n   mexp(r,c)=sin(Rv*pi*freq)/(Rv*pi*freq);\r\n  else\r\n   mexp(r,c)=1;\r\n  end\r\n  end % c\r\n end %r\r\n\r\n%figure(1);imagesc(mexp)\r\n%figure(2);surf(mexp)\r\n\r\nassert(~any(any(isnan(m))))\r\nassert(max(max(abs(m-mexp)))\u003c.01)\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":24,"test_suite_updated_at":"2013-03-01T06:34:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-28T03:54:48.000Z","updated_at":"2025-06-24T13:11:54.000Z","published_at":"2013-02-28T05:36:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to efficiently create the Sombrero function of various sizes, resolutions, and frequencies.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFigure Example:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Freq=1; XY_max_value=3; Num Rows / Cols 64\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate the 2-D array m(row,col)=sin(pi*R*freq)/(pi*R*freq) where R is the distance from the center of the array. The array is [nrc,nrc] with X(1,1)=Y(1,1)= - xymax and X(nrc,nrc)=Y(nrc,nrc)=xymax.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [xymax,nrc,freq]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [m] an array of size(nrc,nrc) representing the sin(x)/x function\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHints:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMatlab provides excellent functions and array operators to readily create vectors and grids. [ linspace, meshgrid ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFuture:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePolar grid creation to produce Zernike surfaces\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray 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\"}]}"},{"id":1284,"title":"Animated GIF Creator","description":"This Challenge is to execute the Function Template which has a fully functional Animated GIF creator of a shape related to a Zernike polynomial, as seen below.\r\n\r\nBased on the Elastic Collision 1 - D I asked my Team how to create a GIF using Matlab.  The response was a splendid implementation. There are multiple links inside mathworks on Animated GIF creation.\r\n\u003chttp://www.mathworks.com/support/solutions/en/data/1-48KECO/ Mathworks Create a GIF\u003e,  \r\n\u003chttp://www.mathworks.com/matlabcentral/fileexchange/21944-animated-gif/content/Animated_GIF/html/AnimatedGif.html Mathworks File Exchange GIF\u003e\r\n\r\nThe Function Template example is courtesy of Team. I added a few comments.\r\nThis implementation uses refreshdata and imwrite.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Zernike_n80_32_256.gif?attredirects=0\u003e\u003e\r\n\r\n*Other methods of GIF creation are welcome. Place a comment with your favorite GIF method and I will try to add example GIF outputs. (Max size 256x256)*\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eThis Challenge is to execute the Function Template which has a fully functional Animated GIF creator of a shape related to a Zernike polynomial, as seen below.\u003c/p\u003e\u003cp\u003eBased on the Elastic Collision 1 - D I asked my Team how to create a GIF using Matlab.  The response was a splendid implementation. There are multiple links inside mathworks on Animated GIF creation. \u003ca href = \"http://www.mathworks.com/support/solutions/en/data/1-48KECO/\"\u003eMathworks Create a GIF\u003c/a\u003e,   \u003ca href = \"http://www.mathworks.com/matlabcentral/fileexchange/21944-animated-gif/content/Animated_GIF/html/AnimatedGif.html\"\u003eMathworks File Exchange GIF\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe Function Template example is courtesy of Team. I added a few comments.\r\nThis implementation uses refreshdata and imwrite.\u003c/p\u003e\u003cimg src = \"https://sites.google.com/site/razapor/matlab_cody/Zernike_n80_32_256.gif?attredirects=0\"\u003e\u003cp\u003e\u003cb\u003eOther methods of GIF creation are welcome. Place a comment with your favorite GIF method and I will try to add example GIF outputs. (Max size 256x256)\u003c/b\u003e\u003c/p\u003e","function_template":"function z=makeGIF()\r\n% Courtesy of Team 02/19/2013\r\nclear all\r\nclose all\r\nclc\r\n\r\nz=0; % Not part of GIF creation \r\n\r\n\r\n%% Variables\r\n%\r\nfile_name = 'test.gif';\r\n% linear samples\r\nsamples = 512;\r\n% image steps\r\nsteps = 20;\r\n% plot scales\r\ndlim = [-1 1];\r\n% gif - frame rate in seconds... supposedly\r\ndtime = 0.01;\r\n\r\n\r\n%% Work\r\n% setup\r\nx = linspace(-1,1,samples);\r\n[X Y] = meshgrid(x,x);\r\nR = sqrt(X.^2 + Y.^2);\r\nT = atan2(Y,X);\r\nmask = double(R\u003c=1);\r\nmask = mask ./ mask;\r\nksteps = linspace(dlim(1),dlim(2),steps);\r\ndk = ksteps(2)-ksteps(1);\r\n\r\n% Setup figure\r\nW = R.^2 .* sin(2*T) .* mask;\r\n% plot setup\r\nh = surf(x,x,W,'ZDataSource','W');\r\n% workaround for stupid win7 aero...\r\nset(gcf,'Renderer','zbuffer')\r\n% white background\r\nset(gcf,'Color',[1 1 1]);\r\n% etc...\r\nshading flat\r\naxis off\r\nview([-10 70]) % semi-flat view\r\nview([0 90]) % Overhead flat view\r\nview([-70 14]) % raz  [az el] from 3D rotation view\r\nxlim(dlim);ylim(dlim);zlim(dlim);caxis(dlim)\r\n\r\n% Initialize first image frame; Simple method\r\nW = -1 * R.^2 .* sin(2*T).*mask;\r\nrefreshdata(h,'caller')\r\ndrawnow\r\nframe = getframe(1);\r\nim = frame2im(frame);\r\n[imind,cm] = rgb2ind(im,256);\r\nimwrite(imind,cm,file_name,'gif', 'Loopcount',inf,'DelayTime',dtime);\r\n\r\n% pumped up loops\r\n% Step through surface function \r\nfor k = ksteps(2:end)\r\n    W = k * R.^2 .* sin(2*T).*mask;\r\n    refreshdata(h,'caller')\r\n    drawnow\r\n    frame = getframe(1);\r\n    im = frame2im(frame);\r\n    [imind,cm] = rgb2ind(im,256);\r\n    imwrite(imind,cm,file_name,'gif','WriteMode','append','DelayTime',dtime);\r\nend\r\n% back dat surface up\r\n% Draw surface in reverse to frame#2 to form smooth GIF cycle\r\nfor k = ksteps(end-1):-dk:ksteps(2)\r\n    W = k * R.^2 .* sin(2*T).*mask;\r\n    refreshdata(h,'caller')\r\n    drawnow\r\n    frame = getframe(1);\r\n    im = frame2im(frame);\r\n    [imind,cm] = rgb2ind(im,256);\r\n    imwrite(imind,cm,file_name,'gif','WriteMode','append','DelayTime',dtime);\r\nend\r\n\r\n\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',0);\r\n\r\nz=makeGIF();","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":26,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-20T20:54:26.000Z","updated_at":"2026-01-01T13:50:15.000Z","published_at":"2013-02-21T01:33:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to execute the Function Template which has a fully functional Animated GIF creator of a shape related to a Zernike polynomial, as seen below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased on the Elastic Collision 1 - D I asked my Team how to create a GIF using Matlab. The response was a splendid implementation. There are multiple links inside mathworks on Animated GIF creation.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/support/solutions/en/data/1-48KECO/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMathworks Create a GIF\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/fileexchange/21944-animated-gif/content/Animated_GIF/html/AnimatedGif.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMathworks File Exchange GIF\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Function Template example is courtesy of Team. I added a few comments. This implementation uses refreshdata and imwrite.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOther methods of GIF creation are welcome. Place a comment with your favorite GIF method and I will try to add example GIF outputs. 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\"}]}"},{"id":690,"title":"Remove the two elements next to NaN value","description":"The aim is to *remove the two elements next to NaN values* inside a vector.\r\n\r\nFor example:\r\n\r\n x = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8]\r\n\r\nThe output y will be:\r\n\r\n y = [6 10 5 8 9 7 3 21 43 7 8]\r\n\r\nNan values and the 2 next elements after the NaN (23-9  and 4-6) have been removed.\r\n\r\nThere will be always NaN values in the input vector followed by at least 2 integers.","description_html":"\u003cp\u003eThe aim is to \u003cb\u003eremove the two elements next to NaN values\u003c/b\u003e inside a vector.\u003c/p\u003e\u003cp\u003eFor example:\u003c/p\u003e\u003cpre\u003e x = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8]\u003c/pre\u003e\u003cp\u003eThe output y will be:\u003c/p\u003e\u003cpre\u003e y = [6 10 5 8 9 7 3 21 43 7 8]\u003c/pre\u003e\u003cp\u003eNan values and the 2 next elements after the NaN (23-9  and 4-6) have been removed.\u003c/p\u003e\u003cp\u003eThere will be always NaN values in the input vector followed by at least 2 integers.\u003c/p\u003e","function_template":"function y = your_fcn_name(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8];\r\ny_correct = [6 10 5 8 9 7 3 21 43 7 8];\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = [25 NaN 1 3];\r\ny_correct = 25;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = [ NaN 15 15 17 ]\r\ny_correct = 17;\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":9,"comments_count":7,"created_by":639,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":703,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":12,"created_at":"2012-05-15T09:33:34.000Z","updated_at":"2026-02-10T12:07:16.000Z","published_at":"2012-05-15T09:33:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe aim is to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eremove the two elements next to NaN values\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e inside a vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [6 10 5 8 9 NaN 23 9 7 3 21 43 NaN 4 6 7 8]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output y will be:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ y = [6 10 5 8 9 7 3 21 43 7 8]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNan values and the 2 next elements after the NaN (23-9 and 4-6) have been removed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere will be always NaN values in the input vector followed by at least 2 integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":909,"title":"Image Processing 003: Interferometer Data Interpolation","description":"This Challenge is to correct a 2-D Interferometer data set for drop-outs.\r\n\r\nInterferometer data is notorious for drop-outs which result in values of zero.\r\n\r\nExamples of Pre and Post WFE drop-out correction:\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_2D_Drop_250.jpg\u003e\u003e\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_2D_Fix_250.jpg\u003e\u003e\r\n\r\n*surf layed flat*\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_3D_Drop_250.jpg\u003e\u003e\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Inter_3D_Fix_250.jpg\u003e\u003e\r\n\r\n*3-D surf*\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/Challenge_WF_500.jpg\u003e\u003e\r\n\r\n*Challenge WFE :Pre Drop-Out insertion*\r\n\r\n\r\n*Input:* \r\n\r\n2-D square array representing surface z-values(NaNs in WFE array)\r\n\r\nCorrupted data points(Drop-Outs) are represented by having the value 0.000\r\n\r\n*Output:*\r\n\r\nsame size 2-D square array as input with Drop-Outs corrected, approximately. \r\n\r\n*Hint:* \r\n\r\nTriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.  \r\n\r\n*Follow-Up:* Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF","description_html":"\u003cp\u003eThis Challenge is to correct a 2-D Interferometer data set for drop-outs.\u003c/p\u003e\u003cp\u003eInterferometer data is notorious for drop-outs which result in values of zero.\u003c/p\u003e\u003cp\u003eExamples of Pre and Post WFE drop-out correction:\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Drop_250.jpg\"\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_2D_Fix_250.jpg\"\u003e\u003cp\u003e\u003cb\u003esurf layed flat\u003c/b\u003e\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Drop_250.jpg\"\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Inter_3D_Fix_250.jpg\"\u003e\u003cp\u003e\u003cb\u003e3-D surf\u003c/b\u003e\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/Challenge_WF_500.jpg\"\u003e\u003cp\u003e\u003cb\u003eChallenge WFE :Pre Drop-Out insertion\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e2-D square array representing surface z-values(NaNs in WFE array)\u003c/p\u003e\u003cp\u003eCorrupted data points(Drop-Outs) are represented by having the value 0.000\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e\u003c/p\u003e\u003cp\u003esame size 2-D square array as input with Drop-Outs corrected, approximately.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHint:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFollow-Up:\u003c/b\u003e Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF\u003c/p\u003e","function_template":"function out = Fix_interferometer(z)\r\n% z is square with a linear square grid\r\n% w=size(z,1) % for square data set\r\n% [xmesh ymesh]=meshgrid(1:w);\r\n% xyz(:,1),xyz(:,2) are subsets of xmesh,ymesh\r\n% size of xyz is [z_nonzero_values,3], [xmesh_subset ymesh_subset z~=0] \r\n% FZ=TriScatteredInterp(xyz(:,1),xyz(:,2),xyz(:,3),'mode');%linear natural\r\n% out=FZ(xmesh,ymesh);\r\n\r\n  out = z;\r\nend","test_suite":"w=100;\r\n[xm ym]=meshgrid(1:w);\r\n% simple ellipsoid\r\nz(xm(:)+(ym(:)-1)*w)=sqrt(1-(xm(:)/142).^2-(ym(:)/142).^2);\r\nz=reshape(z,w,w);\r\nz_orig=z; % used for performance check\r\n%figure(42);surf(z,'LineStyle','none')\r\n\r\n% or ellipsoid by for loops\r\n%for i=1:w\r\n% for j=1:w\r\n%  z(i,j)=sqrt(1-(i/142)^2-(j/142)^2);\r\n% end\r\n%end\r\n\r\nz(50,50)=0;\r\n%figure(43);surf(z,'LineStyle','none')\r\n\r\ntic\r\nout = Fix_interferometer(z);\r\ntoc\r\ndelta=abs(out-z_orig);\r\n\r\nfprintf('Max delta=%e\\n',max(delta(:)))\r\n\r\nassert(max(delta(:))\u003c1e-4,sprintf('Max delta=%f\\n',max(delta(:))))\r\n%%\r\n% Load a real Interferometry Profile\r\n%   Previously TriScatteredInterp corrected\r\n% Randomly induce Drop-Outs\r\n\r\ntic\r\nurlwrite('http://tinyurl.com/matlab-interferometer','Inter_zN.mat')\r\nload Inter_zN.mat\r\ntoc\r\n\r\nzN_orig=zN;\r\n%figure(44);surf(zN_orig,'LineStyle','none')\r\n\r\nzN_idx=find(~isnan(zN));\r\n\r\nfor i=1:20\r\n zN(zN_idx(randi(size(zN_idx,1))))=0;\r\nend\r\n%figure(45);surf(zN,'LineStyle','none')\r\n\r\ntic\r\nout = Fix_interferometer(zN);\r\ntoc\r\ndelta=abs(out-zN_orig);\r\n\r\nfprintf('Max delta=%e\\n',max(delta(:)))\r\n\r\n% Threshold based on a handful of test cases\r\nassert(max(delta(:))\u003c5e-7,sprintf('Max delta=%f\\n',max(delta(:))))\r\n\r\n\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":4,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":0,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-08-13T02:35:40.000Z","updated_at":"2026-03-29T19:25:41.000Z","published_at":"2012-10-13T21:05:14.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId3\",\"target\":\"/media/image3.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId4\",\"target\":\"/media/image4.JPEG\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId5\",\"target\":\"/media/image5.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to correct a 2-D Interferometer data set for drop-outs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInterferometer data is notorious for drop-outs which result in values of zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples of Pre and Post WFE drop-out correction:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esurf layed flat\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3-D surf\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eChallenge WFE :Pre Drop-Out insertion\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2-D square array representing surface z-values(NaNs in WFE array)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCorrupted data points(Drop-Outs) are represented by having the value 0.000\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003esame size 2-D square array as input with Drop-Outs corrected, approximately.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHint:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTriScatteredInterp allows for interpolation onto a grid given a non-grid set of (x,y,z) points.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFollow-Up:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Re-sampling to a grid, Convolving with an Optical Transfer Function and finally Calculating MTF\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" 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\"},{\"partUri\":\"/media/image2.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,/9j/4AAQSkZJRgABAgAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADCAPoDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigAooooAKKKKACiikzQAYo6dKp3eo2tkubidE9ATkn8K5vUPGixsyWkWeOHf/AArelh6lV+6jgxWZYbDfxJa9up15IHU1UuNUsrVcz3UKAddzgV5tfeI727XEl0wH91TtH6f1rDe6+boPxr0aWUTkrzdjw6vEybtRp39T1Cbxno0XHns5H91D/Os+f4gWif6qzmcY7kD/ABrz4zBv4qYZQFb6da7Y5RRW92cE+IMZLay+R27fEOUp8mnKpPQtNn+lMX4g3LcfY4c/75/wrz+a429O1QPfr7CuuOU0GtI/mR/aePlqp/kekD4hXKMu+xiK99rnNSD4korfPpxC9yJf/rV5ZJqny4JGPaq76mvr+tWskoPeP5m9PMMw6zv8kewQ/E3SW2ia2uoyepChgP1z+la9n4z0C94j1GJG/uy5Q/rXz82p+4xT4Z97feqKnD1Jq8W0d8M1xUVeaTPpuOVJV3o6sp6FWyDT818/6Zqt3pzeZaXEkTf7DcH6jofxrudJ8b6iuxLoR3C45bG1j+XH6V42IyetS1i7o6aef0Nqq5fxPR6KyLTX7O5C7nMTHoJBitVWVlypBB9K8mUXF2Z69HE0q8eanJMfRRRSNwooooAKKKKACiiigAooooAKKKKAEopCcLzWDqWubN0NrgsOC/YfShtdTmxOKp4aHPUZpXupW1kv72QBiOFHJNclqvie5ZWEJEEftyx/wqrM29mJJLE5JPesjUCNreuK9fA4aErOSufCZjn+Irz9nSfLHy3+8zrvUD8zEkk9STyaypdR396ZdB2buBmqJiPp+dfU0qMEjmpUYvWW5d+1D++c00vvbqarhPm4H1NOrXlXQ05EtiyG2+1I0wK9e1QM+3r+tV3fLe1ChdjjTuTO6been1rPuJ419M0krnbWVcvlsgHiuqlS1O2hQTe5JcTDb/8AXqg8p7E4prs/ccUIu/nmu+MFFHpwgoocrF1X1qxDI0T96riMryD3qzED0I4pTtYU7WNu0n39a6CwuNsisD0Ncvaso9a2bZX2qR0rysRBO54mKpp3O6trvzV+frWra309u2YZCvbHb8q4i0u3T7wrbg1BGXk18xjMu59YnlU61bCz5oM76x12Kb5LjEb+v8Na4YMMg5rzZL1G/i68Vq6frU9lx/rIumwnp9K8eeEqw3R9Tl/EsW1DEfedt+FBqnZX8F9Hvib6qeoq53rneh9bTqRqRUoO6YtFFFIsKKKKACiiigBKa7hRuJwBzmhmCqSeAOpNYOpXzTHZGcRj/wAerkxWLhh1q9XsjOrUVOLkyPUtRe4/dxsVizzj+KsaQqKlmb5az55tq08uoVcRPnmz4TN8fzSd9RssoWs25w3vntUdxdZ6nvVbz9zck19rRociVj5uMJN8zImtC/zbOPWoTZxr1HPWtJH3rtH3aeVDR8AZ710+0ktDZVpLRnPTQlfuCqskXHPX61vT23y9QKzZIHXoM/7R7V1U6qaO2lWTW5n+RnuTxzSmEBen41aWDZ1bryWqTywegI+ta+0NnU8zFni/+v71Ta13djW/NAnYgnPSqjqFXAA68+1bwrO2h006ztoYr2melAtAO1XX+831pUG5sV0e0djp9rKxUFqPSpY7L5uKvRxj/GtG3tt3asZ12kYVMS4op2+nh+1atvaPF06Vr2NgPvMKviyUNx930Iryq2NV7M8etjnJtGCwDL6VUkmki+7XSXGnxMvygBqyJrR4m2uOKKVWMiaVaEiG3vD68iulsZfNjXJrATT/AJtyDNb+nwmKPBrmxrh7NtGdfkbXLubFm7wzK8blWB6jv9a66wvBdQ/PgOOoFcrapzV6af7JHuRtsmMgjtX5ZmOdPCYlJap7o/Q+HcPUlTstjqKKytH1mLVoWAIWaM4kTPT3+latfQUasa0FOGzPcnCUJOMlZi0UUVqSJRRisvXdVTSNNe5ON+dqKe7Gs6lRU4OctkVCEpyUY7sdfXO7dCh/3qx5lNYVtq5eTJkJZjkk963oJluY+cZ9fWvzfE5jVli3WqbdPJHVjsrmqdupmyg1m3UZK7QK6GW39qpyW3tX3GT57Rikfm+Z5PWvddDkri0k+9jj3qFYCzV1Elv7ZqnJbDtxX3GGzGFZaHztT2lHSaM2GF1bA6VoJboq89fWgxbeFqUH5a1nNvY451HIpy243VUmtd64OcZzitGRgWpgI9RirjNouFSS1MiOxZpuRkA8CmzR7eNmPatTz0hkz1BP5VXnlt5Gzn8+1bRqSbu1odMas29Voc9eLGnIIz3rKlbD5x1rXvQPmAHBP3qyLpoui7mYdlFepR2R7OH1SKxcbsd6er7GqlPlWyM/jULzFWzvxmu1QutDvVK60N2KZFrYs7uNVX9K4db0+ZsJwa07O6LMvz9qwrYZtanPiMG2tT0KxvlZtvathWBXiuIspSNuDXVWExdVDenWvncdh0tUfP1qXs56Fp1DduagktfN+9V9F3U8xg14rzH2bsio4OUveTM6C18rirscVS+XuqWNa48bmjlDQ68NgL1LyLVoPu1Q12Qru5xgcVowcNWb4hQsuTyCK/MMyfPi1J9T9V4aSSUWcJH4gn0bVYb6Llom+Zf7ynqP8969p0XV7bXdJt9RtCTFMuQD1U9wfQg8V8+638u7+VdH8JvFKWOsy6FdTkQ3ZDW+9vlSQdQP94fqPevrMoq8seToz7PPMthUwqxFNe8vy/4B7jRSClr3z4gSvHfiD4jN9r32CBx5Fp8pIP3nPX8v8a9R1vUV0nRbu/faRDEzgMcZIHA/E183m9kurqS4lctJKxZmPck5NeZmc3yezXU+o4ZwKrVZV5LSO3qzp9NuyzZJPX1rtdPvAI1z0rzuwBbac85rrrF/3a96+Ix1JM9rMaEWdpBOsq+tPkj3Vj2LvW3HkR/NzXixlKjP3WfIYzDQd0zPmhrPlj21sTVmTiv0Th7G1JWTZ+cZ5hIRu0jNmO1aoG529Ku3P3fr+lY06hWznp+tfpdCKlFXPjqUE20yaS4/Cqk10V4z+tVZrrb9az5bnPeu+nQud9LD36F6a9DdzVBruVuF5Hqah8wFqeBziuqNOMUdkacYLYCzuuDkn27VTmgkRmEXU8kntWzDEG/lSSxBGzjPvQqiTshxrcrsjm5dPk8vLP8AN1wK5+7llik8t+nbiu1u5E21yV/F5srSNxjpXo4Wo5P3j18FV537yMtLkozIc9cg1qadc571hTHLM68DoM1f0ond3ODXoVYJwuepXppwbO902bO2uy0piy1xGkIzba7vS4SkeT1NfJZk1FO58TmCSlZGvGKsKtRRLVpEr80zGuoybTPVwNFyitBBHTlTBqULUipXzdXHNJ3eh79LBptWQsaVn67/AKvb7c1qcRRsx7CsPVLgTK3P/wBavnKlV1qyfRH2OUYd02mkeX67/F9TXIStJBcx3ELFZY3Dow7EHIP5122uRnc2enqK5C5T5mr67AytFWP0mNNVcNyn0t4L8SReKfDNrqK4E2PLuEBzskHUfyI9jXRV4x8EdXKz6jor52kfao/Y8K3/ALL3r2evqKU+eKZ+Y4/D/V8RKmjgPi5f/ZvB4tw7K1zOqfL3A+Yg/lXhsJ+bAr1b423TpBo9tkeU7SyMMc5AUD/0I15HC3zda8vHa1D7rheKjg13bf8Al+h0+lHDLXX2DgMvvXC6fJt5z3rpbK92t64r5nGU3Jux14+i5N2O+sl+7xWuflj/AArmdOui+0k4H1rpAd8P4V83Ujy1VzHxGYQlFMrymqE/3avSD5apyrX22TSjFp3PzzNouSaMa8QnkcYrAuWb5q6uWP72ay7m0XbwOP5V+o4HExlBHxF/ZTakcVqEro1Z3nnucH0rotRst3QVgXFo6djn6V9HQnCSR7mGnCUUhyS7utXITnb7VnRwye9aluhXqOfpVVLJaFVrJaGlaqflPTBzzUkzheg4606BDtp0sBZea89yXMeW5Lm1Ma78tl6AHrXNahEX3Z5C/wAI7118tlv7VmT6aMYxzmu/D1YxZ6mFxEYPc4l7J9y4XLdh2FdDpOmybV+Qe3HX3rbj0YPIvy849K6Gw0jb0AHHFXicxXLY1xearksiHRrA/LkfpXWW8WxVAqC1tPK/HritSGKvhs5zJJNJnk4alPE1eZrQfElWkSkjjqwq1+X5jj7t2Z9xgcFZK6GqtSKKULTuFXJr56riLo92lh7NIiuji3YeorlL843cfWukuJyyt6Vz9ym9+cc1OG0d2fRYGPJucXqkJbdjv1rkby3KSV6TfWm5W4Fcve2Q3c19Ng8QkrH2GCxCceVkHgC/fR/G2nTYOyZ/s7jHZ+P54NfSg6V8xW8MkGp2s0RKukyFWHYgjBr6bX7o+lfU4Cp7SDXY+V4poxjXhUj1X5f8OeOfHXKnQ2wcfvhnHH8FeRQy17n8cLRpvB9rcK4C292pYHvkMP614EjbWpYqF53OzIcQ1hkl0N61m29+K6OwcN/H36Vx1tL81bVnc7WXHXPNeNiKV07H1c17WndHpWlsW212Fnn7P+lcJoM+/bk54rvbUf6PnPWvj8cuWdj4bNo8raZHKKqvVyUVUcGvo8sn7qufnGYQ1dipItUpY81pOtVmjr7nLcYoqzZ8dj8M27pGFdxbuNnSsiWwL8lePUV1klvv6igWi7duyvpqeYU4RTucFOdWGiRx0OnN5nCcZrSg0x2Zfl49a6FbRF6IB+FSC3qKmcU+jNpPET2RmpYIq082Qb8q01gpTBXmyziCdriWBrtXZhzWA28fpVT+yXLZ2dfUV0/2Ymni19qbz+lBWua08FitkYcOnhdu8dPStCGEIvFXDbUiwmuatnMKsXZ2GsvqqSc1cI1q9FHUCRbavQpXxWc46ybiz6rKsG7pNEkaVJtHrSgbarSyhdxzzmvhalaVSbaZ9tQwySSsTNKqr/jUTzoy1TeXcvU8Um8t0FQodzujRSFd06E1kXUgEnArRmh+XjrVF4dzc/erqpWWp20eValCcF16dawrq13Sc/pXWSWxC8/hVN7HdzjOa7KNdRPRoYlQOZt7ENcR/J/Gp+nIr3dfuj6V5jb6cvnKeBggj869OHQV9fw/X51U+R4ue1/bSh5X/QwPG2lrrPgzVbMpl2t2aP8A31GV/UV8mqdrV9pHmvlX4g+HP+Ec8Y3lpGrLbyHz4MnPyN/gQR+FeziY6XJyWs1KVP5mJbye9bFrKFbj0rAiG2tO1c15VaCaPvcDUbXIzudE1Ioy545r0bTNVRoVV8kGvINPO1txNdlpl1tXqc+lfMZhhlJ3R5ma4KNS7PQ8JKu5DmoWi9qybC+f+/29K2o50defzrzKeKqYd26HwmNypN6Fcxe1J9nq/sU+lIY9vSu2OcySsnY8eWUxvqih9lHpTDBjtWkFpGjGK3p55VvaTMJ5PTaukZwiFPWMVa2Cm7RXT/aUqibTOdZeoPVDBEKXyRUgFSqtefWxtSOtzvpYSEtLFfycUhj9qt7KMpXIs0kt9Tp/s2L20KnlUnl4q7tWm+XWkc0b6kSy1LoVlT1qwg9KQgU5SKwxOIdVXNcPQVN2I55Cq8fSqcg37f1NaLKrdaaI0XsOetefF22R7EasYorRW4ZuRkAdamNsm7rUhYL04FRPJ710U8PUqPQ5a2OUNbimCNuOahNkqtwKd5tHn+9dLy6t9k5oZrFL3mRTWm5enNVlsn6447VorOO/8qUzjtWawuIT5bHUs1pqN7hYaerXMZbHynJH0rpMVl6SN5kmP0H9a1a+/wAgwcsPhff3k7nNUruv7wV5v8XfC76xoMepWsDSXdiSWCKMtEfve5xgEY9+K9IprKGUqQCCMEGvcnFSjYrD1pUKsakeh8eJHj6Vcgwtdj8RvCB8Pa4Z7WIjTrti0RHRG/iX29R7cdq5GOOvErJwbiz9Ry6pCvTjVp7M3LIo61vWspiZcDiuesTs4610VtGZFXFeJibX12Hi7X12Ols5kdV2v8wrXtLjPBOfauXtkeJuvNbtiBL7GvCxFNHzWJpJXZ0dsfxz+lWTWfaJIGw/6Vdc15ihedkfPYlqGo4kUxnqJ3qIy46V6NDATkrrU8mtjYxdticmmZqEzUCQe1ejHBVIq9jgljISdrlgGpFeqnmU5Zayq4CclexrSxsIu1y3voJH0+lVvMFIZa4f7Nm3sdn9oRS3LYIprNVfzqb51XDLJ31RE8xg1uTlhSB6qtNTfPr0oZVNxWh588ygnuXfMpDJVEz5pTPVrJp3V0S82g07MneTFQPNULy+9U57jauf1r6LLslc2lY8LHZtyp2ZZa4296jN0PWsK71dIujVjS6427h/1r7PD8Owa1R4f1vFVXeOx24u/en28r3NzHBHyXOPp6mvO5fEZUff7eteoeBtNmXS11G7z51yoaNGHKJ/9frUYzJMPhYc8lq9j08voY3E1lGTtFbnU28Igt0jXoABU2aBRxXmqy0PvErKyFooopjMrXdFtfEGkT6ddA+XKOGHVSOQR7g18+634cu/DuptY3idyYpB0kX1H9fQ19LVj6/4fsvENg1tdxjcOY5APmjb1B/zmuTFYf2sbrdHt5NnE8BPllrB7/5nz/BgbV/PNdJpv8Oaq6t4fvtAu/JvIuCfkmUfI30Pb6HmnWbFO+c18li4SV4yVmfa1asK9Pnpu6Z1FvAr85ya07WERbWz34rKsZ0ZPpW/Yoku3PTtmvnq7cb3PnMVJxvfY04XBj464pkrVL8sa7QMCo2Aas8HFKXM9j5bHS5laJUkJ7VA7mrbxVA0R9K+xwVeikrnyOLo1buxX3Uoc1L5Ao+zGvUWJw1rM876tiL3RGHNOEtOMBqNoitClhqmiYOOIp6tC+caaZ6iZDTCDXoUsFQlZnFUxdeOhOZjSedUJFNNdEcBReyOeWMqrqTtKaiaaonfavWqEt7tWvQw2WRa2OSrjKk3aJpefQ1xjvXNT6ttkb5sVRn1s7eH7etenHJ4Sa0KhHEyWj3OmudRSIdcmud1TxBtXaCPp6VgXetsd3P61zGo6sW/j717mCymMWtD0cLlcqkk56m3fa4Nv3++etc7c66+5sGsS5vHkbJJxXpXw6+Et9r9xBq2uRNbaSDuWF8rJcemB2XPUnkjgDvXpYmvhcBT5qm59ThsshFWsa/wt8EXWvXK67rMLpp8TBraJ14uDzyQf4Rx9T7V70Bio4IY4IY4okVI0UKiqMBQOgAqWvzvMMdUxlZ1J7dF2PapUo01aKFoooriNQooooAKKKKAKOp6Xa6tZNa3cW+Nu2eQfUe9eXaz4Mu9DaS4iJuLMHIdR8yD/aH9RXr2RSEBuD0rjxeDp4iNno+534LMa2EdoO8ex4xYyKu3cTmujsroI3y5Nb2qeDLG8ZpbUfZpuvy/dP4VzM+kX1g2JY2RQcBgcg/jXxWZZXVo3clddz3FjKGLW9n2Z0kd7E67XOD2qUoG+YciuYjkZe+TWpa3bqvB/PvXg8sqTvFnnYnL4yV0aBBpNtEVyH69fpVgoD0rWOMs7NWPHqYBxK/lCjy9tSEbajZ66qdSpN6O6OKcKcFqtRjVGy09nqJpQteth6dRtcqPMrzp68zGMoqBgKe84qs01fT4LDV3a6PnsZiKK2YjGoX96ZNdIi9ayLzVtu5VIx3NfWYTBTbTaPAqSdV2gizeXkaLjdXN3mqbOlULzUt7tl/1rAvr0tu5r6jC4La56WDy/a5bvdTz35z2rJn1Pb1aqMtyW71lTzbm2e+M17lLDRitT6TD4KK0sX7jUQ3eqEcV1qV5HbWsDz3ErbUjjUlmJ7AV2/hH4Ta94oEd1Ov9n6e4ys0q5Z+n3V6854J44r3rwt4B0Dwim7TbTN0ww91Md0h9cHsPYYFeNmWf4fCxdOj70j2aGFUTzfwH8EXtbqPVPFJjZk2vFYxtuAbg/vGHBx6DI969wxxS0lfB4rFVcTPnqu7O6KSVkLRRRXOMKKKKACiiigAooooAKKKKACo3jR12uoYHggjrUlFJxTVmBhXPhu0lO6EtEf7o5X8qzZdDurds43r2KV11FeNisiwuIu0uV+X+R1QxlWCte6OVitnRuU6etWshV21uSQxyj51BqpLpcT/dZkJ9DXzlbhSup3hNNfcKpipSWi1Ml2FQOwrRl0ab/lnKp+oqk+kX46Kje4auzC5FWp/EeDjKlbpC5Tkf3qrI1WptM1JFz9mY/Qg/1rPmstT/AIbGdj7LX1mX5ao2cmkfJ46WJk7Rpy+5jJJcd6y7i927vmp8+ma/O21dMn5OBnA/rVZ/CPiec7P7OC5H3nmTA/Ik/pX1VCGHpJJzX3o8+nlWLqu8oP7jHvNSPzZeua1DVfvDf9cGuxHwt8UXn+tnsbZSSDukZiPwAx+tWbL4FhpFfVNedxkFo7aELnnkbiSf0FerTzDL6KvKd/TU+iweSTVnJWPIrnVgn8ROfQ1FZ22q65cLDpdjc3Tk7cRISAfc9B+JFfRWnfB/wdpzMW057tiBzdSl8fQDArt4LaC1j2QQRxL/AHUUKKxrcU0oK2Hp6+Z71LAQhufPegfA/wAQalGs2sXcemRHnywPMl/EAgD8z9K9Y8NfDTw14X2yW1l9ouhz9pucO4+nGB+AFdjRXz2MznGYvSpKy7LRHbGlGOyACloorzDQKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAENJRRQiXuFOoooGgooooGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q==\"},{\"partUri\":\"/media/image3.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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\"},{\"partUri\":\"/media/image4.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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Non-Co-Planar Points in an N-Cube","description":"This Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\r\n\r\n  N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\n  N=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\r\nOutput is a Qx3 matrix of the non-co-planar points.\r\n\r\nReference: The \u003chttp://68.173.157.131/Contest/Tetrahedra March 2016 Al Zimmermann Non-Coplanar contest\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\r\n\r\nTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003chttp://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar Cody Co-Planar Check\u003e may improve speed. ","description_html":"\u003cp\u003eThis Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eN=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\u003c/pre\u003e\u003cp\u003eOutput is a Qx3 matrix of the non-co-planar points.\u003c/p\u003e\u003cp\u003eReference: The \u003ca href = \"http://68.173.157.131/Contest/Tetrahedra\"\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/a\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\u003c/p\u003e\u003cp\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar\"\u003eCody Co-Planar Check\u003c/a\u003e may improve speed.\u003c/p\u003e","function_template":"function m=MaxNonCoplanarPts(N,Q);\r\n% Place Q or more points in an 0:N-1 #D grid such that each plane created uses only 3 points from the set provided\r\n% N is Cube size\r\n% Q is expected number of points in solution\r\n% Hint: Point [0,0,0] can be assumed to be in the solution for N=2 and N=3\r\n% N=3 is the 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n  m=[];\r\nend","test_suite":"%%\r\nN=2;\r\nQ=5;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\r\n%%\r\nN=3;\r\nQ=8;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-03-07T00:22:02.000Z","updated_at":"2016-03-07T01:01:33.000Z","published_at":"2016-03-07T01:01:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar. Given the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput is a Qx3 matrix of the non-co-planar points.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReference: The\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://68.173.157.131/Contest/Tetrahedra\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody Co-Planar Check\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may improve speed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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