Lester,

I believe an endfunction clause is necessary to terminate the function definition. Try adding one at the end Regards, Federico Miyara On 2/1/2024 13:28, Lester Anderson wrote: Hello, I have tried to convert the following Matlab code as a whole and even a very basic function, but it fails to generate anything! function [x0,y0,iout,jout] = intersections(x1,y1,x2,y2,robust) %INTERSECTIONS Intersections of curves. % Computes the (x,y) locations where two curves intersect. The curves % can be broken with NaNs or have vertical segments. % % Example: % [X0,Y0] = intersections(X1,Y1,X2,Y2,ROBUST); % % where X1 and Y1 are equal-length vectors of at least two points and % represent curve 1. Similarly, X2 and Y2 represent curve 2. % X0 and Y0 are column vectors containing the points at which the two % curves intersect. % % ROBUST (optional) set to 1 or true means to use a slight variation of the % algorithm that might return duplicates of some intersection points, and % then remove those duplicates. The default is true, but since the % algorithm is slightly slower you can set it to false if you know that % your curves don't intersect at any segment boundaries. Also, the robust % version properly handles parallel and overlapping segments. % % The algorithm can return two additional vectors that indicate which % segment pairs contain intersections and where they are: % % [X0,Y0,I,J] = intersections(X1,Y1,X2,Y2,ROBUST); % % For each element of the vector I, I(k) = (segment number of (X1,Y1)) + % (how far along this segment the intersection is). For example, if I(k) = % 45.25 then the intersection lies a quarter of the way between the line % segment connecting (X1(45),Y1(45)) and (X1(46),Y1(46)). Similarly for % the vector J and the segments in (X2,Y2). % % You can also get intersections of a curve with itself. Simply pass in % only one curve, i.e., % % [X0,Y0] = intersections(X1,Y1,ROBUST); % % where, as before, ROBUST is optional. % Version: 2.0, 25 May 2017 % Author: Douglas M. Schwarz % Email: dmschwarz=ieee*org, dmschwarz=urgrad*rochester*edu % Real_email = regexprep(Email,{'=','*'},{'@','.'}) % Theory of operation: % % Given two line segments, L1 and L2, % % L1 endpoints: (x1(1),y1(1)) and (x1(2),y1(2)) % L2 endpoints: (x2(1),y2(1)) and (x2(2),y2(2)) % % we can write four equations with four unknowns and then solve them. The % four unknowns are t1, t2, x0 and y0, where (x0,y0) is the intersection of % L1 and L2, t1 is the distance from the starting point of L1 to the % intersection relative to the length of L1 and t2 is the distance from the % starting point of L2 to the intersection relative to the length of L2. % % So, the four equations are % % (x1(2) - x1(1))*t1 = x0 - x1(1) % (x2(2) - x2(1))*t2 = x0 - x2(1) % (y1(2) - y1(1))*t1 = y0 - y1(1) % (y2(2) - y2(1))*t2 = y0 - y2(1) % % Rearranging and writing in matrix form, % % [x1(2)-x1(1) 0 -1 0; [t1; [-x1(1); % 0 x2(2)-x2(1) -1 0; * t2; = -x2(1); % y1(2)-y1(1) 0 0 -1; x0; -y1(1); % 0 y2(2)-y2(1) 0 -1] y0] -y2(1)] % % Let's call that A*T = B. We can solve for T with T = A\B. % % Once we have our solution we just have to look at t1 and t2 to determine % whether L1 and L2 intersect. If 0 <= t1 < 1 and 0 <= t2 < 1 then the two % line segments cross and we can include (x0,y0) in the output. % % In principle, we have to perform this computation on every pair of line % segments in the input data. This can be quite a large number of pairs so % we will reduce it by doing a simple preliminary check to eliminate line % segment pairs that could not possibly cross. The check is to look at the % smallest enclosing rectangles (with sides parallel to the axes) for each % line segment pair and see if they overlap. If they do then we have to % compute t1 and t2 (via the A\B computation) to see if the line segments % cross, but if they don't then the line segments cannot cross. In a % typical application, this technique will eliminate most of the potential % line segment pairs. % Input checks. if verLessThan('matlab','7.13') error(nargchk(2,5,nargin)) %#ok<NCHKN> else narginchk(2,5) end % Adjustments based on number of arguments. switch nargin case 2 robust = true; x2 = x1; y2 = y1; self_intersect = true; case 3 robust = x2; x2 = x1; y2 = y1; self_intersect = true; case 4 robust = true; self_intersect = false; case 5 self_intersect = false; end % x1 and y1 must be vectors with same number of points (at least 2). if sum(size(x1) > 1) ~= 1 || sum(size(y1) > 1) ~= 1 || ... length(x1) ~= length(y1) error('X1 and Y1 must be equal-length vectors of at least 2 points.') end % x2 and y2 must be vectors with same number of points (at least 2). if sum(size(x2) > 1) ~= 1 || sum(size(y2) > 1) ~= 1 || ... length(x2) ~= length(y2) error('X2 and Y2 must be equal-length vectors of at least 2 points.') end % Force all inputs to be column vectors. x1 = x1(:); y1 = y1(:); x2 = x2(:); y2 = y2(:); % Compute number of line segments in each curve and some differences we'll % need later. n1 = length(x1) - 1; n2 = length(x2) - 1; xy1 = [x1 y1]; xy2 = [x2 y2]; dxy1 = diff(xy1); dxy2 = diff(xy2); % Determine the combinations of i and j where the rectangle enclosing the % i'th line segment of curve 1 overlaps with the rectangle enclosing the % j'th line segment of curve 2. % Original method that works in old MATLAB versions, but is slower than % using binary singleton expansion (explicit or implicit). % [i,j] = find( ... % repmat(mvmin(x1),1,n2) <= repmat(mvmax(x2).',n1,1) & ... % repmat(mvmax(x1),1,n2) >= repmat(mvmin(x2).',n1,1) & ... % repmat(mvmin(y1),1,n2) <= repmat(mvmax(y2).',n1,1) & ... % repmat(mvmax(y1),1,n2) >= repmat(mvmin(y2).',n1,1)); % Select an algorithm based on MATLAB version and number of line % segments in each curve. We want to avoid forming large matrices for % large numbers of line segments. If the matrices are not too large, % choose the best method available for the MATLAB version. if n1 > 1000 || n2 > 1000 || verLessThan('matlab','7.4') % Determine which curve has the most line segments. if n1 >= n2 % Curve 1 has more segments, loop over segments of curve 2. ijc = cell(1,n2); min_x1 = mvmin(x1); max_x1 = mvmax(x1); min_y1 = mvmin(y1); max_y1 = mvmax(y1); for k = 1:n2 k1 = k + 1; ijc{k} = find( ... min_x1 <= max(x2(k),x2(k1)) & max_x1 >= min(x2(k),x2(k1)) & ... min_y1 <= max(y2(k),y2(k1)) & max_y1 >= min(y2(k),y2(k1))); ijc{k}(:,2) = k; end ij = vertcat(ijc{:}); i = ij(:,1); j = ij(:,2); else % Curve 2 has more segments, loop over segments of curve 1. ijc = cell(1,n1); min_x2 = mvmin(x2); max_x2 = mvmax(x2); min_y2 = mvmin(y2); max_y2 = mvmax(y2); for k = 1:n1 k1 = k + 1; ijc{k}(:,2) = find( ... min_x2 <= max(x1(k),x1(k1)) & max_x2 >= min(x1(k),x1(k1)) & ... min_y2 <= max(y1(k),y1(k1)) & max_y2 >= min(y1(k),y1(k1))); ijc{k}(:,1) = k; end ij = vertcat(ijc{:}); i = ij(:,1); j = ij(:,2); end elseif verLessThan('matlab','9.1') % Use bsxfun. [i,j] = find( ... bsxfun(@le,mvmin(x1),mvmax(x2).') & ... bsxfun(@ge,mvmax(x1),mvmin(x2).') & ... bsxfun(@le,mvmin(y1),mvmax(y2).') & ... bsxfun(@ge,mvmax(y1),mvmin(y2).')); else % Use implicit expansion. [i,j] = find( ... mvmin(x1) <= mvmax(x2).' & mvmax(x1) >= mvmin(x2).' & ... mvmin(y1) <= mvmax(y2).' & mvmax(y1) >= mvmin(y2).'); end % Find segments pairs which have at least one vertex = NaN and remove them. % This line is a fast way of finding such segment pairs. We take % advantage of the fact that NaNs propagate through calculations, in % particular subtraction (in the calculation of dxy1 and dxy2, which we % need anyway) and addition. % At the same time we can remove redundant combinations of i and j in the % case of finding intersections of a line with itself. if self_intersect remove = isnan(sum(dxy1(i,:) + dxy2(j,:),2)) | j <= i + 1; else remove = isnan(sum(dxy1(i,:) + dxy2(j,:),2)); end i(remove) = []; j(remove) = []; % Initialize matrices. We'll put the T's and B's in matrices and use them % one column at a time. AA is a 3-D extension of A where we'll use one % plane at a time. n = length(i); T = zeros(4,n); AA = zeros(4,4,n); AA([1 2],3,:) = -1; AA([3 4],4,:) = -1; AA([1 3],1,:) = dxy1(i,:).'; AA([2 4],2,:) = dxy2(j,:).'; B = -[x1(i) x2(j) y1(i) y2(j)].'; % Loop through possibilities. Trap singularity warning and then use % lastwarn to see if that plane of AA is near singular. Process any such % segment pairs to determine if they are colinear (overlap) or merely % parallel. That test consists of checking to see if one of the endpoints % of the curve 2 segment lies on the curve 1 segment. This is done by % checking the cross product % % (x1(2),y1(2)) - (x1(1),y1(1)) x (x2(2),y2(2)) - (x1(1),y1(1)). % % If this is close to zero then the segments overlap. % If the robust option is false then we assume no two segment pairs are % parallel and just go ahead and do the computation. If A is ever singular % a warning will appear. This is faster and obviously you should use it % only when you know you will never have overlapping or parallel segment % pairs. if robust overlap = false(n,1); warning_state = warning('off','MATLAB:singularMatrix'); % Use try-catch to guarantee original warning state is restored. try lastwarn('') for k = 1:n T(:,k) = AA(:,:,k)\B(:,k); [unused,last_warn] = lastwarn; %#ok<ASGLU> lastwarn('') if strcmp(last_warn,'MATLAB:singularMatrix') % Force in_range(k) to be false. T(1,k) = NaN; % Determine if these segments overlap or are just parallel. overlap(k) = rcond([dxy1(i(k),:);xy2(j(k),:) - xy1(i(k),:)]) < eps; end end warning(warning_state) catch err warning(warning_state) rethrow(err) end % Find where t1 and t2 are between 0 and 1 and return the corresponding % x0 and y0 values. in_range = (T(1,:) >= 0 & T(2,:) >= 0 & T(1,:) <= 1 & T(2,:) <= 1).'; % For overlapping segment pairs the algorithm will return an % intersection point that is at the center of the overlapping region. if any(overlap) ia = i(overlap); ja = j(overlap); % set x0 and y0 to middle of overlapping region. T(3,overlap) = (max(min(x1(ia),x1(ia+1)),min(x2(ja),x2(ja+1))) + ... min(max(x1(ia),x1(ia+1)),max(x2(ja),x2(ja+1)))).'/2; T(4,overlap) = (max(min(y1(ia),y1(ia+1)),min(y2(ja),y2(ja+1))) + ... min(max(y1(ia),y1(ia+1)),max(y2(ja),y2(ja+1)))).'/2; selected = in_range | overlap; else selected = in_range; end xy0 = T(3:4,selected).'; % Remove duplicate intersection points. [xy0,index] = unique(xy0,'rows'); x0 = xy0(:,1); y0 = xy0(:,2); % Compute how far along each line segment the intersections are. if nargout > 2 sel_index = find(selected); sel = sel_index(index); iout = i(sel) + T(1,sel).'; jout = j(sel) + T(2,sel).'; end else % non-robust option for k = 1:n [L,U] = lu(AA(:,:,k)); T(:,k) = U\(L\B(:,k)); end % Find where t1 and t2 are between 0 and 1 and return the corresponding % x0 and y0 values. in_range = (T(1,:) >= 0 & T(2,:) >= 0 & T(1,:) < 1 & T(2,:) < 1).'; x0 = T(3,in_range).'; y0 = T(4,in_range).'; % Compute how far along each line segment the intersections are. if nargout > 2 iout = i(in_range) + T(1,in_range).'; jout = j(in_range) + T(2,in_range).'; end end % Plot the results (useful for debugging). % plot(x1,y1,x2,y2,x0,y0,'ok'); function y = mvmin(x) % Faster implementation of movmin(x,k) when k = 1. y = min(x(1:end-1),x(2:end)); function y = mvmax(x) % Faster implementation of movmax(x,k) when k = 1. y = max(x(1:end-1),x(2:end)); Any ideas? 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