![]() Note that in the latter two cases is a one-dimensional vector, and should be reshaped back into a matrix if necessary (for example, using reshape). ![]() To plot the given matrixs color map, you can use the mesh() function. For instance, if we have: A = Īnd we want to extract A(, ) using logical indexing, we can do either this: Ir = logical() The color order controls the set of colors that MATLAB uses for plotting multiple. The subscript vector must be either of the same dimensions as the original matrix or a vector with the same number of elements. In logical indexing the subscripts are binary, where a logical 1 indicates that the corresponding element is selected, and 0 means it is not. you can consider ax0 has random values assigned in some specific rows and column and rest are zeros. the code in MATLAB is a following : m, nsize(Im1) m961 n220 a ax0 ax0 is a matrix of 961 by 220. For example, if you want to convert the subscripts in matrix A (corresponding to element 30) into a linear index, you can write sub2ind(size(A), 1, 3) (the result in this case should be 7, of course). All I want from the Python code is to perform the same function as Matlab. The resulting matrix is, however always of the same dimensions as the subscript matrix.įor instance, if I =, then A(I) is the same as writing reshape(A(I(:)), size(I)).Ĭonverting from matrix subscripts to linear indices and vice versa:įor that you have sub2ind and ind2sub, respectively. The subscript matrix is simply converted into a column vector, and used for linear indexing. It is also possible to use another matrix for linear indexing. For that reason, A(:) converts any matrix A into a column vector. The special colon and end subscripts are also allowed, of course. ![]() A matrix is a two-dimensional array often used for linear algebra. The equivalent column vector is: A = [10 All MATLAB variables are multidimensional arrays, no matter what type of data. ![]() For instance, we have: A = Īnd we want to compute b = A(2). Linear indexing treats any matrix as if it were a column vector by concatenating the columns into one column vector and assigning indices to the elements respectively. This is especially useful for large matrices.
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