I have a matrix and for each element I want to get the index of its surrounding elements. All these results have to be stored into a matrix in the following way. Each row of the matrix corresponds to a matrix element and each of the columns of this matrix contain s the neighbor indexes. For example, for a 4x4 matrix we will get a 16x8 result array. Some of the matrix elements do not have 8 neighbors.
There is an example, I think it is working, I there any way to avoid for loop?:
ElementNeighbors = [];
for n = 1:numel(Matrix)
NeighborsMask = [ n-1 n+1 n+size(matrix,1) n-size(Matrix,1) n-size(Matrix,1)-1 n-size(Matrix,1)+1 ...
n+size(Matrix,1)-1 n+size(Matrix,1)+1 ];
ElementNeighbors = [ElementNeighbors ; NeighborsMask ];
end
ElementNeighbors (ElementNeighbors ==0|ElementNeighbors <0) = NaN;
Given the linear indices of a matrix M(n,m), you can convince yourself that the top left neighbor of element M(i,j) = M(i-1, j-1) = M(i-1 + n * (j-2))
In "linear index" space that means the offset of this element is
-n-1
Doing this for all other locations, we find
-n-1 | -1 | n-1
-n | x | n => [-n-1, -n, -n+1, -1, +1, +n-1, +n, +n+1]
-n+1 | +1 | n+1
Thus you can create a vector offset with the above values (replacing n with the first dimension). For example, if M is (5x4), then
offset = [-6 -5 -4 -1 1 4 5 6];
You then create all the indices:
indices = bsxfun(#plus, (1:m*n), offset(:));
bsxfun is a cool shorthand for "do this function on these elements; where one element has a singleton dimension and the other doesn't, expand accordingly". You could do the same with repmat, but that creates unnecessary intermediate matrices (which can sometimes be very large).
That command will create a (8 x m*n) matrix of indices of all 8 neighbors, including ones that may not really be the neighbors... something you need to fix.
Several possible approaches:
pad the matrix before you start
don't care about wrapping, and just get rid of the elements that fall off the edge
create a mask for all the ones that are "off the edge".
I prefer the latter. "Off the edge" means:
going up in the top row
going left in the left column
going down in the bottom row
going right in the right column
In each of these four cases there are 3 indices that are 'invalid'. Their position in the above matrix can be determined as follows:
mask = zeros(size(M));
mask(:,1) = 1;
left = find(mask == 1);
mask(:,end) = 2;
right = find(mask == 2);
mask(1,:) = 3;
top = find(mask == 3);
mask(end,:) = 4;
bottom = find(mask == 4);
edgeMask = ones(8,m*n);
edgeMask(1:3, top) = 0;
edgeMask([1 4 6], left) = 0;
edgeMask([3 5 8], right) = 0;
edgeMask(6:8, bottom) = 0;
Now you have everything you need - all the indices, and the "invalid" ones. Without loops.
If you were feeling ambitious you could turn this into a cell array but it will be slower than using the full array + mask. For example if you want to find the average of all the neighbors of a value, you can do
meanNeighbor = reshape(sum(M(indices).*edgeMask, 1)./sum(edgeMask, 1), size(M));
EDIT re-reading your question I see you wanted a M*N, 8 dimension. My code is transposed. I'm sure you can figure out how to adapt it...
ATTRIBUTION #Tin helpfully suggested many great edits to the above post, but they were rejected in the review process. I cannot totally undo that injustice - but would like to record my thanks here.
EXTENDING TO DIFFERENT REGIONS AND MULTIPLE DIMENSIONS
If you have an N-dimensional image matrix M, you could find the neighbors as follows:
temp = zeros(size(M));
temp(1:3,1:3,1:3) = 1;
temp(2,2,2) = 2;
offsets = find(temp==1) - find(temp==2);
If you want a region that is a certain radius in size, you could do
sz = size(M);
[xx yy zz] = meshgrid(1:sz(1), 1:sz(2), 1:sz(3));
center = round(sz/2);
rr = sqrt((xx - center(1)).^2 + (yy - center(2)).^2 + (zz - center(3)).^2);
offsets = find(rr < radius) - find(rr < 0.001);
You can probably figure out how to deal with the problem of edges along the lines shown earlier for the 2D case.
Untested - please see if you notice any problems with the above.
Related
I have a vector that has 1's in some places, and I want to create a diagonal using the vector. The vector is called one_vec_two:
n = 4;
one_vec_two = zeros(n*n, 1);
one_vec_two(1,1) = 1;
for k=0:(n-1)
one_vec_two(k*n+1, 1) = 1;
end
non_zero_vecs = [one_vec_two];
placement = [n-1];
A = spdiags(non_zero_vecs, placement, n*n, n*n);
fullA = full(A);
disp(A)
The first element of the vector one_vec_two is a 1:
>> one_vec_two(1)
ans =
1
And, I placed the vector starting at diagonal n-1, which is 3. But, when I go to column 4, I don't see it:
>> fullA(1,4)
ans =
0
Why isn't MATLAB putting my vector in the correct spot?
As per the doc for spdiag,
Note In this syntax, if a column of B is longer than the diagonal it is replacing, and m >= n, spdiags takes elements of super-diagonals from the lower part of the column of B, and elements of sub-diagonals from the upper part of the column of B.
it is placing the lower part of your vector into the location specified. Hence the result is as expected.
It looks like you want something like
A = spdiags(non_zero_vecs([end-placement+1:end 1:end-placement]), placement, n*n, n*n)
or
A = spdiags(non_zero_vecs, -placement, n*n, n*n)'
which both do the same thing, just in slightly different ways.
I'm currently working on creating a histogram of Altitudes at which a type of atmospheric instability happens. To be specific, it is when the values of what we call, N^2 is less than zero. This is where the problem comes in. I am trying to plot the occurrence frequency against the altitudes.
load /data/matlabst/DavidBloom/N_square_Ri_number_2005.mat
N_square(N_square > 0) = 0;
N_square = abs(N_square);
k = (1:87);
H = 7.5;
p0 = 101325;
nbins = (500);
N_square(N_square==0)=[];
Alt = zeros(1,578594);
PresNew = squeeze(N_square(:,:,k,:));
for lati = 1:32
for long = 1:64
for t = 1:1460
for k = 1:87
Alt(1,:) = -log((PresNew)/p0)*H;
end
end
end
end
So, let me explain what I am doing. I'm loading a file with all these different variables. Link To Image This shows the different variables it displays. Next, I take the 4-D matrix N_square and I filter all values greater than zero to equal 0. Then I take the absolute value of the leftover negative values. I then define several variables and move on to the next filtering.
(N_square(N_square==0)=[];
The goal of this one was give just discard all values of N_square that were 0. I think this is where the problem begins. Jumping down to the for loop, I am then taking the 3rd dimension of N_square and converting pressure to altitude.
My concern is that when I run this, PresNew = squeeze(N_square(:,:,k,:)); is giving me the error.
Error in PlottingN_2 (line 10)
PresNew = squeeze(N_square(:,:,k,:));
And I have no idea why.
Any thoughts or suggestions on how I could avoid this catastrophe and make my code simpler? Thanks.
When you remove random elements from a multi-dimensional array, they are removed but it can no longer be a valid multi-dimensional array because it has holes in it. Because of this, MATLAB will collapse the result into a vector, and you can't index into the third dimension of a vector like you're trying.
data = magic(3);
% 8 1 6
% 3 5 7
% 4 9 2
% Remove all values < 2
data(data < 2) = []
% 8 3 4 5 9 6 7 2
data(2,3)
% Index exceeds matrix dimensions.
The solution is to remove the 0 values after your indexing (i.e. within your loop).
Alt = zeros(1,578594);
for lati = 1:32
for long = 1:64
for t = 1:1460
for k = 1:87
% Index into 4D matrix
PresNew = N_square(:,:,k,:);
% NOW remove the 0 values
PresNew(PresNew == 0) = [];
Alt(1,:) = -log((PresNew)/p0)*H;
end
end
end
end
I am working with a n x 1 matrix, A, that has repeating values inside it:
A = [0;1;2;3;4; 0;1;2;3;4; 0;1;2;3;4; 0;1;2;3;4]
which correspond to an n x 1 matrix of B values:
B = [2;4;6;8;10; 3;5;7;9;11; 4;6;8;10;12; 5;7;9;11;13]
I am attempting to produce a generalised code to place each repetition into a separate column and store it into Aa and Bb, e.g.:
Aa = [0 0 0 0 Bb = [2 3 4 5
1 1 1 1 4 5 6 7
2 2 2 2 6 7 8 9
3 3 3 3 8 9 10 11
4 4 4 4] 10 11 12 13]
Essentially, each repetition from A and B needs to be copied into the next column and then deleted from the first column
So far I have managed to identify how many repetitions there are and copy the entire column over to the next column and then the next for the amount of repetitions there are but my method doesn't shift the matrix rows to columns as such.
clc;clf;close all
A = [0;1;2;3;4;0;1;2;3;4;0;1;2;3;4;0;1;2;3;4];
B = [2;4;6;8;10;3;5;7;9;11;4;6;8;10;12;5;7;9;11;13];
desiredCol = 1; %next column to go to
destinationCol = 0; %column to start on
n = length(A);
for i = 2:1:n-1
if A == 0;
A = [ A(:, 1:destinationCol)...
A(:, desiredCol+1:destinationCol)...
A(:, desiredCol)...
A(:, destinationCol+1:end) ];
end
end
A = [...] retrieved from Move a set of N-rows to another column in MATLAB
Any hints would be much appreciated. If you need further explanation, let me know!
Thanks!
Given our discussion in the comments, all you need is to use reshape which converts a matrix of known dimensions into an output matrix with specified dimensions provided that the number of elements match. You wish to transform a vector which has a set amount of repeating patterns into a matrix where each column has one of these repeating instances. reshape creates a matrix in column-major order where values are sampled column-wise and the matrix is populated this way. This is perfect for your situation.
Assuming that you already know how many "repeats" you're expecting, we call this An, you simply need to reshape your vector so that it has T = n / An rows where n is the length of the vector. Something like this will work.
n = numel(A); T = n / An;
Aa = reshape(A, T, []);
Bb = reshape(B, T, []);
The third parameter has empty braces and this tells MATLAB to infer how many columns there will be given that there are T rows. Technically, this would simply be An columns but it's nice to show you how flexible MATLAB can be.
If you say you already know the repeated subvector, and the number of times it repeats then it is relatively straight forward:
First make your new A matrix with the repmat function.
Then remap your B vector to the same size as you new A matrix
% Given that you already have the repeated subvector Asub, and the number
% of times it repeats; An:
Asub = [0;1;2;3;4];
An = 4;
lengthAsub = length(Asub);
Anew = repmat(Asub, [1,An]);
% If you can assume that the number of elements in B is equal to the number
% of elements in A:
numberColumns = size(Anew, 2);
newB = zeros(size(Anew));
for i = 1:numberColumns
indexStart = (i-1) * lengthAsub + 1;
indexEnd = indexStart + An;
newB(:,i) = B(indexStart:indexEnd);
end
If you don't know what is in your original A vector, but you do know it is repetitive, if you assume that the pattern has no repeats you can use the find function to find when the first element is repeated:
lengthAsub = find(A(2:end) == A(1), 1);
Asub = A(1:lengthAsub);
An = length(A) / lengthAsub
Hopefully this fits in with your data: the only reason it would not is if your subvector within A is a pattern which does not have unique numbers, such as:
A = [0;1;2;3;2;1;0; 0;1;2;3;2;1;0; 0;1;2;3;2;1;0; 0;1;2;3;2;1;0;]
It is worth noting that from the above intuitively you would have lengthAsub = find(A(2:end) == A(1), 1) - 1;, But this is not necessary because you are already effectively taking the one off by only looking in the matrix A(2:end).
I would like to generate a set of all 1 matrices given a matrix of sizes, (dimensions in this example), but I have been having a hard time making the dimensions matrix return a size vector that ones can work with.
My first instinct is: dimensions(:,:)
I read this (incorrectly) as return a matrix of size vectors [x,y]
But this doesn't seem to work -- Is there any way to use dimensions to produce a matrix of size vectors?
I am tempted to use a loop to iterate, for i = 1 to 3 dimensions(i,:) , but I was wondering if this is the only way.
Code:
clear;
%3x2
dimensions = [32,40; %32x40 box of ones
20, 30; %20x30 box of ones
60, 10; %60x10 box of ones
];
Onesboxes = ones(dimensions(1,:));
%this works, but I really want OnesBoxes to be an array such that:
%OnesBoxes(1) = 32x40 box of ones
%OnesBoxes(2) = 20x30 box of ones
%OnesBoxes(3) = 60x10 box of ones
% if I try:
OnesBoxes = ones(dimensions);
%Error using ones: Size vector should be a row vector with real elements.
%what I want to do is pass in sizes as rows in dimensions
%passing in the size of the ones array as a single vector works:
%onessize dimensions: 1x2
onessize = [4,2];
%tTestOnes dimensions: 4x2
tTestOnes = ones(onessize);
%making dimensions a 2x3 matrix instead doesn't seem to make a difference
%(I was thinking that maybe matlab thinks of matricies as an array of
%columns instead of arrays of rows?)
%dimensions2 = [32,20,60; 40,30,10];
%tOnesBoxes2 = ones(dimensions2);
EDIT: Outputs:
Output of
onessize = [4,2];
%tTestOnes dimensions: 4x2
tTestOnes = ones(onessize);
is a 4x2 array of all ones
Output of
dimensions = [32,40; %32x40 box of ones
20, 30; %20x30 box of ones
60, 10; %60x10 box of ones
];
Onesboxes = ones(dimensions(1,:));
Is a 32x40 array of all ones
Output of
dimensions = [32,40; %32x40 box of ones
20, 30; %20x30 box of ones
60, 10; %60x10 box of ones
];
OnesBoxes = ones(dimensions);
Is an error
Error using ones Size vector should be a row vector with real
elements.
If you are okay with having a for loop, the simplest way I can think of is:
dimensions = [32,40; %32x40 box of ones
20, 30; %20x30 box of ones
60, 10; %60x10 box of ones
];
for i=1:size(dimensions,1)
OnesBoxes{i}=ones(dimensions(i,:));
end
This will create OnesBoxes like how you wanted like this:
OnesBoxes{1}% = 32x40 box of ones
OnesBoxes{2}% = 20x30 box of ones
OnesBoxes{3}% = 60x10 box of ones
You need to pass ones a vector that defines the matrix size. If you want several matrices, you need to call ones several times. To collect all resulting matrices you need a cell array because the matrices have different sizes.
You can do that using a for loop, or arrayfun, or cellfun. Here I'm using the latter:
dimensions = [ 2 3;
4 5 ]; %// input data. Each row defines a matrix size
dimCell = mat2cell(dimensions, ones(1,size(dimensions,1)), size(dimensions,2));
%// split each row into a cell, ready to be used as input for `cellfun`
result = cellfun(#(x) ones(x), dimCell, 'uniformoutput', false);
This gives a cell array of the desired matrices. In the example,
>> result
result =
[2x3 double]
[4x5 double]
>> celldisp(result)
result{1} =
1 1 1
1 1 1
result{2} =
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
I have an array with n dimensions, and I have a sequence along one dimension at a certain location on all other dimensions. How do I find the location of this sequence? Preferably without loops.
I use matlab. I know what dimension it should be in, but the sequence isnt necessarily there. Find and == dont work. I could make an nd find function using crosscorrelation but Im guessing this is already implemented and I just dont know what function to call.
example:
ND = rand(10,10,10,10);
V = ND(randi(10),randi(10),randi(10),:);
[I1, I2, I3] = find(ND==V);
Edit: The sequence to be found spans the entire dimension it is on, I did not mention this in my original formulation of the problem. Knedlsepp`s solution solves exactly the problem I had, but Luis' solution solves a more general problem for when the sequence doesn't necessarily span the entire dimension.
As there are multiple ways to interpret your question, I will clarify: This approach assumes a 1D sequence of size: numel(V) == size(ND, dimToSearch). So, for V = [1,2] and ND = [1,2,1,2] it is not applicable. If you want this functionality go with Luis Mendo's answer, if not this will likely be faster.
This will be a perfect opportunity to use bsxfun:
We start with some example data:
ND = rand(10,10,10,10);
V = ND(3,2,:,3);
If you don't have the vector V given in the correct dimension (in this case [1,1,10,1]) you can reshape it in the following way:
dimToSearch = 3;
Vdims = ones(1, ndims(ND));
Vdims(dimToSearch) = numel(V);
V = reshape(V, Vdims);
Now we generate a cell that will hold the indices of the matches:
I = cell(1, ndims(ND));
At this point we compute the size of ND if it were collapsed along the dimension dimToSearch (we compute dimToSearch according to V, as at this point it will have the correct dimensions):
dimToSearch = find(size(V)>1);
collapsedDims = size(ND);
collapsedDims(dimToSearch) = 1;
Finally the part where we actually look for the pattern:
[I{:}] = ind2sub(collapsedDims, find(all(bsxfun(#eq, ND, V), dimToSearch)));
This is done in the following way: bsxfun(#eq, ND, V) will implicitly repmat the array V so it has the same dimensions as ND and do an equality comparison. After this we do a check with all to see if all the entries in the dimension dimToSearch are equal. The calls to find and ind2sub will then generate the correct indices to your data.
Let d be the dimension along which to search. I'm assuming that the sought sequence V may be shorter than size(ND,d). So the sequence may appear once, more than once, or never along each dimension-d- "thread".
The following code uses num2cell to reshape ND into a cell array such that each dimension-d-thread is in a different cell. Then strfind is applied to each cell to determine matches with V, and the result is a cell array with the same dimensions as ND, but where the dimension d is a singleton. The contents of each cell tell the d-dimension-positions of the matches, if any.
Credit goes to #knedlsepp for his suggestion to use num2cell, which greatly simplified the code.
ND = cat(3, [1 2 1 2; 3 4 5 6],[2 1 0 5; 0 0 1 2] ); %// example. 2x4x2
V = 1:2; %// sought pattern. It doesn't matter if it's a row, or a column, or...
d = 2; %// dimension along which to search for pattern V
result = cellfun(#(x) strfind(x(:).', V(:).'), num2cell(ND,d), 'UniformOutput', 0);
This gives
ND(:,:,1) =
1 2 1 2
3 4 5 6
ND(:,:,2) =
2 1 0 5
0 0 1 2
V =
1 2
result{1,1,1} =
1 3 %// V appears twice (at cols 1 and 3) in 1st row, 1st slice
result{2,1,1} =
[] %// V doesn't appear in 2nd row, 1st slice
result{1,1,2} =
[] %// V appears appear in 1st row, 2nd slice
result{2,1,2} =
3 %// V appears once (at col 3) in 2nd row, 2nd slice
One not very optimal way of doing it:
dims = size(ND);
Vrep = repmat(V, [dims(1), dims(2), dims(3), 1]);
ND_V_dist = sqrt(sum(abs(ND.^2-Vrep.^2), 4));
iI = find(ND_V_dist==0);
[I1, I2, I3] = ind2sub([dims(1), dims(2), dims(3)], iI);