consider an area with size m*n. Here the size of m and n is unknown. Now I am extracting data from each point in the area. I am scanning the area first going in the x direction till m point and the again returning to m=0 and n=1, i.e the second row. Again I scan along the x direction till the end of m. An example of the data has been shown below. Here I get value for different x,y coordinates during the scan. I can carry out operation between the first two points in x direction by
p1 = A{1}; %%reading the data from the text file
p2 = A{2};
LA=[p1 p2];
for m=1:length(y)
p= LA(m,1);
t= LA(m,2);
%%and
q=LA(m+1,1)
r=LA(m+1,2)
I want to do the same for y axis. That is I want to operate between first point in x=0 and y=1 then between x=2 and y=1 and so on. Hope you have got it.
g x y
2 0 0
3 1 0
2 2 0
4 3 0
1 4 0
2 m 0
3 0 1
2 1 1
4 2 1
5 3 1
.
.
.
.
2 m 1
now I was thinking of a logic where I will first find the size of n by counting the number of zeros
NUMX = 0;
while y((NUMX+1),:) == 0
NUMX = NUMX + 1;
end
NU= NUMX;
And then I was thinking of applying the following loop
for m=1:NU:n-1
%%and
p= LA(m,1);
t= LA(m,2);
%%and
q=LA(m+1,1)
r=LA(m+1,2)
But its showing error. Please help!!
??? Attempted to access del2(99794,:); index out of bounds because
size(del2)=[99793,1].
Here NUMX=198
Comment: The nomenclature in your question is inconsistent, making it difficult to understand what you are doing. The variable del2 you mention in the error message is nowhere to be seen.
1.) Let's start off by creating a minimal working example that illustrates the data structure and provides knowledge of the dimensions we want to retrieve later. You matrix is not m x n but m*n x 3.
The following example will set up a matrix with data similar to what you have shown in your question:
M = zeros(8,3);
for J=1:4
for I=1:2
M((J-1)*2+I,1) = rand(1);
M((J-1)*2+I,2) = I;
M((J-1)*2+I,3) = J-1;
end
end
M =
0.469 1 0
0.012 2 0
0.337 1 1
0.162 2 1
0.794 1 2
0.311 2 2
0.529 1 3
0.166 2 3
2.) Next, let's determine the number of x and y, to use the nomenclature of your question:
NUMX = 0;
while M(NUMX+1,3) == 0
NUMX = NUMX + 1;
end
NUMY = size(M,1)/NUMX;
NUMX =
2
NUMY =
4
3.) The data processing you want to do still is unclear, but here are two approaches that can be used for different means:
(a)
COUNT = 1;
for K=1:NUMX:size(M,1)
A(COUNT,1) = M(K,1);
COUNT = COUNT + 1;
end
In this case, you step through the first column of M with a step-size corresponding to NUMX. This will result in all the values for x=1:
A =
0.469
0.337
0.794
0.529
(b) You can also use NUMX and NUMY to reorder M:
for J=1:NUMY
for I=1:NUMX
NEW_M(I,J) = M((J-1)*NUMX+I,1);
end
end
NEW_M =
0.469 0.337 0.794 0.529
0.012 0.162 0.311 0.166
The matrix NEW_M now is of size m x n, with the values of constant y in the columns and the values of constant x in the rows.
Concluding remark: It is unclear how you define m and n in your code, so your specific error message cannot be resolved here.
Related
Following this question and the precious help I got from it, I've reached to the following issue:
Using indices of detected peaks and having computed the median of my signal +/-3 datapoints around these peaks, I need to replace my signal in a +/-5 window around the peak with the previously computed median.
I'm only able replace the datapoint at the peak with the median, but not the surrounding +/-5 data points...see figure. Black = original peak; Yellow = data point at peak changed to the median of +/-3 datapoints around it.
Original peak and changed peak
Unfortunately I have not been able to make it work by following suggestions on the previous question.
Any help will be very much appreciated!
Cheers,
M
Assuming you mean the following. Given the array
x = [0 1 2 3 4 5 35 5 4 3 2 1 0]
you want to replace 35 and surrounding +/- 5 entries with the median of 3,4,5,35,5,4,3, which is 4, so the resulting array should be
x = [0 4 4 4 4 4 4 4 4 4 4 4 0]
Following my answer in this question an intuitive approach is to simply replace the neighbors with the median value by offsetting the indicies. This can be accomplished as follows
[~,idx]=findpeaks(x);
med_sz = 3; % Take the median with respect to +/- this many neighbors
repl_sz = 5; % Replace neighbors +/- this distance from peak
if ~isempty(idx)
m = medfilt1(x,med_sz*2+1);
N = numel(x);
for offset = -repl_sz:repl_sz
idx_offset = idx + offset;
idx_valid = idx_offset >= 1 & idx_offset <= N;
x(idx_offset(idx_valid)) = m(idx(idx_valid));
end
end
Alternatively, if you want to avoid loops, an equivalent loopless implementation is
[~,idx]=findpeaks(x);
med_sz = 3;
repl_sz = 5;
if ~isempty(idx)
m = medfilt1(x,med_sz*2+1);
idx_repeat = repmat(idx,repl_sz*2+1,1);
idx_offset = idx_repeat + repmat((-repl_sz:repl_sz)',1,numel(idx));
idx_valid = idx_repeat >= 1 & idx_repeat <= numel(x);
idx_repeat = idx_repeat(idx_valid);
idx_offset = idx_offset(idx_valid);
x(idx_offset) = m(idx_repeat);
end
I have an array (say of 1s and 0s) and I want to find the index, i, for the first location where 1 appears n times in a row.
For example,
x = [0 0 1 0 1 1 1 0 0 0] ;
i = 5, for n = 3, as this is the first time '1' appears three times in a row.
Note: I want to find where 1 appears n times in a row so
i = find(x,n,'first');
is incorrect as this would give me the index of the first n 1s.
It is essentially a string search? eg findstr but with a vector.
You can do it with convolution as follows:
x = [0 0 1 0 1 1 1 0 0 0];
N = 3;
result = find(conv(x, ones(1,N), 'valid')==N, 1)
How it works
Convolve x with a vector of N ones and find the first time the result equals N. Convolution is computed with the 'valid' flag to avoid edge effects and thus obtain the correct value for the index.
Another answer that I have is to generate a buffer matrix where each row of this matrix is a neighbourhood of overlapping n elements of the array. Once you create this, index into your array and find the first row that has all 1s:
x = [0 0 1 0 1 1 1 0 0 0]; %// Example data
n = 3; %// How many times we look for duplication
%// Solution
ind = bsxfun(#plus, (1:numel(x)-n+1).', 0:n-1); %'
out = find(all(x(ind),2), 1);
The first line is a bit tricky. We use bsxfun to generate a matrix of size m x n where m is the total number of overlapping neighbourhoods while n is the size of the window you are searching for. This generates a matrix where the first row is enumerated from 1 to n, the second row is enumerated from 2 to n+1, up until the very end which is from numel(x)-n+1 to numel(x). Given n = 3, we have:
>> ind
ind =
1 2 3
2 3 4
3 4 5
4 5 6
5 6 7
6 7 8
7 8 9
8 9 10
These are indices which we will use to index into our array x, and for your example it generates the following buffer matrix when we directly index into x:
>> x = [0 0 1 0 1 1 1 0 0 0];
>> x(ind)
ans =
0 0 1
0 1 0
1 0 1
0 1 1
1 1 1
1 1 0
1 0 0
0 0 0
Each row is an overlapping neighbourhood of n elements. We finally end by searching for the first row that gives us all 1s. This is done by using all and searching over every row independently with the 2 as the second parameter. all produces true if every element in a row is non-zero, or 1 in our case. We then combine with find to determine the first non-zero location that satisfies this constraint... and so:
>> out = find(all(x(ind), 2), 1)
out =
5
This tells us that the fifth location of x is where the beginning of this duplication occurs n times.
Based on Rayryeng's approach you can loop this as well. This will definitely be slower for short array sizes, but for very large array sizes this doesn't calculate every possibility, but stops as soon as the first match is found and thus will be faster. You could even use an if statement based on the initial array length to choose whether to use the bsxfun or the for loop. Note also that for loops are rather fast since the latest MATLAB engine update.
x = [0 0 1 0 1 1 1 0 0 0]; %// Example data
n = 3; %// How many times we look for duplication
for idx = 1:numel(x)-n
if all(x(idx:idx+n-1))
break
end
end
Additionally, this can be used to find the a first occurrences:
x = [0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 1 1 0 0 0]; %// Example data
n = 3; %// How many times we look for duplication
a = 2; %// number of desired matches
collect(1,a)=0; %// initialise output
kk = 1; %// initialise counter
for idx = 1:numel(x)-n
if all(x(idx:idx+n-1))
collect(kk) = idx;
if kk == a
break
end
kk = kk+1;
end
end
Which does the same but shuts down after a matches have been found. Again, this approach is only useful if your array is large.
Seeing you commented whether you can find the last occurrence: yes. Same trick as before, just run the loop backwards:
for idx = numel(x)-n:-1:1
if all(x(idx:idx+n-1))
break
end
end
One possibility with looping:
i = 0;
n = 3;
for idx = n : length(x)
idx_true = 1;
for sub_idx = (idx - n + 1) : idx
idx_true = idx_true & (x(sub_idx));
end
if(idx_true)
i = idx - n + 1;
break
end
end
if (i == 0)
disp('No index found.')
else
disp(i)
end
I'm trying to elegantly split a vector. For example,
vec = [1 2 3 4 5 6 7 8 9 10]
According to another vector of 0's and 1's of the same length where the 1's indicate where the vector should be split - or rather cut:
cut = [0 0 0 1 0 0 0 0 1 0]
Giving us a cell output similar to the following:
[1 2 3] [5 6 7 8] [10]
Solution code
You can use cumsum & accumarray for an efficient solution -
%// Create ID/labels for use with accumarray later on
id = cumsum(cut)+1
%// Mask to get valid values from cut and vec corresponding to ones in cut
mask = cut==0
%// Finally get the output with accumarray using masked IDs and vec values
out = accumarray(id(mask).',vec(mask).',[],#(x) {x})
Benchmarking
Here are some performance numbers when using a large input on the three most popular approaches listed to solve this problem -
N = 100000; %// Input Datasize
vec = randi(100,1,N); %// Random inputs
cut = randi(2,1,N)-1;
disp('-------------------- With CUMSUM + ACCUMARRAY')
tic
id = cumsum(cut)+1;
mask = cut==0;
out = accumarray(id(mask).',vec(mask).',[],#(x) {x});
toc
disp('-------------------- With FIND + ARRAYFUN')
tic
N = numel(vec);
ind = find(cut);
ind_before = [ind-1 N]; ind_before(ind_before < 1) = 1;
ind_after = [1 ind+1]; ind_after(ind_after > N) = N;
out = arrayfun(#(x,y) vec(x:y), ind_after, ind_before, 'uni', 0);
toc
disp('-------------------- With CUMSUM + ARRAYFUN')
tic
cutsum = cumsum(cut);
cutsum(cut == 1) = NaN; %Don't include the cut indices themselves
sumvals = unique(cutsum); % Find the values to use in indexing vec for the output
sumvals(isnan(sumvals)) = []; %Remove NaN values from sumvals
output = arrayfun(#(val) vec(cutsum == val), sumvals, 'UniformOutput', 0);
toc
Runtimes
-------------------- With CUMSUM + ACCUMARRAY
Elapsed time is 0.068102 seconds.
-------------------- With FIND + ARRAYFUN
Elapsed time is 0.117953 seconds.
-------------------- With CUMSUM + ARRAYFUN
Elapsed time is 12.560973 seconds.
Special case scenario: In cases where you might have runs of 1's, you need to modify few things as listed next -
%// Mask to get valid values from cut and vec corresponding to ones in cut
mask = cut==0
%// Setup IDs differently this time. The idea is to have successive IDs.
id = cumsum(cut)+1
[~,~,id] = unique(id(mask))
%// Finally get the output with accumarray using masked IDs and vec values
out = accumarray(id(:),vec(mask).',[],#(x) {x})
Sample run with such a case -
>> vec
vec =
1 2 3 4 5 6 7 8 9 10
>> cut
cut =
1 0 0 1 1 0 0 0 1 0
>> celldisp(out)
out{1} =
2
3
out{2} =
6
7
8
out{3} =
10
For this problem, a handy function is cumsum, which can create a cumulative sum of the cut array. The code that produces an output cell array is as follows:
vec = [1 2 3 4 5 6 7 8 9 10];
cut = [0 0 0 1 0 0 0 0 1 0];
cutsum = cumsum(cut);
cutsum(cut == 1) = NaN; %Don't include the cut indices themselves
sumvals = unique(cutsum); % Find the values to use in indexing vec for the output
sumvals(isnan(sumvals)) = []; %Remove NaN values from sumvals
output = {};
for i=1:numel(sumvals)
output{i} = vec(cutsum == sumvals(i)); %#ok<SAGROW>
end
As another answer shows, you can use arrayfun to create a cell array with the results. To apply that here, you'd replace the for loop (and the initialization of output) with the following line:
output = arrayfun(#(val) vec(cutsum == val), sumvals, 'UniformOutput', 0);
That's nice because it doesn't end up growing the output cell array.
The key feature of this routine is the variable cutsum, which ends up looking like this:
cutsum =
0 0 0 NaN 1 1 1 1 NaN 2
Then all we need to do is use it to create indices to pull the data out of the original vec array. We loop from zero to max and pull matching values. Notice that this routine handles some situations that may arise. For instance, it handles 1 values at the very beginning and very end of the cut array, and it gracefully handles repeated ones in the cut array without creating empty arrays in the output. This is because of the use of unique to create the set of values to search for in cutsum, and the fact that we throw out the NaN values in the sumvals array.
You could use -1 instead of NaN as the signal flag for the cut locations to not use, but I like NaN for readability. The -1 value would probably be more efficient, as all you'd have to do is truncate the first element from the sumvals array. It's just my preference to use NaN as a signal flag.
The output of this is a cell array with the results:
output{1} =
1 2 3
output{2} =
5 6 7 8
output{3} =
10
There are some odd conditions we need to handle. Consider the situation:
vec = [1 2 3 4 5 6 7 8 9 10 11 12 13 14];
cut = [1 0 0 1 1 0 0 0 0 1 0 0 0 1];
There are repeated 1's in there, as well as a 1 at the beginning and end. This routine properly handles all this without any empty sets:
output{1} =
2 3
output{2} =
6 7 8 9
output{3} =
11 12 13
You can do this with a combination of find and arrayfun:
vec = [1 2 3 4 5 6 7 8 9 10];
N = numel(vec);
cut = [0 0 0 1 0 0 0 0 1 0];
ind = find(cut);
ind_before = [ind-1 N]; ind_before(ind_before < 1) = 1;
ind_after = [1 ind+1]; ind_after(ind_after > N) = N;
out = arrayfun(#(x,y) vec(x:y), ind_after, ind_before, 'uni', 0);
We thus get:
>> celldisp(out)
out{1} =
1 2 3
out{2} =
5 6 7 8
out{3} =
10
So how does this work? Well, the first line defines your input vector, the second line finds how many elements are in this vector and the third line denotes your cut vector which defines where we need to cut in our vector. Next, we use find to determine the locations that are non-zero in cut which correspond to the split points in the vector. If you notice, the split points determine where we need to stop collecting elements and begin collecting elements.
However, we need to account for the beginning of the vector as well as the end. ind_after tells us the locations of where we need to start collecting values and ind_before tells us the locations of where we need to stop collecting values. To calculate these starting and ending positions, you simply take the result of find and add and subtract 1 respectively.
Each corresponding position in ind_after and ind_before tell us where we need to start and stop collecting values together. In order to accommodate for the beginning of the vector, ind_after needs to have the index of 1 inserted at the beginning because index 1 is where we should start collecting values at the beginning. Similarly, N needs to be inserted at the end of ind_before because this is where we need to stop collecting values at the end of the array.
Now for ind_after and ind_before, there is a degenerate case where the cut point may be at the end or beginning of the vector. If this is the case, then subtracting or adding by 1 will generate a start and stopping position that's out of bounds. We check for this in the 4th and 5th line of code and simply set these to 1 or N depending on whether we're at the beginning or end of the array.
The last line of code uses arrayfun and iterates through each pair of ind_after and ind_before to slice into our vector. Each result is placed into a cell array, and our output follows.
We can check for the degenerate case by placing a 1 at the beginning and end of cut and some values in between:
vec = [1 2 3 4 5 6 7 8 9 10];
cut = [1 0 0 1 0 0 0 1 0 1];
Using this example and the above code, we get:
>> celldisp(out)
out{1} =
1
out{2} =
2 3
out{3} =
5 6 7
out{4} =
9
out{5} =
10
Yet another way, but this time without any loops or accumulating at all...
lengths = diff(find([1 cut 1])) - 1; % assuming a row vector
lengths = lengths(lengths > 0);
data = vec(~cut);
result = mat2cell(data, 1, lengths); % also assuming a row vector
The diff(find(...)) construct gives us the distance from each marker to the next - we append boundary markers with [1 cut 1] to catch any runs of zeros which touch the ends. Each length is inclusive of its marker, though, so we subtract 1 to account for that, and remove any which just cover consecutive markers, so that we won't get any undesired empty cells in the output.
For the data, we mask out any elements corresponding to markers, so we just have the valid parts we want to partition up. Finally, with the data ready to split and the lengths into which to split it, that's precisely what mat2cell is for.
Also, using #Divakar's benchmark code;
-------------------- With CUMSUM + ACCUMARRAY
Elapsed time is 0.272810 seconds.
-------------------- With FIND + ARRAYFUN
Elapsed time is 0.436276 seconds.
-------------------- With CUMSUM + ARRAYFUN
Elapsed time is 17.112259 seconds.
-------------------- With mat2cell
Elapsed time is 0.084207 seconds.
...just sayin' ;)
Here's what you need:
function spl = Splitting(vec,cut)
n=1;
j=1;
for i=1:1:length(b)
if cut(i)==0
spl{n}(j)=vec(i);
j=j+1;
else
n=n+1;
j=1;
end
end
end
Despite how simple my method is, it's in 2nd place for performance:
-------------------- With CUMSUM + ACCUMARRAY
Elapsed time is 0.264428 seconds.
-------------------- With FIND + ARRAYFUN
Elapsed time is 0.407963 seconds.
-------------------- With CUMSUM + ARRAYFUN
Elapsed time is 18.337940 seconds.
-------------------- SIMPLE
Elapsed time is 0.271942 seconds.
Unfortunately there is no 'inverse concatenate' in MATLAB. If you wish to solve a question like this you can try the below code. It will give you what you looking for in the case where you have two split point to produce three vectors at the end. If you want more splits you will need to modify the code after the loop.
The results are in n vector form. To make them into cells, use num2cell on the results.
pos_of_one = 0;
% The loop finds the split points and puts their positions into a vector.
for kk = 1 : length(cut)
if cut(1,kk) == 1
pos_of_one = pos_of_one + 1;
A(1,one_pos) = kk;
end
end
F = vec(1 : A(1,1) - 1);
G = vec(A(1,1) + 1 : A(1,2) - 1);
H = vec(A(1,2) + 1 : end);
I am trying to allocate (x, y) points to the cells of a non-uniform rectangular grid. Simply speaking, I have a grid defined as a sorted non-equidistant array
xGrid = [x1, x2, x3, x4];
and an array of numbers x lying between x1 and x4. For each x, I want to find its position in xGrid, i.e. such i that
xGrid(i) <= xi <= xGrid(i+1)
Is there a better (faster/simpler) way to do it than arrayfun(#(x) find(xGrid <= x, 1, 'last'), x)?
You are looking for the second output of histc:
[~,where] = histc(x, xGrid)
This returns the array where such that xGrid(where(i)) <= x(i) < xGrid(where(i)+1) holds.
Example:
xGrid = [2,4,6,8,10];
x = [3,5,6,9,11];
[~,where] = histc(x, xGrid)
Yields the following output:
where =
1 2 3 4 0
If you want xGrid(where(i)) < x(i) <= xGrid(where(i)+1), you need to do some trickery of negating the values:
[~,where] = histc(-x,-flip(xGrid));
where(where~=0) = numel(xGrid)-where(where~=0)
This yields:
where =
1 2 2 4 0
Because x(3)==6 is now counted for the second interval (4,6] instead of [6,8) as before.
Using bsxfun for the comparisons and exploiting find-like capabilities of max's second output:
xGrid = [2 4 6 8]; %// example data
x = [3 6 5.5 10 -10]; %// example data
comp = bsxfun(#gt, xGrid(:), x(:).'); %'// see if "x" > "xGrid"
[~, result] = max(comp, [], 1); %// index of first "xGrid" that exceeds each "x"
result = result-1; %// subtract 1 to find the last "xGrid" that is <= "x"
This approach gives 0 for values of x that lie outside xGrid. With the above example values,
result =
1 3 2 0 0
See if this works for you -
matches = bsxfun(#le,xGrid(1:end-1),x(:)) & bsxfun(#ge,xGrid(2:end),x(:))
[valid,pos] = max(cumsum(matches,2),[],2)
pos = pos.*(valid~=0)
Sample run -
xGrid =
5 2 1 6 8 9 2 1 6
x =
3 7 14
pos =
8
4
0
Explanation on the sample run -
First element of x, 3 occurs last between ...1 6 with the criteria of xGrid(i) <= xi <= xGrid(i+1) at the backend of xGrid and that 1 is at the eight position, so the first element of the output pos is 8. This continues for the second element 7, which is found between 6 and 8 and that 6 is at the fourth place in xGrid, so the second element of the output is 4. For the third element 14 which doesn't find any neighbours to satisfy the criteria xGrid(i) <= xi <= xGrid(i+1) and is therefore outputted as 0.
If x is a column this might help
xg1=meshgrid(xGrid,1:length(x));
xg2=ndgrid(x,1:length(xGrid));
[~,I]=min(floor(abs(xg1-xg2)),[],2);
or a single line implementation
[~,I]=min(floor(abs(meshgrid(xGrid,1:length(x))-ndgrid(x,1:length(xGrid)))),[],2);
Example: xGrid=[1 2 3 4 5], x=[2.5; 1.3; 1.7; 4.8; 3.3]
Result:
I =
2
1
1
4
3
If I have sequence 1 0 0 0 1 0 1 0 1 1 1
how to effectively locate zero which has from both sides 1.
In this sequence it means zero on position 6 and 8. The ones in bold.
1 0 0 0 1 0 1 0 1 1 1
I can imagine algorithm that would loop through the array and look one in back and one in front I guess that means O(n) so probably there is not any more smooth one.
If you can find another way, I am interested.
Use strfind:
pos = strfind(X(:)', [1 0 1]) + 1
Note that this will work only when X is a vector.
Example
X = [1 0 0 0 1 0 1 0 1 1 1 ];
pos = strfind(X(:)', [1 0 1]) + 1
The result:
pos =
6 8
The strfind method that #EitanT suggested is quite nice. Another way to do this is to use find and element-wise bit operations:
% let A be a logical ROW array
B = ~A & [A(2:end),false] & [false,A(1:end-1)];
elements = find(B);
This assumes, based on your example, that you want to exclude boundary elements. The concatenations [A(2:end),false] and [false,A(1:end-1)] are required to keep the array length the same. If memory is a concern, these can be eliminated:
% NB: this will work for both ROW and COLUMN vectors
B = ~A(2:end-1) & A(3:end) & A(1:end-2);
elements = 1 + find(B); % need the 1+ because we cut off the first element above
...and to elaborate on #Eitan T 's answer, you can use strfind for an array if you loop by row
% let x = some matrix of 1's and 0's (any size)
[m n] = size(x);
for r = 1:m;
pos(r,:) = strfind(x(r,:)',[1 0 1]) + 1;
end
pos would be a m x ? matrix with m rows and any returned positions. If there were no zeros in the proper positions though, you might get a NaN ... or an error. Didn't get a chance to test.