I have a simulation running at 50 Hz, and some data that comes in at 10 Hz. I have extra 'in-between' points with dummy data at the following 50 Hz time points, and interpolation set to off. This should in theory ensure that between 10 Hz time steps, the dummy data is being held and only at the 10 Hz steps is the actual data present. For example, my data vector would be
[0.0 0.8 0.1 0.12 0.2 0.22 0.3 0.32 0.4 0.42 0.5 0.52 ...
-1 -1 1 -1 2 -1 3 -1 4 -1 5 -1 ...]
However, with a scope attached directly from the 'from-workspace' block, simulink is returning this:
[0.0 0.8 0.1 0.12 0.2 0.22 0.3 0.32 0.34 0.4 0.42 0.5 0.52...
-1 -1 -1 -1 2 -1 3 3 -1 4 -1 5 5...]
where some values are skipped and others are repeated in a consistent pattern. Is there something with simulinks time-step algorithms that would cause this?
Edit: A solution I ended up finding was to offset the entire time vector by 1/100th of a second so that the sim was taking data between points rather than on points, and that seemed to fix it. Not ideal, but functional.
How can I find the the starting point of A array and calculate average starting from starting points to 1 second
A=[0 0 0 0 0 -0.01 -0.2 0.3 0.4 0.5 0 0 0 0 0 0 0.01 0.02 0.03 0.04 0.1 0.2 0.3 0.4 0.7 0.8 1 1.2 1.3 1.4 1.5]
Time=[0 0.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.8 3 3.1]
By removing the noise the starting point should be A(17) which is equal to 0.01
Then calculate average of A starting from starting point after 1 seconds
Code is self explanatory
A=[0 0 0 0 0 -0.01 -0.2 0.3 0.4 0.5 0 0 0 0 0 0 0.01 0.02 0.03 0.04 0.1 0.2 0.3 0.4 0.7 0.8 1 1.2 1.3 1.4 1.5] ;
Time=[0 0.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.8 3 3.1];
%make negative values zero
A(A<0)=0;
%get non negative values position and add padding
mask=[0,A>0,0];
%get starting points
startingPoints =strfind(mask,[0 1]);
%get length of continuous values from starting points
temp =diff(find(~mask))-1;
length = temp(temp>0);
%get the index of largest length
[~,index]=max(length);
%get starting point
dataStartingIndex = startingPoints(index)
%starting point value
A(dataStartingIndex)
%get ending point after 1 seconds
dataEndingIndex=find((Time(dataStartingIndex)+1)==Time);
%find average
avg=mean(A(dataStartingIndex:dataEndingIndex))
This really depends on your data. It is a bit unclear but in your example it seems that noise can exceed your 'information value'. So you can't detect it just with a threshold.
Maybe get the position where A is always superior to something like 0.01 :
startpos= (A>0).argmax()
truedata=A[startpos:]
time=T[startpos:]
you can calculate average with the method .mean()
I am new to MATLAB and I need help. I have 3 matrices (A, B, and C) and I want to create a new matrix average_ABC that contains average values.
A = [ 0.3 0.5 0.9
0.14 0.36 0.1
0.9 0.5 0.14]
B = [ 0.8 0.9 0.14
0.1 0.25 0.4
0.8 0.14 0.25]
C = [0.25 0.3 0.47
0.12 0.3 0.2
0.14 0.56 0.9]
The resulting matrix will be
average_matrix = [ 0.3 0.5 0.47
0.12 0.25 0.2
0.8 0.5 0.25]
Please, any suggestion, how can I do it?
You can first concatenate your matrices along the third dimension (using cat) and then compute whatever you want using the dim parameter that is available for most functions to specify that you want to perform that operation along the third dimension.
Also you've stated that you want the average (mean), but based on your example you actually want the median. Either way, we can compute them using this method.
data = cat(3, A, B, C);
% Compute the mean
mean(data, 3)
% 0.45 0.56667 0.50333
% 0.12 0.30333 0.23333
% 0.61333 0.4 0.43
% Compute the median (which seems to be what you actually want)
median(data, 3)
% 0.3 0.5 0.47
% 0.12 0.3 0.2
% 0.8 0.5 0.25
I hope this will work
average_matrix=(A+B+C)/3.;
I am trying to make a program in matlab to get this numbers:
0 1 0
0 0.8 0.2
0 0.6 0.4
0 0.4 0.6
0 0.2 0.8
0 0 1
0.1 0.9 0
0.1 0.7 0.2
0.1 0.5 0.4
0.1 0.3 0.6
0.1 0.1 0.8
0.1 0 0.9
and so on but I cant make the program to reduce the values of the second and third column when the first column increases. This is my code. Thanks
lai=0:0.1:1;
laj=1:-0.2:0;
lat=0:0.2:1;
for i=1,length(lai)
for j=1,i
for t=1,j
j
lam1(1,:)=lai;
lam2(1,:)=laj;
lam3(1,:)=lat;
end
end
end
Try this and do some thinking for what you require.
for i=0:0.1:0.1
for j=0:0.2:1
disp([i,j,1-j])
end
end
Let's say I have a 2 by 9 matrix. I want to replace the 2 by 3 matrices inside this matrix with respect to descending sort of a(2,3), a(2,6), and a(2,9) elements. For example:
a =
0.4 0.4 0.5 0.6 0.2 0.2 0.6 0.2 0.6
0.5 0.8 0.9 0.9 0.6 0.6 0.1 0.2 0.8
[b i] = sort(a(2,3:3:end),2,'descend')
b =
0.9 0.8 0.6
i =
1 3 2
So, I want to have the following matrix:
a =
0.4 0.4 0.5 0.6 0.2 0.6 0.6 0.2 0.6
0.5 0.8 0.9 0.1 0.2 0.8 0.9 0.6 0.6
Try converting to a cell matrix first and then using your i to rearrange the cells
[b i] = sort(a(2,3:3:end),2,'descend')
A = mat2cell(a, 2, 3*ones(1,3));
cell2mat(A(i))
If for whatever reason you don't want to convert the whole of a into a cell matrix, you can do it by extending your indexing vector i to index all the columns. In your case you'd need:
I = [1,2,3,7,8,9,4,5,6]
which you could generate using a loop or else use bsxfun to get
[1 7 4
2 8 5
3 9 6]
and then "flatten" using reshape:
I = reshape(bsxfun(#plus, 3*s-2, (0:2)'), 1, [])
and then finally
a(:,I)
Typically, when a 2d matrix is separated into blocks, best practice ist to use more dimensions:
a=reshape(a,size(a,1),3,[]);
Now you can access each block via a(:,:,1)
To sort use:
[~,idx]=sort(a(2,3,:),'descend')
a=a(:,:,idx)
If you really need a 2d matrix, change back:
a=reshape(a,2,[])
sortrows-based approach:
n = 3; %// number of columns per block
m = size(a,1);
a = reshape(sortrows(reshape(a, m*n, []).', -m*n).', m, []);
This works by reshaping each block into a row, sorting rows according to last column, and reshaping back.