I have data at specific points in 3D rectangle and I want to see temperature gradient. I have values at specific points , but I want a continous flow of gradient between each sensor. I am not been able to figure out how to visualize or map data in between each sensors placed at different points. stucked :(
X=[5 0 0 0 0 5 10 10 10 10 0 5 10 10 0 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 0 5 10 0 5 10 10 10 5 0 0]';
Y=[10 10 5 5 10 10 5 10 5 10 0 0 0 0 0 0 3.5 7 3.5 7 3.5 7 3.5 7 3.5 7 3.5 7 3.5 7 3.5 7 0 0 0 0 0 0 5 10 10 10 5 ]';
Z=[20 20 20 14 14 14 14 14 20 20 20 20 20 14 14 14 3.8 3.8 0 0 7.5 7.5 10 10 12.5 12.5 15 15 17.5 17.5 20 20 0 0 0 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5]';
%# temperature vector
T = [20 22 24 22.1 26.1 22.4 15 17 21 22 19 22 18 17 18 20 21 22 21 24 22.3 22.5 22.8 28.9 22 27 26 20 19 24 21 23 19 18 22 25 27 21 29 25 22 21 22];
scatter3(X,Y,Z,[4000],T,'.');
grid off
box off
view(32,18); axis equal tight off vis3d; % azimuth 26
camproj perspective
camlight; lighting gouraud; alpha(0.75);
rotate3d on
Code below just shows how my one side of 3d rectangle should look like(its just a random code)
datagrid = 500*peaks(100);
R = makerefmat('RasterSize',size(datagrid));
[aspect,slope,gradN,gradE] = gradientm(datagrid,R);
figure; axesm eqacyl
meshm(datagrid,R)
colormap (jet(64))
colorbar('vert')
title('Peaks: elevation')
axis square
You can break the problem down into two sub-problems:
Interpolation
Visualization
Let's take a look at interpolation first. There are many methods available but let's try the MATLAB function griddatan. This will interpolate (linearly) values onto a new set of points (here I've used a regular grid constructed using meshgrid).
M = 20;
N = 20;
L = 40;
T = transpose(T);
% Set up the grid of points we wish to interpolate at
[xi,yi,zi] = meshgrid(linspace(0,10,M),linspace(0,10,N),linspace(0,20,L));
% Perform interpolation
ti = griddatan([X,Y,Z],T,[xi(:),yi(:),zi(:)]);
% Reshape back from a column vector to a MxNxL matrix
ti = reshape(ti, size(xi));
% Visualise in some way
scatter3(xi(:),yi(:),zi(:),400,ti(:),'.')
When it comes to visualization then the sky's the limit and 3D volume visualization is more of an art than a science. I'm afraid I can't run your example (I don't have access to makerefmat) but http://www.mathworks.co.uk/help/techdoc/visualize/bqliccy.html has some good starting points.
Related
I want to use randsample to sample values from a matrix, but I want the values sampled to be replaced by zero in the matrix. What do I do/Is there a fuction for this?
I think you don't want to use randsample because you have a given matrix (here M). You can use datasample instead to randomly sample existing data. Then you can use the second output of datasample (here ind) to address the entries in the original matrix M and overwrite them easily.
In the following example operates over the second dimension and takes a selection of columns. If you want a selection of rows, change the third argument of datasample to 1 (this is Matlab's default behaviour when no third argument is given).
% create random data
M = randi(20,4,10)
% randomly sample data
[Y,ind] = datasample(M,4,2)
% write 0 for the sampled data in original matrix
M(:,ind) = 0
This is the result:
M =
20 14 6 18 1 9 4 15 11 11
11 5 9 20 8 19 2 9 3 13
20 20 16 4 1 16 13 11 4 9
15 1 4 12 20 18 4 19 11 13
Y =
18 4 15 14
20 2 9 5
4 13 11 20
12 4 19 1
ind =
4 7 8 2
M =
20 0 6 0 1 9 0 0 11 11
11 0 9 0 8 19 0 0 3 13
20 0 16 0 1 16 0 0 4 9
15 0 4 0 20 18 0 0 11 13
Initialized with rng(4).
I have a question about intensity inhomogeneity. I read a paper, it defined a way to calculate the intensity inhomogeneity based on average filter:
Let see my problem, I have a image I (below code) and a average filter with r=3. I want to calculate image transformation J based on formula (17). Could you help me to implement it by matlab code? Thank you so much.
This is my code
%Create image I
I=[3 5 5 2 0 0 6 13 1
0 3 7 5 0 0 2 8 6
4 5 5 4 2 1 3 5 9
17 10 3 1 3 7 9 9 0
7 25 0 0 5 0 10 13 2
111 105 25 19 13 11 11 8 0
103 105 15 26 0 12 2 6 0
234 238 144 140 51 44 7 8 8
231 227 150 146 43 50 8 16 9
];
%% Create filter AF
size=3; % scale parameter in Average kernel
AF=fspecial('average',[size,size]); % Average kernel
%%How to calculate CN and J
CN=mean(I(:));%Correct?
J=???
You're pretty close! The mean intensity is calculated correctly; all you are missing to calculate J is apply the filter defined with fspecial to your image:
Here is the code:
clc
clear
%Create image I
I=[3 5 5 2 0 0 6 13 1
0 3 7 5 0 0 2 8 6
4 5 5 4 2 1 3 5 9
17 10 3 1 3 7 9 9 0
7 25 0 0 5 0 10 13 2
111 105 25 19 13 11 11 8 0
103 105 15 26 0 12 2 6 0
234 238 144 140 51 44 7 8 8
231 227 150 146 43 50 8 16 9
];
% Create filter AF
size=3; % scale parameter in Average kernel
AF=fspecial('average',[size,size]); % Average kernel
%%How to calculate CN and J
CN=mean(I(:)); % This is correct
J = (CN*I)./imfilter(I,AF); % Apply the filter to the image
figure;
subplot(1,2,1)
image(I)
subplot(1,2,2)
image(J)
Resulting in the following:
My data matrix is large: smt like 180:3000 size.
Each element value is between 0 to 255;
I have to find areas in this matrix where average value is higher than some threshold (lets call it 'P'). And reset each element in these areas to '0'. Another words filter my matrix.
I have width and heigth of filter area.
So I need to loop over data matrix to find appropriate areas (As many as exist).
EDIT:
Please, see an example:
4 6 7 5 6 6 7
10 8 9 8 9 10 9
10 8 9 8 9 10 9
7 4 6 9 7 8 7
4 5 5 5 5 5 5
4 5 5 5 5 5 5
10 12 12 12 13 10 11
14 15 15 16 14 15 15
13 15 15 15 14 14 13
This is given matrix. Lets try to find areas (2, 3) of size where average value is > 15.
So the result will be:
4 6 7 5 6 6 7
10 8 9 8 9 10 9
10 8 9 8 9 10 9
7 4 6 9 7 8 7
4 5 5 5 5 5 5
4 5 5 5 5 5 5
10 12 12 12 13 10 11
14 0 0 0 14 15 15
13 0 0 0 14 14 13
Please, look at bottom of matrix
Please, give me some tips how it is possible to loop throw.
Thank you very much.
One way of doint this is as follows:
% example A with more areas of mean greater than 15
% there are four such areas as shown here: http://i.imgur.com/V6m0NfL.jpg
A = [16 16 16 5 16 16 16
16 16 16 8 16 16 16
10 8 9 8 9 10 9
7 4 6 9 7 8 7
4 5 15.1 15 15 5 5
4 5 15 15 15 5 5
10 12 12 12 13 10 11
14 15 15 16 14 15 15
13 15 15 15 14 14 13];
% filter size
[n,m] = deal(2,3);
% filter center
center = floor(([n,m]+1)/2);
% find where we have areas greater than 15
B = nlfilter(A, [n,m], #(b) mean(b(:)) > 15);
% get coordinates of areas with mean > 15
[rows,cols] = find(B);
% zero out elements in all found areas
for i = 1:size(rows,1)
% calculate starting coordinates for the area to be set to 0
row = rows(i) - center(1) + 1;
col = cols(i) - center(2) + 1;
A(row:row+n-1 , col:col+m-1) = 0;
end
Results in:
A =
0 0 0 5 0 0 0
0 0 0 8 0 0 0
10 8 9 8 9 10 9
7 4 6 9 7 8 7
4 5 0 0 0 5 5
4 5 0 0 0 5 5
10 12 12 12 13 10 11
14 0 0 0 14 15 15
13 0 0 0 14 14 13
try this
a = input_matrix;
ii = 2 ; jj = 3;
threshold = 15;
x = ones(ii,jj)/(ii*jj);
%\\create matrix temp2 with average value of block a(i:i+ii-1,j:j+jj-1) at temp2(i,j)
temp1 = conv2(a,x,'full');
temp2 = temp1(ii:end-ii+1,jj:end-jj+1);
%\\find row and column indices of temp2 with value > threshold
[row_ col_] = find(temp2>threshold);
out = a;
%\\assign zero value to the corresponding blocks
for iii = 1:length(row_)
out(row_(iii):row_(iii)+ii-1,col_(iii):col_(iii)+jj-1) = 0;
end
I've a matrix called datevector containing the year, month, day, hour, minutes, seconds of the timeseries that I would like to plot.
datevector = [...
2009 11 4 11 35 0
2009 11 4 11 36 0
2009 11 4 11 37 0
2009 11 4 11 38 0
2009 11 4 11 39 0
2009 11 4 11 40 0]
To plot my data with respect to this time series I create the array containing the time series
xdate = datenum(datevector);
and then I try to plot my data = [1 2 3 4 5 6]
figure
plot(xdate',data)
datetick('x','yyyy-mm-dd HH:MM:SS')
...well the figure I get is not the one expected...I would like to have a minute resolution as in datavector...can you help me?
Thanks!
I have a function f(x,y) which has certain symmetries that I would like to plot. Here is an example:
This plot can be generated with:
[x,y,z] =
0 0 0.1415
0.1999 0.1999 0.1165
0.2760 0 0.1268
0.3694 0.3694 0.0983
0.4830 0 0.1142
0.5090 0.5090 0.0903
0.5550 0.1871 0.0881
0.6189 0.3558 0.0715
0.6197 0.6197 0.0907
0.6399 0 0.1056
0.7071 0.7071 0.1415
0.7169 0.4835 0.0869
0.7215 0.1200 0.0859
0.7304 0.2392 0.0680
0.7643 0 0.1005
0.7926 0.3574 0.0856
0.8090 0.5878 0.1393
0.8581 0.1122 0.0821
0.8634 0.2343 0.0878
0.8794 0 0.0986
0.8910 0.4540 0.1332
0.9511 0.3090 0.1253
0.9877 0.1564 0.1191
1.0000 0 0.1169
t =
6 4 8
12 6 8
8 4 7
4 2 7
8 7 14
14 7 13
3 2 1
5 7 3
3 7 2
17 12 21
6 12 9
9 17 11
12 17 9
10 15 13
10 7 5
13 7 10
21 12 16
16 12 8
8 14 16
18 14 13
15 20 18
13 15 18
24 23 18
18 20 24
21 16 22
23 22 19
19 18 23
14 18 19
19 16 14
19 22 16
trisurf(t,x,y,z)
So I know that function has a reflection symmetry about y=x and then the resulting function is to be repeated in all the quadrants. Here is the code to do this:
allx = [x; x;-x;-x;y; y;-y;-y];
ally = [y;-y; y;-y;x;-x; x;-x];
allz = [z; z; z; z;z; z; z; z];
These are the new vertices for the surface I want to plot. Now how do I properly generate the faces for this new surface?
When I use a finer mesh and add some pretty lights it should look something like this:
Speculative:
So your question is about how to set-up the first argument of trisurf, i.e. how to define the extended t in your code. According to the docs this is the index into the vertices defined by the remaining arguments. I don't have MATLAB installed on this machine, but what happens if you do:
allx = [x; x;-x;-x];
ally = [y;-y; y;-y];
allz = [z; z; z; z];
s = size(x,1);
t = [t; t + s; t + 2*s; t + 3*s]
Just trying to think if this makes sense and if/how it extends into the other quadrants.