I am working on some fourier transform code in matlab, and have come across the following:
xx = meshgrid(1:N);
% Center on DC
xx = xx - dcN;
% normalize dynamic range from -1 to 1
xx = xx./max(abs(xx(:)));
% form y coordinate from negative transpose of x coordinate (maintains symmetry about DC)
yy = -xx';
% compute the related radius of the x/y coordinates centered on DC
rr = sqrt(xx.^2 + yy.^2);
How can I generalize this for non-square matrices? This code is assuming my matrix is square, so dcN is the center of the square matrix (in other words, with 11x11, dcN = 6).
The math doesnt work out for that yy variable when the transpose is taken for a non-square matrix.
I have tried to figure out if I can make a meshgrid going from "top to bottom" instead of left to right - but I havent been able to figure taht out either.
Thanks
I have tried to figure out if I can
make a meshgrid going from "top to
bottom" instead of left to right - but
I havent been able to figure taht out
either.
>> N=5
N =
5
>> rot90(meshgrid(N:-1:1))
ans =
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
5 5 5 5 5
From your question I guess that you want to find rr, i.e. the distance of any element in the matrix from the center.
If you want this for a M-by-N array, you'd do the following
%# note that using meshgrid instead of ndgrid will swap xx and yy
[xx,yy] = ndgrid(-(M-1)/2:(M-1)/2,-(N-1)/2:(N-1)/2);
%# normalize to the max of xx,yy
nrm = max((M-1)/2,(N-1)/2);
xx = xx./nrm;
yy = yy./nrm;
rr = sqrt(xx.^2+yy.^2)
Related
Consider a set of points (just an example)
x = [0 1 2 5 4 8 5 6];
y = [5 8 4 2 5 6 4 5];
and another reference point:
xc=1;
yc=1;
In which I use to represent these points as vectors:
vec=[x-xc y-yc];
I wish to obtain a matrix with all the angles between all vectors which is obtained by the calculation (for single vectors)
angle = acosd(dot(v,u)/norm(u)*norm(v));
How can I obtain this calculation in a few lines without going vector by vector in a loop? In my calculation the number of points is very very large.
I think you mean vec = [x-xc; y-yc];. To calucate the dotproduct between all rows, you can use
vec.'*vec
The norm (Euclidean) of each vector can be determined as
no = sqrt(sum(vec.*vec,1))
The product of the different norms can be calculated the same as for vec:
no.'*no
The angles can thus be found as
no = sqrt(sum(vec.*vec,1));
angles = acosd(vec.'*vec./(no.'*no));
I am to fit planes through various points in an image, but I am having issues with forcing the line through a particular point in the image. This happens particularly when the line is 90 degrees.
My code is as follows:
I = [3 3 3 3 3 2 2
3 3 3 3 2 2 2
3 3 3 3 2 2 2
3 3 1 2 2 2 2
1 1 1 2 2 2 2
1 1 1 1 1 2 2
1 1 1 1 1 1 1];
% force the line through point p
p = [3,3];
% points to fit plane through
edgeA = [3,3.5; 3,4; 2.5,4; 2,4; 1.5,4];
edgeB = [3.5,3; 4,3; 4.5,3; 5,3];
% fit a plane through p and edgeA
xws = [p(2), edgeA(:,2)']';
yws = [p(1), edgeA(:,1)']';
Cws = [xws ones(size(xws))];
dws = yws;
Aeqws = [p(2) 1];
beqws = [p(1)];
planefitA = lsqlin(Cws ,dws,[],[],Aeqws, beqws);
% fit a plane through p and edgeB
xwn = [p(2), edgeB(:,2)']';
ywn = [p(1), edgeB(:,1)']';
Cwn = [xwn ones(size(xwn))];
dwn = ywn;
Aeqwn = [p(2) 1];
beqwn = [p(1)];
planefitB = lsqlin(Cwn ,dwn,[],[],Aeqwn, beqwn);
%%%%% plot the fitted planes:
xAxis = linspace(0, size(I, 2), 12);
%obtain linear curve
fA = planefitA(1)*xAxis + planefitA(2);
fB = planefitB(1)*xAxis + planefitB(2);
%plot the fitted curve
RI = imref2d(size(I),[0 size(I, 2)],[0 size(I, 1)]);
figure, imshow(I, RI, [], 'InitialMagnification','fit')
grid on;
hold on;
plot(xAxis,fA, 'Color', 'b', 'linewidth', 2);
plot(xAxis,fB, 'Color', 'r', 'linewidth', 2);
All the points in edgeB fall on a 90 degrees line. However, the function ends up fitting a wrong line through those points. I know this because using
planefitB = polyfit([p(2), edgeB(:,2)'], [p(1), edgeB(:,1)'], 1);
works for this particular line but the problem is that i have these process repeated so many times at different locations in my image, hence i do not know how to suggest polyfit when the line would be 90 degrees.
Please, any ideas/suggestions on how i could make this work? Many thanks.
This amounts to the least squares solution of only the angle of the line. The offset is fixed by the fact that it has to go through (3,3). The easiest way to express this is by offsetting your data points by the known crossing. That is, subtract (3,3) from your data points, and fit the best m for y=mx, the b being fixed to 0.
For the non-vertical case, you can use a classic least-squares formulation, but don't augment the constant 1 into the Vandermonde matrix:
slope = (edgeA(:,2) - p(2)) \ (edgeA(:,1) - p(1));
This gives exactly the same answer as your constrained lsq solution.
Now for the vertical line: A non-vertical line can be expressed in the standard functional form of y=mx, where the least squares formulation implicitly assumes an independent and a dependent variable. A vertical line doesn't follow that, so the only general choice is a "Total Least Squares" formulation, where errors in both variables are considered, rather than just the residuals in the dependent (y) variable.
The simplest way to write this is to choose a and b to minimize ax - by, in the least squares sense. [x_k -y_k]*[a b].' should be as close to a zero vector as possible. This is the vector closest to the null space of the [x -y] matrix, which can be computed with the svd. Swapping columns and fudging signs lets us just use svd directly:
[u s v] = svd(bsxfun(#minus, edgeA, p));
The last column of v is the closest to the null space, so mapping back to your x/y definitions, (edgeA-p)*v(:,2) is the line, so the y multiplier is in the top position, and the x in the lower, with a sign flip. To convert to y=mx form, just divide:
slope = -v(2,2)/v(1,2);
Note that this answer will be quite a bit different than the normal least squares answer, since you are treating the residuals differently. Also, the final step of computing "slope" won't work in the vertical case for the reasons we've already discussed (it produces Inf), so you are probably better off leaving the line as a normalized 2-vector, which won't have any corner cases.
Let
input = [0 0 0 5 5 7 8 8];
I now want to transform this vector into the form
output = [3 3 3 3 5 5 6 8];
Which basically is a stairs plot.
Explanation
The input vector is used to plot data points along the x-axis. The y-axis is thereby provided by 1:length(input). So the resulting plot shows the cumulative number of datapoints along the y-axis and the time of occurrence along the x-axis.
I now want to fit a model against my dataset. Therefor I need a vector that provides the correct value for a certain time (x-value).
The desired output vector basically is the result of a stairs plot. I am looking for an efficient way to generate the desired vector in matlab. The result of
[x, y] = stairs(input, 1:length(input));
did not bring me any closer.
It can be done with bsfxun as follows:
x = [0 0 0 5 5 7 8 8];
y = sum(bsxfun(#le, x(:), min(x):max(x)), 1);
This counts, for each element in 1:numel(x), how many elements of x are less than or equal to that.
I have two datasets, one of which is a target position, and the other is the actual position. I would like to plot the target with a +/- acceptable range and then overlay with the actual. This question is only concerning the target position however.
I have unsuccessfully attempted the built in area, fill, and rectangle functions. Using code found on stackoverflow here, it is only correct in certain areas.
For example
y = [1 1 1 2 1 1 3 3 1 1 1 1 1 1 1]; % Target datum
y1 = y+1; %variation in target size
y2 = y-1;
t = 1:15;
X=[t,fliplr(t)]; %create continuous x value array for plotting
Y=[y1,fliplr(y2)]; %create y values for out and then back
fill(X,Y,'b');
The figure produced looks like this:
I would prefer it to be filled within the red boxes drawn on here:
Thank you!
If you would just plot a function y against x, then you could use a stairs plot. Luckily for us, you can use the stairs function like:
[xs,ys] = stairs(x,y);
to create the vectors xs, ys which generate a stairs-plot when using the plot function. We can now use these vectors to generate the correct X and Y vectors for the fill function. Note that stairs generates column vectors, so we have to transpose them first:
y = [1 1 1 2 1 1 3 3 1 1 1 1 1 1 1]; % Target datum
y1 = y+1; %variation in target size
y2 = y-1;
t = 1:15;
[ts,ys1] = stairs(t,y1);
[ts,ys2] = stairs(t,y2);
X=[ts.',fliplr(ts.')]; %create continuous x value array for plotting
Y=[ys1.',fliplr(ys2.')]; %create y values for out and then back
fill(X,Y,'b');
Again, thank you hbaderts. You answered my question perfectly, however when I applied it to the large data set I needed for, I obtained this image
https://dl.dropboxusercontent.com/u/37982601/stair%20fill.png
I think it is because the fill function connects vertices to fill?
In any case, for the potential solution of another individual, combined your suggested code with the stair function and used the area function.
By plotting them on top of one another and setting the color of the lower area to be white, it appears as the rectangular figures I was after.
%sample code. produces image similar to o.p.
y = [1 1 1 2 1 1 3 3 1 1 1 1 1 1 1];
y1 = y+1;
y2 = y-1;
t = 1:15;
[ts,ys1] = stairs(t,y1);
[ts,ys2] = stairs(t,y2);
area(ts,ys1,'FaceColor','b','EdgeColor','none')
hold on
area(ts,ys2,'FaceColor','w','EdgeColor','none')
https://dl.dropboxusercontent.com/u/37982601/stair%20area.png
Thanks again for your help and for pointing me in the right direction!
I have two vectors of the same size. The first one can have any different numbers with any order, the second one is decreasing (but can have the same elements) and consists of only positive integers. For example:
a = [7 8 13 6];
b = [5 2 2 1];
I would like to plot them in the following way: on the x axis I have points from a vector and on the y axis I have the sum of elements from vector b before this points divided by the sum(b). Therefore I will have points:
(7; 0.5) - 0.5 = 5/(5+2+2+1)
(8; 0.7) - 0.7 = (5+2)/(5+2+2+1)
(13; 0.9) ...
(6; 1) ...
I assume that this explanation might not help, so I included the image
Because this looks to me as a cumulative distribution function, I tried to find luck with cdfplot but with no success.
I have another option is to draw the image by plotting each line segment separately, but I hope that there is a better way of doing this.
I find the values on the x axis a little confusing. Leaving that aside for the moment, I think this does what you want:
b = [5 2 2 1];
stairs(cumsum(b)/sum(b));
set(gca,'Ylim',[0 1])
And if you really need those values on the x axis, simply rename the ticks of that axis:
a = [7 8 13 6];
set(gca,'xtick',1:length(b),'xticklabel',a)
Also grid on will add grid to the plot