I need to create a surface generate by rotation on y axis of shape formed by two curves. I already have the equation for this two curves. Here is the shape
I already create the surface but for simetric cone formed by one of this curve, here is my script:
h=20;
rw=1;
hw=5;
L=25;
r=linspace(rw,L,50);
theta=linspace(0,2*pi,25);
[r,theta] = meshgrid(r,theta);
xx=r.*cos(theta);
yy=r.*sin(theta);
z=sqrt(log(r/rw)*(h^2-hw^2)/log(L/rw)+hw^2); %Fuction of curve
surf(xx,yy,z)
Here is my result
In the other curve is the same fuction (z) but change r,h,L
Thanks for your help,
you should use parameter grids instead of scalars:
% parameters
h=[20 25];
rw=1;
hw=5;
L=[25 30];
nr = 50;
nt = 25;
theta=linspace(0,2*pi,nt);
rmax = linspace(L(1),L(2),floor((nt+1)/2));
% generating **grids** for all parameters
l = [linspace(L(1),L(2),floor((nt+1)/2)) linspace(L(2),L(1),floor((nt)/2))];
hh = [linspace(h(1),h(2),floor((nt+1)/2)) linspace(h(2),h(1),floor((nt)/2))];
[~,thetaGrid] = meshgrid(1:nr,theta);
[~,lGrid] = meshgrid(1:nr,l);
[~,hGrid] = meshgrid(1:nr,hh);
rGrid = zeros(size(thetaGrid));
for ii = 1:floor((nt+1)/2)
rGrid(ii,:) = linspace(rw,rmax(ii),nr);
end
rGrid(ceil((nt+1)/2):end,:) = rGrid(floor((nt+1)/2):-1:1,:);
% set coordinate grids
xx=rGrid.*cos(thetaGrid);
yy=rGrid.*sin(thetaGrid);
z=sqrt(log(rGrid./rw).*(hGrid.^2-hw^2)./log(lGrid./rw)+hw.^2); %Fuction of curve
% plot
surf(xx,yy,z)
Related
I have a matrix a=360x360x2048
now I want to plot them in 3d
My attempt
x = 1:size(a,2);
y = 1:size(a,1);
z = 1:size(a,3);
How to plot this in 3d
Now I want to 3d plot this
now I want to plot them all
Instead of using imagesc() which only works in 2D, you could try to use scatter() and its 3D extension scatter3().
nslices = 100;
nelems = 360;
A = rand(nelems, nelems, nslices); % your matrix
% Marker settings for scatter plot
mrkrSize = 10;
mrkr = '.';
figure
hold on
view(3)
for i = 1:nslices
% Get the i-th matrix
Aslice = A(:, :, i);
idx = find(Aslice);
[x, y] = ind2sub(size(Aslice), idx);
z = i.*ones(size(x));
scatter3(x, y, z, mrkrSize, Aslice(idx), 'Marker', mrkr);
end
Using Slices of a Matrix to Create 3D Plot
Given that I saw the question before the multiple edits. Using the slice() function may help to plot the 3D matrix as slices in a 3D plot. The slice() function takes three additional inputs along with the matrix/volume to be plotted. The following three inputs dictate the method used to slice the matrix. In this example, slicing was done so that you can have a set of xy-planes that are stacked along the z-axis.
%Generating a random test array%
X_Dimension = 360; Y_Dimension = 360; Z_Dimension = 5;
Volume = rand(X_Dimension,Y_Dimension,Z_Dimension);
%Configuring the slicing method%
X_Slice = [];
Y_Slice = [];
Z_Slice = 1:size(Volume,3);
%Plotting slices of array%
Slices = slice(Volume,X_Slice,Y_Slice,Z_Slice);
xlabel("x"); ylabel("y"); zlabel("z");
%Removing edge lines%
for Slice = 1: length(Slices)
Slices(Slice).EdgeAlpha = 0;
end
%Angle of 3D plot and colorbar%
Angle = 72; Elevation = 10;
view(Angle, Elevation);
colorbar
Ran using MATLAB R2019b
I'm trying to fill an area between two curves with respect to a function which depends on the values of the curves.
Here is the code of what I've managed to do so far
i=50;
cc = #(xx,x,y) 1./(1+(exp(-xx)/(exp(-x)-exp(-y))));
n_vec = 2:0.1:10;
x_vec = linspace(2,10,length(n_vec));
y_vec = abs(sin(n_vec));
N=[n_vec,fliplr(n_vec)];
X=[x_vec,fliplr(y_vec)];
figure(1)
subplot(2,1,1)
hold on
plot(n_vec,x_vec,n_vec,y_vec)
hp = patch(N,X,'b')
plot([n_vec(i) n_vec(i)],[x_vec(i),y_vec(i)],'linewidth',5)
xlabel('n'); ylabel('x')
subplot(2,1,2)
xx = linspace(y_vec(i),x_vec(i),100);
plot(xx,cc(xx,y_vec(i),x_vec(i)))
xlabel('x'); ylabel('c(x)')
This code produces the following graph
The color code which I've added represent the color coding that each line (along the y axis at a point on the x axis) from the area between the two curves should be.
Overall, the entire area should be filled with a gradient color which depends on the values of the curves.
I've assisted the following previous questions but could not resolve a solution
MATLAB fill area between lines
Patch circle by a color gradient
Filling between two curves, according to a colormap given by a function MATLAB
NOTE: there is no importance to the functional form of the curves, I would prefer an answer which refers to two general arrays which consist the curves.
The surf plot method
The same as the scatter plot method, i.e. generate a point grid.
y = [x_vec(:); y_vec(:)];
resolution = [500,500];
px = linspace(min(n_vec), max(n_vec), resolution(1));
py = linspace(min(y), max(y), resolution(2));
[px, py] = meshgrid(px, py);
Generate a logical array indicating whether the points are inside the polygon, but no need to extract the points:
in = inpolygon(px, py, N, X);
Generate Z. The value of Z indicates the color to use for the surface plot. Hence, it is generated using the your function cc.
pz = 1./(1+(exp(-py_)/(exp(-y_vec(i))-exp(-x_vec(i)))));
pz = repmat(pz',1,resolution(2));
Set Z values for points outside the area of interest to NaN so MATLAB won't plot them.
pz(~in) = nan;
Generate a bounded colourmap (delete if you want to use full colour range)
% generate colormap
c = jet(100);
[s,l] = bounds(pz,'all');
s = round(s*100);
l = round(l*100);
if s ~= 0
c(1:s,:) = [];
end
if l ~= 100
c(l:100,:) = [];
end
Finally, plot.
figure;
colormap(jet)
surf(px,py,pz,'edgecolor','none');
view(2) % x-y view
Feel free to turn the image arround to see how it looks like in the Z-dimention - beautiful :)
Full code to test:
i=50;
cc = #(xx,x,y) 1./(1+(exp(-xx)/(exp(-x)-exp(-y))));
n_vec = 2:0.1:10;
x_vec = linspace(2,10,length(n_vec));
y_vec = abs(sin(n_vec));
% generate grid
y = [x_vec(:); y_vec(:)];
resolution = [500,500];
px_ = linspace(min(n_vec), max(n_vec), resolution(1));
py_ = linspace(min(y), max(y), resolution(2));
[px, py] = meshgrid(px_, py_);
% extract points
in = inpolygon(px, py, N, X);
% generate z
pz = 1./(1+(exp(-py_)/(exp(-y_vec(i))-exp(-x_vec(i)))));
pz = repmat(pz',1,resolution(2));
pz(~in) = nan;
% generate colormap
c = jet(100);
[s,l] = bounds(pz,'all');
s = round(s*100);
l = round(l*100);
if s ~= 0
c(1:s,:) = [];
end
if l ~= 100
c(l:100,:) = [];
end
% plot
figure;
colormap(c)
surf(px,py,pz,'edgecolor','none');
view(2)
You can use imagesc and meshgrids. See comments in the code to understand what's going on.
Downsample your data
% your initial upper and lower boundaries
n_vec_long = linspace(2,10,1000000);
f_ub_vec_long = linspace(2, 10, length(n_vec_long));
f_lb_vec_long = abs(sin(n_vec_long));
% downsample
n_vec = linspace(n_vec_long(1), n_vec_long(end), 1000); % for example, only 1000 points
% get upper and lower boundary values for n_vec
f_ub_vec = interp1(n_vec_long, f_ub_vec_long, n_vec);
f_lb_vec = interp1(n_vec_long, f_lb_vec_long, n_vec);
% x_vec for the color function
x_vec = 0:0.01:10;
Plot the data
% create a 2D matrix with N and X position
[N, X] = meshgrid(n_vec, x_vec);
% evaluate the upper and lower boundary functions at n_vec
% can be any function at n you want (not tested for crossing boundaries though...)
f_ub_vec = linspace(2, 10, length(n_vec));
f_lb_vec = abs(sin(n_vec));
% make these row vectors into matrices, to create a boolean mask
F_UB = repmat(f_ub_vec, [size(N, 1) 1]);
F_LB = repmat(f_lb_vec, [size(N, 1) 1]);
% create a mask based on the upper and lower boundary functions
mask = true(size(N));
mask(X > F_UB | X < F_LB) = false;
% create data matrix
Z = NaN(size(N));
% create function that evaluates the color profile for each defined value
% in the vectors with the lower and upper bounds
zc = #(X, ub, lb) 1 ./ (1 + (exp(-X) ./ (exp(-ub) - exp(-lb))));
CData = zc(X, f_lb_vec, f_ub_vec); % create the c(x) at all X
% put the CData in Z, but only between the lower and upper bound.
Z(mask) = CData(mask);
% normalize Z along 1st dim
Z = normalize(Z, 1, 'range'); % get all values between 0 and 1 for colorbar
% draw a figure!
figure(1); clf;
ax = axes; % create some axes
sc = imagesc(ax, n_vec, x_vec, Z); % plot the data
ax.YDir = 'normal' % set the YDir to normal again, imagesc reverses it by default;
xlabel('n')
ylabel('x')
This already looks kinda like what you want, but let's get rid of the blue area outside the boundaries. This can be done by creating an 'alpha mask', i.e. set the alpha value for all pixels outside the previously defined mask to 0:
figure(2); clf;
ax = axes; % create some axes
hold on;
sc = imagesc(ax, n_vec, x_vec, Z); % plot the data
ax.YDir = 'normal' % set the YDir to normal again, imagesc reverses it by default;
% set a colormap
colormap(flip(hsv(100)))
% set alpha for points outside mask
Calpha = ones(size(N));
Calpha(~mask) = 0;
sc.AlphaData = Calpha;
% plot the other lines
plot(n_vec, f_ub_vec, 'k', n_vec, f_lb_vec, 'k' ,'linewidth', 1)
% set axis limits
xlim([min(n_vec), max(n_vec)])
ylim([min(x_vec), max(x_vec)])
there is no importance to the functional form of the curves, I would prefer an answer which refers to two general arrays which consist the curves.
It is difficult to achieve this using patch.
However, you may use scatter plots to "fill" the area with coloured dots. Alternatively, and probably better, use surf plot and generate z coordinates using your cc function (See my seperate solution).
The scatter plot method
First, make a grid of points (resolution 500*500) inside the rectangular space bounding the two curves.
y = [x_vec(:); y_vec(:)];
resolution = [500,500];
px = linspace(min(n_vec), max(n_vec), resolution(1));
py = linspace(min(y), max(y), resolution(2));
[px, py] = meshgrid(px, py);
figure;
scatter(px(:), py(:), 1, 'r');
The not-interesting figure of the point grid:
Next, extract the points inside the polygon defined by the two curves.
in = inpolygon(px, py, N, X);
px = px(in);
py = py(in);
hold on;
scatter(px, py, 1, 'k');
Black points are inside the area:
Finally, create color and plot the nice looking gradient colour figure.
% create color for the points
cid = 1./(1+(exp(-py)/(exp(-y_vec(i))-exp(-x_vec(i)))));
c = jet(101);
c = c(round(cid*100)+1,:); % +1 to avoid zero indexing
% plot
figure;
scatter(px,py,16,c,'filled','s'); % use size 16, filled square markers.
Note that you may need a fairly dense grid of points to make sure the white background won't show up. You may also change the point size to a bigger value (won't impact performance).
Of cause, you may use patch to replace scatter but you will need to work out the vertices and face ids, then you may patch each faces separately with patch('Faces',F,'Vertices',V). Using patch this way may impact performance.
Complete code to test:
i=50;
cc = #(xx,x,y) 1./(1+(exp(-xx)/(exp(-x)-exp(-y))));
n_vec = 2:0.1:10;
x_vec = linspace(2,10,length(n_vec));
y_vec = abs(sin(n_vec));
% generate point grid
y = [x_vec(:); y_vec(:)];
resolution = [500,500];
px_ = linspace(min(n_vec), max(n_vec), resolution(1));
py_ = linspace(min(y), max(y), resolution(2));
[px, py] = meshgrid(px_, py_);
% extract points
in = inpolygon(px, py, N, X);
px = px(in);
py = py(in);
% generate color
cid = 1./(1+(exp(-py)/(exp(-y_vec(i))-exp(-x_vec(i)))));
c = jet(101);
c = c(round(cid*100)+1,:); % +1 to avoid zero indexing
% plot
figure;
scatter(px,py,16,c,'filled','s');
I have created some MatLab code that plots a plane wave using two different expressions that give the same plane wave. The first expression is in Cartesian coordinates and works fine. However, the second expression is in polar coordinates and when I calculate the plane wave in this case, the plot is distorted. Both plots should look the same. So what am I doing wrong in transforming to/from polar coordinates?
function Plot_Plane_wave()
clc
clear all
close all
%% Step 0. Input paramaters and derived parameters.
alpha = 0*pi/4; % angle of incidence
k = 1; % wavenumber
wavelength = 2*pi/k; % wavelength
%% Step 1. Define various equivalent versions of the incident wave.
f_u_inc_1 = #(alpha,x,y) exp(1i*k*(x*cos(alpha)+y*sin(alpha)));
f_u_inc_2 = #(alpha,r,theta) exp(1i*k*r*cos(theta-alpha));
%% Step 2. Evaluate the incident wave on a grid.
% Grid for field
gridMax = 10;
gridN = 2^3;
g1 = linspace(-gridMax, gridMax, gridN);
g2 = g1;
[x,y] = meshgrid(g1, g2);
[theta,r] = cart2pol(x,y);
u_inc_1 = f_u_inc_1(alpha,x,y);
u_inc_2 = 0*x;
for ir=1:gridN
rVal = r(ir);
for itheta=1:gridN
thetaVal = theta(itheta);
u_inc_2(ir,itheta) = f_u_inc_2(alpha,rVal,thetaVal);
end
end
%% Step 3. Plot the incident wave.
figure(1);
subplot(2,2,1)
imagesc(g1(1,:), g1(1,:), real(u_inc_1));
hGCA = gca; set(hGCA, 'YDir', 'normal');
subplot(2,2,2)
imagesc(g1(1,:), g1(1,:), real(u_inc_2));
hGCA = gca; set(hGCA, 'YDir', 'normal');
end
Your mistake is that your loop is only going through the first gridN values of r and theta. Instead you want to step through the indices of ix and iy and pull out the rVal and thetaVal of the matrices r and theta.
You can change your loop to
for ix=1:gridN
for iy=1:gridN
rVal = r(ix,iy); % Was equivalent to r(ix) outside inner loop
thetaVal = theta(ix,iy); % Was equivalent to theta(iy)
u_inc_2(ix,iy) = f_u_inc_2(alpha,rVal,thetaVal);
end
end
which gives the expected graphs.
Alternatively you can simplify your code by feeding matrices in to your inline functions. To do this you would have to use an elementwise product .* instead of a matrix multiplication * in f_u_inc_2:
alpha = 0*pi/4;
k = 1;
wavelength = 2*pi/k;
f_1 = #(alpha,x,y) exp(1i*k*(x*cos(alpha)+y*sin(alpha)));
f_2 = #(alpha,r,theta) exp(1i*k*r.*cos(theta-alpha));
% Change v
f_old = #(alpha,r,theta) exp(1i*k*r *cos(theta-alpha));
gridMax = 10;
gridN = 2^3;
[x,y] = meshgrid(linspace(-gridMax, gridMax, gridN));
[theta,r] = cart2pol(x,y);
subplot(1,3,1)
contourf(x,y,real(f_1(alpha,x,y)));
title 'Cartesian'
subplot(1,3,2)
contourf(x,y,real(f_2(alpha,r,theta)));
title 'Polar'
subplot(1,3,3)
contourf(x,y,real(f_old(alpha,r,theta)));
title 'Wrong'
I want to calculate the mean and Gaussian curvatures of some points in a point cloud.
I have x,y,z, that are coordinates and are 1d arrays. I want to use the below code src but in the input parameters X, Y and Z are 2d arrays, I don't know what means that, and how I can calculate 2d arrays corresponding to them.
function [K,H,Pmax,Pmin] = surfature(X,Y,Z),
% SURFATURE - COMPUTE GAUSSIAN AND MEAN CURVATURES OF A SURFACE
% [K,H] = SURFATURE(X,Y,Z), WHERE X,Y,Z ARE 2D ARRAYS OF POINTS ON THE
% SURFACE. K AND H ARE THE GAUSSIAN AND MEAN CURVATURES, RESPECTIVELY.
% SURFATURE RETURNS 2 ADDITIONAL ARGUMENTS,
% [K,H,Pmax,Pmin] = SURFATURE(...), WHERE Pmax AND Pmin ARE THE MINIMUM
% AND MAXIMUM CURVATURES AT EACH POINT, RESPECTIVELY.
% First Derivatives
[Xu,Xv] = gradient(X);
[Yu,Yv] = gradient(Y);
[Zu,Zv] = gradient(Z);
% Second Derivatives
[Xuu,Xuv] = gradient(Xu);
[Yuu,Yuv] = gradient(Yu);
[Zuu,Zuv] = gradient(Zu);
[Xuv,Xvv] = gradient(Xv);
[Yuv,Yvv] = gradient(Yv);
[Zuv,Zvv] = gradient(Zv);
% Reshape 2D Arrays into Vectors
Xu = Xu(:); Yu = Yu(:); Zu = Zu(:);
Xv = Xv(:); Yv = Yv(:); Zv = Zv(:);
Xuu = Xuu(:); Yuu = Yuu(:); Zuu = Zuu(:);
Xuv = Xuv(:); Yuv = Yuv(:); Zuv = Zuv(:);
Xvv = Xvv(:); Yvv = Yvv(:); Zvv = Zvv(:);
Xu = [Xu Yu Zu];
Xv = [Xv Yv Zv];
Xuu = [Xuu Yuu Zuu];
Xuv = [Xuv Yuv Zuv];
Xvv = [Xvv Yvv Zvv];
% First fundamental Coeffecients of the surface (E,F,G)
E = dot(Xu,Xu,2);
F = dot(Xu,Xv,2);
G = dot(Xv,Xv,2);
m = cross(Xu,Xv,2);
p = sqrt(dot(m,m,2));
n = m./[p p p];
% Second fundamental Coeffecients of the surface (L,M,N)
L = dot(Xuu,n,2);
M = dot(Xuv,n,2);
N = dot(Xvv,n,2);
[s,t] = size(Z);
% Gaussian Curvature
K = (L.*N - M.^2)./(E.*G - F.^2);
K = reshape(K,s,t);
% Mean Curvature
H = (E.*N + G.*L - 2.*F.*M)./(2*(E.*G - F.^2));
H = reshape(H,s,t);
% Principal Curvatures
Pmax = H + sqrt(H.^2 - K);
Pmin = H - sqrt(H.^2 - K);
You have ways to convert your x,y,z data to surface matrices/ 2D arrays. Way depends on, how and what your data is.
Structured grid data:
(i). If your x,y,z corresponds to a structured grid, then you can straight a way get unique values of x,y which gives number of points along (nx,ny) along x and y axes respectively. With this (nx,ny), you need to reshape x,y,z data into matrices X,Y,Z respectively and use your function.
(ii). If you are not okay with reshaping, you can get min and max values of x,y make your own grid using meshgrid and do interpolation using griddata.
Unstructured grid data: If your data is unstructured/ scattered, get min and max, make your grid using meshgrid and do interpolation using griddata , scatteredInterpolant.
Also have a look in the following links:
https://in.mathworks.com/matlabcentral/fileexchange/56533-xyz2grd
https://in.mathworks.com/matlabcentral/fileexchange/56414-xyz-file-functions
I'm trying to calculate the surface between two circular curves (yellow surface in this picture as simplification) but I'm somehow stuck since I don't have datapoints at the same angular values of the two curves. Any ideas?
Thanks for your help!
Picture:
I assume you have the x,y coordinates which you used to the plot. I obtained them here using imfreehand. I used inpolygon to generate a binary mask for each curve and then apply xor on them to get a mask of the desired area:
% x,y were obtained using imfreehand on 100x100 image and getPosition()
x = [21;22;22;22;22;22;22;23;23;23;23;23;23;24;25;25;26;26;27;28;29;30;30;31;32;32;33;34;35;36;37;38;39;40;41;42;43;44;45;46;47;48;49;50;51;52;53;54;55;56;57;58;59;60;61;62;63;64;65;66;67;68;69;70;71;72;73;74;75;76;77;78;79;79;80;80;81;81;81;82;82;82;82;83;83;83;84;84;85;85;86;86;86;86;86;86;85;84;84;83;82;81;80;79;78;77;76;75;74;73;72;71;70;69;68;67;66;65;64;63;62;61;60;59;58;57;56;55;54;53;52;51;50;49;48;47;46;45;44;43;42;41;40;39;38;37;36;35;34;33;32;31;30;29;28;27;26;25;25;24;24;23;22;21;21;21;21;21;21;21;21;21;21;21;21;21];
y = [44;43;42;41;40;39;38;37;36;35;34;33;32;31;30;29;28;27;26;25;24;23;22;21;20;19;18;18;17;17;17;17;17;16;16;16;16;16;16;15;15;14;14;14;14;14;14;15;15;15;16;16;17;17;17;17;18;18;18;19;20;20;21;22;23;23;24;25;26;27;28;29;30;31;32;33;34;35;36;37;38;39;40;41;42;43;44;45;46;47;48;49;50;51;52;53;54;55;56;56;57;57;58;59;59;60;61;61;61;61;61;60;60;60;59;58;57;56;56;55;55;54;54;54;54;54;54;54;54;54;55;55;55;55;56;57;58;59;60;61;61;62;63;63;64;64;65;65;66;66;66;66;66;66;65;64;63;62;61;60;59;58;57;56;55;54;53;52;51;50;49;48;47;46;45;44];
% generate arbitrary xy
x1 = (x - 50)./10; y1 = (y - 50)./10;
x2 = (x - 50)./10; y2 = (y - 40)./10;
% generate binary masks using poly2mask
pixelSize = 0.01; % resolution
xx = min([x1(:);x2(:)]):pixelSize:max([x1(:);x2(:)]);
yy = min([y1(:);y2(:)]):pixelSize:max([y1(:);y2(:)]);
[xg,yg] = meshgrid(xx,yy);
mask1 = inpolygon(xg,yg,x1,y1);
mask2 = inpolygon(xg,yg,x2,y2);
% add both masks (now their common area pixels equal 2)
combinedMask = mask1 + mask2;
% XOR on both of them
xorMask = xor(mask1,mask2);
% compute mask area in units (rather than pixels)
Area = bwarea(xorMask)*pixelSize^2;
% plot
subplot(131);
plot(x1,y1,x2,y2,'LineWidth',2);
title('Curves');
axis square
set(gca,'YDir','reverse');
subplot(132);
imshow(combinedMask,[]);
title('Combined Mask');
subplot(133);
imshow(xorMask,[]);
title(['XNOR Mask, Area = ' num2str(Area)]);
function area = area_between_curves(initial,corrected)
interval = 0.1;
x = -80:interval:80;
y = -80:interval:80;
[X,Y] = meshgrid(x,y);
in_initial = inpolygon(X,Y,initial(:,1),initial(:,2));
in_corrected = inpolygon(X,Y,corrected(:,1),corrected(:,2));
in_area = xor(in_initial,in_corrected);
area = interval^2*nnz(in_area);
% visualization
figure
hold on
plot(X(in_area),Y(in_area),'r.')
plot(X(~in_area),Y(~in_area),'b.')
end
If I use the lines of the question, this is the result:
area = 1.989710000000001e+03