I am generating a scatter plot containing data from multiple sources, as displayed below.
I would like to be able to generate a curve surrounding an arbitrary query point and passing through points on scatter plot. Final goal is to calculate the area between the lines on the plot.
I have implemented solution using finding points with knnsearch in a circular fashion and then applying hampel filter to eliminate noise. In the example below, I have selected a point right about in the middle of the blue-shaded area. As you can see, the result is far from perfect, and I need more precision.
I am looking for something similar to boundary function, but to work from the inside of the point cloud, not from the outside.
Final goal is to calculate the area between the lines on the plot.
I would do it differently. Just take any two lines of the plot, calculate the area under the curves with some kind of numerical approximation (for example trapezoidal numerical integration), then subtract the areas and obtain the area between the lines.
Thank to idea in Trilarion's answer, I was able to come up with the better solution.
Note that I use notation for YZ plane instead of XY (to keep consistent with robot coordinate system).
Solution
Generate curves for each set of scatter data
% Scatter data is in iy and iz vectors.
curve = fit(iy, iz, 'smoothingspline', 'SmoothingParam', 0.5);
% Remove outliers.
fdata = feval(curve, iy);
I = abs(fdata - iz) > 0.5 * std(iz);
outliers = excludedata(iy, iz, 'indices', I);
% Final curve without outliers.
curve = fit(iy, iz, 'smoothingspline', 'Exclude', outliers, 'SmoothingParam', 0.5);
Plot curves and scatter data
% Color maps generated by MATLAB's colormap function.
h_curve = plot(curve);
set(h_curve, 'Color', color_map_light(i,:));
scatter(iy, iz, '.', 'MarkerFaceColor', color_map(i,:))
Let user provide an input by selecting points
User selects one point as a query point and two points for limits along Y axis. This is because some curves come close, but never intersect.
[cs_position.y, cs_position.z] = ginput(1);
[cs_area_limits, ~] = ginput(2);
if cs_area_limits(1) > cs_area_limits(2)
cs_area_limits = flipud(cs_area_limits);
end
plot_cross_section(cs_position);
Finally calculate and plot surface area
This section uses fantastic answer by Doresoom.
function [ ] = plot_cross_section(query_point)
%PLOT_CROSS_SECTION Calculates and plots cross-section area.
% query_point Query point.
% Find values on query point's Y on each of the curves.
z_values = cellfun(#(x, y) feval(x, y),...
curves, num2cell(ones(size(curves)) * query_point.y))
% Find which curves are right above and below the query point.
id_top = find(z_values >= query_point.z, 1, 'first')
id_bottom = find(z_values < query_point.z, 1, 'last')
if isempty(id_top) || isempty(id_bottom)
return
end
% Generate points along curves on the range over Y.
y_range = cs_area_limits(1):0.1:cs_area_limits(2);
z_top = feval(curves{id_top}, y_range).';
z_bottom = feval(curves{id_bottom}, y_range).';
% Plot area.
Y = [ y_range, fliplr(y_range) ];
Z = [ z_top, fliplr(z_bottom) ];
fill(Y, Z, 'b', 'LineStyle', 'none')
alpha 0.5
hold on
% Calculate area and show to user.
cs_area = polyarea(Y, Z);
area_string = sprintf('%.2f mm^2', cs_area);
text(0, -3, area_string, 'HorizontalAlignment', 'center')
end
Result
Related
I've found this answer, but I can't complete my work. I wanted to plot more precisely the functions I am studying, without overcoloring my function with black ink... meaning reducing the number of mesh lines. I precise that the functions are complex.
I tried to add to my already existing code the work written at the link above.
This is what I've done:
r = (0:0.35:15)'; % create a matrix of complex inputs
theta = pi*(-2:0.04:2);
z = r*exp(1i*theta);
w = z.^2;
figure('Name','Graphique complexe','units','normalized','outerposition',[0.08 0.1 0.8 0.55]);
s = surf(real(z),imag(z),imag(w),real(w)); % visualize the complex function using surf
s.EdgeColor = 'none';
x=s.XData;
y=s.YData;
z=s.ZData;
x=x(1,:);
y=y(:,1);
% Divide the lengths by the number of lines needed
xnumlines = 10; % 10 lines
ynumlines = 10; % 10 partitions
xspacing = round(length(x)/xnumlines);
yspacing = round(length(y)/ynumlines);
hold on
for i = 1:yspacing:length(y)
Y1 = y(i)*ones(size(x)); % a constant vector
Z1 = z(i,:);
plot3(x,Y1,Z1,'-k');
end
% Plotting lines in the Y-Z plane
for i = 1:xspacing:length(x)
X2 = x(i)*ones(size(y)); % a constant vector
Z2 = z(:,i);
plot3(X2,y,Z2,'-k');
end
hold off
But the problem is that the mesh is still invisible. How to fix this? Where is the problem?
And maybe, instead of drawing a grid, perhaps it is possible to draw circles and radiuses like originally on the graph?
I found an old script of mine where I did more or less what you're looking for. I adapted it to the radial plot you have here.
There are two tricks in this script:
The surface plot contains all the data, but because there is no mesh drawn, it is hard to see the details in this surface (your data is quite smooth, this is particularly true for a more bumpy surface, so I added some noise to the data to show this off). To improve the visibility, we use interpolation for the color, and add a light source.
The mesh drawn is a subsampled version of the original data. Because the original data is radial, the XData and YData properties are not a rectangular grid, and therefore one cannot just take the first row and column of these arrays. Instead, we use the full matrices, but subsample rows for drawing the circles and subsample columns for drawing the radii.
% create a matrix of complex inputs
% (similar to OP, but with more data points)
r = linspace(0,15,101).';
theta = linspace(-pi,pi,101);
z = r * exp(1i*theta);
w = z.^2;
figure, hold on
% visualize the complex function using surf
% (similar to OP, but with a little bit of noise added to Z)
s = surf(real(z),imag(z),imag(w)+5*rand(size(w)),real(w));
s.EdgeColor = 'none';
s.FaceColor = 'interp';
% get data back from figure
x = s.XData;
y = s.YData;
z = s.ZData;
% draw circles -- loop written to make sure the outer circle is drawn
for ii=size(x,1):-10:1
plot3(x(ii,:),y(ii,:),z(ii,:),'k-');
end
% draw radii
for ii=1:5:size(x,2)
plot3(x(:,ii),y(:,ii),z(:,ii),'k-');
end
% set axis properties for better 3D viewing of data
set(gca,'box','on','projection','perspective')
set(gca,'DataAspectRatio',[1,1,40])
view(-10,26)
% add lighting
h = camlight('left');
lighting gouraud
material dull
How about this approach?
[X,Y,Z] = peaks(500) ;
surf(X,Y,Z) ;
shading interp ;
colorbar
hold on
miss = 10 ; % enter the number of lines you want to miss
plot3(X(1:miss:end,1:miss:end),Y(1:miss:end,1:miss:end),Z(1:miss:end,1:miss:end),'k') ;
plot3(X(1:miss:end,1:miss:end)',Y(1:miss:end,1:miss:end)',Z(1:miss:end,1:miss:end)','k') ;
I tried generating the cone of influence for a Morlet wavelet power spectrum using the following MATLAB code:
cone = conofinf('morl',1:365,LenSig,[],'plot');
However, all I get is a strange looking shaded area bounded by two linear lines. It doesn't look like a cone of influence for Morlet wavelet power spectrum.
What did I do wrong?
I guess you wanted to output COI at the borders of your wavelet transform. In this case you have to specify the last parameter as non-empty vector, but with coordinates, where you need COI to be computed, e.g.
cone = conofinf('morl',1:365,LenSig,[1 LenSig],'plot');
I had a similar task and here's what I did:
figure;
% plot wavelet transform / scalogram
imagesc(t,scales,WT);
axis square;
colorbar;
% annotate axes and title
title('Coefficients of continuous wavelet transform');
xlabel('Time (or space) b');
ylabel('Scales a');
% get cone of influence
% Here, you have to specify points at which you want to calculate COI
% as the last parameter:
cone = conofinf(wname,scales,LenSig,[1 LenSig]);
% combine left and right edges
cone = [cone{1}(:,floor(1:LenSig/2)) cone{2}(:,ceil(LenSig/2):end)];
% previous steps give you an area under COI
% you can see it with: figure; imagesc(cone);
% now, we want to get the border of this area
coi = zeros(1,LenSig);
for idx = 1:LenSig
valcoi = find(cone(:,idx)==1,1,'last');
if ~isempty(valcoi)
coi(idx) = f(valcoi);
end
end
% now plot COI border on top of your wavelet transform
hold on;
plot(t,coi,'k','LineWidth',1.5);
hold off;
Optionally, you can hatch the area under COI, but it's a bit of a hack and it's not ideal. For this you will need hatchfill function (here's an example how to use it). Once you have this function on your path, you can use it like this:
[~,h] = contourf(t,scales,cone*max(WT(:)),[1 1]*max(WT(:)));
hPatch = findobj(h, 'Type', 'patch');
hh = hatchfill(hPatch, 'cross', 45, 10);
I'm trying to create a surface plot from (x,y,z) data on an irregular grid. The datasets are located along diagonal lines of positive gradient in the (x,y) plane. The method is illustrated below
xi = linspace (min(x), max(x), 1000);
yi = linspace (min(y), max(y), 1000);
zi = linspace (min(z), max(z), 400);
[XI YI]=meshgrid(xi,yi);
F = TriScatteredInterp (x,y,z);
Vi = F(XI,YI);
surf(Xi,Yi,Vi);
shading interp;
view(2)
An example result is shown below:
The data should be smooth in the x direction (so at y=860 there should be a single, continuous blue dip). I think the problem is that the interpolation is being carried out well along each dataset but poorly between them as the spacing between the datasets is far larger than between points within a dataset.
What would be the best way to deal with this?
i just started with my master thesis and i already am in trouble with my capability/understanding of matlab.
The thing is, i have a trajectory on a surface of a planet/moon whatever (a .mat with the time, and the coordinates. Then i have some .mat with time and the measurement at that time.
I am able to plot this as a color coded trajectory (using the measurement and the coordinates) in scatter(). This works awesomely nice.
However my problem is that i need something more sophisticated.
I now need to take the trajectory and instead of color-coding it, i am supposed to add the graph (value) of the measurement (which is given for each point) to the trajectory (which is not always a straight line). I will added a little sketch to explain what i want. The red arrow shows what i want to add to my plot and the green shows what i have.
You can always transform your data yourself: (using the same notation as #Shai)
x = 0:0.1:10;
y = x;
m = 10*sin(x);
So what you need is the vector normal to the curve at each datapoint:
dx = diff(x); % backward finite differences for 2:end points
dx = [dx(1) dx]; % forward finite difference for 1th point
dy = diff(y);
dy = [dy(1) dy];
curve_tang = [dx ; dy];
% rotate tangential vectors 90° counterclockwise
curve_norm = [-dy; dx];
% normalize the vectors:
nrm_cn = sqrt(sum(abs(curve_norm).^2,1));
curve_norm = curve_norm ./ repmat(sqrt(sum(abs(curve_norm).^2,1)),2,1);
Multiply that vector with the measurement (m), offset it with the datapoint coordinates and you're done:
mx = x + curve_norm(1,:).*m;
my = y + curve_norm(2,:).*m;
plot it with:
figure; hold on
axis equal;
scatter(x,y,[],m);
plot(mx,my)
which is imo exactly what you want. This example has just a straight line as coordinates, but this code can handle any curve just fine:
x=0:0.1:10;y=x.^2;m=sin(x);
t=0:pi/50:2*pi;x=5*cos(t);y=5*sin(t);m=sin(5*t);
If I understand your question correctly, what you need is to rotate your actual data around an origin point at a certain angle. This is pretty simple, as you only need to multiply the coordinates by a rotation matrix. You can then use hold on and plot to overlay your plot with the rotated points, as suggested in the comments.
Example
First, let's generate some data that resembles yours and create a scatter plot:
% # Generate some data
t = -20:0.1:20;
idx = (t ~= 0);
y = ones(size(t));
y(idx) = abs(sin(t(idx)) ./ t(idx)) .^ 0.25;
% # Create a scatter plot
x = 1:numel(y);
figure
scatter(x, x, 10, y, 'filled')
Now let's rotate the points (specified by the values of x and y) around (0, 0) at a 45° angle:
P = [x(:) * sqrt(2), y(:) * 100] * [1, 1; -1, 1] / sqrt(2);
and then plot them on top of the scatter plot:
hold on
axis square
plot(P(:, 1), P(:, 2))
Note the additional things have been done here for visualization purposes:
The final x-coordinates have been stretched (by sqrt(2)) to the appropriate length.
The final y-coordinates have been magnified (by 100) so that the rotated plot stands out.
The axes have been squared to avoid distortion.
This is what you should get:
It seems like you are interested in 3D plotting.
If I understand your question correctly, you have a 2D curve represented as [x(t), y(t)].
Additionally, you have some value m(t) for each point.
Thus we are looking at the plot of a 3D curve [x(t) y(t) m(t)].
you can easily achieve this using
plot3( x, y, m ); % assuming x,y, and m are sorted w.r.t t
alternatively, you can use the 3D version of scatter
scatter3( x, y, m );
pick your choice.
Nice plot BTW.
Good luck with your thesis.
I have a problem dealing with 3rd dimension plot for three variables.
I have three matrices: Temperature, Humidity and Power. During one year, at every hour, each one of the above were measured. So, we have for each matrix 365*24 = 8760 points. Then, one average point is taken every day. So,
Tavg = 365 X 1
Havg = 365 X 1
Pavg = 365 X 1
In electrical point of veiw, the power depends on the temperature and humidity. I want to discover this relation using a three dimensional plot.
I tried using mesh, meshz, surf, plot3, and many other commands in MATLAB but unfortunately I couldn't get what I want. For example, let us take first 10 days. Here, every day is represented by average temperature, average humidity and average power.
Tavg = [18.6275
17.7386
15.4330
15.4404
16.4487
17.4735
19.4582
20.6670
19.8246
16.4810];
Havg = [75.7105
65.0892
40.7025
45.5119
47.9225
62.8814
48.1127
62.1248
73.0119
60.4168];
Pavg = [13.0921
13.7083
13.4703
13.7500
13.7023
10.6311
13.5000
12.6250
13.7083
12.9286];
How do I represent these matrices by three dimension plot?
The challenge is that the 3-D surface plotting functions (mesh, surf, etc.) are looking for a 2-D matrix of z values. So to use them you need to construct such a matrix from the data.
Currently the data is sea of points in 3-D space, so, you have to map these points to a surface. A simple approach to this is to divide up the X-Y (temperature-humidity) plane into bins and then take the average of all of the Z (power) data. Here is some sample code for this that uses accumarray() to compute the averages for each bin:
% Specify bin sizes
Tbin = 3;
Hbin = 20;
% Create binned average array
% First create a two column array of bin indexes to use as subscripts
subs = [round(Havg/Hbin)+1, round(Tavg/Tbin)+1];
% Now create the Z (power) estimate as the average value in each bin
Pest = accumarray(subs,Pavg,[],#mean);
% And the corresponding X (temp) & Y (humidity) vectors
Tval = Tbin/2:Tbin:size(Pest,2)*Tbin;
Hval = Hbin/2:Hbin:size(Pest,1)*Hbin;
% And create the plot
figure(1)
surf(Tval, Hval, Pest)
xlabel('Temperature')
ylabel('Humidity')
zlabel('Power')
title('Simple binned average')
xlim([14 24])
ylim([40 80])
The graph is a bit coarse (can't post image yet, since I am new) because we only have a few data points. We can enhance the visualization by removing any empty bins by setting their value to NaN. Also the binning approach hides any variation in the Z (power) data so we can also overlay the orgional point cloud using plot3 without drawing connecting lines. (Again no image b/c I am new)
Additional code for the final plot:
%% Expanded Plot
% Remove zeros (useful with enough valid data)
%Pest(Pest == 0) = NaN;
% First the original points
figure(2)
plot3(Tavg, Havg, Pavg, '.')
hold on
% And now our estimate
% The use of 'FaceColor' 'Interp' uses colors that "bleed" down the face
% rather than only coloring the faces away from the origin
surfc(Tval, Hval, Pest, 'FaceColor', 'Interp')
% Make this plot semi-transparent to see the original dots anb back side
alpha(0.5)
xlabel('Temperature')
ylabel('Humidity')
zlabel('Power')
grid on
title('Nicer binned average')
xlim([14 24])
ylim([40 80])
I think you're asking for a surface fit for your data. The Curve Fitting Toolbox handles this nicely:
% Fit model to data.
ft = fittype( 'poly11' );
fitresult = fit( [Tavg, Havg], Pavg, ft);
% Plot fit with data.
plot( fitresult, [xData, yData], zData );
legend( 'fit 1', 'Pavg vs. Tavg, Havg', 'Location', 'NorthEast' );
xlabel( 'Tavg' );
ylabel( 'Havg' );
zlabel( 'Pavg' );
grid on
If you don't have the Curve Fitting Toolbox, you can use the backslash operator:
% Find the coefficients.
const = ones(size(Tavg));
coeff = [Tavg Havg const] \ Pavg;
% Plot the original data points
clf
plot3(Tavg,Havg,Pavg,'r.','MarkerSize',20);
hold on
% Plot the surface.
[xx, yy] = meshgrid( ...
linspace(min(Tavg),max(Tavg)) , ...
linspace(min(Havg),max(Havg)) );
zz = coeff(1) * xx + coeff(2) * yy + coeff(3);
surf(xx,yy,zz)
title(sprintf('z=(%f)*x+(%f)*y+(%f)',coeff))
grid on
axis tight
Both of these fit a linear polynomial surface, i.e. a plane, but you'll probably want to use something more complicated. Both of these techniques can be adapted to this situation. There's more information on this subject at mathworks.com: How can I determine the equation of the best-fit line, plane, or N-D surface using MATLAB?.
You might want to look at Delaunay triangulation:
tri = delaunay(Tavg, Havg);
trisurf(tri, Tavg, Havg, Pavg);
Using your example data, this code generates an interesting 'surface'. But I believe this is another way of doing what you want.
You might also try the GridFit tool by John D'Errico from MATLAB Central. This tool produces a surface similar to interpolating between the data points (as is done by MATLAB's griddata) but with cleaner results because it smooths the resulting surface. Conceptually multiple datapoints for nearby or overlapping X,Y coordinates are averaged to produce a smooth result rather than noisy "ripples." The tool also allows for some extrapolation beyond the data points. Here is a code example (assuming the GridFit Tool has already been installed):
%Establish points for surface
num_points = 20;
Tval = linspace(min(Tavg),max(Tavg),num_points);
Hval = linspace(min(Havg),max(Havg),num_points);
%Do the fancy fitting with smoothing
Pest = gridfit(Tavg, Havg, Pavg, Tval, Hval);
%Plot results
figure(5)
surfc(XI,YI,Pest, 'FaceColor', 'Interp')
To produce an even nicer plot, you can add labels, some transparancy and overlay the original points:
alpha(0.5)
hold on
plot3(Tavg,Havg,Pavg,'.')
xlabel('Temperature')
ylabel('Humidity')
zlabel('Power')
grid on
title('GridFit')
PS: #upperBound: Thanks for the Delaunay triangulation tip. That seems like the way to go if you want to go through each of the points. I am a newbie so can't comment yet.
Below is your solution:
Save/write the Myplot3D function
function [x,y,V]=Myplot3D(X,Y,Z)
x=linspace(X(1),X(end),100);
y=linspace(Y(1),Y(end),100);
[Xt,Yt]=meshgrid(x,y);
V=griddata(X,Y,Z,Xt,Yt);
Call the following from your command line (or script)
[Tavg_new,Pavg_new,V]=Myplot3D(Tavg,Pavg,Havg);
surf(Tavg_new,Pavg_new,V)
colormap jet;
xlabel('Temperature')
ylabel('Power/Pressure')
zlabel('Humidity')