I don't know if this is possible, but I would like to be able to plot contour lines in a given latitude and longitude.
I have an ocean model that gives me the currents in direction u and v at location x (longitude) and y(latitude).
Using the quiver function (quiver(x,y,u,v)) and the following code, I managed to map the currents in the Gulf of Lions.
Step=8 %Only use 1 in 8 data point so the arrows don't overlap too much
figure
q=quiver(lonu(1:Step:681,1:Step:711),latu(1:Step:681,1:Step:711),U,V,0)
As you can see, the model is more detailed close to the coast, because it uses the following grid:
Source: Briton, Florence, et al. "Highâresolution modelling of ocean circulation can reveal retention spots important for biodiversity conservation." Aquatic Conservation: Marine and Freshwater Ecosystems 28.4 (2018): 882-893.
The problem with this is that when I try to use contour or contourf it completely loses the shape of the gulf of Lions due to the choice of grid:
figure
contourf(sqrt(U.^2+V.^2))%The vector of the current is X=sqrt(U^2+V^2) see pythagoras
colorbar
So eventually, I would like to be able to indicate the strength of the current using contourf while indicating the direction using quiver. So how do I reshape the picture given by contourf into something realistic using coordinates?
I checked question Matlab 2D contour using X-Y coordinate data but I don't understand how to use the proposed function.
You suddenly decided to avoid imputing the data that gives the shape, X and Y input parameters.
contourf(lonu(1:Step:681,1:Step:711),latu(1:Step:681,1:Step:711),sqrt(U.^2+V.^2))%The vector of the current is X=sqrt(U^2+V^2) see pythagoras
Related
I'm trying to plot some 2d points with WA, but with no luck.
I do not want to plot points from data, like [{1,2}, {3,4}, ...] as described here; what i would like to do is to plot points from a function/equation, e.g: (x,y) : y=x, "Every x and y that match y=x".
Of course my example is just a cointinuous straight line, obviously i'm trying to plot a discrete set of points; in fact i would like to plot a lattice of points from a formula describing those points.
Maybe i'm writing my formula wrong, maybe i need to use some specific WA function, maybe WA don't do that, i don't know, but i can't find any tips/clues about it.
In one histc, I have latitude data. In another histc, I have longitude data. I have plotted the bar graphs of these 2 separately. Now, I want to combine them and produce a 3D graph where the x-axis is latitude, y-axis is longitude, and z-axis is the frequency with which each latitude-longitude pair occurs.
The problem is that, while plotting the graphs for latitudes and longitudes separately, I calculated their respective frequencies by taking their individual histcs separately. However, when I want to make it a 3D plot, I can't seem to find a way to take the histc of the latitude-longitude pairs.
EDIT: I am adding my code for plotting the bar graphs here upon being asked by one commenter to do so, though I don't see how that would help. The bar graph for latitude is: bar(unique(M), histc(M,unique(M))) and that for longitude is bar(unique(N), histc(N,unique(N))). And the M and N are nx1 matrices. (Actually, they are 2 columns of a much larger matrix. But for simplicity in comprehension, I have avoided writing complex formulae here.)
EDIT: I reckon what I am looking for might be solved by surface plot, surf. But I am not sure. If it is, then the issue I am facing, speaking in terms of surf, can be stated as an issue in defining the Z parameter.
latitudes=180*(rand(1,10000)-0.5);
longitudes=360*(rand(1,10000)-0.5);
d=[latitudes;longitudes];
minVal_Lat=-90;
maxVal_Lat=90;
minVal_Long=-180;
maxVal_Long=180;
delta=10;
axisLat=minVal_Lat:delta:maxVal_Lat;
axisLong=minVal_Long:delta:maxVal_Long;
nPDF_Lat=length(axisLat);
nPDF_Long=length(axisLong);
PDF=zeros(nPDF_Lat,nPDF_Long);
temp=0;
count_i=1;
count_j=1;
for i=axisLat;
lowlimit_x=i-delta/2;
upperlimit_x =i+delta/2;
for j=axisLong;
lowlimit_y=j-delta/2;
upperlimit_y =j+delta/2;
temp=0;
for k=1:length(d(1,:));
if lowlimit_x<=d(1,k) & d(1,k)<upperlimit_x
if lowlimit_y<=d(2,k) & d(2,k)<upperlimit_y
temp=temp+1;
else
end
else
end
end
PDF(count_i,count_j)=temp;
count_j=count_j+1;
end
count_i=count_i+1;
count_j=1;
end
normFactor=sum(sum(PDF));
PDF=(1/normFactor)*PDF;
randVar_Lat=minVal_Lat:delta:maxVal_Lat;
randVar_Long=minVal_Long:delta:maxVal_Long;
surf(randVar_Lat,randVar_Long,PDF')
If you want get a 2D probability density function with surf plot, these codes will be worked.
I have N 3D observations taken from an optical motion capture system in XYZ form.
The motion that was captured was just a simple circle arc, derived from a rigid body with fixed axis of rotation.
I used the princomp function in matlab to get all marker points on the same plane i.e. the plane on which the motion has been done.
(See a pic representing 3D data on the plane that was found, below)
What i want to do after the previous step is to look the fitted data on the plane that was found and get the curve of the captured motion in 2D.
In the princomp how to, it is said that
The first two coordinates of the principal component scores give the
projection of each point onto the plane, in the coordinate system of
the plane.
(from "Fitting an Orthogonal Regression Using Principal Components Analysis" article on mathworks help site)
So i thought that if i just plot those pc scores -plot(score(:,1),score(:,2))- i'll get the motion curve. Instead what i got is this.
(See a pic representing curve data in 2D derived from pc scores, below)
The 2d curve seems stretched and nonlinear (different y values for same x values) when it shouldn't be. The curve that i am looking for, should be interpolated by just using simple polynomial (polyfit) or circle fit in matlab.
Is this happening because the plane that was found looks like rhombus relative to the original coordinate system and the pc axes are rotated with respect to the basis of plane in such way that produce this stretch?
Then i thought that, this is happening because of the different coordinate systems of optical system and Matlab. Optical system's (ie cameras) co.sys. is XZY oriented and Matlab's default (i think) co.sys is XYZ oriented. I transformed my data to correspond to Matlab's co.sys through a rotation matrix, run again princomp but i got the same stretch in the 2D curve (the new curve just had different orientation now).
Somewhere else i read that
Principal Components Analysis chooses the first PCA axis as that line
that goes through the centroid, but also minimizes the square of the
distance of each point to that line. Thus, in some sense, the line is
as close to all of the data as possible. Equivalently, the line goes
through the maximum variation in the data. The second PCA axis also
must go through the centroid, and also goes through the maximum
variation in the data, but with a certain constraint: It must be
completely uncorrelated (i.e. at right angles, or "orthogonal") to PCA
axis 1.
I know that i am missing something but i have a problem understanding why i get a stretched curve. What i have to do so i can get the curve right?
Thanks in advance.
EDIT: Here is a sample data file (3 columns XYZ coords for 2 markers)
w w w.sendspace.com/file/2hiezc
I have a 3D data set of a surface that is not a function graph. The data is just a bunch of points in 3D, and the only thing I could think of was to try scatter3 in Matlab. Surf will not work since the surface is not a function graph.
Using scatter3 gave a not so ideal result since there is no perspective/shading of any sort.
Any thoughts? It does not have to be Matlab, but that is my go-to source for plotting.
To get an idea of the type of surface I have, consider the four images:
The first is a 3D contour plot, the second is a slice in a plane {z = 1.8} of the contour. My goal is to pick up all the red areas. I have a method to do this for each slice {z = k}. This is the 3rd plot, and I like what I see here a lot.
Iterating this over z give will give a surface, which is the 4th plot, which is a bit noisy (though I have ideas to reduce the noise...). If I plot just the black surface using scatter3 without the contour all I get is a black indistinguishable blob, but for every slice I get a smooth curve, and I have noticed that the curves vary pretty smoothly when I adjust z.
Some fine-tuning will give a much better 4th plot, but still, even if I get the 4th plot to have no noise at all, the result using scatter3 will be a black incomprehensible blob when plotted alone and not on top of the 3D contour. I would like to get a nice picture of the full surface that is not plotted on top of the 3D contour plot
In fact, just to compare and show how bad scatter3 is for surfaces, even if you had exact points on a sphere and used scatter3 the result would be a black blob, and wouldn't even look like a sphere
Can POV-Ray handle this? I've never used it...
If you have a triangulation of your points, you could consider using the trisurf function. I have used that before to generate closed surfaces that have no boundary (such as polyhedra and spheres). The downside is that you have to generate a triangulation of your points. This may not be ideal to your needs but it definitely an option.
EDIT: As #High Performance Mark suggests, you could try using delaunay to generate a triangulation in Matlab
just wanted to follow up on this question. A quick nice way to do this in Matlab is the following:
Consider the function d(x, y, z) defined as the minimum distance from (x, y, z) to your data set. Make sure d(x, y, z) is defined on some grid that contains the data set you're trying to plot.
Then use isosurface to plot a (some) countour(s) of d(x, y, z). For me plotting the contour 0.1 of d(x, y ,z) was enough: Matlab will plot a nice looking surface of all points within a distance 0.1 of the data set with good lighting and all.
In povray, a blob object could be used to display a very dense collection of points, if you make them centers of spheres.
http://www.povray.org/documentation/view/3.6.1/71/
If you want to be able to make slices of "space" and have them colored as per your data, then maybe the object pattern (based on a #declared blob object) might do the trick.
Povray also has a way to work with df3 files, which I've never worked with, but this user appears to have done something similar to your visualization.
http://paulbourke.net/miscellaneous/df3/
I have a formula that depends on theta and phi (spherical coordinates 0<=theta<=2*pi and 0<=phi<=pi). By inserting each engle, I obtained a quantity. Now I have a set of data for different angles and I need to plot the surface. My data is a 180*360 matrix, so I am not sure if I can use SURF or MESH or PLOT3. The figure should be a surface that include all data and the axes should be in terms of the quantity, not the quantity versus the angles. How can I plot such a surface?
I see no reason why you cannot use mesh or surf to plot such data. Another option I tend to use is that of density plots. You basically display the dependent variable (quantity) as an image and include the independent variables (angles) along the axis, much like you would with the aforementioned 3D plotting functions. This can be done with imagesc.
Typically you would want your axes to be the dependent variables. Could you elaborate more on this point?
If I understand you correctly you have calculated a function f(theta,phi) and now you want to plot the surface containing all the points with the polar coordinated (r,theta,phi) where r=f(theta,phi).
If this is what you want to do, the 2D version of such a plot is included in MATLAB under the name polar. Unfortunately, as you pointed out, polar3 on MatlabCentral is not the generalization you are looking for.
I have been able to plot a sphere with the following code, using constant r=1. You can give it a try with your function:
phi1=0:1/(3*pi):pi; %# this would be your 180 points
theta1=-pi:1/(3*pi):pi; % your 360 points
r=ones(numel(theta1),numel(phi1));
[phi,theta]=meshgrid(phi1,theta1);
x=r.*sin(theta).*cos(phi);
y=r.*sin(theta).*sin(phi);
z=r.*cos(theta);
tri=delaunay(x(:),y(:),z(:));
trisurf(tri,x,y,z);
From my tests it seems that delaunay also includes a lot of triangles which go through the volume of my sphere, so it seems this is not optimal. So maybe you can have a look at fill3 and construct the triangles it draws itself: as a first approximation, you could have the points [x(n,m) x(n+1,m) x(n,m+1)] combined into one triangle, and [x(n+1,m) x(n+1,m+1) x(n+1,m+1)] into another...?