A density plot using 2 coordinates columns and a weight column - matlab

I have a matrix with 3 columns. The first two columns are coordinates and the third is weight or intensity.
newmat = [ 27.37 -45.69 14.47
27.37 -45.68 18.58
27.37 -45.67 29.05
27.37 -45.66 51.7
... ... ... ]
I have already created a scatter plot:
However, I'd like to have something like a density plot (as the second plot here). I have tried to use hist3 function as in here, but I didn't figure out how to take into account the third column - weight.

You could create a matrix from the data in newmat (using the functions sortrows, unique, and accumarray) and plot it as an image:
newmat = sortrows(newmat, [1 2]); % Sort the first two columns in ascending order
[x, ~, newmat(:, 1)] = unique(newmat(:, 1)); % Make numeric indices for column 1
[y, ~, newmat(:, 2)] = unique(newmat(:, 2)); % Make numeric indices for column 2
M = accumarray(newmat(:, 1:2), newmat(:, 3)).'; % Build the matrix
imagesc(x, y, M);
Here's some sample data similar to your format:
[X, Y] = meshgrid(0:0.1:2, 3:0.1:5);
Z = peaks(21);
newmat = [X(:) Y(:) Z(:)];
And here's the plot the above code produces from that data:

Related

How to plot a covariance matrix on a 2D plot in MATLAB?

In MATLAB there is a function called cov. If I insert a matrix X into cov like this cov(X), then cov will return a square matrix of covariance.
My question is very simple:
How can I, with MATLAB, plot that matrix cov(X) onto a 2D plot like this.
I can see a lot of covariance matrix plots at Google. But how do they create them?
My best guess is that you're trying to add the principal components to the plot. To do that, you could do something like this.
%% generate data points
S_tru = [2 1; 1 1];
N = 1000;
%% compute mean, covariance, principal components
X = mvnrnd([0,0],S_tru,N);
mu = mean(X);
S = cov(X);
[U,D] = eig(S);
%% specify base points/directions for arrows
base = [mu;mu];
vecs = sqrt(D)*U';
vecs = 2 * vecs;
%% plot
plot(X(:,1),X(:,2), 'r.')
axis equal
hold on
quiver(base(:,1),base(:,2),vecs(:,1),vecs(:,2),'blue','LineWidth',2)
Resulting graph:

Plotting a cell array

I need to plot a cell array with the following format in Matlab:
{[vector1], [vector2], ...}
Into a 2D graph with the index of the vector as the y and the vector as the x
([vector1], 1), ([vector2], 2), ...
Here's a simple option:
% some arbitrary data:
CellData = {rand(10,1)*50,rand(10,1)*50,rand(10,1)*50};
% Define x and y:
x = cell2mat(CellData);
y = ones(size(x,1),1)*(1:size(x,2));
% plot:
plot(x,y,'o')
ylim([0 size(x,2)+1])
so you plot each vector of x on a separate y value:
It will work as long as your cell array is just a list of vectors.
EDIT: For non equal vectors
You'll have to use a for loop with hold:
% some arbitrary data:
CellData = {rand(5,1)*50,rand(6,1)*50,rand(7,1)*50,rand(8,1)*50,rand(9,1)*50};
figure;
hold on
for ii = 1:length(CellData)
x = CellData{ii};
y = ones(size(x,1),1)*ii;
plot(x,y,'o')
end
ylim([0 ii+1])
hold off
Hope this answers your question ;)
Here's my (brute force) interpretation of your request. There are likely more elegant solutions.
This code generates a dot plot that puts the values from the vectors at each index on the y axis—bottom to top. It can accommodate vectors of different lengths. You could make it a dot plot of vector distributions, but you might need to add some jitter to the x value, if multiple occurrences of identical or nearly identical values are possible.
% random data--three vectors from range 1:10 of different lengths
for i = 1:3
dataVals{i} = randi(10,randi(10,1),1);
end
dotSize = 14;
% plot the first vector with dots and increase the dot size
% I happen to like filled circles for this, and this is how I do it.
h = plot(dataVals{1}, ones(length(dataVals{1}), 1),'.r');
set(h,'markers', dotSize);
ax = gca;
axis([0 11 0 4]); % set axis limits
% set the Y axis labels to whole numbers
ax.YTickLabel = {'','','1','','2','','3','','',}';
hold on;
% plot the rest of the vectors
for i=2:length(dataVals)
h = plot(dataVals{i}, ones(length(dataVals{i}),1)*i,'.r');
set(h, 'markers', dotSize);
end
hold off
Without any data this is the best I can come up with for what you want:
yourCell = {[0,0,0],[1,1,1],[2,2,2]}; % 1x3 cell
figure;
plot(cell2mat(yourCell));
ylabel('Vector Values');
xlabel('Index of Vector');
It makes a plot like this:
Hope this helps.

Fitting a n ordered polynomial

I was trying to plot a regression curve:
coef_fit = polyfit(norm_dist,norm_time,7);
y_fit = polyval(coef_fit,xlim);
plot(xlim,y_fit,'r');
But it is always plotting a line respective of the order I pass.
The problem is that the x values you are using are the output ot xlim, which is a length-2 vector. You need to define an x vector with more values:
norm_dist = sort(5*randn(1,50) + (1:50)); %// example x values
norm_time = 5*randn(1,50) + (1:50).^2; %// example y values
x = linspace(min(norm_dist), max(norm_dist), 200); %// define x values for plot
coef_fit = polyfit(norm_dist,norm_time,7);
y_fit = polyval(coef_fit,x);
plot(x,y_fit,'r');
hold on
plot(norm_dist, norm_time, 'b.') %// plot original points for comparison

Matlab's hist3, which axis corresponds to X and which one to Y

So suppose you pass some matrix N to hist3 in Matlab, which is a m-by-2 matrix, simply for an example purposes. Where the first column is your variable X and column 2 corresponds to your variable Y.
When you run the cnt = hist3(N, {bins_X bins_Y}), you would get a m-by-m matrix. Rows here are which variable, X or Y?
OP seems to have solved his problem. However, I am leaving a code snippet exemplifying hist3's output indexing in case anyone finds it useful.
% Simulate random 2-column matrix
X = randn(1e5,2);
% Scale x-axis data to see label distinction
X(:,1) = X(:,1)*10;
% Define bins
bin_x = linspace(-30,30,80);
bin_y = linspace(-3,3,100);
% Get frequency grid
cnt = hist3(X,{bin_x,bin_y});
% Plot frequency values with surf
[x,y] = meshgrid(bin_x,bin_y);
figure
surf(x,y,cnt')
title('Original hist3 output')
xlabel('First Column')
ylabel('Second Column')
zlabel('Frequency')
% Access and modify cnt, and plot again
cnt(end,1:10) = 60;
cnt(25:55,1:55)= 0;
figure
surf(x,y,cnt')
title('Modified hist3 output')
xlabel('First Column')
ylabel('Second Column')
zlabel('Frequency')

Generate heatmap with coordinates and data stored in vectors

Let A be an n by 3 matrix, such that the first two columns are all ordered pairs of the form (5*i,5*i) for i from 1 to 200. The third column contains values from 0 to 1, which I will call intensities. I want to make a 1000 by 1000 plot so that the rectangle at (5*i,5*i) is shaded with intensity described by the third column entry.
I'm familiar with the heatmap function and imshow, but I don't see a way to include this "scaling by 5" to make a nice plot. And of course in general the x and y coordinates may not be scaled by the same amount.
Is there a nice way to do this in Matlab?
With imagesc it's actually pretty simple:
First some example data:
%// generate example data
ii = 1:200;
[xx,yy] = meshgrid(ii);
A(:,1) = 5*xx(:);
A(:,2) = 5*yy(:);
A(:,3) = randi([0,1],1,40000);
Actual answer
n = 200;
%// reshape data
D = reshape( A(:,3),n,n );
%// heatmap
imagesc(A(:,1),A(:,2),D)
colormap(gray)
caxis([0,1])
gives:
Important notice
If your coordinates are not sorted as required for imagesc you can sort them with:
A = sortrows(A,[2,1]);
Clown Example
%// original image
load clown
I = reshape(1:numel(X),size(X));
[R,C] = ind2sub(size(X),I);
A(:,1) = R(:);
A(:,2) = C(:);
A(:,3) = X(:);
D = reshape( A(:,3),200,320 );
figure(1)
subplot(1,3,1)
imagesc(A(:,1),A(:,2),D)
%// shuffled image -> shuffled data
shuffle = randperm(320*200);
A = A(shuffle,:);
D = reshape( A(:,3),200,320 );
subplot(1,3,2)
imagesc(A(:,1),A(:,2),D)
%// sorted image
A = sortrows(A,[2,1]);
D = reshape( A(:,3),200,320 );
subplot(1,3,3)
imagesc(A(:,1),A(:,2),D)
You see, even if your coordinates are sorted like a mess, you can rebuild the image with sortrows.
See this
function DrawHeatmap(X,Y,Z)
%DRAWHEATMAP Draw a 2D heatmap for (X,Y) coordinates whose values are in Z
% X, Y , Z must be columns
% By: Eng. Osama Talaat Abdel-Hafiz - PhD Student
% Egypt - Sept 2017
if size(X,2)==1 && size(Y,2)==1 && size(Z,2)==1
F = scatteredInterpolant(X,Y,Z); % create a function from interpolation
[X,Y] = meshgrid(min(X):0.1:max(X),min(Y):0.1:max(Y));
Z = F(X,Y);
contourf(X, Y, Z, linspace(floor(min(min(Z))),ceil(max(max(Z))),400), 'LineColor','none')
colorbar;
else
error('X, Y , Z must be columns')
end
end