I want to have grid with lines in it like this:
Currently, i have coded to get grid like this:
I just need to add horizontal and vertical lines in it.
MyCode:
[X,Y] = meshgrid(-1:0.1:1, -1:0.1:1);
X = X(:);
Y = Y(:);
plot(X,Y,'b.');
xlabel('X'); % // Label the X and Y axes
ylabel('Y');
title('Initial Grid');
To draw those lines, the easiest approach is two loops:
x = -1:0.1:1;
y = -1:0.1:1;
hold on
for n = 1:numel(x); %// loop over vertical lines
plot([x(n) x(n)], [y(1) y(end)], 'k-'); %// change 'k-' to whatever you need
end
for n = 1:numel(y); %// loop over horizontal lines
plot([x(1) x(end)], [y(n) y(n)], 'k-'); %// change 'k-' to whatever you need
end
Alternatively, you can use grid; but then you don't have control on line type. You get black dotted lines:
x = -1:0.1:1;
y = -1:0.1:1;
figure
set(gca,'xtick',x);
set(gca,'ytick',y);
axis([min(x) max(x) min(y) max(y)])
grid on
Related
I made two 3D plots on the same axis. now I desire to give them different colors for easy identification. How do I do this coloring? The MATLAB code is shown below.
tic
Nx = 50;
Ny = 50;
x = linspace(0,1,Nx);
y = linspace(0,0.5,Ny);
[X,Y] = meshgrid(x,y);
[M,N] = size(X);
for m=1:M
for n=1:N
%get x,y coordinate
x_mn = X(m,n);
y_mn = Y(m,n);
%%% X=D2 and Y=D1
%Check if x_mn and y_mn satisfy requirement
if(x_mn >= y_mn)
%evaluate function 1
Z(m,n) = (x_mn^2 - 2*x_mn*y_mn + y_mn^2);
Z_1(m,n) = (x_mn^2);
elseif(x_mn < y_mn)
%evaluate function 2
Z(m,n) = 0;
Z_1(m,n) = (x_mn^2);
%% Z(m,n) = 2*(2*x_mn*y_mn + y_mn - y_mn^2 - 2*x_mn);
else
Z(m,n) = 0;
end
end
end
%Plot the surface
figure
surf(X,Y,Z) %first plot
surfc(X,Y,Z)
hold on
surf(X,Y,Z_1) %second plot
xlabel('Dm');
ylabel('D');
zlabel('pR');
grid on
shading interp
toc
disp('DONE!')
How can I create two differently colored surfaces?
figure
surf(X,Y,Z) %first plot
surfc(X,Y,Z)
hold on
surf(X,Y,Z_1)
Your surfc() call actually overwrites your surf() call, is this intended?
As to your colour: the documentation is a marvellous thing:
surfc(X,Y,Z,C) additionally specifies the surface color.
In other words: just specify the colour as you want it. C needs to be a matrix of size(Z) with the desired colours, i.e. set all of them equal to create an monocoloured surface:
x = 1:100;
y = 1:100;
z = rand(100);
figure;
surfc(x,y,z,ones(size(z)))
hold on
surfc(x,y,z+6,ones(size(z))+4)
Results in (MATLAB R2007b, but the syntax is the same nowadays)
I created two scatter plots and then used lsline to add regression lines for each plot. I used this code:
for i=1:2
x = ..;
y = ..;
scatter(x, y, 50, 'MarkerFaceColor',myColours(i, :));
end
h_lines = lsline;
However, the darker line extends far beyond the last data point in that scatter plot (which is at around x=0.3):
lsline doesn't seem to have properties that allow its horizontal range to be set. Is there a workaround to set this separately for the two lines, in Matlab 2016a?
For a single data set
This is a workaround rather than a solution. lsline internally calls refline, which plots a line filling the axis as given by their current limits (xlim and ylim). So you can change those limits to the extent you want for the line, call lsline, and then restore the limits.
Example:
x = randn(1,100);
y = 2*x + randn(1,100); % random, correlated data
plot(x, y, '.') % scatter plot
xlim([-1.5 1.5]) % desired limit for line
lsline % plot line
xlim auto % restore axis limit
For several data sets
In this case you can apply the same procedure for each data set sequentially, but you need to keep only one data set visible when you call lsline; otherwise when you call it to create the second line it will also create a new version of the first (with the wrong range).
Example:
x = randn(1,100); y = 2*x + randn(1,100); % random, correlated data
h = plot(x, y, 'b.'); % scatter plot
axis([min(x) max(x) min(y) max(y)]) % desired limit for line
lsline % plot line
xlim auto % restore axis limit
hold on
x = 2*randn(1,100) - 5; y = 1.2*x + randn(1,100) + 6; % random, correlated data
plot(x, y, 'r.') % scatter plot
axis([min(x) max(x) min(y) max(y)]) % desired limit for line
set(h, 'HandleVisibility', 'off'); % hide previous plot
lsline % plot line
set(h, 'HandleVisibility', 'on'); % restore visibility
xlim auto % restore axis limit
Yet another solution: implement your own hsline. It's easy!
In MATLAB, doing a least squares fit of a straight line is trivial. Given column vectors x and y with N elements, b = [ones(N,1),x] \ y; are the parameters to the best fit line. [1,x1;1,x2]*b are the y locations of two points along the line with x-coordinates x1 and x2. Thus you can write (following Luis' example, and getting the exact same output):
N = 100;
x = randn(N,1); y = 2*x + randn(N,1); % random, correlated data
h = plot(x, y, 'b.'); % scatter plot
hold on
b = [ones(N,1),x] \ y;
x = [min(x);max(x)];
plot(x,[ones(2,1),x] * b, 'b-')
x = 2*randn(N,1) - 5; y = 1.2*x + randn(N,1) + 6; % random, correlated data
plot(x, y, 'r.') % scatter plot
b = [ones(N,1),x] \ y;
x = [min(x);max(x)];
plot(x,[ones(2,1),x] * b, 'r-')
You can get the points that define the line using
h_lines =lsline;
h_lines(ii).XData and h_lines(ii).YData will contain 2 points that define the lines for each ii=1,2 line. Use those to create en equation of a line, and plot the line in the range you want.
I am doing some analysis and need to produce a histogram plot. I know how to create the standard histogram plot but I need something like the image below, where each point is an interval on the x axis. Each bin is based on a value from x-x for example.
You can use the histogram function, and then set the XTick positions and XTickLabels accordingly. See the comments in the code for explanation.
% random normally distrubuted data
x = 1*randn(1000,1);
edges = -5:1:5;
% create vector with labels (for XTickLabel ... to ...)
labels = [edges(1:end-1); edges(2:end)];
labels = labels(:);
% plot the histogram
figure();
ax = axes;
h = histogram(x, 'BinEdges', edges, 'Normalization', 'Probability');
ax.XTick = edges + mean(diff(edges)/2);
ax.XTickLabel = sprintf('%.1f to %.1f\n', labels);
ax.XTickLabelRotation = 90;
% set yticks to percentage
ax.YTickLabel = cellfun(#(a) sprintf('%i%%', (str2double(a)*100)), ax.YTickLabel, 'UniformOutput', false);
% text above bars
bin_props = h.BinCounts/numel(x); % determine probabilities per bin in axis units
bin_centers = ax.XTick(1:end-1); % get the bin centers
txt_heigts = bin_props + 0.01; % put the text slightly above the bar
txt_labels = split(sprintf('%.1f%% ', bin_props*100), ' ');
txt_labels(end) = []; % remove last cell, is empty because of split.
text(ax, bin_centers, txt_heigts, txt_labels, 'HorizontalAlignment', 'center')
% set ylim to fit all text (otherwise text is outside axes)
ylim([0 .4]);
Putting the text at the right location may require some tweaking. Most important is the 'HorizontalAlignment' option, and the distance to the bars. I also used the 'Normalization', 'probability' option from the histogram function, and set the y axis to also show percentages.
I figure you can make the addition below yourself when needed.
When your data can be outside of the defined binedges, you can clip your data, and set the XTickLabels with less than or greater than signs.
% when data can be outside of defined edges
x = 5*randn(1000,1);
xclip = x;
xclip(x >= max(edges)) = max(edges);
xclip(x <= min(edges)) = min(edges);
% plot the histogram
figure();
ax = axes;
h = histogram(xclip, 'BinEdges', edges);
ax.XTick = edges + mean(diff(edges)/2);
ax.XTickLabel = sprintf('%.1f to %.1f\n', labels);
ax.XTickLabelRotation = 90;
% set boundary labels
ax.XTickLabel{1} = sprintf('\\leq %.1f', edges(2));
ax.XTickLabel{end-1} = sprintf('\\geq %.1f', edges(end-1));
You can also set the outer edges to -Inf and Inf, as user2305193 pointed out. Since the outer bins are then much wider (because they actually extend to Inf on the x axis), which you can correct by setting the axis xlim. By the default the XTickLabels will display -Inf to -5.0, which I personally don't like, so I set them to lesser (and equal) than and greater than signs.
step = 1;
edges = -5:step:5; % your defined range
edges_inf = [-Inf edges Inf]; % for histogram
edges_ext = [edges(1)-step edges]; % for the xticks
x = 5*randn(1000,1);
% plot the histogram
figure();
ax = axes;
h = histogram(x, 'BinEdges', edges_inf, 'Normalization', 'probability');
labels = [edges_inf(1:end-1); edges_inf(2:end)];
labels = labels(:);
ax.XTick = edges_ext + step/2;
ax.XTickLabel = sprintf('%.1f to %.1f\n', labels);
ax.XTickLabelRotation = 90;
% show all bins with equal width (Inf bins are in fact wider)
xlim([min(edges)-step max(edges)+step])
% set boundary labels
ax.XTickLabel{1} = sprintf('\\leq %.1f', edges(1));
ax.XTickLabel{end-1} = sprintf('\\geq %.1f', edges(end));
I have created a plot in Matlab. Let's assume for simplicity that I have the following plot:
x = 0:pi/100:2*pi;
y = sin(x);
plot(x,y)
Now I would like to add vertical lines (going from the bottom of the figure to the top) at positions x = 1, x = 3 and x = 5. Additionally, the vertical lines should have text (next to the line or on top of the line). For example, for the line at x = 1 I would like to have the text "test 1".
How can this be done? This seems to be a pretty tricky thing in Matlab.
Here are some ways to draw lines:
x = 0:pi/100:2*pi;
y = sin(x);
plot(x,y,[1 1],[-1 1],[3 3],[-1 1],[5 5],[-1 1]);
x = 0:pi/100:2*pi;
y = sin(x);
plot(x,y); hold on;
for ind1 = 1:2:5
line([ind1 ind1],[min(y) max(y)],'Color',[0 0 0]);
end
x = 0:pi/100:2*pi;
y = sin(x);
A = zeros(6); A(sub2ind(size(A),1:6,[2 1 4 3 6 5])) = 1;
plot(x,y); hold on; gplot(A, [repelem(1:2:5,1,2).', reshape(repelem([1 -1],3,1).',[],1)]);
Etc.
Either use hold on and plot in several commands, or provide all inputs to your plot function right aways to get the desired result. Consult the documentation of the above functions for more information.
For texts refer to text.
for i=1:2:5
hold on
plot([i i],[0 1])
s=sprintf('test%1.0f', i)
t=text(i,1,s)
set(t,'Rotation',90)
end
I'd like to draw a curve on an empty (semilog-y) graph by clicking the points I want it to run through, on the X-Y plane.
Is there a function for this?
edit: I'm trying to do this by obtaining the position of last pointer click -
axis([0 3000 0 1000]);
co=get(gcf, 'CurrentPoint');
It seems to return the cursor position at the time of execution, but it does not change later.
edit2: Here's what works for me. The actual drawing I can do by using the arrays of points collected.
clear
clc
h=plot(0);
grid on;
xlim([0 3000]);
ylim([0 1000]);
datacursormode on;
% Enlarge figure to full screen.
screenSize = get(0,'ScreenSize');
set(gcf, 'units','pixels','outerposition', screenSize);
hold on;
% Print the x,y coordinates - will be in plot coordinates
x=zeros(1,10); y=zeros(1,10);
for p=1:10;
[x(p),y(p)] = ginput(1) ;
% Mark where they clicked with a cross.
plot(x(p),y(p), 'r+', 'MarkerSize', 20, 'LineWidth', 3);
% Print coordinates on the plot.
label = sprintf('(%.1f, %.1f)', x(p), y(p));
text(x(p)+20, y(p), label);
end
Not really, but now there is:
function topLevel
%// parameters
xrange = [0 100];
yrange = [1e-4 1e4];
%// initialize figure, plot
figure, clf, hold on
plot(NaN, NaN);
axis([xrange yrange]);
set(gca, 'YScale', 'log')
t = text(sum(xrange)/2, sum(yrange)/2, ...
'<< Need at least 3 points >>',...
'HorizontalAlignment', 'center');
%// Main loop
xs = []; p = [];
ys = []; P = [];
while true
%// Get new user-input, and collect all of them in a list
[x,y] = ginput(1);
xs = [xs; x]; %#ok<AGROW>
ys = [ys; y]; %#ok<AGROW>
%// Plot the selected points
if ishandle(p)
delete(p); end
p = plot(xs, ys, 'rx');
axis([xrange yrange]);
%// Fit curve through user-injected points
if numel(xs) >= 3
if ishandle(t)
delete(t); end
%// Get parameters of best-fit in a least-squares sense
[A,B,C] = fitExponential(xs,ys);
%// Plot the new curve
xp = linspace(xrange(1), xrange(end), 100);
yp = A + B*exp(C*xp);
if ishandle(P)
delete(P); end
P = plot(xp,yp, 'b');
end
end
%// Fit a model of the form y = A + B·exp(C·x) to data [x,y]
function [A, B, C] = fitExponential(x,y)
options = optimset(...
'maxfunevals', inf);
A = fminsearch(#lsq, 0, options);
[~,B,C] = lsq(A);
function [val, B,C] = lsq(A)
params = [ones(size(x(:))) x(:)] \ log(abs(y-A));
B = exp(params(1));
C = params(2);
val = sum((y - A - B*exp(C*x)).^2);
end
end
end
Note that as always, fitting an exponential curve can be tricky; the square of the difference between model and data is exponentially much greater for higher data values than for lower data values, so there will be a strong bias to fit the higher values better than the lower ones.
I just assumed a simple model and used a simple solution, but this gives a biased curve which might not be "optimal" in the sense that you need it to be. Any decent solution really depends on what you want specifically, and I'll leave that up to you ^_^