I have the following code to generate a PRM map that will be used for A* application. There exist 2 problems with the code
It keeps the blue lines representing the original PRM where lines can cross over obstacles. I don't want to keep the blue lines but I couldn't find the way to remove them.
The green lines are going over obstacles even though they shouldn't
The code is as follows
clc;
clear all;
close all;
seed = 123512;
rng(seed);
xaxis = 100;
yaxis = 100;
obstacles = false(xaxis,yaxis);
[X,Y] = meshgrid(1:xaxis,1:yaxis);
obstacles(50:75,50:75) = true;
obstacles(25:35,30:40) = true;
obstacles(25:35,60:80) = true;
figure;
imshow(~obstacles,"InitialMagnification",1000);
axis([0 xaxis 0 yaxis]);
axis xy;
axis on;
%PRM PARAMETERS
max_nodes_connect = 4;
max_connect_len = 40;
segments = 1;
max_nodes_grid = 30;
skipped = 0;
%PRM ALGO
nodes = 0; %Counter
map = zeros(size(obstacles)); %generate map
map(obstacles) = 1; %put the obstacles
Graph_connections = Inf(max_nodes_grid,max_nodes_connect + 1); %rows = # nodes cols = ID and neighbors
while (nodes < max_nodes_grid)
node_x = randi(xaxis);
node_y = randi(yaxis);
if(map(node_y,node_x)==1 || map(node_y,node_x)==2)
continue;
end
nodes = nodes + 1; %a valid node generated
map(node_y,node_x) = 2; %2 means there exists a node at that location
hold on
scatter(node_x,node_y,"red","filled")
%NODES TO CONNECT
nodes_to_connect = [];
distances = [];
for i= 1:numel(Graph_connections(:,1))
if(Graph_connections(i,1)==Inf)
break
end
[row,col] = ind2sub(size(map),Graph_connections(i,1));
%Check if within range
if(norm([node_y,node_x]-[row,col])>max_connect_len)
continue;
end
line_on_obstacle = check_obstacle(map,node_x,node_y,row,col);
%Check if obstacle thru line HAS TO BE WRITTEN
if(line_on_obstacle)
disp("Check Obstacle: " + line_on_obstacle);
skipped = skipped + 1;
continue;
end
nodes_to_connect = [nodes_to_connect, Graph_connections(i,1)];
distances = [distances; [Graph_connections(i,1),norm([node_y,node_x]-[row,col])]];
end
Graph_connections(nodes,1) = sub2ind(size(map),node_y,node_x);
if(size(distances)>0)
sorted_distances = sortrows(distances,2);
for i = 1:min(max_nodes_connect,size(sorted_distances,1))
Graph_connections(nodes,i+1) = sorted_distances(i,1);
[row,col] = ind2sub(size(map),sorted_distances(i,1));
if(line_on_obstacle==false)
disp("Line is not on obstacle")
hold on
plot([node_x,col],[node_y,row],"green","LineWidth",1.5);
continue;
else
disp("Line is on obstacle: " + [node_x,col] + " " + [node_y,row]);
break;
end
end
disp("==========================")
end
end
function on_line = check_obstacle(map,node_x,node_y,row,col)
on_line = 0;
my_line = line([node_x,col],[node_y,row]);
line_spacing = max(abs(my_line.XData(1) - my_line.XData(2))+1,abs(my_line.XData(1) - my_line.XData(2))+1);
x_coordinates_line = round(linspace(my_line.XData(1),my_line.XData(2),line_spacing));
y_coordinates_line = round(linspace(my_line.YData(1),my_line.YData(2),line_spacing));
for i = 1:line_spacing
if(map(x_coordinates_line(i),y_coordinates_line(i))==1)
disp("ON OBSTACLE: " + x_coordinates_line(i) + " " + y_coordinates_line(i));
on_line = true;
break;
end
end
end
The check_obstacle function is used to check if the points on the line are in the boundaries of obstacles. What am I missing here?
close all;clear all;clc
format short
nx=100;ny=100; % grid size
[X,Y]=meshgrid(1:nx,1:ny);
Nobst=3 % amount obstacles
Nnet=30 % amount net points
Dmax=100 % net segment max length
% OBSTACLES
% define obstacles.
% Following are as defined in question
P1=[50 50; 75 50; 75 75; 50 75; 50 50];
P2=[25 30; 25 40; 35 40; 35 30; 25 30];
P3=[25 60; 25 80; 35 80;35 60;25 60];
% obstacle points all in one array
Pobst=[P1(:);P2(:);P3(:)];
Pobst=reshape(Pobst,[size(P1,1),size(P1,2),Nobst]);
% plot obstacles
hp=[]
figure(1)
ax=gca
hp=patch(squeeze(Pobst(:,1,:)),squeeze(Pobst(:,2,:)),[.5 .5 .5])
axis([1 nx 1 ny]);grid on
hp.EdgeAlpha=0;
ax.DataAspectRatio=[1 1 1]
hold(ax,'on')
% obstacle segments list : [x1 y1 x2 y2 d(X1,Y1)]
Lobst1=[]
for k=2:1:size(P1,1)
Lobst1=[Lobst1; [P1(k-1,:) P1(k,:) ((P1(k-1,1)-P1(k,1))^2+(P1(k-1,2)-P1(k,2))^2)^.5]];
end
Lobst2=[]
for k=2:1:size(P2,1)
Lobst2=[Lobst2; [P2(k-1,:) P2(k,:) ((P2(k-1,1)-P2(k,1))^2+(P2(k-1,2)-P2(k,2))^2)^.5]];
end
Lobst3=[]
for k=2:1:size(P3,1)
Lobst3=[Lobst3; [P3(k-1,:) P3(k,:) ((P3(k-1,1)-P3(k,1))^2+(P3(k-1,2)-P3(k,2))^2)^.5]];
end
Lobst=[Lobst1;Lobst2;Lobst3]
%% NETWORK
% all grid points outside obstacles
[in1,on1]=inpolygon(X(:),Y(:),P1(:,1),P1(:,2));
[in2,on2]=inpolygon(X(:),Y(:),P2(:,1),P2(:,2));
[in3,on3]=inpolygon(X(:),Y(:),P3(:,1),P3(:,2));
Xout=X(~in1 & ~in2 & ~in3);Yout=Y(~in1 & ~in2 & ~in3);
% plot(ax,Xout,Yout,'og','LineStyle','none') % check
% choose Nnet points outside obstacles
nP2=randi([1 numel(Xout)],Nnet,1);
Pnet=[Xout(nP2) Yout(nP2)];
plot(ax,Pnet(:,1),Pnet(:,2),'or','LineStyle','none')
% net segments list [x1 y1 x2 y2 d(X2,Y2) 0/1] 6th column [0 1] 1: draw 0: do not draw
nLnet=nchoosek([1:size(Pnet,1)],2);
Lnet=[Pnet(nLnet(:,1),1) Pnet(nLnet(:,1),2) ... % segment 1st point
Pnet(nLnet(:,2),1) Pnet(nLnet(:,2),2) ... % segment 2nd point
((Pnet(nLnet(:,1),1)-Pnet(nLnet(:,1),2)).^2+(Pnet(nLnet(:,2),1)+Pnet(nLnet(:,2),2)).^2).^.5 ... % segment length
ones(size(nLnet,1),1)];
% check all net links are plotted
for k=1:1:size(Lnet,1)
plot(ax,[Lnet(k,1) Lnet(k,3)],[Lnet(k,2) Lnet(k,4)],'b');
hold(ax,'on')
end
% remove segments longer than Dmax
Lnet=sortrows(Lnet,5,'descend');
[~,n1]=max(Lnet(:,5)<=Dmax);
Lnet(1:n1-1,:)=[];
for k=1:1:size(Lnet,1)
plot(ax,[Lnet(k,1) Lnet(k,3)],[Lnet(k,2) Lnet(k,4)],'r');
hold(ax,'on')
end
%%
Redrawing and NOT removing net segments longer than Dmax
close all
hp=[]
figure(1)
ax=gca
hp=patch(squeeze(Pobst(:,1,:)),squeeze(Pobst(:,2,:)),[.5 .5 .5])
axis([1 nx 1 ny]);grid on
hp.EdgeAlpha=0;
ax.DataAspectRatio=[1 1 1]
hold(ax,'on')
plot(ax,Pnet(:,1),Pnet(:,2),'or','LineStyle','none')
Lnet=[Pnet(nLnet(:,1),1) Pnet(nLnet(:,1),2) ... % segment 1st point
Pnet(nLnet(:,2),1) Pnet(nLnet(:,2),2) ... % segment 2nd point
((Pnet(nLnet(:,1),1)-Pnet(nLnet(:,1),2)).^2+(Pnet(nLnet(:,2),1)+Pnet(nLnet(:,2),2)).^2).^.5 ... % segment length
ones(size(nLnet,1),1)];
% check what pair segments intersect
for k2=1:1:size(Lnet,1)
allclear=ones(1,size(Lobst,1));
% allclear=zeros(1,size(Lobst,1));
for k1=1:1:size(Lobst,1)
% segments are contained in lines : check lines intersect
x1o=Lobst(k1,1);y1o=Lobst(k1,2);x2o=Lobst(k1,3);y2o=Lobst(k1,4);
x1n=Lnet(k2,1);y1n=Lnet(k2,2);x2n=Lnet(k2,3);y2n=Lnet(k2,4);
x1=x1o;x2=x2o;y1=y1o;y2=y2o;
x3=x1n;x4=x2n;y3=y1n;y4=y2n;
% t1 : x parameter
t1=((x1-x3)*(y3-y4)-(y1-y3)*(x3-x4))/((x1-x2)*(y3-y4)-(y1-y2)*(x3-x4));
xp=x1+t1*(x2-x1); % xp : crossing x coordinage
% u1 : y parameter
u1=-((x1-x2)*(y1-y3)-(y1-y2)*(x1-x3))/((x1-x2)*(y3-y4)-(y1-y2)*(x3-x4));
yp=y1+t1*(y2-y1); % yp : crossing y coordinate
% checks
plot(ax,x1o,y1o,'c*');plot(ax,x2o,y2o,'c*');plot(ax,[x1o x2o],[y1o y2o],'c')
plot(ax,x1n,y1n,'m*');plot(ax,x2n,y2n,'m*'); % plot(ax2,[x1n x2n],[y1n y2n],'m')
m1o=(y2o-y1o)/(x2o-x1o); % slopes
m2n=(y2n-y1n)/(x2n-x1n) ;
if (t1>=0 && t1<=1 && u1>=0 && u1<=1) && ...
(xp>=0 || xp<=nx || yp>=0 || yp<=ny)
allclear(k1)=0; % then do not plot this segment
end
end
if sum(allclear)==size(Lobst,1) %k2-th net segment hits no obstacles
plot(ax,x1n,y1n,'m*');plot(ax,x2n,y2n,'m*');plot(ax,[x1n x2n],[y1n y2n],'m')
elseif sum(allclear)~=size(Lobst,1)
Lnet(k2,end)=0;
end
end
Comments
1.- Note I have added format short because with format long there is decimal accretion right at the bottom of a few intersection points, that believe it or not, cause some false results.
2.- I like Torsen's explanation to find line segment intersections available here.
3.- There are faster ways to implement the main loop, but I make it go through all net-segment vs obstacle-element in case you may need it this way, to for instance count hits.
4.- There's also room for improvement in the way Lobst is generated.
5.- These lines
x1=x1o;x2=x2o;y1=y1o;y2=y2o;
x3=x1n;x4=x2n;y3=y1n;y4=y2n;
it's just an easy way to plug in formulation to already written lines, reducing amount variables is also left for next version.
Related
I'm currently working on a code that makes use of a 2D cellular automata as an epidemic simulator in MATLAB. The main basic rule I'm trying to implement is that if any neighbour within a Moore Neighbourhood with a 1-cell radius is infected, the cell will become infected. But I can't seem to get a good code working for it.
Basically what I'm trying to do is say with for a cell with a one cell radius Moore neighbourhood, if any values in this neighbourhood = 2, then the initial cell will become 2.
I've tried using the forest fire code on the rosetta code as a basis for my code behaviour but it doesnt work very well. The rules don't really work that well when applying it to mine. I've tried using the mod function and a series of if loops to attach. I'll put in some code of each to give context.
This example doesn't really function well as an epidemic simulator to be honest.
matlab
clear; clc;
n = 200;
N = n/2;
E = 0.001; % Creating an arbitrary number for population exposed to
the disease but not infected
p = 1 + (rand(n,n)<E);
%p = ceil(rand(n,n)*2.12) - 1;
% ratio0 = sum(p(:)==0)/n^2;
% ratio1 = sum(p(:)==1)/n^2;
% ratio2 = sum(p(:)==2)/n^2;
% ratio3 = sum(p(:)==3)/n^2;
S = ones(3); S(2,2) = 0;
ff = 0.00000000002;
p(N,N) = 3;
%% Running the simulation for a set number of loops
colormap([1,1,1;1,0,1;1,0,0]); %Setting colourmap to Green, red and
grey
count = 0;
while(count<365) % Running the simulation with limited number of runs
count = count + 1;
image(p); pause(0.1); % Creating an image of the model
P = (p==1); % Adding empty cells to new array
P = P + (p==2).*((filter2(S,p==3)>0) + (rand(n,n)<ff) + 2); % Setting
2 as a tree, ignites based on proximity of trees and random
chance ff
P = P + (p==3); % Setting 3 as a burning tree, that becomes 1,
p = P;
end
second idea. this basically returns nothing
matlab
clear;clf;clc;
n = 200;
pos = mod((1:n),n) + 1; neg = mod((1:n)-2,n) + 1;
p = (ceil(rand(n,n)*1.0005));
for t = 1:365
if p(neg,neg) ==2
p(:,:) = 2;
end
if p(:,neg)==2
p(:,:) = 2;
end
if p(pos,neg)==2
p(:,:) = 2;
end
if p(neg,:)==2
p(:,:) = 2;
end
if p(pos,:)==2
p(:,:) = 2;
end
if p(neg,pos)==2
p(:,:) = 2;
end
if p(:,pos)==2
p(:,:) = 2;
end
if p(pos,pos)== 2
p(:,:) = 2;
end
image(p)
colormap([1,1,1;1,0,1])
end
third I tried using logic gates to see if that would work. I don't know if commas would work instead.
matlab
clear;clf;clc;
n = 200;
pos = mod((1:n),n) + 1; neg = mod((1:n)-2,n) + 1;
p = (ceil(rand(n,n)*1.0005));
%P = p(neg,neg) + p(:,neg) + p(pos,neg) + p(neg,:) + p(:,:) + p(pos,:)
+ p(neg,pos) + p(:,pos) + p(pos,pos)
for t=1:365
if p(neg,neg)|| p(:,neg) || p(pos,neg) || p(neg,:) || p(pos,:) ||
p(neg,pos) || p(:,pos) || p(pos,pos) == 2
p(:,:) = 2;
end
image(p)
colormap([1,1,1;1,0,1])
end
I expected the matrix to just gradually become more magenta but nothing happens in the second one. I get this error for the third.
"Operands to the || and && operators must be convertible to logical scalar values."
I just have no idea what to do!
Cells do not heal
I assume that
Infected is 2, non-infected is 1;
An infected cell remains infected;
A non-infected cell becomes infected if any neighbour is.
A simple way to achieve this is using 2-D convolution:
n = 200;
p = (ceil(rand(n,n)*1.0005));
neighbourhood = [1 1 1; 1 1 1; 1 1 1]; % Moore plus own cell
for t = 1:356
p = (conv2(p-1, neighbourhood, 'same')>0) + 1; % update
image(p), axis equal, axis tight, colormap([.4 .4 .5; .8 0 0]), pause(.1) % plot
end
Cells heal after a specified time
To model this, it is better to use 0 for a non-infected cell and a positive integer for an infected cell, which indicated how long it has been infected.
A cell heals after it has been infected for a specified number of iterations (but can immediately become infeced again...)
The code uses convolution, as the previous one, but now already infected cells need to be dealt with separately from newly infected cells, and so a true Moore neighbourhood is used.
n = 200;
p = (ceil(rand(n,n)*1.0005))-1; % 0: non-infected. 1: just infected
T = 20; % time to heal
neighbourhood = [1 1 1; 1 0 1; 1 1 1]; % Moore
for t = 1:356
already_infected = p>0; % logical index
p(already_infected) = p(already_infected)+1; % increase time count for infected
newly_infected = conv2(p>0, neighbourhood, 'same')>0; % logical index
p(newly_infected & ~already_infected) = 1; % these just became infected
newly_healed = p==T; % logical index
p(newly_healed) = 0; % these are just healed
image(p>0), axis equal, axis tight, colormap([.4 .4 .5; .8 0 0]), pause(.1) % plot
% infected / non-infected state
end
I have a question about changing the color of lines in a plot in MATLAB. I have written a code where I want to change the color of lines in a plot that is embedded in several for loops (the code is shown below). In the code, a new plot is created (with its own figure) when the "if loop" condition is satisfied. I want each plot to have lines with different colors (from other plots), so I created a variable = "NewColor" to increment and change the line colors. However, the following problem has been occurring:
Suppose I am in debug mode and I have stopped at the plot command. I run the next step and a plot is created with a blue line. I check the value of NewColor and find NewColor = 0.1. Next, I use the "run to cursor" command to step to the next time the plot command is activated. When I do this I am still within the "i for loop", so NewColor has not changed. I check the editor to find that NewColor = 0.1. Therefore, when I run the next step a blue line should show up on the current plot. To the contrary and my disbelief an orange line shows up (in addition to the blue line). I don't understand since in both steps of the debugger NewColor = 0.1, and the code is written so the color of the lines = [0,NewColor,0]. If anyone can find the error of my ways it would be greatly appreciated. Thanks
ThetaPlot = [40,50]; % Put incident angle as input
Count1 = 0;
Count2 = 0;
NewColor = 0;
for m = 1:length(ThetaPlot)
NewColor = 0.1;
Title = sprintf('Angle(%d)',ThetaPlot(m));
figure('name',Title)
Count1 = 0;
for i = 1:length(xrange)-1 % X Coordinate of Start Node
for j = 1:length(yrange)-1 % Y Coordinate of Start Node
Count1 = Count1+1;
for k = 2:length(xrange) % X Coordinate of End Node
for l = 2:length(yrange) % Y Coordinate of End Node
Count2 = Count2+1;
if ReflRayData(Count2,ThetaPlot(m) - ThetaIncident(1) + 1,Count1) == 1
x = [xrange(i),xrange(k)];
y = [yrange(j),yrange(l)];
plot(x,y,['-',[0,NewColor,0],'o']);
hold on;
end
end
Count2 = 0;
end
end
end
NewColor = NewColor + 0.02;
end
Full Code:
%% Calculating Angles of Reflection
run = 1; % Set run = 1 for calculations
if run == 1
xrange = [0:1:14.5]'; % Coordinates to try for Panel Geometry (in)
yrange = [0:1:36]'; % Coordinates to try for Panel Geometry (in)
ThetaIncident = [-90:1:90]'; % Incident Angle of Ray (measured relative to normal direction with clockwise postive)
OvenGlassXrange = [14.5:0.1:36.5]; %Range of X coordinates for Oven Glass
ReflRayData = zeros((length(xrange)-1)*(length(yrange)-1),length(ThetaIncident),(length(xrange)-1)*(length(yrange)-1)); % Matrix containing Reflected Ray Data
Count1 = 0;
Count2 = 0;
for i = 1:length(xrange)-1 % X Coordinate of Start Node
for j = 1:length(yrange)-1 % Y Coordinate of Start Node
Count1 = Count1+1;
for k = 2:length(xrange) % X Coordinate of End Node
for l = 2:length(yrange) % Y Coordinate of End Node
Count2 = Count2+1;
for m = 1:length(ThetaIncident)
xStart = xrange(i);
yStart = yrange(j);
xEnd = xrange(k);
yEnd = yrange(l);
m1 = (yEnd - yStart)/(xEnd - xStart); % Slope between Start and End Nodes
b1 = yStart - m1*xStart;
m2 = 1/m1; % Slope of normal direction
b2 = (yEnd - 0.5*(yEnd - yStart)) - m2*(xEnd - 0.5*(xEnd - xStart));
ArbXCoor = 1; % Arbitary Point X Coordinate on Normal Line
ArbYCoor = m2*ArbXCoor+b2; % Arbitary Point Y Coordinate on Normal Line
ThetaReflected = -ThetaIncident(m); % Reflected Angle
ArbXCoorRot = ArbXCoor*cosd(ThetaReflected) - ArbYCoor*sind(ThetaReflected); % Arbitary Point X Coordinate on Reflected Line
ArbYCoorRot = ArbYCoor*cosd(ThetaReflected) + ArbXCoor*sind(ThetaReflected); % Arbitary Point Y Coordinate on Reflected Line
m3 = (ArbYCoorRot - (yEnd - 0.5*(yEnd - yStart)))/(ArbXCoorRot - (xEnd - 0.5*(xEnd - xStart))); % Slope of Reflected Line
b3 = (yEnd - 0.5*(yEnd - yStart)) - m3*(xEnd - 0.5*(xEnd - xStart));
ElemLength = sqrt((yEnd - yStart)^2 + (xEnd - xStart)^2);
if min(OvenGlassXrange) < -b3/m3 && -b3/m3 < max(OvenGlassXrange) && -1 < m1 && m1 < 0 && m1 ~= -Inf && m1 ~= Inf && ElemLength < 3
ReflRayData(Count2,m,Count1) = 1;
end
end
end
end
Count2 = 0;
end
end
%% Plotting
ThetaPlot = [40,50]; % Put incident angle as input
Count1 = 0;
Count2 = 0;
NewColor = 0;
for m = 1:length(ThetaPlot)
NewColor = 0.1;
Title = sprintf('Angle(%d)',ThetaPlot(m));
figure('name',Title)
Count1 = 0;
for i = 1:length(xrange)-1 % X Coordinate of Start Node
for j = 1:length(yrange)-1 % Y Coordinate of Start Node
Count1 = Count1+1;
for k = 2:length(xrange) % X Coordinate of End Node
hold on;
for l = 2:length(yrange) % Y Coordinate of End Node
Count2 = Count2+1;
if ReflRayData(Count2,ThetaPlot(m) - ThetaIncident(1) + 1,Count1) == 1
x = [xrange(i),xrange(k)];
y = [yrange(j),yrange(l)];
plot(x,y,['-',[0,NewColor,0],'o']);
hold on;
end
end
Count2 = 0;
end
end
end
NewColor = NewColor + 0.02;
end
instead of plot(x,y,['-',[0,NewColor,0],'o']); try:
plot(x,y,'linestyle','-','marker','o','color',[0,NewColor,0])
According to the Matlab documentation, by calling hold on Matlab uses the next color.
hold on retains plots in the current axes so that new plots added to the axes do not delete existing plots. New plots use the next colors and line styles based on the ColorOrder and LineStyleOrder properties of the axes.
Therefore, in your code when you call hold on inside the for, it just uses the next color.
My solution is to put figure; hold on; before for loop and remove that one in you loop
I am using subplot function of MATLAB. Surprisingly the last plot in each subplot set becomes over-sized. Can anybody help me to resolve this issue? I have experimented with the parameters a little, but no luck. I am not able to post the plot figure.
function plotFluxVariabilityByGene(cRxn,KeggID,geneName)
load iJO1366; % Load the model iJO1366
%Find 'Gene' associated reactions from 'model'
reactions = rxnNamesFromKeggID(model,KeggID);
nCheck = 0; % Initialize counter
% Determine initial subplot dimensions
[R C setSize] = subplotSize(numel(reactions));
for n = 1 : numel(reactions)
% Get the name of nth reaction
rxn = reactions{n};
% Define the array for control reaction fluxes
cRxnArray = getCrxnArray(model,cRxn);
% Initialize storage for lower and upper limit-values
L = []; U = []; Avg = [];
% Get the fluxVariability values
for i = 1 : numel(cRxnArray)
modelMod = changeRxnBounds(model,cRxn,cRxnArray(i),'b');
[L(i) U(i)] = fluxVariability(modelMod,100,'max',{rxn});
Avg(i) = (L(i) + U(i))/2;
%fprintf('mthfcFlux = %f; Li = %f; Ui = %f\n',array(i),L(i),U(i));
end
% adjust the subplot number
nCheck = nCheck + 1;
% Determine the range of n to be saved in one file
if nCheck == 1
start = n;
elseif nCheck == setSize;
stop = n;
end
subplot(R,C,nCheck)
plot(cRxnArray,L,'-r','LineWidth',1); hold on;
plot(cRxnArray,L,'^r','MarkerSize',3,'LineWidth',2);
plot(cRxnArray,U,'-.b','LineWidth',1);
plot(cRxnArray,U,'^b','MarkerSize',2,'LineWidth',2);
plot(cRxnArray,Avg,'-','Color',[0.45,0.45,0.45],'LineWidth',2.5);
% Label X and Y axes
%xlabel([cRxn ' Flux']);
%ylabel(['fluxVariability ' char(rxn)]);
xlabel('Flux');
ylabel('fluxVariability');
hold off;
% Adjust X and Y axes limits
%xmn = min(cRxnArray) - ((max(cRxnArray) - min(cRxnArray))*0.05);
%xmx = max(cRxnArray) + ((max(cRxnArray) - min(cRxnArray))*0.05);
%ymn = min([U L]) - ((max([U L]) - min([U L]))*0.05);
%ymx = max([U L]) + ((max([U L]) - min([U L]))*0.05);
%if xmn ~= xmx
% xlim([xmn xmx]);
%end
%if ymn ~= ymx
% ylim([ymn ymx]);
%end
% Print which reactions are done
fprintf('\n......done for %s',char(rxn));
% If 'setSize' subplots are done then save the set in a file
if nCheck == setSize
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.fig']);
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.eps']); close(gcf);
% Determine initial subplot dimensions
[R C setSize] = subplotSize(numel(reactions)-n);
% Return nCheck to zero;
nCheck = 0;
end
end
% If nCheck is not equal to 16 then there are subplot that is not saved
% inside the for loop. Let's save it here.
if nCheck ~= setSize
stop = n;
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.fig']);
saveas(gcf,['TEST/' cRxn 'flux-Vs-' geneName '_fluxVariability' num2str(start) '-' num2str(stop) '.eps']); close(gcf);
end
fprintf('\nAll done\n');
end
%####################################################
%# Other functions ##
%####################################################
function rxnNames = rxnNamesFromKeggID(model,KeggID)
% Find 'Gene' associated reactions from 'model'
associatedRxns = findRxnsFromGenes(model,KeggID);
% Extract the reaction details from the structure to a cell
rxnDetails = eval(sprintf('associatedRxns.%s',KeggID));
% Extract only the reaction names from the cell
rxnNames = rxnDetails(:,1);
end
%####################################################
function cRxnArray = getCrxnArray(model,cRxn)
% Define the solver
changeCobraSolver('glpk');
% Find solution for the model
sol = optimizeCbModel(model);
% Change the objective of the default model to 'cRxn'
tmpModel = changeObjective(model,cRxn);
% Find slution for the changed model. This gives the maximum and
% minimum possible flux through the reaction 'cRxn' when the model is
% still viable
%solMax = optimizeCbModel(tmpModel,'max');
solMin = optimizeCbModel(tmpModel,'min');
% Create an array of 20 euqally spaced flux values between 'solMin' and
% 'sol.x'
%array = linspace(solMin.f,solMax.f,10);
cRxnArray = linspace(solMin.f,sol.x(findRxnIDs(model,cRxn)),20);
end
%####################################################
function [R C setSize] = subplotSize(remainingPlots)
% Sets number of columns and rows to 3 if total subplot >= 9
if remainingPlots > 7
R = 3; C = 3; setSize = 9;
elseif remainingPlots < 7
R = 2; C = 3; setSize = 6;
elseif remainingPlots < 5
R = 2; C = 2; setSize = 4;
elseif remainingPlots < 4
R = 1; C = 3; setSize = 3;
elseif remainingPlots < 3
R = 1; C = 2; setSize = 2;
end
end
%####################################################
My subplot looks like this:
I suspect its because you are calling subplotSize a second time inside your loop. This could be changing your R and C variables.
I would advise to check the R and C variables at the subplot command on each loop.
I'm trying to output Voronoi cells to a format which can be used for 3D printing.
MATLAB generates voronoi cells from lists of X and Y coordinates. My script generates such a list of points, but getting to a format I can export is seeming problematic.
My main hopes lie with stlwrite, http://www.mathworks.com/matlabcentral/fileexchange/20922-stlwrite-write-binary-or-ascii-stl-file
This function/script requires a surface to export.
function [lines, LineData, pOut] = makeSurfaceFromVorVerts(Lx, Ly, varargin)
p = inputParser;
addRequired(p,'Lx',...
#(x) (isequal(class(x),'double') && isequal(size(x,1),2)));
addRequired(p,'Ly',...
#(x) (isequal(class(x),'double') && isequal(size(x,1),2)));
defaultResolution = 100;
addOptional(p,'Resolution',defaultResolution,...
#(x) (isequal(class(x),'double') && isequal(size(x),[1 1])));
defaultBoundary = [0 110; 0 110];
addOptional(p,'Boundaries',defaultBoundary,...
#(x) (isequal(class(x),'double') && isequal(size(x),[2 2])));
parse(p,Lx,Ly,varargin{:});
pOut = p;
LX = p.Results.Lx;
LY = p.Results.Ly;
Bounds = p.Results.Boundaries;
% Strip high values
reducedXdat = [];
reducedYdat = [];
for i=1:size(LX,2)
if LX(1,i) > Bounds(1,1) && LX(1,i) < Bounds(1,2) && ... % X value of start of line
LX(2,i) > Bounds(2,1) && LX(2,i) < Bounds(2,2) && ... % Y value of start of line
LY(1,i) > Bounds(1,1) && LY(1,i) < Bounds(1,2) && ... % X value of end of line
LY(2,i) > Bounds(2,1) && LY(2,i) < Bounds(2,2), % Y value of end of line
reducedXdat = [reducedXdat, LX(:,i)];
reducedYdat = [reducedYdat, LY(:,i)];
end
end
% Initialise a grid of points
%sXnew = (Bounds(1,2) - Bounds(1,1)) * p.Results.Resolution;
%sYnew = (Bounds(2,2) - Bounds(2,1)) * p.Results.Resolution;
%Z = zeros(sXnew, sYnew,'uint8');
%for x=1:size(X,1)
% for y=1:size(Y,1)
% nX = floor(X(x)*p.Results.Resolution);
% nY = floor(Y(y)*p.Results.Resolution);
% Z(nX,nY) = 1;
% end
%end
%surface = Z;
%coords = [X,Y];
lines = line(reducedXdat,reducedYdat);
LineData = [reducedXdat; reducedYdat];
end
My processing script, above, takes the points generated by the command
[Lx, Ly] = voronoi(xValuesOfCellCentres, yValuesOfCellCentres);
along with an optional 'boundary' matrix (there's also a check for resolution, for the commented section) and then outputs lines.
I would like these lines to form the surface. I considered creating a mesh using a binary Z value (1 for points, 0 for everywhere else), but I don't know how I could also include the positions between points, ie those covered by the lines.
I expect that there is some relevant middle step I can take, to create a frame based on extrusion of the lines drawn (either by this script, which has cut away the extra lines to infinity, or by voronoi(X,Y), but I can't work it out.
Found a way to do this.
Changed the script, new script is pasted at the bottom.
Workflow goes:
Matlab (create line plot, then for i=1:size(lineHandles,1), set(lineHandles(i),'lineWidth',4),end). Change the line width as required.)
Save as .png file.
Gimp (crop file to a nice rectangle)
InkScape (open png file, click image, menu Path -> Trace Bitmap -> Color Quantization (2 colors) -> Drag the path aside (it shows path in the status bar at the bottom when you have this selected). Click image and delete. Place path (with nodes) back onto the page (there's an x/y coordinates setting in a toolbar at the top, set these both to 0.
Save as a .dxf file. To get these to input more nicely, use the extension provided at http://www.thingiverse.com/thing:14221. This should be installed by copying the .py and .inx files to /usr/share/inkscape/extensions (or similar)
Open in OpenSCAD using the command import("/path/to/filename.dxf");
This object can be extruded using the linear_extrude(height = x) where x is a height in mm (other lengths probably configurable)
Render using CGAL (F6 is the shortcut for this in OpenSCAD.)
Export to .stl file (menu Design > Export as STL...)
MATLAB script (edit as needed and take outputs as required, if you want the line handles you'll need to put in almost all of the output arguments (or reorder them):
%voronoiScriptHex generates voronoi cells in a hexagonal tesselation grid
% [X, Y, Fig, Axis, Lx, Ly, lH, lD] = voronoiScript(Bnd, Spc, D, ...)
%
% Output variables
% X is the x coordinate of the voronoi cell centres
% Y is the y coordinate of the voronoi cell centres
% Fig is the handle of the figure generated
% Axis is the handle of the axes used
% Lx gives the start and end x coordinates of the voronoi lines
% Ly gives the start and end y coordinates of the voronoi lines
% lH is the set of handles for the voronoi lines
% lD is constructed from [Lx; Ly], it is a [4 by Length] array
%
% Bnd specifies the boundaries for the region to be covered. It should be
% either one number, or a pair of numbers in a [1x2] vector, specifying
% [maxX, maxY]. 0 is taken as the minimum value.
%
% Spc specifies the average spacing. For a hex grid, it only accepts one
% value.
%
% D specifies the variation from a uniform grid. It is multiplied by a
% random number between -0.5 and 0.5 and added to the [x,y] coordinates
% of a point. If size(D) = [1x2], then the values are for [Dx, Dy].
%
% Optional arguments can be used to place some points exactly on the grid
% they would lie on with D[x/y] = 0. The first should be 'PartFixed' -
% this is a boolean and if true, some points are fixed to the grid.
%
% The second argument is 'FractionFixed'. This is an integer value
% (double class variables are accepted, but floor(arg) must be equal to
% (arg)). It specifies inversely how often points should be fixed, eg a
% value of 1 fixes every point, whilst a value of 5 fixes 1/5 of the
% points.
%
% PlotScatter is another boolean value, which sets if a scatter plot of
% the X,Y values corresponding to the cell centres should be included in
% the figure.
function [X, Y, Figure, Axis, Lx, Ly, lineHandles, lineData] = ...
voronoiScriptHex(Boundary, Spacing, Delta, varargin)
p = inputParser;
p.FunctionName = 'voronoiScript';
addRequired(p, 'Boundary', #checkTypes);
addRequired(p, 'Spacing', #isnumeric);
addRequired(p, 'Delta', #checkTypes);
defaultPart = false;
addOptional(p, 'PartFixed', defaultPart, #islogical);
defaultFraction = 2;
addOptional(p, 'FractionFixed', defaultFraction, #isAnInt);
defaultScatter = false;
addOptional(p, 'PlotScatter', defaultScatter, #islogical);
parse(p, Boundary, Spacing, Delta, varargin{:});
% Get values for boundaries and delta
% (Can be vectors or scalars)
if isequal(size(p.Results.Boundary),[1,2])
% Boundary is a vector [maxX, maxY]
BoundaryY = p.Results.Boundary(1,2);
else
BoundaryY = p.Results.Boundary(1,1);
end
if isequal(size(p.Results.Delta),[1,2])
% Delta is a vector [dX, dY]
DeltaY = p.Results.Delta(1,2);
else
DeltaY = p.Results.Delta(1,1);
end
Spacing = p.Results.Spacing;
BoundaryX = p.Results.Boundary(1,1);
DeltaX = p.Results.Delta(1,1);
D1 = [2*Spacing*cosd(30), Spacing];
numP = [ceil(BoundaryX/D1(1,1)) ceil(BoundaryY/D1(1,2))];
D2 = D1 ./ 2;
% Create the values
counter = 1;
xList(numP(1,1)*numP(1,2)) = 0;
yList(numP(1,1)*numP(1,2)) = 0;
for x=1:numP(1,1)
for y = 1:numP(1,2)
xList(counter) = (getPointValue(x, D1(1,1), DeltaX)-D2(1,1));
xList(counter+1) = getPointValue(x, D1(1,1), DeltaX);
yList(counter) = (getPointValue(y, D1(1,2), DeltaY)-D2(1,2));
yList(counter+1) = getPointValue(y, D1(1,2), DeltaY);
counter = counter + 2;
end
end
% Set some of the points to be without random change
if (p.Results.PartFixed),
for counter=1:p.Results.FractionFixed:size(xList,2),
[x, y] = getXYfromC(counter, numP(1,2));
xList(counter) = x*Spacing;
yList(counter) = y*Spacing;
end
end
X = xList;
Y = yList;
% Set manual ticks for the figure axes
ticksX = zeros(1,numP(1,1)+1);
for i=1:numP(1,1)+1,
ticksX(i) = i*D1(1,1);
end
ticksY = zeros(1,numP(1,2)+1);
for i=1:numP(1,2)+1,
ticksY(i) = i*D1(1,2);
end
BoundCoeff = 1.08;
Bounds = [0 BoundCoeff*BoundaryX; 0 BoundCoeff*BoundaryY];
% Give the figure a handle that is returned, and set axes values
Figure = figure;
Axis = axes;
axis equal;
minor = 'off';
gridtoggle = 'off';
set(Axis,'XTickMode','manual','YTickMode','manual', ...
'XGrid',gridtoggle,'YGrid',gridtoggle, ...
'XMinorGrid',minor,'YMinorGrid',minor, ...
'XTick',ticksX,'YTick',ticksY, ...
'XMinorTick',minor,'YMinorTick',minor, ...
'XLim',[0 Bounds(1,2)],'YLim',[0 Bounds(2,2)]);
%set(Axis,'XLim',[0 Bounds(1,2)],'YLim',[0 Bounds(2,2)]);
% Create the voronoi cells, returning the line points
[Lx, Ly] = voronoi(X,Y);
% Strip high values
counter = 1;
reducedXdat = zeros(2,size(Lx,2));
reducedYdat = zeros(2,size(Lx,2));
for i=1:size(Lx,2)
if Lx(1,i) > Bounds(1,1) && Lx(1,i) < Bounds(1,2) && ... % X value of start of line
Lx(2,i) > Bounds(2,1) && Lx(2,i) < Bounds(2,2) && ... % Y value of start of line
Ly(1,i) > Bounds(1,1) && Ly(1,i) < Bounds(1,2) && ... % X value of end of line
Ly(2,i) > Bounds(2,1) && Ly(2,i) < Bounds(2,2), % Y value of end of line
reducedXdat(:,counter) = Lx(:,i);
reducedYdat(:,counter) = Ly(:,i);
counter = counter + 1;
end
end
Lx = reducedXdat(:,1:counter-1);
Ly = reducedYdat(:,1:counter-1);
% Plot the voronoi lines
lineHandles = line(Lx, Ly);
% Set colours to black
if (1)
for i=1:size(lineHandles,1)
set(lineHandles(i),...
'LineWidth',3, ...
'LineSmoothing','on', ...
'Color',[0 0 0]);
end
end
lineData = [Lx; Ly];
if (p.Results.PlotScatter)
hold on;
scatter(X,Y);
end
end
function bool = checkTypes(arg)
bool = (isequal(class(arg),'double') && ...
(isequal(size(arg),[1,1]) || isequal(size(arg),[1,2])));
end
function bool = isAnInt(arg)
bool = (isequal(floor(arg),arg) && ...
isequal(size(arg),[1,1]) && ...
isnumeric(arg));
end
function val = getPointValue(intV, spacing, delta)
val = (((rand(1)-0.5)*delta)+(intV*spacing));
end
function [x,y] = getXYfromC(counter, sizeY)
x = floor(counter/sizeY)+1;
y = counter - ((x-1)*sizeY);
end
SCAD script file to place the voronoi cells within a pair of cylinders for 3D printing a grid. Edit as needed with path, or changes to shapes etc:
$fn = 360;
inkFile = "/path/to/my/file";
scl = 1.05; // Scale variable
transVec = [-100, -98, 0]; // Move the imported image pattern so that it's centred.
union(){
makeRing([0,0,3],inR=96, outR=99, height=6);
makeRing([0,0,1.5], inR=99, outR=102, height=3);
intersection() {
#linear_extrude(height = 6.05, convexity=40, center=false) // Removing the # will get rid of the overlay, which helps see where the grid is.
scale([scl, scl, 1])
translate(transVec)
import(inkFile);
makeCylinder([0,0,3], 96, 6, $fn=360);
}
}
module makeCylinder(centre, radius, height) {
translate(centre){
cylinder(h = height, r = radius, center=true);
}
}
module makeRing(centre,inR, outR, height) {
difference() {
makeCylinder(centre, outR, height);
makeCylinder(centre, inR, height+0.1);
}
}
I have a numeric dataset and I want to cluster data with a non-parametric algorithm. Basically, I would like to cluster without specifying the number of clusters for the input. I am using this code that I accessed through the MathWorks File Exchange network which implements the Mean Shift algorithm. However, I don't Know how to adapt my data to this code as my dataset has dimensions 516 x 19.
function [clustCent,data2cluster,cluster2dataCell] =MeanShiftCluster(dataPts,bandWidth,plotFlag)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
%perform MeanShift Clustering of data using a flat kernel
%
% ---INPUT---
% dataPts - input data, (numDim x numPts)
% bandWidth - is bandwidth parameter (scalar)
% plotFlag - display output if 2 or 3 D (logical)
% ---OUTPUT---
% clustCent - is locations of cluster centers (numDim x numClust)
% data2cluster - for every data point which cluster it belongs to (numPts)
% cluster2dataCell - for every cluster which points are in it (numClust)
%
% Bryan Feldman 02/24/06
% MeanShift first appears in
% K. Funkunaga and L.D. Hosteler, "The Estimation of the Gradient of a
% Density Function, with Applications in Pattern Recognition"
%*** Check input ****
if nargin < 2
error('no bandwidth specified')
end
if nargin < 3
plotFlag = true;
plotFlag = false;
end
%**** Initialize stuff ***
%[numPts,numDim] = size(dataPts);
[numDim,numPts] = size(dataPts);
numClust = 0;
bandSq = bandWidth^2;
initPtInds = 1:numPts
maxPos = max(dataPts,[],2); %biggest size in each dimension
minPos = min(dataPts,[],2); %smallest size in each dimension
boundBox = maxPos-minPos; %bounding box size
sizeSpace = norm(boundBox); %indicator of size of data space
stopThresh = 1e-3*bandWidth; %when mean has converged
clustCent = []; %center of clust
beenVisitedFlag = zeros(1,numPts,'uint8'); %track if a points been seen already
numInitPts = numPts %number of points to posibaly use as initilization points
clusterVotes = zeros(1,numPts,'uint16'); %used to resolve conflicts on cluster membership
while numInitPts
tempInd = ceil( (numInitPts-1e-6)*rand) %pick a random seed point
stInd = initPtInds(tempInd) %use this point as start of mean
myMean = dataPts(:,stInd); % intilize mean to this points location
myMembers = []; % points that will get added to this cluster
thisClusterVotes = zeros(1,numPts,'uint16'); %used to resolve conflicts on cluster membership
while 1 %loop untill convergence
sqDistToAll = sum((repmat(myMean,1,numPts) - dataPts).^2); %dist squared from mean to all points still active
inInds = find(sqDistToAll < bandSq); %points within bandWidth
thisClusterVotes(inInds) = thisClusterVotes(inInds)+1; %add a vote for all the in points belonging to this cluster
myOldMean = myMean; %save the old mean
myMean = mean(dataPts(:,inInds),2); %compute the new mean
myMembers = [myMembers inInds]; %add any point within bandWidth to the cluster
beenVisitedFlag(myMembers) = 1; %mark that these points have been visited
%*** plot stuff ****
if plotFlag
figure(12345),clf,hold on
if numDim == 2
plot(dataPts(1,:),dataPts(2,:),'.')
plot(dataPts(1,myMembers),dataPts(2,myMembers),'ys')
plot(myMean(1),myMean(2),'go')
plot(myOldMean(1),myOldMean(2),'rd')
pause
end
end
%**** if mean doesnt move much stop this cluster ***
if norm(myMean-myOldMean) < stopThresh
%check for merge posibilities
mergeWith = 0;
for cN = 1:numClust
distToOther = norm(myMean-clustCent(:,cN)); %distance from posible new clust max to old clust max
if distToOther < bandWidth/2 %if its within bandwidth/2 merge new and old
mergeWith = cN;
break;
end
end
if mergeWith > 0 % something to merge
clustCent(:,mergeWith) = 0.5*(myMean+clustCent(:,mergeWith)); %record the max as the mean of the two merged (I know biased twoards new ones)
%clustMembsCell{mergeWith} = unique([clustMembsCell{mergeWith} myMembers]); %record which points inside
clusterVotes(mergeWith,:) = clusterVotes(mergeWith,:) + thisClusterVotes; %add these votes to the merged cluster
else %its a new cluster
numClust = numClust+1 %increment clusters
clustCent(:,numClust) = myMean; %record the mean
%clustMembsCell{numClust} = myMembers; %store my members
clusterVotes(numClust,:) = thisClusterVotes;
end
break;
end
end
initPtInds = find(beenVisitedFlag == 0); %we can initialize with any of the points not yet visited
numInitPts = length(initPtInds); %number of active points in set
end
[val,data2cluster] = max(clusterVotes,[],1); %a point belongs to the cluster with the most votes
%*** If they want the cluster2data cell find it for them
if nargout > 2
cluster2dataCell = cell(numClust,1);
for cN = 1:numClust
myMembers = find(data2cluster == cN);
cluster2dataCell{cN} = myMembers;
end
end
This is the test code I am using to try and get the Mean Shift program to work:
clear
profile on
nPtsPerClust = 250;
nClust = 3;
totalNumPts = nPtsPerClust*nClust;
m(:,1) = [1 1];
m(:,2) = [-1 -1];
m(:,3) = [1 -1];
var = .6;
bandwidth = .75;
clustMed = [];
%clustCent;
x = var*randn(2,nPtsPerClust*nClust);
%*** build the point set
for i = 1:nClust
x(:,1+(i-1)*nPtsPerClust:(i)*nPtsPerClust) = x(:,1+(i-1)*nPtsPerClust:(i)*nPtsPerClust) + repmat(m(:,i),1,nPtsPerClust);
end
tic
[clustCent,point2cluster,clustMembsCell] = MeanShiftCluster(x,bandwidth);
toc
numClust = length(clustMembsCell)
figure(10),clf,hold on
cVec = 'bgrcmykbgrcmykbgrcmykbgrcmyk';%, cVec = [cVec cVec];
for k = 1:min(numClust,length(cVec))
myMembers = clustMembsCell{k};
myClustCen = clustCent(:,k);
plot(x(1,myMembers),x(2,myMembers),[cVec(k) '.'])
plot(myClustCen(1),myClustCen(2),'o','MarkerEdgeColor','k','MarkerFaceColor',cVec(k), 'MarkerSize',10)
end
title(['no shifting, numClust:' int2str(numClust)])
The test script generates random data X. In my case. I want to use the matrix D of size 516 x 19 but I am not sure how to adapt my data to this function. The function is returning results that are not agreeing with my understanding of the algorithm.
Does anyone know how to do this?