1 %lydia barnes, 20191107
   2 %email: lydiabarnes01@gmail.com
   3 %adapted from sneha shashidhara, 20181025
   4 
   5 %this script contains example code for an introductory MATLAB visulisation
   6 %course held at the MRC CBU, 08 Nov 2019. 
   7 
   8 %clear the command window, figures, and workspace
   9 clc; close all; clear; 
  10 
  11 %in case of errors, make sure we can pause to see what went wrong
  12 dbstop if error
  13 
  14 %% scatter plot
  15 
  16 %make some example data (or load your own data here)
  17 x = linspace(0,1,100); %go from 0 to 1 in 100 steps
  18 y = x + 0.1*rand(1,100); %copy x, then modify it by some random values between 0 and .1
  19 
  20 %make an empty figure
  21 figure
  22 
  23 %->what does 'scatter' take as its first two inputs?
  24 
  25 %plot!
  26 scatter(x,y);
  27 
  28 %add a least-squares fit line
  29 lsline;
  30 
  31 %->clear the plot, workspace, and command window
  32 
  33 %% line plot
  34 
  35 %make some example data
  36 x = linspace(0,360,100); %0:360 in 100 steps
  37 y = sind(x); 
  38 z = cosd(x);
  39 
  40 %set your colours. MATLAB plots expect colours in a vector of three values
  41 %between 0 and 1, indicating red, green, and blue intensities
  42 red=[1 0 0];
  43 green=[0 1 0];
  44 blue=[0 0 1];
  45 black=[0 0 0];
  46 white=[1 1 1];
  47 %->choose your own three colours! ie purple = [.5 0 .5]...
  48 
  49 %make an empty figure. this time, store a 'handle' to the figure
  50 h = figure;
  51 %->look at h to see what 'properties' a figure automatically has
  52 
  53 %->learn about name-value pairs
  54 
  55 %plot!
  56 plot(x,y,'Color',red)
  57 
  58 %'hold' the plot so that the next plot commands layer over the top of this
  59 %one
  60 hold on
  61 
  62 %make some fresh data
  63 plot(x,z,'Color',blue);
  64 
  65 %describe the plot contents
  66 title('trigonometry','FontSize',20);
  67 xlabel('angle in degrees');
  68 ylabel('trig functions');
  69 legend('sine','cosine','Location','best'); %put a legend where it fits best
  70 legend boxoff
  71 
  72 %because we have a handle for the figure, we can ask matlab to save
  73 %everything from that handle to an image file
  74 saveas(h,'trigLinePlot');
  75 
  76 %% barplots
  77 
  78 %make some example data
  79 %   assume we've collected reaction times from 10 participants for 3
  80 %   different tasks, each of which has an easy and a difficult version.
  81 %   we'll organise our data into one subjects x tasks matrix for easy, and
  82 %   another subjects x tasks matrix for the hard condition:
  83 x = randi(10,[10,3]); %x = easy. get integers between 0 and 10, for 10 subjects (rows) and 3 tasks (columns)
  84 y = x + randi(3,[10,3]); %y = hard. assume this evoked slightly larger responses than the easy condition.
  85 %   get the group average of each condition
  86 data = [mean(x,1); mean(y,1)];
  87 %   estimate the standard error (the standard deviation/sq root of n)
  88 %   of the group means
  89 errorData = [std(x,1)/sqrt(size(x,1)); std(y,1)/sqrt(size(y,1))];
  90 %-> view your data matrices
  91 
  92 %make a new figure
  93 h = figure; %store the figure handle in h
  94 
  95 %plot
  96 b = bar(data); %store the plot handle in b
  97 hold on %make sure subsequent plotting commands apply to this figure
  98 %->look at b's properties by typing b into the command window
  99 %->what properties are in h (the figure handle) vs b (the plot handle)?
 100 
 101 %set the colours for each task
 102 set(b(1),'FaceColor',blue);
 103 set(b(2),'FaceColor',green);
 104 set(b(3),'FaceColor',red);
 105 
 106 %label your figure and axes
 107 title('reaction times across three cognitive tasks (n=10)','FontSize',15);
 108 xlabel('conditions','FontSize',15); 
 109 ylabel('RT (s)','FontSize',15);
 110 
 111 %get the axis handle
 112 ax=gca; 
 113 %->look at the axis handle 
 114 %->what properties does the axis (ax) have? compare to the figure
 115 %properties in h
 116 
 117 %modify the axis properties
 118 set(ax,'XTick',[1, 2]) %only put ticks at 1 and 2
 119 set(ax,'XTickLabel',{'easy','hard'}) %label those ticks with our conditions
 120 
 121 %add error bars
 122 %   record where the centre of each bar is along the x-axis
 123 X = [1-(2/9) 1 1+(2/9); 2-(2/9) 2 2+(2/9)];
 124 %   use the errorbar function to plot the standard errors you calculated
 125 %   earlier. inputs are the x-axis positions, y-axis positions (our group 
 126 %   means), and the standard errors for each bar
 127 %   (by default, errorbar plots bars 2*the standard error in either
 128 %   direction)
 129 eb = errorbar(X,data,errorData,'.','Color',black); 
 130 %-> use name-value pairs to set the width of the errorbars
 131 set(eb,'LineWidth',1.5)
 132 
 133 %do a ttest! OR invent a p-value and skip this step...
 134 %   average the three tasks to get one column of values each for the easy
 135 %   and hard conditions
 136 allxdata = mean(x,2); %easy
 137 allydata = mean(y,2); %hard
 138 %   use the ttest function. inputs are condition 1 and condition 2 as
 139 %   vectors. outputs are [h,p,stats], where h is 1 if you can reject the
 140 %   null at alpha = .05, and p is the p-value. discard h but store p
 141 [~,p] = ttest(allxdata,allydata);
 142 
 143 p=.01; %make a p-value
 144 
 145 %plot a line indicating what we compared, along with the p-value
 146 %   calculate the highest value on the plot: the largest group mean plus
 147 %   its error bar
 148 ymax = max(max(data)) + max(max(errorData));
 149 %   plot a line at that height (ie the top of the plot), stretching between
 150 %   our two conditions
 151 line([1 2],[ymax ymax],'linewidth',2,'Color',black);
 152 %   add text for our p-value
 153 t=text(1.5,ymax,sprintf('p = %.2f',p));
 154 %->change Vertical Alignment so that the line and text don't overlap
 155 %->adjust the font size
 156 t.FontSize=15;
 157 t.HorizontalAlignment='center';
 158 t.VerticalAlignment='bottom';
 159 
 160 %-> use ylim to adjust the y-axis limits to make room for the p-value
 161 ylim([0 ymax+1])
 162 
 163 %save the plot
 164 %->look at "help saveas" and work out how to save the figure as a jpeg
 165 saveas(h,'responseTimesGrpBarPlot.jpg');
 166 
 167 
 168 %% subplots
 169 
 170 %make a new figure
 171 h = figure;
 172 
 173 %make sure the subsequent plotting commands are applied to this figure
 174 hold on;
 175 
 176 %we're going to reuse the data from the previous barplot. rather than take
 177 %the group average, we'll plot each subject on their own 'subplot' within
 178 %this figure.
 179 
 180 %->find out how many subjects there are with the size function. remember,
 181 %we had one subject per row in the data matrices x and y.
 182 nsubjects = size(x,1);
 183 
 184 %make an empty array of axes with 'gobjects' (for speed - not essential)
 185 ax = gobjects(1,nsubjects); %1 row, a column per subject
 186 
 187 %loop through each subject
 188 for subjid = 1:nsubjects %as the index increases from 1 to the number of subjects
 189     
 190     %select this subject's data from our easy and hard condition matrices
 191     data = [x(subjid,:); y(subjid,:)]; %row 1 is from x, row 2 is from y
 192     
 193     %->use help look at the first three inputs we can give the subplot function
 194     
 195     %make a subplot for this person, and store its handle in the empty axis array
 196     %we made earlier
 197     %   subplot breaks a figure into parts. its first two inputs dictate
 198     %   how many rows and columns of subplots you want: in our case, 2
 199     %   rows, 5 columns. the third input selects which subplot you want to
 200     %   work with (moving left-right, top-bottom, as though you're reading)
 201     ax(subjid) = subplot(2,5,subjid);
 202     
 203     %now that we've selected a subplot, plot this subject's data there
 204     b=bar(data);
 205     
 206     %->use your favourite colours for the three bars
 207     
 208     %label the plot with this subject's ID
 209     title(sprintf('subject %d',subjid));
 210 end
 211 
 212 %label the full plot
 213 suptitle('all subjects');
 214 
 215 %calculate the group range and use it to set the y-axis limits
 216 lower=min([min(x),min(y)])-1;  
 217 upper=max([max(x),max(y)])+1;
 218 set(ax,'ylim',[lower upper]);
 219 
 220 
 221 %% matrices
 222 
 223 %make some data
 224 x=randn(5,5); %fill a 5x5 matrix with random values
 225 %->when would we want to plot full matrices?
 226 
 227 %make a new plot
 228 figure
 229 imagesc(x); %treat the matrix as an image, giving each value a color based on its magnitude
 230 colorbar; %show the color scale