Merge pull request #52 from Julie-Fabre/bleeding_edge

update prettify_matlab subtree

prettify-matlab/README.md

@@ -43,8 +43,6 @@ Uses the matrices from:
 #### To do list 
 - [ ] Perceptually-uniform, colorblind friendly colormaps.
 - [ ] Prettify colorbars and labels
-- [ ] Add pvalue bars above plots 
 - [ ] wrap titles/labels 
-- [ ] bar plots, violin plots 
 - [ ] shaded, shaded gradual 
 

prettify-matlab/examples/prettify_plot_demo.m

@@ -0,0 +1,29 @@
+
+% generate an uglyPlot
+uglyPlot;
+
+% Make it pretty with default settings 
+prettify_plot;
+
+% Change the figure color, fonts, fontsizes, ticks 
+prettify_plot('FigureColor', 'k')
+prettify_plot('FigureColor', 'none', 'Font', 'Times','TitleFontSize', 12, 'LabelFontSize', 14,'GeneralFontSize', 12,...
+    'AxisTicks', 'in', 'TickLength', 0.01, 'TickWidth', 1)
+
+% let's go back to the defaults
+prettify_plot;
+
+% Homogenize the x, y and climits by rows of subplots (other options include by columns
+% ('col') and for all aubplots ('all')
+prettify_plot('YLimits', 'cols', 'XLimits', 'all')
+
+% Change colormaps, make symmetric 
+prettify_plot('CLimits', 'all', 'SymmetricalCLimits', true, 'ChangeColormaps', true)
+
+% Change the legends
+prettify_plot('LegendReplace', true)
+
+% Replace axis by scale bars 
+prettify_addScaleBars
+
+% Add p-values 

prettify-matlab/examples/uglyPlot.m

@@ -1,87 +1,98 @@
-%% uglyPlot
-
-% Define x values
-x = linspace(0, 2*pi, 1000);
-
-% Define y values for various functions
-y1 = sin(x);
-y2 = cos(x);
-y3 = tan(x);
-y4 = log(x + 1); % Adding 1 to avoid negative values and log(0)
-y_cot = cot(x); % Cotangent values
-
-% Create a new figure
-figure(2); clf;
-
-
-%% line plots 
-% First subplot: sine curve
-subplot(2, 3, 1);
-plot(x, y1, 'r'); 
-hold on; % Keep the current plot and add new plots to it
-plot(x, 0.5*ones(size(x)), 'k--'); % Dashed line at y = 0.5
-title('Sine Curve');
-xlabel('x');
-ylabel('sin(x)');
-legend('sin(x)', 'y = 0.5');
-
-% Second subplot: cosine curve
-subplot(2, 3, 4);
-plot(x, y2, 'b'); 
-hold on;
-% Sample points
-x_points = [pi/4, pi/2, 3*pi/4];
-y_points = cos(x_points);
-plot(x_points, y_points, 'ro'); % red circles
-title('Cosine Curve');
-xlabel('x');
-ylabel('cos(x)');
-
-% scatter plots
-
-numDots = 20;
-subplot(2, 3, 2);
-x = rand(numDots, 1); % Random numbers between 0 and 1 for the x-axis
-y = rand(numDots, 1); % Random numbers between 0 and 1 for the y-axis
-scatter(x, y);
-xlabel('X-axis');
-ylabel('Y-axis');
-title('Scatter Plot of Random Dots');
-
-
-
-numDots = 25;
-subplot(2, 3, 5);
-x = rand(numDots, 1); % Random numbers between 0 and 1 for the x-axis
-y = rand(numDots, 1); % Random numbers between 0 and 1 for the y-axis
-scatter(x, y);
-xlabel('X-axis');
-ylabel('Y-axis');
-title('Scatter Plot of Random Dots');
-
-%% images 
-time = linspace(0, 24, 100); % Time from 0 to 24 hours
-activity = cumsum(randn(15, 100)); % Random walk for activity
-subplot(2, 3, 3);
-uglyColors = [1 0 1; 0 1 0; 0 0 1; 1 1 0; 1 0.5 0.2];
-colormap(uglyColors);
-imagesc(time, [], activity);
-ylabel('Activity');
-xlabel('Time (ms)');
-c = colorbar;
-c.Title.String = 'Zscore';
-
-% Activity plot 
-time = linspace(0, 24, 100); % Time from 0 to 24 hours
-activity = cumsum(randn(15, 100)).*2; % Random walk for activity
-subplot(2, 3, 6);
-uglyColors = [1 0 1; 0 1 0; 0 0 1; 1 1 0; 1 0.5 0.2];
-colormap(uglyColors);
-% Plot the activity data
-imagesc(time, [], activity);
-ylabel('Activity');
-xlabel('Time (ms)');
-c2 = colorbar;
-c2.Title.String = 'Zscore';
-
-prettify_plot;
+% Create a 3x4 subplot
+figure;
+
+% Parameters for simulation
+time = linspace(0, 2, 2000);  % Time vector
+stimulus_onset = 0.5;  % Time of stimulus onset
+stimulus_duration = 0.2;  % Duration of the stimulus
+response_delay = 0.3;  % Response delay after stimulus onset
+response_duration = 0.25;  % Duration of the response
+noise_amplitude = 0.5;  % Amplitude of noise
+
+% Simulate stimulus
+stimulus = zeros(size(time));
+stimulus((time >= stimulus_onset) & (time < stimulus_onset + stimulus_duration)) = 1;
+
+% Simulate neurons
+neuron1_response = 12 * exp(-(time - (stimulus_onset + response_delay)) / response_duration) .* (time >= (stimulus_onset + response_delay)) +...
+     noise_amplitude * randn(size(time));
+neuron2_response = 0.5 * exp(-(time - (stimulus_onset + response_delay)) / response_duration) .* (time >= (stimulus_onset + response_delay)) +...
+    + noise_amplitude * randn(size(time));
+neuron3_response = 22 * -exp(-(time - (stimulus_onset + response_delay)) / response_duration) .* (time >= (stimulus_onset + response_delay))+...
+    noise_amplitude * randn(size(time));
+
+% Plot the simulated responses
+subplot(3, 3, 1);
+plot(time, neuron1_response);
+xlim([0 1])
+xlabel('Time');
+ylabel('Response');
+
+subplot(3, 3, 4);
+plot(time, neuron2_response);
+xlabel('Time');
+ylabel('Response');
+
+subplot(3, 3, 7);
+plot(time, neuron3_response);
+xlabel('Time');
+ylabel('Response');
+
+% simulate PSTH 
+% Define a function to generate neuron responses
+generate_neuron_response = @(amplitude) amplitude * exp(-(time - (stimulus_onset + response_delay)) / response_duration) .* ...
+    (time >= (stimulus_onset + response_delay)) + amplitude * randn(size(time));
+
+noise_amplitude = 0.1; 
+% Use arrayfun to generate responses for all neurons
+neurons1_amplitudes = [-2:0.5:12];
+neurons_1 = arrayfun(generate_neuron_response, neurons1_amplitudes, 'UniformOutput', false);
+neurons_1_matrix = cell2mat(neurons_1');
+
+neurons2_amplitudes = [-0.5:0.01:0.5];
+neurons_2 = arrayfun(generate_neuron_response, neurons2_amplitudes, 'UniformOutput', false);
+neurons_2_matrix = cell2mat(neurons_2');
+
+neurons3_amplitudes = [-22:1:1];
+neurons_3 = arrayfun(generate_neuron_response, neurons3_amplitudes, 'UniformOutput', false);
+neurons_3_matrix = cell2mat(neurons_3');
+
+
+% plot PSTHs
+subplot(3, 3, 2);
+imagesc(time, [], neurons_1_matrix)
+colormap("jet")
+cb = colorbar;
+cb.Title.String = 'Activity';
+
+subplot(3, 3, 5);
+imagesc(time, [], neurons_2_matrix)
+colormap("jet")
+colorbar;
+
+subplot(3, 3, 8);
+imagesc(time, [], neurons_3_matrix)
+colormap("jet")
+colorbar;
+
+% plot some lines 
+average_psth1 = smoothdata(mean(neurons_1_matrix, 1),'gaussian', [0, 100]);
+average_psth2 =  smoothdata(mean(neurons_2_matrix, 1), 'gaussian',[0, 100]);
+average_psth3 =  smoothdata(mean(neurons_3_matrix, 1), 'gaussian',[0, 100]);
+
+
+subplot(3, 3, [3,6,9]); hold on;
+plot(time, average_psth2);
+plot(time, average_psth3);
+plot(time, average_psth1);
+
+
+legend('Type 1', 'Type 2', 'Type 3')
+
+
+
+
+
+
+% Adjust the layout of the subplots
+sgtitle('Demo');