goPlotVert

PURPOSE ^

This script produces the figure 5a. It plot the vertical propagation versus

SYNOPSIS ^

This is a script file.

DESCRIPTION ^

 This script produces the figure 5a. It plot the vertical propagation versus
 propagation levels in C. Elegans neural network, small-world network, random
 network, regular network and Drosophila's CX network. The curves are based on
 fitting Gompertz function. The errorbar is standard deviation.

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 % This script produces the figure 5a. It plot the vertical propagation versus
0002 % propagation levels in C. Elegans neural network, small-world network, random
0003 % network, regular network and Drosophila's CX network. The curves are based on
0004 % fitting Gompertz function. The errorbar is standard deviation.
0005 
0006 %   ---------
0007 %   Yen-Nan Lin, NTHU, 2010-2014, Matlab 2012a
0008 
0009 close all;
0010 
0011 % plot setting
0012 FIG_SIZE = [0 0 1200 600];
0013 lineWidth = 3;
0014 markerSize = 10;
0015 markerScale = 1.5;
0016 
0017 % fitting with Gompertz function
0018 cfit.CE = fitGompertz(0:(MAX_LEVEL - 2), ceNetwork.VertProp);
0019 cfit.Rand = fitGompertz(0:(MAX_LEVEL - 2), meanRandNet.VertProp);
0020 cfit.Regular = fitGompertz(0:(MAX_LEVEL - 2), meanRegularNet.VertProp);
0021 cfit.Ring = fitGompertz(0:(MAX_LEVEL - 2), meanRingNet.VertProp);
0022 cfit.CX = fitGompertz(0:(MAX_LEVEL - 2), cxNetwork.VertProp);
0023 
0024 % save cfit object to each network
0025 ceNetwork.cfitVertProp = cfit.CE;
0026 meanRandNet.cfitVertProp = cfit.Rand;
0027 meanRegularNet.cfitVertProp = cfit.Regular;
0028 meanRingNet.cfitVertProp = cfit.Ring;
0029 cxNetwork.cfitVertProp = cfit.CX;
0030 
0031 % plot marker and errorbar of each networks
0032 figure('name', 'Vertical Propagation', 'outerPosition', FIG_SIZE, 'color', 'w');
0033 hold on;
0034 tmpP = linspace(0, MAX_LEVEL - 2, 100);
0035 plot(0:(MAX_LEVEL - 2), ceNetwork.VertProp, 'Marker', netMarker('CE'), ...
0036     'Color', netColor('CE'), 'MarkerSize', markerSize, ...
0037     'MarkerFaceColor', netMarkerFace('CE'), ...
0038     'LineWidth', lineWidth, 'LineStyle', 'none');
0039 errorbar(0:(MAX_LEVEL - 2), meanRandNet.VertProp, ...
0040     stdRandNet.VertProp, 'Marker', netMarker('ER'), ...
0041     'Color', netColor('ER'), 'MarkerSize', markerSize * markerScale, ...
0042     'MarkerFaceColor', netMarkerFace('ER'), ...
0043     'LineWidth', lineWidth, 'LineStyle', 'none');
0044 errorbar(0:(MAX_LEVEL - 2), meanRegularNet.VertProp, ...
0045     stdRegularNet.VertProp, 'Marker', netMarker('RL'), ...
0046     'Color', netColor('RL'), 'MarkerSize', markerSize, ...
0047     'MarkerFaceColor', netMarkerFace('RL'), ...
0048     'LineWidth', lineWidth, 'LineStyle', 'none');
0049 errorbar(0:(MAX_LEVEL - 2), meanRingNet.VertProp, ...
0050     stdRingNet.VertProp, 'Marker', netMarker('SW'), ...
0051     'Color', netColor('SW'), 'MarkerSize', markerSize, ...
0052     'MarkerFaceColor', netMarkerFace('SW'), ...
0053     'LineWidth', lineWidth, 'LineStyle', 'none');
0054 plot(0:(MAX_LEVEL - 2), cxNetwork.VertProp, 'Marker', netMarker('CX'), ...
0055     'Color', netColor('CX'), 'MarkerSize', markerSize * markerScale, ...
0056     'MarkerFaceColor', netMarkerFace('CX'), ...
0057     'LineWidth', lineWidth, 'LineStyle', 'none');
0058 
0059 % plot fitting curve of each network
0060 plot(tmpP, cfit.CE(tmpP), 'Color', netColor('CE'), 'LineWidth', 4);
0061 plot(tmpP, cfit.Rand(tmpP), 'Color', netColor('ER'), 'LineWidth', 4);
0062 plot(tmpP, cfit.Regular(tmpP), 'Color', netColor('RL'), 'LineWidth', 4);
0063 plot(tmpP, cfit.Ring(tmpP), 'Color', netColor('SW'), 'LineWidth', 4);
0064 plot(tmpP, cfit.CX(tmpP), 'Color', netColor('CX'), 'LineWidth', 4);
0065 plot([2 2], [0 cxNetwork.cfitVertProp(2)], '--k', 'lineWidth', 3);
0066 set(gca, 'FontSize', 18, 'lineWidth', 4, 'FontWeight', 'bold');
0067 
0068 hL = legend('CE', 'ER', 'RL', 'SW', 'CX', ...
0069     'location', 'southEast');
0070 legend(gca, 'boxoff');
0071 % to show the correct linewidth of errorbar's symbol due to matlab's bug
0072 set(findobj(hL, 'type', 'line'), 'LineWidth', lineWidth);
0073 
0074 xlim([0 4]);
0075 ylim([0 1.1]);
0076 xlabel('Propagation level');
0077 ylabel('Vertical propagation');
0078 set(gca, 'xTick', [0 1 2 3 4], 'yTick', [0 0.4 0.8 1]);
0079 box off;

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