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Cove Sturtevant
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Part 4
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‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/display_figures.m‎

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function display_figures(figure_list, output_mode, output_dir)
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% display_figures -- Display figure scripts to screen or write to file
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%
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% $Id: display_figures.m 580 2005-09-13 05:50:42Z ccornish $
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default_output_mode = 'screen';
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default_output_dir = './images';
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default_image_format_list = {'jpeg', 'epsc'};
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[default_figure_list] = textread('figure_list', '%s');
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if ( ~exist('figure_list', 'var') || isempty(figure_list))
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figure_list = default_figure_list;
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end
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if ( ~exist('output_mode', 'var') || ~isempty(output_mode))
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output_mode = default_output_mode;
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end
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if ( ~exist('output_dir', 'var') || ~isempty(output_dir))
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output_dir = default_output_dir;
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end
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image_format_list = default_image_format_list;
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if (strcmp(output_mode, 'screen'))
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visible = 'on';
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else
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visible = 'off';
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end
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for (ifig = 1:length(figure_list))
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fig_name = figure_list{ifig};
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disp(['Running ', fig_name, '...']);
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hfigure = figure('Name', fig_name, 'Visible', visible);
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eval(['run ', fig_name]);
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switch output_mode
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case 'screen'
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disp('Pausing ... Hit any key to continue.');
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pause;
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case 'file'
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for (ifor = 1:length(image_format_list))
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out_format = image_format_list{ifor};
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out_filename = [output_dir, filesp, fig, '.', out_format];
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try
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print(['-d', out_format], out_filename);
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catch
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disp(['Error encountered while printing format ', out_format]);
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end
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end
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otherwise
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end
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close(hfigure);
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end
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‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/figure098a.m‎

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% figure098a - Calculate and plot equivalent D(4) wavelet and scaling filters.
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%
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% Usage:
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% figure098a
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%
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% $Id: figure098a.m 303 2004-03-25 23:40:38Z ccornish $
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plot_equivalent_filter('DWT', 'd6', 7);
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figure_datestamp(mfilename, gcf);

‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/figure098b.m‎

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% figure098b - Calculate and plot equivalent LA(8) wavelet and scaling filters.
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%
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% Usage:
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% figure098b
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%
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% $Id: figure098b.m 353 2004-06-14 20:53:21Z ccornish $
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plotOpts.PlotAutoCorrelationWidth = 0;
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plot_equivalent_filter('DWT', 'la8', 7, ...
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'', '', ...
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'', '', plotOpts);
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figure_datestamp(mfilename, gcf);

‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/figure099.m‎

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% figure099 - Calculate and plot squared gain functiosn of DWT LA(8) filters.
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%
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% Usage:
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% figure099
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%
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% $Id: figure099.m 353 2004-06-14 20:53:21Z ccornish $
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plot_filter_sgf('DWT', 'la8', 4);
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figure_datestamp(mfilename, gcf);

‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/figure126.m‎

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% figure126 - Calculate and plot partial DWT coefficients W of level J0 = 6 for
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% ECG time series using Harr, D(4), C(6) and LA(8) wavelets.
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%
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% Usage:
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% figure126
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%
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% $Id: figure126.m 569 2005-09-12 19:37:21Z ccornish $
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% Load the data
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[X, x_att] = wmtsa_data('ecg');
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% Calculate DWT coefficient vectors
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J0 = 6;
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boundary = 'periodic';
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opts.RetainVJ = 0;
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[WJ_haar, VJ_haar, att_haar, NJ_haar] = dwt(X, 'Haar', J0, 'periodic', opts);
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[WJ_d4, VJ_d4, att_d4, NJ_d4] = dwt(X, 'd4', J0, 'periodic', opts);
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[WJ_c6, VJ_c6, att_c6, NJ_c6] = dwt(X, 'c6', J0, 'periodic', opts);
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[WJ_la8, VJ_la8, att_la8, NJ_la8] = dwt(X, 'la8', J0, 'periodic', opts);
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[W_haar] = dwt2vector(WJ_haar, VJ_haar, att_haar);
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[W_d4] = dwt2vector(WJ_d4, VJ_d4, att_d4);
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[W_c6] = dwt2vector(WJ_c6, VJ_c6, att_c6);
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[W_la8] = dwt2vector(WJ_la8, VJ_la8, att_la8);
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% Plot the DWT coefficients vectors
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nsubplots = 4;
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XLim = [0, 2048];
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XTick = [0, 512, 1024, 1536, 2048];
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XTickLabel = XTick;
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YLim = [-4, 4];
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YTick = [-4, 0, 4];
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YTickLabel = YTick;
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axesProp.XLim = XLim;
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axesProp.XTick = XTick;
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axesProp.XTickLabel = XTickLabel;
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axesProp.YLim = YLim;
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axesProp.YTick = YTick;
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axesProp.YTickLabel = YTickLabel;
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axesProp.YMinorTick = 'off';
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plotOpts.labelSubvectors = 1;
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plotOpts.subvectorLabels = zeros(J0+1, 1);
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plotOpts.subvectorLabels(1:4) = [1:4];
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plotOpts.subvectorLabels(J0+1) = J0;
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subplot(nsubplots, 1, 1);
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[haxes, hlineDWT, hlineBdry] = plot_dwt_vector(W_haar, NJ_haar, '', '', axesProp, ...
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plotOpts);
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htext = ylabel('Haar');
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set(htext, 'Rotation', 0, ...
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'HorizontalAlignment', 'right', ...
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'VerticalAlignment', 'middle');
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hold on;
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plotOpts.labelSubvectors = 0;
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subplot(nsubplots, 1, 2);
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[haxes, hlineDWT, hlineBdry] = plot_dwt_vector(W_d4, NJ_d4, '', '', axesProp, ...
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plotOpts);
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htext = ylabel('D(4)');
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set(htext, 'Rotation', 0, ...
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'HorizontalAlignment', 'right', ...
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'VerticalAlignment', 'middle');
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hold on;
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subplot(nsubplots, 1, 3);
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[haxes, hlineDWT, hlineBdry] = plot_dwt_vector(W_c6, NJ_c6, '', '', axesProp, ...
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plotOpts);
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htext = ylabel('C(6)');
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set(htext, 'Rotation', 0, ...
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'HorizontalAlignment', 'right', ...
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'VerticalAlignment', 'middle');
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hold on;
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subplot(nsubplots, 1, 4);
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[haxes, hlineDWT, hlineBdry] = plot_dwt_vector(W_la8, NJ_la8, '', '', axesProp, ...
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plotOpts);
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htext = ylabel('LA(8)');
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set(htext, 'Rotation', 0, ...
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'HorizontalAlignment', 'right', ...
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'VerticalAlignment', 'middle');
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hold on;
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title_str = ...
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{['Figure 126.'], ...
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['Partial DWT coefficients W for J0 = 6'], ...
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['for ECG time series'], ...
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['using Harr, D(4), C(6) and LA(8) wavelets.']};
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suptitle(title_str);
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figure_datestamp(mfilename, gcf);
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‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/figure183.m‎

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% figure183 - Calculate and plot MODWT coefficients of ECG time series to replicate Figure 183 of WMTSA.
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%
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% Usage:
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% figure183
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%
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% $Id: figure183.m 599 2005-10-04 22:46:44Z ccornish $
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% Load the data
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[X, x_att] = wmtsa_data('ecg');
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% Compute MODWT coefficients
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wtfname = 'la8';
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J0 = 6;
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% boundary = 'reflection';
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boundary = 'periodic';
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[WJt, VJt, att] = modwt(X, wtfname, J0, boundary);
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% Setup time axis
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delta_t = 1 / 180; % sampling interval in seconds
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time_offset = 0.31; % seconds
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xaxis = (1:length(X)) * delta_t + time_offset;
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xlabel_str = 't (seconds)';
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% Setup plotting parameters
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title_str = {};
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title_str(1) = {'\bfFigure 183.\rm MODWT wavelet analysis of ECG time series'};
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title_str(2) = {['Wavelet: ' wtfname ', NLevels: ', int2str(J0), ...
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', Boundary: ', boundary]};
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XplotAxesProp.XLim = [0 12];
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XplotAxesProp.XTick = [0:1:12];
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XplotAxesProp.XTickLabel = [0:1:12];
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MRAplotAxesProp.XLim = XplotAxesProp.XLim;
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MRAplotAxesProp.XTick = XplotAxesProp.XTick;
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MRAplotAxesProp.XTickLabel = XplotAxesProp.XTickLabel;
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XplotAxesProp.YLim = [-1.5 1.5];
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XplotAxesProp.YTick = [-1.5 -.5 .5 1.5];
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XplotAxesProp.YTickLabel = [-1.5 -.5 .5 1.5];
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% Plot MODWT coefficients
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[hWplotAxes, hXplotAxes] = plot_modwt_coef(WJt, VJt, X, att, ...
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title_str, xaxis, xlabel_str, ...
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MRAplotAxesProp, XplotAxesProp);
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figure_datestamp(mfilename, gcf);

‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/figure184.m‎

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% figure184 - Calculate and plot MODWT MRA of ECG time series to replicate Figure 184 of WMTSA.
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%
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% Usage:
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% figure184
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%
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% $Id: figure184.m 569 2005-09-12 19:37:21Z ccornish $
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% Load the data
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[X, x_att] = wmtsa_data('ecg');
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% Compute MODWT coefficients
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wtfname = 'la8';
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J0 = 6;
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boundary = 'reflection';
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[WJt, VJt, att] = modwt(X, wtfname, J0, boundary);
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% Compute inverse MODWT MRA reconstruction.
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[DJt, SJt] = imodwt_mra(WJt, VJt, att);
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% Setup x-axis for plotting.
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delta_t = 1 / 180; % sampling interval in seconds
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time_offset = 0.31; % seconds
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xaxis = (1:length(X)) * delta_t + time_offset;
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xlabel_str = 't (sec)';
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% Setup plotting parameters
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title_str = {};
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title_str(1) = {'\bfFigure 184.\rm MODWT multiresolution analysis of ECG time series'};
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title_str(2) = {['Wavelet: ' wtfname ', NLevels: ', int2str(J0), ...
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', Boundary: ', boundary]};
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XplotAxesProp.XLim = [0 12];
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XplotAxesProp.XTick = [0:1:12];
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% XplotAxesProp.XTickLabel = [0:2:12];
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% XplotAxesProp.XMinorTick = 'off';
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XplotAxesProp.YLim = [-1.5 1.5];
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XplotAxesProp.YTick = [-1.5 -.5 .5 1.5];
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XplotAxesProp.YTickLabel = [-1.5 -.5 .5 1.5];
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% Plot MRA coefficients
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[hMRAplotAxes, hXplotAxes] = plot_imodwt_mra(DJt, SJt, X, att, ...
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title_str, xaxis, xlabel_str, ...
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[], XplotAxesProp);
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figure_datestamp(mfilename, gcf);
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‎wmtsa-matlab-0.2.6/wmtsa/Examples/WMTSA_Book/Figures/figure186.m‎

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% figure186 - Calculate and plot MODWT MRA for Crescent City subtidal variations to replicate Figure 186 of WMTSA.
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%
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% Usage:
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% figure186
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%
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% $Id: figure186.m 569 2005-09-12 19:37:21Z ccornish $
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% Load the data
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[X, x_att] = wmtsa_data('subtidal');
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% Compute MODWT coefficients
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wtfname = 'la8';
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J0 = 7;
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boundary = 'reflection';
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[WJt, VJt, att] = modwt(X, wtfname, J0, boundary);
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% Compute inverse MODWT MRA reconstruction.
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[DJt, SJt] = imodwt_mra(WJt, VJt, att);
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% Setup x-axis for plotting.
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% Sample interval is 1/2 day -- Convert to years
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delta_t = .5 / 365;
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% Start time is 0800 PST 6 Jan 1980 = 1980 + 6.333333 days
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time_offset = 1980 + 6.33333 / 365;
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xaxis = (1:length(X)) * delta_t + time_offset;
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xlabel_str = 'years';
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% Setup plotting parameters
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title_str = {};
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title_str(1) = {['\bfFigure 186.\rm MODWT multiresolution analysis ', ...
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'for CrescentCity subtidal variations']};
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title_str(2) = {['Wavelet: ' wtfname ', NLevels: ', int2str(J0), ...
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', Boundary: ', boundary]};
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XplotAxesProp.XLim = [1980, 1992];
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XplotAxesProp.XTick = [1980:1:1992];
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XplotAxesProp.XTickLabel = [1980:1:1992];
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XplotAxesProp.YLim = [-40 80];
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XplotAxesProp.YTick = [-40:20:80];
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XplotAxesProp.YTickLabel = [-40:20:80];
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MRAplotAxesProp.XLim = XplotAxesProp.XLim;
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MRAplotAxesProp.XTick = XplotAxesProp.XTick;
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MRAplotAxesProp.XTickLabel = XplotAxesProp.XTickLabel;
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% Plot MRA coefficients
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[hMRAplotAxes, hXplotAxes] = plot_imodwt_mra(DJt, SJt, X, att, ...
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title_str, xaxis, xlabel_str, ...
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MRAplotAxesProp, XplotAxesProp);
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% Plot overlay of lines marking start of years 1981-1991
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year_range = [1981:1:1991];
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x1 = year_range;
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x2 = year_range;
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lineProp.Color = 'green';
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lineProp.LineWidth = .35;
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lineProp.LineStyle = '--';
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% Plot overlay on MRA plot.
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ylim = get(hMRAplotAxes, 'YLim');
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y1 = ylim(1) * ones(length(x1),1);
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y2 = ylim(2) * ones(length(x1),1);
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set(get(hMRAplotAxes, 'Parent'), 'CurrentAxes', hMRAplotAxes);
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linesegment_plot(x1, y1, x2, y2, lineProp);
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linesegment_plot(x1, y1, x2, y2, lineProp);
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% Plot overlay on X plot.
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ylim = get(hXplotAxes, 'YLim');
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y1 = ylim(1) * ones(length(x1),1);
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y2 = ylim(2) * ones(length(x1),1);
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set(get(hXplotAxes, 'Parent'), 'CurrentAxes', hXplotAxes);
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linesegment_plot(x1, y1, x2, y2, lineProp);
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linesegment_plot(x1, y1, x2, y2, lineProp);
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figure_datestamp(mfilename, gcf);

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