sig_raster() - Plots 2D (electrode x time/frequency) raster diagram of
variables that show significant effects according to t-tests.
Significant positive effects are represented
with white squares (red if color is requested).
Significant negative effects are represented with
black squares (blue if color is requested).
Usage:
>> img=sig_raster_coefs(regress_results_or_fname,varargin)
Required Inputs:
regress_results - A GND/GRP/specGND structure variable or the filename of
sucah a variable that has been saved to disk.
To create a GND variable from Kutaslab ERP files (e.g.,
*.nrm files) use avgs2GND.m. To do the same from
EEGLAB *.set files use sets2GND.m. To create a
a GRP structure use GNDs2GRP.m. See Mass Univariate ERP
Toolbox documentation for detailed information about the
format of GND and GRP variables. If you specifiy a filename
be sure to include the file's path, unless the file is
in the current working directory.
test_id - [integer] The index # of the t-test results
stored in the GND/GRP/specGND variable that you wish to visualize.
To see what test results are available, look at
the "t_tests" field of your variable (e.g., GND.t_tests)
or use the functions headinfo.m or headinfo_spec.m.
Optional Inputs:
x_ticks - [vector] The times/frequencies you want marked with ticks
on the x-axis. Note, because the EEG has been sampled
at discrete time points, not all times between the
minimum and maximum analyzed times/frequencies can be
used. When a tick is requested that is not available,
the closest possible value is used. If not
specified, x_ticks are automatically chosen based
on the time/frequency range covered by the diagram.
This option has no effect if the t-test was
executed based on mean voltages/power within specified
time windows/frequency bands.
fig_id - [integer] The index # of the MATLAB figure in which
the diagram will be produced. Useful for overwriting
old figures. {default: lowest unused index}
use_color - [integer] If non-zero, non-gray scale colors will be
used in the diagram. {default: 0}
plot_vert_lines - [integer] If non-zero, vertical lines separating all
time points included in the t-test will be
shown. If 0, the only vertical lines drawn will be
those that separate times/frequencies included in a test
from those not included. This option has no effect if
the t-test was executed based on mean voltage/power
within specified time windows. {default: 1}
lr_sym - [integer] If non-zero, left hemisphere electrodes are
plot from most anterior to most posterior and right
hemisphere electrodes are plot from most posterior to
most anterior. This may make it easier to visualize
the degree of effect left-right symmetry. If 0, all
electrodes are plot from most anterior to most
posterior.
verblevel - An integer specifiying the amount of information you want
this function to provide about what it is doing during runtime.
Options are:
0 - quiet, only show errors, warnings, and EEGLAB reports
1 - stuff anyone should probably know
2 - stuff you should know the first time you start working
with a data set {default value}
3 - stuff that might help you debug (show all
reports)
Outputs:
img - Two-dimensional matrix (channel x time/frequency)
representing the visualized raster. This could be
useful for making your own personalized raster plot
(e.g., >> imagesc(img);). Note that the matrix
includes rows for any blank rows separating groups of
channels (e.g., left electrodes from midline
electrodes).
Global Variable:
VERBLEVEL - Mass Univariate ERP Toolbox level of verbosity (i.e., tells functions
how much to report about what they're doing during
runtime) set by the optional function argument 'verblevel'
Notes:
-The printed/exported figure will have the same dimensions as the figure
on the display. Thus you can undo figure clutter by re-sizing it.
-Some image viewers (e.g., Apple's Preview) may have trouble viewing
postscript raster images since they downsample the image to make the file
smaller, which blurs the lines. Ghostview and Adobe Illustrator should
be able to view the files just fine. If viewing the raster as a
postscript file is producing problems, you can save the figure in jpg
format.
-Assignment of electrode to left, midline, or right grouping of
electrodes is based on the regress_results.chanlocs(x).theta coordinate. Anterior to
posterior organization of electrodes is based on regress_results.chanlocs(x).radius.
Author:
David Groppe
Kutaslab, 3/20100001 % sig_raster() - Plots 2D (electrode x time/frequency) raster diagram of 0002 % variables that show significant effects according to t-tests. 0003 % Significant positive effects are represented 0004 % with white squares (red if color is requested). 0005 % Significant negative effects are represented with 0006 % black squares (blue if color is requested). 0007 % 0008 % Usage: 0009 % >> img=sig_raster_coefs(regress_results_or_fname,varargin) 0010 % 0011 % Required Inputs: 0012 % regress_results - A GND/GRP/specGND structure variable or the filename of 0013 % sucah a variable that has been saved to disk. 0014 % To create a GND variable from Kutaslab ERP files (e.g., 0015 % *.nrm files) use avgs2GND.m. To do the same from 0016 % EEGLAB *.set files use sets2GND.m. To create a 0017 % a GRP structure use GNDs2GRP.m. See Mass Univariate ERP 0018 % Toolbox documentation for detailed information about the 0019 % format of GND and GRP variables. If you specifiy a filename 0020 % be sure to include the file's path, unless the file is 0021 % in the current working directory. 0022 % test_id - [integer] The index # of the t-test results 0023 % stored in the GND/GRP/specGND variable that you wish to visualize. 0024 % To see what test results are available, look at 0025 % the "t_tests" field of your variable (e.g., GND.t_tests) 0026 % or use the functions headinfo.m or headinfo_spec.m. 0027 % 0028 % Optional Inputs: 0029 % x_ticks - [vector] The times/frequencies you want marked with ticks 0030 % on the x-axis. Note, because the EEG has been sampled 0031 % at discrete time points, not all times between the 0032 % minimum and maximum analyzed times/frequencies can be 0033 % used. When a tick is requested that is not available, 0034 % the closest possible value is used. If not 0035 % specified, x_ticks are automatically chosen based 0036 % on the time/frequency range covered by the diagram. 0037 % This option has no effect if the t-test was 0038 % executed based on mean voltages/power within specified 0039 % time windows/frequency bands. 0040 % fig_id - [integer] The index # of the MATLAB figure in which 0041 % the diagram will be produced. Useful for overwriting 0042 % old figures. {default: lowest unused index} 0043 % use_color - [integer] If non-zero, non-gray scale colors will be 0044 % used in the diagram. {default: 0} 0045 % plot_vert_lines - [integer] If non-zero, vertical lines separating all 0046 % time points included in the t-test will be 0047 % shown. If 0, the only vertical lines drawn will be 0048 % those that separate times/frequencies included in a test 0049 % from those not included. This option has no effect if 0050 % the t-test was executed based on mean voltage/power 0051 % within specified time windows. {default: 1} 0052 % lr_sym - [integer] If non-zero, left hemisphere electrodes are 0053 % plot from most anterior to most posterior and right 0054 % hemisphere electrodes are plot from most posterior to 0055 % most anterior. This may make it easier to visualize 0056 % the degree of effect left-right symmetry. If 0, all 0057 % electrodes are plot from most anterior to most 0058 % posterior. 0059 % verblevel - An integer specifiying the amount of information you want 0060 % this function to provide about what it is doing during runtime. 0061 % Options are: 0062 % 0 - quiet, only show errors, warnings, and EEGLAB reports 0063 % 1 - stuff anyone should probably know 0064 % 2 - stuff you should know the first time you start working 0065 % with a data set {default value} 0066 % 3 - stuff that might help you debug (show all 0067 % reports) 0068 % 0069 % Outputs: 0070 % img - Two-dimensional matrix (channel x time/frequency) 0071 % representing the visualized raster. This could be 0072 % useful for making your own personalized raster plot 0073 % (e.g., >> imagesc(img);). Note that the matrix 0074 % includes rows for any blank rows separating groups of 0075 % channels (e.g., left electrodes from midline 0076 % electrodes). 0077 % 0078 % 0079 % Global Variable: 0080 % VERBLEVEL - Mass Univariate ERP Toolbox level of verbosity (i.e., tells functions 0081 % how much to report about what they're doing during 0082 % runtime) set by the optional function argument 'verblevel' 0083 % 0084 % Notes: 0085 % -The printed/exported figure will have the same dimensions as the figure 0086 % on the display. Thus you can undo figure clutter by re-sizing it. 0087 % 0088 % -Some image viewers (e.g., Apple's Preview) may have trouble viewing 0089 % postscript raster images since they downsample the image to make the file 0090 % smaller, which blurs the lines. Ghostview and Adobe Illustrator should 0091 % be able to view the files just fine. If viewing the raster as a 0092 % postscript file is producing problems, you can save the figure in jpg 0093 % format. 0094 % 0095 % -Assignment of electrode to left, midline, or right grouping of 0096 % electrodes is based on the regress_results.chanlocs(x).theta coordinate. Anterior to 0097 % posterior organization of electrodes is based on regress_results.chanlocs(x).radius. 0098 % 0099 % Author: 0100 % David Groppe 0101 % Kutaslab, 3/2010 0102 0103 %%%%%%%%%%%%%%%% REVISION LOG %%%%%%%%%%%%%%%%% 0104 % 3/27/2010-Comments revised to accommodate GRP variable 0105 % 0106 % 4/8/2010-chanloc theta values standardized to a range of +/- 180 (A2 had 0107 % a theta of 240 for some reason) 0108 % 0109 % 10/5/2010-channels lacking a theta coordinate are assigned a theta 0110 % coordinate of 0 and radius of 2 0111 % 0112 % 12/14/2010-Compatible with specGND variables now too 0113 % 0114 % 3/29/2011-Classifies any electrode with a radius of 0 as a midline 0115 % electrode 0116 0117 %%%%%%%%%%%%%%%% FUTURE WORK %%%%%%%%%%%%%%%%% 0118 % -When you click on figure, box with time and electrode appear behind edges 0119 % of figure. Fix this somehow? 0120 % -Make it possible to further exclude channels from plot? 0121 % -Make it possible to deal with overlapping time windows when mean time 0122 % windows are used? 0123 0124 0125 function img=sig_raster_dif_coefs(regress_results_or_fname,varargin) 0126 0127 p=inputParser; 0128 p.addRequired('regress_results_or_fname',@(x) isstruct(x) || ischar(x)); 0129 p.addParamValue('x_ticks',[],@isnumeric); 0130 p.addParamValue('predictor',[],@isnumeric); 0131 p.addParamValue('fig_id',[],@(x) isnumeric(x) && (length(x)==1)); 0132 p.addParamValue('use_color',0,@(x) isnumeric(x) || ischar(x)); 0133 p.addParamValue('plot_vert_lines',1,@(x) isnumeric(x) || ischar(x)); 0134 p.addParamValue('lr_sym',0,@(x) isnumeric(x) || ischar(x)); 0135 p.addParamValue('verblevel',[],@(x) isnumeric(x) && (length(x)==1)); 0136 0137 p.parse(regress_results_or_fname,varargin{:}); 0138 0139 % Manage VERBLEVEL 0140 if isempty(p.Results.verblevel), 0141 VERBLEVEL=2; %not global, just local 0142 else 0143 VERBLEVEL=p.Results.verblevel; 0144 end 0145 0146 use_color=str2bool(p.Results.use_color); 0147 plot_vert_lines=str2bool(p.Results.plot_vert_lines); 0148 lr_sym=str2bool(p.Results.lr_sym); 0149 0150 %Load "regress_results" variable 0151 if ischar(regress_results_or_fname), 0152 fprintf('Loading GND, GRP, or specGND struct variable from file %s.\n',regress_results_or_fname); 0153 load(regress_results_or_fname,'-MAT'); 0154 if ~exist('regress_results','var') 0155 error('File %s does not contain a regress_results variable.',regress_results_or_fname); 0156 end 0157 else 0158 regress_results=regress_results_or_fname; 0159 clear regress_results_or_fname; 0160 end 0161 0162 %FDR or permutation test correction for multiple comparisons 0163 if VERBLEVEL, 0164 fprintf('Correcting for multiple comparisons via FDR procedure: %s\n', ... 0165 regress_results.crctn_method); 0166 end 0167 0168 fprintf('White squares indicate time point/electrode pairs in which Group %s has larger coefficients than Group %s.\n', ... 0169 regress_results.group_descr{1},regress_results.group_descr{2}); 0170 fprintf('Black squares indicate time point/electrode pairs in which Group %s has larger coefficients than Group %s.\n', ... 0171 regress_results.group_descr{2},regress_results.group_descr{1}); 0172 0173 %Clean up code by copying some GND.t_tests variables to their own 0174 %variable 0175 use_bin=regress_results.bins; 0176 [n_use_chans, n_tpt, n_pred]=size(regress_results.mn_dif); 0177 use_tpts=linspace(regress_results.time_wind(1),regress_results.time_wind(2), ... 0178 n_tpt); 0179 pval=regress_results.p_cor; 0180 grands_t=regress_results.t_dif; 0181 use_chans=1:n_use_chans; 0182 0183 if isempty(p.Results.predictor) 0184 for a=1:n_pred, 0185 fprintf('%d) %s\n',a,regress_results.predictor_names{a}); 0186 end 0187 use_pred=input('Enter the number of the predictor you wish to plot: '); 0188 else 0189 use_pred=p.Results.predictor; 0190 end 0191 if use_pred>n_pred, 0192 error('This regress_results variable only has %d precitors in it. You cannot plot #%d.', ... 0193 n_pred,use_pred); 0194 end 0195 0196 % Standardize theta to -180<theta<=180 degrees 0197 for c=1:n_use_chans, 0198 theta=mod(regress_results.chanlocs(c).theta,360); 0199 if isempty(theta), 0200 watchit(sprintf('Channel #%d (Label: %s) does not have a theta coordinate. To produce raster, I''m assigning it temporary coordinate of: theta=0.',c,regress_results.chanlocs(c).labels)); 0201 theta=0; 0202 end 0203 if theta>180, 0204 theta=theta-360; 0205 elseif theta<=-180, 0206 theta=360+theta; 0207 end 0208 regress_results.chanlocs(c).theta=theta; 0209 0210 if isempty(regress_results.chanlocs(c).radius), 0211 regress_results.chanlocs(c).radius=2; 0212 watchit(sprintf('Channel #%d (Label: %s) does not have a radius coordinate. To produce raster, I''m assigning it temporary coordinate of: radius=2.',c,regress_results.chanlocs(c).labels)); 0213 end 0214 end 0215 0216 % Get midline electrodes from front to back 0217 midline=[]; 0218 midline_dist=[]; %distance from MiCe 0219 for c=1:n_use_chans, 0220 theta=regress_results.chanlocs(c).theta; 0221 radius=regress_results.chanlocs(c).radius; 0222 0223 if theta==0 || radius==0, 0224 midline=[midline c]; 0225 midline_dist=[midline_dist radius]; 0226 elseif theta==180, 0227 midline=[midline c]; 0228 midline_dist=[midline_dist -radius]; 0229 end 0230 end 0231 [midline_dist id]=sort(midline_dist,2,'descend'); 0232 midline=midline(id); 0233 n_midline=length(midline); 0234 0235 % Get left electrodes from front to back 0236 left=[]; 0237 left_dist=[]; 0238 for c=1:n_use_chans, 0239 %standardize theta to a range between +/-180 0240 theta=regress_results.chanlocs(c).theta; 0241 0242 if (theta<0) && (theta>-180) 0243 if regress_results.chanlocs(c).radius, 0244 %make sure electrode isn't midline or right 0245 left=[left c]; 0246 ang=90-abs(theta); 0247 dist=regress_results.chanlocs(c).radius*sin(ang*2*pi/360); 0248 left_dist=[left_dist dist]; 0249 end 0250 end 0251 end 0252 [left_dist id]=sort(left_dist,2,'descend'); 0253 left=left(id); 0254 n_left=length(left); 0255 0256 % Get right electrodes from front to back 0257 right=[]; 0258 right_dist=[]; 0259 for c=1:n_use_chans, 0260 %standardize theta to a range between +/-180 0261 theta=regress_results.chanlocs(c).theta; 0262 0263 if (theta>0) && (theta<180) 0264 if regress_results.chanlocs(c).radius, 0265 %make sure electrode isn't midline or right 0266 right=[right c]; 0267 ang=90-abs(theta); 0268 dist=regress_results.chanlocs(c).radius*sin(ang*2*pi/360); 0269 right_dist=[right_dist dist]; 0270 end 0271 end 0272 end 0273 % Make right electrodes back to front if requested 0274 if lr_sym, 0275 right_dist=-right_dist; 0276 end 0277 [right_dist id]=sort(right_dist,2,'descend'); 0278 right=right(id); 0279 n_right=length(right); 0280 0281 % Compute time/frequency tick mark locations and labels 0282 %show_tpts=use_tpts(1):use_tpts(end); %show_tpts differs from use_tpts if multiple non-contiguous time windows were used 0283 %n_show_tpts=length(show_tpts); 0284 % Test was based on each time point in time window(s)/frequency band(s) 0285 srate=1/((use_tpts(2)-use_tpts(1))/1000); 0286 if isempty(p.Results.x_ticks), 0287 tm_range=use_tpts(end)-use_tpts(1); 0288 omag=orderofmag(tm_range); 0289 tm_step=round(omag*srate/1000); 0290 xtick=1:tm_step:n_tpt; 0291 xtick_lab=cell(1,length(xtick)); 0292 for a=1:length(xtick), 0293 xtick_lab{a}=num2str(use_tpts(xtick(a))); 0294 end 0295 else 0296 tk_ct=0; 0297 xtick=zeros(1,length(p.Results.x_ticks)); 0298 for t=p.Results.x_ticks, 0299 tk_ct=tk_ct+1; 0300 xtick(tk_ct)=find_tpt(use_tpts,t); 0301 end 0302 xtick=unique(xtick); %get rid of any redundant ticks 0303 xtick_lab=cell(1,length(xtick)); 0304 for a=1:length(xtick), 0305 xtick_lab{a}=num2str(use_tpts(xtick(a))); 0306 end 0307 end 0308 0309 fdr_crct=~strcmpi(regress_results.crctn_method,'perm'); 0310 if VERBLEVEL, 0311 if fdr_crct, 0312 fprintf('q level of critical t-scores: %f.\n',regress_results.family_alpha); 0313 else 0314 fprintf('Estimated alpha level of critical t-scores: %f.\n',regress_results.perm_test_info.est_alpha); 0315 end 0316 end 0317 0318 %Initialize variables 0319 n_blank=(n_right>0)+(n_left>0)+(n_midline>0)-1; 0320 if use_color, 0321 img=-.25*ones(n_use_chans+n_blank,n_tpt);%makes skipped lines grey (non-sig rectangles are white) 0322 else 0323 img=zeros(n_use_chans+n_blank,n_tpt); %makes skipped lines grey 0324 end 0325 chan_lab=cell(1,n_use_chans); 0326 dat.chan_lab=cell(1,n_use_chans+n_blank); %channel labels including empty cells for skipped lines 0327 %dat_chan_lab is needed to make sig_raster plot interactive 0328 0329 %Get IDs of time points/frequencies used in perm test (some shown time points/frequencies may not 0330 %have been included 0331 used_tpt_ids=zeros(1,n_tpt); 0332 for a=1:n_tpt, 0333 if ismember(use_tpts(a),use_tpts), 0334 used_tpt_ids(a)=1; 0335 end 0336 end 0337 used_tpt_ids=find(used_tpt_ids); 0338 0339 %Construct raster for left electrodes 0340 ct=0; 0341 skipped=[]; 0342 for a=left, 0343 ct=ct+1; 0344 elec_id=find(use_chans==a); 0345 if fdr_crct, 0346 img(ct,used_tpt_ids)=(pval(elec_id,:,use_pred)<=regress_results.family_alpha).*sign(grands_t(elec_id,:,use_pred)); 0347 else 0348 img(ct,used_tpt_ids)=(pval(elec_id,:,use_pred)<regress_results.perm_test_info.est_alpha).*sign(grands_t(elec_id,:,use_pred)); 0349 end 0350 chan_lab{ct}=regress_results.chanlocs(use_chans(elec_id)).labels; 0351 dat.chan_lab{ct}=regress_results.chanlocs(use_chans(elec_id)).labels; 0352 end 0353 0354 if ~isempty(midline), 0355 if ct>0, 0356 %skip line to separate left and midline electrodes 0357 ct=ct+1; 0358 skipped=ct; 0359 end 0360 for a=midline, 0361 ct=ct+1; 0362 elec_id=find(use_chans==a); 0363 if fdr_crct, 0364 img(ct,used_tpt_ids)=(pval(elec_id,:,use_pred)<=regress_results.family_alpha).*sign(grands_t(elec_id,:,use_pred)); 0365 else 0366 img(ct,used_tpt_ids)=(pval(elec_id,:,use_pred)<regress_results.perm_test_info.est_alpha).*sign(grands_t(elec_id,:,use_pred)); 0367 end 0368 chan_lab{ct-length(skipped)}=regress_results.chanlocs(use_chans(elec_id)).labels; 0369 dat.chan_lab{ct}=regress_results.chanlocs(use_chans(elec_id)).labels; 0370 end 0371 end 0372 0373 if ~isempty(right), 0374 if ct>0, 0375 %skip line to separate right from left or midline electrodes 0376 ct=ct+1; 0377 skipped=[skipped ct]; 0378 end 0379 for a=right, 0380 ct=ct+1; 0381 elec_id=find(use_chans==a); 0382 if fdr_crct, 0383 img(ct,used_tpt_ids)=(pval(elec_id,:,use_pred)<=regress_results.family_alpha).*sign(grands_t(elec_id,:,use_pred)); 0384 else 0385 img(ct,used_tpt_ids)=(pval(elec_id,:,use_pred)<regress_results.perm_test_info.est_alpha).*sign(grands_t(elec_id,:,use_pred)); 0386 end 0387 chan_lab{ct-length(skipped)}=regress_results.chanlocs(use_chans(elec_id)).labels; 0388 dat.chan_lab{ct}=regress_results.chanlocs(use_chans(elec_id)).labels; 0389 end 0390 end 0391 0392 0393 if isempty(p.Results.fig_id) 0394 fig_h=figure; 0395 else 0396 fig_h=figure(p.Results.fig_id); clf; 0397 end 0398 % if isfield(GND,'bin_info') 0399 % set(fig_h,'name',['Bin ' int2str(use_bin) ' [' GND.bin_info(use_bin).bindesc ']'],'paperpositionmode','auto'); 0400 % else 0401 % set(fig_h,'name',['Bin ' int2str(use_bin) ' [' GND.bindesc{use_bin} ']'],'paperpositionmode','auto'); 0402 % end 0403 %setting paperpositionmode to 'auto' means that if the figure is manually 0404 %resized, the printed version of the figure will reflect the whatever the 0405 %shown size was (at the time of printing) 0406 0407 if use_color, 0408 %skipped rows/columns=grey, nonsig=white, +sig=red, -sig=blue 0409 h_img=imagesc(img,[-1 1]);colormap([0 0 1; .5 .5 .5; 1 1 1; 1 0 0]); 0410 else 0411 h_img=imagesc(img,[-1 1]);colormap([0 0 0; .6 .6 .6; 1 1 1]); 0412 end 0413 bdfcn=['Cp = get(gca,''CurrentPoint''); ' ... 0414 'Xp=Cp(2,1);', ... 0415 'Yp=Cp(2,2);', ... 0416 'dat=get(gcbo,''userdata'');', ... 0417 'ht=text(Xp,Yp,[int2str(dat.times(round(Xp))) '' ms, '' dat.chan_lab{round(Yp)}]);' ... 0418 'set(ht,''backgroundcolor'',''w'',''horizontalalignment'',''center'',''verticalalignment'',''middle'',''buttondownfcn'',''delete(gcbo);'');']; 0419 dat.times=use_tpts; %dat.chan_lab has been set above 0420 set(h_img,'buttondownfcn',bdfcn,'userdata',dat); 0421 set(gca,'xtick',xtick,'xticklabel',xtick_lab,'tickdir','out'); 0422 0423 set(gca,'ytick',setdiff(1:(n_use_chans+n_blank),skipped),'yticklabel',chan_lab,'box','off'); 0424 0425 h=xlabel('Time (ms)'); 0426 set(h,'fontsize',12,'fontweight','bold'); 0427 0428 h=ylabel('Electrode'); 0429 set(h,'fontsize',12,'fontweight','bold'); 0430 0431 % 0432 % if isfield(GND,'bin_info') 0433 % h=title(['Bin ' int2str(use_bin) ': ' GND.bin_info(use_bin).bindesc]); 0434 % else 0435 0436 % end 0437 h=title([regress_results.predictor_names{use_pred} ', Bin(s) ' num2str(regress_results.bins)]); 0438 set(h,'interpreter','none','fontsize',14,'fontweight','bold'); 0439 0440 hold on; 0441 v=axis; 0442 %rightmost vertical line to cover up weird white lip that peeks out from 0443 %under imagesc 0444 h=plot([1 1]*v(2),v(3:4)); 0445 set(h,'color',[1 1 1]*.6); 0446 0447 %horizontal lines 0448 %show_times=GND.time_pts(use_tpts); 0449 %start_tpt=find_tpt(GND.t_tests(test_id).time_wind(a,1),use_tpts); 0450 %stop_tpt=find_tpt(GND.t_tests(test_id).time_wind(a,2),use_tpts); 0451 %start_tpt=use_tpts(1); 0452 %stop_tpt=use_tpts(end); 0453 v=axis; 0454 start_tpt=v(1); 0455 stop_tpt=v(2); 0456 for c=0:(n_use_chans+n_blank-1), 0457 h=plot([start_tpt-.5 stop_tpt+.5],[1 1]*c+.5); 0458 if ismember(c+1,skipped) || ismember(c,skipped), 0459 %make lines near boundaries different?? currently not used 0460 set(h,'color',[0 0 0],'buttondownfcn',bdfcn,'userdata',dat); 0461 else 0462 set(h,'color',[0 0 0],'buttondownfcn',bdfcn,'userdata',dat); 0463 end 0464 end 0465 0466 %vertical lines 0467 if plot_vert_lines 0468 for t=1:(n_tpt), 0469 if ismember(use_tpts(t),use_tpts), 0470 if ismember(t+1,xtick) || ismember(t,xtick), 0471 %draw vertical lines at time tick marks differently?? 0472 %currently option isn't used 0473 c=[0 0 0]; 0474 else 0475 c=[0 0 0]; 0476 end 0477 if isempty(skipped), 0478 h=plot([1 1]*t+.5,v(3:4)); 0479 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0480 if t==1 || ~ismember(use_tpts(t-1),use_tpts), 0481 h=plot([1 1]*t-.5,v(3:4)); 0482 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0483 end 0484 else 0485 h=plot([1 1]*t+.5,[v(3) skipped(1)-.5]); 0486 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0487 if length(skipped)==2, 0488 h=plot([1 1]*t+.5,[skipped(1)+.5 skipped(2)-.5]); 0489 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0490 end 0491 h=plot([1 1]*t+.5,[skipped(end)+.5 v(4)]); 0492 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0493 0494 if t==1 || ~ismember(use_tpts(t-1),use_tpts), 0495 h=plot([1 1]*t-.5,[v(3) skipped(1)-.5]); 0496 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0497 if length(skipped)==2, 0498 h=plot([1 1]*t-.5,[skipped(1)+.5 skipped(2)-.5]); 0499 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0500 end 0501 h=plot([1 1]*t-.5,[skipped(end)+.5 v(4)]); 0502 set(h,'color',c,'buttondownfcn',bdfcn,'userdata',dat); 0503 end 0504 end 0505 end 0506 end 0507 end 0508 0509 0510 % 0511 %% %%%%%%%%%%%%%%%%%%%%% function orderofmag() %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0512 % 0513 function ord=orderofmag(val) 0514 %function ord=orderofmag(val) 0515 % 0516 % Returns the order of magnitude of the value of 'val' in multiples of 10 0517 % (e.g., 10^-1, 10^0, 10^1, 10^2, etc ...) 0518 % used for computing erpimage trial axis tick labels as an alternative for 0519 % plotting sorting variable 0520 0521 val=abs(val); 0522 if val>=1 0523 ord=1; 0524 val=floor(val/10); 0525 while val>=1, 0526 ord=ord*10; 0527 val=floor(val/10); 0528 end 0529 return; 0530 else 0531 ord=1/10; 0532 val=val*10; 0533 while val<1, 0534 ord=ord/10; 0535 val=val*10; 0536 end 0537 return; 0538 end 0539 0540 0541 % 0542 %% %%%%%%%%%%%%%%%%%%%%% function str2bool() %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 0543 % 0544 function bool=str2bool(str) 0545 %function bool=str2bool(str) 0546 0547 if ischar(str), 0548 if strcmpi(str,'yes') || strcmpi(str,'y') 0549 bool=1; 0550 else 0551 bool=0; 0552 end 0553 else 0554 bool=str; 0555 end