PLOTWAVEFORMS - plot waveforms in various ways depending on what data was provided.
Usage:
dsPlotWaveforms(data,'option',value)
Inputs:
- data: DynaSim data structure (see dsCheckData)
- options:
'variable': name of field containing data to plot (default: all pops with
state variable of variable in data.labels)
'time_limits': in units of data.time {[beg,end]}
'max_num_overlaid': maximum # of waveforms to overlay per plot
'max_num_rows': maximum # of subplot rows per figure
Notes:
if Nsims>1: one sim per row
elseif Npops>1: one pop per row
else: one cell per row
Examples for specifying 'variable' option:
[] : plot all data.labels with same variable name as first element of
data.labels (eg, 'pop1_v' and 'pop2_v')
'*' : plot all data.labels
'*_v' : plot all data.labels ending in _v (i.e., all state variables 'v'
for all populations)
'pop1_*': plot all data.labels starting with pop1_ (i.e., all variables for
population 'pop1')
'pop1_v': plot only variable 'pop1_v'
'v' : look for all data.labels ending in _v then starting with v_ (eg,
all populations with variable 'v')
'pop1' : look for all data.labels ending in _pop1 then starting with pop1_
(eg, all variables for population 'pop1')
'*_iNa_*': plot all data.labels for the 'iNa' mechanism (for all populations)
Examples:
- Example 1: One cell:
data=dsSimulate('dv/dt=@current+10; {iNa,iK}');
dsPlotWaveforms(data); % plot first state variable ('v')
dsPlotWaveforms(data,'variable','*'); % plot all state variables
% plot all variables and time 30-60ms
dsPlotWaveforms(data,'variable','*','time_limits',[30 60]);
- Example 2: One population: with noisy input
data=dsSimulate('dv[5]/dt=@current+10*(1+randn(1,Npop)); {iNa,iK}');
dsPlotWaveforms(data);
dsPlotWaveforms(data,'variable','*'); % plot all state variables (all cells)
dsPlotWaveforms(data,'variable','m'); % plot state variable 'm' (all cells)
% plot all variables and time 30-60ms
dsPlotWaveforms(data,'variable','*','time_limits',[30 60]);
- Example 3: One population varying one parameter (input amplitude):
eqns='dv[5]/dt=@current+amp*(1+randn(1,Npop)); {iNa,iK}';
vary={'','amp',[0 10 20]};
data=dsSimulate(eqns,'vary',vary);
dsPlotWaveforms(data);
dsPlotWaveforms(data,'variable','m');
dsPlotWaveforms(data,'variable','*');
- Example 4: One population varying two parameters (input amplitude and
membrane capacitance):
eqns='dv[5]/dt=@current/Cm+amp*(1+randn(1,Npop)); {iNa,iK}';
vary={'','Cm',[1 2]; '','amp',[0 10 20]};
data=dsSimulate(eqns,'vary',vary);
dsPlotWaveforms(data);
dsPlotWaveforms(data,'variable','*');
- Example 5: Two populations: noisy input to E and excitatory connection from E to I
spec=[];
spec.populations(1).name='E1';
spec.populations(1).equations='dv[5]/dt=@current+amp*(1+randn(1,Npop)); amp=10; {iNa,iK}';
spec.populations(2).name='E2';
spec.populations(2).equations='dv[2]/dt=@current; {iNa,iK}';
spec.connections(1).direction='E1->E2';
spec.connections(1).mechanism_list='iAMPA';
data=dsSimulate(spec);
dsPlotWaveforms(data); % plot first state variable
dsPlotWaveforms(data,'variable','*');
% plot monitored synaptic current with post-synaptic voltages:
dsPlotWaveforms(data,'variable',{'E2_v','ISYN'});
% plot monitored synaptic current with pre- and post-synaptic voltages:
dsPlotWaveforms(data,'variable',{'v','ISYN'});
- Example 6: Two populations varying one parameter (input amplitude):
vary={'E1','amp',[0 10 20]};
data=dsSimulate(spec,'vary',vary);
dsPlotWaveforms(data);
dsPlotWaveforms(data,'variable','*');
dsPlotWaveforms(data,'variable','*_iNa_*');
- Example 7: Two populations varying two parameters (input amplitude and
synaptic conductance):
vary={'E1','amp',[0 10 20]; 'E1->E2','gSYN',[0 .05 .1]};
data=dsSimulate(spec,'vary',vary);
dsPlotWaveforms(data,'variable','v');
dsPlotWaveforms(data,'variable','ISYN');
dsPlotWaveforms(data,'variable','E1_v');
dsPlotWaveforms(data,'variable','*');
See also: dsPlotFR, dsCheckData0001 function plotWaveforms(data,varargin) 0002 %PLOTWAVEFORMS - plot waveforms in various ways depending on what data was provided. 0003 % 0004 % Usage: 0005 % dsPlotWaveforms(data,'option',value) 0006 % 0007 % Inputs: 0008 % - data: DynaSim data structure (see dsCheckData) 0009 % - options: 0010 % 'variable': name of field containing data to plot (default: all pops with 0011 % state variable of variable in data.labels) 0012 % 'time_limits': in units of data.time {[beg,end]} 0013 % 'max_num_overlaid': maximum # of waveforms to overlay per plot 0014 % 'max_num_rows': maximum # of subplot rows per figure 0015 % 0016 % Notes: 0017 % if Nsims>1: one sim per row 0018 % elseif Npops>1: one pop per row 0019 % else: one cell per row 0020 % 0021 % Examples for specifying 'variable' option: 0022 % [] : plot all data.labels with same variable name as first element of 0023 % data.labels (eg, 'pop1_v' and 'pop2_v') 0024 % '*' : plot all data.labels 0025 % '*_v' : plot all data.labels ending in _v (i.e., all state variables 'v' 0026 % for all populations) 0027 % 'pop1_*': plot all data.labels starting with pop1_ (i.e., all variables for 0028 % population 'pop1') 0029 % 'pop1_v': plot only variable 'pop1_v' 0030 % 'v' : look for all data.labels ending in _v then starting with v_ (eg, 0031 % all populations with variable 'v') 0032 % 'pop1' : look for all data.labels ending in _pop1 then starting with pop1_ 0033 % (eg, all variables for population 'pop1') 0034 % '*_iNa_*': plot all data.labels for the 'iNa' mechanism (for all populations) 0035 % 0036 % Examples: 0037 % - Example 1: One cell: 0038 % data=dsSimulate('dv/dt=@current+10; {iNa,iK}'); 0039 % dsPlotWaveforms(data); % plot first state variable ('v') 0040 % dsPlotWaveforms(data,'variable','*'); % plot all state variables 0041 % % plot all variables and time 30-60ms 0042 % dsPlotWaveforms(data,'variable','*','time_limits',[30 60]); 0043 % 0044 % - Example 2: One population: with noisy input 0045 % data=dsSimulate('dv[5]/dt=@current+10*(1+randn(1,Npop)); {iNa,iK}'); 0046 % dsPlotWaveforms(data); 0047 % dsPlotWaveforms(data,'variable','*'); % plot all state variables (all cells) 0048 % dsPlotWaveforms(data,'variable','m'); % plot state variable 'm' (all cells) 0049 % % plot all variables and time 30-60ms 0050 % dsPlotWaveforms(data,'variable','*','time_limits',[30 60]); 0051 % 0052 % - Example 3: One population varying one parameter (input amplitude): 0053 % eqns='dv[5]/dt=@current+amp*(1+randn(1,Npop)); {iNa,iK}'; 0054 % vary={'','amp',[0 10 20]}; 0055 % data=dsSimulate(eqns,'vary',vary); 0056 % dsPlotWaveforms(data); 0057 % dsPlotWaveforms(data,'variable','m'); 0058 % dsPlotWaveforms(data,'variable','*'); 0059 % 0060 % - Example 4: One population varying two parameters (input amplitude and 0061 % membrane capacitance): 0062 % eqns='dv[5]/dt=@current/Cm+amp*(1+randn(1,Npop)); {iNa,iK}'; 0063 % vary={'','Cm',[1 2]; '','amp',[0 10 20]}; 0064 % data=dsSimulate(eqns,'vary',vary); 0065 % dsPlotWaveforms(data); 0066 % dsPlotWaveforms(data,'variable','*'); 0067 % 0068 % - Example 5: Two populations: noisy input to E and excitatory connection from E to I 0069 % spec=[]; 0070 % spec.populations(1).name='E1'; 0071 % spec.populations(1).equations='dv[5]/dt=@current+amp*(1+randn(1,Npop)); amp=10; {iNa,iK}'; 0072 % spec.populations(2).name='E2'; 0073 % spec.populations(2).equations='dv[2]/dt=@current; {iNa,iK}'; 0074 % spec.connections(1).direction='E1->E2'; 0075 % spec.connections(1).mechanism_list='iAMPA'; 0076 % data=dsSimulate(spec); 0077 % dsPlotWaveforms(data); % plot first state variable 0078 % dsPlotWaveforms(data,'variable','*'); 0079 % % plot monitored synaptic current with post-synaptic voltages: 0080 % dsPlotWaveforms(data,'variable',{'E2_v','ISYN'}); 0081 % % plot monitored synaptic current with pre- and post-synaptic voltages: 0082 % dsPlotWaveforms(data,'variable',{'v','ISYN'}); 0083 % 0084 % - Example 6: Two populations varying one parameter (input amplitude): 0085 % vary={'E1','amp',[0 10 20]}; 0086 % data=dsSimulate(spec,'vary',vary); 0087 % dsPlotWaveforms(data); 0088 % dsPlotWaveforms(data,'variable','*'); 0089 % dsPlotWaveforms(data,'variable','*_iNa_*'); 0090 % 0091 % - Example 7: Two populations varying two parameters (input amplitude and 0092 % synaptic conductance): 0093 % vary={'E1','amp',[0 10 20]; 'E1->E2','gSYN',[0 .05 .1]}; 0094 % data=dsSimulate(spec,'vary',vary); 0095 % dsPlotWaveforms(data,'variable','v'); 0096 % dsPlotWaveforms(data,'variable','ISYN'); 0097 % dsPlotWaveforms(data,'variable','E1_v'); 0098 % dsPlotWaveforms(data,'variable','*'); 0099 % 0100 % See also: dsPlotFR, dsCheckData 0101 0102 % Check inputs 0103 data=dsCheckData(data, varargin{:}); 0104 fields=fieldnames(data); 0105 0106 options=dsCheckOptions(varargin,{... 0107 'time_limits',[-inf inf],[],... 0108 'variable',[],[],... 0109 'max_num_overlaid',50,[],... 0110 'max_num_rows',20,[],... 0111 'plot_mode','trace',{'trace','image'},... 0112 },false); 0113 0114 % todo: add option 'plot_mode' {'trace','image'} 0115 0116 % variables to plot 0117 var_fields=dsSelectVariables(data(1).labels,options.variable, varargin{:}); 0118 tmp=regexp(var_fields,'_(.+)$','tokens','once'); 0119 variables=unique([tmp{:}]); 0120 0121 % populations to plot 0122 pop_names={data(1).model.specification.populations.name}; % list of populations 0123 % restrict to populations with variables to plot 0124 pop_indices=[]; % indices of populations to plot 0125 pop_var_indices={}; % indices of var_fields to plot per population 0126 % pop_var_indices{pop}(variable): index into var_fields 0127 for i=1:length(pop_names) 0128 % do any variables start with this population name? 0129 var_inds=find(~cellfun(@isempty,regexp(var_fields,['^' pop_names{i}]))); 0130 if any(var_inds) 0131 inds=cellfun(@(x)find(~cellfun(@isempty,regexp(var_fields(var_inds),['_' x '$'],'once'))),variables,'uni',0); 0132 varsel=~cellfun(@isempty,inds); 0133 fldind=[inds{:}]; 0134 pop_indices(end+1)=i; 0135 pop_var_indices{end+1}=nan(1,length(variables)); 0136 pop_var_indices{end}(varsel)=var_inds(fldind); 0137 end 0138 end 0139 pop_names=pop_names(pop_indices); 0140 0141 % data set info 0142 time=data(1).time; % time vector 0143 pop_sizes=[data(1).model.specification.populations(pop_indices).size]; 0144 num_pops=length(pop_names); % number of populations to plot 0145 num_sims=length(data); % number of simulations 0146 num_vars=length(variables); 0147 num_labels=length(var_fields); % number of labels to plot 0148 num_times=length(time); 0149 0150 MRPF = options.max_num_rows; % max rows per fig 0151 MTPP = options.max_num_overlaid; % max traces per plot 0152 0153 % how many plots: 0154 if num_sims==1 && num_pops==1 && num_vars==1 0155 num_fig_sets=1; num_figs=ceil(pop_sizes/MRPF); num_rows=min(pop_sizes,MRPF); 0156 elseif num_sims==1 && num_pops==1 && num_vars>1 0157 num_fig_sets=1; num_figs=ceil(num_vars/MRPF); num_rows=min(num_vars,MRPF); 0158 elseif num_sims==1 && num_pops>1 && num_vars==1 0159 num_fig_sets=1; num_figs=ceil(num_pops/MRPF); num_rows=min(num_pops,MRPF); 0160 elseif num_sims==1 && num_pops>1 && num_vars>1 0161 num_fig_sets=num_vars; num_figs=ceil(num_pops/MRPF); num_rows=min(num_pops,MRPF); 0162 elseif num_sims>1 && num_pops==1 && num_vars==1 0163 num_fig_sets=1; num_figs=ceil(num_sims/MRPF); num_rows=min(num_sims,MRPF); 0164 elseif num_sims>1 && num_pops==1 && num_vars>1 0165 num_fig_sets=num_vars; num_figs=ceil(num_sims/MRPF); num_rows=min(num_sims,MRPF); 0166 elseif num_sims>1 && num_pops>1 && num_vars==1 0167 num_fig_sets=1; num_figs=ceil(num_sims/MRPF); num_rows=min(num_sims,MRPF); 0168 elseif num_sims>1 && num_pops>1 && num_vars>1 0169 num_fig_sets=num_vars; num_figs=ceil(num_sims/MRPF); num_rows=min(num_sims,MRPF); 0170 end 0171 0172 % make subplot adjustments for varied parameters 0173 if num_sims>1 && isfield(data,'varied') 0174 % collect info on parameters varied 0175 varied=data(1).varied; 0176 num_varied=length(varied); % number of model components varied across simulations 0177 num_sims=length(data); 0178 % collect info on parameters varied 0179 param_mat=zeros(num_sims,num_varied); 0180 param_cell=cell(1,num_varied); 0181 for j=1:num_varied 0182 if isnumeric(data(1).(varied{j})) 0183 param_mat(:,j)=[data.(varied{j})]; 0184 param_cell{j}=unique([data.(varied{j})]); 0185 else 0186 % todo: handle sims varying non-numeric model components 0187 % (eg, mechanisms) (also in dsPlotFR) 0188 end 0189 end 0190 param_size=cellfun(@length,param_cell); 0191 % varied parameter with most elements goes along the rows (everything else goes along columns) 0192 row_param_index=find(param_size==max(param_size),1,'first'); 0193 row_param_name=varied{row_param_index}; 0194 row_param_values=param_cell{row_param_index}; 0195 num_rows=length(row_param_values); 0196 num_cols=num_sims/num_rows; 0197 sim_indices=[]; 0198 for row=1:num_rows 0199 sim_indices=[sim_indices find(param_mat(:,row_param_index)==row_param_values(row))]; 0200 end 0201 else 0202 sim_indices=ones(1,num_rows); % index into data array 0203 num_cols=1; 0204 end 0205 0206 % set x-axis limits 0207 if isempty(options.time_limits) 0208 options.time_limits=[min(time) max(time)]; 0209 end 0210 0211 for figset=1:num_fig_sets 0212 for fig=1:num_figs 0213 ylims=[nan nan]; 0214 % create figure 0215 figure('units','normalized','outerposition',[0 0 1 1]) 0216 % position axes 0217 haxes=tight_subplot(num_rows,num_cols,[.01 .03],[.05 .01],[.03 .01]); 0218 axis_counter=0; 0219 % draw plots 0220 text_string=''; % string to add to subplot (set below) 0221 legend_string=''; % legend for subplot (set below) 0222 shared_ylims_flag=1; 0223 for row=1:num_rows 0224 for col=1:num_cols 0225 sim_index=sim_indices(col,row); % index into data array for this subplot 0226 axis_counter=axis_counter+1; % number subplot axis we're on 0227 % what to plot 0228 if num_sims==1 && num_pops==1 && num_vars==1 0229 % one cell per row: dat = data(s=1).(var)(:,c=r) where var=vars{v=1} 0230 var=var_fields{1}; 0231 dat=data(sim_index).(var)(:,row); 0232 if num_rows>1 0233 text_string{row,col}=sprintf('cell %g',row); 0234 end 0235 elseif num_sims==1 && num_pops==1 && num_vars>1 0236 % one variable per row: dat = data(s=1).(var)(:,1:MTPP) where var=vars{v=r} 0237 var=var_fields{row}; 0238 dat=data(sim_index).(var); 0239 shared_ylims_flag=0; 0240 elseif num_sims==1 && num_pops>1 && num_vars==1 0241 % one population per row: dat = data(s=1).(var)(:,1:MTPP) where var=vars{v=r} 0242 var=var_fields{row}; 0243 dat=data(sim_index).(var); 0244 elseif num_sims==1 && num_pops>1 && num_vars>1 0245 % one population per row: dat = data(s=1).(var)(:,1:MTPP) where var=vars{these(p=r)} 0246 if isnan(pop_var_indices{row}(figset)) 0247 continue; 0248 end 0249 var=var_fields{pop_var_indices{row}(figset)}; 0250 dat=data(sim_index).(var); 0251 elseif num_sims>1 && num_pops==1 && num_vars==1 0252 % one simulation per row: dat = data(s=r).(var)(:,1:MTPP) where var=vars{v=1} 0253 var=var_fields{1}; 0254 dat=data(sim_index).(var); 0255 elseif num_sims>1 && num_pops==1 && num_vars>1 0256 % one simulation per row: dat = data(s=r).(var)(:,1:MTPP) where var=vars{v++} 0257 if isnan(pop_var_indices{1}(figset)) 0258 continue; 0259 end 0260 var=var_fields{pop_var_indices{1}(figset)}; 0261 dat=data(sim_index).(var); 0262 elseif num_sims>1 && num_pops>1 && num_vars==1 0263 % one simulation per row, overlay pops: dat = <data(s=r).(var)(:,1:MTPP),2|vars> 0264 % calculate averages across populations 0265 dat=nan(num_times,num_pops); 0266 for k=1:num_pops 0267 if ~strcmp(reportUI,'matlab') && exist('nanmean') ~= 2 % 'nanmean is not in Octave's path 0268 try 0269 pkg load statistics; % trying to load octave forge 'statistics' package before using nanmean function 0270 catch 0271 error('nanmean function is needed, please install the statistics package from Octave Forge'); 0272 end 0273 end 0274 dat(:,k)=nanmean(data(sim_index).(var_fields{k}),2); 0275 end 0276 var=['<' variables{1} '>']; 0277 elseif num_sims>1 && num_pops>1 && num_vars>1 0278 % one simulation per row, overlay pops: dat = <data(s=r).(var)(:,1:MTPP),2|vars(these)> 0279 % calculate averages across populations 0280 dat=nan(num_times,num_pops); 0281 for k=1:num_pops 0282 if isnan(pop_var_indices{k}(figset)) 0283 continue; 0284 end 0285 var=var_fields{pop_var_indices{k}(figset)}; 0286 try 0287 pkg load statistics; % trying to load octave forge 'statistics' package before using nanmean function 0288 catch 0289 error('nanmean function is needed, please install the statistics package from Octave Forge'); 0290 end 0291 end 0292 dat(:,k)=nanmean(data(sim_index).(var),2); 0293 end 0294 var=['<' variables{figset} '>']; 0295 end 0296 if size(dat,2)>1 0297 legend_string=cellfun(@(x)['cell ' num2str(x)],num2cell(1:min(size(dat,2),10)),'uni',0); 0298 end 0299 if num_sims>1 && isfield(data,'varied') 0300 % list the parameter varied along the rows first 0301 str=[row_param_name '=' num2str(row_param_values(row)) ': ']; 0302 for k=1:num_varied 0303 fld=data(sim_index).varied{k}; 0304 if ~strcmp(fld,row_param_name) 0305 val=data(sim_index).(fld); 0306 str=[str fld '=' num2str(val) ', ']; 0307 end 0308 end 0309 text_string{row,col}=['(' strrep(str(1:end-2),'_','\_') ')']; 0310 if num_pops>1 0311 legend_string=cellfun(@(x)[x ' (mean)'],pop_names,'uni',0); 0312 end 0313 end 0314 % plot data 0315 axes(haxes(axis_counter)); 0316 if strcmp(options.plot_mode,'trace') 0317 % select max subset allowed 0318 dat=dat(:,1:min(size(dat,2),MTPP)); % select max subset to plot 0319 plot(time,dat); 0320 else 0321 imagesc(dat); 0322 end 0323 % format axes 0324 if row==num_rows 0325 xlabel('time (ms)'); 0326 else 0327 set(haxes(axis_counter),'XTickLabel',''); 0328 %set(haxes(row),'YTickLabel',''); 0329 end 0330 xlim(options.time_limits); 0331 ylabel(strrep(var,'_','\_')); 0332 if shared_ylims_flag 0333 % update max/min 0334 ylims(1)=min(ylims(1),min(dat(:))); 0335 ylims(2)=max(ylims(2),max(dat(:))); 0336 else 0337 % set ylim to max/min of this data set 0338 ylims=[min(dat(:)) max(dat(:))]; 0339 if ylims(1)~=ylims(2) 0340 ylim(ylims); 0341 end 0342 % add text 0343 if ~isempty(text_string) 0344 xmin=min(time); xmax=max(time); 0345 ymin=min(dat(:)); ymax=max(dat(:)); 0346 text_xpos=xmin+.05*(xmax-xmin); 0347 text_ypos=ymin+.9*(ymax-ymin); 0348 text(text_xpos,text_ypos,text_string{row,col}); 0349 end 0350 end 0351 if ~isempty(legend_string) 0352 legend(legend_string); 0353 end 0354 end % end loop over subplot columns 0355 end % end loop over subplot rows 0356 % set y-limits to max/min over data in this figure 0357 if shared_ylims_flag 0358 if ylims(1)~=ylims(2) 0359 set(haxes,'ylim',ylims); 0360 end 0361 if ~isempty(text_string) 0362 axis_counter=0; 0363 for row=1:num_rows 0364 for col=1:num_cols 0365 axis_counter=axis_counter+1; 0366 axes(haxes(axis_counter)); 0367 xmin=min(time); xmax=max(time); 0368 ymin=min(ylim); ymax=max(ylim); 0369 text_xpos=xmin+.05*(xmax-xmin); 0370 text_ypos=ymin+.9*(ymax-ymin); 0371 text(text_xpos,text_ypos,text_string{row,col}); 0372 end 0373 end 0374 end 0375 end 0376 end % end loop over figures in this set 0377 end % end loop over figure sets 0378 0379 % 1 sim, 1 pop, 1 var (X) 0380 % N=1 one fig, one row (plot var X) 0381 % N>1 one row per cell (plot var X), enough figs for all cells 0382 % (nsims=1, num_pops=1, num_vars=1, pop_sizes>=1): num_fig_sets=1, num_figs=ceil(pop_sizes/MRPF), num_rows=min(pop_sizes,MRPF): row r: dat = data(s=1).(var)(:,c=r) where var=vars{v=1} 0383 % 0384 % 1 sim, 1 pop, >1 vars (X,Y,...) 0385 % N=1 one row per var (plot cell 1), enough figs for all vars 0386 % N>1 one row per var (overlay cells), enough figs for all vars 0387 % (nsims=1, num_pops=1, num_vars>=1, pop_sizes>=1): num_fig_sets=1, num_figs=ceil(num_vars/MRPF), num_rows=min(num_vars,MRPF): row r: dat = data(s=1).(var)(:,1:MTPP) where var=vars{v=r} 0388 % 0389 % 1 sim, >1 pops, 1 var (X) 0390 % all N=1 one row per pop (plot var X, cell 1), enough figs for all pops 0391 % any N>1 one row per pop (plot var X, overlay cells), enough figs for all pops 0392 % (nsims=1, num_pops>=1, num_vars=1, pop_sizes>=1): num_fig_sets=1, num_figs=ceil(num_pops/MRPF), num_rows=min(num_pops,MRPF): row r: dat = data(s=1).(var)(:,1:MTPP) where var=vars{v=r} 0393 % 0394 % 1 sim, >1 pops, >1 vars (X,Y,...) 0395 % all N=1 one row per pop (plot var X, cell 1), enough figs for all pops, separate figs for each var 0396 % any N>1 one row per pop (plot var X, overlay cells), enough figs for all pops, separate figs for each var 0397 % (nsims=1, num_pops>=1, num_vars>=1, pop_sizes>=1): num_fig_sets=num_vars, num_figs=ceil(num_pops/MRPF), num_rows=min(num_pops,MRPF): row r: dat = data(s=1).(var)(:,1:MTPP) where var=vars{these(p=r)} 0398 % 0399 % >1 sim, 1 pop, 1 var (X) 0400 % N=1 one row per sim (plot var X, cell 1), enough figs for all sims 0401 % N>1 one row per sim (plot var X, overlay cells), enough figs for all sims 0402 % (nsims>1, num_pops=1, num_vars=1, pop_sizes>=1): num_fig_sets=1, num_figs=ceil(nsims/MRPF), num_rows=min(nsims,MRPF): row r: dat = data(s=r).(var)(:,1:MTPP) where var=vars{v=1} 0403 % 0404 % >1 sim, 1 pop, >1 vars (X,Y,...) 0405 % N=1 one row per sim (plot var X, cell 1), enough figs for all sims, separate figs for each var 0406 % N>1 one row per sim (plot var X, overlay cells), enough figs for all sims, separate figs for each var 0407 % (nsims>1, num_pops=1, num_vars=1, pop_sizes>=1): num_fig_sets=num_vars, num_figs=ceil(nsims/MRPF), num_rows=min(nsims,MRPF): row r: dat = data(s=r).(var)(:,1:MTPP) where var=vars{v++} 0408 % 0409 % >1 sim, >1 pops, 1 var (X) 0410 % all N=1 one row per sim (plot var X, overlay pops), enough figs for all sims 0411 % any N>1 one row per sim (plot var <X>, overlay pops), enough figs for all sims 0412 % (nsims>1, num_pops=1, num_vars=1, pop_sizes>=1): num_fig_sets=1, num_figs=ceil(nsims/MRPF), num_rows=min(nsims,MRPF): row r: dat = <data(s=r).(var)(:,1:MTPP),2|vars> 0413 % 0414 % >1 sim, >1 pops, >1 vars (X,Y,...) 0415 % all N=1 one row per sim (plot var X, overlay pops), enough figs for all sims, separate figs for each var 0416 % any N>1 one row per sim (plot var <X>, overlay pops), enough figs for all sims, separate figs for each var 0417 % (nsims>1, num_pops=1, num_vars=1, pop_sizes>=1): num_fig_sets=num_vars, num_figs=ceil(nsims/MRPF), num_rows=min(nsims,MRPF): row r: dat = <data(s=r).(var)(:,1:MTPP),2|vars(these)> 0418 0419 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5