Unify help

src/pspm_cfg/pspm_cfg_combine_markerchannels.m

@@ -23,9 +23,7 @@
 combine_markerchannels.val    = {datafile, channel_action, marker_chan};
 combine_markerchannels.prog   = @pspm_cfg_run_combine_markerchannels;
 combine_markerchannels.vout   = @pspm_cfg_vout_outchannel;
-combine_markerchannels.help   = {['The feature combine marker channels ',...
-  'can combine all the marker channels of a data file and add the ',...
-  'result to the original data file, specified by channel action.']};
+combine_markerchannels.help   = pspm_cfg_help_format('pspm_combine_markerchannels');
 
 
 

src/pspm_cfg/pspm_cfg_data_editor.m

@@ -38,4 +38,4 @@
 data_editor.tag  = 'data_editor';
 data_editor.val  = {datafile, epochfile, outputfile};
 data_editor.prog = @pspm_cfg_run_data_editor;
-data_editor.help = {['']};
+data_editor.help = pspm_cfg_help_format('pspm_data_editor');

src/pspm_cfg/pspm_cfg_dcm.m

@@ -17,38 +17,21 @@
 timingfile.tag     = 'timingfile';
 timingfile.num     = [1 1];
 %timingfile.filter  = '.*\.(mat|MAT)$';
-timingfile.help    = {['The timing file has to be a .mat file containing a cell array called "epochs". ' ...
-    'For a design with n trials and m events per trial (n x m events in total), the cell array has to be ' ...
-    'structured in the following way:'], '', ['Create m cells in the cell array (one per event ' ...
-    'type that occurs in each trial). Each cell defines either a fixed or a flexible event type.', ...
-    'A cell that defines a fixed event type has to contain a vector with n entries, i.e. one time point per ' ...
-    'trial, in seconds, samples or markers.'], ['A cell that defines a flexible type has to contain an ' ...
-    'array with n rows and two columns. The first column specifies the onsets of the time windows for each trial, ' ...
-    'while the second column specifies the offsets of the time windows.'] ,'', ['It is assumed that ' ...
-    'all trials have the same structure, i.e. the same number of fixed and flexible event types. For individual ' ...
-    'trials with a different structure you can enter negative values as time information to omit estimation of a ' ...
-    'response.'], '', ['For later comparison between trials of different conditions, it is absolutely mandatory ' ...
-    'that they contain the same types of events, to avoid bias. Hence, if one condition omits an event (e. g. ' ...
-    'unreinforced trials in conditioning experiments), the omitted event needs to be modelled as well.']};
+timingfile.help    = {settings.help.pspm_dcm.Arguments.model.timing};
 
 name         = cfg_entry;
 name.name    = 'Name';
 name.tag     = 'name';
 name.strtype = 's';
 name.val     = {''};
-name.help    = {'Optional: Enter a name of the event.  This name can later be used for display and export.'};
+name.help    = {settings.help.pspm_dcm.Arguments.options.trlnames};
 
 onsets         = cfg_entry;
 onsets.name    = 'Onsets';
 onsets.tag     = 'onsets';
 onsets.strtype = 'r';
 onsets.num     = [Inf Inf];
-onsets.help    = {['For events with a fixed response, specify a vector of onsets. The length of the ' ...
-    'vector corresponds to the number of trials (n).'], '', ['For events with a flexible response, ' ...
-    'specify a two column array. The first column defines the onset of the time window in which the ' ...
-    'response occurs. The second column defines the offset. The number of rows of the array corresponds ' ...
-    'to the number of trials (n).'], '', ['All timings have to be indicated in seconds.']};
-
+onsets.help    = {settings.help.pspm_dcm.Arguments.model.timing};
 
 timing_man       = cfg_branch;
 timing_man.name  = 'Event';
@@ -109,29 +92,22 @@
 condition_rep.values  = {condition};
 condition_rep.num     = [0 Inf];
 condition_rep.check   = @pspm_cfg_dcm_check_conditions;
-condition_rep.help    = {['Optional: Specify the conditions that the individual trials belong to.'], ['This information ' ...
-    'is not used for the parameter estimation of the DCM routine, but it allows you to later access the ' ...
-    'conditions in the contrast manager.']};
+condition_rep.help    = {settings.help.pspm_dcm.Arguments.options.trlnames};
 
 % Missing epochs
 no_epochs         = cfg_const;
 no_epochs.name    = 'No Missing Epochs';
 no_epochs.tag     = 'no_epochs';
 no_epochs.val     = {0};
-no_epochs.help    = {['Missing epochs are detected automatically ', ...
-    'according to the data option ''Subsession threshold''.']};
+no_epochs.help    = {};
 
 
 epochentry         = cfg_entry;
 epochentry.name    = 'Enter Missing Epochs Manually (discouraged)';
 epochentry.tag     = 'epochentry';
 epochentry.strtype = 'i';
 epochentry.num     = [Inf 2];
-epochentry.help    = {['Enter the start and end points of missing epochs ', ...
-    '(m) manually.'], ['Specify an m x 2 array, where m is the number ', ...
-    'of missing epochs. The first column marks the ' ...
-    'start points of the epochs that are excluded from the analysis ', ...
-    'and the second column the end points.']};
+epochentry.help    = {settings.help.pspm_dcm.Arguments.model.missing};
 
 epochs        = cfg_choice;
 epochs.name   = 'Define Missing Epochs';
@@ -407,16 +383,7 @@
 dcm.val  = {modelfile, chan, session_rep, data_options, resp_options, inv_options, disp_options};
 dcm.prog = @pspm_cfg_run_dcm;
 dcm.vout = @pspm_cfg_vout_modelfile;
-dcm.help = {['Non-linear models for SCR are powerful if response timing is not precisely known and has to be ' ...
-    'estimated. A typical example are anticipatory SCR in fear conditioning � they must occur at some point ' ...
-    'within a time-window of several seconds duration, but that time point may vary over trials. Dynamic ' ...
-    'causal modelling (DCM) is the framework for parameter estimation. PsPM implements an iterative ' ...
-    'trial-by-trial algorithm. Different from GLM, response parameters are estimated per trial, not per ' ...
-    'condition, and the algorithm must not be informed about the condition. Trial-by-trial response parameters ' ...
-    'can later be summarized across trials, and compared between conditions, using the contrast manager.'], '', ...
-    'References:', '', ...
-    'Bach, Daunizeau et al. (2010) Biological Psychology (Model development)', '', ...
-    'Staib et al. (2015) Journal of Neuroscience Methods (Optimising a model-based approach)'};
+dcm.help = pspm_cfg_help_format('pspm_dcm');
 
 
 function [sts, val] = pspm_cfg_dcm_check_conditions(val)

src/pspm_cfg/pspm_cfg_display.m

@@ -10,10 +10,5 @@
 display.tag  = 'display';
 display.val  = {datafile};
 display.prog = @pspm_cfg_run_display;
-display.help = {'Display PsPM data file in a new figure.'};
+display.help = pspm_cfg_help_format('pspm_display');
 
-
-    function pspm_cfg_run_display(job)
-        pspm_display(job.datafile{1});
-    end
-end

src/pspm_cfg/pspm_cfg_export.m

@@ -1,5 +1,8 @@
 function export = pspm_cfg_export
 
+%% Initialise
+global settings
+
 %% Standard items
 outfile           = pspm_cfg_selector_outputfile();
 modelfile         = pspm_cfg_selector_datafile('model', inf);
@@ -26,29 +29,17 @@
 datatype.val    = {'param'};
 datatype.labels = {'All parameters','One parameter per condition','Reconstructed amplitude estimate'};
 datatype.values = {'param','cond','recon'};
-datatype.help   = {['Normally, all parameter estimates are exported. For GLM, you can choose to ' ...
-    'only export the first basis function per condition, or the reconstructed response per condition. ' ...
-    'For DCM, you can specify contrasts based on conditions as well. This will average within conditions. ', ...
-    'This argument cannot be used for other first-level models.'], ...
-    '', ...
-    '- All parameters: Export all parameter estimates.', '', ...
-    ['- One parameter per condition: Export conditions in a GLM, automatically detects number ' ...
-    'of basis functions and uses only the first one (i.e. without derivatives), ', ...
-    'or export condition averages in DCM.'], '', ...
-    ['- Reconstructed amplitude estimate: Export all conditions in a GLM, reconstructs estimated response ' ...
-    'from all basis functions and export the peak amplitude of the estimated response.'], ''};
+datatype.help   = pspm_cfg_help_format('pspm_export', 'options.statstype');
 
 %Exclude conditions with too many NaN
 exclude_missing         = cfg_menu;
-exclude_missing.name    = 'Exclude condtitions with too many NaN';
+exclude_missing.name    = 'Exclude conditions with too many NaN';
 exclude_missing.tag     = 'exclude_missing';
 exclude_missing.val     = {false};
 exclude_missing.labels  = {'No', 'Yes'};
 exclude_missing.values  = {false, true};
-exclude_missing.help  ={['Exclude parameters from conditions with too many NaN ',...
-                 'values. This option can only be used for GLM file for ',...
-                 'which the corresponding option was used during model ',...
-                 'setup. Otherwise this argument is ignored.']};
+exclude_missing.help    = pspm_cfg_help_format('pspm_export', 'options.exclude_missing');
+
 % Delimiter
 tab         = cfg_const;
 tab.name    = 'Tab';
@@ -88,12 +79,11 @@
 delim.help   =  {''};
 
 
-
 %% Executable Branch
 export      = cfg_exbranch;
 export.name = 'Export Statistics';
 export.tag  = 'export';
 export.val  = {modelfile, datatype, exclude_missing, target, delim};
 export.prog = @pspm_cfg_run_export;
 export.vout = @pspm_cfg_vout_outfile;
-export.help = {'Export statistics to a file for further analysis in statistical software, or to the screen.'};
+export.help = pspm_cfg_help_format('pspm_export');

src/pspm_cfg/pspm_cfg_extract_segments.m

@@ -23,27 +23,23 @@
 mode_manual.tag         = 'mode_manual';
 
 mode_manual.val         = {channel, timeunits, design};
-mode_manual.help        = {['Specify all the settings manually.']};
+mode_manual.help        = {};
 
 
 %% Automatic mode
 mode_automatic          = cfg_branch;
 mode_automatic.name     = 'Automatically read from model file (GLM, or non-linear SCR model)';
 mode_automatic.tag      = 'mode_automatic';
 mode_automatic.val      = {modelfile};
-mode_automatic.help     = {['Extracts all relevant information from a GLM or']...
-                           ['non-linear SCR model file. To distinguish between conditions in a']...
-                           ['non-linear model, trialnames must be specified in the model definition ']...
-                           ['(before running it)']};
+mode_automatic.help     = {};
 
 %% Mode
 extract_mode            = cfg_choice;
 extract_mode.name       = 'Mode';
 extract_mode.tag        = 'mode';
 extract_mode.val        = {mode_automatic};
 extract_mode.values     = {mode_automatic, mode_manual};
-extract_mode.help       = {['Either extract all information from a ', ...
-    'model file or define the relevant information manually. ']};
+extract_mode.help       = {['Extract from model, or define onsets explicitly.']};
 
 
 %% Segment length
@@ -53,8 +49,7 @@
 segment_length.strtype  = 'r';
 segment_length.num      = [1 1];
 segment_length.val      = {10};
-segment_length.help     = {['Length of segments in seconds. Default: 10 s.']};
-
+segment_length.help     = pspm_cfg_help_format('pspm_extract_segments', 'options.length');
 
 %% Outputfile for nan-percentage
 nan_none                = cfg_const;
@@ -67,22 +62,22 @@
 nan_screen.name         = 'Screen';
 nan_screen.tag          = 'nan_screen';
 nan_screen.val          = {'screen'};
-nan_screen.help         = {'Output to screen.'};
+nan_screen.help         = {};
 
 %% NaN output
 nan_output              = cfg_choice;
-nan_output.name         = 'NaN-output';
+nan_output.name         = 'NaN output';
 nan_output.tag          = 'nan_output';
 nan_output.val          = {nan_none};
 nan_output.values       = {nan_none, nan_screen, nan_outputfile};
-nan_output.help         = {'Option to output the percentages of NaN values of each trial and over all trials per condition'};
+nan_output.help         = pspm_cfg_help_format('pspm_extract_segments', 'options.nan_output');
 
 %% Options
 options                 = cfg_branch;
 options.name            = 'Options';
 options.tag             = 'options';
 options.val             = {segment_length, nan_output};
-options.help            = {['Change values of optional settings.']};
+options.help            = {};
 
 %% Plot
 plot                    = cfg_menu;
@@ -91,14 +86,13 @@
 plot.val                = {false};
 plot.labels             = {'No', 'Yes'};
 plot.values             = {false, true};
-plot.help               = {['Plot means over conditions with standard error of the mean.']};
-
+plot.help               = pspm_cfg_help_format('pspm_extract_segments', 'options.plot');
 %% Output
 output                 = cfg_branch;
 output.name            = 'Output';
 output.tag             = 'output';
 output.val             = {outputfile, plot};
-output.help            = {['Output settings.']};
+output.help            = {};
 
 %% Executable branch
 extract_segments      = cfg_exbranch;
@@ -107,7 +101,6 @@
 extract_segments.val  = {extract_mode, options, output};
 extract_segments.prog = @pspm_cfg_run_extract_segments;
 extract_segments.vout = @pspm_cfg_vout_outfile;
-extract_segments.help = {['This function extracts data segments ', ...
-    '(e.g., for visual inspection of mean responses per condition).']};
+extract_segments.help = pspm_cfg_help_format('pspm_extract_segments');
 
 

src/pspm_cfg/pspm_cfg_filtering.m

@@ -14,20 +14,22 @@
 tau.tag           = 'tau';
 tau.strtype       = 'r';
 tau.num           = [1 1];
-tau.help          = {'Time constant in seconds.'};
+tau.help          = pspm_cfg_help_format('pspm_pp', 'tau');
 
 FilterLeaky             = cfg_branch;
 FilterLeaky.name        = 'Leaky Integrator';
 FilterLeaky.tag         = 'leaky_integrator';
 FilterLeaky.val         = {tau};
 FilterLeaky.help        = {''};
+
 %% Medianfilter
 nr_time_pt               = cfg_entry;
 nr_time_pt.name          = 'Number of Time Points';
 nr_time_pt.tag           = 'nr_time_pt';
 nr_time_pt.strtype       = 'i';
 nr_time_pt.num           = [1 1];
-nr_time_pt.help          = {'Number of time points over which the median is taken.'};
+nr_time_pt.help          = pspm_cfg_help_format('pspm_pp', 'n');
+
 % Medianfilter
 FilterMedian             = cfg_branch;
 FilterMedian.name        = 'Median Filter';
@@ -39,13 +41,7 @@
 filtertype.name          = 'Filter Type';
 filtertype.tag           = 'filtertype';
 filtertype.values        = {FilterMedian,FilterButter,FilterLeaky};
-filtertype.help          = {['Currently, median and Butterworth ',...
-                           'filters and a leaky integrator are implemented. A median filter is ' ...
-                           'recommended for short spikes, generated ' ...
-                           'for example in MRI scanners by gradient ' ...
-                           'switching. A butterworth filter is already applied ' ...
-                           'in most psychophysiological models; check there to see whether ' ...
-                           'an additional filtering is meaningful. A leaky integrater is often used for EMG or neural data.']};
+filtertype.help          = {};
 
 %% Executable branch
 filtering             = cfg_exbranch;
@@ -55,12 +51,5 @@
 filtering.val         = {datafile,chan_nr, chan_action, filtertype};
 filtering.prog        = @pspm_cfg_run_filtering;
 filtering.vout        = @pspm_cfg_vout_outchannel;
-filtering.help        = {['This module offers several basic filtering functions. ',...
-                           'Currently, a median filter and a butterworth low pass ' ...
-                           'filter are implemented. The median filter is useful to ' ...
-                           'remove short "spikes" in the data, for example from gradient ' ...
-                           'switching in MRI. The Butterworth filter can be used to get ' ...
-                           'rid of high frequency noise that is not sufficiently ',...
-                           'filtered away by the filters implemented on-the-fly during ',...
-                           'first level modelling.']};
+filtering.help        = pspm_cfg_help_format('pspm_pp');