Home > database > legacy > repack_formdata.m

repack_formdata

PURPOSE ^

repackages data from one or more forms into a more useable format

SYNOPSIS ^

function as = repack_formdata(as,ed)

DESCRIPTION ^

 repackages data from one or more forms into a more useable format

 as = repack_formdata(as,ed)

 as -- analysis structure (see init_analysis_struct.m for details)
 ed -- experiment data

 Multiple instances of a stimulus are associated with unique entries along the 3rd dimension
 of the .data field and the 2nd dimension of the .stimidx field.  Scripts that
 subsequently operate on the as structure can easily locate the multiple
 instances by searching for a specific stimidx in the stimidx field.

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function as = repack_formdata(as,ed)
0002 % repackages data from one or more forms into a more useable format
0003 %
0004 % as = repack_formdata(as,ed)
0005 %
0006 % as -- analysis structure (see init_analysis_struct.m for details)
0007 % ed -- experiment data
0008 %
0009 % Multiple instances of a stimulus are associated with unique entries along the 3rd dimension
0010 % of the .data field and the 2nd dimension of the .stimidx field.  Scripts that
0011 % subsequently operate on the as structure can easily locate the multiple
0012 % instances by searching for a specific stimidx in the stimidx field.
0013 
0014 % 08/19/05 Petr Janata
0015 % 02/19/06 PJ - Added checking to make sure that there are entries in the form
0016 %               to be processed.
0017 % 03/05/06 PJ - Added code to make sure that the rows in the .num and .txt data
0018 %               matrices correspond to the same subjects.
0019 %               Added writing of datenum information.
0020 % 07/18/06 PJ - Improved handling of stimulus vs non-stimulus numerical
0021 %               responses
0022 % 07/19/06 PJ - Resolved handling of multiple iterations of a stimulus.
0023 % 07/27/06 PJ - Added provisions for two types of timestamps, one that is
0024 %               stimulus based, and one that is question based
0025 % 08/03/06 PJ - Eliminated separate handling of single iteration data since
0026 %               this was causing minor problems.
0027 
0028 NUMERIC = 1;
0029 
0030 DEBUG = 2;
0031 
0032 numeric_types = {'int16','int32','int64','double','enum'};
0033 
0034 p = as.params;
0035 
0036 nforms = length(as.forms);
0037 
0038 iter_cnt_num = [];
0039 iter_cnt_txt = [];
0040 
0041 % We need to go through the forms twice.
0042 % The first time is to extract all of the existing subject IDs and then create
0043 % a sorted list of these.  This list will structure the .data tables and will
0044 % ensure that the rows of the .num and .txt data structures are in register.
0045 % It is much easier to ensure that this reasonable assumption is true at this
0046 % stage rather than having to do explicit matching by subject ID in scripts
0047 % that analyze the analysis structure. Note that mismatches in subids may
0048 % persist across elements of an analysis structure, e.g. as(1) vs as(2).
0049 
0050 master_sublist = {};
0051 for ifrm = 1:nforms
0052   cfname = as.forms{ifrm};  % get the current form name
0053   frm_idx = strmatch(cfname,ed.form_names,'exact');
0054 
0055   % copy the current formdata
0056   fd = ed.form_data{frm_idx};
0057 
0058   % make sure there are entries to process
0059   nentries = size(fd.data{1},1);
0060   if nentries == 0
0061     continue
0062   end
0063   
0064   % Map our form column constants
0065   FD = set_form_col_const(fd.vars);
0066 
0067   % Get the list of subjects for whom we have data in this form
0068   subids = fd.data{FD.SUB_ID};   
0069   
0070   % Update the master list
0071   master_sublist = union(master_sublist,subids);
0072 end  % for ifrm=
0073 
0074 % set the subid fields to have the master list
0075 as.num.subid = master_sublist;
0076 as.txt.subid = master_sublist;
0077 
0078 for ifrm = 1:nforms
0079   cfname = as.forms{ifrm};  % get the current form name
0080   frm_idx = strmatch(cfname,ed.form_names,'exact');
0081   
0082   % Make a local copy of the form data
0083   fd = ed.form_data{frm_idx};
0084   nentries = size(fd.data{1},1);
0085   
0086   if nentries == 0
0087     continue
0088   end
0089   
0090   % Map our form column constants
0091   FD = set_form_col_const(fd.vars);
0092 
0093   % Get some basic subject info
0094   subids = fd.data{FD.SUB_ID};   
0095   unique_subs = unique(subids);
0096   nsub = length(unique_subs);
0097   
0098   % Determine what the questions are on this form and create appropriate
0099   % entries in the output structures if they don't yet exist
0100   
0101   qid = cat(2,fd.data{[FD.QUEST_ID FD.SUBQUEST_ID]});  % get question IDs
0102   compqid = qid(:,1)+qid(:,2)/10;
0103 
0104   [unique_quest_ids, quest_idxs] = unique(qid,'rows'); % get the unique questions
0105   nquest = size(unique_quest_ids,1);
0106   
0107   % Create the output question ID field
0108   out_ids = unique_quest_ids(:,1)+unique_quest_ids(:,2)/10; % qid.sqid
0109   
0110   % Retrieve the data formats
0111   qid_str = sprintf('(question_id=%d AND subquestion=%d) OR ', unique_quest_ids');
0112   qid_str(end-3:end) = [];  
0113   mysql_str = sprintf(['SELECT type, data_format_id FROM data_format ' ...
0114     'RIGHT JOIN question_x_data_format ON' ...
0115     ' data_format.data_format_id=question_x_data_format.answer_format_id ' ...
0116     'WHERE (%s);'], qid_str);
0117   [types, dfid] = mysql(p.conn_id,mysql_str);
0118   
0119   % Find out which of the entries have to do with a stimulus ID
0120   stim_mask = ~isnan(fd.data{FD.STIM_ID});
0121   
0122   % Determine which ones are numeric
0123   is_numeric = ismember(types,numeric_types);
0124   
0125   for itype = 1:2
0126     
0127     % Define a few variables that depend on the data type
0128     if itype == NUMERIC  % numeric data
0129       ts = as.num;
0130       type_mask = is_numeric;
0131       src_col = FD.RESP_ENUM;  % column in fd.data that we pull data from
0132       iter_cnt = iter_cnt_num;
0133     else  % non-numeric data
0134       ts = as.txt;
0135       type_mask = ~is_numeric;
0136       src_col = FD.RESP_TXT;  % column in fd.data that we pull data from
0137       iter_cnt = iter_cnt_txt;
0138     end
0139     
0140     % Check to see if we need to handle any data of this type
0141     if ~any(type_mask)
0142       continue
0143     end
0144     
0145     % Check to see which ones we need to add to the numeric and text output structures
0146     proc_qid_idxs = find(~ismember(out_ids,ts.qid) & type_mask);
0147     nproc = length(proc_qid_idxs);
0148   
0149     if nproc
0150       % copy the new question IDs
0151       insert_idxs = (length(ts.qid)+1):(length(ts.qid)+nproc);
0152       ts.qid(insert_idxs) = out_ids(proc_qid_idxs);
0153       
0154       % copy the new data format IDs (for decoding enums)
0155       if itype == 1
0156     ts.dfid(insert_idxs) = dfid(proc_qid_idxs);
0157       end
0158       
0159       % copy the question text
0160       tmp = cat(2,fd.data{[FD.QUEST_TXT FD.SUBQUEST_TXT]});
0161       for iproc = 1:nproc
0162     if ~strcmp(tmp{quest_idxs(iproc),1},tmp{quest_idxs(proc_qid_idxs(iproc)),2})
0163       ts.qtxt{insert_idxs(iproc)} = ...
0164           cell2str(tmp(quest_idxs(proc_qid_idxs(iproc)),:),'\n');
0165     else
0166       ts.qtxt{insert_idxs(iproc)} = tmp{quest_idxs(proc_qid_idxs(iproc)),1};
0167     end
0168       end
0169       
0170       % Initialize some iteration handling variables
0171 %       if isempty(ts.niter)
0172 %     last_idx = nsub;
0173 %       else
0174 %     last_idx = size(ts.niter,1);
0175 %       end
0176 %       ts.niter(last_idx,insert_idxs) = 0  % Total number of iterations/subject/question
0177 %       iter_cnt(last_idx,insert_idxs) = 0;
0178     end % if nproc
0179     
0180     %
0181     % COPY THE DATA FOR EACH SUBJECT.
0182     %
0183     % Exactly how this happens depends on the how we want to handle multiple
0184     % responses to the same questions, as happens when the same form is
0185     % presented multiple times. This can happen either in the context of
0186     % non-stimulus based forms, e.g. PANAS, or with forms that are associated
0187     % with a stimulus ID.
0188     %
0189     % The easiest way to handle this might be to place repetitions into a 3rd
0190     % dimension in the data field.
0191     %
0192     
0193     for isub = 1:nsub
0194       sid = unique_subs{isub};
0195       
0196       % Check to see if we already have an entry for this subject and create if
0197       % necessary.
0198       % This conditional code should not be entered given the changes made of
0199       % initializing the subid lists in the .num and .txt structures to be the
0200       % same (same elements, same order).
0201       
0202       row_idx = find(strcmp(ts.subid,sid));
0203       if isempty(row_idx)
0204     if DEBUG == 2
0205       fprintf('Creating Type %d entry for subject: %s\n', itype, sid);
0206     end
0207     ts.subid{end+1} = sid;
0208     row_idx = length(ts.subid);
0209       end
0210 
0211       % Make sure our iteration counters are up to date
0212       if (size(iter_cnt,1) < row_idx) | (size(iter_cnt,2) < length(ts.qid))
0213     ts.niter(row_idx,length(ts.qid)) = 0;
0214     iter_cnt(row_idx,length(ts.qid)) = 0;
0215       end
0216       
0217       % Determine which entries in the form data belong to this subject
0218       submask = strcmp(subids,sid);
0219       
0220       % Determine destination columns for the data. compqid(submask) pulls out
0221       % the question IDs that we have responses for for this subject, and
0222       % ts.qid is a vector that maps each question ID to a column in the output
0223       % data matrix.  have_dest indicates which of the responses can be copied,
0224       % and dest_idx gives the appropriate column in the output data matrix.
0225 
0226       destmask = zeros(size(submask));
0227       [have_dest,dest_idx] = ismember(compqid(submask),ts.qid);
0228 
0229       % Make sure we have some data to enter
0230       if any(have_dest)
0231     destmask(submask) = have_dest;
0232     
0233     if itype == NUMERIC & (DEBUG == NUMERIC)
0234       [dest_idx(have_dest) fd.data{src_col}(submask&destmask)]
0235       [sum(submask&destmask) length(dest_idx(have_dest))]
0236     end
0237     
0238     % update the iteration count
0239     dest_cols = unique(dest_idx(have_dest));
0240     ndest = length(dest_cols);
0241     if ndest > 1
0242       ts.niter(row_idx,dest_cols) = ...
0243           ts.niter(row_idx,dest_cols) + ...
0244           hist(dest_idx(have_dest),dest_cols);
0245     else
0246       ts.niter(row_idx,dest_cols) = ...
0247           ts.niter(row_idx,dest_cols) + ...
0248           sum(have_dest);
0249     end
0250     
0251     % Now copy the data.
0252     
0253       % Now loop over destination columns (individual questions)
0254       for idest = 1:ndest
0255         col_idx = dest_cols(idest);
0256         % Find which responses pertain to this question.
0257         colmask = compqid == ts.qid(col_idx);
0258         
0259         curr_mask = colmask&submask;
0260         clear colmask
0261         
0262         % check to see if we are dealing with stimulus IDs
0263         is_stim = any(fd.data{FD.STIM_ID}(curr_mask&stim_mask));
0264         
0265         if is_stim
0266           curr_mask = curr_mask&stim_mask;
0267         end
0268       
0269         % Figure out how many responses we have to this particular question
0270         nresp = sum(curr_mask);
0271         
0272         % Copy the data. If we aren't dealing with a stimulus, then it is easy
0273         % because we don't have to go into specific stim_id slots, and can
0274         % just pack things into the last place we left off.
0275         %
0276         % NOTE: This is a somewhat weak solution, meaning that there might
0277         % be situations where we want things aligned by iteration even if we
0278         % aren't dealing with a stimulus.  So, the handling of this may need
0279         % to be changed in the future.
0280         if ~is_stim
0281           % Figure out where the responses are going to go in the 3D data
0282           % field.
0283           rep_idxs = ...
0284           iter_cnt(row_idx,col_idx)+1:iter_cnt(row_idx,col_idx)+nresp;
0285           
0286           % update the iteration count
0287           iter_cnt(row_idx,col_idx) = max(rep_idxs);
0288           
0289           % copy the data
0290           ts.data(row_idx,col_idx,rep_idxs) = ...
0291           fd.data{src_col}(curr_mask);
0292           
0293           ts.datenum.by_question(row_idx,col_idx,rep_idxs) = fd.data{FD.DATE_TIME}(curr_mask);
0294           %diff(fd.data{FD.RESP_ID}(curr_mask)) < 0
0295         else
0296           % Handle each stimulus individually.
0297           % We have to go through some extra hoops to preserve the temporal
0298           % order in which they occurred.
0299           %
0300           % HANDLING OF MULTIPLE ITERATIONS OF A STIMULUS
0301           %
0302           % Multiple iterations of a stimulus ID are handled by simply
0303           % adding another instance of the stimulus ID to the .data and
0304           % .stimidx matrices (the 3rd and 2nd dimensions, respectively),
0305           % rather than creating an extra dimension to handle multiple
0306           % stimulus iterations.  The advantage of this scheme is that it
0307           % preserves the temporal ordering of stimulus presentation in
0308           % these variables. Thus, unique instances of a stimulus are now
0309           % identified by a combination of their stimulus ID and timestamp.
0310           
0311           [curr_stim_ids,idx1,idx2] = ...
0312           unique(fd.data{FD.STIM_ID}(curr_mask));
0313           curr_mask_idxs = find(curr_mask);
0314           
0315           % Make sure we have no NaNs among the stim ids
0316           bad_ids = isnan(curr_stim_ids);
0317           if any(bad_ids)
0318         warning(sprintf('Found %d NaNs among the stim IDs. subid=%s\n', sum(bad_ids), subid))
0319           end
0320           
0321           % sort them into original temporal order
0322           curr_stim_ids = curr_stim_ids(idx2);
0323           curr_stim_times = fd.data{FD.DATE_TIME}(curr_mask_idxs);
0324           
0325           % Make sure that there are no duplicate stimulus submissions
0326           dup_idxs = find((diff(curr_stim_ids)==0) & ...
0327           (diff(curr_stim_times)==0))+1;
0328 
0329           if ~isempty(dup_idxs)
0330         warning(sprintf(['\n%d duplicated responses with same stimulus ' ...
0331               'ID (%s) and timestamp: subject (%s)\n'], length(dup_idxs), sprintf('%d,', curr_stim_ids(dup_idxs)), sid))
0332         curr_stim_ids(dup_idxs) = [];
0333         curr_stim_times(dup_idxs) = [];
0334         nresp = nresp-1;
0335           end
0336           
0337           num_presented_stims = length(curr_stim_ids);
0338           
0339           % issue a warning if there are more responses than stimuli,
0340           % i.e. some stimulus was responded to more than once
0341           if num_presented_stims ~= nresp
0342         warning(sprintf('Encountered %d stimuli and %d responses\n', num_presented_stims, nresp))
0343           end
0344           
0345           for istim = 1:num_presented_stims
0346         curr_stim_id = curr_stim_ids(istim);
0347         
0348         % Check to see if this particular stim_id already exists in the
0349         % master stimulus list. If not, add it to the list.
0350         master_stim_idx = find(as.stims.ids==curr_stim_id);
0351         
0352         if isempty(master_stim_idx)
0353           master_stim_idx = length(as.stims.ids)+1;
0354           
0355           % Copy the stim ID to this location
0356           as.stims.ids(master_stim_idx) = curr_stim_id;
0357         end % if isempty(master_stim_idx)
0358         
0359         % Now, check to see if this particular master_stim_idx already
0360         % exists in the subject's stimulus list, and if it doesn't,
0361         % pack it into the first available slot
0362         
0363         % Make sure we've started a row in the stimidx matrix for this subject
0364         if size(ts.stimidx,1) < row_idx
0365           ts.stimidx(row_idx,1) = 0;
0366         end
0367         
0368         ts.stimidx(row_idx,istim) = master_stim_idx;
0369         ts.datenum.by_stim(row_idx,istim) = curr_stim_times(istim);
0370         
0371         % make a mask for this instance of the stimulus
0372         curr_stimmask = (fd.data{FD.STIM_ID} == curr_stim_id) & ...
0373             (fd.data{FD.DATE_TIME} == curr_stim_times(istim)); 
0374         
0375         % Copy the data to this location
0376         tmpdata = fd.data{src_col}(curr_mask&curr_stimmask);
0377         
0378         % Only 1 data value should be returned, though in the case of a
0379         % doubly submitted response, there will be more than
0380         % one. Therefore, explicitly take the first.
0381         ts.data(row_idx,col_idx,istim) = tmpdata(1);
0382         
0383         % update the iteration count
0384         iter_cnt(row_idx,col_idx) = iter_cnt(row_idx,col_idx)+1;
0385           end % for istim=
0386         end % if ~is_stim
0387       end % for idest=
0388 
0389       end % if any(have_dest)
0390     end % for isub=
0391 
0392     % put the temporary structure back into the proper output structure
0393     if itype == NUMERIC
0394       as.num = ts; 
0395       iter_cnt_num = iter_cnt;
0396     else 
0397       as.txt = ts; 
0398       iter_cnt_txt;
0399     end
0400     
0401   end % for itype=
0402 end % for ifrm=

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