Adapted code to accomodate pixel_distance in lieu of previous scaling…

… functions

preprocessing/behavior/general_behavior_file_SNlab.m

@@ -21,13 +21,6 @@ function general_behavior_file_SNlab(varargin)
 basename = basenameFromBasepath(basepath);
 session = loadSession(basepath,basename);
 
-% try
-%     load(fullfile(basepath,'digitalIn.events.mat'))
-% catch
-%     disp('no digitalin.events found. Can''t run general behavior file for ephys')
-%     return
-% end
-
 % check if file was already made
 if force_overwrite
     disp('Overwriting previous runs')
@@ -39,7 +32,7 @@ function general_behavior_file_SNlab(varargin)
 
 % run update_behavioralTracking to make sure dlc files and associated
 % epochs are indicated in basename.session.behavioralTracking
-update_behavioralTracking('basepath',basepath)
+update_behavioralTracking('basepath',basepath,'force',force_overwrite)
 session = loadSession(basepath,basename);
 
 
@@ -48,34 +41,39 @@ function general_behavior_file_SNlab(varargin)
     tracking_files{i} = session.behavioralTracking{1, i}.filenames;
 end
 
+% extract tracking for godot tracking
 if any(contains({tracking_files{:}},'godot'))
     [t,x,y,v,a,angle,units,source,fs,notes,extra_points,vidnames] = ...
         tracking.extract_godot_tracking(basepath);
 end
 
+
+% extract tracking for Deeplabcut 
 if any(contains({tracking_files{:}},'DLC'))
     [t_dlc,x_dlc,y_dlc,v_dlc,a_dlc,angle_dlc,units_dlc,source_dlc,fs_dlc,notes_dlc,extra_points_dlc,vidnames_dlc] = ...
         tracking.extract_tracking(basepath,primary_coords_dlc,likelihood_dlc,smooth_factor);
 
     % deeplabcut will often have many tracking points, add them here
     if ~isempty(extra_points_dlc)
         for field = fieldnames(extra_points_dlc)'
-            field = field{1};
-            behavior.position.(field) = extra_points_dlc.(field)';
+            behavior.position.(field{1}) = extra_points_dlc.(field{1})';
         end
     end
+
 end
 
-if exist('t','var') & isrow(t)
+% transpose timestamps
+if exist('t','var') && isrow(t)
     t = t';
 end
 
-if exist('t_dlc','var') & isrow(t_dlc)
+% transpose timestamps from DLC
+if exist('t_dlc','var') && isrow(t_dlc)
     t_dlc = t_dlc';
 end
 
 % combine both
-if exist('t','var') & exist('t_dlc','var')
+if exist('t','var') && exist('t_dlc','var')
 
     if min(t) < min(t_dlc)
         % concatenate
@@ -91,6 +89,7 @@ function general_behavior_file_SNlab(varargin)
         notes = {notes; notes_dlc};
         vidnames = {vidnames; vidnames_dlc};
         extra_points = {extra_points; extra_points_dlc};
+
     else
         % concatenate
         t = [t_dlc; t];
@@ -107,8 +106,9 @@ function general_behavior_file_SNlab(varargin)
         extra_points = {extra_points_dlc;extra_points};
     end
 end
-% for dlc 
-if ~exist('t','var') & exist('t_dlc','var')
+
+% for DLC only 
+if ~exist('t','var') && exist('t_dlc','var')
         % rename
     t = t_dlc;
     x = x_dlc;
@@ -150,7 +150,6 @@ function general_behavior_file_SNlab(varargin)
 behavior.processinginfo.function = 'general_behavioral_file_SNlab.mat';
 behavior.processinginfo.source = source;
 
-
 if save_mat
     save([basepath,filesep,[basename,'.animal.behavior.mat']],'behavior');
 end

preprocessing/behavior/get_maze_XY.m

@@ -36,6 +36,7 @@ function get_maze_XY(varargin)
 p = inputParser;
 p.addParameter('basepath',pwd,@isfolder);
 p.addParameter('vid_type','.avi',@ischar);
+p.addParameter('re-do_rescale',false,@islogical);
 p.addParameter('overwrite',false,@islogical);
 p.addParameter('vid_time',300,@isnumeric); % time of video to load in seconds
 p.addParameter('config_path','C:\Users\schafferlab\github\SNLab_ephys\behavior\behavior_configs',@isfolder); % time of video to load in seconds
@@ -44,6 +45,7 @@ function get_maze_XY(varargin)
 p.parse(varargin{:})
 basepath = p.Results.basepath; 
 vid_type = p.Results.vid_type; 
+rescale = p.Results.re-do_rescale;
 overwrite = p.Results.overwrite;
 vid_time = p.Results.vid_time; 
 config_path = p.Results.config_path;
@@ -57,14 +59,23 @@ function get_maze_XY(varargin)
     return
 end
 
+
 if ~isfield(session,'behavioralTracking')
     warning('No tracking items found. No maze coords to return.')
     return
 end
 
 % check if behavior file exsists and if not make one
 if ~exist([basepath,filesep,[basename,'.animal.behavior.mat']],'file')
-    general_behavior_file_SNlab(basepath)
+    error('Cannot update behavior file as one does not exist.')
+end
+
+% if maze_coords exist, but you want to correct the rescale 
+if length(dir(fullfile(basepath,'*maze_coords.csv'))) == length(session.behavioralTracking) && rescale
+    disp('Maze coords found for each behavioralTracking entry, rescaling coordinates')
+
+
+
 end
 
 % get coords
@@ -85,7 +96,63 @@ function main(session, config_path, vid_time, vid_type)
 basename = basenameFromBasepath(basepath);
 
 % load animal behavior file
-load(fullfile(basepath,[basename,'.animal.behavior.mat']))
+load(fullfile(basepath,[basename,'.animal.behavior.mat']),'beahvior')
+
+
+% loop through video
+for file = 1:length(session.behavioralTracking) %loop through folders containing subject videos
+
+
+    vid_path = fullfile(basepath,session.behavioralTracking{1,file}.notes);
+    vid_type = extractAfter(session.behavioralTracking{1,file}.notes,'.');
+    coords_table = readtable(fullfile(basepath,[extractBefore(session.behavioralTracking{1,file}.notes,vid_type),'_maze_coords.csv']))
+
+    % create image save to current directory
+    sys_cmd = ['ffmpeg -ss ', num2str(vid_time),' -i ',vid_path,' -vframes 1 ',img_path];
+    system(sys_cmd)
+
+    epoch = session.behavioralTracking{1,file}.epoch;
+    % choose config based on epoch
+    config = get_behavior_config(session,epoch,config_path);
+    crop_params = session.behavioralTracking{1, file}.crop_params;
+    % pulls up video frame and grabs coords
+    coords_table = grab_coords(img_path,session.behavioralTracking{1,file}.notes,config,crop_params);
+
+    % load pixel distance and pixel_reference 
+    pixel_distance = session.behavioralTracking{1, file}.pixel_distance;
+    pixel_dist_reference = session.behavioralTracking{1, file}.pixel_dist_reference;
+
+    coords_table.x_scaled = coords_table.x * (pixel_dist_reference/pixel_distance);
+    coords_table.y_scaled = coords_table.y * (pixel_dist_reference/pixel_distance);
+    % save to session 
+    session.behavioralTracking{1,file}.maze_coords = coords_table;
+
+    % save data to csv 
+    save_file = fullfile(basepath,[extractBefore(session.behavioralTracking{1,file}.notes,vid_type),'_maze_coords.csv']);
+    writetable(coords_table,save_file);
+
+    % delete the image you created
+    delete(img_path)
+end
+
+% save session back to basepath 
+save(fullfile(basepath,[basename,'.session.mat']),'session');
+
+end
+
+function main(session, config_path, vid_time, vid_type)
+% runs main process of looping through videos in basepath, pulling up
+% image via local grab_coords function which allows user to collect coordinate data
+% and outputs coords for object A center, object A edge, object B center,
+% object B edge, corner center A-D (inner corner of maze).
+%
+
+% LB 2022
+basepath = session.general.basePath;
+basename = basenameFromBasepath(basepath);
+
+% load animal behavior file
+load(fullfile(basepath,[basename,'.animal.behavior.mat']),'beahvior')
 
 
 % loop through video
@@ -98,19 +165,24 @@ function main(session, config_path, vid_time, vid_type)
 
     vid_path = fullfile(basepath,session.behavioralTracking{1,file}.notes);
     img_path = fullfile(basepath,'temp_img.png');
+
     % create image save to current directory
     sys_cmd = ['ffmpeg -ss ', num2str(vid_time),' -i ',vid_path,' -vframes 1 ',img_path];
     system(sys_cmd)
-    %Pull up video
-%     videoObj = VideoReader(vid_path,'CurrentTime',vid_time); % load video starting at vid_time
-
+
     epoch = session.behavioralTracking{1,file}.epoch;
     % choose config based on epoch
     config = get_behavior_config(session,epoch,config_path);
     crop_params = session.behavioralTracking{1, file}.crop_params;
     % pulls up video frame and grabs coords
     coords_table = grab_coords(img_path,session.behavioralTracking{1,file}.notes,config,crop_params);
 
+    % load pixel distance and pixel_reference 
+    pixel_distance = session.behavioralTracking{1, file}.pixel_distance;
+    pixel_dist_reference = session.behavioralTracking{1, file}.pixel_dist_reference;
+
+    coords_table.x_scaled = coords_table.x * (pixel_dist_reference/pixel_distance);
+    coords_table.y_scaled = coords_table.y * (pixel_dist_reference/pixel_distance);
     % save to session 
     session.behavioralTracking{1,file}.maze_coords = coords_table;
 

preprocessing/behavior/restrict_and_transform.m

@@ -4,11 +4,9 @@ function restrict_and_transform(basepath,varargin)
 %
 % Assumes general behavior file and *maze_coords.csv is in basepath. 
 p = inputParser;
-p.addParameter('maze_size',[],@isnumeric)
 p.addParameter('overwrite',false,@islogical)
 
 p.parse(varargin{:});
-maze_size = p.Results.maze_size;
 overwrite = p.Results.overwrite;
 
 % session basename to load animal behavior file and sessions file 

preprocessing/behavior/update_behavior_from_metadata.m

@@ -4,13 +4,9 @@ function update_behavior_from_metadata(metadata_path,varargin)
 % input parser
 p = inputParser;
 addParameter(p,'basepath',pwd);
-addParameter(p,'batch',false); % flag for batch processing
-addParameter(p,'data_folder',[]); % directory for batch
 
 parse(p,varargin{:});
 basepath = p.Results.basepath;
-batch = p.Results.batch;
-data_folder = p.Results.data_folder; % for batch in progress
 
 % read in metadata csv
 df = readtable(metadata_path);
@@ -22,75 +18,19 @@ function update_behavior_from_metadata(metadata_path,varargin)
     return
 end
 
-if batch
-    disp('batch processing option a work in progress')
-    return
-
-end
-
-% update trials
+% update trials in behavior file
 update_trials(basepath,df)
 
-% update maze size
+% update maze size in session and in behavior file
 update_maze_size(basepath,df)
 
-% update behavior.epochs.scale_measurement from maze_size column
+% update pixel in session and in behavior file
 update_pixel_distance(basepath,df)
 
 
 end
 
 
-
-% Batch function in progress
-function main_batch(data_folder,df)
-% loop through and update animal behavior file for all subfolders in
-% data_folder
-%
-basenames = unique(df.basename);
-% loop through unique basenamesl
-for i = 1:length(basenames)
-    % obtain subject dir from basename
-    basename = basenames{i};
-
-    sub = split(basename,'_');
-    sub = sub{1};
-
-    % create basepath
-    basepath = [data_folder,filesep,sub,filesep,basename];
-    session = loadSession(basepath,basename);
-
-    % load animal behavior file
-    if exist(fullfile(basepath,[basename,'.animal.behavior.mat']),'file')
-        load(fullfile(basepath,[basename,'.animal.behavior.mat']),'behavior')
-    else
-        try
-            general_behavior_file_SNlab('basepath',basepath)
-            load(fullfile(basepath,[basename,'.animal.behavior.mat']),'behavior')
-        catch e
-            disp(e)
-            continue
-        end
-
-    end
-
-    if ~isempty(behavior.trials)
-        continue
-    else 
-        % update behavior.trials from trial_start/stop columns
-        update_trials(basepath,df)
-    end
-
-    % update behavior.epochs.maze_size from maze_size column
-    update_maze_size(basepath,df)
-
-    % update behavior.epochs.scale_measurement from maze_size column
-    update_pixel_distance(basepath,df)
-
-
-end
-end
-
 function update_pixel_distance(basepath,df)
 % updates behavior.trials from frames in df
 
@@ -106,7 +46,7 @@ function update_pixel_distance(basepath,df)
 
 % setup
 vars = fieldnames(temp_df);
-col_idx = contains(vars,{'pixel_distance'});
+col_idx = contains(vars,{'pixel_distance','pixel_dist_reference'});
 
 % loop through videos indicated in session.behavioralTracking
 for ii = 1:length(session.behavioralTracking)
@@ -117,11 +57,19 @@ function update_pixel_distance(basepath,df)
     vidname = session.behavioralTracking{1,ii}.notes;
     row_idx = contains(temp_df.vidname,extractBefore(vidname,'.avi'));
 
-    behavior.epochs{1, epoch}.pixel_distance = table2array(temp_df(row_idx,col_idx));
+    values = table2array(temp_df(row_idx,col_idx));
+    behavior.epochs{1, epoch}.pixel_distance = values(1);
+    behavior.epochs{1, epoch}.pixel_dist_reference = values(2);
+
+    session.behavioralTracking{1,ii}.pixel_distance = values(1);
+    session.behavioralTracking{1,ii}.pixel_dist_reference = values(2);
+
 
 end
 % save behavior file
 save(fullfile(basepath,[basename,'.animal.behavior.mat']),'behavior')
+save(fullfile(basepath,[basename,'.session.mat']),'session')
+
 end
 
 function update_maze_size(basepath,df)
@@ -150,11 +98,14 @@ function update_maze_size(basepath,df)
     vidname = session.behavioralTracking{1,ii}.notes;
     row_idx = contains(temp_df.vidname,extractBefore(vidname,'.avi'));
 
+    session.behavioralTracking{1,ii}.maze_size = table2array(temp_df(row_idx,col_idx));
+
     behavior.epochs{1, epoch}.maze_size = table2array(temp_df(row_idx,col_idx));
 
 end
-% save behavior file
+% save behavior and session file
 save(fullfile(basepath,[basename,'.animal.behavior.mat']),'behavior')
+save(fullfile(basepath,[basename,'.session.mat']),'session')
 end
 
 function update_trials(basepath,df)

preprocessing/functions/update_epochs.m

@@ -79,9 +79,8 @@ function update_epochs(varargin)
         else
             % loop through the other epochs
             for i = start_idx:1:size(digitalIn.timestampsOn{1, 2},1) % by default 2nd column is events
-                session.epochs{i}.name =  char(i);
-                session.epochs{i}.startTime =  digitalIn.timestampsOn{1, 2}(i)
-                session.epochs{i}.stopTime =  digitalIn.timestampsOff{1, 2}(i)
+                session.epochs{i}.startTime =  digitalIn.timestampsOn{1, 2}(i);
+                session.epochs{i}.stopTime =  digitalIn.timestampsOff{1, 2}(i);
             end
         end