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@@ -1,113 +0,0 @@
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-function [ newNeurons, fluorescenceData, classifications, binaryPullTimes,pulls,options] = processDFFInitVars(dir, pullFrames, fr, autoClassifyNeurons, pTA)
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-% This function initializes variables needed for processDFFPipeline.m
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-% Detailed explanation goes here
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-
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- %% Find the json file and Load in variables for dff extraction from same directory.
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- if isempty(dir)
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- disp('Pick Centroid json file');
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- [foldername, dir] = uigetfile('.json', 'Pick Centroid json file');
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- jsonFilePath = fullfile(dir,foldername); % Set the file name using this variable
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- else
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- jsonFilePath = fullfile(dir, 'centroids.json');
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- end
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-
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- if(exist(fullfile(dir, 'Fdf.mat'),'file'))
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- load(fullfile(dir, 'Fdf.mat'))
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- end
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- if(exist(fullfile(dir, 'Cd.mat'),'file'))
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- load(fullfile(dir, 'Cd.mat'))
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- end
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- if(exist(fullfile(dir, 'Sp.mat'),'file'))
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- load(fullfile(dir, 'Sp.mat'))
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- end
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-
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- %% Create a new struct with neuron data (nid, dff, Cd, and Sp) concatenated by time (optional)
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- Fdf_concat = [];
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- Sp_concat = [];
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- Cd_concat = [];
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- for i = 1:length(F_df)
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- if(exist('F_df','var'))
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- Fdf_concat = horzcat(Fdf_concat, cell2mat(F_df(i)));
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- end
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- if(exist('Sp','var'))
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- Sp_concat = horzcat(Sp_concat, cell2mat(Sp(i)));
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- end
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- if(exist('Cd','var'))
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- Cd_concat = horzcat(Cd_concat, cell2mat(Cd(i)));
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- end
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- end
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-
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- fluorescenceData = struct('Fdf',Fdf_concat,'Cd',Cd_concat,'Sp',Sp_concat);
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-
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- newNeurons = struct('nid',[],'dff',[],'Cd',[],'Sp',[]);
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- neurons = jsonread(jsonFilePath);
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- numNeurons = length(neurons.jmesh);
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- for i = 1:numNeurons
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- newNeurons(i).nid = i;
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- %newNeurons(i).dff = Fdf_concat(i,:)';
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- newNeurons(i).Sp = Sp_concat(i,:)';
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- newNeurons(i).Cd = Cd_concat(i,:)';
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- end
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-
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- %% Initialize Variables
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- numFrames = length(Cd_concat);
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- xpoints = 1:numFrames;
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-
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- if isempty(pTA)
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- pTA = 1; % Default Value - frames before and after pull to average
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- end
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- if isempty(fr)
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- fr = 3; %% If no framerate set, just use frame numbers.
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- end
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- options = struct('numFrames',numFrames,'numNeurons',numNeurons,'pTA',pTA,'xpoints',xpoints,'framerate',fr);
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-
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- %% Pull Time Data
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-
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- binaryPullTimes = zeros(1,numFrames);
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- for i = 1:2:length(pullFrames)
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- binaryPullTimes(pullFrames(i) - pTA :pullFrames(i+1) + pTA) = 1;
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- end
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- pulls= struct('pullNum',[],'pullFrames',[],'average',[]);
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- pullNum = 1;
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- for i = 1:2:length(pullFrames)
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- thisPull = Cd_concat(:,pullFrames(i) - pTA : pullFrames(i+1) + pTA);
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- meanPull = mean(thisPull,1);
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- pulls(pullNum).pullNum = pullNum;
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- pulls(pullNum).pullFrames = [pullFrames(i) pullFrames(i+1)];
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- pulls(pullNum).average = meanPull;
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- pullNum = pullNum + 1;
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- end
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- %% Initialize Data Frame for Classifying Cells as Active or Quiescent Active
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- % data(3).im(2).roi_trace_thresh(10,:) % Third Animal on second days 10th roi
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- % Data Struct - first input
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- data=struct('im',[]);
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- data.im = struct('roi_trace_thresh',Sp_concat,'roi_trace_df',Sp_concat);
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-
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- % Analysis Struct - second input
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- % analysis(3).lever(2).lever_move_frames(:,1) % Third Animal on the Second
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- % day - binarized movement frames
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- analysis = struct('lever',[]);
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- analysis.lever = struct('lever_move_frames',[]);
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- analysis(1).lever(1).lever_move_frames = binaryPullTimes';
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-
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- [classified_rois, classified_p] = AP_classify_movement_cells_continuous(data,analysis); % Seems to work pretty well
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-
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- %% Neuron Classification Variables
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- if isempty(autoClassifyNeurons)
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- autoClassifyNeurons = true;
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- end
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- if autoClassifyNeurons
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- active = find(classified_rois.movement);
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- quiesc = find(classified_rois.quiescent);
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- indisc = find(classified_rois.unclassified_active);
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- else % Have a csv file with the data for
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- disp('Pick neuron classification file');
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- nCfile = uigetfile(fullfile(dir,'*.csv'),'Pick neuron classification file');
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- neuronClass = csvread(fullfile(dir,nCfile));
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- active = find(neuronClass==1);
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- quiesc = find(neuronClass==2);
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- indisc = find(neuronClass==3);
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- end
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- classifications = struct('classified_rois',classified_rois,'classified_p',classified_p,'active',active,'quiescent',quiesc,'indisc',indisc);
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-end
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-
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