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Trial.m
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Trial.m
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classdef Trial <handle
% TRIAL A trial has handle behavior to save memory. It can be reset to its orginal
% state by calling restoreToOriginal;
%
% Usage:
% tObj=Trial(nspikeColl, covarColl, event, hist)
% hist: History Object. Optional argument;
% event: Events Object. Optional argument;
% covarColl: CovColl Object. Required.
% nspikeColl: nstColl Obj
%
% <a href="matlab: methods('Trial')">methods</a>
% <a href="matlab:web('TrialExamples.html', '-helpbrowser')">Trial Examples</a>
%
% see also <a href="matlab:help('CovColl')">CovColl</a>,
% <a href="matlab:help('Covariate')">Covariate</a>,
% <a href="matlab:help('SignalObj')">SignalObj</a>,
% <a href="matlab:help('nspikeTrain')">nspikeTrain</a>
%
% Reference page in Help browser
% <a href="matlab: doc('Trial')">doc Trial</a>
%
% nSTAT v1 Copyright (C) 2012 Masschusetts Institute of Technology
% Cajigas, I, Malik, WQ, Brown, EN
% This program is free software; you can redistribute it and/or
% modify it under the terms of the GNU General Public License as published
% by the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
% See the GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software Foundation,
% Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
properties
% A trial consists of
nspikeColl; % a collection of neural spikes
covarColl; % a collection covariates
ensCovHist; % the history structure used to create the ensCovColl
ev; % events
history ; % current history settings for this trial
sampleRate; % sampleRate for all the covariates and nstColl
minTime; % minTime of all data or minTime of interest
maxTime; % maxTime of all data or maxTime of interest
covMask; % mask indicating visible covariates
ensCovMask; % mask indicating which neurons to consider for neighbor effcts;
neuronMask; % mask indicating visible neurons
trainingWindow % start and endtimes for training data
validationWindow % start and endtime for validation data
end
properties (Hidden)
ensCovColl; % a covariate collection of neighboring neuron spiking activity
end
methods
function tObj=Trial(nspikeColl, covarColl, event, hist,ensCovHist,ensCovMask)
% tObj=Trial(nspikeColl, covarColl, event, hist,ensCovHist)
% nspikeColl: is an <a href="matlab:help('nstColl')">nstColl</a> object
% containing all of the relevant spike trains for this experimental trial.
% covarColl: is a <a href="matlab:help('CovColl')">CovColl</a>
% object containing all of the covariates associated with the trial
% event: <a href="matlab:help('Events')">Events</a> object.
% hist: <a href="matlab:help('History')">History</a>
% object for any given spike train
% ensCovHist:<a href="matlab:help('History')">History</a>
% object that specifies how much history to include for the ensemble effect.
if(nargin<6)
nSpikes = nspikeColl.numSpikeTrains;
ensCovMask = ones(nSpikes,nSpikes)-eye(nSpikes,nSpikes);
end
if(nargin<5)
ensCovHist = [];
end
if(nargin<4)
hist = [];
end
if(nargin<3)
event=[];
end
if(isa(nspikeColl,'nstColl'))
tObj.nspikeColl = nspikeColl;
else
error('nstColl is a required argument');
end
if(isa(covarColl,'CovColl'))
tObj.covarColl = covarColl;
else
error('CovColl is a required argument');
end
tObj.setTrialEvents(event);
if(isa(hist,'History') || isa(hist,'double'))
tObj.setHistory(hist);
else
tObj.history = [];
end
if(isa(ensCovHist,'History')|| isa(ensCovHist,'double'))
tObj.setEnsCovHist(ensCovHist);
else
tObj.ensCovHist=[];
end
tObj.covMask = covarColl.covMask;
tObj.neuronMask = nspikeColl.neuronMask;
tObj.ensCovMask = ensCovMask;
if(~tObj.isSampleRateConsistent)
tObj.makeConsistentSampleRate;
else
tObj.sampleRate = tObj.covarColl.sampleRate;
end
tObj.makeConsistentTime;
tObj.setTrialPartition([]); %default to all training data
tObj.setTrialTimesFor('training');
% tObj.setBatchMode('off'); % Turn batchMode off by default
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Set functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function setTrialEvents(tObj,event)
if(isa(event,'Events'))
tObj.ev = event;
else
tObj.ev = [];
end
end
%
% function setBatchMode(tObj,mode)
% if(isa(mode,'char'))
% if(strcmp(mode,'on'))
% tObj.batchMode = 'on';
% elseif(strcmp(mode,'off'));
% tObj.batchMode ='off';
% end
% elseif(isa(mode,'double'))
% if(mode==1)
% tObj.batchMode='on';
% elseif(mode==0)
% tObj.batchMode='off';
% end
% end
%
% end
%
function setTrialPartition(tObj,partitionTimes)
%partitionTimes is an array containing three or four elements
% 4 elements :[startTraining stopTraining startValidation endValidation]
% 3 elements [startTraining stopTraining/startValidation endValidation]
% the middle value is used for end of training period and start
% of validation period
if(nargin<2 || isempty(partitionTimes))
partitionTimes = tObj.getTrialPartition;
end
if(~isempty(partitionTimes))
%partitionTimes = round(partitionTimes*tObj.sampleRate/10)./tObj.sampleRate*10; %make sure that the partition time are achievable at the current sampleRate;
% if sampleRate is 100 then we multiply partionTimes by 10
% and so we keep only the first decimal place.
if(length(partitionTimes)==4)
trainingWindow = [partitionTimes(1) partitionTimes(2)];
validationWindow = [partitionTimes(3) partitionTimes(4)];
elseif(length(partitionTimes)==3)
trainingWindow = [partitionTimes(1) partitionTimes(2)];
validationWindow = [partitionTimes(2) partitionTimes(3)];
else
error('partitionTimes must be length 3 or 4');
end
tObj.trainingWindow = trainingWindow;
tObj.validationWindow = validationWindow;
tObj.setMinTime(trainingWindow(1));
tObj.setMaxTime(trainingWindow(2)); %default to ready for training
end
end
function p=getTrialPartition(tObj)
p1=tObj.trainingWindow;
p2=tObj.validationWindow;
p=[p1 p2];
if(isempty(p))
p1 = [tObj.minTime, tObj.maxTime];
p2 = [tObj.maxTime, tObj.maxTime];
p = [p1, p2];
end
end
function setTrialTimesFor(tObj,partitionName)
if(nargin<2)
partitionName = 'training';
end
p = tObj.getTrialPartition;
if(strcmp(partitionName,'training'))
timeWindow = p(1:2);
elseif(strcmp(partitionName,'validation'))
timeWindow = p(3:4);
else
error('partitionName must be either training or validation');
end
if(~isempty(timeWindow))
%currSamplingRate = tObj.sampleRate;
%tObj.restoreToOriginal;
%tObj.resample(currSamplingRate);
tObj.setMinTime(timeWindow(1));
tObj.setMaxTime(timeWindow(2));
% tObj.makeConsistentTime;
% tObj.restoreToOriginal;
% tObj.resample(currSamplingRate);
end
end
function setMinTime(tObj,minTime)
% setMinTime(tObj,minTime)
% sets the minTime of interest of the trial to minTime
if(nargin<2)
minTime = tObj.findMinTime;
end;
tObj.nspikeColl.setMinTime(minTime);
tObj.covarColl.setMinTime(minTime);
if(~isempty(tObj.ensCovColl))
tObj.ensCovColl.setMinTime(minTime);
end
%tObj.covarColl.covArray{1}.standardRep
tObj.minTime = minTime;
end
function setMaxTime(tObj,maxTime)
% setMaxTime(tObj,maxTime)
% sets the maxTime of interest of the trial to maxTime
if(nargin<2)
maxTime=tObj.findMaxTime;
end
tObj.nspikeColl.setMaxTime(maxTime);
tObj.covarColl.setMaxTime(maxTime);
if(~isempty(tObj.ensCovColl))
tObj.ensCovColl.setMaxTime(maxTime);
end
tObj.maxTime = maxTime;
end
function updateTimePartitions(tObj)
if((~isempty(tObj.minTime))&& (~isempty(tObj.maxTime))) %avoid calling before maxTime and minTime are set
p = tObj.getTrialPartition;
if(~isempty(p))
training = p(1:2);
validation=p(3:end);
newTrainMin = max(tObj.minTime,training(1));
newTrainMax = min(tObj.maxTime,training(2));
newValMin = max(tObj.minTime,validation(1));
newValMax = min(tObj.maxTime,validation(2));
tObj.setTrialPartition([newTrainMin newTrainMax newValMin newValMax]);
end
end
end
function setSampleRate(tObj,sampleRate)
% setSampleRate(tObj,sampleRate)
% sets the sampleRate of the trial and all of its components to
% sampleRate
tObj.sampleRate=sampleRate;
tObj.nspikeColl.resample(sampleRate);
tObj.covarColl.resample(sampleRate);
tObj.resampleEnsColl;
end
function setEnsCovMask(tObj,mask)
% setEnsCovMask(tObj, mask)
% sets the mask of neighboring neurons to be considered when
% ensCovHist is set
if(nargin<2 || isempty(mask))
nSpikes = tObj.nspikeColl.numSpikeTrains;
mask = ones(nSpikes,nSpikes)-eye(nSpikes,nSpikes);
end
tObj.ensCovMask=mask; % needs to be a nSpikeTrain x nSpikeTrain matrix with zeroes along diagonal if all neurons are possible neighbors
end
function setCovMask(tObj,mask)
% setCovMask(tObj,mask)
% sets the covariate mask of the trial and of the covColl to
% mask
if(isa(mask,'char'))
if(strcmp(mask,'all'));
tObj.covarColl.resetMask;
end
else
tObj.covarColl.setMask(mask);
end
tObj.covMask = tObj.covarColl.covMask;
end
function setNeuronMask(tObj,mask)
% setNeuronMask(tObj,mask)
% sets the neuron mask of the trial and of the nstColl to mask
tObj.nspikeColl.setMask(mask);
tObj.neuronMask = tObj.nspikeColl.neuronMask;
end
function setNeighbors(tObj,varargin)
tObj.nspikeColl.setNeighbors(varargin{:});
end
function setHistory(tObj,hist)
% setHistory(tObj,hist)
% sets the history object of the trial to hist.
% hist can be of class History or a vector of doubles
% specifying the windowTimes for the History object
if(isempty(hist))
tObj.history = [];
else
if(isa(hist,'History'))
tObj.history = hist;
elseif(isa(hist,'cell'))
if(isa(hist{1},'History'))
tObj.history = hist;
end
elseif(isa(hist,'double')) %then we got windowTimes
[l,w]=size(hist);
if(min(l,w)>1)
error('Only one of the dimension of the windowTimes can be greater than 1.');
else
if(length(hist)>1)
tObj.history = History(hist);
else
error('At least two times points must be specified to determine a window');
end
end
else
error('Can only set trial history to be an instantiation of the History object class or by using windowTimes');
end
end
end
function setEnsCovHist(tObj, hist)
% ensCovHist(tObj,hist)
% sets the ensCovHist of the trial to hist.
% hist can be of class History or a vector of doubles
% specifying the windowTimes for the History object
if(nargin<2)
hist =[];
end
if(isempty(hist))
tObj.ensCovHist = [];
tObj.ensCovColl = [];
else
if(isa(hist,'History'))
tObj.ensCovHist = hist;
elseif(isa(hist,'double')) %then we got windowTimes
[l,w]=size(hist);
if(min(l,w)>1)
error('Only one of the dimension of the windowTimes can be greater than 1.');
else
if(length(hist)>1)
tObj.ensCovHist = History(hist);
else
error('At least two times points must be specified to determine a window');
end
end
else
error('Can only set trial ensCovHist to be an instantiation of the History object class or by using windowTimes');
end
% getEnsembleNeuronCovariates(nstCollObj,neuronNum,neighbor
% Index,windowTimes)
tObj.ensCovColl=tObj.getEnsembleNeuronCovariates(1,[],tObj.ensCovHist);
% initialize ensCovColl to first neuron and its default
% neighbors
end
end
function answer=isNeuronMaskSet(tObj)
% answer=isNeuronMaskSet(tObj)
% 1 if neuronMask of nstColl is set;
% 0 otherwise
answer=tObj.nspikeColl.isNeuronMaskSet;
end
function answer=isCovMaskSet(tObj)
% answer=isCovMaskSet(tObj)
% 1 if the covMask of the covColl is set
% 0 otherwise
answer = tObj.covarColl.isCovMaskSet;
end
function answer=isMaskSet(tObj)
% answer=isMaskSet(tObj)
% 1 if either neuronMask or covMask is set for the trial
% 0 if neither is set
answer=tObj.isNeuronMaskSet||tObj.isCovMaskSet;
end
function answer=isHistSet(tObj)
% answer=isHistSet(tObj)
% 1 if the history object of the trial is non-empty
% 0 if it is empty
if(~isempty(tObj.history))
if(isa(tObj.history,'History'))
answer =1;
elseif(isa(tObj.history,'cell'))
if(isa(tObj.history{1},'History'))
answer=1;
else
answer=0;
end
end
else
answer =0;
end
end
function answer=isEnsCovHistSet(tObj)
% answer=isHistSet(tObj)
% 1 if the history object of the trial is non-empty
% 0 if it is empty
answer=(~isempty(tObj.ensCovHist))&& (isa(tObj.ensCovHist,'History'));
end
function addCov(tObj,cov)
tObj.covarColl.addToColl(cov);
tObj.covMask = tObj.covarColl.covMask;
if(~tObj.isSampleRateConsistent)
tObj.makeConsistentSampleRate;
end
tObj.makeConsistentTime;
end
function removeCov(tObj,identifier)
tObj.covarColl.removeCovariate(identifier);
tObj.covMask = tObj.covarColl.covMask;
if(~tObj.isSampleRateConsistent)
tObj.makeConsistentSampleRate;
end
tObj.makeConsistentTime;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Get functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function y=getSpikeVector(tObj,varargin)
% y=getSpikeVector(tObj,varargin)
% returns a matrix of spikes by calling the dataToMatrix method
% of nstColl with any arguments passed in
% see also <a href="matlab:help('nstColl.dataToMatrix')">nstColl.dataToMatrix</a>,
y=tObj.nspikeColl.dataToMatrix(varargin{:});
end
function X=getDesignMatrix(tObj,neuronNum,varargin)
% X=getDesignMatrix(tObj,varargin)
% returns a matrix X by calling covColl.getDataMatrix with repType='zero-mean'
% along with any parameters passed in.
%
% dataSelector must be in the following format
% dataSelector{1} = {'Position','x','y'};
% dataSelector{2} = {'Force','fx','fy','fz'};
% see also <a
% href="matlab:help('covColl.dataToMatrix')">covColl.dataToMatrix</a>
if(nargin<2)
error('Must specify neuronNumber to getDesignMatrix!');
end
%repType='zero-mean';
repType='standard';
X=tObj.covarColl.dataToMatrix(repType,varargin{:});
if(tObj.isHistSet)
H=tObj.getHistMatrices(neuronNum);
if(isempty(X))
X=H;
else
X=[X,H];
end
end
if(tObj.isEnsCovHistSet)
includedNeurons = find(tObj.ensCovMask(:,neuronNum)==1);
E = tObj.getEnsCovMatrix(neuronNum,includedNeurons);
if(isempty(X))
X=E;
else
X=[X,E];
end
end
end
function ensCovMatOut = getEnsCovMatrix(tObj,neuronNum,includedNeurons,varargin)
if(nargin<3)
includedNeurons = find(tObj.ensCovMask(:,neuronNum)==1);
end
if(nargin<2)
error('Must specify neuronNumber to get the right neighbors!');
end
if(tObj.isEnsCovHistSet && ~isempty(neuronNum))
if(neuronNum>0 && neuronNum<=tObj.nspikeColl.numSpikeTrains)
ensCovCollTemp = tObj.ensCovColl;
neighbors = tObj.getNeuronNeighbors(neuronNum);
neighbors = intersect(neighbors, includedNeurons);
ensCovCollTemp.maskAwayAllExcept(neighbors);
repType='standard';
ensCovMatOut=ensCovCollTemp.dataToMatrix(repType,varargin{:});
end
else
ensCovMatOut=[]; %dont return matrix if neuron number not specified.
% display('isEnsCovEnabled=0 - empty matrix returnecd');
end
end
function histCovColl = getHistForNeurons(tObj,neuronIndex)
% histCovColl = getHistForNeurons(tObj,neuronIndex)
% returns a CovColl with number of Covariates equal to the
% length of neuronIndex.
% CovColl.getCov(i) corresponds to the History for the
% nspikeTrain corresponding to neuronIndex(i)
%
if(tObj.isHistSet)
nst=tObj.nspikeColl.getNST(neuronIndex);
if(length(neuronIndex)>=1)
if(isa(tObj.history,'History'))
histCovColl = tObj.history.computeHistory(nst);
elseif(isa(tObj.history,'cell'))
for i=1:length(tObj.history)
if(i==1)
histCovColl = tObj.history{1}.computeHistory(nst,i);
% histCovColl.getCov
else
tempHistCovColl = tObj.history{i}.computeHistory(nst,i);
histCovColl.addToColl(tempHistCovColl);
end
end
end
end
else
histCovColl = []; %returns an empty array to indicate no History
display('History has not been specified. Empty array returned');
display('Set Trial history and retry');
end
if(~isempty(histCovColl))
if(isa(nst,'cell'))
histCovColl.setSampleRate(nst{1}.sampleRate);
else
histCovColl.setSampleRate(nst.sampleRate);
end
end
end
function matrices = getHistMatrices(tObj,neuronIndex)
% matrices = getHistMatrices(tObj,neuronIndex)
% return a matrix representation of the History Covariates
% corresponding the History object specified for the trial and
% the data corresponding to neuronIndex
if(tObj.isHistSet)
histCovColl = tObj.getHistForNeurons(neuronIndex);
matrices = cell(1,length(neuronIndex));
if(length(histCovColl)==1)
matrices = histCovColl.dataToMatrix;
else
for i=1:length(histCovColl)
matrices{i} = histCovColl{i}.dataToMatrix;
end
end
else
matrices = cell(1, length(neuronIndex));
for i=1:length(neuronIndex)
matrices{i} = zeros(length(tObj.nspikeColl.getNST(neuronIndex(i)).sigRep.time),0);
end
end
end
function ensembleCovariates = getEnsembleNeuronCovariates(tObj,varargin)
% getEnsembleNeuronCovariates(nstCollObj,neuronNum,neighborIndex,windowTimes)
ensembleCovariates = tObj.nspikeColl.getEnsembleNeuronCovariates(varargin{:});
end
function index = getNeuronIndFromMask(tObj)
% index = getNeuronIndFromMask(tObj)
% see also <a href="matlab:help('nstColl.getIndFromMask')">nstColl.getIndFromMask</a>
index=tObj.nspikeColl.getIndFromMask;
end
function num = getNumUniqueNeurons(tObj)
num = length(tObj.nspikeColl.uniqueNeuronNames);
end
function names = getNeuronNames(tObj)
names = tObj.nspikeColl.getNSTnames;
end
function unames = getUniqueNeuronNames(tObj)
unames = tObj.nspikeColl.getUniqueNSTnames;
end
function index = getNeuronIndFromName(tObj,neuronName)
tempInd = tObj.nspikeColl.getNSTIndicesFromName(neuronName);
currMask = find(tObj.neuronMask==1);
if(isa(tempInd,'cell'))
for i=1:length(tempInd)
index{i} = intersect(currMask,tempInd{i});
end
elseif(isa(tempInd,'double'))
index = intersect(currMask,tempInd);
end
end
function n = getNeuronNeighbors(tObj, neuronNum)
if(nargin<2)
neuronNum = tObj.getNeuronIndFromMask;
end
n=tObj.nspikeColl.getNeighbors(neuronNum);
end
function selector = getCovSelectorFromMask(tObj)
% selector = getCovSelectorFromMask(tObj)
% see also <a href="matlab:help('CovColl.getSelectorFromMasks')">CovColl.getSelectorFromMasks</a>
selector = tObj.covarColl.getSelectorFromMasks;
end
function cov = getCov(tObj,identifier)
% cov = getCov(tObj,identifier)
% see also <a href="matlab:help('CovColl.getCov')">CovColl.getCov</a>
cov=tObj.covarColl.getCov(identifier);
end
function NST = getNeuron(tObj,identifier)
% NST = getNeuron(tObj,identifier)
% see also <a href="matlab:help('nstColl.getNST')">nstColl.getNST</a>
NST = tObj.nspikeColl.getNST(identifier);
end
function e = getEvents(tObj)
% e = getEvents(tObj)
% Returns the Events object associated with the Trial
% e is either [] or an Events object
e=tObj.ev;
end
function l = getAllLabels(tObj)
l=tObj.getAllCovLabels;
offset=length(l);
if(tObj.isHistSet)
l2=tObj.getHistLabels;
for i=1:length(l2)
l{offset+i} = l2{i};
end
end
offset=length(l);
if(tObj.isEnsCovHistSet)
l3=tObj.getEnsCovLabels;
for i=1:length(l3)
l{offset+i} = l3{i};
end
end
end
function n = getNumHist(tObj)
% n = getNumHist(tObj)
% Return the number of history windows (length windowTimes - 1)
if(tObj.isHistSet)
if(isa(tObj.history,'History'))
n=length(tObj.history.windowTimes)-1;
elseif(and(isa(tObj.history,'cell'),isa(tObj.history{1},'History')))
for i=1:length(tObj.history)
n(i) = length(tObj.history{i}.windowTimes)-1;
end
end
else
n=0;
end
end
function l = getAllCovLabels(tObj)
l =tObj.covarColl.getAllCovLabels;
end
function l = getCovLabelsFromMask(tObj)
l = tObj.covarColl.getCovLabelsFromMask;
end
function l = getHistLabels(tObj)
if(tObj.isHistSet)
histCovColl = tObj.getHistForNeurons(1);
if(isa(histCovColl,'Covariate'))
l= histCovColl.dataLabels;
elseif(isa(histCovColl,'CovColl'))
l = histCovColl.getAllCovLabels;
else
error('histCovColl must be either a Covariate or a CovColl');
end
else
l={};
end
end
function l = getEnsCovLabels(tObj)
if(tObj.isEnsCovHistSet)
ensCovCollTemp = tObj.ensCovColl;
if(isa(ensCovCollTemp,'CovColl'))
% if(nargin<2)
% %l = ensCovCollTemp.getCovLabelsFromMask; %return labels from current mask
% getAllCovLabels
% else %set mask and return those labels
% ensCovCollTemp.maskAwayAllExcept(tObj.getNeuronNeighbors(neuronNum));
% l = ensCovCollTemp.getCovLabelsFromMask;
% end
l=ensCovCollTemp.getAllCovLabels;
else
error('ensCovColl must be either a CovColl');
end
else
l={};
end
end
function l = getEnsCovLabelsFromMask(tObj,neuronNum)
if(nargin<2)
error('Must specify neuron number!');
end
if(isa(neuronNum,'char'))
tempIndices = tObj.getNeuronIndFromName(neuronNum);
if(length(tempIndices)>1)
neuronNum=tempIndices(1);
else
neuronNum=tempIndices;
end
end
if(tObj.isEnsCovHistSet)
ensCovCollTemp = tObj.ensCovColl;
if(isa(ensCovCollTemp,'CovColl'))
% if(nargin<2)
% %l = ensCovCollTemp.getCovLabelsFromMask; %return labels from current mask
% getAllCovLabels
% else %set mask and return those labels
% ensCovCollTemp.maskAwayAllExcept(tObj.getNeuronNeighbors(neuronNum));
% l = ensCovCollTemp.getCovLabelsFromMask;
% end
if(neuronNum>0 && neuronNum<=tObj.nspikeColl.numSpikeTrains)
ensCovCollTemp.maskAwayAllExcept(tObj.getNeuronNeighbors(neuronNum));
l=ensCovCollTemp.getCovLabelsFromMask;
else
error('NeuronNum is out of bounds!');
end
else
error('ensCovColl must be either a CovColl');
end
else
l={};
end
end
function l = getLabelsFromMask(tObj,neuronNum)
if(nargin<2)
error('To get right labels need to specify neuronNum'); %because of ensemble
end
l = tObj.getCovLabelsFromMask;
offset=length(l);
if(tObj.isHistSet)
l2=tObj.getHistLabels;
l((1:length(l2))+offset) = l2;
end
offset=length(l);
if(tObj.isEnsCovHistSet)
l3=tObj.getEnsCovLabelsFromMask(neuronNum);
l((1:length(l3))+offset) = l3;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Utility functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function shiftTrial(tObj,deltaT)
% tObj.covarColl.shiftCovariates(deltaT);
% tObj.nspikeColl.shift(deltaT);
% tObj.makeConsistentTime;
% tObj.setTrialPartition([]); %default to all training data
% %tObj.setTrialTimesFor('training');
% end
%
function flatMask=flattenCovMask(tObj)
flatMask = tObj.covarColl.flattenCovMask;
% if(tObj.isHistSet)
% histCovColl = tObj.getHistForNeurons(1);
% if(isa(histCovColl,'Covariate'))
% flatMask = [flatMask histCovColl.dataMask];
% elseif(isa(histCovColl,'CovColl'))
% flatMask = [flatMask histCovColl.flattenCovMask];
% else
% error('histCovColl must be either a Covariate or a CovColl');
% end
% end
end
function flatMask=flattenMask(tObj)
flatMask = tObj.flattenCovMask;
if(tObj.isHistSet)
histCovColl = tObj.getHistForNeurons(1);
if(isa(histCovColl,'Covariate'))
flatMask = [flatMask histCovColl.dataMask];
elseif(isa(histCovColl,'CovColl'))
flatMask = [flatMask histCovColl.flattenCovMask];
else
error('histCovColl must be either a Covariate or a CovColl');
end
end
if(tObj.isEnsCovHistSet)
ensCovCollTemp = tObj.ensCovColl;
if(isa(ensCovCollTemp,'CovColl'))
flatMask = [flatMask ensCovCollTemp.flattenCovMask];
else
error('ensCovCollTemp must be either a CovColl');
end
end
end
function shiftCovariates(tObj, varargin)
tObj.covarColl.setCovShift(varargin{:});
tObj.makeConsistentTime;
end
function resetEnsCovMask(tObj)
nSpikes = tObj.nspikeColl.numSpikeTrains;
tObj.ensCovMask = ones(nSpikes,nSpikes)-eye(nSpikes,nSpikes);
end
function resetCovMask(tObj)
% resetCovMask(tObj)
% see also <a href="matlab:help('CovColl.resetMask')">CovColl.resetMask</a>
tObj.covarColl.resetMask;
end
function resetNeuronMask(tObj)
% resetNeuronMask(tObj)
% see also <a href="matlab:help('nstColl.resetMask')">nstColl.resetMask</a>
tObj.nspikeColl.resetMask;
end
function resetHistory(tObj)
% resetHistory(tObj)
% Sets the History object associated with this Trial equal to
% the empty array [].
tObj.history = [];
end
% function shiftCovs(tObj, covLag)
% % shiftCovs(tObj, covLag)
% % if covLag is not specified or isempty ([]) all covariates are
% % href="matlab:help('CovColl.restoreToOriginal')">CovColl.restoreToOriginal</a>
% % If specified, all covariates are shifted by covLag
% % href="matlab:help('Covariate.shift')">Covariate.shift</a>
% %
% % The trial minTime and maxTime properties are updated to
% % reflect any changes.
%
% if(nargin<2)
% covLag = [];
% end
% if(isempty(covLag))
% for i =1:tObj.covarColl.numCov
% tCov = tObj.covarColl.getCov(i);
% tCov.restoreToOriginal;
% end
% else
% for i =1:tObj.covarColl.numCov
% tCov = tObj.covarColl.getCov(i);
% tCov.shift(covLag);
% end
% end
% tObj.makeConsistentTime;
% end
function resample(tObj,sampleRate)
% resample(tObj,sampleRate)
% calls <a
% href="matlab:help('Trial.setSampleRate')">Trial.setSampleRate</a>
tObj.setSampleRate(sampleRate);
end
function resampleEnsColl(tObj)
if(~isempty(tObj.ensCovColl) && ~isempty(tObj.ensCovHist))
tObj.ensCovColl=tObj.getEnsembleNeuronCovariates(1,[],tObj.ensCovHist);
else
tObj.setEnsCovHist; %set to empty;
end
end
function restoreToOriginal(tObj)
% restoreToOriginal(tObj)
% calls <a
% href="matlab:help('nstColl.restoreToOriginal')">nstColl.resto
% reToOriginal</a>, <a
% href="matlab:help('CovColl.restoreToOriginal')">CovColl.restoreToOriginal</a>
% resets minTime and maxTime of the Trial according to the
% changes that happened when restoring the nstColl and CovColl
% to their original states.
tObj.nspikeColl.restoreToOriginal;
tObj.covarColl.restoreToOriginal;
if(~tObj.isSampleRateConsistent)
tObj.makeConsistentSampleRate;
end
tObj.resampleEnsColl; % compute at the new sampling rate
tObj.makeConsistentTime;
end
function makeConsistentSampleRate(tObj)
tObj.resample(tObj.findMaxSampleRate);
end
function makeConsistentTime(tObj)
tObj.setMinTime;
tObj.setMaxTime;
%tObj.setTrialPartition;
%tObj.updateTimePartitions;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Plot functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function plotRaster(tObj,handle)
if(nargin<2)
handle =gcf;
end
end
function plotCovariates(tObj,handle)
if(nargin<2)
handle =gcf;
end
numCovars = tObj.covarColl.nActCovar; %accounts for masks
figure(handle);
if(numCovars==1)
tObj.covarColl.plot; hold on;
if(~isempty(tObj.ev))
tObj.ev.plot;
end
elseif(numCovars==2)
a1=subplot(1,2,1);
a2=subplot(1,2,2);
tObj.covarColl.plot([a1,a2]); hold on;
if(~isempty(tObj.ev))
tObj.ev.plot([a1,a2]);
end
elseif(numCovars==3)
a1=subplot(3,2,[1 3 5]);
tObj.nspikeColl.plot;hold on;
a2=subplot(3,2,2); a3=subplot(3,2,4); a4=subplot(3,2,6);
tObj.covarColl.plot([a2,a3 a4]); hold on;
if(~isempty(tObj.ev))
tObj.ev.plot([a1,a2,a3,a4]);
end
else
figure(handle);
tObj.nspikeColl.plot; hold on;
if(~isempty(tObj.ev))
tObj.ev.plot;
end
figure;
tObj.covarColl.plot; hold on;
if(~isempty(tObj.ev))
tObj.ev.plot;
end
end
end
function plot(tObj,handle)
% plot(tObj,handle)
% plots the Trial on the figure handle specified.
% if handle is not specified, then handle = gcf;
if(nargin<2)
handle =gcf;
end
numCovars = tObj.covarColl.nActCovar; %accounts for masks
figure(handle);
if(numCovars==1)
a1=subplot(2,2,[1 3]);
tObj.nspikeColl.plot;hold on;
a2=subplot(2,2,[2 4]);
tObj.covarColl.plot(a2); hold on;
if(~isempty(tObj.ev))
tObj.ev.plot([a1,a2]);
end
elseif(numCovars==2)
a1=subplot(2,2,[1 3]);
tObj.nspikeColl.plot;hold on;
a2=subplot(2,2,2); a3=subplot(2,2,4);
tObj.covarColl.plot([a2,a3]); hold on;
if(~isempty(tObj.ev))
tObj.ev.plot([a1,a2,a3]);
end
elseif(numCovars==3)
a1=subplot(3,2,[1 3 5]);
tObj.nspikeColl.plot;hold on;
a2=subplot(3,2,2); a3=subplot(3,2,4); a4=subplot(3,2,6);
tObj.covarColl.plot([a2,a3 a4]); hold on;
if(~isempty(tObj.ev))
tObj.ev.plot([a1,a2,a3,a4]);
end
else
figure(handle);
tObj.nspikeColl.plot; hold on;
if(~isempty(tObj.ev))
tObj.ev.plot;
end
figure;
tObj.covarColl.plot; hold on;
if(~isempty(tObj.ev))
tObj.ev.plot;
end
end
%make sure events plot on every covariate plot
%add events and covariate labels to the legend or mark events
%with text on the screen.
end