Large data files removed

iahncajigas · Dec 22, 2013 · 6afebe5 · 6afebe5
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diff --git a/.gitattributes b/.gitattributes
@@ -0,0 +1,2 @@
+# Disable LF normalization for all files
+* -text
diff --git a/Analysis.m b/Analysis.m
diff --git a/CIF.m b/CIF.m
@@ -249,8 +249,8 @@
                 elseif(strcmp(fitType,'binomial'))
                     cifObj.lambdaDelta = simplify(exp(beta*cifObj.varIn  + cifObj.histCoeffs*cifObj.histVars)./(1+exp(beta*cifObj.varIn  + cifObj.histCoeffs*cifObj.histVars)));
                     cifObj.lambdaDeltaGamma = simplify(exp(beta*cifObj.varIn  + cifObj.histCoeffVars*cifObj.histVars)./(1+exp(beta*cifObj.varIn  + cifObj.histCoeffVars*cifObj.histVars)));
-                    cifObj.lambdaDeltaFunction = matlabFunction(cifObj.lambdaDelta,'vars',[cifObj.varIn; cifObj.histVars]);
-                    cifObj.lambdaDeltaGammaFunction = matlabFunction(cifObj.lambdaDeltaGamma,'vars',[cifObj.varIn; cifObj.histVars; histCoeffsVarsTrans]);
+                    cifObj.lambdaDeltaFunction = matlabFunction(cifObj.lambdaDelta,'vars',symvar([cifObj.varIn; cifObj.histVars]));
+                    cifObj.lambdaDeltaGammaFunction = matlabFunction(cifObj.lambdaDeltaGamma,'vars',symvar([cifObj.varIn; cifObj.histVars; histCoeffsVarsTrans]));
                 end
 
 

diff --git a/ConfidenceInterval.m b/ConfidenceInterval.m
diff --git a/ConfigColl.m b/ConfigColl.m
diff --git a/CovColl.m b/CovColl.m
diff --git a/Covariate.m b/Covariate.m
diff --git a/DecodingAlgorithms.m b/DecodingAlgorithms.m
@@ -38,29 +38,29 @@
         %all of the computations for the PPAF are done symbolically based
         %on the CIF object. However, it also means that this version is
         %must slower than the linear version below.
-        function [x_p, W_p, x_u, W_u, x_uT,W_uT,x_pT, W_pT] = PPDecodeFilter(A, Q, dN,lambdaCIFColl,binwidth,x0,Pi0, yT,PiT,estimateTarget)  
+        function [x_p, W_p, x_u, W_u, x_uT,W_uT,x_pT, W_pT] = PPDecodeFilter(A, Q, Px0, dN,lambdaCIFColl,binwidth,x0,Pi0, yT,PiT,estimateTarget)  
             % A can be static or can be a different matrix for each time N
 
             [C,N]   = size(dN); % N time samples, C cells
 
             ns=size(A,1); % number of states
 
 
-            if(nargin<11 || isempty(estimateTarget))
+            if(nargin<12 || isempty(estimateTarget))
                 estimateTarget=0;
             end
 
-            if(nargin<10 || isempty(PiT))
+            if(nargin<11 || isempty(PiT))
                 if(estimateTarget==1)
                     PiT = zeros(size(Q));
                 else
                     PiT = 0*diag(ones(ns,1))*1e-6;
                 end
             end
-            if(nargin<8 || isempty(Pi0))
+            if(nargin<9 || isempty(Pi0))
                 Pi0 = zeros(ns,ns);
             end
-            if(nargin<9 || isempty(yT))
+            if(nargin<10 || isempty(yT))
                 yT=[];
                 Amat = A;
                 Qmat = Q;
@@ -136,11 +136,11 @@
 
             end
 
-            if(nargin<7 || isempty(x0))
+            if(nargin<8 || isempty(x0))
                 x0=zeros(size(A,2),1);
             end
 
-            if(nargin<6)
+            if(nargin<7)
                 binwidth = .001; % in seconds
             end
 

diff --git a/Events.m b/Events.m
diff --git a/FitResSummary.m b/FitResSummary.m
diff --git a/FitResult.m b/FitResult.m
diff --git a/History.m b/History.m
diff --git a/PointProcessSimulation.mdl.r2010b b/PointProcessSimulation.mdl.r2010b
diff --git a/PointProcessSimulation.mdl.r2011a b/PointProcessSimulation.mdl.r2011a
diff --git a/PointProcessSimulation.mdl.r2011b b/PointProcessSimulation.mdl.r2011b
diff --git a/PointProcessSimulationThinning.mdl b/PointProcessSimulationThinning.mdl
diff --git a/README.txt b/README.txt
diff --git a/SignalObj.m b/SignalObj.m
@@ -1294,7 +1294,9 @@ function setMask(sObj, mask)
             %sObjOut is a copy of sObj with the newSampleRate specified;
             if(sObj.sampleRate ~=newSampleRate)
                 sObjOut = sObj.copySignal;
-                sObjOut.resampleMe(newSampleRate);
+                if(or(~isnan(sObjOut.sampleRate),size(sObjOut.data,1)>1))
+                    sObjOut.resampleMe(newSampleRate);
+                end
             else
                 sObjOut = sObj.copySignal;
             end

diff --git a/Trial.m b/Trial.m
diff --git a/TrialConfig.m b/TrialConfig.m
diff --git a/data/Explicit Stimulus/Dir16/Neuron1/Stim1/testdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron1/Stim1/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron1/Stim1/trngdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron1/Stim1/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron1/Stim2/testdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron1/Stim2/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron1/Stim2/trngdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron1/Stim2/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron1/Stim3/testdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron1/Stim3/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron1/Stim3/trngdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron1/Stim3/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron2/Stim1/testdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron2/Stim1/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron2/Stim1/trngdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron2/Stim1/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron2/Stim2/testdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron2/Stim2/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron2/Stim2/trngdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron2/Stim2/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron2/Stim3/testdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron2/Stim3/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir16/Neuron2/Stim3/trngdataBis.mat b/data/Explicit Stimulus/Dir16/Neuron2/Stim3/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim1/testdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron1/Stim1/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim1/trngdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron1/Stim1/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim2/Experiment2-Data.eps b/data/Explicit Stimulus/Dir3/Neuron1/Stim2/Experiment2-Data.eps
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim2/Experiment2-Results.eps b/data/Explicit Stimulus/Dir3/Neuron1/Stim2/Experiment2-Results.eps
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim2/testdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron1/Stim2/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim2/trngdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron1/Stim2/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim3/testdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron1/Stim3/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron1/Stim3/trngdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron1/Stim3/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron2/Stim1/testdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron2/Stim1/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron2/Stim1/trngdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron2/Stim1/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron2/Stim2/testdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron2/Stim2/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron2/Stim2/trngdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron2/Stim2/trngdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron2/Stim3/testdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron2/Stim3/testdataBis.mat
diff --git a/data/Explicit Stimulus/Dir3/Neuron2/Stim3/trngdataBis.mat b/data/Explicit Stimulus/Dir3/Neuron2/Stim3/trngdataBis.mat
diff --git a/data/Explicit Stimulus/GenCovMat.m b/data/Explicit Stimulus/GenCovMat.m
diff --git a/data/PSTH/Data Description.pdf b/data/PSTH/Data Description.pdf
diff --git a/data/PSTH/Results.mat b/data/PSTH/Results.mat
diff --git a/data/Place Cells/PlaceCellDataAnimal1.mat b/data/Place Cells/PlaceCellDataAnimal1.mat
diff --git a/data/Place Cells/PlaceCellDataAnimal2.mat b/data/Place Cells/PlaceCellDataAnimal2.mat
diff --git a/data/Place Cells/Readme.txt b/data/Place Cells/Readme.txt
diff --git a/data/Place Cells/license.txt b/data/Place Cells/license.txt
diff --git a/data/glm_data.mat b/data/glm_data.mat
diff --git a/data/glm_data_orig.mat b/data/glm_data_orig.mat
diff --git a/data/mEPSCs/epsc2.txt b/data/mEPSCs/epsc2.txt
diff --git a/data/mEPSCs/mEPSCAnalysis.asv b/data/mEPSCs/mEPSCAnalysis.asv
diff --git a/data/mEPSCs/washout1.txt b/data/mEPSCs/washout1.txt
diff --git a/data/mEPSCs/washout2.txt b/data/mEPSCs/washout2.txt
diff --git a/helpfiles/Analysis.html b/helpfiles/Analysis.html
diff --git a/helpfiles/AnalysisExamples.html b/helpfiles/AnalysisExamples.html
@@ -6,61 +6,64 @@
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+      --><title>Analysis Examples</title><meta name="generator" content="MATLAB 8.2"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2013-11-20"><meta name="DC.source" content="AnalysisExamples.m"><style type="text/css">
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   </style></head><body><div class="content"><h1>Analysis Examples</h1><!--introduction--><p>This is an example on the standard approach to fitting GLM models to spike train data. This data set was obtained at the Society For Neuroscience '08 Workshop on <a href="http://www.sfn.org/index.aspx?pagename=ShortCourse3_2008">Workshop on Neural Signal Processing</a> Compare to analysis with <a href="matlab:web('AnalysisExamples2.html','-helpbrowser')">Neural Spike Analysis Toolbox</a></p><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Example 1: Tradition Preliminary Analysis</a></li></ul></div><h2>Example 1: Tradition Preliminary Analysis<a name="1"></a></h2><pre class="codeinput"><span class="comment">% Script glm_part1.m</span>
 <span class="comment">% MATLAB code to visualize data, fit a GLM model of the relation between</span>
@@ -138,8 +141,7 @@
 ylabel(<span class="string">'Empirical CDF of Rescaled ISIs'</span>);
 title(<span class="string">'KS Plot with 95% Confidence Intervals'</span>);
 legend(<span class="string">'Linear'</span>,<span class="string">'Quadratic'</span>);
-</pre><img vspace="5" hspace="5" src="AnalysisExamples_04.png" alt=""> <p class="footer"><br>
-      Published with MATLAB&reg; 7.13<br></p></div><!--
+</pre><img vspace="5" hspace="5" src="AnalysisExamples_04.png" alt=""> <p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2013b</a><br></p></div><!--
 ##### SOURCE BEGIN #####
 %% Analysis Examples 
 % This is an example on the standard approach to fitting GLM models to

diff --git a/helpfiles/AnalysisExamples.m b/helpfiles/AnalysisExamples.m
diff --git a/helpfiles/AnalysisExamples.png b/helpfiles/AnalysisExamples.png
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-span.syscmd {color: #B28C00}
 
-pre.codeoutput {
-  color: #666666;
-  padding: 10px;
-}
 
-pre.error {
-  color: red;
-}
 
-p.footer {
-  text-align: right;
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   </style></head><body><div class="content"><h2>Contents</h2><div><ul><li><a href="#1">Analysis Examples 2</a></li><li><a href="#8">Toolbox vs. Standard GLM comparison</a></li><li><a href="#9">Compute the history effect</a></li></ul></div><h2>Analysis Examples 2<a name="1"></a></h2><p>Compare with traditional Neural Spike Train Analysis <a href="matlab:web('AnalysisExamples.html','-helpbrowser')">here</a></p><pre class="codeinput"><span class="comment">% load the rat trajectory and spiking data;</span>
 close <span class="string">all</span>;
@@ -86,7 +89,7 @@
 covarColl = CovColl({baseline,radial});
 trial     = Trial(spikeColl,covarColl);
 clear <span class="string">tc</span>;
-sampleRate=30;
+sampleRate=1000;
 <span class="comment">% tcObj=TrialConfig(covMask,sampleRate, history,minTime,maxTime)</span>
 tc{1} = TrialConfig({{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>},{<span class="string">'Radial'</span>,<span class="string">'x'</span>,<span class="string">'y'</span>}},sampleRate,[]); tc{1}.setName(<span class="string">'Linear'</span>);
 tc{2} = TrialConfig({{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>},{<span class="string">'Radial'</span>,<span class="string">'x'</span>,<span class="string">'y'</span>,<span class="string">'x^2'</span>,<span class="string">'y^2'</span>,<span class="string">'x*y'</span>}},sampleRate,[]); tc{2}.setName(<span class="string">'Quadratic'</span>);
@@ -129,27 +132,32 @@
 </pre><pre class="codeoutput">
 ans =
 
-   -0.0011
-    0.0050
-    0.0034
-    0.0069
-    0.0077
-    0.0165
+    3.5041
+    0.0099
+    0.0102
+    0.0210
+    0.0215
+    0.0172
 
-</pre><h2>Compute the history effect<a name="9"></a></h2><pre class="codeinput">sampleRate=30;  makePlot=1; neuronNum = 1;
-covLabels = {{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>}};
+</pre><h2>Compute the history effect<a name="9"></a></h2><pre class="codeinput">sampleRate=1000;  makePlot=1; neuronNum = 1;
+covLabels = {{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>},{<span class="string">'Radial'</span>,<span class="string">'x'</span>,<span class="string">'y'</span>,<span class="string">'x^2'</span>,<span class="string">'y^2'</span>,<span class="string">'x*y'</span>}};
 Algorithm = <span class="string">'GLM'</span>;
 batchMode=0;
-windowTimes =(0:2:10)./sampleRate;
+windowTimes =(0:1:10)./sampleRate;
+<span class="comment">% [fitResults,tcc] = computeHistLag(tObj,neuronNum,windowTimes,CovLabels,Algorithm,batchMode,sampleRate,makePlot,histMinTimes,histMaxTimes)</span>
 [fitResults,tcc] = Analysis.computeHistLag(trial,neuronNum,windowTimes,covLabels,Algorithm,batchMode,sampleRate,makePlot);
 </pre><pre class="codeoutput">Analyzing Configuration #1: Neuron #1
 Analyzing Configuration #2: Neuron #1
 Analyzing Configuration #3: Neuron #1
 Analyzing Configuration #4: Neuron #1
 Analyzing Configuration #5: Neuron #1
 Analyzing Configuration #6: Neuron #1
-</pre><img vspace="5" hspace="5" src="AnalysisExamples2_04.png" alt=""> <p class="footer"><br>
-      Published with MATLAB&reg; 7.13<br></p></div><!--
+Analyzing Configuration #7: Neuron #1
+Analyzing Configuration #8: Neuron #1
+Analyzing Configuration #9: Neuron #1
+Analyzing Configuration #10: Neuron #1
+Analyzing Configuration #11: Neuron #1
+</pre><img vspace="5" hspace="5" src="AnalysisExamples2_04.png" alt=""> <p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2013b</a><br></p></div><!--
 ##### SOURCE BEGIN #####
 %% Analysis Examples 2
 % Compare with traditional Neural Spike Train Analysis
@@ -196,7 +204,7 @@
 covarColl = CovColl({baseline,radial});
 trial     = Trial(spikeColl,covarColl);
 clear tc;
-sampleRate=30;
+sampleRate=1000;
 % tcObj=TrialConfig(covMask,sampleRate, history,minTime,maxTime)
 tc{1} = TrialConfig({{'Baseline','mu'},{'Radial','x','y'}},sampleRate,[]); tc{1}.setName('Linear');
 tc{2} = TrialConfig({{'Baseline','mu'},{'Radial','x','y','x^2','y^2','x*y'}},sampleRate,[]); tc{2}.setName('Quadratic');
@@ -247,11 +255,12 @@
 
 
 %% Compute the history effect
-sampleRate=30;  makePlot=1; neuronNum = 1;
-covLabels = {{'Baseline','mu'}};
+sampleRate=1000;  makePlot=1; neuronNum = 1;
+covLabels = {{'Baseline','mu'},{'Radial','x','y','x^2','y^2','x*y'}};
 Algorithm = 'GLM';
 batchMode=0;
-windowTimes =(0:2:10)./sampleRate;
+windowTimes =(0:1:10)./sampleRate;
+% [fitResults,tcc] = computeHistLag(tObj,neuronNum,windowTimes,CovLabels,Algorithm,batchMode,sampleRate,makePlot,histMinTimes,histMaxTimes)
 [fitResults,tcc] = Analysis.computeHistLag(trial,neuronNum,windowTimes,covLabels,Algorithm,batchMode,sampleRate,makePlot);
 
 ##### SOURCE END #####