docs for +Utils

+CMBHOME/+Utils/AddChronuxPackage.m

@@ -1,5 +1,5 @@
 function AddChronuxPackage
-
+% Add the chronux pacage
 if ~isempty(findstr('chronux', path)), return; end % check that it isnt already there
 
 a = what('CMBHOME');

+CMBHOME/+Utils/AutoCorrRotation.m

@@ -1,7 +1,4 @@
 function [phi, max_cor, phi_v_cor] = AutoCorrRotation(ac1, ac2, varargin)
-% [phi] = AutoCorrRotation(ac1, ac2);
-% [phi, max_cor, phi_v_cor] = AutoCorrRotation(ac1, ac2);
-%
 % Calculates angle phi for which ac1 and ac2 are maximally correlated.
 % Useful for checking for rotation of grid field orientation between
 % conditions
@@ -35,7 +32,8 @@
 %   3. Finds max of the correlation score, and returns both vectors, as
 %   well.
 %   
-
+% [phi] = AutoCorrRotation(ac1, ac2);
+% [phi, max_cor, phi_v_cor] = AutoCorrRotation(ac1, ac2);
 import CMBHOME.Utils.*
 
 p = inputParser;

+CMBHOME/+Utils/CatCA.m

@@ -1,6 +1,4 @@
-function matout = CatCA(cellin)
-% matout = CMBHOME.Utils.CatCA(cellin)
-%
+function matout = CatCA(cellin)%
 % Converts a cell array of vectors 'cellin' of size MxN to a matrix padded by NaNs of
 % size max_vector x N x M. This is useful for data structures in CMBHOME.
 % If cellin is a matrix, matout = cellin. The reason M (epochs) gets pushed

+CMBHOME/+Utils/Cell2MatPlus.m

@@ -1,5 +1,4 @@
 function matout = Cell2MatPlus(cellin)
-
 % returns a matrix from cell cellin, where differently sized vectors are
 % padded with NaNs
 

+CMBHOME/+Utils/ContinuizeEpochs.m

@@ -1,8 +1,7 @@
 function varargout = ContinuizeEpochs(varargin)
-% varargout = CMBHOME.Utils.ContinuizeEpochs(varargin);
-%
 % Converts all cell arrays passed through varagin to arrays in varargout
 %
+%
 % If the cell array is Mx1, then that vararg output is a column vector. If
 % the cell array is MxN, then the vararg output is a matrix max(cat(M) x N
 % padded by zeros.
@@ -11,6 +10,7 @@
 
 % takes all data from cell arrays in vargin, and continuizes them into
 % vectors in vargout
+% varargout = CMBHOME.Utils.ContinuizeEpochs(varargin);
 
 varargout = cell(length(varargin),1);
 

+CMBHOME/+Utils/EllipseDirectFit.m

@@ -1,6 +1,4 @@
 function A = EllipseDirectFit(XY)
-% coefficients = EllipseDirectFit(XY)
-%
 %  Direct ellipse fit, proposed in article
 %    A. W. Fitzgibbon, M. Pilu, R. B. Fisher
 %     "Direct Least Squares Fitting of Ellipses"
@@ -22,6 +20,8 @@
 %  better approximated by a hyperbola.
 %  It is somewhat biased toward smaller ellipses.
 %
+% % coefficients = EllipseDirectFit(XY)
+
 
 centroid = mean(XY);   % the centroid of the data set
 

+CMBHOME/+Utils/FilterEpochs.m

@@ -1,11 +1,10 @@
 function epoch = FilterEpochs(epoch, mindur, minsep)
-%
-% epoch = CMBHOME.Utils.Filterepoch(epoch, mindur, minsep)
-%
-% For an Nx2 array of epoch values (time or otherwise), merges those closer
+%  For an Nx2 array of epoch values (time or otherwise), merges those closer
 % than 'minsep' and removes those less than 'mindur'
 %
 % andrew 6/29/2011
+% epoch = CMBHOME.Utils.Filterepoch(epoch, mindur, minsep)
+
 
 [~, inds] = sort(epoch(:,1)); % sort ascending
 

+CMBHOME/+Utils/IntersectEpochs.m

@@ -1,6 +1,4 @@
 function epochs = IntersectEpochs(root, epochs)
-% epochs = root.IntersectEpochs(epochs)
-%
 % Returns list of epochs that are contained in both epochs lists { epochs1, epochs2 }
 %
 % INPUTS

+CMBHOME/+Utils/IntersectEpochs2.m

@@ -1,12 +1,9 @@
 function epochs = IntersectEpochs2(varargin)
+% Finds the intersection of all sets of epochs.
 % epochs = IntersectEpochs2(epochs1, epochs2, epochs3...)
 %
 % Must pass at least
 %
-% Finds the intersection of all sets of epochs.
-%
-% andrew october 14 2011
-
 import CMBHOME.Utils.*
 
 if length(varargin)<2, error('you must pass at least 2 input arguments of type ''epoch'''); end

+CMBHOME/+Utils/LinearRegression.m

@@ -1,9 +1,8 @@
 function [y_m, R, P, b, y_int] = LinearRegression(x, y, ifPlot)
-%  [y_m, R, P, b, y_int] = LinearRegression(x, y)
 % fits x and y to a linear model, and returns the R and P values, along
 % with y_m, model y values
-
-% andrew 26 january 2010
+%
+%  [y_m, R, P, b, y_int] = LinearRegression(x, y)
 
 degree=2; % linear regression
 

+CMBHOME/+Utils/MergeEpochs.m

@@ -1,16 +1,12 @@
 function root = MergeEpochs(root)
-%
-% root = CMBHOME.Utils.MergeEpochs(root);
-%
 % Returns root with epochs that do not overlap. Touching epochs are merged.
 %
+%
 % Assumes that all epochs are on the timescale of root.b_ts (all epochs
 % have samples within them. any epochs without samples within them are
 % deleted)
-%
-% v1. andrew
-% v2. ehren
-
+% root = CMBHOME.Utils.MergeEpochs(root);
+
 if ~strcmp(class(root), 'CMBHOME.Session'), error('MergeEpochs has been updated to require new syntax.'); end
 
 if size(root.epoch,1)<2, return; end

+CMBHOME/+Utils/MergeEpochs2.m

@@ -1,7 +1,8 @@
 function epochs = MergeEpochs2(epochs, mergetouching)
+% merge epochs of arbirary precision without creating binary vector
+%
 % epochs = CMBHOME.Utils.MergeEpochs2(epochs, mergetouching)
 %
-% merge epochs of arbirary precision without creating binary vector
 %
 % returned sorted
 %

+CMBHOME/+Utils/MyvarHolder.m

@@ -1,5 +1,6 @@
 classdef MyvarHolder
-
+    % Class for arbitrary time series variables. 
+
     properties (SetAccess=private, Hidden) 
         p_var       % Backup of the original variable, from the time of making the object
         p_ts        % Backup of the original timestamps, from the time o fmaking the object

+CMBHOME/+Utils/OverThresholdDetect.m

@@ -1,10 +1,9 @@
 function [ind,tf] = OverThresholdDetect(A, thresh, min_sep, min_length)
-% ind = ThresholdDetect(A, thresh, min_sep, min_length);
-% [ind, epochs] = ThresholdDetect(A, thresh, min_sep, min_length);
-%
 % Searches for continuous epochs for which A is at least thresh, merges those
 % separated by less than min_sep indices, and returns ind, an Nx2 array of
 % indices for continuous epochs over thresh
+% ind = ThresholdDetect(A, thresh, min_sep, min_length);
+% [ind, epochs] = ThresholdDetect(A, thresh, min_sep, min_length);
 
 A = A(:);
 

+CMBHOME/+Utils/PipeRaster.m

@@ -1,5 +1,4 @@
 function PipeRaster(cellin, varargin)
-
 % Scatters the time course of a single element on ydim with the pipe '|'
 % character. Existing plot must be 'held'
 

+CMBHOME/+Utils/RemoveJumpsAndSmooth.m

@@ -1,6 +1,5 @@
 function x = RemoveJumpsAndSmooth(x, max_allowed_flips, jitter_threshold, smooth_std,filter_def)
-% x = RemoveJumpsAndSmooth(x, max_flip_length, jitter_threshold, smooth_std);
-%
+%  Removes large jumps in data, smoothes the removed regions
 %   ARGUMENTS
 %       x                   a vector of something. NaNs may be used in place of
 %                           known bad samples
@@ -17,7 +16,7 @@
 % then linearly interpolate the missed samples and smooth by convolution
 % with a gaussion of standard deviation smooth_std bins.
 %
-% andrew june 15 2011
+% x = RemoveJumpsAndSmooth(x, max_flip_length, jitter_threshold, smooth_std);
 
     import CMBHOME.Utils.*