Step 1: Find flow direction (find min. potential of all adjacent cells) Step 2: Follow flow direction to sum the cumulative # cells flowing into a given "minimum" cell. Step 3: Find the maximum likely channel location in each E-W direction for a given number of channels.
NOTE: The user should make sure that the upstream portion of the potential matrix Psi corresponds to first row of the...
Computes an orthogonal basis from a given criteria. Options are identical to those of sortwvfrms: basis are computed by signal width, localization, and noise. The vectors are sorted by the given criteria, projected onto a subspace...
This function is a more robust and improved version of my previous submission, matchingpursuit.m
This function computes the projection of a given input vector or matrix onto a "dictionary" of other vectors or matrices using a...
Estimates the signal width of data input, using definition: rho = cumtrapz(t,x.^2)/trapz(t,x.^2)
The integration limits go from mean(t)-w/2 and mean(t)+w/2; w is defined as the signal width, and gives the time at which rho...
Sorts input matrix of data so that each column is re-ordered by user-specified criteria. Options are (1) sort data by signal width (2) sort data by energy localization (3) sort data by dissimilarity of data to a normal probability...
Dilates a time series input and resizes it to the orignal sample length input. If a matrix is input, dilation is done along columns. % The up/down sampling is done via resample.m, so low-pass filtering of the up/downsampling is...
Plots columns of matrix data as distinct time series. User should normalize data first to minimize overlap.
This function treats a matrix as a column-wise set of signals and circularly shifts each column so that it aligns with the first column of data so that the inner product between those columns is maximized with respect to all other shifts. ...
Circularly shifts input data in matrix by columns, according to cross correlation maximum index.
[h,varargout]=plotColumns(data,varargin) plots columns of a matrix as if each column is a time series
USAGE: plotColumns(data); [h] = plotColumns(t,data); [h] = plotColumns(t,data,{colors}); [h] =... |