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plotXmatrix.m

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Plots columns of matrix data as distinct time series. User should normalize data first to minimize overlap.

Free download from Shareware Connection - Plots columns of matrix data as distinct time series.

Publisher: Joshua Carmichael | License: Freeware | Price: 0.00
Version: 1.0 | Size: 10 KB | Platform: Matlab, Scripts
Released Date: 23-03-2013 | Rating: 0 | Title: plotXmatrix.m

Author Url: http://www.mathworks.com
Program Info Url: http://www.mathworks.com
Download Url: http://www.mathworks.com/matlabcentral/fx_files/27455/2/plotXmatrix.zip

More downloads from plotXmatrix.m publisher Joshua Carmichael:

Coherently aligns data - 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

xcorAlign.m - Circularly shifts input data in matrix by columns, according to cross correlation maximum index.

normColumns.m - Data stored column-wise in an input matrix are normalized as time series, with options to output just the normalized data, or the norm of each column as well

D8 Algorithm for Hydrological Models - Find flow direction (find min. potential of all adjacent cells)

matchPurs.m - This function is a more robust and improved version of my previous submission, matchingpursuit.m

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rand_extended computes a random number in the interval [a,b] - rand_extended computes a random number in the interval [a,b] It is basically an extended version of the command rand Inputs: a: the lower bound of the interval b: the upper bound of the interval m,n(optional): dimensions of the matrix of random nu

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plotColumns.m: plots columns in matrix as time series - [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] = plotColumns(t,data,'plotopt'); [h] = plotColumns(...

maxalign.m - Aligns matrix entries so that the center of the time series corresponds to the maximum value of the time series.

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