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Procedure: minvar.pro This program computes the principal variance directions and variances of a vector quantity (can be 2D or 3D). This routine is a simple version designed to be used by a tplot wrapper with the contrans_var library Input: Vxyz, an (ndim,npoints) array of data Output: eigenVijk, an (ndim,ndim) array containing the principal axes vectors Maximum variance direction eigenvector, Vi=eigenVijk(*,0) Intermediate variance direction, Vj=eigenVijk(*,1) (descending order) Vrot: if set to a name, that name becomes an array of (ndim,npoints) containing the rotated data in the new coordinate system, ijk. Vi(maximum direction)=Vrot(0,*) Vj(intermediate direction)=Vrot(1,*) Vk(minimum variance direction)=Vrot(2,*) lambdas2=if set to a name returns the eigenvalues of the computation Written by: Vassilis Angelopoulos $LastChangedBy: pcruce $ $LastChangedDate: 2007-10-04 15:40:27 -0700 (Thu, 04 Oct 2007) $ $LastChangedRevision: 1667 $ $URL: svn+ssh://thmsvn@ambrosia.ssl.berkeley.edu/repos/ssl_general/tags/tdas_3_01/cotrans/special/minvar/minvar.pro $
(See ssl_general/cotrans/special/minvar/minvar.pro)
Procedure: minvar_matrix_make Purpose: wrapper for minvar.pro, uses tplot variables, can generate multiple transformation matrices using sliding average Arguments: in_var_name: the name of the tplot variable holding the input data, can be any sort of timeseries 3-d data tstart(optional): the start time of the data you'd like to consider for generating the transformation matrix(defaults to minimum time of in_var timeseries) tstop(optional): the stop time of the data you'd like to consider for generating the transformation matrix(defaults to maximum time of in_var timeseries) twindow(optional): the size of the window(in seconds) you'd like to consider when using a moving boxcar average to generate multiple transformations. (defaults to the entire time series) tslide(optional): the number of seconds the boxcar should slide forward after each average.(defaults to twindow/2xo) set tslide=0 to cause the program to generate only a single matrix newname(optional): the name of the tplot variable in which to store the transformation matrix(matrices) (defaults to in_var_name+'_mva_mat' evname(optional): the name of the tplot variable in which to store the eigenvalues of the mva matrix(matrices) (defaults to nowhere, ie if unset doesn't store them error(optional): named variable that holds the error state of the computation, 1=success 0 = failure tminname(optional): name of a tplot variable in which you would like to store the minimum variance direction vectors this vector will be represented in the original coordinate system tmidname(optional): name of a tplot variable in which you would like to store the intermediate variance direction vectors this vector will be represented in the original coordinate system tmaxname(optional): name of a tplot variable in which you would like to store the minimum variance direction vectors this vector will be represented in the original coordinate system $LastChangedBy: pcruce $ $LastChangedDate: 2007-08-29 14:25:09 -0700 (Wed, 29 Aug 2007) $ $LastChangedRevision: 1512 $ $URL: svn+ssh://thmsvn@ambrosia.ssl.berkeley.edu/repos/ssl_general/tags/tdas_3_01/cotrans/special/minvar/minvar_matrix_make.pro $
(See ssl_general/cotrans/special/minvar/minvar_matrix_make.pro)
Procedure: mva_crib Purpose: A crib on showing how to transform into minimum variance analysis coordinates Notes: $LastChangedBy: pcruce $ $LastChangedDate: 2007-08-29 14:25:09 -0700 (Wed, 29 Aug 2007) $ $LastChangedRevision: 1512 $ $URL: svn+ssh://thmsvn@ambrosia.ssl.berkeley.edu/repos/ssl_general/tags/tdas_3_01/cotrans/special/minvar/mva_crib.pro $
(See ssl_general/cotrans/special/minvar/mva_crib.pro)