# eofcor_ts

Calculates the time series of the amplitudes associated with each eigenvalue in an EOF which was calculated using a correlation matrix.

## Prototype

function eofcor_ts ( data : numeric, evec : numeric ) return_val : numeric

## Arguments

*data*

A multi-dimensioned array in which the rightmost dimension is the number of observations. Generally, this is the time dimension.

*evec*

A multi-dimensional array containing the EOFs calculated using
**eofcor**.

## Return value

A two-dimensional array dimensioned by the number of eigen values selected in
**eofcor** by the size of the time dimension of *data*.

## Description

This function is deprecated and has been replaced by the faster
**eofunc_ts**.

Calculates the time series of the amplitudes associated with each eigenvalue in
an EOF which was calculated using a correlation matrix. They are derived via the dot
product of the data and the EOF spatial patterns.

## See Also

This function is deprecated and has been replaced by the faster
**eofunc_ts**.

## Examples

**Example 1**

Let x be two dimensional with dimensions variables (size = nvar) and time:

neval = 3 ; calculate 3 EOFs out of 7 ev =eofcor(x,neval) ; ev(neval,nvar) ev_ts =eofcor_ts(x,ev_cor)

**Example 2**

Let x be three-dimensional with dimensions of time, lat, lon. Reorder x so that time is the rightmost dimension:

y!0 = "time" ; name dimensions if not already done y!1 = "lat" ; must be named to reorder y!2 = "lon" neval = nvar ; calculate all EOFs ev =eofcor(y(lat|:,lon|:,time|:),neval) ; ev(neval,nlat,nlon) ev_ts =eofcor_ts(y,ev)

**Example 3**

Let z be four-dimensional with dimensions lev, lat, lon, and time:

neval = 3 ; calculate 3 EOFs out of klev*nlat*mlon ev =eofcor(z,neval) ; ev will be dimensioned neval, level, lat, lon ev_ts =eofcor_ts(z,ev)

**Example 4**

Calculate the EOFs at every other point rather. Use of a temporary array is NOT necessary but it avoids having to reorder the array twice in this example:

neval = 5 ; calculate 5 EOFs out of nlat*mlon zTemp = z(lat|::2,lon|::2,time|:) ; reorder and use temporary array ev =eofcor(zTemp,neval) ; ev(neval,nlat/2,mlon/2) ev_ts =eofcor_ts(zTemp,ev)

**Example 5**

Let z be four-dimensional with dimensions level, lat, lon, time. Calculate the EOFs at one specified level:

kl = 3 ; specify level neval = 8 ; calculate 8 EOFs out of nlat*mlon ev =eofcor(z(kl,:,:,:),neval) ; ev will be dimensioned neval, lat, lon optETS = True optETS@jopt = 1 ev_rot =eofcor_ts(z,ev)

**Example 6**

Let z be four-dimensional with dimensions time, lev, lat, lon. Reorder x so that time is the rightmost dimension and calculate on one specified level:

kl = 3 ; specify level neval = 8 ; calculate 8 EOFs out of nlat*mlon zTemp = z(lev|kl,lat|:,lon|:,time|:) ev =eofcor(zTemp,neval) ; ev will be dimensioned neval, lat, lon ev_ts =eofcor_ts(zTemp,ev)

**Example 7**

Area weight the data prior to calculation. Let p be four-dimensional with dimensions lat, lon, and time. The array lat contains the latitudes.

; calculate the weights using the square root of the cosine of the latitude and ; also convert degrees to radians wgt =sqrt(cos(lat*0.01745329)) ; reorder data so time is fastest varying pt = p(lat|:,lon|:,time|:) ; (lat,lon,time) ptw = pt ; create an array with metadata ; weight each point prior to calculation. ;conformis used to make wgt the same size as pt ptw = pt*conform(pt,wgt,0) evec = eofcor(ptw,neval) evec_ts =eofcor_ts(ptw,evec)