;***************************************************
;Computes explained variance of filtered time series
;**************************************************
begin
filename = "test_jjas.csv"
lines    = asciiread(filename,-1,"string")
data     = lines(1:)
rain    = tofloat(str_get_field(data,1,","))

;************************************************
; create BandPass Filter
;************************************************
  ihp      = 2                                ; bpf=>band pass filter
  nWgt     = 141
  sigma    = 1.0                              ; Lanczos sigma
  fca      = 1./90.
  fcb      = 1./25.
  wgt      = filwgts_lanczos (nWgt, ihp, fca, fcb, sigma )

;************************************************
; Apply the Lanczos band pass filter weights to the original anomalies
;************************************************

 xBPF = wgt_runave_n_Wrap (rain, wgt, 0, 0) 

;***********************************************
; Compute variance of original anomalies
;***********************************************

xVarSummer = dim_variance_n_Wrap(rain,0)

;********************************************************************
; Compute explained variance (filtered_variance)/(unfiltered_variance 
;********************************************************************

xRatioVarSummer = (xVarSummer/xBPF)*100

print(xRatioVarSummer)

end