;+ ; NAME: ; ssl_correlate_tplot ; ; PURPOSE: ; Identified the amount of shift required to correlate two ; time series tplot variables and stores the result in a tplot ; variable. Only compares 1-d to 1-d data at a time So for example you ; can, compare the fge x and the fgl_x but can't do all three ; at a time. ; ; ; Works by binning the timeseries data, then calculating the ; time shift required to maximally correlate each bin. ; When too few points overlap bins are rejected. ; ; CATEGORY: ; THEMIS-SOC ; ; CALLING SEQUENCE: ; pro thm_correlate_tplot,var1_name, var1_y_dim, var2_name, var2_y_dim, store_name, correlation_floor = correlation_floor, point_number = point_number, lag_step_number = lag_step_number, time_step_size = time_step_size, bin_size = bin_size ; ; INPUTS: ; ; var1_name: the tplot name of the first variable to be compared ; ; var1_y_dim: the numerical dimension of the first tplot y_var to look ; at(from 0 to n-1) ; ; var2_name: the tplot name of the second variable to be ; compared ; ; var2_y_dim: the numerical dimension of the second tplot y_var ; to look at(from 0 to n-1) ; ; store_name: the name of a tplot variable in which to store the result ; ; correlation_floor: optional, if set filters all results where ; the correlation between functions is too poor(default:.9) ; ; point_number: optional, the minimum number of points of ; overlap necessary to try correlating a bin(default:200) ; ; lag_step_number: optional, checks plus or minus lag_steps * time steps ; to correlate the vectors (default:64) ; ; time_step_size: optional, the size of the time step to use when ; interpolating and correlating the vectors, in ; seconds(default:1/128 seconds) ; ; bin_size: optional, the size of each bin in seconds ; (default:60 seconds) ; ; ; OUTPUTS: ; ; stores the time and the shift values in the select tplot_var ; ; KEYWORDS: ; ; COMMENTS: This function will probably die horribly if time ; values are not monotonic. ; ; ; PROCEDURE: ; ; EXAMPLE: ; ; MODIFICATION HISTORY: ; Written by: Patrick Cruce(pcruce@gmail.com) ; 2007-05-24 V1.0 ; ;;$LastChangedBy: lphilpott $ ;$LastChangedDate: 2012-06-25 15:20:30 -0700 (Mon, 25 Jun 2012) $ ;$LastChangedRevision: 10638 $ ;$URL: svn+ssh://thmsvn@ambrosia.ssl.berkeley.edu/repos/spdsoft/trunk/general/misc/ssl_correlate_tplot.pro $ ; ; KNOWN BUGS: ;- ;tests if argument is array, 1L on success 0L on failure ;function tcs_is_array, a ; ; if 0 eq (size(a))(0) then return, 0L ; ; return, 1L ; ;end ;the main function pro ssl_correlate_tplot,var1_name, var1_y_dim, var2_name, var2_y_dim, store_name, correlation_floor = correlation_floor, point_number = point_number, lag_step_number = lag_step_number, time_step_size = time_step_size, bin_size = bin_size if not keyword_set(correlation_floor) then correlation_floor = 0.9D if not keyword_set(point_number) then point_number = 200 if not keyword_set(lag_step_number) then lag_step_number = 64 if not keyword_set(time_step_size) then time_step_size = 1.0D/128.0D if not keyword_set(bin_size) then bin_size = 60 get_data, var1_name, data = d times1 = d.X values1 = transpose(d.Y[*, var1_y_dim]) get_data, var2_name, data = d times2 = d.X values2 = transpose(d.Y[*, var2_y_dim]) ;below is a little idl magic to concatenate for input into thm_correlation_shift ts1 = transpose([[times1], [reform(values1)]]) ts2 = transpose([[times2], [reform(values2)]]) ;x_ts1 is a 2xn matrix(column major) of x-value/timestamp pairs from ;tha_fgl ;x_ts2 is a 2xn matrix(column major) of x-value/timestamp pairs from ;tha_fge ;x_cor is a 3xn matrix(column major of timestamp/shift/correlation ;triplets between tha_fgl and tha_fge x_cor = ssl_correlation_shift(ts1, ts2, n_pts = point_number, lag_steps = lag_step_number, time_step = time_step_size, bin_size = bin_size) if not tcs_is_array(x_cor) then begin dprint, 'error calculating correlation shift, probable 0 overlap between variables' return endif ;filter all values that do not correlate better than correlation_floor ;this line prevent arithmetic error messages index = where(finite(x_cor[2, *])) ;tcs_is array is a utility function that just makes sure the indices ;returned aren't -1L ;find the values that have acceptable correlation if(tcs_is_array(index)) then index = where(x_cor[2, index] ge correlation_floor) ;make proper assignments if(tcs_is_array(index)) then x_cor[2, index] = !VALUES.F_NAN $ else x_cor[*] = !VALUES.F_NAN ;store the data store_data, store_name, DATA = {X:transpose(x_cor[0, *]), Y:transpose(x_cor[1, *])} end