vdt: Merged Day Complex SHT Time Series
A network day time series (mrvdt) is created by merging the contemporaneous mode coefficients from site days that contribute to the network day using a weighted average at each time sample based on the Modulation Transfer Function and its estimated error.
Dimensionally, the x-axis is time (sec), the y-axis is the order of the spherical harmonic, M, from 0 to L, with the window function at the last row, and the z-axis contains the real [*,*,1], and imaginary parts [*,*,2]. The real part of the row containing the window function is the observed window function and the imaginary part is the window function after gap filling.
Starting with network day 960606, these data products were no longer archived to a DSDS tape. L-nu diagrams are still produced.
Steps involved: 1. Calibrate the measured MTF files. lpar mtfcal input = "" List of input MTF[k(r)] files output = "" List of output MTF[l] files (trim_file = "grasplib$plm_0200.txt") ASCII triming file (data_type = "velocity") Image data type |velocity|intensity| (correct_type = "full") Correction type |full|sky_optics|pixel_only| (mask = yes) Apply moving mask? (mask_cbr = 0.05) Moving mask radius for cosine-bell apodization (mask_EWrad = 0.95) Maximum mask fractional radius in E-W direction (mask_NSrad = 0.95) Maximum mask fractional radius in N-S direction 2. Create the day time series template with mkts. lpar mkts rootname = Rootname of monthly times series template ts_start = TS_START value: yyyy/mm/dd hh:mm:ss ts_end = TS_END value: yyyy/mm/dd hh:mm:ss (gong_month = INDEF) GONG month number (gong_day = "0000/00/00") GONG date for merged daily template (YY/MM/DD) (l_start = 0) Starting L-value (l_end = 200) Ending L-value (l_max = INDEF) L_max of SHT decomposition (default= l_end) (m_type = "all") M-type: all|even (dispersion = 60.) Time dispersion (seconds) (merge_templa = no) Merge time series template output mrvdtYYMMDDd[000-200] sample FITS header output TS_START= '1996/05/26 00:00:16' TS_END = '1996/05/26 23:59:16' (data product id: mtvdt960526d[000-200]) 3. Merge the sites into the day template with tsmerge. lpar tsmerge input = Input list of site day times series images ots_path = Full path name of network day L=0 image mtf_path = "./" Full path MTF image directory (type_weight = "mtferr") Type of weighting (mtferr|equal|oldmtferr) (threshold = 2.) Reject data where MTF <= threshold * MTF_ERROR sample FITS header output MERGE_BB= T MERGE_LE= T MERGE_ML= T (Each merged site will have an entry similar to this) 4. Normalize the time series from a 3-plane image (with real, imaginary, and weights as the three planes) to a 2-plane image with tsnorm. lpar tsnorm input = Input list of network day times series images output = Output list of normalized network day times series sample FITS header output GHIST000= 'Task TSNORM (96/08/06 15:24:06, V93.2.32)' GHIST001= 'input=tmrvdt960523d000.imh' 5. Fill gaps in GONG time series via autoregressive filter with gapfill. lpar gapfill maxlen = 20 Maximum Auto-regressive filter length fill_factor = 0.1 Filling factor (max time length gap filled by gapfill = maxlen * fill_factor) sample FITS header output TS_NEFF = 1354 GTS_NEFF= 1440 GHIST002= 'Task GAPFILL (96/08/06 15:31:51, V93.2.32)' GHIST003= 'input=mrvdt960523d000.imh,maxlen=20,fill_factor=0.1' 6. Find the fraction of the month time series that is filled. Fill factor = GTS_NEFF / ts_length 7. Write the time series to a DSDS tape. Starting with network day 960606, this data product will no longer be archived to a tape.