| 1 | ; |
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| 2 | ; make std dev from 2D macro_base_fld monthly time serie (defined in macro_read) |
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| 3 | ; |
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| 4 | FUNCTION make_stddev, file_name, ncdf_db, TIME_1 = time_1, TIME_2 = time_2, ALL_DATA = all_data, ZMTYP = zmtyp |
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| 5 | |
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| 6 | @common |
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| 7 | @com_eg |
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| 8 | ; |
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| 9 | ; |
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| 10 | ; Read time serie |
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| 11 | ; |
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| 12 | IF debug_w THEN print, 'keyword_set(ALL_DATA) : ', keyword_set(ALL_DATA) |
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| 13 | |
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| 14 | mfld = nc_read(file_name, macro_base_fld, ncdf_db, TIME_1 = time_1, TIME_2 = time_2, ALL_DATA = all_data) |
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| 15 | |
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| 16 | mfld.data = trends(mfld.data, '412', 'xyt') |
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| 17 | |
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| 18 | print, ' Warning: standard deviation assuming monthly time serie of base field ',macro_base_fld |
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| 19 | |
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| 20 | ; Modif MK |
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| 21 | |
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| 22 | IF stddev_mth NE '00' THEN BEGIN |
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| 23 | |
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| 24 | mth = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] |
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| 25 | CASE STRMID(stddev_mth, 0, 2) OF |
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| 26 | '01': strd = 1 |
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| 27 | '02': strd = 2 |
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| 28 | '03': strd = 3 |
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| 29 | '04': strd = 4 |
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| 30 | '05': strd = 5 |
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| 31 | '06': strd = 6 |
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| 32 | '07': strd = 7 |
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| 33 | '08': strd = 8 |
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| 34 | '09': strd = 9 |
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| 35 | '10': strd = 10 |
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| 36 | '11': strd = 11 |
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| 37 | '12': strd = 12 |
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| 38 | ENDCASE |
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| 39 | |
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| 40 | print, ' Warning: standard deviation computed for ', mth[strd-1] |
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| 41 | |
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| 42 | ; Sampling of data and computation of new numbers of values |
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| 43 | data = (mfld.data)[*, *, strd-1:*:12] |
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| 44 | nval = (size(data))[3] |
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| 45 | ENDIF ELSE BEGIN |
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| 46 | data = mfld.data |
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| 47 | nval = jpt |
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| 48 | ENDELSE |
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| 49 | |
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| 50 | std = a_timecorrelate(data, 0,/covariance) |
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| 51 | std = sqrt( nval/(nval-1)*std ) |
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| 52 | idx = WHERE (std GT 1.0e+10) |
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| 53 | |
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| 54 | IF idx(0) NE -1 THEN std(idx) = valmask |
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| 55 | |
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| 56 | IF stddev_mth NE '00' AND stddev_diff EQ 1 THEN BEGIN |
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| 57 | print, ' Warning: standard deviation diff between ', mth[strd-1], ' and the whole serie' |
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| 58 | std_tot = a_timecorrelate(mfld.data, 0,/covariance) |
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| 59 | std_tot = sqrt( jpt/(jpt-1)*std_tot ) |
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| 60 | IF idx(0) NE -1 THEN std_tot(idx) = valmask |
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| 61 | idx_diff = WHERE (std NE valmask) |
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| 62 | std(idx_diff) = std(idx_diff) - std_tot(idx_diff) |
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| 63 | ENDIF |
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| 64 | |
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| 65 | field = {name: '', data: std, legend: '', units: '', origin: '', dim: 0, direc:''} |
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| 66 | |
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| 67 | field.origin = mfld.origin |
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| 68 | |
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| 69 | field.dim = 2 |
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| 70 | |
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| 71 | IF stddev_mth NE '00' THEN BEGIN |
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| 72 | field.legend = ' for '+mth[strd-1]+' in ['+cmdm.date1+'-'+cmdm.spec+'] -' |
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| 73 | IF stddev_diff EQ 1 THEN BEGIN |
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| 74 | field.legend = ' diff between '+mth[strd-1]+' and total TS ['+cmdm.date1+' - '+cmdm.spec+'] -' |
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| 75 | ENDIF |
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| 76 | ENDIF ELSE BEGIN |
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| 77 | field.legend = ' ['+cmdm.date1+'-'+cmdm.spec+']' |
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| 78 | ENDELSE |
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| 79 | |
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| 80 | ; Fin Modif MK |
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| 81 | |
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| 82 | ; data and plot are 2D |
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| 83 | |
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| 84 | field.direc = 'xy' |
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| 85 | |
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| 86 | return, field |
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| 87 | END |
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