Changeset 23 for trunk


Ignore:
Timestamp:
06/02/14 18:40:18 (10 years ago)
Author:
lahlod
Message:

modifs

Location:
trunk/src/scripts_Laura
Files:
2 edited

Legend:

Unmodified
Added
Removed

  • trunk/src/scripts_Laura/diff_polarsHV_SSMIS_test.py

    r22 r23  
    3737lonch13_FEB = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
    3838latch13_FEB = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
     39## ch17 ## 
     40bbemis_ch17_FEB = nonzero((emis17_FEB != -500.) & (emis17_FEB <= 1.)) 
     41OUTZCH17_FEB = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx)),len_month[imo]], float) 
     42outzch17_FEB = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx))], float) 
     43lonch17_FEB = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
     44latch17_FEB = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
     45## ch18 ## 
     46bbemis_ch18_FEB = nonzero((emis18_FEB != -500.) & (emis18_FEB <= 1.)) 
     47OUTZCH18_FEB = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx)),len_month[imo]], float) 
     48outzch18_FEB = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx))], float) 
     49lonch18_FEB = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
     50latch18_FEB = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
    3951for ijr in range (0, len_month[imo]): 
    4052    print 'jour=', ijr+1 
     53    ## ch17 ## 
     54    ind_jr17_FEB = np.where(jjr17_FEB[bbemis_ch17_FEB] == ijr+1)[0] 
     55    xx = lon17_FEB[bbemis_ch17_FEB][ind_jr17_FEB] 
     56    yy = lat17_FEB[bbemis_ch17_FEB][ind_jr17_FEB] 
     57    zz = emis17_FEB[bbemis_ch17_FEB][ind_jr17_FEB] 
     58    zz0 = min(zz) 
     59    zz1 = max(zz) 
     60    outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0, x1, y0, y1, zz0, zz1) 
     61    outzch17_FEB = outz 
     62    lonch17_FEB = outx 
     63    latch17_FEB = outy 
     64    OUTZCH17_FEB[:,:,ijr] = outzch17_FEB[:,:] 
     65    ## ch18 ## 
     66    ind_jr18_FEB = np.where(jjr18_FEB[bbemis_ch18_FEB] == ijr+1)[0] 
     67    xx = lon18_FEB[bbemis_ch18_FEB][ind_jr18_FEB] 
     68    yy = lat18_FEB[bbemis_ch18_FEB][ind_jr18_FEB] 
     69    zz = emis18_FEB[bbemis_ch18_FEB][ind_jr18_FEB] 
     70    zz0 = min(zz) 
     71    zz1 = max(zz) 
     72    outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0, x1, y0, y1, zz0, zz1) 
     73    outzch18_FEB = outz 
     74    lonch18_FEB = outx 
     75    latch18_FEB = outy 
     76    OUTZCH18_FEB[:,:,ijr] = outzch18_FEB[:,:] 
    4177    ## ch12 ## 
    4278    ind_jr12_FEB = np.where(jjr12_FEB[bbemis_ch12_FEB] == ijr+1)[0] 
    … …  
    78114lonch13_APR = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
    79115latch13_APR = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
     116## ch17 ## 
     117bbemis_ch17_APR = nonzero((emis17_APR != -500.) & (emis17_APR <= 1.)) 
     118OUTZCH17_APR = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx)),len_month[imo]], float) 
     119outzch17_APR = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx))], float) 
     120lonch17_APR = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
     121latch17_APR = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
     122## ch18 ## 
     123bbemis_ch18_APR = nonzero((emis18_APR != -500.) & (emis18_APR <= 1.)) 
     124OUTZCH18_APR = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx)),len_month[imo]], float) 
     125outzch18_APR = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx))], float) 
     126lonch18_APR = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
     127latch18_APR = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
    80128for ijr in range (0, len_month[imo]): 
    81129    print 'jour=', ijr+1 
     130    ## ch17 ## 
     131    ind_jr17_APR = np.where(jjr17_APR[bbemis_ch17_APR] == ijr+1)[0] 
     132    xx = lon17_APR[bbemis_ch17_APR][ind_jr17_APR] 
     133    yy = lat17_APR[bbemis_ch17_APR][ind_jr17_APR] 
     134    zz = emis17_APR[bbemis_ch17_APR][ind_jr17_APR] 
     135    zz0 = min(zz) 
     136    zz1 = max(zz) 
     137    outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0, x1, y0, y1, zz0, zz1) 
     138    outzch17_APR = outz 
     139    lonch17_APR = outx 
     140    latch17_APR = outy 
     141    OUTZCH17_APR[:,:,ijr] = outzch17_APR[:,:] 
     142    ## ch18 ## 
     143    ind_jr18_APR = np.where(jjr18_APR[bbemis_ch18_APR] == ijr+1)[0] 
     144    xx = lon18_APR[bbemis_ch18_APR][ind_jr18_APR] 
     145    yy = lat18_APR[bbemis_ch18_APR][ind_jr18_APR] 
     146    zz = emis18_APR[bbemis_ch18_APR][ind_jr18_APR] 
     147    zz0 = min(zz) 
     148    zz1 = max(zz) 
     149    outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0, x1, y0, y1, zz0, zz1) 
     150    outzch18_APR = outz 
     151    lonch18_APR = outx 
     152    latch18_APR = outy 
     153    OUTZCH18_APR[:,:,ijr] = outzch18_APR[:,:] 
    82154    ## ch12 ## 
    83155    ind_jr12_APR = np.where(jjr12_APR[bbemis_ch12_APR] == ijr+1)[0] 
    … …  
    91163    lonch12_APR = outx 
    92164    latch12_APR = outy 
    93     OUTZCH12_APR[:,:,ijr] = outzch13_APR[:,:] 
     165    OUTZCH12_APR[:,:,ijr] = outzch12_APR[:,:] 
    94166    ## ch13 ## 
    95167    ind_jr13_APR = np.where(jjr13_APR[bbemis_ch13_APR] == ijr+1)[0] 
    … …  
    122194lonch13_JUL = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
    123195latch13_JUL = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
     196## ch17 ## 
     197bbemis_ch17_JUL = nonzero((emis17_JUL != -500.) & (emis17_JUL <= 1.)) 
     198OUTZCH17_JUL = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx)),len_month[imo]], float) 
     199outzch17_JUL = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx))], float) 
     200lonch17_JUL = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
     201latch17_JUL = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
     202## ch18 ## 
     203bbemis_ch18_JUL = nonzero((emis18_JUL != -500.) & (emis18_JUL <= 1.)) 
     204OUTZCH18_JUL = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx)),len_month[imo]], float) 
     205outzch18_JUL = np.zeros([len(np.arange(y0, y1+1, dy)),len(np.arange(x0, x1+1, dx))], float) 
     206lonch18_JUL = np.zeros([len(np.arange(x0, x1+1, dx))], float) 
     207latch18_JUL = np.zeros([len(np.arange(y0, y1+1, dy))], float) 
    124208for ijr in range (0, len_month[imo]): 
    125209    print 'jour=', ijr+1 
     210    ## ch17 ## 
     211    ind_jr17_JUL = np.where(jjr17_JUL[bbemis_ch17_JUL] == ijr+1)[0] 
     212    xx = lon17_JUL[bbemis_ch17_JUL][ind_jr17_JUL] 
     213    yy = lat17_JUL[bbemis_ch17_JUL][ind_jr17_JUL] 
     214    zz = emis17_JUL[bbemis_ch17_JUL][ind_jr17_JUL] 
     215    zz0 = min(zz) 
     216    zz1 = max(zz) 
     217    outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0, x1, y0, y1, zz0, zz1) 
     218    outzch17_JUL = outz 
     219    lonch17_JUL = outx 
     220    latch17_JUL = outy 
     221    OUTZCH17_JUL[:,:,ijr] = outzch17_JUL[:,:] 
     222    ## ch18 ## 
     223    ind_jr18_JUL = np.where(jjr18_JUL[bbemis_ch18_JUL] == ijr+1)[0] 
     224    xx = lon18_JUL[bbemis_ch18_JUL][ind_jr18_JUL] 
     225    yy = lat18_JUL[bbemis_ch18_JUL][ind_jr18_JUL] 
     226    zz = emis18_JUL[bbemis_ch18_JUL][ind_jr18_JUL] 
     227    zz0 = min(zz) 
     228    zz1 = max(zz) 
     229    outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0, x1, y0, y1, zz0, zz1) 
     230    outzch18_JUL = outz 
     231    lonch18_JUL = outx 
     232    latch18_JUL = outy 
     233    OUTZCH18_JUL[:,:,ijr] = outzch18_JUL[:,:] 
    126234    ## ch12 ## 
    127235    ind_jr12_JUL = np.where(jjr12_JUL[bbemis_ch12_JUL] == ijr+1)[0] 
    … …  
    139247    ind_jr13_JUL = np.where(jjr13_JUL[bbemis_ch13_JUL] == ijr+1)[0] 
    140248    xx = lon13_JUL[bbemis_ch13_JUL][ind_jr13_JUL] 
    141     yy = lat12_JUL[bbemis_ch13_JUL][ind_jr13_JUL] 
     249    yy = lat13_JUL[bbemis_ch13_JUL][ind_jr13_JUL] 
    142250    zz = emis13_JUL[bbemis_ch13_JUL][ind_jr13_JUL] 
    143251    zz0 = min(zz) 
    … …  
    150258 
    151259 
    152 ####################################### 
    153 ## calcul de la climatologie moyenne ## 
    154 ####################################### 
    155  
    156 ## FEBRUARY ## 
    157 ## ch12 ## 
    158 mean_outzch12_FEB = np.zeros([len(latch12_FEB), len(lonch12_FEB)], float) 
    159 for ilon in range (0, len(lonch12_FEB)): 
    160     for ilat in range (0, len(latch12_FEB)): 
    161         mean_outzch12_FEB[ilat, ilon] = mean(OUTZCH12_FEB[ilat, ilon, :]) 
    162  
    163 ## ch13 ## 
    164 mean_outzch13_FEB = np.zeros([len(latch13_FEB), len(lonch13_FEB)], float) 
    165 for ilon in range (0, len(lonch13_FEB)): 
    166     for ilat in range (0, len(latch13_FEB)): 
    167         mean_outzch13_FEB[ilat, ilon] = mean(OUTZCH13_FEB[ilat, ilon, :]) 
    168  
    169  
    170 ## APRIL ## 
    171 ## ch12 ## 
    172 mean_outzch12_APR = np.zeros([len(latch12_APR), len(lonch12_APR)], float) 
    173 for ilon in range (0, len(lonch12_APR)): 
    174     for ilat in range (0, len(latch12_APR)): 
    175         mean_outzch12_APR[ilat, ilon] = mean(OUTZCH12_APR[ilat, ilon, :][nonzero(isnan(OUTZCH12_APR[ilat, ilon, :]) == False)]) 
    176  
    177 ## ch13 ## 
    178 mean_outzch13_APR = np.zeros([len(latch13_APR), len(lonch13_APR)], float) 
    179 for ilon in range (0, len(lonch13_APR)): 
    180     for ilat in range (0, len(latch13_APR)): 
    181         mean_outzch13_APR[ilat, ilon] = mean(OUTZCH13_APR[ilat, ilon, :][nonzero(isnan(OUTZCH13_APR[ilat, ilon, :]) == False)]) 
    182  
    183  
    184  
    185 ## JULY ## 
    186 ## ch12 ## 
    187 mean_outzch12_JUL = np.zeros([len(latch12_JUL), len(lonch12_JUL)], float) 
    188 for ilon in range (0, len(lonch12_JUL)): 
    189     for ilat in range (0, len(latch12_JUL)): 
    190         mean_outzch12_JUL[ilat, ilon] = mean(OUTZCH12_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH12_JUL[ilat, ilon, :]) == False)]) 
    191  
    192 ## ch13 ## 
    193 mean_outzch13_JUL = np.zeros([len(latch13_JUL), len(lonch13_JUL)], float) 
    194 for ilon in range (0, len(lonch13_JUL)): 
    195     for ilat in range (0, len(latch13_JUL)): 
    196         mean_outzch13_JUL[ilat, ilon] = mean(OUTZCH13_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH13_JUL[ilat, ilon, :]) == False)]) 
    197  
    198  
    199 ################## 
    200 ## cartographie ## 
    201 ################## 
     260################################################ 
     261## calcul de la climatologie moyenne par mois ## 
     262################################################ 
     263 
    202264lon = lonch12_FEB = lonch12_APR = lonch12_JUL = lonch13_FEB = lonch13_APR = lonch13_JUL 
    203265lat = latch12_FEB = latch12_APR = latch12_JUL = latch13_FEB = latch13_APR = latch13_JUL 
     266#lon = lonch17_FEB = lonch17_APR = lonch17_JUL = lonch18_FEB = lonch18_APR = lonch18_JUL 
     267#lat = latch17_FEB = latch17_APR = latch17_JUL = latch18_FEB = latch18_APR = latch18_JUL 
    204268 
    205269## FEBRUARY ## 
     270## ch12 ## 
     271mean_outzch12_FEB = np.zeros([len(lat), len(lon)], float) 
     272for ilon in range (0, len(lon)): 
     273    for ilat in range (0, len(lat)): 
     274        mean_outzch12_FEB[ilat, ilon] = mean(OUTZCH12_FEB[ilat, ilon, :]) 
     275 
     276## ch13 ## 
     277mean_outzch13_FEB = np.zeros([len(lat), len(lon)], float) 
     278for ilon in range (0, len(lon)): 
     279    for ilat in range (0, len(lat)): 
     280        mean_outzch13_FEB[ilat, ilon] = mean(OUTZCH13_FEB[ilat, ilon, :]) 
     281 
     282## ch17 ## 
     283mean_outzch17_FEB = np.zeros([len(lat), len(lon)], float) 
     284for ilon in range (0, len(lon)): 
     285    for ilat in range (0, len(lat)): 
     286        mean_outzch17_FEB[ilat, ilon] = mean(OUTZCH17_FEB[ilat, ilon, :]) 
     287 
     288## ch18 ## 
     289mean_outzch18_FEB = np.zeros([len(lat), len(lon)], float) 
     290for ilon in range (0, len(lon)): 
     291    for ilat in range (0, len(lat)): 
     292        mean_outzch18_FEB[ilat, ilon] = mean(OUTZCH18_FEB[ilat, ilon, :]) 
     293 
     294 
     295## APRIL ## 
     296## ch12 ## 
     297mean_outzch12_APR = np.zeros([len(lat), len(lon)], float) 
     298for ilon in range (0, len(lon)): 
     299    for ilat in range (0, len(lat)): 
     300        mean_outzch12_APR[ilat, ilon] = mean(OUTZCH12_APR[ilat, ilon, :][nonzero(isnan(OUTZCH12_APR[ilat, ilon, :]) == False)]) 
     301 
     302## ch13 ## 
     303mean_outzch13_APR = np.zeros([len(lat), len(lon)], float) 
     304for ilon in range (0, len(lon)): 
     305    for ilat in range (0, len(lat)): 
     306        mean_outzch13_APR[ilat, ilon] = mean(OUTZCH13_APR[ilat, ilon, :][nonzero(isnan(OUTZCH13_APR[ilat, ilon, :]) == False)]) 
     307 
     308## ch17 ## 
     309mean_outzch17_APR = np.zeros([len(lat), len(lon)], float) 
     310for ilon in range (0, len(lon)): 
     311    for ilat in range (0, len(lat)): 
     312        mean_outzch17_APR[ilat, ilon] = mean(OUTZCH17_APR[ilat, ilon, :][nonzero(isnan(OUTZCH17_APR[ilat, ilon, :]) == False)]) 
     313 
     314## ch18 ## 
     315mean_outzch18_APR = np.zeros([len(lat), len(lon)], float) 
     316for ilon in range (0, len(lon)): 
     317    for ilat in range (0, len(lat)): 
     318        mean_outzch18_APR[ilat, ilon] = mean(OUTZCH18_APR[ilat, ilon, :][nonzero(isnan(OUTZCH18_APR[ilat, ilon, :]) == False)]) 
     319 
     320 
     321 
     322## JULY ## 
     323## ch12 ## 
     324mean_outzch12_JUL = np.zeros([len(lat), len(lon)], float) 
     325for ilon in range (0, len(lon)): 
     326    for ilat in range (0, len(lat)): 
     327        mean_outzch12_JUL[ilat, ilon] = mean(OUTZCH12_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH12_JUL[ilat, ilon, :]) == False)]) 
     328 
     329## ch13 ## 
     330mean_outzch13_JUL = np.zeros([len(lat), len(lon)], float) 
     331for ilon in range (0, len(lon)): 
     332    for ilat in range (0, len(lat)): 
     333        mean_outzch13_JUL[ilat, ilon] = mean(OUTZCH13_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH13_JUL[ilat, ilon, :]) == False)]) 
     334 
     335## ch17 ## 
     336mean_outzch17_JUL = np.zeros([len(lat), len(lon)], float) 
     337for ilon in range (0, len(lon)): 
     338    for ilat in range (0, len(lat)): 
     339        mean_outzch17_JUL[ilat, ilon] = mean(OUTZCH17_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH17_JUL[ilat, ilon, :]) == False)]) 
     340 
     341## ch18 ## 
     342mean_outzch18_JUL = np.zeros([len(lat), len(lon)], float) 
     343for ilon in range (0, len(lon)): 
     344    for ilat in range (0, len(lat)): 
     345        mean_outzch18_JUL[ilat, ilon] = mean(OUTZCH18_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH18_JUL[ilat, ilon, :]) == False)]) 
     346 
     347 
     348 
     349###################### 
     350## calculs de stats ## 
     351###################### 
     352 
     353### VARIANCE - COVARIANCE ## 
     354 
     355## FEBRUARY ## 
     356imo = 0 
     357## ch12 ## 
     358varF12 = np.zeros([len(lat), len(lon)], float) 
     359for ilon in range (0, len(lon)): 
     360    for ilat in range (0, len(lat)): 
     361        varF12[ilat, ilon] = np.sum((OUTZCH12_FEB[ilat, ilon, :] - mean_outzch12_FEB[ilat, ilon])**2) 
     362 
     363var_FEB_12 = varF12 / len_month[imo] 
     364 
     365## ch13 ## 
     366varF13 = np.zeros([len(lat), len(lon)], float) 
     367for ilon in range (0, len(lon)): 
     368    for ilat in range (0, len(lat)): 
     369        varF13[ilat, ilon] = np.sum((OUTZCH13_FEB[ilat, ilon, :] - mean_outzch13_FEB[ilat, ilon])**2) 
     370 
     371var_FEB_13 = varF13 / len_month[imo] 
     372 
     373## ch12-ch13 ## 
     374cov_FEB = np.zeros([len(lat), len(lon)], float) 
     375for ilon in range (0, len(lon)): 
     376    for ilat in range (0, len(lat)): 
     377        cov_FEB[ilat, ilon] = np.sum((OUTZCH12_FEB[ilat, ilon, :] - mean_outzch12_FEB[ilat, ilon])*(OUTZCH13_FEB[ilat, ilon, :] - mean_outzch13_FEB[ilat, ilon])) 
     378 
     379cov_FEB_1213 = cov_FEB / len_month[imo] 
     380 
     381## ch17 ## 
     382varF17 = np.zeros([len(lat), len(lon)], float) 
     383for ilon in range (0, len(lon)): 
     384    for ilat in range (0, len(lat)): 
     385        varF17[ilat, ilon] = np.sum((OUTZCH17_FEB[ilat, ilon, :] - mean_outzch17_FEB[ilat, ilon])**2) 
     386 
     387var_FEB_17 = varF17 / len_month[imo] 
     388 
     389## ch18 ## 
     390varF18 = np.zeros([len(lat), len(lon)], float) 
     391for ilon in range (0, len(lon)): 
     392    for ilat in range (0, len(lat)): 
     393        varF18[ilat, ilon] = np.sum((OUTZCH18_FEB[ilat, ilon, :] - mean_outzch18_FEB[ilat, ilon])**2) 
     394 
     395var_FEB_18 = varF18 / len_month[imo] 
     396 
     397## ch17-ch18 ## 
     398cov_FEB = np.zeros([len(lat), len(lon)], float) 
     399for ilon in range (0, len(lon)): 
     400    for ilat in range (0, len(lat)): 
     401        cov_FEB[ilat, ilon] = np.sum((OUTZCH17_FEB[ilat, ilon, :] - mean_outzch17_FEB[ilat, ilon])*(OUTZCH18_FEB[ilat, ilon, :] - mean_outzch18_FEB[ilat, ilon])) 
     402 
     403cov_FEB_1718 = cov_FEB / len_month[imo] 
     404 
     405## ch13-ch17 ## 
     406cov_FEB = np.zeros([len(lat), len(lon)], float) 
     407for ilon in range (0, len(lon)): 
     408    for ilat in range (0, len(lat)): 
     409        cov_FEB[ilat, ilon] = np.sum((OUTZCH13_FEB[ilat, ilon, :] - mean_outzch13_FEB[ilat, ilon])*(OUTZCH17_FEB[ilat, ilon, :] - mean_outzch17_FEB[ilat, ilon])) 
     410 
     411cov_FEB_1317 = cov_FEB / len_month[imo] 
     412 
     413## ch12-ch18 ## 
     414cov_FEB = np.zeros([len(lat), len(lon)], float) 
     415for ilon in range (0, len(lon)): 
     416    for ilat in range (0, len(lat)): 
     417        cov_FEB[ilat, ilon] = np.sum((OUTZCH12_FEB[ilat, ilon, :] - mean_outzch12_FEB[ilat, ilon])*(OUTZCH18_FEB[ilat, ilon, :] - mean_outzch18_FEB[ilat, ilon])) 
     418 
     419cov_FEB_1218 = cov_FEB / len_month[imo] 
     420 
     421 
     422## APRIL ## 
     423imo = 1 
     424## ch12 ## 
     425varA12 = np.zeros([len(lat), len(lon)], float) 
     426for ilon in range (0, len(lon)): 
     427    for ilat in range (0, len(lat)): 
     428        varA12[ilat, ilon] = np.sum((OUTZCH12_APR[ilat, ilon, :] - mean_outzch12_APR[ilat, ilon])**2) 
     429 
     430var_APR_12 = varA12 / len_month[imo] 
     431 
     432## ch13 ## 
     433varA13 = np.zeros([len(lat), len(lon)], float) 
     434for ilon in range (0, len(lon)): 
     435    for ilat in range (0, len(lat)): 
     436        varA13[ilat, ilon] = np.sum((OUTZCH13_APR[ilat, ilon, :] - mean_outzch13_APR[ilat, ilon])**2) 
     437 
     438var_APR_13 = varA13 / len_month[imo] 
     439 
     440## ch17 ## 
     441varA17 = np.zeros([len(lat), len(lon)], float) 
     442for ilon in range (0, len(lon)): 
     443    for ilat in range (0, len(lat)): 
     444        varA17[ilat, ilon] = np.sum((OUTZCH17_APR[ilat, ilon, :] - mean_outzch17_APR[ilat, ilon])**2) 
     445 
     446var_APR_17 = varA17 / len_month[imo] 
     447 
     448## ch18 ## 
     449varA18 = np.zeros([len(lat), len(lon)], float) 
     450for ilon in range (0, len(lon)): 
     451    for ilat in range (0, len(lat)): 
     452        varA18[ilat, ilon] = np.sum((OUTZCH18_APR[ilat, ilon, :] - mean_outzch18_APR[ilat, ilon])**2) 
     453 
     454var_APR_18 = varA18 / len_month[imo] 
     455 
     456## ch17-ch18 ## 
     457cov_APR = np.zeros([len(lat), len(lon)], float) 
     458for ilon in range (0, len(lon)): 
     459    for ilat in range (0, len(lat)): 
     460        cov_APR[ilat, ilon] = np.sum((OUTZCH17_APR[ilat, ilon, :] - mean_outzch17_APR[ilat, ilon])*(OUTZCH18_APR[ilat, ilon, :] - mean_outzch18_APR[ilat, ilon])) 
     461 
     462cov_APR_1718 = cov_APR / len_month[imo] 
     463 
     464## ch12-ch18 ## 
     465cov_APR = np.zeros([len(lat), len(lon)], float) 
     466for ilon in range (0, len(lon)): 
     467    for ilat in range (0, len(lat)): 
     468        cov_APR[ilat, ilon] = np.sum((OUTZCH12_APR[ilat, ilon, :] - mean_outzch12_APR[ilat, ilon])*(OUTZCH18_APR[ilat, ilon, :] - mean_outzch18_APR[ilat, ilon])) 
     469 
     470cov_APR_1218 = cov_APR / len_month[imo] 
     471 
     472 
     473 
     474## JULY ## 
     475imo = 2 
     476## ch17 ## 
     477varJ17 = np.zeros([len(lat), len(lon)], float) 
     478for ilon in range (0, len(lon)): 
     479    for ilat in range (0, len(lat)): 
     480        varJ17[ilat, ilon] = np.sum((OUTZCH17_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH17_JUL[ilat, ilon, :]) == False)] - mean_outzch17_JUL[ilat, ilon])**2) 
     481 
     482var_JUL_17 = varJ17 / len_month[imo] 
     483 
     484## ch18 ## 
     485varJ18 = np.zeros([len(lat), len(lon)], float) 
     486for ilon in range (0, len(lon)): 
     487    for ilat in range (0, len(lat)): 
     488        varJ18[ilat, ilon] = np.sum((OUTZCH18_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH18_JUL[ilat, ilon, :]) == False)] - mean_outzch18_JUL[ilat, ilon])**2) 
     489 
     490var_JUL_18 = varJ18 / len_month[imo] 
     491 
     492## ch17-ch18 ## 
     493cov_JUL = np.zeros([len(lat), len(lon)], float) 
     494for ilon in range (0, len(lon)): 
     495    for ilat in range (0, len(lat)): 
     496        cov_JUL[ilat, ilon] = np.sum((OUTZCH17_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH17_JUL[ilat, ilon, :]) == False)] - mean_outzch17_JUL[ilat, ilon]) * (OUTZCH18_JUL[ilat, ilon, :][nonzero(isnan(OUTZCH18_JUL[ilat, ilon, :]) == False)] - mean_outzch18_JUL[ilat, ilon])) 
     497 
     498cov_JUL_1718 = cov_JUL / len_month[imo] 
     499 
     500 
     501plt.ion() 
    206502figure() 
    207 plt.ion() 
    208 ## ch12 ## 
    209 subplot(2,1,1) 
    210503m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    211504m.drawcoastlines(linewidth = 1) 
    … …  
    213506m.drawmeridians(np.arange(-180., 180., 20)) 
    214507xii,yii = m(*np.meshgrid(lon, lat)) 
    215 clevs = arange(0.3, 1., 0.001) 
    216 cs = m.contourf(xii, yii, mean_outzch12_FEB, clevs, cmap=cm.s3pcpn_l_r) 
     508clevs = arange(0., 0.0035, 0.00001) 
     509cs = m.contourf(xii, yii, cov_JUL_1718, clevs, cmap=cm.s3pcpn_l_r) 
    217510cbar = colorbar(cs) 
    218 cbar.set_label('Mean emissivity FEB [CH12] - SSMIS') 
    219 ## ch13 ## 
    220 subplot(2,1,2) 
    221 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    222 m.drawcoastlines(linewidth = 1) 
    223 m.drawparallels(np.arange(-90., -30., 20)) 
    224 m.drawmeridians(np.arange(-180., 180., 20)) 
    225 clevs = arange(0.3, 1., 0.001) 
    226 cs = m.contourf(xii, yii, mean_outzch13_FEB, clevs, cmap=cm.s3pcpn_l_r) 
    227 cbar = colorbar(cs) 
    228 cbar.set_label('Mean emissivity FEB [CH13] - SSMIS') 
    229 ## BIAIS ch12-ch13 ## 
    230 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    231 m.drawcoastlines(linewidth = 1) 
    232 m.drawparallels(np.arange(-90., -30., 20)) 
    233 m.drawmeridians(np.arange(-180., 180., 20)) 
    234 biais_FEB = mean_outzch12_FEB - mean_outzch13_FEB 
    235 clevs = arange(-0.35, 0., 0.001) 
    236 cs = m.contourf(xii, yii, biais_FEB, clevs, cmap=cm.s3pcpn_l_r) 
    237 cbar = colorbar(cs) 
    238 cbar.set_label('Bias of Mean emissivity FEB [CH12-CH13] - SSMIS') 
    239 ## VARIANCE ch12 - ch13 ## 
    240 stddev = np.zeros([len(lat), len(lon)], float) 
    241 N = len(lon)*len(lat) 
    242 for ilat in range (0, len(lat)): 
    243     for ilon in range (0, len(lon)): 
    244         stddev[ilat, ilon] =(mean_outzch12_FEB[ilat, ilon]-mean_outzch13_FEB[ilat, ilon])**2 
    245  
    246 std_FEB = sqrt(stddev/N) 
    247 figure () 
    248 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    249 m.drawcoastlines(linewidth = 1) 
    250 m.drawparallels(np.arange(-90., -30., 20)) 
    251 m.drawmeridians(np.arange(-180., 180., 20)) 
    252 clevs = arange(0., 0.01, 0.0001) 
    253 cs = m.contourf(xii, yii, std_FEB, clevs, cmap=cm.s3pcpn_l_r) 
    254 cbar = colorbar(cs) 
    255 cbar.set_label('Stantard deviation of Mean emissivity FEB [CH12-CH13] - SSMIS') 
    256  
     511cbar.set_label('covar(emis) JUL [CH17-CH18] - SSMIS') 
     512xticks(arange(-180, 200, 20), rotation = 45) 
     513yticks(arange(-90, -10, 20)) 
     514 
     515 
     516## BIAIS ## 
     517 
     518## FEBRUARY ## 
     519biais_FEB_polar1 = mean_outzch12_FEB - mean_outzch13_FEB 
     520biais_FEB_polar2 = mean_outzch17_FEB - mean_outzch18_FEB 
     521biais_FEB_freq1 = mean_outzch12_FEB - mean_outzch18_FEB 
     522biais_FEB_freq2 = mean_outzch13_FEB - mean_outzch17_FEB 
     523 
     524## APRIL ## 
     525biais_APR_polar1 = mean_outzch12_APR - mean_outzch13_APR 
     526biais_APR_polar2 = mean_outzch17_APR - mean_outzch18_APR 
    257527 
    258528## JULY ## 
     529biais_JUL_polar1 = mean_outzch12_JUL - mean_outzch13_JUL 
     530biais_JUL_polar2 = mean_outzch17_JUL - mean_outzch18_JUL 
     531 
     532 
     533 
    259534figure() 
    260 plt.ion() 
    261 ## ch12 ## 
    262 subplot(2,1,1) 
    263535m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    264536m.drawcoastlines(linewidth = 1) 
    … …  
    266538m.drawmeridians(np.arange(-180., 180., 20)) 
    267539xii,yii = m(*np.meshgrid(lon, lat)) 
    268 clevs = arange(0.3, 1., 0.001) 
    269 cs = m.contourf(xii, yii, mean_outzch12_JUL, clevs, cmap=cm.s3pcpn_l_r) 
     540clevs = arange(-0.24, 0.32, 0.001) 
     541cs = m.contourf(xii, yii, biais_FEB_freq2, clevs, cmap=cm.s3pcpn_l_r) 
    270542cbar = colorbar(cs) 
    271 cbar.set_label('Mean emissivity JUL [CH12] - SSMIS') 
    272 ## ch13 ## 
    273 subplot(2,1,2) 
    274 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    275 m.drawcoastlines(linewidth = 1) 
    276 m.drawparallels(np.arange(-90., -30., 20)) 
    277 m.drawmeridians(np.arange(-180., 180., 20)) 
    278 clevs = arange(0.3, 1., 0.001) 
    279 cs = m.contourf(xii, yii, mean_outzch13_JUL, clevs, cmap=cm.s3pcpn_l_r) 
    280 cbar = colorbar(cs) 
    281 cbar.set_label('Mean emissivity JUL [CH13] - SSMIS') 
    282 ## BIAIS ch12-ch13 ## 
    283 figure() 
    284 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    285 m.drawcoastlines(linewidth = 1) 
    286 m.drawparallels(np.arange(-90., -30., 20)) 
    287 m.drawmeridians(np.arange(-180., 180., 20)) 
    288 biais_JUL = mean_outzch12_JUL - mean_outzch13_JUL 
    289 clevs = arange(-0.30, 0., 0.001) 
    290 cs = m.contourf(xii, yii, biais_JUL, clevs, cmap=cm.s3pcpn_l_r) 
    291 cbar = colorbar(cs) 
    292 cbar.set_label('Bias of Mean emissivity JUL [CH12-CH13] - SSMIS') 
    293 ## VARIANCE ch12 - ch13 ## 
    294 stddev = np.zeros([len(lat), len(lon)], float) 
    295 N = len(lon)*len(lat) 
    296 for ilat in range (0, len(lat)): 
    297     for ilon in range (0, len(lon)): 
    298         stddev[ilat, ilon] =(mean_outzch12_JUL[ilat, ilon]-mean_outzch13_JUL[ilat, ilon])**2 
    299  
    300 std_JUL = sqrt(stddev/N) 
    301 figure () 
    302 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    303 m.drawcoastlines(linewidth = 1) 
    304 m.drawparallels(np.arange(-90., -30., 20)) 
    305 m.drawmeridians(np.arange(-180., 180., 20)) 
    306 clevs = arange(0., 0.01, 0.0001) 
    307 cs = m.contourf(xii, yii, std_JUL, clevs, cmap=cm.s3pcpn_l_r) 
    308 cbar = colorbar(cs) 
    309 cbar.set_label('Stantard deviation of Mean emissivity JUL [CH12-CH13] - SSMIS') 
    310  
    311  
    312 ## APRIL ## 
    313 figure() 
    314 plt.ion() 
    315 ## ch12 ## 
    316 subplot(2,1,1) 
    317 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    318 m.drawcoastlines(linewidth = 1) 
    319 m.drawparallels(np.arange(-90., -30., 20)) 
    320 m.drawmeridians(np.arange(-180., 180., 20)) 
    321 xii,yii = m(*np.meshgrid(lon, lat)) 
    322 clevs = arange(0.5, 1., 0.001) 
    323 cs = m.contourf(xii, yii, mean_outzch12_APR, clevs, cmap=cm.s3pcpn_l_r) 
    324 cbar = colorbar(cs) 
    325 cbar.set_label('Mean emissivity APR [CH12] - SSMIS') 
    326 ## ch13 ## 
    327 subplot(2,1,2) 
    328 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    329 m.drawcoastlines(linewidth = 1) 
    330 m.drawparallels(np.arange(-90., -30., 20)) 
    331 m.drawmeridians(np.arange(-180., 180., 20)) 
    332 clevs = arange(0.5, 1., 0.001) 
    333 cs = m.contourf(xii, yii, mean_outzch13_APR, clevs, cmap=cm.s3pcpn_l_r) 
    334 cbar = colorbar(cs) 
    335 cbar.set_label('Mean emissivity APR [CH13] - SSMIS') 
    336 ## BIAIS ch12-ch13 ## 
    337 figure() 
    338 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    339 m.drawcoastlines(linewidth = 1) 
    340 m.drawparallels(np.arange(-90., -30., 20)) 
    341 m.drawmeridians(np.arange(-180., 180., 20)) 
    342 biais_APR = mean_outzch12_APR - mean_outzch13_APR 
    343 clevs = arange(-0.04, -0.017, 0.0001) 
    344 cs = m.contourf(xii, yii, biais_APR, clevs, cmap=cm.s3pcpn_l_r) 
    345 cbar = colorbar(cs) 
    346 cbar.set_label('Bias of Mean emissivity APR [CH12-CH13] - SSMIS') 
    347 ## VARIANCE ch12 - ch13 ## 
    348 stddev = np.zeros([len(lat), len(lon)], float) 
    349 N = len(lon)*len(lat) 
    350 for ilat in range (0, len(lat)): 
    351     for ilon in range (0, len(lon)): 
    352         stddev[ilat, ilon] =(mean_outzch12_APR[ilat, ilon] - mean_outzch13_APR[ilat, ilon])**2 
    353  
    354 std_APR = sqrt(stddev/N) 
    355 figure () 
    356 m = Basemap(llcrnrlon=-180, urcrnrlon=180, llcrnrlat=-90, urcrnrlat=-30, projection='cyl', resolution='c', fix_aspect=True) 
    357 m.drawcoastlines(linewidth = 1) 
    358 m.drawparallels(np.arange(-90., -30., 20)) 
    359 m.drawmeridians(np.arange(-180., 180., 20)) 
    360 clevs = arange(0., 0.0015, 0.00001) 
    361 cs = m.contourf(xii, yii, std_APR, clevs, cmap=cm.s3pcpn_l_r) 
    362 cbar = colorbar(cs) 
    363 cbar.set_label('Stantard deviation of Mean emissivity APR [CH12-CH13] - SSMIS') 
    364  
    365  
    366 ########################## 
    367 # DIAGRAMME DE HOVMOLLER # 
    368 ########################## 
    369 # shape(tbch17_anom_JUN) = [ilat, ilon, ijr] 
    370 ## sur la bande de latitude (-80° / -85°) 
    371 ## FEBRUARY ## 
    372 imo = 0 
    373 bbtranche_FEB = nonzero((lat >= -85.) & (lat <= -80)) 
    374 mean_std_FEB = np.zeros([len(lon), len_month[imo]], float) 
    375 for ilon in range (0, len(lon)): 
    376     for ijr in range (0,len_month[imo]): 
    377         mean_std_FEB[ilon,ijr] = mean(std_FEB[bbtranche_FEB][:,ilon,ijr]) 
    378  
    379 y_time, x_space = np.meshgrid(arange(0, len_month[imo], 1), lon) 
    380 fig = plt.figure() 
    381 plt.pcolor(x_space, y_time, mean_std_FEB, cmap=cm.s3pcpn_l_r, vmin = -10., vmax = 15.) 
    382 plt.axis([-180., 180., 0, 28]) 
    383 cb = plt.colorbar() 
    384 cb.set_label('std of emissivity - FEB SSMIS CH17') 
    385 plt.xticks(arange(-180.,200.,40)) 
    386 plt.yticks(arange(0, 28, 1), arange (1, 29, 1)) 
    387 plt.xlabel('longitude') 
    388 plt.ylabel('FEBRUARY 2010') 
    389  
    390 ## MAY ## 
    391 bbtranche17_MAY = nonzero((latch17_MAY >= -85.) & (latch17_MAY <= -75)) 
    392 mean_tbch17_anom_MAY = np.zeros([len(lonch17_MAY), 31], float) 
    393 for ilon in range (0, len(lonch17_MAY)): 
    394     for ijr in range (0,31): 
    395         mean_tbch17_anom_MAY[ilon,ijr] = mean(tbch17_anom_MAY[bbtranche17_MAY][:,ilon,ijr]) 
    396  
    397 y_time, x_space = np.meshgrid(arange(0,31,1), lonch17_MAY) 
    398 fig = plt.figure() 
    399 plt.pcolor(x_space, y_time, mean_tbch17_anom_MAY, cmap=cm.s3pcpn_l_r, vmin = -12., vmax = 25.) 
    400 plt.axis([-180., 180., 0, 30]) 
    401 cb = plt.colorbar() 
    402 cb.set_label('Tb anomaly - SSMIS CH17') 
    403 plt.xticks(arange(-180.,200.,40)) 
    404 plt.yticks(arange(0, 30, 1), arange(1,31,1)) 
    405 plt.xlabel('longitude') 
    406 plt.ylabel('MAY 2010') 
    407  
    408 ## JUNE ## 
    409 bbtranche17_JUN = nonzero((latch17_JUN >= -85.) & (latch17_JUN <= -75)) 
    410 mean_tbch17_anom_JUN = np.zeros([len(lonch17_JUN), 30], float) 
    411 for ilon in range (0, len(lonch17_JUN)): 
    412     for ijr in range (0,30): 
    413         mean_tbch17_anom_JUN[ilon,ijr] = tbch17_anom_JUN[bbtranche17_JUN][:,ilon,ijr].mean() 
    414  
    415  
    416 y_time, x_space = np.meshgrid(arange(0,30,1), lonch17_APR) 
    417 fig = plt.figure() 
    418 plt.pcolor(x_space, y_time, mean_tbch17_anom_APR, cmap=cm.s3pcpn_l_r, vmin = -30., vmax = 25.) 
    419 plt.axis([-180., 180., 0, 29]) 
    420 cb = plt.colorbar() 
    421 cb.set_label('Tb anomaly - SSMIS CH17') 
    422 plt.xticks(arange(-180.,220.,40)) 
    423 plt.yticks(arange(0, 30, 1), arange(1,31,1)) 
    424 plt.xlabel('longitude') 
    425 plt.ylabel('JUNE 2010') 
    426  
     543cbar.set_label('biais(emis) FEB [CH13-CH17] - SSMIS') 
     544xticks(arange(-180, 200, 20), rotation = 45) 
     545yticks(arange(-90, -10, 20)) 

  • trunk/src/scripts_Laura/read_SSMIS_test.py

    r22 r23  
    1111 
    1212 
     13 
     14################ fichiers par canal - mois de juin ################################################### 
     15 
     16 
    1317f1 = '/net/dedale/usr/dedale/surf/lelod/ANTARC/SSMIS_CH' 
    1418f3 = '_ANTARC_JUNE2010.DAT' 
    … …  
    2630 
    2731 
    28 ################ fichiers par canal - mois de juin ################################################### 
     32 
    2933 
    3034 
    … …  
    186190 
    187191 
    188 ################ fichiers par mois - canal 17 ################################################### 
     192 
     193############### fichiers par mois pour deux canaux (polars H et V) ################################## 
     194 
     195######## 
     196# ch17 # 
     197######## 
    189198 
    190199f1 = '/net/dedale/usr/dedale/surf/lelod/ANTARC/SSMIS_CH17_ANTARC_' 
    … …  
    323332 
    324333 
    325 ############### fichiers par mois pour deux canaux (polars H et V) ################################## 
     334######## 
     335# ch18 # 
     336######## 
     337 
     338f1 = '/net/dedale/usr/dedale/surf/lelod/ANTARC/SSMIS_CH18_ANTARC_' 
     339f3 = '2010.DAT' 
     340date=np.array(['FEBRUARY', 'APRIL', 'JULY']) 
     341numlines = np.zeros([len(date)],int) 
     342 
     343for imo in range (0, len(date)): 
     344     print date[imo]  
     345     f = f1 + str(date[imo]) + f3 
     346     fichier = open(f, 'r') 
     347     numlines[imo] = 0 
     348     for line in fichier: numlines[imo] += 1 
     349 
     350      fichier.close() 
     351 
     352 
     353imo = 0 # FEBUARY 
     354fichier = open(f1 + str(date[imo]) + f3, 'r') 
     355ssmis = np.zeros([18, numlines[imo]], float) 
     356for iligne in range (0,numlines[imo]): 
     357    line = fichier.readline() 
     358    liste = line.split() 
     359    for j in range(0,18): 
     360        ssmis[j,iligne] = float(liste[j]) 
     361 
     362    fichier.close 
     363 
     364ssch18_FEB=ssmis 
     365lon18_FEB=ssch18_FEB[0,:] 
     366lat18_FEB=ssch18_FEB[1,:] 
     367jjr18_FEB=ssch18_FEB[4,:] 
     368ts18_FEB=ssch18_FEB[8,:] 
     369emis18_FEB=ssch18_FEB[14,:] 
     370tb18_FEB=ssch18_FEB[13,:] 
     371tup18_FEB=ssch18_FEB[16,:] 
     372tdn18_FEB=ssch18_FEB[15,:] 
     373trans18_FEB=ssch18_FEB[17,:] 
     374orog18_FEB=ssch18_FEB[11,:] 
     375 
     376 
     377imo = 1 # APRIL 
     378fichier = open(f1 + str(date[imo]) + f3, 'r') 
     379ssmis = np.zeros([18, numlines[imo]], float) 
     380for iligne in range (0,numlines[imo]): 
     381    line = fichier.readline() 
     382    liste = line.split() 
     383    for j in range(0,18): 
     384        ssmis[j,iligne] = float(liste[j]) 
     385 
     386    fichier.close 
     387 
     388ssch18_APR=ssmis 
     389lon18_APR=ssch18_APR[0,:] 
     390lat18_APR=ssch18_APR[1,:] 
     391jjr18_APR=ssch18_APR[4,:] 
     392ts18_APR=ssch18_APR[8,:] 
     393emis18_APR=ssch18_APR[14,:] 
     394tb18_APR=ssch18_APR[13,:] 
     395tup18_APR=ssch18_APR[16,:] 
     396tdn18_APR=ssch18_APR[15,:] 
     397trans18_APR=ssch18_APR[17,:] 
     398orog18_APR=ssch18_APR[11,:] 
     399 
     400 
     401 
     402imo = 2 # JULY 
     403fichier = open(f1 + str(date[imo]) + f3, 'r') 
     404ssmis = np.zeros([18, numlines[imo]], float) 
     405for iligne in range (0,numlines[imo]): 
     406    line = fichier.readline() 
     407    liste = line.split() 
     408    for j in range(0,18): 
     409        ssmis[j,iligne] = float(liste[j]) 
     410 
     411    fichier.close 
     412 
     413ssch18_JUL=ssmis 
     414lon18_JUL=ssch18_JUL[0,:] 
     415lat18_JUL=ssch18_JUL[1,:] 
     416jjr18_JUL=ssch18_JUL[4,:] 
     417ts18_JUL=ssch18_JUL[8,:] 
     418emis18_JUL=ssch18_JUL[14,:] 
     419tb18_JUL=ssch18_JUL[13,:] 
     420tup18_JUL=ssch18_JUL[16,:] 
     421tdn18_JUL=ssch18_JUL[15,:] 
     422trans18_JUL=ssch18_JUL[17,:] 
     423orog18_JUL=ssch18_JUL[11,:] 
     424 
    326425 
    327426########## 
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