| 1 | ### =========================================================================== |
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| 2 | ### |
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| 3 | ### Compute calving weights. |
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| 4 | ### |
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| 5 | ### =========================================================================== |
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| 6 | ## |
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| 7 | ## Warning, to install, configure, run, use any of Olivier Marti's |
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| 8 | ## software or to read the associated documentation you'll need at least |
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| 9 | ## one (1) brain in a reasonably working order. Lack of this implement |
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| 10 | ## will void any warranties (either express or implied). |
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| 11 | ## O. Marti assumes no responsability for errors, omissions, |
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| 12 | ## data loss, or any other consequences caused directly or indirectly by |
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| 13 | ## the usage of his software by incorrectly or partially configured |
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| 14 | ## personal. |
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| 15 | ## |
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| 16 | import netCDF4, numpy as np |
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| 17 | import sys, os, platform, argparse, textwrap, time |
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| 18 | from scipy import ndimage |
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| 19 | import nemo |
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| 20 | |
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| 21 | ## SVN information |
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| 22 | __Author__ = "$Author$" |
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| 23 | __Date__ = "$Date$" |
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| 24 | __Revision__ = "$Revision$" |
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| 25 | __Id__ = "$Id$" |
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| 26 | __HeadURL__ = "$HeadURL$" |
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| 27 | __SVN_Date__ = "$SVN_Date: $" |
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| 28 | |
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| 29 | ### |
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| 30 | |
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| 31 | ### ===== Handling command line parameters ================================================== |
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| 32 | # Creating a parser |
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| 33 | parser = argparse.ArgumentParser ( |
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| 34 | description = """Compute calving weights""", |
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| 35 | epilog='-------- End of the help message --------') |
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| 36 | |
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| 37 | # Adding arguments |
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| 38 | parser.add_argument ('--oce' , help='oce model name', type=str, default='eORCA1.2', choices=['ORCA2.3', 'eORCA1.2', 'eORCA025'] ) |
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| 39 | parser.add_argument ('--atm' , help='atm model name (ICO* or LMD*)', type=str, default='ICO40' ) |
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| 40 | parser.add_argument ('--type' , help='Type of distribution', type=str, choices=['iceshelf', 'iceberg', 'nosouth', 'full'], default='full' ) |
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| 41 | parser.add_argument ('--dir' , help='Directory of input file', type=str, default='./' ) |
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| 42 | parser.add_argument ('--repartition_file', help='Data files with iceberg melting' , type=str, default='./runoff-icb_DaiTrenberth_Depoorter_eORCA1_JD.nc' ) |
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| 43 | parser.add_argument ('--repartition_var' , help='Variable name for iceshelf' , type=str, default=None) |
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| 44 | |
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| 45 | # Parse command line |
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| 46 | myargs = parser.parse_args() |
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| 47 | |
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| 48 | # Model Names |
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| 49 | src_Name = myargs.atm ; dst_Name = myargs.oce |
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| 50 | |
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| 51 | # Default vars |
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| 52 | if myargs.repartition_var == None : |
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| 53 | # Runoff from Antarctica iceshelves (Depoorter, 2013) |
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| 54 | if myargs.type == 'iceshelf' : repartitionVar = 'sornfisf' |
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| 55 | |
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| 56 | # Runoff from Antarctica iceberg (Depoorter, 2013) |
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| 57 | if myargs.type == 'iceberg' : repartitionVar = 'Icb_Flux' |
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| 58 | |
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| 59 | else : repartitionVar = myargs.repartition_var |
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| 60 | |
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| 61 | # Latitude limits of each calving zone |
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| 62 | limit_lat = ( (40.0, 90.0), (-50.0, 40.0), ( -90.0, -50.0) ) |
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| 63 | |
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| 64 | nb_zone = len(limit_lat) |
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| 65 | ### ========================================================================================== |
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| 66 | |
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| 67 | # Model short names |
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| 68 | src_name = None ; dst_name = None |
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| 69 | if src_Name.count('ICO') != 0 : src_name = 'ico' |
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| 70 | if src_Name.count('LMD') != 0 : src_name = 'lmd' |
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| 71 | if dst_Name.count('ORCA') != 0 : dst_name = 'orc' |
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| 72 | |
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| 73 | CplModel = dst_Name + 'x' + src_Name |
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| 74 | |
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| 75 | print ('Atm names : ' + src_name + ' ' + src_Name ) |
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| 76 | print ('Oce names : ' + dst_name + ' ' + dst_Name ) |
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| 77 | print (' ') |
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| 78 | |
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| 79 | # Reading domains characteristics |
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| 80 | areas = myargs.dir + '/areas_' + dst_Name + 'x' + src_Name + '.nc' |
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| 81 | masks = myargs.dir + '/masks_' + dst_Name + 'x' + src_Name + '.nc' |
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| 82 | grids = myargs.dir + '/grids_' + dst_Name + 'x' + src_Name + '.nc' |
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| 83 | |
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| 84 | print ('Opening ' + areas) |
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| 85 | f_areas = netCDF4.Dataset ( areas, "r" ) |
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| 86 | print ('Opening ' + masks) |
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| 87 | f_masks = netCDF4.Dataset ( masks, "r" ) |
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| 88 | print ('Opening ' + grids) |
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| 89 | f_grids = netCDF4.Dataset ( grids, "r" ) |
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| 90 | |
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| 91 | src_lon = f_grids.variables ['t' + src_name + '.lon'][:] |
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| 92 | src_lat = f_grids.variables ['t' + src_name + '.lat'][:] |
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| 93 | dst_lon = f_grids.variables ['t' + dst_name + '.lon'][:] |
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| 94 | dst_lat = f_grids.variables ['t' + dst_name + '.lat'][:] |
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| 95 | |
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| 96 | src_msk = f_masks.variables ['t' + src_name + '.msk'][:] |
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| 97 | dst_msk = f_masks.variables ['t' + dst_name + '.msk'][:] |
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| 98 | dst_mskutil = 1-dst_msk # Reversing the convention : 0 on continent, 1 on ocean |
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| 99 | print ('dst_msk : ' + str(np.sum(dst_msk))) |
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| 100 | print ('dst_mskutil : ' + str(np.sum(dst_mskutil))) |
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| 101 | |
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| 102 | # Periodicity masking for NEMO |
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| 103 | if dst_Name == 'ORCA2.3' : nperio_dst = 4 |
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| 104 | if dst_Name == 'eORCA1.2' : nperio_dst = 6 |
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| 105 | if dst_Name == 'ORCA025' : nperio_dst = 6 |
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| 106 | if dst_Name == 'eORCA025' : nperio_dst = 6 |
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| 107 | print ('nperio_dst: ' + str(nperio_dst) ) |
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| 108 | dst_mskutil = nemo.lbc_mask (dst_mskutil, nperio=nperio_dst, cd_type='T' ) |
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| 109 | print ('dst_mskutil : ' + str(np.sum(dst_mskutil))) |
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| 110 | |
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| 111 | ## |
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| 112 | ## Fill Closed seas and other |
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| 113 | ## ================================================== |
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| 114 | |
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| 115 | # Preparation by closing some straits |
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| 116 | # ----------------------------------- |
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| 117 | if dst_Name == 'eORCA025' : |
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| 118 | print ('Filling some seas for eORCA025') |
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| 119 | # Set Gibraltar strait to 0 to fill Mediterranean sea |
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| 120 | dst_mskutil[838:841,1125] = 0 |
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| 121 | # Set Bal-El-Mandeb to 0 to fill Red Sea |
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| 122 | dst_mskutil[736,1321:1324] = 0 |
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| 123 | # Set Stagerak to 0 to fill Baltic Sea |
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| 124 | dst_mskutil[961,1179:1182] = 0 |
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| 125 | # Set Ormuz Strait to 0 to fill Arabo-Persian Gulf |
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| 126 | dst_mskutil[794:798,1374] = 0 |
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| 127 | # Set Hudson Strait to 0 to fill Hudson Bay |
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| 128 | dst_mskutil[997:1012,907] = 0 |
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| 129 | |
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| 130 | if dst_Name == 'eORCA1.2' : |
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| 131 | print ('Filling some seas for eORCA1.2') |
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| 132 | # Set Gibraltar strait to 0 to fill Mediterrean sea |
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| 133 | dst_mskutil[240, 283] = 0 |
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| 134 | # Set Bal-El-Mandeb to 0 to fill Red Sea |
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| 135 | dst_mskutil[211:214, 332] = 0 |
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| 136 | # Set Stagerak to 0 to fill Baltic Sea |
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| 137 | dst_mskutil[272:276, 293] = 0 |
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| 138 | # Set Ormuz Strait to 0 to fill Arabo-Persian Gulf |
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| 139 | dst_mskutil[227:230, 345] = 0 |
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| 140 | # Set Hudson Strait to 0 to fill Hudson Bay |
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| 141 | dst_mskutil[284,222:225] = 0 |
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| 142 | |
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| 143 | if dst_Name == 'ORCA2.3' : |
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| 144 | print ('Filling some seas for ORCA2.3') |
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| 145 | # Set Gibraltar strait to 0 to fill Mediterrean sea |
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| 146 | dst_mskutil[101,139] = 0 |
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| 147 | # Set Black Sea to zero. At the edge of the domain : binary_fill_holes fails |
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| 148 | dst_mskutil[ 99:111, 0: 5] = 0 |
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| 149 | dst_mskutil[106:111, 173:182] = 0 |
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| 150 | # Set Stagerak to 0 to fill Baltic Sea |
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| 151 | dst_mskutil[115,144] = 0 |
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| 152 | # Set Hudson Strait to 0 to fill Hudson Bay |
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| 153 | dst_mskutil[120:123,110] = 0 |
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| 154 | # Set Bal-El-Mandeb to 0 to fill Red Sea |
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| 155 | dst_mskutil[87:89,166] = 0 |
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| 156 | |
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| 157 | dst_closed = dst_mskutil |
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| 158 | |
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| 159 | # Fill closed seas with image processing library |
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| 160 | # ---------------------------------------------- |
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| 161 | dst_mskutil = nemo.lbc_mask ( 1-ndimage.binary_fill_holes (1-nemo.lbc(dst_mskutil, nperio=nperio_dst, cd_type='T')), nperio=nperio_dst, cd_type='T' ) |
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| 162 | |
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| 163 | # Surfaces |
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| 164 | src_srf = f_areas.variables ['t' + src_name + '.srf'] |
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| 165 | dst_srf = f_areas.variables ['t' + dst_name + '.srf'] |
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| 166 | dst_srfutil = dst_srf * np.float64 (dst_mskutil) |
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| 167 | |
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| 168 | dst_srfutil_sum = np.sum( dst_srfutil) |
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| 169 | |
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| 170 | src_clo = f_grids.variables ['t' + src_name + '.clo'][:] |
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| 171 | src_cla = f_grids.variables ['t' + src_name + '.cla'][:] |
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| 172 | dst_clo = f_grids.variables ['t' + dst_name + '.clo'][:] |
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| 173 | dst_cla = f_grids.variables ['t' + dst_name + '.cla'][:] |
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| 174 | |
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| 175 | # Indices |
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| 176 | ( src_jpj, src_jpi) = src_lat.shape ; src_grid_size = src_jpj*src_jpi |
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| 177 | ( dst_jpj, dst_jpi) = dst_lat.shape ; dst_grid_size = dst_jpj*dst_jpi |
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| 178 | orc_index = np.arange (dst_jpj*dst_jpi, dtype=np.int32) + 1 # Fortran indices (starting at one) |
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| 179 | |
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| 180 | ### ===== Reading needed data ================================================== |
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| 181 | if myargs.type in ['iceberg', 'iceshelf' ]: |
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| 182 | # Reading data file for calving or iceberg geometry around Antarctica |
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| 183 | print ( 'Opening ' + myargs.repartition_file) |
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| 184 | f_repartition = netCDF4.Dataset ( myargs.repartition_file, "r" ) |
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| 185 | repartition = np.sum ( f_repartition.variables [repartitionVar][:], axis=0 ) |
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| 186 | |
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| 187 | ## Before loop on basins |
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| 188 | remap_matrix = np.empty ( shape=(0), dtype=np.float64 ) |
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| 189 | src_address = np.empty ( shape=(0), dtype=np.int32 ) |
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| 190 | dst_address = np.empty ( shape=(0), dtype=np.int32 ) |
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| 191 | |
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| 192 | print (' ') |
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| 193 | |
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| 194 | ### ===== Starting loop on basins============================================== |
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| 195 | |
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| 196 | # Initialise some fields |
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| 197 | remap_matrix = np.empty ( shape=(0), dtype=np.float64 ) |
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| 198 | src_address = np.empty ( shape=(0), dtype=np.int32 ) |
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| 199 | dst_address = np.empty ( shape=(0), dtype=np.int32 ) |
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| 200 | |
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| 201 | basin_msk = np.zeros( shape=(nb_zone, dst_jpj, dst_jpi), dtype=np.float32) |
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| 202 | key_repartition = np.zeros( shape=(nb_zone, dst_jpj, dst_jpi), dtype=np.float64) |
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| 203 | |
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| 204 | ## Loop |
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| 205 | for n_bas in np.arange ( nb_zone ) : |
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| 206 | south = False ; ok_run = False |
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| 207 | lat_south = np.min(limit_lat[n_bas]) ; lat_north = np.max(limit_lat[n_bas]) |
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| 208 | if lat_south <= -60.0 : south = True |
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| 209 | |
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| 210 | print ( 'basin: {:2d} -- Latitudes: {:+.1f} {:+.1f} --'.format(n_bas, lat_south, lat_north) ) |
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| 211 | ## |
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| 212 | if myargs.type == 'iceberg' and south : ok_run = True ; print ('Applying iceberg to south' ) |
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| 213 | if myargs.type == 'iceshelf' and south : ok_run = True ; print ('Applying iceshelf to south' ) |
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| 214 | if myargs.type == 'iceberg' and not south : ok_run = False ; print ('Skipping iceberg: not south ') |
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| 215 | if myargs.type == 'iceshelf' and not south : ok_run = False ; print ('Skipping iceshelf: not south ') |
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| 216 | if myargs.type == 'nosouth' and south : ok_run = False ; print ('Skipping south: nosouth case' ) |
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| 217 | if myargs.type == 'nosouth' and not south : ok_run = True ; print ('Running: not in south, uniform repartition') |
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| 218 | if myargs.type == 'full' : ok_run = True ; print ('Running general trivial case, uniform repartition' ) |
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| 219 | |
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| 220 | if ok_run : |
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| 221 | # Calving integral send to one point per latitude bands |
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| 222 | index_src = ((src_grid_size - 1)*n_bas) // (nb_zone-1) + 1 |
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| 223 | |
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| 224 | # Basin mask |
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| 225 | basin_msk[n_bas] = np.where ( (dst_lat > lat_south ) & (dst_lat <= lat_north ), dst_mskutil, 0 ) |
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| 226 | |
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| 227 | # Repartition pattern |
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| 228 | if myargs.type in ['iceberg', 'iceshelf' ] : key_repartition[n_bas] = repartition * basin_msk[n_bas] |
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| 229 | else : key_repartition[n_bas] = basin_msk[n_bas] |
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| 230 | |
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| 231 | # Integral and normalisation |
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| 232 | sum_repartition = np.sum ( key_repartition[n_bas] * dst_srfutil ) |
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| 233 | key_repartition = key_repartition / sum_repartition |
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| 234 | |
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| 235 | print ( 'Sum of repartition key : {:12.3e}'.format (np.sum (key_repartition[n_bas] )) ) |
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| 236 | print ( 'Integral (area weighted) of repartition key : {:12.3e}'.format (np.sum (key_repartition[n_bas] * dst_srfutil )) ) |
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| 237 | |
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| 238 | # Basin surface (modulated by repartition key) |
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| 239 | basin_srfutil = np.sum ( key_repartition[n_bas] * dst_srfutil ) |
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| 240 | |
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| 241 | # Weights and links |
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| 242 | poids = 1.0 / basin_srfutil |
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| 243 | matrix_local = np.where ( basin_msk[n_bas].ravel() > 0.5, key_repartition[n_bas].ravel()*poids, 0. ) |
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| 244 | matrix_local = matrix_local[matrix_local > 0.0] # Keep only non zero values |
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| 245 | num_links = np.count_nonzero (matrix_local) |
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| 246 | # Address on source grid : all links points to the same LMDZ point. |
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| 247 | src_address_local = np.ones(num_links, dtype=np.int32 )*index_src |
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| 248 | # Address on destination grid : each NEMO point with non zero link |
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| 249 | dst_address_local = np.where ( key_repartition[n_bas].ravel() > 0.0, orc_index, 0) |
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| 250 | dst_address_local = dst_address_local[dst_address_local > 0] |
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| 251 | # Append to global tabs |
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| 252 | remap_matrix = np.append ( remap_matrix, matrix_local ) |
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| 253 | src_address = np.append ( src_address , src_address_local ) |
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| 254 | dst_address = np.append ( dst_address , dst_address_local ) |
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| 255 | # |
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| 256 | #print ( 'Sum of remap_matrix : {:12.3e}'.format(np.sum(matrix_local)) ) |
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| 257 | print ( 'Point in atmospheric grid : {:4d} -- num_links: {:6d}'.format(index_src, num_links) ) |
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| 258 | print (' ') |
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| 259 | |
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| 260 | ## End of loop |
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| 261 | print (' ') |
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| 262 | |
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| 263 | # |
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| 264 | num_links = np.count_nonzero (remap_matrix) |
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| 265 | print ( 'Total num_links : {:10d}'.format(num_links) ) |
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| 266 | |
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| 267 | ### ===== Writing the weights file, for OASIS MCT ========================================== |
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| 268 | |
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| 269 | # Output file |
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| 270 | calving = 'rmp_t' + src_Name + '_to_' + 't' + dst_Name + '_calving_' + myargs.type + '.nc' |
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| 271 | f_calving = netCDF4.Dataset ( calving, 'w', format='NETCDF3_64BIT' ) |
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| 272 | |
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| 273 | print ('Output file: ' + calving ) |
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| 274 | |
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| 275 | f_calving.Conventions = "CF-1.6" |
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| 276 | f_calving.source = "IPSL Earth system model" |
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| 277 | f_calving.group = "ICMC IPSL Climate Modelling Center" |
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| 278 | f_calving.Institution = "IPSL https.//www.ipsl.fr" |
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| 279 | f_calving.Ocean = dst_Name + " https://www.nemo-ocean.eu" |
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| 280 | f_calving.Atmosphere = src_Name + " http://lmdz.lmd.jussieu.fr" |
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| 281 | if myargs.type in ['iceberg', 'iceshelf' ]: f_calving.originalFiles = myargs.repartition_file |
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| 282 | f_calving.associatedFiles = grids + " " + areas + " " + masks |
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| 283 | f_calving.directory = os.getcwd () |
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| 284 | f_calving.description = "Generated with XIOS http://forge.ipsl.jussieu.fr/ioserver and MOSAIX https://forge.ipsl.jussieu.fr/igcmg/browser/TOOLS/MOSAIX" |
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| 285 | f_calving.title = calving |
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| 286 | f_calving.Program = "Generated by " + sys.argv[0] + " with flags " + str(sys.argv[1:]) |
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| 287 | f_calving.timeStamp = time.asctime() |
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| 288 | f_calving.uuid = f_areas.uuid |
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| 289 | f_calving.HOSTNAME = platform.node() |
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| 290 | #f_calving.LOGNAME = os.getlogin() |
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| 291 | f_calving.Python = "Python version " + platform.python_version() |
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| 292 | f_calving.OS = platform.system() |
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| 293 | f_calving.release = platform.release() |
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| 294 | f_calving.hardware = platform.machine() |
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| 295 | f_calving.Comment = "Preliminary attempt - Do not trust !" |
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| 296 | f_calving.conventions = "SCRIP" |
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| 297 | if src_name == 'lmd' : f_calving.source_grid = "curvilinear" |
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| 298 | if src_name == 'ico' : f_calving.source_grid = "unstructured" |
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| 299 | f_calving.dest_grid = "curvilinear" |
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| 300 | f_calving.Model = "IPSL CM6" |
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| 301 | f_calving.SVN_Author = "$Author$" |
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| 302 | f_calving.SVN_Date = "$Date$" |
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| 303 | f_calving.SVN_Revision = "$Revision$" |
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| 304 | f_calving.SVN_Id = "$Id$" |
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| 305 | f_calving.SVN_HeadURL = "$HeadURL$" |
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| 306 | |
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| 307 | d_nb_zone = f_calving.createDimension ('nb_zone' , nb_zone ) |
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| 308 | d_num_links = f_calving.createDimension ('num_links' , num_links ) |
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| 309 | d_num_wgts = f_calving.createDimension ('num_wgts' , 1 ) |
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| 310 | |
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| 311 | d_src_grid_size = f_calving.createDimension ('src_grid_size' , src_grid_size ) |
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| 312 | d_src_grid_corners = f_calving.createDimension ('src_grid_corners', src_clo.shape[0] ) |
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| 313 | D_src_grid_rank = f_calving.createDimension ('src_grid_rank' , 2 ) |
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| 314 | |
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| 315 | d_dst_grid_size = f_calving.createDimension ('dst_grid_size' , dst_grid_size ) |
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| 316 | d_dst_grid_corners = f_calving.createDimension ('dst_grid_corners', dst_clo.shape[0] ) |
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| 317 | d_dst_grid_rank = f_calving.createDimension ('dst_grid_rank' , 2 ) |
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| 318 | |
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| 319 | v_remap_matrix = f_calving.createVariable ( 'remap_matrix', 'f8', ('num_links', 'num_wgts') ) |
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| 320 | v_src_address = f_calving.createVariable ( 'src_address' , 'i4', ('num_links',) ) |
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| 321 | v_src_address = f_calving.createVariable ( 'dst_address' , 'i4', ('num_links',) ) |
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| 322 | |
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| 323 | v_remap_matrix[:] = remap_matrix |
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| 324 | v_src_address [:] = src_address |
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| 325 | v_src_address [:] = src_address |
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| 326 | |
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| 327 | v_src_grid_dims = f_calving.createVariable ( 'src_grid_dims' , 'i4', ('src_grid_rank',) ) |
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| 328 | v_src_grid_center_lon = f_calving.createVariable ( 'src_grid_center_lon', 'i4', ('src_grid_size',) ) |
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| 329 | v_src_grid_center_lat = f_calving.createVariable ( 'src_grid_center_lat', 'i4', ('src_grid_size',) ) |
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| 330 | v_src_grid_center_lon.units='degrees_east' ; v_src_grid_center_lon.long_name='Longitude' |
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| 331 | v_src_grid_center_lon.long_name='longitude' ; v_src_grid_center_lon.bounds="src_grid_corner_lon" |
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| 332 | v_src_grid_center_lat.units='degrees_north' ; v_src_grid_center_lat.long_name='Latitude' |
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| 333 | v_src_grid_center_lat.long_name='latitude ' ; v_src_grid_center_lat.bounds="src_grid_corner_lat" |
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| 334 | v_src_grid_corner_lon = f_calving.createVariable ( 'src_grid_corner_lon', 'f8', ('src_grid_size', 'src_grid_corners') ) |
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| 335 | v_src_grid_corner_lat = f_calving.createVariable ( 'src_grid_corner_lat', 'f8', ('src_grid_size', 'src_grid_corners') ) |
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| 336 | v_src_grid_corner_lon.units="degrees_east" |
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| 337 | v_src_grid_corner_lat.units="degrees_north" |
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| 338 | v_src_grid_area = f_calving.createVariable ( 'src_grid_area' , 'f8', ('src_grid_size',) ) |
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| 339 | v_src_grid_area.long_name="Grid area" ; v_src_grid_area.standard_name="cell_area" ; v_src_grid_area.units="m2" |
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| 340 | v_src_grid_imask = f_calving.createVariable ( 'src_grid_imask' , 'f8', ('src_grid_size',) ) |
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| 341 | v_src_grid_imask.long_name="Land-sea mask" ; v_src_grid_imask.units="Land:1, Ocean:0" |
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| 342 | |
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| 343 | v_src_grid_dims [:] = ( src_jpi, src_jpi ) |
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| 344 | v_src_grid_center_lon[:] = src_lon[:].ravel() |
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| 345 | v_src_grid_center_lat[:] = src_lat[:].ravel() |
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| 346 | v_src_grid_corner_lon[:] = src_clo.reshape( (src_jpi*src_jpj, d_src_grid_corners.__len__()) ) |
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| 347 | v_src_grid_corner_lat[:] = src_cla.reshape( (src_jpi*src_jpj, d_src_grid_corners.__len__()) ) |
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| 348 | v_src_grid_area [:] = src_srf[:].ravel() |
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| 349 | v_src_grid_imask [:] = src_msk[:].ravel() |
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| 350 | |
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| 351 | # -- |
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| 352 | |
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| 353 | v_dst_grid_dims = f_calving.createVariable ( 'dst_grid_dims' , 'i4', ('dst_grid_rank',) ) |
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| 354 | v_dst_grid_center_lon = f_calving.createVariable ( 'dst_grid_center_lon', 'i4', ('dst_grid_size',) ) |
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| 355 | v_dst_grid_center_lat = f_calving.createVariable ( 'dst_grid_center_lat', 'i4', ('dst_grid_size',) ) |
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| 356 | v_dst_grid_center_lon.units='degrees_east' ; v_dst_grid_center_lon.long_name='Longitude' |
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| 357 | v_dst_grid_center_lon.long_name='longitude' ; v_dst_grid_center_lon.bounds="dst_grid_corner_lon" |
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| 358 | v_dst_grid_center_lat.units='degrees_north' ; v_dst_grid_center_lat.long_name='Latitude' |
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| 359 | v_dst_grid_center_lat.long_name='latitude' ; v_dst_grid_center_lat.bounds="dst_grid_corner_lat" |
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| 360 | v_dst_grid_corner_lon = f_calving.createVariable ( 'dst_grid_corner_lon', 'f8', ('dst_grid_size', 'dst_grid_corners') ) |
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| 361 | v_dst_grid_corner_lat = f_calving.createVariable ( 'dst_grid_corner_lat', 'f8', ('dst_grid_size', 'dst_grid_corners') ) |
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| 362 | v_dst_grid_corner_lon.units="degrees_east" |
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| 363 | v_dst_grid_corner_lat.units="degrees_north" |
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| 364 | v_dst_grid_area = f_calving.createVariable ( 'dst_grid_area' , 'f8', ('dst_grid_size',) ) |
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| 365 | v_dst_grid_area.long_name="Grid area" ; v_dst_grid_area.standard_name="cell_area" ; v_dst_grid_area.units="m2" |
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| 366 | v_dst_grid_imask = f_calving.createVariable ( 'dst_grid_imask' , 'f8', ('dst_grid_size',) ) |
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| 367 | v_dst_grid_imask.long_name="Land-sea mask" ; v_dst_grid_imask.units="Land:1, Ocean:0" |
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| 368 | |
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| 369 | v_dst_bassin = f_calving.createVariable ( 'dst_bassin' , 'f8', ('nb_zone', 'dst_grid_size',) ) |
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| 370 | v_dst_repartition = f_calving.createVariable ( 'dst_repartition' , 'f8', ('nb_zone', 'dst_grid_size',) ) |
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| 371 | |
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| 372 | v_dst_grid_dims [:] = ( dst_jpi, dst_jpi ) |
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| 373 | v_dst_grid_center_lon[:] = dst_lon[:].ravel() |
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| 374 | v_dst_grid_center_lat[:] = dst_lat[:].ravel() |
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| 375 | v_dst_grid_corner_lon[:] = dst_clo.reshape( (dst_jpi*dst_jpj, d_dst_grid_corners.__len__()) ) |
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| 376 | v_dst_grid_corner_lat[:] = dst_cla.reshape( (dst_jpi*dst_jpj, d_dst_grid_corners.__len__()) ) |
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| 377 | v_dst_grid_area [:] = dst_srf[:].ravel() |
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| 378 | v_dst_grid_imask [:] = dst_msk[:].ravel() |
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| 379 | |
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| 380 | v_dst_bassin[:] = basin_msk.reshape ( (nb_zone,dst_grid_size) ) |
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| 381 | v_dst_repartition[:] = key_repartition.reshape( (nb_zone,dst_grid_size) ) |
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| 382 | |
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| 383 | # Additoonnal fields for debugging |
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| 384 | # ================================ |
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| 385 | v_dst_grid_imaskutil = f_calving.createVariable ( 'dst_grid_imaskutil' , 'f8', ('dst_grid_size',) ) |
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| 386 | v_dst_grid_imaskutil.long_name="Land-sea mask" ; v_dst_grid_imaskutil.units="Land:1, Ocean:0" |
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| 387 | v_dst_grid_imaskclose = f_calving.createVariable ( 'dst_grid_imaskclose' , 'f8', ('dst_grid_size',) ) |
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| 388 | v_dst_grid_imaskclose.long_name="Land-sea mask" ; v_dst_grid_imaskclose.units="Land:1, Ocean:0" |
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| 389 | v_dst_grid_imaskutil [:] = dst_mskutil[:].ravel() |
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| 390 | v_dst_grid_imaskclose[:] = dst_closed[:].ravel() |
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| 391 | |
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| 392 | v_dst_lon_addressed = f_calving.createVariable ( 'dst_lon_addressed', 'f8', ('num_links',) ) |
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| 393 | v_dst_lat_addressed = f_calving.createVariable ( 'dst_lat_addressed', 'f8', ('num_links',) ) |
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| 394 | v_dst_lon_addressed.long_name="Longitude" ; v_dst_lon_addressed.standard_name="longitude" ; v_dst_lon_addressed.units="degrees_east" |
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| 395 | v_dst_lat_addressed.long_name="Latitude" ; v_dst_lat_addressed.standard_name="latitude" ; v_dst_lat_addressed.units="degrees_north" |
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| 396 | v_dst_area_addressed = f_calving.createVariable ( 'dst_area_addressed', 'f8', ('num_links',) ) |
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| 397 | v_dst_area_addressed.long_name="Grid area" ; v_dst_area_addressed.standard_name="cell_area" ; v_dst_grid_area.units="m2" |
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| 398 | v_dst_imask_addressed = f_calving.createVariable ( 'dst_imask_addressed', 'i4', ('num_links',) ) |
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| 399 | v_dst_imask_addressed.long_name="Land-sea mask" ; v_dst_imask_addressed.units="Land:1, Ocean:0" |
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| 400 | v_dst_imaskutil_addressed = f_calving.createVariable ( 'dst_imaskutil_addressed', 'i4', ('num_links',) ) |
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| 401 | v_dst_imaskutil_addressed.long_name="Land-sea mask" ; v_dst_imaskutil_addressed.units="Land:1, Ocean:0" |
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| 402 | |
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| 403 | v_dst_lon_addressed [:] = dst_lon.ravel()[dst_address-1].ravel() |
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| 404 | v_dst_lat_addressed [:] = dst_lat.ravel()[dst_address-1].ravel() |
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| 405 | v_dst_area_addressed [:] = dst_srf[:].ravel()[dst_address-1].ravel() |
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| 406 | v_dst_imask_addressed[:] = dst_msk[:].ravel()[dst_address-1].ravel() |
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| 407 | v_dst_imaskutil_addressed[:] = dst_mskutil.ravel()[dst_address-1].ravel() |
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| 408 | |
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| 409 | f_calving.close () |
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| 410 | |
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| 411 | |
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| 412 | ### ===== That's all Folk's !! ============================================== |
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