| 1 | #!/usr/bin/env python |
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| 2 | |
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| 3 | ### A. Colaitis |
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| 4 | |
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| 5 | ## |
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| 6 | # This routine transforms a diagfi.nc file into a diagfi_MCS.nc file where |
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| 7 | # the fields are directly comparable to those contained in MCS data, i.e. |
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| 8 | # fields are re-binned at times over the ranges specified in the MCS file. |
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| 9 | ### |
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| 10 | |
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| 11 | ########################################################################################### |
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| 12 | ########################################################################################### |
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| 13 | ### What is below relate to running the file as a command line executable (very convenient) |
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| 14 | if __name__ == "__main__": |
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| 15 | import sys |
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| 16 | from optparse import OptionParser ### to be replaced by argparse |
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| 17 | from netCDF4 import Dataset |
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| 18 | from os import system,path |
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| 19 | from times import sol2ls |
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| 20 | import numpy as np |
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| 21 | from mymath import find_nearest |
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| 22 | from myplot import getfield,separatenames |
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| 23 | from make_netcdf import make_gcm_netcdf |
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| 24 | from zrecast_wrapper import call_zrecast |
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| 25 | parser = OptionParser() |
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| 26 | |
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| 27 | ############################# |
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| 28 | ### Options |
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| 29 | parser.add_option('-f', '--file', action='store',dest='file', type="string", default=None, help='[NEEDED] filename.') |
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| 30 | parser.add_option('-m', '--mfile', action='store',dest='mcsfile', type="string", default=None, help='[NEEDED] filename for MCS comparison.') |
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| 31 | parser.add_option('-v', '--var', action='append',dest='var', type="string", default=None, help='[NEEDED] Variables to process. (coma-separated list. aps and bps are always included.)') |
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| 32 | parser.add_option('-x', action='store_true',dest='recast', default=False, help='Force aps and bps to be ommited in output file (usefull if your file is already recasted along z) [False]') |
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| 33 | parser.add_option('-i', '--zrecast', action='store_true', dest='zrecast', default=False, help='Cast zrecast.e on diagfi file with MCS pressure levels. Will pass this operation is recasted file is already present, unless --override is specified. [False]') |
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| 34 | parser.add_option('--override', action='store_true', dest='override', default=False, help='Force zrecast.e to act even if recasted file is already present(will erase previous recasted file) [False]') |
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| 35 | |
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| 36 | ############################# |
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| 37 | ### Get options and variables |
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| 38 | (opt,args) = parser.parse_args() |
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| 39 | |
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| 40 | ############################# |
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| 41 | ### Load and check data |
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| 42 | |
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| 43 | if opt.var is None: |
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| 44 | print "You must specify at least a field to process with -v." |
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| 45 | exit() |
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| 46 | |
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| 47 | # Zrecast |
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| 48 | |
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| 49 | varznames=separatenames(opt.var[0]) |
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| 50 | |
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| 51 | if opt.zrecast: |
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| 52 | if (path.exists(opt.file[0:len(opt.file)-3]+"_P.nc") and (not opt.override)): |
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| 53 | print "--> "+opt.file[0:len(opt.file)-3]+"_P.nc" |
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| 54 | print "Recasted file is already there, skipping interpolation. [use --override to force interpolation]" |
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| 55 | filename=opt.file[0:len(opt.file)-3]+"_P.nc" |
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| 56 | else: |
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| 57 | print "--> "+opt.file[0:len(opt.file)-3]+"_P.nc" |
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| 58 | filename=call_zrecast ( interp_mode = 2, \ |
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| 59 | input_name = [opt.file], \ |
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| 60 | fields = varznames, \ |
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| 61 | predifined = 'mcs')[0] |
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| 62 | else:filename=opt.file |
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| 63 | # Files |
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| 64 | |
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| 65 | print "--> Loading diagfi dataset." |
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| 66 | |
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| 67 | nc=Dataset(filename) |
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| 68 | ncmcs=Dataset(opt.mcsfile) |
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| 69 | |
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| 70 | # Dimensions |
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| 71 | |
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| 72 | lon=nc.variables["longitude"][:] |
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| 73 | lat=nc.variables["latitude"][:] |
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| 74 | alt=nc.variables["altitude"][:] |
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| 75 | time=nc.variables["Time"][:] # in fraction of sols |
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| 76 | if "controle" in nc.variables: |
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| 77 | controle=nc.variables["controle"][:] |
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| 78 | day_ini=controle[3]%669 |
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| 79 | else: |
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| 80 | if opt.zrecast: |
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| 81 | nccontrol=Dataset(opt.file) |
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| 82 | if "controle" in nccontrol.variables: |
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| 83 | controle=nccontrol.variables["controle"][:] |
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| 84 | day_ini=controle[3]%669 |
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| 85 | else: |
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| 86 | print "Error: could not find controle variable in diagfi." |
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| 87 | day_ini=input("Please type initial sol number:")%669 |
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| 88 | time[:]=time[:]+day_ini |
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| 89 | nx=len(lon) |
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| 90 | ny=len(lat) |
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| 91 | nz=len(alt) |
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| 92 | nt=len(time) |
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| 93 | lstime=sol2ls(time) |
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| 94 | |
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| 95 | # MCS |
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| 96 | |
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| 97 | print "--> Loading and preparing MCS dataset." |
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| 98 | |
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| 99 | dtimemintmp=ncmcs.variables["dtimemin"][:,:,:] |
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| 100 | dtimemaxtmp=ncmcs.variables["dtimemax"][:,:,:] |
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| 101 | ntimemintmp=ncmcs.variables["ntimemin"][:,:,:] |
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| 102 | ntimemaxtmp=ncmcs.variables["ntimemax"][:,:,:] |
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| 103 | lonmcs=ncmcs.variables["longitude"][:] |
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| 104 | latmcs=ncmcs.variables["latitude"][:] |
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| 105 | timemcs=ncmcs.variables["time"][:]%360 # IN LS |
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| 106 | |
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| 107 | dtimemin=np.ma.masked_where(dtimemintmp < 0.,dtimemintmp) |
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| 108 | dtimemin.set_fill_value([np.NaN]) |
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| 109 | dtimemax=np.ma.masked_where(dtimemaxtmp < 0.,dtimemaxtmp) |
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| 110 | dtimemax.set_fill_value([np.NaN]) |
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| 111 | ntimemin=np.ma.masked_where(ntimemintmp < 0.,ntimemintmp) |
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| 112 | ntimemin.set_fill_value([np.NaN]) |
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| 113 | ntimemax=np.ma.masked_where(ntimemaxtmp < 0.,ntimemaxtmp) |
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| 114 | ntimemax.set_fill_value([np.NaN]) |
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| 115 | |
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| 116 | # Variables to treat |
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| 117 | |
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| 118 | print "--> Preparing diagfi dataset." |
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| 119 | |
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| 120 | varz=[] |
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| 121 | n=0 |
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| 122 | for zn in varznames: |
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| 123 | load=getfield(nc,zn) |
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| 124 | load=np.ma.masked_where(load < -1.e-20,load) |
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| 125 | load.set_fill_value([np.NaN]) |
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| 126 | load=load.filled() |
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| 127 | load=np.ma.masked_invalid(load) |
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| 128 | load.set_fill_value([np.NaN]) |
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| 129 | load=load.filled() |
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| 130 | varz.append(load) |
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| 131 | load=0. |
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| 132 | print "Found: "+zn+" with dimensions: " |
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| 133 | print np.array(varz[n]).shape |
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| 134 | n=n+1 |
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| 135 | |
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| 136 | nzvar=len(varz) |
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| 137 | dimensions={} |
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| 138 | vv=0 |
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| 139 | for var in varz: |
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| 140 | a=len(np.array(var).shape) |
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| 141 | if a == 3: dimensions[vv]=a |
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| 142 | elif a == 4: dimensions[vv]=a |
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| 143 | else: |
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| 144 | print "Warning, only 3d and 4d variables are supported for time-recasting" |
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| 145 | exit() |
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| 146 | vv=vv+1 |
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| 147 | |
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| 148 | # Variables to save but not treated (only along z, or phisinit-like files) |
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| 149 | |
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| 150 | aps=nc.variables["aps"][:] |
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| 151 | bps=nc.variables["bps"][:] |
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| 152 | fullnames=["aps","bps"] |
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| 153 | for name in varznames: |
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| 154 | fullnames.append("d"+name) |
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| 155 | fullnames.append("n"+name) |
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| 156 | print "Will output: " |
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| 157 | if opt.recast: print fullnames[2:] |
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| 158 | else: print fullnames |
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| 159 | ############################# |
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| 160 | ### Building |
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| 161 | ############################# |
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| 162 | |
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| 163 | ### We loop over chunks of gcm data corresponding to MCS time dimension |
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| 164 | ### Bin sizes for mcs data is 5 degrees ls centered on value |
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| 165 | varday=np.zeros([len(timemcs),nz,ny,nx]) |
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| 166 | varnight=np.zeros([len(timemcs),nz,ny,nx]) |
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| 167 | vardayout=np.zeros([nzvar,len(timemcs),nz,ny,nx]) |
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| 168 | varnightout=np.zeros([nzvar,len(timemcs),nz,ny,nx]) |
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| 169 | vardayout=np.ma.masked_invalid(vardayout) |
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| 170 | varnightout=np.ma.masked_invalid(varnightout) |
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| 171 | i=0 |
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| 172 | for ls in timemcs: |
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| 173 | lsstart=ls-2.5 |
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| 174 | lsstop=ls+2.5 |
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| 175 | istart=find_nearest(lstime,lsstart,strict=True) |
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| 176 | istop=find_nearest(lstime,lsstop,strict=True) |
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| 177 | varchk=0 |
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| 178 | if ((istart is np.NaN) or (istop is np.NaN)): |
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| 179 | vardayout[:,i,:,:,:]=np.NaN |
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| 180 | varnightout[:,i,:,:,:]=np.NaN |
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| 181 | print "Time interval skipped. Ls MCS: (",lsstart,';',lsstop,')',"// Ls diagfi: (",lstime.min(),';',lstime.max(),')' |
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| 182 | i=i+1 |
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| 183 | continue |
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| 184 | print "--->> Processing Data. Ls MCS: (",lsstart,';',lsstop,')',"// Ls diagfi: (",lstime.min(),';',lstime.max(),')' |
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| 185 | # warning, python's convention is that the second index of array[a:b] is the array index of element after the one being picked last. |
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| 186 | # for that reason, array[0:0] is nan and array[0:1] is only one value. Hence, len(array[a:b+1]) is b-a+1 and not b-a+2 |
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| 187 | print " .initialisation." |
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| 188 | |
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| 189 | |
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| 190 | varchk=np.zeros([nzvar,istop-istart+1,nz,ny,nx]) |
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| 191 | vv=0 |
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| 192 | for variable in varz: |
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| 193 | if dimensions[vv] is 3: |
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| 194 | varchk[vv,:,0,:,:]=variable[istart:istop+1,:,:] |
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| 195 | else: |
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| 196 | varchk[vv,:,:,:,:]=variable[istart:istop+1,:,:,:] |
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| 197 | vv=vv+1 |
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| 198 | varchk=np.ma.masked_invalid(varchk) |
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| 199 | varchk.set_fill_value([np.NaN]) |
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| 200 | varchktime=time[istart:istop+1] |
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| 201 | ndays=np.floor(varchktime[len(varchktime)-1])-np.floor(varchktime[0]) |
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| 202 | dtmichk=dtimemin[i,:,:] |
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| 203 | dtmachk=dtimemax[i,:,:] |
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| 204 | ntmichk=ntimemin[i,:,:] |
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| 205 | ntmachk=ntimemax[i,:,:] |
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| 206 | dtmichk.set_fill_value([np.NaN]) |
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| 207 | dtmachk.set_fill_value([np.NaN]) |
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| 208 | ntmichk.set_fill_value([np.NaN]) |
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| 209 | ntmachk.set_fill_value([np.NaN]) |
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| 210 | dtmichk=dtmichk.filled() |
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| 211 | dtmachk=dtmachk.filled() |
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| 212 | ntmichk=ntmichk.filled() |
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| 213 | ntmachk=ntmachk.filled() |
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| 214 | |
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| 215 | ### We iterate for each day in the chunk, on each grid point we find |
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| 216 | ### the closest corresponding MCS grid point and the index of the |
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| 217 | ### time in the chunk closest to the time in the closest MCS grid point. |
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| 218 | ### (yea it's complicated) |
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| 219 | |
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| 220 | vartmpnight=np.zeros([nzvar,ndays,nz,ny,nx]) |
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| 221 | vartmpday=np.zeros([nzvar,ndays,nz,ny,nx]) |
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| 222 | vartmpnight=np.ma.masked_invalid(vartmpnight) |
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| 223 | vartmpday=np.ma.masked_invalid(vartmpday) |
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| 224 | vartmpnight.set_fill_value([np.NaN]) |
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| 225 | vartmpday.set_fill_value([np.NaN]) |
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| 226 | |
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| 227 | nd=0 |
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| 228 | print " .time indices MCS grid -> diagfi grid." |
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| 229 | while nd < ndays: |
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| 230 | |
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| 231 | daystart=find_nearest(varchktime-varchktime[0],nd) |
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| 232 | daystop=find_nearest(varchktime-varchktime[0],nd+1) |
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| 233 | # varchktime_lon=np.zeros([daystop-daystart+1,len(lon)]) |
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| 234 | ix=0 |
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| 235 | for x in lon: |
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| 236 | |
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| 237 | varchktime_lon = 24.*(varchktime[daystart:daystop+1]-varchktime[daystart]) + x/15. |
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| 238 | |
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| 239 | iy=0 |
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| 240 | for y in lat: |
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| 241 | niy=find_nearest(latmcs,y) |
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| 242 | nix=find_nearest(lonmcs,x) |
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| 243 | nitdtmichk=find_nearest(varchktime_lon,dtmichk[niy,nix]) |
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| 244 | nitdtmachk=find_nearest(varchktime_lon,dtmachk[niy,nix]) |
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| 245 | nitntmichk=find_nearest(varchktime_lon,ntmichk[niy,nix]) |
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| 246 | nitntmachk=find_nearest(varchktime_lon,ntmachk[niy,nix]) |
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| 247 | for vv in np.arange(nzvar): |
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| 248 | if ((nitdtmichk is np.NaN) or (nitdtmachk is np.NaN)): |
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| 249 | vartmpday[vv,nd,:,iy,ix]=np.NaN |
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| 250 | elif nitdtmichk > nitdtmachk: |
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| 251 | vartmpday[vv,nd,:,iy,ix]=(np.ma.mean(varchk[vv,daystart+nitdtmichk:daystop+1,:,iy,ix],axis=0)+np.ma.mean(varchk[vv,daystart:daystart+nitdtmachk+1,:,iy,ix],axis=0))/2. |
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| 252 | else: |
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| 253 | vartmpday[vv,nd,:,iy,ix]=np.ma.mean(varchk[vv,daystart+nitdtmichk:daystart+nitdtmachk+1,:,iy,ix],axis=0) |
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| 254 | if ((nitntmichk is np.NaN) or (nitntmachk is np.NaN)): |
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| 255 | vartmpnight[vv,nd,:,iy,ix]=np.NaN |
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| 256 | elif nitntmichk > nitntmachk: |
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| 257 | vartmpnight[vv,nd,:,iy,ix]=(np.ma.mean(varchk[vv,daystart+nitntmichk:daystop+1,:,iy,ix],axis=0)+np.ma.mean(varchk[vv,daystart:daystart+nitntmachk+1,:,iy,ix],axis=0))/2. |
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| 258 | else: |
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| 259 | vartmpnight[vv,nd,:,iy,ix]=np.ma.mean(varchk[vv,daystart+nitntmichk:daystart+nitntmachk+1,:,iy,ix],axis=0) |
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| 260 | iy=iy+1 |
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| 261 | ix=ix+1 |
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| 262 | nd=nd+1 |
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| 263 | |
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| 264 | print " .creating bins." |
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| 265 | |
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| 266 | vartmpdaymasked=np.ma.masked_invalid(vartmpday) |
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| 267 | vartmpnightmasked=np.ma.masked_invalid(vartmpnight) |
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| 268 | vartmpdaymasked.set_fill_value([np.NaN]) |
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| 269 | vartmpnightmasked.set_fill_value([np.NaN]) |
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| 270 | for vv in np.arange(nzvar): |
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| 271 | vardayout[vv,i,:,:,:]=np.ma.mean(vartmpdaymasked[vv,:,:,:,:],axis=0) |
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| 272 | varnightout[vv,i,:,:,:]=np.ma.mean(vartmpnightmasked[vv,:,:,:,:],axis=0) |
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| 273 | print " ."+varznames[vv]+".done" |
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| 274 | i=i+1 |
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| 275 | |
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| 276 | print "--->> Preparing Data for ncdf. Missing value is NaN." |
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| 277 | |
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| 278 | vardayout=np.ma.masked_invalid(vardayout) |
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| 279 | varnightout=np.ma.masked_invalid(varnightout) |
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| 280 | vardayout.set_fill_value([np.NaN]) |
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| 281 | varnightout.set_fill_value([np.NaN]) |
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| 282 | |
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| 283 | all=[aps,bps] |
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| 284 | for vv in np.arange(nzvar): |
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| 285 | if dimensions[vv] == 3: |
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| 286 | all.append(vardayout[vv,:,0,:,:].filled()) |
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| 287 | all.append(varnightout[vv,:,0,:,:].filled()) |
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| 288 | elif dimensions[vv] == 4: |
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| 289 | all.append(vardayout[vv,:,:,:,:].filled()) |
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| 290 | all.append(varnightout[vv,:,:,:,:].filled()) |
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| 291 | |
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| 292 | if opt.recast: |
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| 293 | all=all[2:] |
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| 294 | fullnames=fullnames[2:] |
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| 295 | |
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| 296 | make_gcm_netcdf (zfilename="diagfi_MCS.nc", \ |
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| 297 | zdescription="Temperatures from diagfi reworked to match MCS format", \ |
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| 298 | zlon=lon, \ |
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| 299 | zlat=lat, \ |
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| 300 | zalt=alt, \ |
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| 301 | ztime=timemcs, \ |
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| 302 | zvariables=all, \ |
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| 303 | znames=fullnames) |
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