1 | #!/usr/bin/env python3 |
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2 | # -*- Mode: Python; coding: utf-8; indent-tabs-mode: nil; tab-width: 4 -*- |
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3 | # |
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4 | ########################################################################################## |
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5 | # Post-diagnostic of STATION_ASF for air-sea fluxes (over open ocean) |
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6 | # |
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7 | # L. Brodeau, 2020 |
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8 | ########################################################################################## |
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9 | |
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10 | import sys |
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11 | from os import path, listdir |
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12 | import argparse as ap |
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13 | from math import floor, ceil, copysign, log |
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14 | import numpy as nmp |
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15 | from netCDF4 import Dataset,num2date |
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16 | import matplotlib as mpl |
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17 | mpl.use('Agg') |
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18 | import matplotlib.pyplot as plt |
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19 | import matplotlib.dates as mdates |
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20 | |
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21 | dir_figs='.' |
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22 | size_fig=(13,8.5) |
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23 | size_fig0=(12,10) |
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24 | |
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25 | clr_red = '#AD0000' |
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26 | clr_mod = '#008ab8' |
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27 | |
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28 | rDPI=100. |
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29 | |
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30 | #l_color = [ '0.85' , '#ffed00' , '#008ab8' , '0.4' ] ; # colors to differentiate algos on the plot |
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31 | #l_width = [ 4 , 3 , 2 , 1 ] ; # line-width to differentiate algos on the plot |
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32 | #l_style = [ '-' , '-' , '-' , '--' ] ; # line-style |
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33 | |
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34 | #ffed00: yellow ON |
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35 | #E8A727: ornage |
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36 | |
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37 | l_color = [ '0.3' , '#E8A727', '0.1' , '#008ab8' ] ; # colors to differentiate algos on the plot |
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38 | l_width = [ 2 , 2 , 1.5 , 2 ] ; # line-width to differentiate algos on the plot |
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39 | l_style = [ '-' , '-' , '--' , '-' ] ; # line-style |
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40 | |
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41 | |
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42 | # Variables to compare between algorithms |
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43 | ############################################ |
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44 | crealm = 'open-ocean' |
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45 | L_VNEM = [ 'Cd_oce' , 'Ce_oce' , 'qla_oce' , 'qsb_oce' , 'qt_oce' , 'qlw_oce' , 'taum' , 'dt_skin' ] |
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46 | L_VARO = [ 'Cd' , 'Ce' , 'Qlat' , 'Qsen' , 'Qnet' , 'Qlw' , 'Tau' , 'dT_skin' ] ; # name of variable on figure |
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47 | L_VARL = [ r'$C_{D}$' , r'$C_{E}$' , r'$Q_{lat}$', r'$Q_{sens}$' , r'$Q_{net}$' , r'$Q_{lw}$' , r'$|\tau|$' , r'$\Delta T_{skin}$' ] ; # name of variable in latex mode |
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48 | L_VUNT = [ '' , '' , r'$W/m^2$' , r'$W/m^2$' , r'$W/m^2$' , r'$W/m^2$' , r'$N/m^2$' , 'K' ] |
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49 | L_BASE = [ 0.0005 , 0.0005 , 5. , 5. , 5 , 5. , 0.05 , 0.05 ] |
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50 | L_PREC = [ 3 , 3 , 0 , 0 , 0 , 0 , 2 , 3 ] |
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51 | L_ANOM = [ False , False , True , True , True , True , True , False ] |
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52 | L_MAXT = [ 10000. , 10000., 10000. , 10000. , 10000. , 10000. , 10000. , 1.5 ] |
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53 | L_MINT = [ 0.001 , 0.001 , -10000. , -10000. , -10000. , -10000. ,-10000. , -10000. ] |
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54 | |
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55 | # About STATION_ASF output files to read: |
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56 | cpref = 'STATION_ASF-' ; np = len(cpref) |
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57 | csuff = '_gridT.nc' ; ns = len(csuff) |
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58 | cclnd = '_1h_YYYY0101_YYYY1231' ; nc = len(cclnd) |
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59 | |
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60 | |
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61 | ################## ARGUMENT PARSING / USAGE ################################################################################################ |
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62 | parser = ap.ArgumentParser(description='Generate pixel maps of a given scalar.') |
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63 | # |
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64 | requiredNamed = parser.add_argument_group('required arguments') |
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65 | requiredNamed.add_argument('-d', '--dirout' , required=True, help='Path to (production) directory where STATION_ASF was run') |
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66 | requiredNamed.add_argument('-f', '--forcing', required=True, default="PAPA", help='Name of forcing (ex: PAPA, ERA5_arctic') |
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67 | # |
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68 | parser.add_argument('-C', '--conf', default="STATION_ASF", help='specify NEMO config (ex: STATION_ASF)') |
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69 | parser.add_argument('-s', '--ystart', default="2018", help='specify first year of experiment (ex: 2018)') |
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70 | parser.add_argument('-e', '--yend', default="2018", help='specify last year of experiment (ex: 2018)') |
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71 | # |
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72 | parser.add_argument('-t', '--itype', default="png", help='specify the type of image you want to create (ex: png, svg, etc.)') |
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73 | #parser.add_argument('-l', '--lev' , type=int, default=0, help='specify the level to use if 3D field (default: 0 => 2D)') |
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74 | #parser.add_argument('-I', '--ice' , action='store_true', help='draw sea-ice concentration layer onto the field') |
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75 | # |
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76 | args = parser.parse_args() |
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77 | # |
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78 | cdir_data = args.dirout |
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79 | cforcing = args.forcing |
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80 | # |
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81 | CONF = args.conf |
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82 | cy1 = args.ystart |
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83 | cy2 = args.yend |
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84 | # |
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85 | fig_ext = args.itype |
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86 | #jk = args.lev |
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87 | #lshow_ice = args.ice |
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88 | # |
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89 | #print(''); print(' *** cdir_data = ', cdir_data); print(' *** cforcing = ', cforcing) |
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90 | #print(' *** CONF = ', CONF); print(' *** cy1 = ', cy1); print(' *** cy2 = ', cy2) |
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91 | ############################################################################################################################################### |
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92 | |
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93 | |
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94 | |
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95 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> |
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96 | # Populating and checking existence of files to be read |
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97 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> |
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98 | |
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99 | dir_out = cdir_data+'/output' |
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100 | ldir = listdir(dir_out) |
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101 | |
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102 | cf_in = [] |
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103 | list_exp = [] |
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104 | list_frc = [] |
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105 | for fn in ldir: |
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106 | fpn = dir_out+'/'+fn |
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107 | if path.isfile(dir_out+'/'+fn): |
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108 | if fn[:np]==cpref and fn[-ns:]==csuff and cforcing in fn: |
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109 | print('\n file: '+fn) |
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110 | clab = fn[np:-nc-ns] |
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111 | [ cexp, cfrc ] = str.split(clab, '_', 1) |
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112 | print(' ===> Experiment = '+cexp+', Forcing = '+cfrc) |
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113 | list_exp.append(cexp) |
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114 | list_frc.append(cfrc) |
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115 | cf_in.append(fpn) |
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116 | nbf = len( set(list_frc) ) |
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117 | if not nbf == 1: |
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118 | print('PROBLEM: we found files for more that one forcing: ', set(list_frc)) |
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119 | sys.exit(0) |
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120 | |
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121 | nb_exp = len(list_exp) |
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122 | |
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123 | |
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124 | print('\n\nThere are '+str(nb_exp)+' experiments to compare:') |
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125 | for ja in range(nb_exp): print(' * '+list_exp[ja]+'\n'+' ==> '+cf_in[ja]+'\n') |
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126 | |
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127 | if nb_exp > len(l_color): |
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128 | print('PROBLEM: the max number of experiments for comparison is '+str(len(l_color))+' for now...') |
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129 | sys.exit(0) |
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130 | |
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131 | |
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132 | #----------------------------------------------------------------- |
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133 | |
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134 | |
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135 | def round_bounds( x1, x2, base=5, prec=3 ): |
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136 | rmin = base * round( floor(float(x1)/base), prec ) |
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137 | rmax = base * round( ceil(float(x2)/base), prec ) |
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138 | return rmin, rmax |
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139 | |
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140 | |
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141 | # Getting time array from the first file: |
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142 | id_in = Dataset(cf_in[0]) |
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143 | vt = id_in.variables['time_counter'][:] |
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144 | cunit_t = id_in.variables['time_counter'].units ; print(' "time_counter" is in "'+cunit_t+'"') |
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145 | id_in.close() |
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146 | Nt = len(vt) |
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147 | |
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148 | vtime = num2date(vt, units=cunit_t) ; # something human! |
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149 | vtime = vtime.astype(dtype='datetime64[D]') |
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150 | |
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151 | ii=Nt/300 |
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152 | ib=max(ii-ii%10,1) |
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153 | xticks_d=int(30*ib) |
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154 | |
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155 | rat = 100./float(rDPI) |
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156 | params = { 'font.family':'Open Sans', |
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157 | 'font.size': int(15.*rat), |
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158 | 'legend.fontsize': int(15.*rat), |
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159 | 'xtick.labelsize': int(15.*rat), |
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160 | 'ytick.labelsize': int(15.*rat), |
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161 | 'axes.labelsize': int(16.*rat) |
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162 | } |
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163 | mpl.rcParams.update(params) |
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164 | font_inf = { 'fontname':'Open Sans', 'fontweight':'normal', 'fontsize':18.*rat } |
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165 | font_x = { 'fontname':'Open Sans', 'fontweight':'normal', 'fontsize':15.*rat } |
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166 | |
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167 | |
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168 | |
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169 | |
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170 | |
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171 | # First for each algorithm we compare some input vs out put variables: |
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172 | |
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173 | # t_skin |
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174 | vtemp_in = [ 'sst' , 'theta_zu' , 'theta_zt' , 't_skin' ] |
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175 | vtemp_lb = [ 'SST (bulk SST)', r'$\theta_{zu}$ (pot. air temp. at zu)', r'$\theta_{zt}$ (pot. air temp. at zt)', 'Skin temperature' ] |
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176 | vtemp_cl = [ 'k' , clr_mod , 'purple' , clr_red ] |
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177 | vtemp_lw = [ 3 , 1.3 , 1.3 , 0.7 ] |
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178 | vtemp_ls = [ '-' , '-' , '--' , '-' ] |
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179 | ntemp = len(vtemp_in) |
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180 | |
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181 | xxx = nmp.zeros((Nt,ntemp)) |
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182 | |
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183 | for ja in range(nb_exp): |
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184 | # |
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185 | # Temperatures... |
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186 | id_in = Dataset(cf_in[ja]) |
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187 | for jv in range(ntemp): |
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188 | xxx[:,jv] = id_in.variables[vtemp_in[jv]][:,1,1] # only the center point of the 3x3 spatial domain! |
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189 | id_in.close() |
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190 | |
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191 | fig = plt.figure(num = 1, figsize=size_fig0, facecolor='w', edgecolor='k') |
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192 | ax1 = plt.axes([0.07, 0.2, 0.9, 0.75]) |
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193 | ax1.set_xticks(vtime[::xticks_d]) |
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194 | ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S')) |
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195 | plt.xticks(rotation='60', **font_x) |
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196 | |
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197 | for jv in range(ntemp): |
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198 | plt.plot(vtime, xxx[:,jv], '-', color=vtemp_cl[jv], linestyle=vtemp_ls[jv], linewidth=vtemp_lw[jv], label=vtemp_lb[jv], zorder=10) |
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199 | |
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200 | idx_okay = nmp.where( nmp.abs(xxx) < 1.e+10 ) |
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201 | fmin, fmax = round_bounds( nmp.min(xxx[idx_okay]) , nmp.max(xxx[idx_okay]), base=5, prec=0 ) |
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202 | ax1.set_ylim(fmin, fmax) ; ax1.set_xlim(vtime[0],vtime[Nt-1]) |
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203 | plt.ylabel(r'Temperature [$^{\circ}$C]') |
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204 | |
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205 | ax1.grid(color='k', linestyle='-', linewidth=0.3) |
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206 | plt.legend(loc='best', ncol=1, shadow=True, fancybox=True) |
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207 | ax1.annotate('Algo: '+list_exp[ja]+', station: '+cforcing, xy=(0.5, 1.), xycoords='axes fraction', ha='center', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf) |
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208 | plt.savefig('01_temperatures_'+list_exp[ja]+'_'+cforcing+'.'+fig_ext, dpi=int(rDPI), transparent=False) |
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209 | plt.close(1) |
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210 | |
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211 | |
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212 | del xxx |
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213 | |
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214 | |
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215 | |
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216 | # Now we compare output variables from bulk algorithms between them: |
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217 | |
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218 | nb_var = len(L_VNEM) |
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219 | |
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220 | xF = nmp.zeros((Nt,nb_exp)) |
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221 | xFa = nmp.zeros((Nt,nb_exp)) |
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222 | |
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223 | |
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224 | for jv in range(nb_var): |
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225 | print('\n *** Treating variable: '+L_VARO[jv]+' !') |
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226 | |
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227 | for ja in range(nb_exp): |
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228 | # |
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229 | id_in = Dataset(cf_in[ja]) |
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230 | xF[:,ja] = id_in.variables[L_VNEM[jv]][:,1,1] ; # only the center point of the 3x3 spatial domain! |
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231 | if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name |
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232 | id_in.close() |
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233 | # |
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234 | id_toolarge, = nmp.where( xF[:,ja] > L_MAXT[jv] ) # |
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235 | xF[id_toolarge,ja] = L_MAXT[jv] |
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236 | id_toosmall, = nmp.where( xF[:,ja] < L_MINT[jv] ) ; #print("id_toosmall =", id_toosmall) |
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237 | xF[id_toosmall,ja] = L_MINT[jv] |
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238 | |
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239 | idx_okay = nmp.where( nmp.abs(xF) < 1.e+10 ) |
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240 | |
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241 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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242 | fig = plt.figure(num = jv, figsize=size_fig, facecolor='w', edgecolor='k') |
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243 | ax1 = plt.axes([0.083, 0.23, 0.9, 0.7]) |
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244 | ax1.set_xticks(vtime[::xticks_d]) |
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245 | ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S')) |
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246 | plt.xticks(rotation='60', **font_x) |
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247 | |
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248 | for ja in range(nb_exp): |
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249 | fplot = nmp.ma.masked_where( xF[:,ja]==0., xF[:,ja] ) |
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250 | plt.plot(vtime, fplot, '-', color=l_color[ja], \ |
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251 | linestyle=l_style[ja], linewidth=l_width[ja], label=list_exp[ja], alpha=0.6 ) #zorder=10+ja) |
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252 | |
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253 | fmin, fmax = round_bounds( nmp.min(xF[idx_okay]) , nmp.max(xF[idx_okay]), base=L_BASE[jv], prec=L_PREC[jv]) |
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254 | ax1.set_ylim(fmin, fmax) ; ax1.set_xlim(vtime[0],vtime[Nt-1]) |
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255 | plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']') |
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256 | |
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257 | ax1.grid(color='k', linestyle='-', linewidth=0.3) |
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258 | plt.legend(loc='best', ncol=1, shadow=True, fancybox=True) |
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259 | ax1.annotate(cvar_lnm+', station: '+cforcing, xy=(0.5, 1.04), xycoords='axes fraction', \ |
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260 | ha='center', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, \ |
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261 | zorder=50, **font_inf) |
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262 | plt.savefig(L_VARO[jv]+'_'+cforcing+'_'+crealm+'.'+fig_ext, dpi=int(rDPI), transparent=False) |
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263 | plt.close(jv) |
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264 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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265 | |
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266 | |
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267 | |
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268 | |
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269 | def symetric_range( pmin, pmax ): |
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270 | # Returns a symetric f-range that makes sense for the anomaly of "f" we're looking at... |
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271 | from math import floor, copysign, log, ceil |
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272 | zmax = max( abs(pmax) , abs(pmin) ) |
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273 | romagn = floor(log(zmax, 10)) ; # order of magnitude of the anomaly we're dealing with |
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274 | rmlt = 10.**(int(romagn)) / 2. |
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275 | frng = copysign( ceil(abs(zmax)/rmlt)*rmlt , zmax) |
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276 | return frng |
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277 | |
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278 | |
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279 | if L_ANOM[jv]: |
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280 | |
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281 | for ja in range(nb_exp): xFa[:,ja] = xF[:,ja] - nmp.mean(xF,axis=1) |
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282 | |
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283 | if nmp.sum(nmp.abs(xFa[:,:])) == 0.0: |
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284 | print(' Well! Seems that for variable '+L_VARO[jv]+', choice of algo has no impact a all!') |
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285 | print(' ==> skipping anomaly plot...') |
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286 | |
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287 | else: |
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288 | |
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289 | yrng = symetric_range( nmp.min(xFa) , nmp.max(xFa) ) |
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290 | |
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291 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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292 | fig = plt.figure(num = 10+jv, figsize=size_fig, facecolor='w', edgecolor='k') |
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293 | ax1 = plt.axes([0.09, 0.23, 0.9, 0.7]) |
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294 | ax1.set_xticks(vtime[::xticks_d]) |
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295 | ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S')) |
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296 | plt.xticks(rotation='60', **font_x) |
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297 | |
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298 | for ja in range(nb_exp): |
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299 | fplot = nmp.ma.masked_where( xFa[:,ja]==0., xFa[:,ja] ) |
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300 | plt.plot(vtime, fplot, '-', color=l_color[ja], \ |
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301 | linewidth=l_width[ja], label=list_exp[ja], alpha=0.6) #, zorder=10+ja) |
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302 | |
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303 | ax1.set_ylim(-yrng,yrng) ; ax1.set_xlim(vtime[0],vtime[Nt-1]) |
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304 | plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']') |
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305 | ax1.grid(color='k', linestyle='-', linewidth=0.3) |
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306 | plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True) |
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307 | ax1.annotate('Anomaly of '+cvar_lnm+', station: '+cforcing, xy=(0.5, 1.04), xycoords='axes fraction', \ |
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308 | ha='center', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, \ |
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309 | zorder=50, **font_inf) |
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310 | plt.savefig(L_VARO[jv]+'_'+cforcing+'_anomaly_'+crealm+'.'+fig_ext, dpi=int(rDPI), transparent=False) |
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311 | plt.close(10+jv) |
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312 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
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