[13675] | 1 | #!/usr/bin/env python3 |
---|
| 2 | # -*- Mode: Python; coding: utf-8; indent-tabs-mode: nil; tab-width: 4 -*- |
---|
[13690] | 3 | # |
---|
| 4 | ########################################################################################## |
---|
| 5 | # Post-diagnostic of STATION_ASF with sea-ice support (ex: run with forcing "ERA5_arctic") |
---|
| 6 | # |
---|
| 7 | # L. Brodeau, 2020 |
---|
| 8 | ########################################################################################## |
---|
[13675] | 9 | |
---|
| 10 | import sys |
---|
| 11 | from os import path as path |
---|
| 12 | import math |
---|
| 13 | import numpy as nmp |
---|
| 14 | from netCDF4 import Dataset,num2date |
---|
| 15 | import matplotlib as mpl |
---|
| 16 | mpl.use('Agg') |
---|
| 17 | import matplotlib.pyplot as plt |
---|
| 18 | import matplotlib.dates as mdates |
---|
| 19 | |
---|
| 20 | CONFIG='STATION_ASF' |
---|
[13690] | 21 | |
---|
| 22 | cforcing = 'PAPA' ; # name of forcing ('PAPA', 'ERA5_arctic', etc...) |
---|
| 23 | |
---|
| 24 | # (files are: output/3x3/'+CONFIG+'-'+algo+'_1h_'+year+'0101_'+year+'1231_gridT_'+cforcing+'.nc' ) |
---|
[13675] | 25 | cstation = 'ERA5 81N, 36.75E' |
---|
| 26 | |
---|
| 27 | cy1 = '2018' ; # First year |
---|
| 28 | cy2 = '2018' ; # Last year |
---|
| 29 | |
---|
| 30 | jt0 = 0 |
---|
| 31 | |
---|
| 32 | dir_figs='.' |
---|
| 33 | size_fig=(13,8) |
---|
| 34 | size_fig0=(12,10) |
---|
| 35 | fig_ext='png' |
---|
| 36 | |
---|
| 37 | clr_red = '#AD0000' |
---|
| 38 | clr_sat = '#ffed00' |
---|
| 39 | clr_mod = '#008ab8' |
---|
| 40 | |
---|
| 41 | rDPI=100. |
---|
| 42 | |
---|
| 43 | L_ALGOS = [ 'ECMWF-LG15', 'ECMWF-LU12', 'ECMWF-CSTC' ] |
---|
| 44 | l_color = [ '#ffed00' , '#008ab8' , '0.4' ] ; # colors to differentiate algos on the plot |
---|
| 45 | l_width = [ 3 , 2 , 1 ] ; # line-width to differentiate algos on the plot |
---|
| 46 | l_style = [ '-' , '-' , '--' ] ; # line-style |
---|
| 47 | |
---|
| 48 | |
---|
| 49 | # Variables to compare for A GIVEN algorithm |
---|
| 50 | ############################################### |
---|
| 51 | #L_VNEM0 = [ |
---|
| 52 | |
---|
| 53 | |
---|
| 54 | |
---|
| 55 | # Variables to compare between algorithms |
---|
| 56 | ############################################ |
---|
| 57 | L_VNEM = [ 'Cd_ice', 'Ce_ice', 'qla_ice' , 'qsb_ice' , 'qt_ice' , 'qlw_ice' , 'qsr_ice' , 'taum_ai' ] |
---|
| 58 | L_VARO = [ 'Cd' , 'Ce' , 'Qlat' , 'Qsen' , 'Qnet' , 'Qlw' , 'Qsw' , 'Tau' ] |
---|
| 59 | L_VARL = [ r'$C_{D}$', r'$C_{E}$', r'$Q_{lat}$', r'$Q_{sens}$' , r'$Q_{net}$' , r'$Q_{lw}$' , r'$Q_{sw}$' , r'$|\tau|$' ] |
---|
| 60 | L_VUNT = [ '' , '' , r'$W/m^2$' , r'$W/m^2$' , r'$W/m^2$' , r'$W/m^2$' , r'$W/m^2$' , r'$N/m^2$' ] |
---|
| 61 | L_VMAX = [ 0.003 , 0.003 , 75. , 75. , 800. , 200. , 200. , 1.2 ] |
---|
| 62 | L_VMIN = [ 0. , 0. , -250. , -125. , -400. , -200. , 0. , 0. ] |
---|
| 63 | L_ANOM = [ False , False , True , True , True , True , True , True ] |
---|
| 64 | |
---|
| 65 | |
---|
| 66 | nb_algos = len(L_ALGOS) |
---|
| 67 | |
---|
| 68 | # Getting arguments: |
---|
| 69 | narg = len(sys.argv) |
---|
| 70 | if narg != 2: |
---|
| 71 | print('Usage: '+sys.argv[0]+' <DIR_OUT_SASF>'); sys.exit(0) |
---|
| 72 | cdir_data = sys.argv[1] |
---|
| 73 | |
---|
| 74 | |
---|
| 75 | |
---|
| 76 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> |
---|
| 77 | # Populating and checking existence of files to be read |
---|
| 78 | # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> |
---|
| 79 | def chck4f(cf): |
---|
| 80 | cmesg = 'ERROR: File '+cf+' does not exist !!!' |
---|
| 81 | if not path.exists(cf): print(cmesg) ; sys.exit(0) |
---|
| 82 | |
---|
| 83 | cf_in = [] |
---|
| 84 | for ja in range(nb_algos): |
---|
[13690] | 85 | cfi = cdir_data+'/output/3x3/'+CONFIG+'-'+L_ALGOS[ja]+'_1h_'+cy1+'0101_'+cy2+'1231_icemod_'+cforcing+'.nc' |
---|
[13675] | 86 | chck4f(cfi) |
---|
| 87 | cf_in.append(cfi) |
---|
| 88 | print('Files we are goin to use:') |
---|
| 89 | for ja in range(nb_algos): print(cf_in[ja]) |
---|
| 90 | #----------------------------------------------------------------- |
---|
| 91 | |
---|
| 92 | # Getting time array from the first file: |
---|
| 93 | id_in = Dataset(cf_in[0]) |
---|
| 94 | vt = id_in.variables['time_counter'][jt0:] |
---|
| 95 | cunit_t = id_in.variables['time_counter'].units ; print(' "time_counter" is in "'+cunit_t+'"') |
---|
| 96 | id_in.close() |
---|
| 97 | Nt = len(vt) |
---|
| 98 | |
---|
| 99 | vtime = num2date(vt, units=cunit_t) ; # something understandable! |
---|
| 100 | vtime = vtime.astype(dtype='datetime64[D]') |
---|
| 101 | |
---|
| 102 | ii=Nt/300 |
---|
| 103 | ib=max(ii-ii%10,1) |
---|
| 104 | xticks_d=int(30*ib) |
---|
| 105 | |
---|
| 106 | rat = 100./float(rDPI) |
---|
| 107 | params = { 'font.family':'Open Sans', |
---|
| 108 | 'font.size': int(15.*rat), |
---|
| 109 | 'legend.fontsize': int(15.*rat), |
---|
| 110 | 'xtick.labelsize': int(15.*rat), |
---|
| 111 | 'ytick.labelsize': int(15.*rat), |
---|
| 112 | 'axes.labelsize': int(16.*rat) |
---|
| 113 | } |
---|
| 114 | mpl.rcParams.update(params) |
---|
| 115 | font_inf = { 'fontname':'Open Sans', 'fontweight':'normal', 'fontsize':18.*rat } |
---|
| 116 | font_x = { 'fontname':'Open Sans', 'fontweight':'normal', 'fontsize':15.*rat } |
---|
| 117 | |
---|
| 118 | |
---|
| 119 | # Now we compare output variables from bulk algorithms between them: |
---|
| 120 | |
---|
| 121 | nb_var = len(L_VNEM) |
---|
| 122 | |
---|
| 123 | xF = nmp.zeros((Nt,nb_algos)) |
---|
| 124 | xFa = nmp.zeros((Nt,nb_algos)) |
---|
| 125 | |
---|
| 126 | |
---|
| 127 | for jv in range(nb_var): |
---|
| 128 | print('\n *** Treating variable: '+L_VARO[jv]+' !') |
---|
| 129 | |
---|
| 130 | for ja in range(nb_algos): |
---|
| 131 | # |
---|
| 132 | id_in = Dataset(cf_in[ja]) |
---|
| 133 | xF[:,ja] = id_in.variables[L_VNEM[jv]][jt0:,1,1] # only the center point of the 3x3 spatial domain! |
---|
| 134 | if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name |
---|
| 135 | id_in.close() |
---|
| 136 | |
---|
| 137 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
---|
| 138 | fig = plt.figure(num = jv, figsize=size_fig, facecolor='w', edgecolor='k') |
---|
| 139 | ax1 = plt.axes([0.08, 0.25, 0.9, 0.7]) |
---|
| 140 | ax1.set_xticks(vtime[::xticks_d]) |
---|
| 141 | ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S')) |
---|
| 142 | plt.xticks(rotation='60', **font_x) |
---|
| 143 | |
---|
| 144 | for ja in range(nb_algos): |
---|
| 145 | plt.plot(vtime, xF[:,ja], '-', color=l_color[ja], linestyle=l_style[ja], linewidth=l_width[ja], label=L_ALGOS[ja], zorder=10+ja) |
---|
| 146 | |
---|
| 147 | ax1.set_ylim(L_VMIN[jv], L_VMAX[jv]) ; ax1.set_xlim(vtime[0],vtime[Nt-1]) |
---|
| 148 | plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']') |
---|
| 149 | |
---|
| 150 | ax1.grid(color='k', linestyle='-', linewidth=0.3) |
---|
| 151 | plt.legend(loc='best', ncol=1, shadow=True, fancybox=True) |
---|
| 152 | ax1.annotate(cvar_lnm+', station: '+cstation, xy=(0.3, 1.), xycoords='axes fraction', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf) |
---|
| 153 | plt.savefig(L_VARO[jv]+'.'+fig_ext, dpi=int(rDPI), transparent=False) |
---|
| 154 | plt.close(jv) |
---|
| 155 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
---|
| 156 | |
---|
| 157 | def symetric_range( pmin, pmax ): |
---|
| 158 | # Returns a symetric f-range that makes sense for the anomaly of "f" we're looking at... |
---|
| 159 | from math import floor, copysign, log, ceil |
---|
| 160 | zmax = max( abs(pmax) , abs(pmin) ) |
---|
| 161 | romagn = floor(log(zmax, 10)) ; # order of magnitude of the anomaly we're dealing with |
---|
| 162 | rmlt = 10.**(int(romagn)) / 2. |
---|
| 163 | frng = copysign( ceil(abs(zmax)/rmlt)*rmlt , zmax) |
---|
| 164 | return frng |
---|
| 165 | |
---|
| 166 | |
---|
| 167 | |
---|
| 168 | if L_ANOM[jv]: |
---|
| 169 | |
---|
| 170 | for ja in range(nb_algos): xFa[:,ja] = xF[:,ja] - nmp.mean(xF,axis=1) |
---|
| 171 | |
---|
| 172 | if nmp.sum(nmp.abs(xFa[:,:])) == 0.0: |
---|
| 173 | print(' Well! Seems that for variable '+L_VARO[jv]+', choice of algo has no impact a all!') |
---|
| 174 | print(' ==> skipping anomaly plot...') |
---|
| 175 | |
---|
| 176 | else: |
---|
| 177 | |
---|
| 178 | yrng = symetric_range( nmp.min(xFa) , nmp.max(xFa) ) |
---|
| 179 | |
---|
| 180 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
---|
| 181 | fig = plt.figure(num = 10+jv, figsize=size_fig, facecolor='w', edgecolor='k') |
---|
| 182 | ax1 = plt.axes([0.08, 0.25, 0.9, 0.7]) |
---|
| 183 | ax1.set_xticks(vtime[::xticks_d]) |
---|
| 184 | ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S')) |
---|
| 185 | plt.xticks(rotation='60', **font_x) |
---|
| 186 | |
---|
| 187 | for ja in range(nb_algos): |
---|
| 188 | plt.plot(vtime, xFa[:,ja], '-', color=l_color[ja], linewidth=l_width[ja], label=L_ALGOS[ja], zorder=10+ja) |
---|
| 189 | |
---|
| 190 | ax1.set_ylim(-yrng,yrng) ; ax1.set_xlim(vtime[0],vtime[Nt-1]) |
---|
| 191 | plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']') |
---|
| 192 | ax1.grid(color='k', linestyle='-', linewidth=0.3) |
---|
| 193 | plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True) |
---|
| 194 | ax1.annotate('Anomaly of '+cvar_lnm, xy=(0.3, 0.97), xycoords='axes fraction', bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf) |
---|
| 195 | plt.savefig(L_VARO[jv]+'_anomaly.'+fig_ext, dpi=int(rDPI), transparent=False) |
---|
| 196 | plt.close(10+jv) |
---|
| 197 | #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |
---|
| 198 | |
---|
| 199 | |
---|
| 200 | |
---|
| 201 | |
---|