1 | #!/usr/bin/env python |
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2 | # -*- coding: utf-8 -*- |
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3 | import string |
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4 | import numpy as np |
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5 | import matplotlib.pyplot as plt |
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6 | #import ffgrid2_gc |
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7 | from pylab import * |
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8 | from mpl_toolkits.basemap import Basemap |
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9 | from mpl_toolkits.basemap import shiftgrid, cm |
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10 | import netCDF4 |
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11 | |
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12 | |
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13 | fichier=open('/net/dedale/usr/dedale/surf/lelod/ANTARC/SSMIS_CH2_ANTARC_JUNE2010.DAT','r') |
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14 | numlines = 0 |
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15 | for line in fichier: numlines += 1 |
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16 | |
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17 | fichier.close |
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18 | |
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19 | |
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20 | fichier=open('/net/dedale/usr/dedale/surf/lelod/ANTARC/SSMIS_CH2_ANTARC_JUNE2010.DAT','r') |
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21 | nbtotal=numlines |
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22 | |
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23 | iligne=0 |
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24 | lat=np.zeros([nbtotal],float) |
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25 | lon=np.zeros([nbtotal],float) |
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26 | jjr=np.zeros([nbtotal],float) |
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27 | zen=np.zeros([nbtotal],float) |
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28 | fov=np.zeros([nbtotal],float) |
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29 | ts=np.zeros([nbtotal],float) |
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30 | emis=np.zeros([nbtotal],float) |
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31 | tb=np.zeros([nbtotal],float) |
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32 | tup=np.zeros([nbtotal],float) |
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33 | tdn=np.zeros([nbtotal],float) |
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34 | trans=np.zeros([nbtotal],float) |
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35 | orog=np.zeros([nbtotal],float) |
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36 | |
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37 | while (iligne <= nbtotal-1) : |
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38 | line=fichier.readline() |
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39 | # exemple : line = "0.22 2.3 5.0 6" |
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40 | liste = line.split() |
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41 | # exemple : listeCoord ['0.22', '2.3', '5.0', '6'] (liste de chaine de caract?es) |
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42 | lat[iligne] = float(liste[1]) |
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43 | lon[iligne] = float(liste[0]) |
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44 | jjr[iligne] = float(liste[4]) |
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45 | ts[iligne] = float(liste[8]) |
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46 | tb[iligne] = float(liste[13]) |
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47 | emis[iligne] = float(liste[14]) |
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48 | orog[iligne] = float(liste[11]) |
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49 | iligne=iligne+1 |
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50 | |
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51 | |
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52 | fichier.close |
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53 | |
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54 | x=lon |
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55 | y=lat |
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56 | z=tb |
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57 | zgrid, zzgrid, ngrid, nngrid, sigma_grid, xvec, yvec =ffgrid2_gc.ffgrid(xx,yy,zz) |
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58 | |
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59 | |
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60 | #antes |
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61 | |
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62 | |
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63 | dx=0.1 |
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64 | dy=1.0 |
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65 | x0, x1 = -180, 180 |
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66 | y0, y1 = -90, 90 |
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67 | |
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68 | monthly_outz=np.zeros([40,3600],float) |
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69 | monthly_lon=np.zeros([3600]) |
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70 | monthly_lat=np.zeros([40]) |
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71 | xx = lon |
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72 | yy = lat |
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73 | zz = tb |
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74 | zz0 = 100 |
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75 | zz1= 300 |
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76 | outz, outx, outy, Nil = ffgrid3.ffgrid(xx, yy, zz, dx, dy, x0,x1,y0,y1,zz0, zz1) |
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77 | x=outx |
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78 | y=outy |
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79 | z = np.transpose(outz) |
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80 | z[Nil]=600 |
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81 | z=np.transpose(z) |
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82 | |
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83 | del outz, outx, outy, zz, xx, yy |
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84 | |
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85 | |
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86 | # ici je fais des cartes moyennes en melangeant les polars |
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87 | |
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88 | xx = lon_ssmis |
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89 | yy = lat_ssmis |
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90 | zz = 0.5*(emis_ssmis[1,:]+emis_ssmis[2,:]) |
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91 | outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0,x1,y0,y1,0.1, 1) |
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92 | monthly_outz_ssmis_polar[0,:,:]=outz |
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93 | del outz, outx, outy, zz |
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94 | |
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95 | zz = 0.5*(emis_ssmis[4,:]+emis_ssmis[5,:]) |
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96 | outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0,x1,y0,y1,0.1, 1) |
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97 | monthly_outz_ssmis_polar[1,:,:]=outz |
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98 | del outz, outx, outy, zz |
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99 | |
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100 | zz = 0.5*(emis_ssmis[6,:]+emis_ssmis[7,:]) |
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101 | outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0,x1,y0,y1,0.1, 1) |
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102 | monthly_outz_ssmis_polar[2,:,:]=outz |
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103 | del outz, outx, outy, zz, xx, yy |
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104 | |
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105 | # ici je fais des cartes moyennes des differences des polars |
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106 | xx = lon_ssmis |
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107 | yy = lat_ssmis |
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108 | zz = emis_ssmis[1,:]-emis_ssmis[2,:] |
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109 | outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0,x1,y0,y1,-0.05, 0.2) |
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110 | monthly_outz_ssmis_diff[0,:,:]=outz |
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111 | del outz, outx, outy, zz |
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112 | |
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113 | zz = emis_ssmis[4,:]-emis_ssmis[5,:] |
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114 | outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0,x1,y0,y1,-0.05, 0.2) |
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115 | monthly_outz_ssmis_diff[1,:,:]=outz |
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116 | del outz, outx, outy, zz |
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117 | |
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118 | zz = emis_ssmis[6,:]-emis_ssmis[7,:] |
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119 | outz, outx, outy = ffgrid2.ffgrid(xx, yy, zz, dx, dy, x0,x1,y0,y1,-0.05, 0.2) |
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120 | monthly_outz_ssmis_diff[2,:,:]=outz |
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121 | del outz, outx, outy, zz, xx, yy |
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122 | |
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123 | draw_map.draw(monthly_outz_ssmis_polar[0,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_19GHzmpolar_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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124 | draw_map.draw(monthly_outz_ssmis_polar[1,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_37GHzmpolar_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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125 | draw_map.draw(monthly_outz_ssmis_polar[2,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_85GHzmpolar_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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126 | |
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127 | draw_map.draw(monthly_outz_ssmis_diff[0,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_19V-H_HN_'+le_mois+'.png', '',0,0.2,0.002,cm.s3pcpn_l_r) |
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128 | draw_map.draw(monthly_outz_ssmis_diff[1,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_37V-H_HN_'+le_mois+'.png', '',0,0.2,0.002,cm.s3pcpn_l_r) |
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129 | draw_map.draw(monthly_outz_ssmis_diff[2,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_85V-H_HN_'+le_mois+'.png', '',0,0.2,0.002,cm.s3pcpn_l_r) |
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130 | |
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131 | draw_map.draw(monthly_outz_ssmis[0,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_50V_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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132 | draw_map.draw(monthly_outz_ssmis[1,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_19V_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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133 | draw_map.draw(monthly_outz_ssmis[2,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_19H_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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134 | draw_map.draw(monthly_outz_ssmis[3,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_22V_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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135 | draw_map.draw(monthly_outz_ssmis[4,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_37V_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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136 | draw_map.draw(monthly_outz_ssmis[5,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_37H_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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137 | draw_map.draw(monthly_outz_ssmis[6,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_85V_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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138 | draw_map.draw(monthly_outz_ssmis[7,:], monthly_lon_ssmis, monthly_lat_ssmis, '..\FIG\mean_ssmis_85H_HN_'+le_mois+'.png', '',0.6,1.01,0.002,cm.s3pcpn_l_r) |
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139 | |
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140 | |
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141 | bins=arange(0.3,1,0.001) |
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142 | bb=(lat_ssmis >= 0) |
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143 | |
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144 | plt.hist(emis_ssmis[0,nonzero(bb)[0]], bins=bins,histtype='step', label='e50V',normed='True',color='#4BB5C1') |
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145 | plt.hist(emis_ssmis[1,nonzero(bb)[0]], bins=bins,histtype='step', label='e19V',normed='True',color='black') |
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146 | plt.hist(emis_ssmis[3,nonzero(bb)[0]], bins=bins,histtype='step', label='e22V',normed='True',color='#B9121B') |
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147 | plt.hist(emis_ssmis[4,nonzero(bb)[0]], bins=bins,histtype='step', label='e37V',normed='True',color='#9748D4') |
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148 | plt.hist(emis_ssmis[6,nonzero(bb)[0]], bins=bins,histtype='step', label='e91V',normed='True',color='#060DE5') |
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149 | plt.legend(loc='upper left') |
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150 | plt.show() |
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151 | plt.savefig('..\FIG\hist_ssmis_V_NH_'+le_mois+'.png') |
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152 | close() |
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153 | |
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154 | bins=arange(0.3,1,0.001) |
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155 | plt.hist(emis_ssmis[2,nonzero(bb)[0]], bins=bins,histtype='step', label='e19H',normed='True',color='black') |
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156 | plt.hist(emis_ssmis[5,nonzero(bb)[0]], bins=bins,histtype='step', label='e37H',normed='True',color='#9748D4') |
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157 | plt.hist(emis_ssmis[7,nonzero(bb)[0]], bins=bins,histtype='step', label='e91H',normed='True',color='#060DE5') |
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158 | plt.legend(loc='upper left') |
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159 | plt.show() |
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160 | plt.savefig('..\FIG\hist_ssmis_H_NH_'+le_mois+'.png') |
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161 | close() |
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162 | |
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163 | bins=arange(0.3,1,0.001) |
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164 | plt.hist(0.5*(emis_ssmis[1,nonzero(bb)[0]]+emis_ssmis[2,nonzero(bb)[0]]), bins=bins,histtype='step', label='e19',normed='True',color='black') |
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165 | plt.hist(0.5*(emis_ssmis[4,nonzero(bb)[0]]+emis_ssmis[5,nonzero(bb)[0]]), bins=bins,histtype='step', label='e37',normed='True',color='#9748D4') |
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166 | plt.hist(0.5*(emis_ssmis[6,nonzero(bb)[0]]+emis_ssmis[7,nonzero(bb)[0]]), bins=bins,histtype='step', label='e91',normed='True',color='#060DE5') |
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167 | plt.legend(loc='upper left') |
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168 | plt.show() |
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169 | plt.savefig('..\FIG\hist_ssmis_mpolar_NH_'+le_mois+'.png') |
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170 | close() |
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171 | |
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172 | |
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173 | # stats quotidienne autour de la station Thulé |
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174 | lat_stations=[76.32, 74.43, 78.13, 58.45, 68.6, 64.58] |
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175 | lon_stations=[-68.3, -94.59, 15.35, -78.08, 33.1, 40.5] |
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176 | nom_stations=['Thule', 'Resolute', 'Longyearbyen', 'Iqaluit', 'Murmansk', 'Arkhangelsk'] |
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177 | |
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178 | |
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179 | for sta in range(0,6): |
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180 | lat0=lat_stations[sta] |
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181 | lon0=lon_stations[sta] |
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182 | stat_jour=np.zeros([8,7,31],float) |
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183 | clear bb |
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184 | for canal in range(0,8): |
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185 | for jjr in range(0,31): |
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186 | jour_obs=jjr+1 |
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187 | bb=(jjr_ssmis == jour_obs) & (abs(lat_ssmis-lat0) < 2.) & (abs(lon_ssmis-lon0) < 2.) |
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188 | stat_jour[canal,0,jjr]=mean(emis_ssmis[canal,nonzero(bb)[0]]) |
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189 | stat_jour[canal,1,jjr]=std(emis_ssmis[canal,nonzero(bb)[0]]) stat_jour[canal,2,jjr]=size(nonzero(bb)) |
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190 | stat_jour[canal,3,jjr]=mean(ts_ssmis[nonzero(bb)[0]]) |
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191 | stat_jour[canal,4,jjr]=std(ts_ssmis[nonzero(bb)[0]]) |
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192 | stat_jour[canal,5,jjr]=mean(tb_ssmis[canal,nonzero(bb)[0]]) |
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193 | stat_jour[canal,6,jjr]=std(tb_ssmis[canal,nonzero(bb)[0]]) |
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194 | del bb |
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195 | np.save('STAT_SSMIS_'+nom_stations[sta]+'_'+le_mois+'.dat', stat_jour) |
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196 | del stat_jour |
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197 | |
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198 | mpolar_ssmis=np.zeros([3,nbtotal],float) |
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199 | mpolar_ssmis[0,:]=0.5*(emis_ssmis[1,:]+emis_ssmis[2,:]) |
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200 | mpolar_ssmis[1,:]=0.5*(emis_ssmis[4,:]+emis_ssmis[5,:]) |
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201 | mpolar_ssmis[2,:]=0.5*(emis_ssmis[6,:]+emis_ssmis[7,:]) |
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202 | |
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203 | mpolarTB_ssmis=np.zeros([3,nbtotal],float) |
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204 | mpolarTB_ssmis[0,:]=0.5*(tb_ssmis[1,:]+tb_ssmis[2,:]) |
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205 | mpolarTB_ssmis[1,:]=0.5*(tb_ssmis[4,:]+tb_ssmis[5,:]) |
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206 | mpolarTN_ssmis[2,:]=0.5*(tb_ssmis[6,:]+tb_ssmis[7,:]) |
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207 | |
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208 | for sta in range(0,6): |
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209 | lat0=lat_stations[sta] |
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210 | lon0=lon_stations[sta] |
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211 | stat2_jour=np.zeros([3,7,31],float) |
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212 | clear bb |
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213 | for canal in range(0,3): |
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214 | for jjr in range(0,31): |
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215 | jour_obs=jjr+1 |
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216 | bb=(jjr_ssmis == jour_obs) & (abs(lat_ssmis-lat0) < 2.) & (abs(lon_ssmis-lon0) < 2.) |
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217 | stat2_jour[canal,0,jjr]=mean(mpolar_ssmis[canal,nonzero(bb)[0]]) |
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218 | stat2_jour[canal,1,jjr]=std(mpolar_ssmis[canal,nonzero(bb)[0]]) |
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219 | stat2_jour[canal,2,jjr]=size(nonzero(bb)) |
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220 | stat2_jour[canal,3,jjr]=mean(ts_ssmis[nonzero(bb)[0]]) |
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221 | stat2_jour[canal,4,jjr]=std(ts_ssmis[nonzero(bb)[0]]) |
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222 | stat2_jour[canal,5,jjr]=mean(tb_ssmis[canal,nonzero(bb)[0]]) stat2_jour[canal,6,jjr]=std(tb_ssmis[canal,nonzero(bb)[0]]) |
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223 | del bb |
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224 | np.save('STAT_SSMIS-MPOLAR_'+nom_stations[sta]+'_'+le_mois+'.dat', stat_jour) |
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225 | del stat_jour |
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226 | |
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227 | # ecriture sous format nc |
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228 | from netCDF4 import Dataset |
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229 | rootgrp = Dataset('..\EMIS\EMIS_SSMIS_'+le_mois+'.nc', 'w', format='NETCDF4') |
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230 | |
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231 | rootgrp.createDimension('longitude', len(monthly_lon_ssmis)) |
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232 | rootgrp.createDimension('latitude', len(monthly_lat_ssmis)) |
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233 | rootgrp.createDimension('channels', 8) |
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234 | rootgrp.createDimension('bchannels', 3) |
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235 | |
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236 | # createVariable (nom de la variable, type, dimensions) |
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237 | # Si 1 dimension, ne pas oublier la virgule |
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238 | nclon = rootgrp.createVariable('longitude', 'f8', ('longitude',)) |
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239 | nclat = rootgrp.createVariable('latitude', 'f8', ('latitude',)) |
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240 | ncchan=rootgrp.createVariable('channels', 'f', ('channels',)) |
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241 | ncchan2=rootgrp.createVariable('bchannels', 'f', ('bchannels',)) |
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242 | nctemp = rootgrp.createVariable('emissivity', 'f8', ('channels','latitude', 'longitude')) |
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243 | nctemp2 = rootgrp.createVariable('emissivity melange polar', 'f8', ('bchannels','latitude', 'longitude')) |
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244 | |
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245 | nclon[:] = monthly_lon_ssmis |
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246 | nclat[:] = monthly_lat_ssmis |
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247 | ncchan[:]=[50,19.1,19.2,22,37.1,37.2,91.1,91.2] |
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248 | ncchan2[:]=[19,37,91] |
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249 | nctemp[:] = monthly_outz_ssmis |
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250 | nctemp2[:] = monthly_outz_ssmis_polar |
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251 | |
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252 | rootgrp.close() |
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253 | |
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254 | |
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