Changeset 53
- Timestamp:
- 08/12/14 18:10:16 (10 years ago)
- Location:
- trunk/src/scripts_Laura/ARCTIC/Travail_CEN
- Files:
-
- 2 added
- 3 edited
Legend:
- Unmodified
- Added
- Removed
-
trunk/src/scripts_Laura/ARCTIC/Travail_CEN/daily_emis_AMSUA_AMSUB_89.py
r52 r53 53 53 54 54 55 55 ''' 56 56 ############################################ 57 57 # time evolution (monthly) in a given zone # … … 62 62 63 63 # select borders of zone 64 yi = 960.65 yf = 1360.66 xi = - 680.67 xf = - 320.64 yi = 1880. 65 yf = 2280. 66 xi = -480. 67 xf = -120. 68 68 69 69 #find corresponding index in xvec and yvec... … … 207 207 ion() 208 208 figure() 209 subplot(2, 1, 1) 209 210 plot(mean_year_spec_a, '+-r', label = 'AMSUA89') 210 211 plot(mean_year_spec_a23, '+-m', label = 'AMSUA23') 211 212 plot(mean_year_spec_b, '+-g', label = 'AMSUB89') 212 213 xlim(0, 365) 213 ylim(0. 5, 1.)214 ylim(0.4, 1.) 214 215 xticks(vec_months, month, rotation = 25) 215 yticks(np.arange(0. 5, 1., 0.05))216 yticks(np.arange(0.4, 1., 0.05)) 216 217 fontP = FontProperties() 217 218 fontP.set_size('small') … … 219 220 grid() 220 221 ylabel('emissivity spec') 221 title(' BeaufortSea')222 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/study_by_zones/monthly_evolution_emis_AMSUA89_AMSUB89_zone_Beaufort_Sea.png')222 title('Chukchi Sea') 223 #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/study_by_zones/monthly_evolution_emis_AMSUA89_AMSUB89_AMSUA23_zone_Chukchi_Sea.png') 223 224 ################################ 224 225 # plot mean difference and std # 225 226 ################################ 226 figure() 227 plot(mean_year_spec_a - mean_year_spec_b, 'r', label = 'mean spec AMSUA - mean spec AMSUB]') 227 #figure() 228 subplot(2, 1, 2) 229 plot(mean_year_spec_a - mean_year_spec_b, '+-b', label = 'mean spec AMSUA89 - mean spec AMSUB89') 228 230 #plot(mean_year_lamb_a - mean_year_lamb_b, 'b', label = 'lamb AMSUA - AMSUB') 229 231 plot(np.zeros([365], float), '--k') 230 plot(std_year_spec_a, ' b', label = 'std spec AMSUA')231 plot(std_year_spec_b, ' c', label = 'std spec AMSUB')232 plot(std_year_spec_a, '+-r', label = 'std spec AMSUA89') 233 plot(std_year_spec_b, '+-g', label = 'std spec AMSUB89') 232 234 #plot(std_year_lamb_a, 'c', label = 'lamb AMSUA') 233 235 #plot(std_year_lamb_b, '--c', label = 'lamb AMSUB') … … 241 243 grid() 242 244 ylabel('Zonal mean and std of emissivity') 243 title('Chukchi Sea')244 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/study_by_zones/monthly_evolution_emis_AMSUA -AMSUB89_zone_Chukchi_Sea.png')245 #title('Chukchi Sea') 246 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/study_by_zones/monthly_evolution_emis_AMSUA89_AMSUB89_AMSUA23_bias_std_mean_zone_Chukchi_Sea.png') 245 247 ''' 246 248 … … 254 256 # monthly mean and std in whole arctic # 255 257 ######################################## 256 bias_spec = np.zeros([M, ny_a, nx_a], float) 258 std_spec_a89 = np.zeros([M, ny_a, nx_a], float) 259 std_spec_a23 = np.zeros([M, ny_a, nx_a], float) 260 std_spec_b = np.zeros([M, ny_a, nx_a], float) 261 std_lamb_a89 = np.zeros([M, ny_a, nx_a], float) 262 std_lamb_a23 = np.zeros([M, ny_a, nx_a], float) 263 std_lamb_b = np.zeros([M, ny_a, nx_a], float) 264 '''bias_spec = np.zeros([M, ny_a, nx_a], float) 257 265 bias_anom = np.zeros([M, ny_a, nx_a], float) 258 bias_mean = np.zeros([M], float) 266 bias_mean = np.zeros([M], float)''' 259 267 for imo in range (0, M): 260 268 print 'month ' + month[imo] … … 264 272 spec_a89 = fichier_a89.variables['mean_spec'][:] 265 273 lamb_a89 = fichier_a89.variables['mean_lamb'][:] 266 std_spec_a89 = fichier_a89.variables['std_spec'][:]267 std_lamb_a89 = fichier_a89.variables['std_spec'][:]274 std_spec_a89[imo, :, :] = fichier_a89.variables['std_spec'][:] 275 std_lamb_a89[imo, :, :] = fichier_a89.variables['std_lamb'][:] 268 276 fichier_a89.close() 269 277 # AMSUA 23 270 fichier_ a23 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_mean-std_data_lamb_spec_near_nadir_AMSUA89_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC')278 fichier_b89 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_mean-std_data_lamb_spec_near_nadir_AMSUA23_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') 271 279 spec_a23 = fichier_a23.variables['mean_spec'][:] 272 280 lamb_a23 = fichier_a23.variables['mean_lamb'][:] 273 std_spec_a23 = fichier_a23.variables['std_spec'][:]274 std_lamb_a23 = fichier_a23.variables['std_spec'][:]281 std_spec_a23[imo, :, :] = fichier_a23.variables['std_spec'][:] 282 std_lamb_a23[imo, :, :] = fichier_a23.variables['std_lamb'][:] 275 283 fichier_a23.close() 276 284 # AMSUB … … 278 286 spec_b = fichier_b.variables['mean_spec'][:] 279 287 lamb_b = fichier_b.variables['mean_lamb'][:] 280 std_spec_b = fichier_b.variables['std_spec'][:]281 std_lamb_b = fichier_b.variables['std_spec'][:]288 std_spec_b[imo, :, :] = fichier_b.variables['std_spec'][:] 289 std_lamb_b[imo, :, :] = fichier_b.variables['std_lamb'][:] 282 290 fichier_b.close() 283 bias_spec[imo, :, :] = spec_a - spec_b291 '''bias_spec[imo, :, :] = spec_a - spec_b 284 292 bias_mean[imo] = mean(bias_spec[imo, :, :][nonzero(isnan(bias_spec[imo, :, :]) == False)]) 285 293 for ilon in range (0, nx_a): 286 294 for ilat in range (0, ny_a): 287 bias_anom[imo, ilat, ilon] = bias_spec[imo, ilat, ilon] - bias_mean[imo] 295 bias_anom[imo, ilat, ilon] = bias_spec[imo, ilat, ilon] - bias_mean[imo]''' 288 296 297 # calculate difference of std between spec and lamb for amsua and amsub 298 c = np.zeros([M], float) 299 f = np.zeros([M], float) 300 for imo in range (0, M): 301 a = mean(std_spec_a89[imo, :, :][nonzero(isnan(std_spec_a89[imo, :, :])==False)]) 302 b = mean(std_lamb_a89[imo, :, :][nonzero(isnan(std_lamb_a89[imo, :, :])==False)]) 303 c[imo] = a - b 304 d = mean(std_spec_b[imo, :, :][nonzero(isnan(std_spec_b[imo, :, :])==False)]) 305 e = mean(std_lamb_b[imo, :, :][nonzero(isnan(std_lamb_b[imo, :, :])==False)]) 306 f[imo] = d - e 307 308 # calculate difference of std between spec amsua and spec amsub 309 k = np.zeros([M], float) 310 for imo in range (0, M): 311 g = mean(std_spec_a89[imo, :, :][nonzero(isnan(std_spec_a89[imo, :, :])==False)]) 312 h = mean(std_spec_b[imo, :, :][nonzero(isnan(std_spec_b[imo, :, :])==False)]) 313 k[imo] = abs(g - h) 289 314 290 315 … … 300 325 for imo in range (0, M): 301 326 print 'map for month ' + month[imo] 302 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec_a, yvec_a, bias_anom[imo, :, :], -0.018, 0.012, 0.001, cm.s3pcpn_l_r, 'Bias anomaly of emis spec AMSUA89-AMSUB89')327 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec_a, yvec_a, std_lamb_a89[imo, :, :], 0., 0.12, 0.001, cm.s3pcpn_l_r, 'std emis lamb AMSUA89') 303 328 title(month[imo] + ' 2009') 304 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ ice_class_AMSUA89_AMSUB89/maps/map_bias_anomaly_AMSUA89-AMSUB89_arctic_' + month[imo] + '2009.png')305 329 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/comparison_lamb_spec/space_evolution/EMIS/cartesian_grid/monthly_std/std_emis_lamb_AMSUA89_' + month[imo] + '2009.png') 330 -
trunk/src/scripts_Laura/ARCTIC/Travail_CEN/map_monthly_mean_emis_parameters_whole_arctic.py
r51 r53 54 54 std_ratio_anom_89 = np.zeros([M, ny, nx], float) 55 55 56 for imo in range (5, M): 57 # daily read gradient ratio file 56 std_spec_a = np.zeros([M, ny, nx], float) 57 std_spec_b = np.zeros([M, ny, nx], float) 58 59 for imo in range (0, M): 60 # AMSUA 89 std 61 fichier_a = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_mean-std_data_lamb_spec_near_nadir_AMSUA89_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') 62 spec_a = fichier_a.variables['emis_spec_mean'][:] 63 lamb_a = fichier_a.variables['emis_lamb_mean'][:] 64 std_spec_a[imo, :, :] = fichier_a.variables['emis_spec_std'][:] 65 #std_lamb_a[imo, :, :] = fichier_a.variables['emis_lamb_std'][:] 66 fichier_a.close() 67 # AMSUB 89 std 68 fichier_b = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_mean-std_data_lamb_spec_near_nadir_AMSUB89_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') 69 spec_b = fichier_b.variables['emis_spec_mean'][:] 70 lamb_b = fichier_b.variables['emis_lamb_mean'][:] 71 std_spec_b[imo, :, :] = fichier_b.variables['emis_spec_std'][:] 72 #std_lamb_b[imo, :, :] = fichier_b.variables['emis_lamb_std'][:] 73 fichier_b.close() 74 # read daily gradient ratio file 58 75 print 'read daily gradient ratio for month ' + month[imo] 59 76 fichier_grad_ratio = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_daily_grad_ratio_spec_23-89_near_nadir_AMSUA_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') … … 72 89 fichier_anom89.close() 73 90 print 'compute montly mean and std ' + month[imo] 91 # calculate monthly means out of daily data 74 92 for ilat in range (0, ny): 75 93 for ilon in range (0, nx): … … 86 104 ratio_anom_89[imo, ilat, ilon] = mean(ra_89[0 : month_day[imo], ilat, ilon][nonzero(isnan(ra_89[0 : month_day[imo], ilat, ilon]) == False)]) 87 105 std_ratio_anom_89[imo, ilat, ilon] = sqrt((1. / (month_day[imo] - 1.)) * sum((ra_89[0 : month_day[imo], ilat, ilon][nonzero(isnan(ra_89[0 : month_day[imo], ilat, ilon]) == False)] - ratio_anom_89[imo, ilat, ilon])**2)) 88 89 90 106 # take out erroneous values of std (on land) 107 if ((xvec[ilon] > -2400.) & (xvec[ilon] < -2160.) & (yvec[ilat] > 1240.) & (yvec[ilat] < 1560.)): 108 grad_ratio[imo, ilat, ilon] = NaN 109 spec_anom_23[imo, ilat, ilon] = NaN 110 spec_anom_89[imo, ilat, ilon] = NaN 111 ratio_anom_89[imo, ilat, ilon] = NaN 112 std_spec_a[imo, ilat, ilon] = NaN 113 std_spec_b[imo, ilat, ilon] = NaN 114 115 116 ######################################################## 117 # map correlation over the year between each parameter # 118 ######################################################## 119 corr_map1 = np.zeros([ny, nx], float) 120 corr_map2 = np.zeros([ny, nx], float) 121 corr_map3 = np.zeros([ny, nx], float) 122 corr_map4 = np.zeros([ny, nx], float) 123 corr_map5 = np.zeros([ny, nx], float) 124 corr_map6 = np.zeros([ny, nx], float) 125 corr_map7 = np.zeros([ny, nx], float) 126 corr_map8 = np.zeros([ny, nx], float) 127 corr_map9 = np.zeros([ny, nx], float) 128 corr_map10 = np.zeros([ny, nx], float) 129 for ilat in range (0, ny): 130 for ilon in range (0, nx): 131 corr_map1[ilat, ilon] = corrcoef(grad_ratio[:, ilat, ilon], spec_anom_23[:, ilat, ilon])[0][1] 132 corr_map2[ilat, ilon] = corrcoef(grad_ratio[:, ilat, ilon], spec_anom_89[:, ilat, ilon])[0][1] 133 corr_map3[ilat, ilon] = corrcoef(grad_ratio[:, ilat, ilon], ratio_anom_89[:, ilat, ilon])[0][1] 134 corr_map4[ilat, ilon] = corrcoef(grad_ratio[:, ilat, ilon], std_spec_a[:, ilat, ilon])[0][1] 135 corr_map5[ilat, ilon] = corrcoef(spec_anom_23[:, ilat, ilon], spec_anom_89[:, ilat, ilon])[0][1] 136 corr_map6[ilat, ilon] = corrcoef(spec_anom_23[:, ilat, ilon], ratio_anom_89[:, ilat, ilon])[0][1] 137 corr_map7[ilat, ilon] = corrcoef(spec_anom_23[:, ilat, ilon], std_spec_a[:, ilat, ilon])[0][1] 138 corr_map8[ilat, ilon] = corrcoef(spec_anom_89[:, ilat, ilon], ratio_anom_89[:, ilat, ilon])[0][1] 139 corr_map9[ilat, ilon] = corrcoef(spec_anom_89[:, ilat, ilon], std_spec_a[:, ilat, ilon])[0][1] 140 corr_map10[ilat, ilon] = corrcoef(ratio_anom_89[:, ilat, ilon], std_spec_a[:, ilat, ilon])[0][1] 141 142 143 ion() 144 x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() 145 x_coast = x_ind 146 y_coast = y_ind 147 z_coast = z_ind 148 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map1[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation grad ratio - spec A23 anom') 149 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param1.png') 150 151 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map2[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation grad ratio - spec A89 anom') 152 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param2.png') 153 154 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map3[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation grad ratio - ratio A89 anom') 155 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param3.png') 156 157 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map4[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation grad ratio - spec std A89') 158 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param4.png') 159 160 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map5[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation spec A23 anom - spec A89 anom') 161 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param5.png') 162 163 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map6[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation spec A23 anom - ratio A89 anom') 164 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param6.png') 165 166 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map7[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation spec A23 anom - spec std A89') 167 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param7.png') 168 169 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map8[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation spec A89 anom - ratio A89 anom') 170 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param8.png') 171 172 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map9[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation spec A89 anom - std spec A89') 173 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param9.png') 174 175 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, corr_map10[:, :], -1.1, 1.1, 0.1, cm.s3pcpn_l_r, 'correlation ratio A89 anom - std spec A89') 176 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/maps/correlation_maps/correl_map_grad_ratio-param10.png') 177 178 179 ''' 180 # test 181 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, std_spec_a[imo, :, :], 0., 0.12, 0.001, cm.s3pcpn_l_r, 'test') 182 ''' 183 184 ############################################################ 185 # correlation matrix between each parameter for each month # 186 ############################################################ 187 # reshape matrix into vector fo calculation or correlation per month between each parameter 188 corr_mat = np.zeros([M, 5, 5], float) 189 for imo in range (0, M): 190 print 'month ' + month[imo] 191 a = reshape(std_spec_a[imo, :, :], size(std_spec_a[imo, :, :])) 192 b = reshape(std_spec_b[imo, :, :], size(std_spec_b[imo, :, :])) 193 c = reshape(grad_ratio[imo, :, :], size(grad_ratio[imo, :, :])) 194 d = reshape(spec_anom_23[imo, :, :], size(spec_anom_23[imo, :, :])) 195 e = reshape(spec_anom_89[imo, :, :], size(spec_anom_89[imo, :, :])) 196 f = reshape(ratio_anom_89[imo, :, :], size(ratio_anom_89[imo, :, :])) 197 aa = a[nonzero(isnan(a) == False)] 198 bb = b[nonzero(isnan(b) == False)] 199 cc = c[nonzero(isnan(c) == False)] 200 dd = d[nonzero(isnan(d) == False)] 201 ee = e[nonzero(isnan(e) == False)] 202 ff = f[nonzero(isnan(f) == False)] 203 print len(aa), len(bb), len(cc), len(dd), len(ee), len(ff) 204 params = np.array([aa, cc, dd, ee, ff]) 205 for ii in range (0, 5): 206 for jj in range (0, 5): 207 corr_mat[imo, ii, jj] = corrcoef(params[ii, :], params[jj, :])[0][1] 208 209 210 ion() 211 for imo in range (0, M): 212 figure() 213 pc = pcolor(corr_mat[imo, :, :], vmin = -1., vmax = 1., cmap = 'RdBu') 214 cbar = colorbar(pc) 215 xticks(np.arange(0.5, 5.5, 1.), np.array(['std spec A89', 'grad ratio A', 'spec A23 anom', 'spec A89 anom', 'ratio A89 anom']), rotation = 20) 216 yticks(np.arange(0.5, 5.5, 1.), np.array(['std spec A89', 'grad ratio A', 'spec A23 anom', 'spec A89 anom', 'ratio A89 anom']), rotation = 70) 217 cbar.set_label('correlation') 218 title(month[imo] + ' 2009') 219 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA89_AMSUB89/correlation_matrix/corr_matrix_emis_params_temporal_std89_' + MONTH[imo] + month[imo] + '2009.png') 220 221 222 ''' 91 223 ############################ 92 224 # map monthly mean and std # … … 97 229 y_coast = y_ind 98 230 z_coast = z_ind 99 for imo in range (5, M): 231 for imo in range (0, M): 232 print 'map month ' + month[imo] 233 # emis spec std AMSUA89 234 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, std_spec_a[imo, :, :], 0., 0.12, 0.001, cm.s3pcpn_l_r, 'emis spec monthly std') 235 title('AMSUA 89GHz - ' + month[imo] + ' 2009') 236 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/comparison_lamb_spec/space_evolution/EMIS/cartesian_grid/monthly_std/std_emis_spec_AMSUA89_' + MONTH[imo] + month[imo] + '2009.png') 237 # emis spec std AMSUB89 238 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, std_spec_b[imo, :, :], 0., 0.12, 0.001, cm.s3pcpn_l_r, 'emis spec monthly std') 239 title('AMSUB 89GHz - ' + month[imo] + ' 2009') 240 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/comparison_lamb_spec/space_evolution/EMIS/cartesian_grid/monthly_std/std_emis_spec_AMSUB89_' + MONTH[imo] + month[imo] + '2009.png') 100 241 # gradient ratio 101 242 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, grad_ratio[imo, :, :], grad_ratio[nonzero(isnan(grad_ratio) == False)].min(), grad_ratio[nonzero(isnan(grad_ratio) == False)].max(), 0.001, cm.s3pcpn_l_r, 'Gradient ratio monthly mean') … … 114 255 title('AMSUA - ' + month[imo] + ' 2009') 115 256 plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/maps/ratio_anom_89/map_monthly_mean_ratio_anomaly_AMSUA89_' + month[imo] + '2009.png') 116 117 118 119 ''' 120 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, std_ grad_ratio[imo, :, :], std_grad_ratio[imo, :, :][nonzero(isnan(std_grad_ratio[imo, :, :]) == False)].min(), std_grad_ratio[imo, :, :][nonzero(isnan(std_grad_ratio[imo, :, :]) == False)].max(), 0.001, cm.s3pcpn_l_r, 'Gradient ratio monthly std')257 ''' 258 259 260 ''' 261 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, std_spec_a[imo, :, :], std_spec_a[imo, :, :][nonzero(std_spec_a[imo, :, :] != 0.)].min(), std_spec_a[imo, :, :][nonzero(std_spec_a[imo, :, :] != 0.)].max(), 0.001, cm.s3pcpn_l_r, 'Gradient ratio monthly std') 121 262 122 263 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, gr[15, :, :], gr[15, :, :][nonzero(isnan(gr[15, :, :]) == False)].min(), gr[15, :, :][nonzero(isnan(gr[15, :, :]) == False)].max(), 0.001, cm.s3pcpn_l_r, 'daily gradient ratio (01-01-2009)') -
trunk/src/scripts_Laura/ARCTIC/Travail_CEN/read_spec_lamb_nadir.py
r46 r53 31 31 x0 = -3000. # min limit of grid 32 32 x1 = 2500. # max limit of grid 33 dx = 40.33 dx = 100. 34 34 xvec = np.arange(x0, x1+dx, dx) 35 35 nx = len(xvec) 36 36 y0 = -3000. # min limit of grid 37 37 y1 = 3000. # max limit of grid 38 dy = 40.38 dy = 100. 39 39 yvec = np.arange(y0, y1+dy, dy) 40 40 ny = len(yvec) … … 45 45 # grid data from .dat files # 46 46 ############################# 47 tsu = np.zeros([ny, nx, 31, M], float) 47 48 '''tu = np.zeros([ny, nx, 31, M], float) 48 49 td = np.zeros([ny, nx, 31, M], float) 49 50 tbs = np.zeros([ny, nx, 31, M], float) 50 tbl = np.zeros([ny, nx, 31, M], float) '''51 tbl = np.zeros([ny, nx, 31, M], float) 51 52 es = np.zeros([ny, nx, 31, M], float) 52 53 el = np.zeros([ny, nx, 31, M], float) 53 54 esl = np.zeros([ny, nx, 31, M], float) 54 '''esl00 = np.zeros([ny, nx, 31, M], float)55 esl00 = np.zeros([ny, nx, 31, M], float) 55 56 esl25 = np.zeros([ny, nx, 31, M], float) 56 57 esl50 = np.zeros([ny, nx, 31, M], float) … … 59 60 for imo in range (0, M): 60 61 print 'month: ' + month[imo] 61 fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/ monthly_GLACE/lamb_spec_param_near_nadir_' + month[imo] + '2009_AMSUA30.dat','r')62 fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/GLACE/AMSUA/GLACE_AMSUA_EMIS_' + month[imo] + '2009.DAT','r') 62 63 numlines = 0 63 64 for line in fichier: numlines += 1 64 fichier.close 65 fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/ monthly_GLACE/lamb_spec_param_near_nadir_' + month[imo] + '2009_AMSUA30.dat','r')65 fichier.close() 66 fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/GLACE/AMSUA/GLACE_AMSUA_EMIS_' + month[imo] + '2009.DAT','r') 66 67 nbtotal = numlines-1 67 68 iligne = 0 … … 69 70 lat = np.zeros([nbtotal],float) 70 71 lon = np.zeros([nbtotal],float) 71 e = np.zeros([nbtotal],float)72 '''ts = np.zeros([nbtotal],float)73 tup = np.zeros([nbtotal],float)72 '''e = np.zeros([nbtotal],float)''' 73 ts = np.zeros([nbtotal],float) 74 '''tup = np.zeros([nbtotal],float) 74 75 tdn = np.zeros([nbtotal],float) 75 76 tb = np.zeros([nbtotal],float) … … 78 79 tdn_lamb = np.zeros([nbtotal],float) 79 80 tb_spec = np.zeros([nbtotal],float) 80 tb_lamb = np.zeros([nbtotal],float) '''81 tb_lamb = np.zeros([nbtotal],float) 81 82 e_spec = np.zeros([nbtotal],float) 82 83 e_lamb = np.zeros([nbtotal],float) 83 84 e_spec_lamb = np.zeros([nbtotal],float) 84 '''e_sl_00 = np.zeros([nbtotal],float)85 e_sl_00 = np.zeros([nbtotal],float) 85 86 e_sl_25 = np.zeros([nbtotal],float) 86 87 e_sl_50 = np.zeros([nbtotal],float) … … 90 91 line=fichier.readline() 91 92 liste = line.split() 92 jjr[iligne] = float(liste[ 0])93 lat[iligne] = float(liste[ 2])94 lon[iligne] = float(liste[ 1])95 e[iligne] = float(liste[5])96 '''ts[iligne] = float(liste[6])97 tup[iligne] = float(liste[7])93 jjr[iligne] = float(liste[4]) 94 lat[iligne] = float(liste[1]) 95 lon[iligne] = float(liste[0]) 96 '''e[iligne] = float(liste[5])''' 97 ts[iligne] = float(liste[8]) 98 '''tup[iligne] = float(liste[7]) 98 99 tdn[iligne] = float(liste[8]) 99 100 tb[iligne] = float(liste[10]) … … 102 103 tdn_lamb[iligne] = float(liste[13]) 103 104 tb_spec[iligne] = float(liste[14]) 104 tb_lamb[iligne] = float(liste[15]) '''105 tb_lamb[iligne] = float(liste[15]) 105 106 e_spec[iligne] = float(liste[16]) 106 107 e_lamb[iligne] = float(liste[17]) 107 108 e_spec_lamb[iligne] = float(liste[18]) 108 '''e_sl_00[iligne] = float(liste[19])109 e_sl_00[iligne] = float(liste[19]) 109 110 e_sl_25[iligne] = float(liste[20]) 110 111 e_sl_50[iligne] = float(liste[21]) … … 113 114 iligne=iligne+1 114 115 fichier.close() 116 print 'ts' 117 z0 = ts.min() 118 z1 = ts.max() 119 zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, ts, z0, z1, dx, dy) 120 ts_day = zgrid_output 121 tsu[:, :, 0 : month_day[imo], imo] = ts_day[:, :, :] 115 122 '''print 'tup' 116 123 z0 = tup.min() … … 136 143 zgrid_output, ngrid_output, z2grid_output, sigmagrid_output, xvec, yvec, xgrid_cart, ygrid_cart = cartesian_grid_test.new_cartesian_grid(month_day[imo], jjr, month[imo], lon, lat, tb_lamb, z0, z1, dx, dy) 137 144 tb_lamb_day = zgrid_output 138 tbl[:, :, 0 : month_day[imo], imo] = tb_lamb_day[:, :, :] '''145 tbl[:, :, 0 : month_day[imo], imo] = tb_lamb_day[:, :, :] 139 146 print 'emis spec' 140 147 z0 = e_spec.min() … … 155 162 e_spec_lamb_day = zgrid_output 156 163 esl[:, :, 0 : month_day[imo], imo] = e_spec_lamb_day[:, :, :] 157 '''print 'emis spec lamb 00'164 print 'emis spec lamb 00' 158 165 z0 = e_sl_00.min() 159 166 z1 = e_sl_00.max() … … 189 196 ############################################### 190 197 print 'stacking of gridded data' 191 rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_ 40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSUA30_' + month[imo] + '2009.nc', 'w', format='NETCDF3_CLASSIC')198 rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_100/cartesian_grid_monthly_surf-temp_' + month[imo] + '2009.nc', 'w', format='NETCDF3_CLASSIC') 192 199 rootgrp.createDimension('longitude', nx) 193 200 rootgrp.createDimension('latitude', ny) … … 196 203 nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) 197 204 nc_day = rootgrp.createVariable('days', 'f', ('days',)) 205 nc_ts = rootgrp.createVariable('ts', 'f', ('latitude', 'longitude', 'days')) 198 206 '''nc_tup = rootgrp.createVariable('tup', 'f', ('latitude', 'longitude', 'days')) 199 207 nc_tdn = rootgrp.createVariable('tdn', 'f', ('latitude', 'longitude', 'days')) 200 208 nc_tb_spec = rootgrp.createVariable('tb_spec', 'f', ('latitude', 'longitude', 'days')) 201 nc_tb_lamb = rootgrp.createVariable('tb_lamb', 'f', ('latitude', 'longitude', 'days')) '''209 nc_tb_lamb = rootgrp.createVariable('tb_lamb', 'f', ('latitude', 'longitude', 'days')) 202 210 nc_e_spec = rootgrp.createVariable('e_spec', 'f', ('latitude', 'longitude', 'days')) 203 211 nc_e_lamb = rootgrp.createVariable('e_lamb', 'f', ('latitude', 'longitude', 'days')) 204 212 nc_e_spec_lamb = rootgrp.createVariable('e_spec_lamb', 'f', ('latitude', 'longitude', 'days')) 205 '''nc_e_sl_00 = rootgrp.createVariable('e_mixed_s00', 'f', ('latitude', 'longitude', 'days'))213 nc_e_sl_00 = rootgrp.createVariable('e_mixed_s00', 'f', ('latitude', 'longitude', 'days')) 206 214 nc_e_sl_25 = rootgrp.createVariable('e_mixed_s25', 'f', ('latitude', 'longitude', 'days')) 207 215 nc_e_sl_50 = rootgrp.createVariable('e_mixed_s50', 'f', ('latitude', 'longitude', 'days')) … … 210 218 nc_lon[:] = xvec 211 219 nc_lat[:] = yvec 220 nc_ts[:] = tsu[:, :, 0 : month_day[imo], imo] 212 221 '''nc_tup[:] = tu[:, :, 0 : month_day[imo], imo] 213 222 nc_tdn[:] = td[:, :, 0 : month_day[imo], imo] 214 223 nc_tb_spec[:] = tbs[:, :, 0 : month_day[imo], imo] 215 nc_tb_lamb[:] = tbl[:, :, 0 : month_day[imo], imo] '''224 nc_tb_lamb[:] = tbl[:, :, 0 : month_day[imo], imo] 216 225 nc_e_spec[:] = es[:, :, 0 : month_day[imo], imo] 217 226 nc_e_lamb[:] = el[:, :, 0 : month_day[imo], imo] 218 227 nc_e_spec_lamb[:] = esl[:, :, 0 : month_day[imo], imo] 219 '''nc_e_sl_00[:] = esl00[:, :, 0 : month_day[imo], imo]228 nc_e_sl_00[:] = esl00[:, :, 0 : month_day[imo], imo] 220 229 nc_e_sl_25[:] = esl25[:, :, 0 : month_day[imo], imo] 221 230 nc_e_sl_50[:] = esl50[:, :, 0 : month_day[imo], imo]
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