- Timestamp:
- 08/13/14 19:00:17 (10 years ago)
- File:
-
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trunk/src/scripts_Laura/ARCTIC/Travail_CEN/choose_new_classified_points.py
r49 r54 44 44 ################################################################################################################## 45 45 # We devide the loop in two steps : 46 # - first loop concerns all months except for AUGUST and SEPTEMBER - use of AMSUA23GHz SPEC emissivity to seperate ice from no-ice zones47 # - second loop concerns AUGUST and SEPTEMBER - use of AMSUA30GHz SPEC emissivity to seperate ice from no_ice zones46 # - first loop concerns JANUARY, FEBRUARY, MARCH, APRIL, SEPTEMBER, OCTOBER, NOVEMBER, DECEMBER - use of AMSUA23GHz SPEC emissivity to seperate ice from no-ice zones 47 # - second loop concerns MAY, JUNE, JULY, AUGUST - use of AMSUA89GHz SPEC emissivity to seperate ice from no_ice zones 48 48 ################################################################################################################## 49 frequ = 23 # apply threshold on this frequency 50 # open .dat file to stack data (see end of loop) 51 #data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/AMSUA'+str(frequ)+'_data_classification_parameters_ice_no-ice_with_AMSUA23-and-30-spec_2009.dat', 'a') 49 frequ = 89 # apply threshold on this frequency 50 ''' 51 #open .dat file to stack data (see end of loop) 52 data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/AMSUA'+str(frequ)+'_data_classification_parameters_ice_no-ice_with_AMSUA23-and-30-spec_2009.dat', 'a') 52 53 bin = 50 53 54 55 # monthly mean parameter (2D-array) on ARCTIC area 56 spec_month = np.zeros([M, ny, nx], float) 57 lamb_month = np.zeros([M, ny, nx], float) 58 diff_month = np.zeros([M, ny, nx], float) 59 ratio_month = np.zeros([M, ny, nx], float) 60 # monthly mean parameter (2D-array) on ARCTIC SEA ICE area 61 spec_ice = np.zeros([M, ny, nx], float) 62 lamb_ice = np.zeros([M, ny, nx], float) 63 diff_ice = np.zeros([M, ny, nx], float) 64 ratio_ice = np.zeros([M, ny, nx], float) 54 ''' 55 56 57 # daily parameter (2D-array) on ARCTIC area 58 emis_spec = np.zeros([M, ny, nx, 31], float) 59 emis_lamb = np.zeros([M, ny, nx, 31], float) 60 emis_diff = np.zeros([M, ny, nx, 31], float) 61 emis_ratio = np.zeros([M, ny, nx, 31], float) 62 63 # daily parameter (2D-array) on ARCTIC SEA ICE area 64 daily_spec_ice = np.zeros([M, ny, nx, 31], float) 65 daily_lamb_ice = np.zeros([M, ny, nx, 31], float) 66 daily_diff_ice = np.zeros([M, ny, nx, 31], float) 67 daily_ratio_ice = np.zeros([M, ny, nx, 31], float) 68 69 ''' 65 70 # monthly mean parameter (1D-array) on ARCTIC SEA ICE area transformed into vector 66 71 spec_vec = np.zeros([M, ny * nx], float) … … 68 73 diff_vec = np.zeros([M, ny * nx], float) 69 74 ratio_vec = np.zeros([M, ny * nx], float) 75 70 76 # histogram distribution (intensity of occurence) of parameter in SEA ICE area (1D-array, bins = 200) 71 77 hist_vals_spec = np.zeros([M, bin], float) … … 73 79 hist_vals_diff = np.zeros([M, bin], float) 74 80 hist_vals_ratio = np.zeros([M, bin], float) 81 75 82 # histogram distribution (emissivity value) of parameter in SEA ICE area (1D-array, bins = 200) 76 83 corresp_emis_spec = np.zeros([M, bin], float) … … 78 85 corresp_emis_diff = np.zeros([M, bin], float) 79 86 corresp_emis_ratio = np.zeros([M, bin], float) 80 months1 = np.array([0, 1, 2, 3, 4, 5, 6, 9, 10, 11]) # use AMSUA 23GHz to delimit ice/no_ice for all months except for AUGUST and SEPTEMBER 87 ''' 88 months1 = np.array([0, 1, 2, 3, 8, 9, 10, 11]) # use AMSUA 23GHz to delimit ice/no_ice for JANUARY, FEBRUARY, MARCH, APRIL, SEPTEMBER, OCTOBER, NOVEMBER, DECEMBER 81 89 for imo in months1: 82 90 print 'month ' + month[imo] … … 94 102 print 'open emissivity to sub_classify file' 95 103 ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB) 96 fichier_e = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSU A' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC')104 fichier_e = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSUB' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC') 97 105 day = fichier_e.variables['days'][:] 98 emis_spec = fichier_e.variables['e_spec'][:]99 emis_lamb = fichier_e.variables['e_lamb'][:]100 emis_diff = fichier_e.variables['e_spec_lamb'][:]106 emis_spec[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec'][:] 107 emis_lamb[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_lamb'][:] 108 emis_diff[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec_lamb'][:] 101 109 fichier_e.close() 102 for ilon in range (0, nx): 103 for ilat in range (0, ny): 104 spec_month[imo, ilat, ilon] = mean(emis_spec[ilat, ilon, :][nonzero(isnan(emis_spec[ilat, ilon, :]) == False)]) 105 lamb_month[imo, ilat, ilon] = mean(emis_lamb[ilat, ilon, :][nonzero(isnan(emis_lamb[ilat, ilon, :]) == False)]) 106 diff_month[imo, ilat, ilon] = mean(emis_diff[ilat, ilon, :][nonzero(isnan(emis_diff[ilat, ilon, :]) == False)]) 107 ## data of emis RATIO 108 fichier_r = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_lamb-spec_ratio_near_nadir_AMSUA' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC') 109 ratio_month[imo, :, :] = fichier_r.variables['emis_ratio'][:] 110 fichier_r.close() 111 print 'compute matrix of parameter on SEA ICE area' 112 for ilon in range (0, nx): 113 for ilat in range (0, ny): 114 if (isnan(spec_lim[ilat, ilon]) == True): 115 spec_ice[imo, ilat, ilon] = NaN 116 lamb_ice[imo, ilat, ilon] = NaN 117 diff_ice[imo, ilat, ilon] = NaN 118 ratio_ice[imo, ilat, ilon] = NaN 119 else: 120 spec_ice[imo, ilat, ilon] = spec_month[imo, ilat, ilon] 121 lamb_ice[imo, ilat, ilon] = lamb_month[imo, ilat, ilon] 122 diff_ice[imo, ilat, ilon] = diff_month[imo, ilat, ilon] 123 ratio_ice[imo, ilat, ilon] = ratio_month[imo, ilat, ilon] 110 # calculate emis ratio daily 111 for ijr in range (0, month_day[imo]): 112 for ilon in range (0, nx): 113 for ilat in range (0, ny): 114 emis_ratio[imo, ilat, ilon, ijr] = ((emis_lamb[imo, ilat, ilon, ijr] - emis_spec[imo, ilat, ilon, ijr]) / emis_spec[imo, ilat, ilon, ijr]) * 100. 115 if (isnan(spec_lim[ilat, ilon]) == True): 116 daily_spec_ice[imo, ilat, ilon, ijr] = NaN 117 daily_lamb_ice[imo, ilat, ilon, ijr] = NaN 118 daily_diff_ice[imo, ilat, ilon, ijr] = NaN 119 daily_ratio_ice[imo, ilat, ilon, ijr] = NaN 120 else: 121 daily_spec_ice[imo, ilat, ilon, ijr] = emis_spec[imo, ilat, ilon, ijr] 122 daily_lamb_ice[imo, ilat, ilon, ijr] = emis_lamb[imo, ilat, ilon, ijr] 123 daily_diff_ice[imo, ilat, ilon, ijr] = emis_diff[imo, ilat, ilon, ijr] 124 daily_ratio_ice[imo, ilat, ilon, ijr] = emis_ratio[imo, ilat, ilon, ijr] 125 ''' 126 # ATTENTION : previous part of script has been modified ==> cannot be applied directly to this following part of script (modification of arrays and names.... 124 127 print 'compute SPEC distribution' 125 128 ######## … … 163 166 print 'start stacking in .dat file' 164 167 #data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/AMSUB'+str(frequ)+'_data_classification_parameters_ice_no-ice_with_AMSUA23-spec_2009.dat', 'a') 165 '''for ii in range (0, bin):168 for ii in range (0, bin): 166 169 data_classif.write(('%(months)10s %(hist_vals_spec)10.5f %(corresp_emis_spec)10.5f %(hist_vals_lamb)10.5f %(corresp_emis_lamb)10.5f %(hist_vals_diff)10.5f %(corresp_emis_diff)10.5f %(hist_vals_rate)10.5f %(corresp_emis_rate)10.5f \n' % { 167 170 'months':month[imo], … … 174 177 'hist_vals_rate':hist_vals_ratio[imo, ii], 175 178 'corresp_emis_rate':corresp_emis_ratio[imo, ii], 176 }))''' 179 })) 180 ''' 177 181 ######################## 178 182 # stack in netcdf file # 179 183 ######################## 180 184 print 'stack in file month ' + str(month[imo]) 181 rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-30_' + month[imo] + '2009_AMSUA' + str(frequ) + '_spec_lamb_thresholds.nc', 'w', format='NETCDF3_CLASSIC') 182 rootgrp.createDimension('longitude', len(xvec)) 183 rootgrp.createDimension('latitude', len(yvec)) 185 rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUB' + str(frequ) + '_spec_thresholds.nc', 'w', format='NETCDF3_CLASSIC') 186 rootgrp.createDimension('longitude', nx) 187 rootgrp.createDimension('latitude', ny) 188 rootgrp.createDimension('days', month_day[imo]) 184 189 nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',)) 185 190 nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) 186 nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude')) 187 nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude')) 188 nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude')) 189 nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude')) 191 nc_days = rootgrp.createVariable('days', 'f', ('days',)) 192 nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude', 'days')) 193 nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude', 'days')) 194 nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude', 'days')) 195 nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude', 'days')) 190 196 nc_lon[:] = xvec 191 197 nc_lat[:] = yvec 192 nc_ice_spec[:] = spec_ice[imo, :, :] 193 nc_ice_lamb[:] = lamb_ice[imo, :, :] 194 nc_ice_diff[:] = diff_ice[imo, :, :] 195 nc_ice_ratio[:] = ratio_ice[imo, :, :] 198 nc_days[:] = np.arange(0, month_day[imo]) 199 nc_ice_spec[:] = daily_spec_ice[imo, :, :, 0 : month_day[imo]] 200 nc_ice_lamb[:] = daily_lamb_ice[imo, :, :, 0 : month_day[imo]] 201 nc_ice_diff[:] = daily_diff_ice[imo, :, :, 0 : month_day[imo]] 202 nc_ice_ratio[:] = daily_ratio_ice[imo, :, :, 0 : month_day[imo]] 196 203 rootgrp.close() 197 204 198 205 199 months2 = np.array([7, 8])# use AMSUA 30GHz to delimit ice/no_ice for AUGUST and SEPTEMBER 206 207 208 months2 = np.array([4, 5, 6, 7])# use AMSUA 89GHz to delimit ice/no_ice for MAY, JUNE, JULY, AUGUST 200 209 for imo in months2: 201 210 print 'month ' + month[imo] … … 204 213 ################################################################################## 205 214 print 'open threshold file' 206 fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/cartesian_grid_map_ice_no-ice_' + str(month[imo]) + '2009_AMSUA 23_spec_lamb_thresholds.nc', 'r', format='NETCDF3_CLASSIC')215 fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/cartesian_grid_map_ice_no-ice_' + str(month[imo]) + '2009_AMSUA89_spec_lamb_thresholds.nc', 'r', format='NETCDF3_CLASSIC') 207 216 spec_lim = fichier_emis.variables['spec_ice_area'][:] 208 217 #lamb_lim = fichier_emis.variables['lamb_ice_area'][:] … … 213 222 print 'open emissivity to sub_classify file' 214 223 ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB) 215 fichier_e = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSU A' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC')224 fichier_e = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSUB' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC') 216 225 day = fichier_e.variables['days'][:] 217 emis_spec = fichier_e.variables['e_spec'][:]218 emis_lamb = fichier_e.variables['e_lamb'][:]219 emis_diff = fichier_e.variables['e_spec_lamb'][:]226 emis_spec[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec'][:] 227 emis_lamb[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_lamb'][:] 228 emis_diff[imo, :, :, 0 : month_day[imo]] = fichier_e.variables['e_spec_lamb'][:] 220 229 fichier_e.close() 221 for ilon in range (0, nx): 222 for ilat in range (0, ny): 223 spec_month[imo, ilat, ilon] = mean(emis_spec[ilat, ilon, :][nonzero(isnan(emis_spec[ilat, ilon, :]) == False)]) 224 lamb_month[imo, ilat, ilon] = mean(emis_lamb[ilat, ilon, :][nonzero(isnan(emis_lamb[ilat, ilon, :]) == False)]) 225 diff_month[imo, ilat, ilon] = mean(emis_diff[ilat, ilon, :][nonzero(isnan(emis_diff[ilat, ilon, :]) == False)]) 226 ## data of emis RATIO 227 fichier_r = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_lamb-spec_ratio_near_nadir_AMSUA' + str(frequ) + '_' + str(month[imo]) + '2009.nc', 'r', format='NETCDF3_CLASSIC') 228 ratio_month[imo, :, :] = fichier_r.variables['emis_ratio'][:] 229 fichier_r.close() 230 print 'compute matrix of parameter on SEA ICE area' 231 for ilon in range (0, nx): 232 for ilat in range (0, ny): 233 if (isnan(spec_lim[ilat, ilon]) == True): 234 spec_ice[imo, ilat, ilon] = NaN 235 lamb_ice[imo, ilat, ilon] = NaN 236 diff_ice[imo, ilat, ilon] = NaN 237 ratio_ice[imo, ilat, ilon] = NaN 238 else: 239 spec_ice[imo, ilat, ilon] = spec_month[imo, ilat, ilon] 240 lamb_ice[imo, ilat, ilon] = lamb_month[imo, ilat, ilon] 241 diff_ice[imo, ilat, ilon] = diff_month[imo, ilat, ilon] 242 ratio_ice[imo, ilat, ilon] = ratio_month[imo, ilat, ilon] 230 # calculate emis ratio daily 231 for ijr in range (0, month_day[imo]): 232 for ilon in range (0, nx): 233 for ilat in range (0, ny): 234 emis_ratio[imo, ilat, ilon, ijr] = ((emis_lamb[imo, ilat, ilon, ijr] - emis_spec[imo, ilat, ilon, ijr]) / emis_spec[imo, ilat, ilon, ijr]) * 100. 235 if (isnan(spec_lim[ilat, ilon]) == True): 236 daily_spec_ice[imo, ilat, ilon, ijr] = NaN 237 daily_lamb_ice[imo, ilat, ilon, ijr] = NaN 238 daily_diff_ice[imo, ilat, ilon, ijr] = NaN 239 daily_ratio_ice[imo, ilat, ilon, ijr] = NaN 240 else: 241 daily_spec_ice[imo, ilat, ilon, ijr] = emis_spec[imo, ilat, ilon, ijr] 242 daily_lamb_ice[imo, ilat, ilon, ijr] = emis_lamb[imo, ilat, ilon, ijr] 243 daily_diff_ice[imo, ilat, ilon, ijr] = emis_diff[imo, ilat, ilon, ijr] 244 daily_ratio_ice[imo, ilat, ilon, ijr] = emis_ratio[imo, ilat, ilon, ijr] 245 ''' 246 # ATTENTION : previous part of script has been modified ==> cannot be applied directly to this following part of script (modification of arrays and names.... 243 247 print 'compute SPEC distribution' 244 248 ######## … … 282 286 print 'start stacking in .dat file' 283 287 #data_classif = open ('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/AMSUB'+str(frequ)+'_data_classification_parameters_ice_no-ice_with_AMSUA23-spec_2009.dat', 'a') 284 '''for ii in range (0, bin):288 for ii in range (0, bin): 285 289 data_classif.write(('%(months)10s %(hist_vals_spec)10.5f %(corresp_emis_spec)10.5f %(hist_vals_lamb)10.5f %(corresp_emis_lamb)10.5f %(hist_vals_diff)10.5f %(corresp_emis_diff)10.5f %(hist_vals_rate)10.5f %(corresp_emis_rate)10.5f \n' % { 286 290 'months':month[imo], … … 293 297 'hist_vals_rate':hist_vals_ratio[imo, ii], 294 298 'corresp_emis_rate':corresp_emis_ratio[imo, ii], 295 }))''' 299 })) 300 ''' 296 301 ######################## 297 302 # stack in netcdf file # 298 303 ######################## 299 304 print 'stack in file month ' + str(month[imo]) 300 rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-30_' + month[imo] + '2009_AMSUA' + str(frequ) + '_spec_lamb_thresholds.nc', 'w', format='NETCDF3_CLASSIC') 301 rootgrp.createDimension('longitude', len(xvec)) 302 rootgrp.createDimension('latitude', len(yvec)) 305 rootgrp = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUB_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUB' + str(frequ) + '_spec_thresholds.nc', 'w', format='NETCDF3_CLASSIC') 306 rootgrp.createDimension('longitude', nx) 307 rootgrp.createDimension('latitude', ny) 308 rootgrp.createDimension('days', month_day[imo]) 303 309 nc_lon = rootgrp.createVariable('longitude', 'f', ('longitude',)) 304 310 nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) 305 nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude')) 306 nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude')) 307 nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude')) 308 nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude')) 311 nc_days = rootgrp.createVariable('days', 'f', ('days',)) 312 nc_ice_spec = rootgrp.createVariable('spec_ice_area', 'f', ('latitude', 'longitude', 'days')) 313 nc_ice_lamb = rootgrp.createVariable('lamb_ice_area', 'f', ('latitude', 'longitude', 'days')) 314 nc_ice_diff = rootgrp.createVariable('diff_ice_area', 'f', ('latitude', 'longitude', 'days')) 315 nc_ice_ratio = rootgrp.createVariable('ratio_ice_area', 'f', ('latitude', 'longitude', 'days')) 309 316 nc_lon[:] = xvec 310 317 nc_lat[:] = yvec 311 nc_ice_spec[:] = spec_ice[imo, :, :] 312 nc_ice_lamb[:] = lamb_ice[imo, :, :] 313 nc_ice_diff[:] = diff_ice[imo, :, :] 314 nc_ice_ratio[:] = ratio_ice[imo, :, :] 318 nc_days[:] = np.arange(0, month_day[imo]) 319 nc_ice_spec[:] = daily_spec_ice[imo, :, :, 0 : month_day[imo]] 320 nc_ice_lamb[:] = daily_lamb_ice[imo, :, :, 0 : month_day[imo]] 321 nc_ice_diff[:] = daily_diff_ice[imo, :, :, 0 : month_day[imo]] 322 nc_ice_ratio[:] = daily_ratio_ice[imo, :, :, 0 : month_day[imo]] 315 323 rootgrp.close() 316 324 … … 320 328 321 329 322 323 ''' 330 ''' 331 fichier = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/sub_classification/cartesian_grid_map_sea_ice_extent_with-AMSUA23-and-89_' + month[imo] + '2009_AMSUA' + str(frequ) + '_spec_thresholds.nc', 'r', format='NETCDF3_CLASSIC') 332 ice_spec = fichier.variables['spec_ice_area'][:] 333 ice_lamb = fichier.variables['lamb_ice_area'][:] 334 ice_ratio = fichier.variables['ratio_ice_area'][:] 335 fichier.close() 336 mean_ratio = np.zeros([ny, nx], float) 337 for ilon in range (0, nx): 338 for ilat in range (0, ny): 339 mean_ratio[ilat, ilon] = mean(ice_ratio[ilat, ilon, 0 : month_day[imo]][nonzero(isnan(ice_ratio[ilat, ilon, 0 : month_day[imo]]) == False)]) 340 341 342 ion() 343 x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() 344 x_coast = x_ind 345 y_coast = y_ind 346 z_coast = z_ind 347 map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, mean_ratio[:, :], -3., 5., 0.1, cm.s3pcpn_l_r, 'test') 348 349 350 351 324 352 # test: 325 353 ion()
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