Changeset 53


Ignore:
Timestamp:
08/12/14 18:10:16 (10 years ago)
Author:
lahlod
Message:

modifs

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  
    5353 
    5454 
    55  
     55''' 
    5656############################################ 
    5757# time evolution (monthly) in a given zone # 
     
    6262 
    6363# select borders of zone 
    64 yi = 960.  
    65 yf = 1360. 
    66 xi = -680. 
    67 xf = -320. 
     64yi = 1880.  
     65yf = 2280. 
     66xi = -480. 
     67xf = -120. 
    6868 
    6969#find corresponding index in xvec and yvec... 
     
    207207ion() 
    208208figure() 
     209subplot(2, 1, 1) 
    209210plot(mean_year_spec_a, '+-r', label = 'AMSUA89') 
    210211plot(mean_year_spec_a23, '+-m', label = 'AMSUA23') 
    211212plot(mean_year_spec_b, '+-g', label = 'AMSUB89') 
    212213xlim(0, 365) 
    213 ylim(0.5, 1.) 
     214ylim(0.4, 1.) 
    214215xticks(vec_months, month, rotation = 25) 
    215 yticks(np.arange(0.5, 1., 0.05)) 
     216yticks(np.arange(0.4, 1., 0.05)) 
    216217fontP = FontProperties() 
    217218fontP.set_size('small') 
     
    219220grid() 
    220221ylabel('emissivity spec') 
    221 title('Beaufort Sea') 
    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') 
     222title('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') 
    223224################################ 
    224225# plot mean difference and std # 
    225226################################ 
    226 figure() 
    227 plot(mean_year_spec_a - mean_year_spec_b, 'r', label = 'mean spec AMSUA - mean spec AMSUB]') 
     227#figure() 
     228subplot(2, 1, 2) 
     229plot(mean_year_spec_a - mean_year_spec_b, '+-b', label = 'mean spec AMSUA89 - mean spec AMSUB89') 
    228230#plot(mean_year_lamb_a - mean_year_lamb_b, 'b', label = 'lamb AMSUA - AMSUB') 
    229231plot(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') 
     232plot(std_year_spec_a, '+-r', label = 'std spec AMSUA89') 
     233plot(std_year_spec_b, '+-g', label = 'std spec AMSUB89') 
    232234#plot(std_year_lamb_a, 'c', label = 'lamb AMSUA') 
    233235#plot(std_year_lamb_b, '--c', label = 'lamb AMSUB') 
     
    241243grid() 
    242244ylabel('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') 
     246plt.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') 
    245247''' 
    246248 
     
    254256# monthly mean and std in whole arctic #  
    255257######################################## 
    256 bias_spec = np.zeros([M, ny_a, nx_a], float) 
     258std_spec_a89 = np.zeros([M, ny_a, nx_a], float) 
     259std_spec_a23 = np.zeros([M, ny_a, nx_a], float) 
     260std_spec_b = np.zeros([M, ny_a, nx_a], float) 
     261std_lamb_a89 = np.zeros([M, ny_a, nx_a], float) 
     262std_lamb_a23 = np.zeros([M, ny_a, nx_a], float) 
     263std_lamb_b = np.zeros([M, ny_a, nx_a], float) 
     264'''bias_spec = np.zeros([M, ny_a, nx_a], float) 
    257265bias_anom = np.zeros([M, ny_a, nx_a], float) 
    258 bias_mean = np.zeros([M], float) 
     266bias_mean = np.zeros([M], float)''' 
    259267for imo in range (0, M): 
    260268    print 'month ' + month[imo] 
     
    264272    spec_a89 = fichier_a89.variables['mean_spec'][:] 
    265273    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'][:] 
    268276    fichier_a89.close() 
    269277    # 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') 
    271279    spec_a23 = fichier_a23.variables['mean_spec'][:] 
    272280    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'][:] 
    275283    fichier_a23.close() 
    276284    # AMSUB 
     
    278286    spec_b = fichier_b.variables['mean_spec'][:] 
    279287    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'][:] 
    282290    fichier_b.close() 
    283     bias_spec[imo, :, :] = spec_a - spec_b 
     291    '''bias_spec[imo, :, :] = spec_a - spec_b 
    284292    bias_mean[imo] = mean(bias_spec[imo, :, :][nonzero(isnan(bias_spec[imo, :, :]) == False)]) 
    285293    for ilon in range (0, nx_a): 
    286294        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]''' 
    288296    
     297# calculate difference of std between spec and lamb for amsua and amsub 
     298c = np.zeros([M], float) 
     299f = np.zeros([M], float) 
     300for 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 
     309k = np.zeros([M], float) 
     310for 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) 
    289314 
    290315 
     
    300325for imo in range (0, M): 
    301326    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') 
    303328    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  
    5454std_ratio_anom_89 = np.zeros([M, ny, nx], float) 
    5555 
    56 for imo in range (5, M):  
    57     # daily read gradient ratio file 
     56std_spec_a = np.zeros([M, ny, nx], float) 
     57std_spec_b = np.zeros([M, ny, nx], float) 
     58 
     59for 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 
    5875    print 'read daily gradient ratio for month ' + month[imo] 
    5976    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') 
     
    7289    fichier_anom89.close() 
    7390    print 'compute montly mean and std ' + month[imo] 
     91    # calculate monthly means out of daily data 
    7492    for ilat in range (0, ny): 
    7593        for ilon in range (0, nx): 
     
    86104            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)]) 
    87105            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######################################################## 
     119corr_map1 = np.zeros([ny, nx], float) 
     120corr_map2 = np.zeros([ny, nx], float) 
     121corr_map3 = np.zeros([ny, nx], float) 
     122corr_map4 = np.zeros([ny, nx], float) 
     123corr_map5 = np.zeros([ny, nx], float) 
     124corr_map6 = np.zeros([ny, nx], float) 
     125corr_map7 = np.zeros([ny, nx], float) 
     126corr_map8 = np.zeros([ny, nx], float) 
     127corr_map9 = np.zeros([ny, nx], float) 
     128corr_map10 = np.zeros([ny, nx], float) 
     129for 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 
     143ion() 
     144x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() 
     145x_coast = x_ind 
     146y_coast = y_ind 
     147z_coast = z_ind 
     148map_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') 
     149plt.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 
     151map_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') 
     152plt.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 
     154map_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') 
     155plt.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 
     157map_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') 
     158plt.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 
     160map_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') 
     161plt.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 
     163map_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') 
     164plt.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 
     166map_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') 
     167plt.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 
     169map_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') 
     170plt.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 
     172map_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') 
     173plt.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 
     175map_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') 
     176plt.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 
     181map_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 
     188corr_mat = np.zeros([M, 5, 5], float) 
     189for 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 
     210ion() 
     211for 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''' 
    91223############################ 
    92224# map monthly mean and std # 
     
    97229y_coast = y_ind 
    98230z_coast = z_ind 
    99 for imo in range (5, M): 
     231for 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') 
    100241    # gradient ratio 
    101242    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') 
     
    114255    title('AMSUA - ' + month[imo] + ' 2009') 
    115256    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''' 
     261map_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') 
    121262 
    122263map_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  
    3131x0 = -3000. # min limit of grid 
    3232x1 = 2500. # max limit of grid 
    33 dx = 40. 
     33dx = 100. 
    3434xvec = np.arange(x0, x1+dx, dx) 
    3535nx = len(xvec)  
    3636y0 = -3000. # min limit of grid 
    3737y1 = 3000. # max limit of grid 
    38 dy = 40. 
     38dy = 100. 
    3939yvec = np.arange(y0, y1+dy, dy) 
    4040ny = len(yvec) 
     
    4545# grid data from .dat files # 
    4646############################# 
     47tsu = np.zeros([ny, nx, 31, M], float) 
    4748'''tu = np.zeros([ny, nx, 31, M], float) 
    4849td = np.zeros([ny, nx, 31, M], float) 
    4950tbs = np.zeros([ny, nx, 31, M], float) 
    50 tbl = np.zeros([ny, nx, 31, M], float)''' 
     51tbl = np.zeros([ny, nx, 31, M], float) 
    5152es = np.zeros([ny, nx, 31, M], float) 
    5253el = np.zeros([ny, nx, 31, M], float) 
    5354esl = np.zeros([ny, nx, 31, M], float) 
    54 '''esl00 = np.zeros([ny, nx, 31, M], float) 
     55esl00 = np.zeros([ny, nx, 31, M], float) 
    5556esl25 = np.zeros([ny, nx, 31, M], float) 
    5657esl50 = np.zeros([ny, nx, 31, M], float) 
     
    5960for imo in range (0, M): 
    6061    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') 
    6263    numlines = 0 
    6364    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')   
    6667    nbtotal = numlines-1 
    6768    iligne = 0 
     
    6970    lat = np.zeros([nbtotal],float) 
    7071    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) 
    7475    tdn = np.zeros([nbtotal],float) 
    7576    tb = np.zeros([nbtotal],float) 
     
    7879    tdn_lamb = np.zeros([nbtotal],float) 
    7980    tb_spec = np.zeros([nbtotal],float) 
    80     tb_lamb = np.zeros([nbtotal],float)''' 
     81    tb_lamb = np.zeros([nbtotal],float) 
    8182    e_spec = np.zeros([nbtotal],float) 
    8283    e_lamb = np.zeros([nbtotal],float) 
    8384    e_spec_lamb = np.zeros([nbtotal],float) 
    84     '''e_sl_00 = np.zeros([nbtotal],float) 
     85    e_sl_00 = np.zeros([nbtotal],float) 
    8586    e_sl_25 = np.zeros([nbtotal],float) 
    8687    e_sl_50 = np.zeros([nbtotal],float) 
     
    9091         line=fichier.readline() 
    9192         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]) 
    9899         tdn[iligne] = float(liste[8]) 
    99100         tb[iligne] = float(liste[10]) 
     
    102103         tdn_lamb[iligne] = float(liste[13]) 
    103104         tb_spec[iligne]  = float(liste[14]) 
    104          tb_lamb[iligne] = float(liste[15])''' 
     105         tb_lamb[iligne] = float(liste[15]) 
    105106         e_spec[iligne] = float(liste[16]) 
    106107         e_lamb[iligne] = float(liste[17]) 
    107108         e_spec_lamb[iligne] = float(liste[18]) 
    108          '''e_sl_00[iligne] = float(liste[19]) 
     109         e_sl_00[iligne] = float(liste[19]) 
    109110         e_sl_25[iligne] = float(liste[20]) 
    110111         e_sl_50[iligne] = float(liste[21]) 
     
    113114         iligne=iligne+1 
    114115    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[:, :, :] 
    115122    '''print 'tup' 
    116123    z0 = tup.min() 
     
    136143    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) 
    137144    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[:, :, :] 
    139146    print 'emis spec' 
    140147    z0 = e_spec.min() 
     
    155162    e_spec_lamb_day = zgrid_output 
    156163    esl[:, :, 0 : month_day[imo], imo] = e_spec_lamb_day[:, :, :] 
    157     '''print 'emis spec lamb 00' 
     164    print 'emis spec lamb 00' 
    158165    z0 = e_sl_00.min() 
    159166    z1 = e_sl_00.max() 
     
    189196    ############################################### 
    190197    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') 
    192199    rootgrp.createDimension('longitude', nx) 
    193200    rootgrp.createDimension('latitude', ny) 
     
    196203    nc_lat = rootgrp.createVariable('latitude', 'f', ('latitude',)) 
    197204    nc_day = rootgrp.createVariable('days', 'f', ('days',)) 
     205    nc_ts = rootgrp.createVariable('ts', 'f', ('latitude', 'longitude', 'days')) 
    198206    '''nc_tup = rootgrp.createVariable('tup', 'f', ('latitude', 'longitude', 'days')) 
    199207    nc_tdn = rootgrp.createVariable('tdn', 'f', ('latitude', 'longitude', 'days')) 
    200208    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')) 
    202210    nc_e_spec = rootgrp.createVariable('e_spec', 'f', ('latitude', 'longitude', 'days')) 
    203211    nc_e_lamb = rootgrp.createVariable('e_lamb', 'f', ('latitude', 'longitude', 'days')) 
    204212    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')) 
    206214    nc_e_sl_25 = rootgrp.createVariable('e_mixed_s25', 'f', ('latitude', 'longitude', 'days')) 
    207215    nc_e_sl_50 = rootgrp.createVariable('e_mixed_s50', 'f', ('latitude', 'longitude', 'days')) 
     
    210218    nc_lon[:] = xvec 
    211219    nc_lat[:] = yvec 
     220    nc_ts[:] = tsu[:, :, 0 : month_day[imo], imo] 
    212221    '''nc_tup[:] = tu[:, :, 0 : month_day[imo], imo] 
    213222    nc_tdn[:] = td[:, :, 0 : month_day[imo], imo] 
    214223    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] 
    216225    nc_e_spec[:] = es[:, :, 0 : month_day[imo], imo] 
    217226    nc_e_lamb[:] = el[:, :, 0 : month_day[imo], imo] 
    218227    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] 
    220229    nc_e_sl_25[:] = esl25[:, :, 0 : month_day[imo], imo] 
    221230    nc_e_sl_50[:] = esl50[:, :, 0 : month_day[imo], imo] 
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