Changeset 49

Timestamp:

08/04/14 18:12:02 (10 years ago)

Author:

lahlod

Message:

new scripts

Location:

trunk/src/scripts_Laura/ARCTIC/Travail_CEN

Files:

• choose_new_classified_points.py (modified) (1 diff)
• map_statistic_parameters.py (added)
• param_anomaly.py (added)
• read_sub_classification_cross_correlation.py (modified) (3 diffs)

trunk/src/scripts_Laura/ARCTIC/Travail_CEN/choose_new_classified_points.py

@@ -47 +47 @@
 # - second loop concerns AUGUST and SEPTEMBER - use of AMSUA30GHz SPEC emissivity to seperate ice from no_ice zones
 ##################################################################################################################
-frequ = 89 # apply threshold on this frequency
+frequ = 23 # apply threshold on this frequency
 # open .dat file to stack data (see end of loop)
 #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')

trunk/src/scripts_Laura/ARCTIC/Travail_CEN/read_sub_classification_cross_correlation.py

@@ -106 +106 @@
 
 
-frequ = 89
-spec_ice_seas = np.zeros([M, ny, nx], float)
-lamb_ice_seas = np.zeros([M, ny, nx], float)
-diff_ice_seas = np.zeros([M, ny, nx], float)
-ratio_ice_seas = np.zeros([M, ny, nx], float)
-for imo in range (0, 1):
-    print 'month ' + month[imo]
-    ########################
-    # read ice extent data #
-    ########################
-    print 'open emissivity to sub_classify file'
-    ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB)
-    fichier_e = 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', 'r', format='NETCDF3_CLASSIC')
-    emis_spec = fichier_e.variables['spec_ice_area'][:]
-    emis_lamb = fichier_e.variables['lamb_ice_area'][:]
-    emis_diff = fichier_e.variables['diff_ice_area'][:]
-    emis_ratio = fichier_e.variables['ratio_ice_area'][:]
-    fichier_e.close()
-    for ilon in range (0, nx):
-        for ilat in range (0, ny):
-            if (isnan(emis_spec[ilat, ilon]) == True):
-               spec_ice_seas[imo, ilat, ilon] = NaN
-               lamb_ice_seas[imo, ilat, ilon] = NaN
-               diff_ice_seas[imo, ilat, ilon] = NaN
-               ratio_ice_seas[imo, ilat, ilon] = NaN
-            if (emis_spec[ilat, ilon] >= 0.87):
-                spec_ice_seas[imo, ilat, ilon] = emis_spec[ilat, ilon]
-                lamb_ice_seas[imo, ilat, ilon] = emis_lamb[ilat, ilon]
-                diff_ice_seas[imo, ilat, ilon] = emis_diff[ilat, ilon]
-                ratio_ice_seas[imo, ilat, ilon] = emis_ratio[ilat, ilon]
-            else:
-                spec_ice_seas[imo, ilat, ilon] = NaN
-                lamb_ice_seas[imo, ilat, ilon] = NaN
-                diff_ice_seas[imo, ilat, ilon] = NaN
-                ratio_ice_seas[imo, ilat, ilon] = NaN
-
-
-
-
+frequ = np.array(['23', '30', '50', '89'], int)
+emis_spec = np.zeros([4, M, ny, nx], float)
+emis_lamb = np.zeros([4, M, ny, nx], float)
+emis_diff = np.zeros([4, M, ny, nx], float)
+emis_ratio = np.zeros([4, M, ny, nx], float)
+spec_ice = np.zeros([4, M, ny * nx], float)
+lamb_ice = np.zeros([4, M, ny * nx], float)
+diff_ice = np.zeros([4, M, ny * nx], float)
+ratio_ice = np.zeros([4, M, ny * nx], float)
+for ifr in range (0, 4):
+    for imo in range (0, M):
+        print 'month ' + month[imo]
+        ########################
+        # read ice extent data #
+        ########################
+        print 'open emissivity to sub_classify file'
+        ## data of emis SPEC, LAMB, DIFF(SPEC-LAMB)
+        fichier_e = 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[ifr])+'_spec_lamb_thresholds.nc', 'r', format='NETCDF3_CLASSIC')
+        emis_spec[ifr, imo, :, :] = fichier_e.variables['spec_ice_area'][:]
+        emis_lamb[ifr, imo, :, :] = fichier_e.variables['lamb_ice_area'][:]
+        emis_diff[ifr, imo, :, :] = fichier_e.variables['diff_ice_area'][:]
+        emis_ratio[ifr, imo, :, :] = fichier_e.variables['ratio_ice_area'][:]
+        fichier_e.close()
+        a = reshape(emis_spec[ifr, imo, :, :], size(emis_spec[ifr, imo, :, :]))
+        spec_ice[ifr, imo, 0 : len(a[nonzero(isnan(a) == False)])] = a[nonzero(isnan(a) == False)]
+        b = reshape(emis_lamb[ifr, imo, :, :], size(emis_lamb[ifr, imo, :, :]))
+        lamb_ice[ifr, imo, 0 : len(a[nonzero(isnan(a) == False)])] = b[nonzero(isnan(b) == False)]
+        c = reshape(emis_diff[ifr, imo, :, :], size(emis_diff[ifr, imo, :, :]))
+        diff_ice[ifr, imo, 0 : len(a[nonzero(isnan(a) == False)])] = c[nonzero(isnan(c) == False)]
+        d = reshape(emis_ratio[ifr, imo, :, :], size(emis_ratio[ifr, imo, :, :]))
+        ratio_ice[ifr, imo, 0 : len(a[nonzero(isnan(a) == False)])] = d[nonzero(isnan(d) == False)]
+
+
+
+
+
+######################
+# correlation matrix #
+######################
+params = np.zeros([M, 16, 5673], float)
+for imo in range (0, M):
+    params[imo, :, :] = np.array([spec_ice[0, imo, :][nonzero(spec_ice[0, imo, :] != 0.)], spec_ice[1, imo, :][nonzero(spec_ice[1, imo, :] != 0.)], spec_ice[2, imo, :][nonzero(spec_ice[2, imo, :] != 0.)], spec_ice[3, imo, :][nonzero(spec_ice[3, imo, :] != 0.)], lamb_ice[0, imo, :][nonzero(lamb_ice[0, imo, :] != 0.)], lamb_ice[1, imo, :][nonzero(lamb_ice[1, imo, :] != 0.)], lamb_ice[2, imo, :][nonzero(lamb_ice[2, imo, :] != 0.)], lamb_ice[3, imo, :][nonzero(lamb_ice[3, imo, :] != 0.)], diff_ice[0, imo, :][nonzero(diff_ice[0, imo, :] != 0.)], diff_ice[1, imo, :][nonzero(diff_ice[1, imo, :] != 0.)], diff_ice[2, imo, :][nonzero(diff_ice[2, imo, :] != 0.)], diff_ice[3, imo, :][nonzero(diff_ice[3, imo, :] != 0.)], ratio_ice[0, imo, :][nonzero(ratio_ice[0, imo, :] != 0.)], ratio_ice[1, imo, :][nonzero(ratio_ice[1, imo, :] != 0.)], ratio_ice[2, imo, :][nonzero(ratio_ice[2, imo, :] != 0.)], ratio_ice[3, imo, :][nonzero(ratio_ice[3, imo, :] != 0.)], ])
+
+
+corr_mat = np.zeros([M, 16, 16], float)
+for imo in range (0, M): 
+    for i in range (0, 16):
+        for j in range (0, 16):
+            corr_mat[imo, i, j] = np.corrcoef(params[imo, i, :], params_c[imo, j, :])[0][1]
+
+
+
+name_params = np.array(['spec89', 'lamb89', 'diff89', 'ratio89', 'spec50', 'lamb50', 'diff50', 'ratio50', 'spec30', 'lamb30', 'diff30', 'ratio30', 'spec23', 'lamb23', 'diff23', 'ratio23'])
+figure()
+cs = pcolor(corr_mat[imo, :, :], cmap = cm.s3pcpn_l_r)
+xticks(np.arange(0, 16, 1), name_params, rotation = 45)
+yticks(np.arange(0, 16, 1), name_params, rotation = 45)
+colorbar(cs)
+#ticks = np.arange(corr_mat[imo, :, :].min(), 1., 0.1)
+grid()
 
 
@@ -154 +175 @@
 z_coast = z_ind
 for imo in range (0, M):
-    map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, spec_ice_seas[0, :, :], 0.7, 1.02, 0.01, cm.s3pcpn_l_r, 'Sea ice extent - emissivity spec')
+    map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, emis_ratio[0, :, :], 0.07, 3.40, 0.01, cm.s3pcpn_l_r, 'Sea ice extent - emissivity spec')
     title('AMSUA ' + str(frequ) + ' - ' + str(month[imo]) + ' 2009')
     #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps_sea_ice_extent/emiss_ratio_map_AMSUA'+str(frequ)+'_with_AMSUA23-and-30-spec_'+str(month[imo])+'_2009.png')
@@ -268 +289 @@
 
 
-'''
-######################
-# correlation matrix #
-######################
-params_c = np.zeros([M, 16, 200], float)
-params_h = np.zeros([M, 16, 200], float)
-for imo in range (0, M):
-    params_c[imo, :, :] = np.array([c_s[imo, 0, :], c_l[imo, 0, :], c_d[imo, 0, :], c_r[imo, 0, :], c_s[imo, 1, :], c_l[imo, 1, :], c_d[imo, 1, :], c_r[imo, 1, :], c_s[imo, 2, :], c_l[imo, 2, :], c_d[imo, 2, :], c_r[imo, 2, :], c_s[imo, 3, :], c_l[imo, 3, :], c_d[imo, 3, :], c_r[imo, 3, :]])
-    params_h[imo, :, :] = np.array([h_s[imo, 0, :], h_l[imo, 0, :], h_d[imo, 0, :], h_r[imo, 0, :], h_s[imo, 1, :], h_l[imo, 1, :], h_d[imo, 1, :], h_r[imo, 1, :], h_s[imo, 2, :], h_l[imo, 2, :], h_d[imo, 2, :], h_r[imo, 2, :], h_s[imo, 3, :], h_l[imo, 3, :], h_d[imo, 3, :], h_r[imo, 3, :]])
-
-
-corr_mat = np.zeros([M, 16, 16], float)
-for imo in range (0, M): 
-    for i in range (0, 16):
-        for j in range (0, 16):
-            corr_mat[imo, i, j] = np.corrcoef(params_c[imo, i, :], params_c[imo, j, :])[0][1]
-
-'''
-
-name_params = np.array(['spec89', 'lamb89', 'diff89', 'ratio89', 'spec50', 'lamb50', 'diff50', 'ratio50', 'spec30', 'lamb30', 'diff30', 'ratio30', 'spec23', 'lamb23', 'diff23', 'ratio23'])
-figure()
-cs = pcolor(corr_mat[imo, :, :], cmap = cm.s3pcpn_l_r)
-xticks(np.arange(0, 16, 1), name_params, rotation = 45)
-yticks(np.arange(0, 16, 1), name_params, rotation = 45)
-colorbar(cs)
-#ticks = np.arange(corr_mat[imo, :, :].min(), 1., 0.1)
-grid()
+
 
 ticks=np.arange(corr_mat.min(), corr_mat.max(), 0.01)