| 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 | from pylab import * |
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| 7 | from mpl_toolkits.basemap import Basemap |
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| 8 | from mpl_toolkits.basemap import shiftgrid, cm |
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| 9 | from netCDF4 import Dataset |
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| 10 | import arctic_map # function to regrid coast limits |
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| 11 | import cartesian_grid_test # function to convert grid from polar to cartesian |
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| 12 | import scipy.special |
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| 13 | import ffgrid2 |
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| 14 | import map_ffgrid |
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| 15 | from matplotlib.font_manager import FontProperties |
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| 16 | import map_cartesian_grid |
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| 17 | |
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| 18 | |
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| 19 | |
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| 20 | |
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| 21 | |
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| 22 | |
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| 23 | def filtering(frequ): |
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| 24 | |
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| 25 | |
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| 26 | |
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| 27 | MONTH = np.array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']) |
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| 28 | month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER']) |
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| 29 | month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) |
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| 30 | M = len(month) |
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| 31 | |
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| 32 | |
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| 33 | ######################## |
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| 34 | # grid characteristics # |
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| 35 | ######################## |
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| 36 | x0 = -3000. # min limit of grid |
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| 37 | x1 = 2500. # max limit of grid |
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| 38 | dx = 40. |
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| 39 | xvec = np.arange(x0, x1+dx, dx) |
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| 40 | nx = len(xvec) |
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| 41 | y0 = -3000. # min limit of grid |
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| 42 | y1 = 3000. # max limit of grid |
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| 43 | dy = 40. |
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| 44 | yvec = np.arange(y0, y1+dy, dy) |
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| 45 | ny = len(yvec) |
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| 46 | |
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| 47 | |
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| 48 | ######################### |
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| 49 | # emissivity thresholds # |
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| 50 | ######################### |
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| 51 | fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/AMSUA'+ str(frequ) + '_data_classification_parameters_ice_no-ice_2009.dat', 'r') |
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| 52 | numlines = 0 |
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| 53 | for line in fichier: numlines += 1 |
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| 54 | |
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| 55 | fichier.close() |
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| 56 | |
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| 57 | fichier=open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/AMSUA' + str(frequ) + '_data_classification_parameters_ice_no-ice_2009.dat','r') |
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| 58 | nbtotal = numlines-1 |
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| 59 | iligne = 0 |
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| 60 | mois = np.zeros([nbtotal],object) |
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| 61 | emis_lim_spec = np.zeros([nbtotal],float) |
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| 62 | emis_lim_lamb = np.zeros([nbtotal],float) |
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| 63 | while (iligne < nbtotal) : |
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| 64 | line=fichier.readline() |
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| 65 | liste = line.split() |
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| 66 | mois[iligne] = str(liste[0]) |
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| 67 | emis_lim_spec[iligne] = float(liste[7]) |
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| 68 | emis_lim_lamb[iligne] = float(liste[8]) |
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| 69 | iligne=iligne+1 |
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| 70 | |
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| 71 | fichier.close() |
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| 72 | vec = np.arange(0, nbtotal + 1, 50) |
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| 73 | lim_spec = np.zeros([M], float) |
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| 74 | lim_lamb = np.zeros([M], float) |
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| 75 | for imo in range (0, M): |
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| 76 | lim_spec[imo] = emis_lim_spec[vec[imo]] |
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| 77 | lim_lamb[imo] = emis_lim_lamb[vec[imo]] |
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| 78 | |
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| 79 | |
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| 80 | |
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| 81 | |
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| 82 | emis_spec_moy = np.zeros([ny, nx, M], float) |
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| 83 | emis_lamb_moy = np.zeros([ny, nx, M], float) |
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| 84 | emis_diff = np.zeros([ny, nx, M], float) |
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| 85 | emis_ratio = np.zeros([ny, nx, M], float) |
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| 86 | for imo in range (0, M): |
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| 87 | #print 'month ' + month[imo] |
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| 88 | ############## |
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| 89 | # emissivity # |
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| 90 | ############## |
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| 91 | #print 'read file for emiss' |
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| 92 | fichier_emis = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_monthly_data_lamb_spec_near_nadir_AMSUA' + str(frequ) + '_' + month[imo] + '2009.nc', 'r', format='NETCDF3_CLASSIC') |
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| 93 | xdist = fichier_emis.variables['longitude'][:] |
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| 94 | ydist = fichier_emis.variables['latitude'][:] |
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| 95 | day = fichier_emis.variables['days'][:] |
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| 96 | emis_spec = fichier_emis.variables['e_spec'][:] |
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| 97 | emis_lamb = fichier_emis.variables['e_lamb'][:] |
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| 98 | fichier_emis.close() |
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| 99 | #print 'calculation of monthly mean' |
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| 100 | for ilon in range (0, nx): |
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| 101 | for ilat in range (0, ny): |
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| 102 | emis_spec_moy[ilat, ilon, imo] = mean(emis_spec[ilat, ilon, :][nonzero(isnan(emis_spec[ilat, ilon, :]) == False)]) |
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| 103 | emis_lamb_moy[ilat, ilon, imo] = mean(emis_lamb[ilat, ilon, :][nonzero(isnan(emis_lamb[ilat, ilon, :]) == False)]) |
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| 104 | #print 'calculation of monthly mean difference lamb-spec' |
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| 105 | emis_diff[:, :, imo] = emis_lamb_moy[:, :, imo] - emis_spec_moy[:, :, imo] |
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| 106 | ######### |
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| 107 | # ratio # |
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| 108 | ######### |
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| 109 | #print 'read file for ratio' |
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| 110 | fichier_ratio = 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) + '_' + month[imo] + '2009.nc', 'r', format='NETCDF3_CLASSIC') |
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| 111 | xdist = fichier_ratio.variables['longitude'][:] |
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| 112 | ydist = fichier_ratio.variables['latitude'][:] |
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| 113 | emis_ratio[:, :, imo] = fichier_ratio.variables['emis_ratio'][:] |
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| 114 | fichier_ratio.close() |
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| 115 | |
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| 116 | |
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| 117 | |
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| 118 | ########################################### |
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| 119 | # emissivity distribution after filtering # |
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| 120 | ########################################### |
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| 121 | ''' |
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| 122 | hist_val_spec = np.zeros([50, M], float) |
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| 123 | hist_val_lamb = np.zeros([50, M], float) |
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| 124 | hist_val_ratio = np.zeros([50, M], float) |
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| 125 | hist_val_diff = np.zeros([50, M], float) |
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| 126 | corresp_val_spec = np.zeros([50, M], float) |
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| 127 | corresp_val_lamb = np.zeros([50, M], float) |
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| 128 | corresp_val_ratio = np.zeros([50, M], float) |
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| 129 | corresp_val_diff = np.zeros([50, M], float) |
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| 130 | ''' |
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| 131 | emis_spec_f = np.zeros([7000, M], float) |
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| 132 | emis_lamb_f = np.zeros([7000, M], float) |
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| 133 | emis_ratio_f = np.zeros([7000, M], float) |
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| 134 | emis_diff_f = np.zeros([7000, M], float) |
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| 135 | L_spec = np.zeros([M], int) |
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| 136 | for imo in range (0, M): |
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| 137 | #print 'month ' + month[imo] |
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| 138 | # choice of spec emissivity as the threshold for the study // definition of x and y index corresponding to the points which emissivity value is over threshold |
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| 139 | y_index_spec = np.where(emis_spec_moy[:, :, imo] >= lim_spec[imo])[0] |
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| 140 | x_index_spec = np.where(emis_spec_moy[:, :, imo] >= lim_spec[imo])[1] |
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| 141 | L_spec[imo] = len(x_index_spec) # = len(y_index) |
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| 142 | #print 'length of x and y index ', L_spec[imo] |
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| 143 | # definition of filtered values (vectors) with the previous threshold // values of filtered emissivity SPEC, LAMB, rate and difference LAMB-SPEC |
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| 144 | for ii in range (0, L_spec[imo]): |
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| 145 | emis_spec_f[ii, imo] = emis_spec_moy[y_index_spec[ii], x_index_spec[ii], imo] |
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| 146 | emis_lamb_f[ii, imo] = emis_lamb_moy[y_index_spec[ii], x_index_spec[ii], imo] |
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| 147 | emis_ratio_f[ii, imo] = emis_ratio[y_index_spec[ii], x_index_spec[ii], imo] |
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| 148 | emis_diff_f[ii, imo] = emis_diff[y_index_spec[ii], x_index_spec[ii], imo] |
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| 149 | ''' |
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| 150 | # definition of their distribution within the new filtered values |
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| 151 | hist_val_spec[:, imo] = hist(emis_spec_f, bins = 50, normed = True, histtype='step')[0] |
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| 152 | hist_val_lamb[:, imo] = hist(emis_lamb_f, bins = 50, normed = True, histtype='step')[0] |
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| 153 | hist_val_ratio[:, imo] = hist(emis_ratio_f, bins = 50, normed = True, histtype='step')[0] |
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| 154 | hist_val_diff[:, imo] = hist(emis_diff_f, bins = 50, normed = True, histtype='step')[0] |
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| 155 | for ibin in range (0, 50): |
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| 156 | corresp_val_spec[ibin, imo] = mean(hist(emis_spec_f, bins = 50, normed = True, histtype='step')[1][ibin : ibin + 2]) |
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| 157 | corresp_val_lamb[ibin, imo] = mean(hist(emis_lamb_f, bins = 50, normed = True, histtype='step')[1][ibin : ibin + 2]) |
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| 158 | corresp_val_ratio[ibin, imo] = mean(hist(emis_ratio_f, bins = 50, normed = True, histtype='step')[1][ibin : ibin + 2]) |
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| 159 | corresp_val_diff[ibin, imo] = mean(hist(emis_diff_f, bins = 50, normed = True, histtype='step')[1][ibin : ibin + 2]) |
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| 160 | ''' |
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| 161 | |
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| 162 | return(emis_spec_f, emis_lamb_f, emis_ratio_f, emis_diff_f, L_spec) |
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| 163 | |
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| 164 | |
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| 165 | |
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| 166 | ''' |
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| 167 | ######## |
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| 168 | # plot # |
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| 169 | ######## |
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| 170 | c = np.array(['r', 'b', 'c', 'm', 'y', 'g']) |
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| 171 | figure() |
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| 172 | for imo in range (0, 6): |
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| 173 | plot(corresp_val[:, imo], hist_val[:, imo], c = str(c[imo]), label = str(month[imo])) |
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| 174 | |
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| 175 | grid() |
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| 176 | xlim(corresp_val.min() - 0.02, corresp_val.max() + 0.02) |
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| 177 | xlabel('emissivity parameter') |
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| 178 | ylabel('frequency of occurence') |
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| 179 | fontP = FontProperties() |
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| 180 | fontP.set_size('small') |
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| 181 | legend(prop = fontP) |
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| 182 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_rate_AMSUA30_JANUARY-JUNE_2009.png') |
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| 183 | ## plot six following months of spec emissivity histograms ## |
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| 184 | figure() |
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| 185 | for imo in range (6, M): |
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| 186 | plot(corresp_val[:, imo], hist_val[:, imo], c = str(c[imo - 6]), label = str(month[imo])) |
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| 187 | |
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| 188 | grid() |
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| 189 | xlim(corresp_val.min() - 0.02, corresp_val.max() + 0.02) |
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| 190 | xlabel('emissivity parameter') |
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| 191 | ylabel('frequency of occurence') |
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| 192 | legend(loc = 1, prop = fontP) |
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| 193 | #plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/emiss_rate_AMSUA30_JULY-DECEMBER_2009.png') |
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| 194 | ''' |
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