| 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 import colors |
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| 16 | from matplotlib.font_manager import FontProperties |
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| 17 | import map_cartesian_grid |
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| 18 | |
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| 19 | |
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| 20 | ############################### |
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| 21 | # time period characteristics # |
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| 22 | ############################### |
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| 23 | MONTH = np.array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']) |
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| 24 | month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER']) |
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| 25 | month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]) |
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| 26 | M = len(month) |
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| 27 | |
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| 28 | |
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| 29 | ######################## |
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| 30 | # grid characteristics # |
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| 31 | ######################## |
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| 32 | x0 = -3000. # min limit of grid |
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| 33 | x1 = 2500. # max limit of grid |
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| 34 | dx = 40. |
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| 35 | xvec = np.arange(x0, x1+dx, dx) |
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| 36 | nx = len(xvec) |
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| 37 | y0 = -3000. # min limit of grid |
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| 38 | y1 = 3000. # max limit of grid |
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| 39 | dy = 40. |
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| 40 | yvec = np.arange(y0, y1+dy, dy) |
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| 41 | ny = len(yvec) |
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| 42 | |
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| 43 | |
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| 44 | |
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| 45 | ''' |
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| 46 | ############################################ |
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| 47 | # time evolution (monthly) in a given zone # |
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| 48 | ############################################ |
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| 49 | #zone 1 (seasonal ice) : yi = 960. // yf = 1360. // xi = -680. // xf = -320. (North Beaufort Sea) |
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| 50 | #zone 2 (multiyear ice) : yi = 320. // yf = 720. // xi = -1080. // xf = -720. (North Canadian Archipelago) |
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| 51 | #zone 3 (young ice) : yi = 1880. // yf = 2280. // xi = -480. // xf = -120. (Chukchi Sea) |
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| 52 | |
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| 53 | # select borders of zone |
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| 54 | yi = 320. |
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| 55 | yf = 720. |
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| 56 | xi = -1080. |
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| 57 | xf = -720. |
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| 58 | |
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| 59 | #find corresponding index in xvec and yvec |
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| 60 | xxi = np.where(xvec == xi)[0][0] |
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| 61 | xxf = np.where(xvec == xf)[0][0] |
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| 62 | yyi = np.where(yvec == yi)[0][0] |
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| 63 | yyf = np.where(yvec == yf)[0][0] |
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| 64 | |
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| 65 | len(xvec[xxi:xxf+1]) |
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| 66 | len(yvec[yyi:yyf+1]) |
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| 67 | |
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| 68 | mean_grad_ratio_zone = np.zeros([M, 31], float) |
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| 69 | std_grad_ratio_zone = np.zeros([M, 31], float) |
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| 70 | |
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| 71 | mean_spec_anom_23_zone = np.zeros([M, 31], float) |
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| 72 | std_spec_anom_23_zone = np.zeros([M, 31], float) |
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| 73 | |
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| 74 | mean_spec_anom_89_zone = np.zeros([M, 31], float) |
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| 75 | std_spec_anom_89_zone = np.zeros([M, 31], float) |
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| 76 | |
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| 77 | mean_ratio_anom_89_zone = np.zeros([M, 31], float) |
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| 78 | std_ratio_anom_89_zone = np.zeros([M, 31], float) |
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| 79 | |
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| 80 | S = np.zeros([M, 31], float) |
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| 81 | |
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| 82 | for imo in range (0, M): |
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| 83 | # daily read gradient ratio file |
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| 84 | print 'read daily gradient ratio for month ' + month[imo] |
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| 85 | 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') |
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| 86 | gr = fichier_grad_ratio.variables['grad_ratio'][:] |
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| 87 | fichier_grad_ratio.close() |
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| 88 | # read daily emis anomaly file for 23GHz |
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| 89 | print 'read daily emis anomaly 23GHz for month ' + month[imo] |
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| 90 | fichier_anom23 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_daily_data_lamb_spec_ratio_anomaly_near_nadir_AMSUA23_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') |
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| 91 | sa_23 = fichier_anom23.variables['spec_anomaly'][:] |
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| 92 | fichier_anom23.close() |
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| 93 | # read daily emis anomaly file for 89GHz |
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| 94 | print 'read daily emis anomaly 89GHz for month ' + month[imo] |
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| 95 | fichier_anom89 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_daily_data_lamb_spec_ratio_anomaly_near_nadir_AMSUA89_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') |
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| 96 | sa_89 = fichier_anom89.variables['spec_anomaly'][:] |
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| 97 | ra_89 = fichier_anom89.variables['ratio_anomaly'][:] |
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| 98 | fichier_anom89.close() |
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| 99 | grad_ratio_vec = np.zeros([month_day[imo], len(xvec[xxi : xxf+1]) * len(yvec[yyi : yyf+1])], float) |
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| 100 | spec_anom_23_vec = np.zeros([month_day[imo], len(xvec[xxi : xxf+1]) * len(yvec[yyi : yyf+1])], float) |
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| 101 | spec_anom_89_vec = np.zeros([month_day[imo], len(xvec[xxi : xxf+1]) * len(yvec[yyi : yyf+1])], float) |
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| 102 | ratio_anom_89_vec = np.zeros([month_day[imo], len(xvec[xxi : xxf+1]) * len(yvec[yyi : yyf+1])], float) |
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| 103 | print 'calculate daily mean and std zonal gradient ratio' |
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| 104 | for ijr in range (0, month_day[imo]): |
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| 105 | print 'date ' + str(ijr+1) + ' ' + month[imo] + ' 2009' |
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| 106 | S[imo, ijr] = shape(gr[ijr, yyi : yyf + 1, xxi : xxf + 1][nonzero(isnan(gr[ijr, yyi : yyf + 1, xxi : xxf + 1]) == False)])[0] |
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| 107 | # gradient ratio |
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| 108 | grad_ratio_vec[ijr, :] = reshape(gr[ijr, yyi : yyf + 1, xxi : xxf + 1], size(gr[ijr, yyi : yyf + 1, xxi : xxf + 1])) |
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| 109 | mean_grad_ratio_zone[imo, ijr] = mean(grad_ratio_vec[ijr, :][nonzero(isnan(grad_ratio_vec[ijr, :]) == False)]) |
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| 110 | std_grad_ratio_zone[imo, ijr] = sqrt((1. / (size(gr[ijr, yyi : yyf+1, xxi : xxf+1]) - 1.)) * sum((grad_ratio_vec[ijr, :][nonzero(isnan(grad_ratio_vec[ijr, :]) == False)] - mean_grad_ratio_zone[imo, ijr])**2)) |
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| 111 | # spec anomaly 23GHz |
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| 112 | spec_anom_23_vec[ijr, :] = reshape(sa_23[ijr, yyi : yyf + 1, xxi : xxf + 1], size(sa_23[ijr, yyi : yyf + 1, xxi : xxf + 1])) |
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| 113 | mean_spec_anom_23_zone[imo, ijr] = mean(spec_anom_23_vec[ijr, :][nonzero(isnan(spec_anom_23_vec[ijr, :]) == False)]) |
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| 114 | std_spec_anom_23_zone[imo, ijr] = sqrt((1. / (size(sa_23[ijr, yyi : yyf+1, xxi : xxf+1]) - 1.)) * sum((spec_anom_23_vec[ijr, :][nonzero(isnan(spec_anom_23_vec[ijr, :]) == False)] - mean_spec_anom_23_zone[imo, ijr])**2)) |
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| 115 | # spec anomaly 89GHz |
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| 116 | spec_anom_89_vec[ijr, :] = reshape(sa_89[ijr, yyi : yyf + 1, xxi : xxf + 1], size(sa_89[ijr, yyi : yyf + 1, xxi : xxf + 1])) |
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| 117 | mean_spec_anom_89_zone[imo, ijr] = mean(spec_anom_89_vec[ijr, :][nonzero(isnan(spec_anom_89_vec[ijr, :]) == False)]) |
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| 118 | std_spec_anom_89_zone[imo, ijr] = sqrt((1. / (size(sa_89[ijr, yyi : yyf+1, xxi : xxf+1]) - 1.)) * sum((spec_anom_89_vec[ijr, :][nonzero(isnan(spec_anom_89_vec[ijr, :]) == False)] - mean_spec_anom_89_zone[imo, ijr])**2)) |
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| 119 | # ratio anomaly 89GHz |
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| 120 | ratio_anom_89_vec[ijr, :] = reshape(ra_89[ijr, yyi : yyf + 1, xxi : xxf + 1], size(ra_89[ijr, yyi : yyf + 1, xxi : xxf + 1])) |
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| 121 | mean_ratio_anom_89_zone[imo, ijr] = mean(ratio_anom_89_vec[ijr, :][nonzero(isnan(ratio_anom_89_vec[ijr, :]) == False)]) |
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| 122 | std_ratio_anom_89_zone[imo, ijr] = sqrt((1. / (size(ra_89[ijr, yyi : yyf+1, xxi : xxf+1]) - 1.)) * sum((ratio_anom_89_vec[ijr, :][nonzero(isnan(ratio_anom_89_vec[ijr, :]) == False)] - mean_ratio_anom_89_zone[imo, ijr])**2)) |
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| 123 | if (isnan(mean_grad_ratio_zone[imo, ijr]) == True): |
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| 124 | std_grad_ratio_zone[imo, ijr] = NaN |
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| 125 | if (isnan(mean_spec_anom_23_zone[imo, ijr]) == True): |
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| 126 | std_spec_anom_23_zone[imo, ijr] = NaN |
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| 127 | if (isnan(mean_spec_anom_89_zone[imo, ijr]) == True): |
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| 128 | std_spec_anom_89_zone[imo, ijr] = NaN |
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| 129 | if (isnan(mean_ratio_anom_89_zone[imo, ijr]) == True): |
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| 130 | std_ratio_anom_89_zone[imo, ijr] = NaN |
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| 131 | |
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| 132 | |
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| 133 | |
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| 134 | # append daily zonal gradient ratio for study of the whole year 2009 |
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| 135 | mean_year_grad_ratio = np.append(mean_grad_ratio_zone[0, 0 : month_day[0]], mean_grad_ratio_zone[1, 0 : month_day[1]]) |
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| 136 | std_year_grad_ratio = np.append(std_grad_ratio_zone[0, 0 : month_day[0]], std_grad_ratio_zone[1, 0 : month_day[1]]) |
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| 137 | |
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| 138 | mean_year_spec_anom_23 = np.append(mean_spec_anom_23_zone[0, 0 : month_day[0]], mean_spec_anom_23_zone[1, 0 : month_day[1]]) |
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| 139 | std_year_spec_anom_23 = np.append(std_spec_anom_23_zone[0, 0 : month_day[0]], std_spec_anom_23_zone[1, 0 : month_day[1]]) |
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| 140 | |
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| 141 | mean_year_spec_anom_89 = np.append(mean_spec_anom_89_zone[0, 0 : month_day[0]], mean_spec_anom_89_zone[1, 0 : month_day[1]]) |
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| 142 | std_year_spec_anom_89 = np.append(std_spec_anom_89_zone[0, 0 : month_day[0]], std_spec_anom_89_zone[1, 0 : month_day[1]]) |
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| 143 | |
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| 144 | mean_year_ratio_anom_89 = np.append(mean_ratio_anom_89_zone[0, 0 : month_day[0]], mean_ratio_anom_89_zone[1, 0 : month_day[1]]) |
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| 145 | std_year_ratio_anom_89 = np.append(std_ratio_anom_89_zone[0, 0 : month_day[0]], std_ratio_anom_89_zone[1, 0 : month_day[1]]) |
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| 146 | |
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| 147 | year_S = np.append(S[0, 0 : month_day[0]], S[1, 0 : month_day[1]]) |
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| 148 | |
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| 149 | for imo in range (2, M): |
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| 150 | # gradient ratio |
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| 151 | mean_year_grad_ratio = np.append(mean_year_grad_ratio, mean_grad_ratio_zone[imo, 0 : month_day[imo]]) |
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| 152 | std_year_grad_ratio = np.append(std_year_grad_ratio, std_grad_ratio_zone[imo, 0 : month_day[imo]]) |
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| 153 | # spec anomaly 23GHz |
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| 154 | mean_year_spec_anom_23 = np.append(mean_year_spec_anom_23, mean_spec_anom_23_zone[imo, 0 : month_day[imo]]) |
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| 155 | std_year_spec_anom_23 = np.append(std_year_spec_anom_23, std_spec_anom_23_zone[imo, 0 : month_day[imo]]) |
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| 156 | # spec anomaly 89GHz |
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| 157 | mean_year_spec_anom_89 = np.append(mean_year_spec_anom_89, mean_spec_anom_89_zone[imo, 0 : month_day[imo]]) |
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| 158 | std_year_spec_anom_89 = np.append(std_year_spec_anom_89, std_spec_anom_89_zone[imo, 0 : month_day[imo]]) |
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| 159 | # ratio anomaly 89GHz |
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| 160 | mean_year_ratio_anom_89 = np.append(mean_year_ratio_anom_89, mean_ratio_anom_89_zone[imo, 0 : month_day[imo]]) |
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| 161 | std_year_ratio_anom_89 = np.append(std_year_ratio_anom_89, std_ratio_anom_89_zone[imo, 0 : month_day[imo]]) |
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| 162 | # number of data points in area of study |
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| 163 | year_S = np.append(year_S, S[imo, 0 : month_day[imo]]) |
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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 | # calculate standard deviation of emissivity parameters over the year 2009 # |
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| 169 | ############################################################################ |
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| 170 | print 'calculate standard deviation of emissivity parameters over the year 2009' |
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| 171 | time_std1 = sqrt((1./364.)*sum((mean_year_grad_ratio[nonzero(isnan(mean_year_grad_ratio) == False)] - mean(mean_year_grad_ratio[nonzero(isnan(mean_year_grad_ratio) == False)]))**2)) |
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| 172 | |
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| 173 | time_std2 = sqrt((1./364.)*sum((mean_year_spec_anom_23[nonzero(isnan(mean_year_spec_anom_23) == False)] - mean(mean_year_spec_anom_23[nonzero(isnan(mean_year_spec_anom_23) == False)]))**2)) |
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| 174 | |
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| 175 | time_std3 = sqrt((1./364.)*sum((mean_year_spec_anom_89[nonzero(isnan(mean_year_spec_anom_89) == False)] - mean(mean_year_spec_anom_89[nonzero(isnan(mean_year_spec_anom_89) == False)]))**2)) |
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| 176 | |
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| 177 | time_std4 = sqrt((1./364.)*sum((mean_year_ratio_anom_89[nonzero(isnan(mean_year_ratio_anom_89) == False)] - mean(mean_year_ratio_anom_89[nonzero(isnan(mean_year_ratio_anom_89) == False)]))**2)) |
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| 178 | |
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| 179 | |
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| 180 | |
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| 181 | |
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| 182 | ######################### |
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| 183 | # plot daily parameters # |
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| 184 | ######################### |
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| 185 | vec_months = np.array([0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334]) |
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| 186 | ion() |
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| 187 | figure() |
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| 188 | subplot(2, 1, 1) |
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| 189 | plot(mean_year_grad_ratio, 'b', label = 'grad ratio') |
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| 190 | plot(mean_year_spec_anom_23, 'r', label = 'spec anom 23GHz') |
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| 191 | plot(mean_year_spec_anom_89, 'g', label = 'spec anom 89GHz') |
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| 192 | plot(np.zeros([365], float), '--k') |
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| 193 | xticks(vec_months, month, rotation = 25) |
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| 194 | xlim(0, 365) |
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| 195 | fontP = FontProperties() |
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| 196 | fontP.set_size('small') |
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| 197 | legend(loc = 3, prop = fontP) |
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| 198 | grid() |
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| 199 | subplot(2, 1, 2) |
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| 200 | plot(mean_year_ratio_anom_89, 'y', label = 'ratio anom 89GHz') |
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| 201 | plot(np.zeros([365], float), '--k') |
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| 202 | xlim(0, 365) |
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| 203 | xticks(vec_months, month, rotation = 25) |
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| 204 | legend(loc = 1, prop = fontP) |
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| 205 | grid() |
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| 206 | 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_grad_ratio_emis_anomaly_params_zone_Chukchi_Sea.png') |
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| 207 | ############################################ |
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| 208 | # plot daily number of data points in area # |
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| 209 | ############################################ |
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| 210 | vec_months = np.array([0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334]) |
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| 211 | figure() |
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| 212 | plot(year_S, label = 'mean=' + str(mean(year_S))[0:5] + ' ; std=' + str(std(year_S))[0:5]) |
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| 213 | xticks(vec_months, month, rotation = 25) |
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| 214 | xlim(0, 365) |
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| 215 | ylabel('Number of data points in area') |
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| 216 | fontP = FontProperties() |
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| 217 | fontP.set_size('small') |
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| 218 | legend(loc = 1, prop = fontP) |
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| 219 | title('North Canadian Archipelago') |
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| 220 | 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_nber_data_points_zone_North_Canadian_Archipelago.png') |
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| 221 | |
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| 222 | |
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| 223 | |
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| 224 | subplot(2, 1, 2) |
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| 225 | plot(std_year_grad_ratio, '--b', label = 'std grad ratio') |
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| 226 | plot(std_year_grad_ratio, '--r', label = 'std grad ratio') |
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| 227 | plot(std_year_grad_ratio, '--g', label = 'std grad ratio') |
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| 228 | plot(std_year_grad_ratio, '--y', label = 'std grad ratio') |
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| 229 | xticks(vec_months, month, rotation = 25) |
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| 230 | legend(loc = 2, prop = fontP) |
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| 231 | xlim(0, 365) |
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| 232 | |
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| 233 | |
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| 234 | |
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| 235 | #################### |
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| 236 | # map studied zone # |
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| 237 | #################### |
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| 238 | ion() |
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| 239 | x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() |
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| 240 | x_coast = x_ind |
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| 241 | y_coast = y_ind |
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| 242 | z_coast = z_ind |
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| 243 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec[xxi : xxf + 1], yvec[yyi : yyf + 1], gr[0, yyi : yyf + 1, xxi : xxf + 1], gr[0, :, :][nonzero(isnan(gr[0, :, :]) == False)].min(), gr[0, :, :][nonzero(isnan(gr[0, :, :]) == False)].max(), 0.001, cm.s3pcpn_l_r, 'daily gradient ratio (01-01-2009)') |
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| 244 | title('area of study - Chukchi Sea') |
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| 245 | plt.savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/study_by_zones/map_grad_ratio_zone_Chukchi_Sea.png') |
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| 246 | ''' |
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| 247 | |
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| 248 | |
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| 249 | ################################ |
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| 250 | # map parameters in all Arctic # |
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| 251 | ################################ |
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| 252 | cumul_params = np.zeros([M, ny, nx], float) |
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| 253 | grad_ratio = np.zeros([M, ny, nx], float) |
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| 254 | spec_anom_23 = np.zeros([M, ny, nx], float) |
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| 255 | spec_anom_89 = np.zeros([M, ny, nx], float) |
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| 256 | ratio_anom_89 = np.zeros([M, ny, nx], float) |
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| 257 | CP = np.zeros([M, ny, nx], float) |
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| 258 | for imo in range (0, M): |
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| 259 | # daily read gradient ratio file |
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| 260 | print 'read daily gradient ratio for month ' + month[imo] |
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| 261 | 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') |
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| 262 | gr = fichier_grad_ratio.variables['grad_ratio'][:] |
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| 263 | fichier_grad_ratio.close() |
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| 264 | # read daily emis anomaly file for 23GHz |
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| 265 | print 'read daily emis anomaly 23GHz for month ' + month[imo] |
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| 266 | fichier_anom23 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_daily_data_lamb_spec_ratio_anomaly_near_nadir_AMSUA23_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') |
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| 267 | sa_23 = fichier_anom23.variables['spec_anomaly'][:] |
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| 268 | fichier_anom23.close() |
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| 269 | # read daily emis anomaly file for 89GHz |
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| 270 | print 'read daily emis anomaly 89GHz for month ' + month[imo] |
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| 271 | fichier_anom89 = Dataset('/net/argos/data/parvati/lahlod/ARCTIC/monthly_GLACE/gridded_data/cartesian_grid/res_40/cartesian_grid_daily_data_lamb_spec_ratio_anomaly_near_nadir_AMSUA89_' + month[imo] + '2009.nc', 'r', format = 'NETCDF3_CLASSIC') |
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| 272 | sa_89 = fichier_anom89.variables['spec_anomaly'][:] |
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| 273 | ra_89 = fichier_anom89.variables['ratio_anomaly'][:] |
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| 274 | fichier_anom89.close() |
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| 275 | for ilon in range (0, nx): |
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| 276 | for ilat in range (0, ny): |
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| 277 | grad_ratio[imo, ilat, ilon] = mean(gr[:, ilat, ilon][nonzero(isnan(gr[:, ilat, ilon]) == False)]) |
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| 278 | spec_anom_23[imo, ilat, ilon] = mean(sa_23[:, ilat, ilon][nonzero(isnan(sa_23[:, ilat, ilon]) == False)]) |
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| 279 | spec_anom_89[imo, ilat, ilon] = mean(sa_89[:, ilat, ilon][nonzero(isnan(sa_89[:, ilat, ilon]) == False)]) |
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| 280 | ratio_anom_89[imo, ilat, ilon] = mean(ra_89[:, ilat, ilon][nonzero(isnan(ra_89[:, ilat, ilon]) == False)]) |
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| 281 | cumul_params[imo, ilat, ilon] = abs(grad_ratio[imo, ilat, ilon]) + abs(spec_anom_23[imo, ilat, ilon]) + abs(spec_anom_89[imo, ilat, ilon]) + abs(ratio_anom_89[imo, ilat, ilon]) |
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| 282 | CP[imo, :, :] = (cumul_params[imo, :, :]/max(cumul_params[imo, :, :][nonzero(isnan(cumul_params[imo, :, :]) == False)])) * 100. |
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| 283 | |
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| 284 | |
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| 285 | #ion() |
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| 286 | x_ind, y_ind, z_ind, volume = arctic_map.arctic_map_lat50() |
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| 287 | x_coast = x_ind |
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| 288 | y_coast = y_ind |
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| 289 | z_coast = z_ind |
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| 290 | for imo in range (0, M): |
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| 291 | print 'map month ' + month[imo] |
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| 292 | map_cartesian_grid.draw_map_cartes_l(x_coast, y_coast, z_coast, volume, xvec, yvec, CP[imo, :, :], 0., 100., 1., cm.s3pcpn_l_r, 'Cumulation index (%)') |
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| 293 | title('AMSUA - ' + month[imo] + ' 2009') |
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| 294 | savefig('/usr/home/lahlod/twice_d/fig_output_ARCTIC/fig_output_sea_ice_study/ice_class_AMSUA/sub_classification/maps/cumul_params/cumul_all_parameters/map_cumulation_index_'+ str(MONTH[imo]) + month[imo] + '2009.png') |
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