source: trunk/src/scripts_Laura/ARCTIC/Travail_CEN/compare_ice_area_different_data.py @ 45

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import string
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import shiftgrid, cm
from netCDF4 import Dataset
import arctic_map # function to regrid coast limits
import cartesian_grid_test # function to convert grid from polar to cartesian
import scipy.special
import ffgrid2
import map_ffgrid
from matplotlib import colors
from matplotlib.font_manager import FontProperties
import map_cartesian_grid




MONTH = np.array(['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'])
month = np.array(['JANUARY', 'FEBRUARY', 'MARCH', 'APRIL', 'MAY', 'JUNE', 'JULY', 'AUGUST', 'SEPTEMBER', 'OCTOBER', 'NOVEMBER', 'DECEMBER'])
month_day = np.array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
M = len(month)

frequ = np.array([23, 50, 89])


#################################
# read .dat files of AMSUA data #
#################################
AS = np.zeros([len(frequ), M], float)
AL = np.zeros([len(frequ), M], float)
for ifr in range (0, len(frequ)):
    fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/AMSUA' + str(frequ[ifr]) + '_data_classification_parameters_ice_no-ice_2009.dat','r')
    numlines = 0
    for line in fichier: numlines += 1
    fichier.close()
    fichier = open('/net/argos/data/parvati/lahlod/ARCTIC/AMSUA_ice_class/AMSUA' + str(frequ[ifr]) + '_data_classification_parameters_ice_no-ice_2009.dat','r')
    nbtotal = numlines - 1
    iligne = 0
    mo = np.zeros([nbtotal],object)
    tot_area_spec = np.zeros([nbtotal],float)
    tot_area_lamb = np.zeros([nbtotal],float)
    while (iligne < nbtotal) :
         line=fichier.readline()
         # exemple : line = "0.22 2.3 5.0 6"
         liste = line.split()
         # exemple : listeCoord ['0.22', '2.3', '5.0', '6'] (liste de chaine de caract?es)
         mo[iligne] = str(liste[0])
         tot_area_spec[iligne] = float(liste[9])
         tot_area_lamb[iligne] = float(liste[10])
         iligne=iligne+1
    fichier.close()
    vec = np.arange(0, nbtotal + 51, 50)
    area_s = np.zeros([M], float)
    area_l = np.zeros([M], float)
    for imo in range (0, M):
        area_s[imo] = tot_area_spec[imo + vec[imo]]
        area_l[imo] = tot_area_lamb[imo + vec[imo]]
    AS[ifr, :] = area_s[:]
    AL[ifr, :] = area_l[:]



#################################
# read .dat file of OSISAF data #
#################################
fichier_osi = open('/net/argos/data/parvati/lahlod/ARCTIC/OSISAF_Ice_Types/OSISAF_daily_ice_no-ice_2009.dat','r')
numlines = 0
for line in fichier_osi: numlines += 1

fichier_osi.close()
fichier_osi = open('/net/argos/data/parvati/lahlod/ARCTIC/OSISAF_Ice_Types/OSISAF_daily_ice_no-ice_2009.dat','r')
nbtotal = numlines - 1
iligne = 0
mo = np.zeros([nbtotal],object)
tot_area_osi = np.zeros([nbtotal],float)
while (iligne < nbtotal) :
         line=fichier_osi.readline()
         # exemple : line = "0.22 2.3 5.0 6"
         liste = line.split()
         # exemple : listeCoord ['0.22', '2.3', '5.0', '6'] (liste de chaine de caract?es)
         mo[iligne] = str(liste[0])
         tot_area_osi[iligne] = float(liste[2])
         iligne=iligne+1

fichier_osi.close()
vec_osi = np.array([0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334])
area_osi = np.zeros([M], float)
for imo in range (0, M):
    area_osi[imo] = tot_area_osi[imo + vec_osi[imo]]


# calculation of bias and std between spec and lamb 
bias_area = np.zeros([len(frequ), M], float)
for ifr in range (0, len(frequ)):
    for imo in range (0, M):
        bias_area[ifr, imo] = (AL[ifr, imo] - AS[ifr, imo]) / 

figure()
plot(bias_area[0, :], 'c', label = 'spec_23GHz')
plot(bias_area[1, :], 'm', label = 'spec_50GHz')
plot(bias_area[2, :], 'g', label = 'spec_89GHz')



###################################
# plot time evolution of ice area #
###################################
color = np.array(['c', 'c--' 'm', 'm--', 'g', 'g--', 'k'])
lbl = np.array(['spec_23GHz', 'lamb_23GHz', 'spec_50GHz', 'lamb_50GHz', 'spec_89GHz', 'lamb_89GHz', 'OSISAF'])
figure()
plot(AS[0, :], 'c', label = 'spec_23GHz')
plot(AL[0, :], 'c--', label = 'lamb_23GHz')
plot(AS[1, :], 'm', label = 'spec_50GHz')
plot(AL[1, :], 'm--', label = 'lamb_50GHz')
plot(AS[2, :], 'g', label = 'spec_89GHz')
plot(AL[2, :], 'g--', label = 'lamb_89GHz')
plot(area_osi, 'k', label = 'OSISAF')
fontP = FontProperties()
fontP.set_size('small')
legend(loc = 3, prop = fontP)
ylabel('total ice area (in square km)')
xticks(np.arange(0, M, 1), month, rotation = 25)
xlim(-1, M)
grid()
plt.savefig('/usr/home/lahlod/twice_d/figure_output_ARCTIC/figure_output_CEN/compar_total_ice_area_AMSUA_SPEC_LAMB_OSISAF_2009.png')