| 1 | #!/usr/bin/env python |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | |
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| 4 | # import |
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| 5 | from pylab import * |
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| 6 | from ReadRawFile import * |
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| 7 | from datetime import date |
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| 8 | import netCDF4 |
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| 9 | from scipy import spatial |
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| 10 | from mpl_toolkits.basemap import pyproj |
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| 11 | import itertools as it |
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| 12 | import time |
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| 13 | |
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| 14 | ##-------------------------------## |
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| 15 | # Class for satellite name # |
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| 16 | ##-------------------------------## |
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| 17 | class SatName: |
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| 18 | """ ### Defined a number for each satellite """ |
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| 19 | def __init__(self): |
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| 20 | self.satname = array(['AMSUBN15','AMSUBN16','MHSN18','MHSN19','MHSM01','MHSM02']) |
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| 21 | self.satnum = arange(size(self.satname))+1 |
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| 22 | |
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| 23 | def get_satnum(self,name): |
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| 24 | return concatenate([self.satnum[nn == self.satname] for nn in name]) |
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| 25 | |
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| 26 | def get_satname(self,num): |
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| 27 | return concatenate([self.satname[nn == self.satnum] for nn in num]) |
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| 28 | |
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| 29 | ##-------------------------------## |
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| 30 | # Correction for one orbit # |
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| 31 | ##-------------------------------## |
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| 32 | def applycorr2orb(fname,lonvec,latvec,ch=['All'],area=[-35.,35.,-180.,180.]): |
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| 33 | """ ### Apply correction to AMSU or MHS orbit |
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| 34 | ## Inputs |
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| 35 | # fname = file name |
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| 36 | # tstep = time resolution for new grid in minutes |
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| 37 | # lonvec = longitude vecteur of new grid |
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| 38 | # latvec = latitude vecteur of new grid |
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| 39 | # ch = list of extrated channel ('B1','B2','B3','B4','B5' or 'All'), default 'All' |
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| 40 | # area = limits [LS,LN,LW,LE] for extracted area, default is world (ie [-35.,35.,-180.,180.]) |
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| 41 | # |
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| 42 | # Outputs |
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| 43 | # tbcorrm = brightness temperature of orbit in new grid |
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| 44 | # trefm = ordinal time |
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| 45 | # |
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| 46 | """ |
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| 47 | |
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| 48 | t=time.time() |
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| 49 | # ## Read orbit file |
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| 50 | # print 'Read ', fname |
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| 51 | lon, lat, dt, tb, fov = load_amsub_mhs(fname,ch,area) |
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| 52 | |
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| 53 | try: |
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| 54 | # ## Class of each orbit points |
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| 55 | # print 'Classification' |
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| 56 | clz = find_class(lat,lon) |
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| 57 | nozero = (clz != 0) |
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| 58 | |
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| 59 | except ValueError: |
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| 60 | print 'Orbit is out of the area:', fname |
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| 61 | return [], [], [] |
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| 62 | |
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| 63 | else: |
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| 64 | # ## Coef of correction as a function of classification, fov and day of year |
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| 65 | # print 'Correction' |
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| 66 | coefz = find_corrcoef(dt[nozero],fov[nozero],clz[nozero],ch=ch) |
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| 67 | tbcorr = array(tb)[:,nozero] - coefz |
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| 68 | # ## Average on new grid |
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| 69 | tbcorrm, tobsm = newgrid(lonvec,latvec,lon[nozero],lat[nozero],dt[nozero],tbcorr) |
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| 70 | |
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| 71 | print 'Deal', fname, 'in:', time.time()-t |
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| 72 | |
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| 73 | return tbcorrm, tobsm, fname.split('/')[3] |
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| 74 | |
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| 75 | ##----------------------------------## |
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| 76 | # Read AMSU-B and MHS files # |
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| 77 | ##----------------------------------## |
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| 78 | def load_amsub_mhs(fname,ch,area): |
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| 79 | """ ### Read AMSU-B and MHS files |
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| 80 | ## Inputs |
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| 81 | # fname = file name |
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| 82 | # ch = list of extrated channel ('B1','B2','B3','B4','B5' or 'All'), default 'All' |
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| 83 | # area = limits [LS,LN,LW,LE] for extracted area, default is world (ie [-35,35,-180.,180]) |
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| 84 | # |
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| 85 | ## Outputs |
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| 86 | # lon = Longitude |
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| 87 | # lat = Latitude |
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| 88 | # yday = Day of year |
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| 89 | # tms = UTC time in milliseconds |
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| 90 | # tb = Brightness temperature for each channel |
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| 91 | # err = 1 if reading error, 0 else |
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| 92 | # |
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| 93 | """ |
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| 94 | |
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| 95 | # Index of channel |
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| 96 | ch=[cch.lower() for cch in ch] |
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| 97 | if (ch[0] == 'all'): |
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| 98 | ich = arange(5) |
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| 99 | else: |
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| 100 | ich = array([int(cch[1])-1 for cch in ch]) |
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| 101 | |
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| 102 | # Variables |
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| 103 | dset_names = ['amb1c_latlon','amb1c_btemps','amb1c_scnlindy','amb1c_scnlintime','amb1c_scnlinyr'] |
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| 104 | try: |
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| 105 | headers, datasets = ReadRawFile(fname,'/home/mleduc/python/ReadRawFile/data/amsub-mhs_1c_header.txt','/home/mleduc/python/ReadRawFile/data/amsub-mhs_1c_record.txt', False,dset_names) |
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| 106 | except: |
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| 107 | print 'Error with %s'%fname |
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| 108 | # continue |
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| 109 | return |
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| 110 | |
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| 111 | # Latitude |
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| 112 | lat = float32(datasets['amb1c_latlon'][:,:,0]/1.E4) |
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| 113 | # Longitude |
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| 114 | lon = float32(datasets['amb1c_latlon'][:,:,1]/1.E4) |
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| 115 | # Year |
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| 116 | yyyy= datasets['amb1c_scnlinyr'] |
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| 117 | # Day of Year |
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| 118 | yday= datasets['amb1c_scnlindy'] |
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| 119 | # Time in milliseconds |
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| 120 | tms = datasets['amb1c_scnlintime'] |
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| 121 | # Tb |
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| 122 | tb = float32(datasets['amb1c_btemps'][:,:,ich]/1.E2) |
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| 123 | # Remplace fill values -9999.99 by NaN |
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| 124 | tb[(tb == -9999.99)] = NaN |
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| 125 | |
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| 126 | ### Extract Area |
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| 127 | # Index in the defined area |
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| 128 | LS, LN, LW, LE = area |
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| 129 | zone = (lon>=LW) & (lon<=LE) & (lat>=LS) & (lat<=LN) |
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| 130 | nscn, nfov, nch = tb.shape |
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| 131 | # Extraction of Tb for each channel |
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| 132 | tb = [(tb[:,:,ii])[zone] for ii in xrange(nch)] |
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| 133 | # day of year and time |
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| 134 | yyyy = transpose([yyyy]*nfov)[zone] |
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| 135 | yday = transpose([yday]*nfov)[zone] |
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| 136 | tms = transpose([tms]*nfov)[zone] |
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| 137 | # Find location in fov |
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| 138 | fov = array(range(nfov)*nscn).reshape(nscn,nfov)[zone]+1 |
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| 139 | |
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| 140 | ### Convert in python date/time |
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| 141 | dd = num2date(yday) |
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| 142 | tt = [ms2time(ms) for ms in tms] |
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| 143 | dt = array([d.replace(year=y,hour=h,minute=m,second=s) for d,y,(h,m,s) in zip(dd,yyyy,tt)]) |
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| 144 | |
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| 145 | return lon[zone], lat[zone], dt, tb, fov |
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| 146 | |
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| 147 | ##-----------------------------------------------------## |
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| 148 | # Convert milliseconds in hour, minute, second # |
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| 149 | ##-----------------------------------------------------## |
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| 150 | def ms2time(ms): |
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| 151 | """ ### Convert milliseconds in hour, minute, second |
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| 152 | # Warning 1: lost millisecond because second is integer 2) no day |
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| 153 | # Warning 2: no day |
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| 154 | ## Inputs |
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| 155 | # ms = millisecond |
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| 156 | # |
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| 157 | ## Output |
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| 158 | # h = hour |
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| 159 | # m = minute |
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| 160 | # s = second |
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| 161 | # |
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| 162 | """ |
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| 163 | s = ms/1000 |
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| 164 | m, s = divmod(s,60) |
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| 165 | h, m = divmod(m,60) |
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| 166 | #d, h = divmod(h,24) |
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| 167 | return h, m, s |
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| 168 | |
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| 169 | ##---------------------## |
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| 170 | # Find classes # |
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| 171 | ##---------------------## |
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| 172 | def find_class(lat,lon): |
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| 173 | """ ### Find class of each given (lat,lon) |
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| 174 | ## Inputs |
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| 175 | # lat = Latitude (degrees) |
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| 176 | # lon = Longitude (degrees) |
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| 177 | # |
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| 178 | ## Output |
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| 179 | # clpt = class of each point (same shape as lat and lon) |
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| 180 | # |
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| 181 | """ |
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| 182 | |
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| 183 | # Read the classification file |
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| 184 | fic_class = '/homedata/mleduc/CorrTb/classifERAI_Trop.nc' |
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| 185 | fileID = netCDF4.Dataset(fic_class) |
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| 186 | lat_class = fileID.variables['lat'][:] |
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| 187 | lon_class = fileID.variables['lon'][:] |
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| 188 | classif = int16(fileID.variables['classif_tot'][:]) |
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| 189 | fileID.close() |
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| 190 | |
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| 191 | # Latitude limits of classification (ie tropical band) |
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| 192 | latstep = abs(diff(lat_class))[0] |
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| 193 | latmin = lat_class.min()-latstep/2. |
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| 194 | latmax = lat_class.max()+latstep/2. |
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| 195 | # Only points in the area of classification |
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| 196 | inclass = logical_and(lat>=latmin,lat<=latmax) |
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| 197 | |
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| 198 | # Geographic projection (lon/lat in degrees to x/y in m) |
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| 199 | projG = pyproj.Proj("+proj=merc +ellps=WGS84 +datum=WGS84") |
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| 200 | longd_class,latgd_class = meshgrid(lon_class,lat_class) |
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| 201 | xclass,yclass = projG(longd_class,latgd_class) |
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| 202 | xx,yy = projG(lon[inclass],lat[inclass]) |
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| 203 | |
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| 204 | # Find the nearest neighbour (euclidian distance) with a kd-tree |
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| 205 | tree = spatial.cKDTree(zip(ravel(xclass),ravel(yclass))) |
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| 206 | _, ind = tree.query(zip(xx,yy)) |
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| 207 | clpt = zeros(shape(lat),dtype=int16) |
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| 208 | clpt[inclass] = classif.flatten()[ind] |
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| 209 | |
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| 210 | return clpt |
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| 211 | |
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| 212 | ##-------------------------------------------## |
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| 213 | # Find the coefficient of correction # |
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| 214 | ##-------------------------------------------## |
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| 215 | def find_corrcoef(dt,fov,cl,ch=['All']): |
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| 216 | """ ### Find the coefficient of correction |
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| 217 | ## Inputs |
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| 218 | # dt = Python date |
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| 219 | # fov = Location in the swath |
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| 220 | # cl = Class |
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| 221 | # ch = list of extrated channel ('B1','B2','B3','B4','B5' or 'All'), default 'All' |
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| 222 | # |
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| 223 | ## Outputs |
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| 224 | # coef = Coefficient of correction |
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| 225 | # |
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| 226 | """ |
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| 227 | # Index of channel |
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| 228 | ch=[cch.lower() for cch in ch] |
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| 229 | if (ch[0] == 'all'): |
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| 230 | ich = arange(5) |
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| 231 | else: |
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| 232 | ich = array([int(cch[1])-1 for cch in ch]) |
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| 233 | nch = ich.size |
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| 234 | |
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| 235 | # Read the classification file |
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| 236 | fic_corr = '/homedata/mleduc/CorrTb/corrTb_AMSUB_MHS_L1C.nc' |
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| 237 | fileID = netCDF4.Dataset(fic_corr) |
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| 238 | day_corr = fileID.variables['day'][:] |
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| 239 | coef_corr = fileID.variables['coefcorr'][:,:,:,ich]#[:] |
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| 240 | fileID.close() |
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| 241 | |
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| 242 | # Find the day of year |
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| 243 | yday = array([d.timetuple().tm_yday for d in dt]) |
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| 244 | # Find the day of correction |
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| 245 | nday = day_corr.size |
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| 246 | daystep = diff(day_corr)[0] |
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| 247 | icorr = list(flatten([[ii]*daystep for ii in xrange(nday)])) + [0]*6 |
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| 248 | iday = array(icorr)[yday-1] |
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| 249 | |
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| 250 | # Find correction |
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| 251 | noclass = (cl == 0) |
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| 252 | icl = array(cl-1,dtype=int) |
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| 253 | ifov = array(fov-1,dtype=int) |
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| 254 | coef = [] |
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| 255 | for ii in ich: |
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| 256 | dd = zeros(cl.shape)+NaN |
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| 257 | dd[~noclass] = coef_corr[iday[~noclass],icl[~noclass],ifov[~noclass],ii] |
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| 258 | coef.append(dd) |
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| 259 | |
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| 260 | return float32(coef) |
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| 261 | |
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| 262 | ##---------------------------## |
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| 263 | # New grid and time # |
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| 264 | ##---------------------------## |
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| 265 | def newgrid(lonvec,latvec,lon,lat,dt,tb): |
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| 266 | """ ### Find the coefficient of correction |
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| 267 | ## Inputs |
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| 268 | # |
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| 269 | # |
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| 270 | ## Outputs |
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| 271 | # |
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| 272 | # |
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| 273 | """ |
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| 274 | |
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| 275 | # from date to ordinal |
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| 276 | dtord = date2num(dt) |
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| 277 | |
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| 278 | ### Find index of each pixel in new grid |
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| 279 | # Geographic projection (lon/lat in degrees to x/y in m) |
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| 280 | projG = pyproj.Proj("+proj=merc +ellps=WGS84 +datum=WGS84") |
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| 281 | longd, latgd = meshgrid(lonvec,latvec) |
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| 282 | xgd, ygd = projG(longd,latgd) |
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| 283 | xx, yy = projG(lon,lat) |
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| 284 | # Find the nearest neighbour (euclidian distance) with a kd-tree |
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| 285 | tree = spatial.cKDTree(zip(ravel(xgd),ravel(ygd))) |
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| 286 | _, indll = tree.query(zip(xx,yy)) |
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| 287 | |
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| 288 | # Mean |
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| 289 | nch = tb.shape[0] |
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| 290 | tbm = zeros((nch,lonvec.size*latvec.size),dtype=float32)+NaN |
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| 291 | dtm = zeros((lonvec.size*latvec.size))+NaN |
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| 292 | for ii in unique(indll): |
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| 293 | tbm[:,ii] = nanmean(tb[:,(indll==ii)],axis=1) |
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| 294 | dtm[ii] = nanmean(dtord[(indll==ii)]) |
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| 295 | |
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| 296 | return tbm, dtm |
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