Mercurial > repos > ecology > argo_getdata

annotate divandfull.jl @ 0:8a1b829b5d27 draft

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author ecology
date Thu, 01 Aug 2024 09:46:31 +0000
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8a1b829b5d27 planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/ocean commit e395cfee9cab90bbed58ac52fb8467c896f51824
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1 #Julia script
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2
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3 ###############################
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4 ## DIVAndrun analsysis ##
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5 ###############################
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6 import Pkg;
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7 using Pkg
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8 Pkg.status()
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9
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10 ### Import packages
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11 using DIVAnd
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12 using Dates
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13 using Printf
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14 # Getting the arguments from the command line
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15 args = ARGS
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16
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17 # Import data
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18 if length(args) < 4
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19 error("This tool needs at least 4 arguments")
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20 else
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21 netcdf_data = args[1]
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22 longmin = parse(Float64, args[2])
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23 longmax = parse(Float64, args[3])
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24 latmin = parse(Float64, args[4])
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25 latmax = parse(Float64, args[5])
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26 startdate = args[6] # yyyy,mm,dd
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27 enddate = args[7]
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28 varname = args[8]
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29 selmin = parse(Float64, args[9])
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30 selmax = parse(Float64, args[10])
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31 bathname = args[11]
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32 end
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33
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34 ## This script will create a climatology:
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35 # 1. ODV data reading.
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36 # 2. Extraction of bathymetry and creation of mask
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37 # 3. Data download from other sources and duplicate removal.
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38 # 4. Quality control.
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39 # 5. Parameter optimisation.
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40 # 6. Spatio-temporal interpolation with DIVAnd.
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41
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42
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43 ### Configuration
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44 # Define the horizontal, vertical (depth levels) and temporal resolutions.
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45 # Select the variable of interest
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46
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47 dx, dy = 0.125, 0.125
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48 lonr = longmin:dx:longmax
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49 latr = latmin:dy:latmax
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50
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51 # Convert string in date
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52 startdate = Date(startdate, "yyyy-mm-dd")
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53
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54 # extract year month day
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55 startyear = year(startdate)
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56 startmonth = month(startdate)
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57 startday = day(startdate)
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58
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59 # Convert string in date
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60 enddate = Date(enddate, "yyyy-mm-dd")
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61
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62 # extract year month day
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63 endyear = year(enddate)
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64 endmonth = month(enddate)
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65 endday = day(enddate)
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66
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67 timerange = [Date(startyear, startmonth, startday),Date(endyear, endmonth, endday)];
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68
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69 depthr = [0.,5., 10., 15., 20., 25., 30., 40., 50., 66,
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70 75, 85, 100, 112, 125, 135, 150, 175, 200, 225, 250,
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71 275, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750,
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72 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250,
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73 1300, 1350, 1400, 1450, 1500, 1600, 1750, 1850, 2000];
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74 depthr = [0.,10.,20.];
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75
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76 varname = varname
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77 yearlist = [1900:2023];
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78 monthlist = [[1,2,3],[4,5,6],[7,8,9],[10,11,12]];
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79
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80 # We create here the variable TS (for "tDataset(netcdf_data,"r")ime selector"), which allows us to work with the observations corresponding to each period of interest.
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81
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82 TS = DIVAnd.TimeSelectorYearListMonthList(yearlist,monthlist);
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83 @show TS;
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84
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85 figdir = "outputs/"
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86 if ~(isdir(figdir))
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87 mkdir(figdir)
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88 else
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89 @info("Figure directory already exists")
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90 end
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91 ### 1. Read your ODV file
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92 # Adapt the datadir and datafile values.
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93 # The example is based on a sub-setting of the Mediterranean Sea aggregated dataset.
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94 # The dataset has been extracted around the Adriatic Sea and exported to a netCDF using Ocean Data
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95 datadir = "../data"
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96
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97 datafile = netcdf_data
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98
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99 # Then you can read the full file:
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100 @time obsval,obslon,obslat,obsdepth,obstime,obsid = NCODV.load(Float64, datafile,
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101 "Water body $(varname)");
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102
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103 # Check the extremal values of the observations
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104 checkobs((obslon,obslat,obsdepth,obstime),obsval,obsid)
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105
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106 ### 2. Extract the bathymetry
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107
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108 # It is used to delimit the domain where the interpolation is performed.
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109 ## 2.1 Choice of bathymetry
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110
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111 # Modify bathname according to the resolution required.
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112
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113 @time bx,by,b = load_bath(bathname,true,lonr,latr);
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114
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115 ## 2.2 Create mask
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116 # False for sea
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117 # True for land
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118
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119 mask = falses(size(b,1),size(b,2),length(depthr))
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120 for k = 1:length(depthr)
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121 for j = 1:size(b,2)
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122 for i = 1:size(b,1)
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123 mask[i,j,k] = b[i,j] >= depthr[k]
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124 end
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125 end
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126 end
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127 @show size(mask)
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128
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129 ### 3. Quality control
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130 # We check the salinity value.
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131 # Adapt the criteria to your region and variable.
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132
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133 sel = (obsval .<= selmax) .& (obsval .>= selmin);
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134
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135 obsval = obsval[sel]
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136 obslon = obslon[sel]
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137 obslat = obslat[sel]
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138 obsdepth = obsdepth[sel]
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139 obstime = obstime[sel]
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140 obsid = obsid[sel];
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141
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142 ### 4. Analysis parameters
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143 # Correlation lengths and noise-to-signal ratio
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144
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145 # We will use the function diva3D for the calculations.
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146 # With this function, the correlation length has to be defined in meters, not in degrees.
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147
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148 sz = (length(lonr),length(latr),length(depthr));
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149 lenx = fill(100_000.,sz) # 100 km
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150 leny = fill(100_000.,sz) # 100 km
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151 lenz = fill(25.,sz); # 25 m
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152 len = (lenx, leny, lenz);
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153 epsilon2 = 0.1;
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154
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155 ### Output file name
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156 outputdir = "outputs_netcdf/"
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157 if !isdir(outputdir)
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158 mkpath(outputdir)
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159 end
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160 filename = joinpath(outputdir, "Water_body_$(replace(varname," "=>"_")).nc")
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161
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162 ### 7. Analysis
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163 # Remove the result file before running the analysis, otherwise you'll get the message
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164 if isfile(filename)
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165 rm(filename) # delete the previous analysis
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166 @info "Removing file $filename"
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167 end
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168
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169 ## 7.1 Plotting function
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170 # Define a plotting function that will be applied for each time index and depth level.
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171 # All the figures will be saved in a selected directory.
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172
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173 function plotres(timeindex,sel,fit,erri)
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174 tmp = copy(fit)
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175 nx,ny,nz = size(tmp)
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176 for i in 1:nz
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177 figure("Additional-Data")
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178 ax = subplot(1,1,1)
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179 ax.tick_params("both",labelsize=6)
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180 ylim(39.0, 46.0);
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181 xlim(11.5, 20.0);
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182 title("Depth: (timeindex)", fontsize=6)
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183 pcolor(lonr.-dx/2.,latr.-dy/2, permutedims(tmp[:,:,i], [2,1]);
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184 vmin = 33, vmax = 40)
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185 colorbar(extend="both", orientation="vertical", shrink=0.8).ax.tick_params(labelsize=8)
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186
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187 contourf(bx,by,permutedims(b,[2,1]), levels = [-1e5,0],colors = [[.5,.5,.5]])
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188 aspectratio = 1/cos(mean(latr) * pi/180)
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189 gca().set_aspect(aspectratio)
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190
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191 figname = varname * @sprintf("_%02d",i) * @sprintf("_%03d.png",timeindex)
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192 plt.savefig(joinpath(figdir, figname), dpi=600, bbox_inches="tight");
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193 plt.close_figs()
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194 end
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195 end
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196
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197 ## 7.2 Create the gridded fields using diva3d
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198 # Here only the noise-to-signal ratio is estimated.
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199 # Set fitcorrlen to true to also optimise the correlation length.
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200 @time dbinfo = DIVAnd.diva3d((lonr,latr,depthr,TS),
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201 (obslon,obslat,obsdepth,obstime), obsval,
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202 len, epsilon2,
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203 filename,varname,
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204 bathname=bathname,
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205 fitcorrlen = false,
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206 niter_e = 2,
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207 surfextend = true
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208 );
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209
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210 # Save the observation metadata in the NetCDF file.
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211 DIVAnd.saveobs(filename,(obslon,obslat,obsdepth,obstime),obsid);