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Copy pathmodis_grid_lyh.jl
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modis_grid_lyh.jl
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using ArgParse
using Base, Dates, Printf
# NetCDF tools for reading and writing
using NCDatasets
# Basic statistics
using Statistics
# File search and completion
using Glob
# JSON files
import JSON
# Parallel computing
using Distributed, SharedArrays, DataStructures
# Profiler
using Profile
using ProgressMeter
function parse_commandline()
s = ArgParseSettings()
@add_arg_table s begin
"--Dict"
help = "JSON dictionary file to use"
arg_type = String
#default = "/home/cfranken/code/gitHub/Gridding/gridding/modis_all.json"
default = "/home/lyh/modis_all.json"
"--outFile", "-o"
help = "output filename"
arg_type = String
#default = "MODIS_map.nc"
default = "/home/lyh/MODIS_map.nc"
"--monthly"
help = "Use time-steps in terms of months (not days)"
action = :store_true
"--latMin"
help = "Lower latitude bound"
arg_type = Float32
default = -90.0f0
"--latMax"
help = "Upper latitude bound"
arg_type = Float32
default = 90.0f0
"--lonMin"
help = "Lower longitude bound"
arg_type = Float32
default = -180.0f0
"--lonMax"
help = "Upper longitude bound"
arg_type = Float32
default = 180.0f0
"--dLat"
help = "latitude resolution"
arg_type = Float32
#default = 1.0f0
default = 0.5f0
"--dLon"
help = "longitude resolution"
arg_type = Float32
#default = 1.0f0
default = 0.5f0
"--startDate"
help = "Start Date (in YYYY-MM-DD)"
arg_type = String
#default = "2018-03-07"
default = "2018-07-15"
"--stopDate"
help = "Stop Date (in YYYY-MM-DD)"
arg_type = String
#default = "2018-10-31"
default = "2018-07-16"
"--dDays"
help = "Time steps in days (or months if --monthly is set)"
arg_type = Int64
#default = 8
default = 1
end
return parse_args(s)
end
L=1/0.0001
C1 = 6.
C2 = 7.5
G = 2.5
function aver!(mat_data, mat_in, iLon_,iLat_, idx)
NDVI = zeros(Float32,1)
d = size(mat_in)
for i = 1:length(idx)
A = view(mat_in,idx[i][2],idx[i][1],1:d[3]-1)
if !any(A.==32767)
# NIR reflectance > 0.01, gets rid of water
if(A[2]*0.0001>0.03)
for j=1:d[3]-1
mat_data[iLon_[i],iLat_[i],j] += mat_in[idx[i][2],idx[i][1],j]*0.0001
end
# Compute vegetation indices here:
# EVI
mat_data[iLon_[i],iLat_[i],d[3]] +=G*(A[2]-A[1])/(A[2]+C1*A[1]-C2*A[4]+L)
# NDVI
NDVI[1] = (A[2]-A[1])/(A[2]+A[1])
#println(NDVI, " ", A[2]*0.0001, " ", A[1]*0.0001, " ", NDVI[1]*A[2]*0.0001," ", (A[2]*0.0001-A[1]*0.0001)/(A[2]*0.0001+A[1]*0.0001))
mat_data[iLon_[i],iLat_[i],d[3]+1] += NDVI[1]
#println(mat_data[iLon_[i],iLat_[i],d[3]+1] ," ", mat_data[iLon_[i],iLat_[i],2] )
# NIRv
mat_data[iLon_[i],iLat_[i],d[3]+2] += NDVI[1]*A[2]*0.0001
# NWDI
mat_data[iLon_[i],iLat_[i],d[3]+3] +=(A[2]-A[5])/(A[2]+A[5])
# N
mat_data[iLon_[i],iLat_[i],end] += mat_in[idx[i][2],idx[i][1],end]
end
end
end
end
function main()
#addprocs()
# Parse command line arguments
ar = parse_commandline()
# Find files to be processed
startDate = DateTime(ar["startDate"])
stopDate = DateTime(ar["stopDate"])
if ar["monthly"]
dDay = Dates.Month(ar["dDays"])
else
dDay = Dates.Day(ar["dDays"])
end
println(startDate, " ", stopDate)
cT = length(startDate:dDay:stopDate)
# Just lazy (too cumbersome in code as often used variables here)
latMax = ar["latMax"]
latMin = ar["latMin"]
lonMax = ar["lonMax"]
lonMin = ar["lonMin"]
dLat = ar["dLat"]
dLon = ar["dLon"]
eps = dLat/100
# Define spatial grid:
lat = collect(latMin+dLat/2.:dLat:latMax-dLat/2.0+eps)
lon = collect(lonMin+dLon/2.:dLon:lonMax-dLon/2.0+eps)
println("Output file dimension (time/lon/lat):")
println(cT, "/", length(lon),"/", length(lat))
# Create output file:
dsOut = Dataset(ar["outFile"],"c")
defDim(dsOut,"lon",length(lon))
defDim(dsOut,"lat",length(lat))
defDim(dsOut,"time", cT)
dsLat = defVar(dsOut,"lat",Float32,("lat",), attrib = ["units" => "degrees_north","long_name" => "Latitude"])
dsLon = defVar(dsOut,"lon",Float32,("lon",), attrib = ["units" => "degrees_east","long_name" => "Longitude"])
dsTime= defVar(dsOut,"time",Float32,("time",),attrib = ["units" => "days since 1970-01-01","long_name" => "Time (UTC), start of interval"])
dsLat[:]=lat
dsLon[:]=lon
# Define a global attribute
dsOut.attrib["title"] = "Awesome gridded file"
# Define gridded variables:
n=zeros(Float32,(length(lat),length(lon)))
SIF = zeros(Float32,(length(lat),length(lon)))
# Parse JSON files as dictionary
jsonDict = JSON.parsefile(ar["Dict"], dicttype=DataStructures.OrderedDict)
#d2 = jsonDict["basic"]
dGrid = jsonDict["grid"]
# Get file naming pattern (needs YYYY MM and DD in there)
fPattern = jsonDict["filePattern"]
# Get main folder for files:
folder = jsonDict["folder"]
NCDict= Dict{String, NCDatasets.CFVariable}()
println("Creating NC datasets in output:")
for (key, value) in dGrid
print(key," ")
NCDict[key] = defVar(dsOut,key,Float32,("time","lon","lat"),deflatelevel=4, fillvalue=-999)
end
ds_ndvi = defVar(dsOut,"NDVI",Float32,("time","lon","lat"),deflatelevel=4, fillvalue=-999)
ds_evi = defVar(dsOut,"EVI",Float32,("time","lon","lat"),deflatelevel=4, fillvalue=-999)
ds_nirv = defVar(dsOut,"NIRv",Float32,("time","lon","lat"),deflatelevel=4, fillvalue=-999)
ds_ndwi = defVar(dsOut,"NDWI",Float32,("time","lon","lat"),deflatelevel=4, fillvalue=-999)
println(" ")
#dSIF = defVar(dsOut,"sif",Float32,("lon","lat"),deflatelevel=4, fillvalue=-999)
dN = defVar(dsOut,"n",Float32,("time","lon","lat"),deflatelevel=4, fillvalue=-999)
# Define data array
# How many additional dataset (here, NDVI, EVI, NIRv and NDWI)
addData = 4
mat_data= zeros(Float32,(length(lon),length(lat),addData+1+length(dGrid)))
mat_in = zeros(Float32,2400,2400,length(dGrid)+1)
ds = Dataset("/home/lyh/test_wholedata.nc","r")
lon_table = ds["longitude"]
lat_table = ds["latitude"]
# Loop through time:
# Time counter
cT = 1
p1 = Progress(cT)
for d in startDate:dDay:stopDate
ProgressMeter.next!(p1; showvalues = [(:Time, d)])
files = String[];
# Step through 8 days here, otherwise overkill
for di in d:Dates.Day(1):d+dDay-Dates.Day(1)
#********* This needs to be updated to use the Day of Year (and not MM and DD)!! *********#
# filePattern = reduce(replace,["YYYY" => lpad(Dates.year(di),4,"0"), "MM" => lpad(Dates.month(di),2,"0"), "DD" => lpad(Dates.day(di),2,"0")], init=fPattern)
filePattern = reduce(replace,["YYYY" => lpad(Dates.year(di),4,"0"), "MM" => lpad(Dates.month(di),2,"0"), "DD" => lpad(Dates.day(di),2,"0"), "DOY" => lpad(Dates.dayofyear(di),3,"0")], init=fPattern)
folderPattern = reduce(replace,["YYYY" => lpad(Dates.year(di),4,"0"), "MM" => lpad(Dates.month(di),2,"0"), "DD" => lpad(Dates.day(di),2,"0")], init=folder)
println(filePattern)
files = [files;glob(filePattern, folderPattern)]
end
#println(files)
# Loop through all files
n = length(files)
p = Progress(n) # minimum update interval: 1 second
for a in files
# Read NC file
fin = Dataset(a)
# Check lat/lon first to see what data to read in
#********* Here you need to read in lat/lon from MODIS (table or calculate on the fly) *********#
#lat_in = fin[d2["lat"]].var[:]
#lon_in = fin[d2["lon"]].var[:]
pos_start = findfirst("MCD43A4.A", a)
filename = a[pos_start[1]:end]
h = parse(Int32,filename[19:20])
v = parse(Int32,filename[22:23])
lon_in_ = lon_table[h+1,v+1,:,:]
lat_in_ = lat_table[h+1,v+1,:,:]
# Call the variables lat_in and lon_in and then best
#lat_in_ = fin[d2["lat_bnd"]].var[:]
#lon_in_ = fin[d2["lon_bnd"]].var[:]
#dim = size(lat_in_)
# Get indices within the lat/lon boudning box:
#idx = findall((minLat[:,1].>latMin).&(maxLat[:,1].<latMax).&(minLon[:,1].>lonMin).&(maxLon[:,1].<lonMax))
idx = findall((lat_in_.>latMin).&(lat_in_.<latMax).&(lon_in_.>lonMin).&(lon_in_.<lonMax))
ProgressMeter.next!(p; showvalues = [(:File, a), (:N_pixels, size(idx))])
#println("Size of idx ", size(idx), " lat_in_ ", size(lat_in_))
# Read data only for non-empty indices
if length(idx) > 0
#print(size(lat_in_))
dim = size(mat_in)
# Read in all entries defined in JSON file:
co = 1
# This should just read in the datasets:
for (key, value) in dGrid
#println(key, value)
mat_in[:,:,co]=fin[value].var[:]
co += 1
end
mat_in[:,:,end].=1
# This computes the indices into which the respective lats and lons are falling into.
#iLat_ = ((lat_in[idx].-latMin)/(latMax-latMin)*length(lat)).+1
#iLon_ = ((lon_in[idx].-lonMin)/(lonMax-lonMin)*length(lon)).+1
iLat_ = round.(Int64,((lat_in_[idx].-latMin)/(latMax-latMin)*length(lat)).+0.5.-1e-6,RoundNearestTiesAway)
iLon_ = round.(Int64,((lon_in_[idx].-lonMin)/(lonMax-lonMin)*length(lon)).+0.5.-1e-6,RoundNearestTiesAway)
# Once you have done this, we can chat about the gridding itself (just a few lines of code here)
aver!(mat_data, mat_in, iLon_,iLat_, idx)
fill!(mat_in,0.0)
#println("Read ", a, " ", length(idx))
else
#println("Read ", a, " ", length(idx))
end
close(fin)
end
# Filter all data, set averages
dims = size(mat_data)
println("Averaging final product...")
NN = mat_data[:,:,end]
dN[cT,:,:]=NN
# Write out time:
dsTime[cT]=d
co = 1
thr = 5
for (key, value) in dGrid
da = round.(mat_data[:,:,co]./mat_data[:,:,end],sigdigits=5)
#println(maximum(da), " ", maximum(NN))
#da[NN.<1e-10].=-999
da[NN.<thr].=-999
NCDict[key][cT,:,:]=da
co += 1
end
d = size(mat_in)
println(d)
da = round.(mat_data[:,:,d[3]]./mat_data[:,:,end],sigdigits=5)
da[NN.<thr].=-999
ds_evi[cT,:,:] = da
da = round.(mat_data[:,:,d[3]+1]./mat_data[:,:,end],sigdigits=5)
da[NN.<thr].=-999
ds_ndvi[cT,:,:] = da
da = round.(mat_data[:,:,d[3]+2]./mat_data[:,:,end],sigdigits=5)
da[NN.<thr].=-999
ds_nirv[cT,:,:] = da
da = round.(mat_data[:,:,d[3]+3]./mat_data[:,:,end],sigdigits=5)
da[NN.<thr].=-999
ds_ndwi[cT,:,:] = da
cT += 1
fill!(mat_data,0.0)
end
close(dsOut)
close(ds)
end
main()