# By using this script, it is possible to import temperature data
# from http://www.worldclim.org/current
# first, you have to download the data from the website
# in Esri-Grid format.

# After that unpack the data into a directory of your choice.

# Change this script according to your data

# This affects three lines of this script


# the first one is the line

# let basedir = ....

# change the text to the directory where the data are stored,
# (this directory contains some subdirectories ending with _1 .. _12)
# the directory can be given as an absolute pathname or
# as an relative path from Secondo's bin directory

# the second line to change is

# let datakind = ...

# Set this value according to the data you want to import
# e.g. tmean, tmin, tmax 

# in the third line change the name of the created object
# this is useful if more than one moving raster 
# should be created within the same database
#




# Note: The time informations in this script are for
# the tmean dataset


{create database worldclim |  open database worldclim}


# set directory of the data

let basedir =  'prec'

# set kind of data

let datakind = 'prec'

# set the name of the msint

let msintname = "precipitation"


# after that, no changes are required

let tmpname = msintname +"_tmp";


# import the first raster (required to extract the spatial grid information) 

query letObject(tmpname, 'importEsriGrid( basedir + \'/\' + datakind + \'_1\')', FALSE)




# 2 seconds at resolution of 10 arc minutes
# 8:40 min at  resolution  of 30 arc seconds

# create a duration of about 1 month

let onemonth = [const duration value (30 0)]


# create the 3D grid for the time depending raster

query letObject("g3", 'createGrid3(getgrid('+tmpname+'),onemonth)',FALSE) 

# create an empty moving raster, 
# data coming from worldclim are always integer raster, thus the raster must be an msint

query letObject(msintname, "createRaster(1,g3)", FALSE)

# create an instant at the first of january.
# the year does not play a role because the value on worldclim are
# the mean over 50 years

let inst1 = [const instant value "2000-1-1-12:00"]


# add the january raster to the moving raster

query evaluate(msintname + ' addLayer[' + tmpname + ', inst1 + onemonth * 0.0 ]',FALSE) count



# 40:13 min at resolution of 30 arc seconds

# the main part of this script:
# imports the months february - december into the moving raster

query intstream(2,12) 
      namedtransformstream[Month] 
      extend[ Q : msintname +' addLayer[ importEsriGrid(\''+basedir+'/'+datakind+'_' + num2string(.Month) + '\') , inst1 + onemonth * ' + num2string(.Month - 1) + '.0]'] 
      extend[ Success : evaluate(.Q,FALSE) extract[Success] ]
      consume


# 45 seconds for a resolution of 10 arc minutes without transaction control
# 407:13 minutes at resolution 30 arc seconds

# clean up temporary objects

delete basedir

delete datakind

delete inst1

delete g3

delete onemonth

query deleteObject(tmpname)

delete tmpname

delete msintname



# Note:  the moving raster is a big structure.
# If you want to display the complete raster in the Javagui 
# even for the resolution of 10 arc minutes, the JVM heap space of the
# Javagui must be increased to 4 GB (change the -xMx option within the sgui script)
# Displaying of the complete grid in best resolution is not an option.