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user = "group7"
pwd = "test123"
# connect to the back-end and login either via explicit call of login, or use your credentials in the connect function
gee = connect(host = "",version = "1.0.0", user = user,password = pwd)
# get the process collection to use the predefined processes of the back-end
p = processes()
# get the collection list to get easier access to the collection ids, via auto completion
collections = list_collections()
# get the formats
formats = list_file_formats()
# load the intial data collection and limit the amount of data loaded
# note: for the collection id and later the format you can also use the its character value
data = p$load_collection(id = collections$`COPERNICUS/S1_GRD`,
spatial_extent = list(west=16.06,
temporal_extent = c("2017-03-01", "2017-06-01"),
bands = c("VV"))
# create three monthly sub data sets, which will be merged back into a single data cube later
march = p$filter_temporal(data = data,
extent = c("2017-03-01", "2017-04-01"))
april = p$filter_temporal(data = data,
extent = c("2017-04-01", "2017-05-01"))
may = p$filter_temporal(data = data,
extent = c("2017-05-01", "2017-06-01"))
# The aggregation function for the following temporal reducer
agg_fun_mean = function(data, context) {
march_reduced = p$reduce_dimension(data = march,
reducer = agg_fun_mean,
dimension = "t")
april_reduced = p$reduce_dimension(data = april,
reducer = agg_fun_mean,
dimension = "t")
may_reduced = p$reduce_dimension(data = may,
reducer = agg_fun_mean,
dimension = "t")
# Each band is currently called VV. We need to rename at least the label of one dimension,
# because otherwise identity of the data cubes is assumed. The bands dimension consists
# only of one label, so we can rename this to be able to merge those data cubes.
march_renamed = p$rename_labels(data = march_reduced,
dimension = "bands",
target = c("R"),
source = c("VV"))
april_renamed = p$rename_labels(data = april_reduced,
dimension = "bands",
target = c("G"),
source = c("VV"))
may_renamed = p$rename_labels(data = may_reduced,
dimension = "bands",
target = c("B"),
source = c("VV"))
# combine the individual data cubes into one
# this is done one by one, since the dimensionalities have to match between each of the data cubes
merge_1 = p$merge_cubes(cube1 = march_renamed,cube2 = april_renamed)
merge_2 = p$merge_cubes(cube1 = merge_1, cube2 = may_renamed)
# rescale the the back scatter measurements into 8Bit integer to view the results as PNG
rescaled = p$apply(data = merge_2,
process = function(data,context) {
p$linear_scale_range(x=data, inputMin = -20,inputMax = -5, outputMin = 0, outputMax = 255)
# export shall be format PNG
# look at the format description
# store the results using the format and set the create options
result = p$save_result(data = rescaled,format = formats$output$PNG, options = list(red="R",green="G",blue="B"))
# create a job
job = create_job(graph = result, title = "S1 Example R", description = "Getting Started example on for R-client")
# then start the processing of the job and turn on logging (messages that are captured on the back-end during the process execution)
start_job(job = job, log = TRUE)