In flahn/openeo-r-client: openEO client interface

UC 1

RClient v0.6.0 -> EURAC version: 0.4.2

Enter valid credentials for euracHost, user and password.

library(openeo)
library(tibble)

# enter valid credentials
euracHost = ""
user = ""
password = ""

api_versions(url=euracHost)

eurac = connect(host = euracHost, version="0.4.2", user = user,password = password, login_type = "basic")

Explorative calls as described in the Use Case 1.

list_processes()

capabilities()

list_file_types()

descriptions = list_collections()
descriptions

d = describe_collection(con = eurac, id="openEO_S2_32632_10m_L2A")
d

describe_process(con = eurac,"load_collection")
describe_process(con = eurac,"ndvi")
describe_process(con = eurac,"min_time")

We will later use the data set that we have stored on d. Since we have to do some dimensional operations, we shoul have a look at the dimensionality of the data set. Especially, how the dimensions are named.

dims_d = dimensions(d)
dims_d
names(dims_d)

Build a process graph: Maximum NDVI with a linear scale into a png file

p = processes()

data1 = p$load_collection(id = p$data$openEO_S2_32632_10m_L2A, 
                              spatial_extent = list(west = 11.2792, 
                                                    south = 46.4643, 
                                                    east = 11.4072, 
                                                    north = 46.5182), 
                              temporal_extent = c("2018-06-04T00:00:00Z","2018-06-23T00:00:00Z"))

ndvi = p$ndvi(data = data1)

max_t = p$reduce(data = ndvi,dimension = "DATE",reducer = function(x) {
  max(x)})

apply_linear_transform = p$apply(data = max_t, process = function(value) {
  p$linear_scale_range(x = value, 
                       inputMin = -1, inputMax = 1, 
                       outputMin = 0, outputMax = 255)
})

result = p$save_result(data = apply_linear_transform, format = "PNG")

graph = as(result,"Graph")

compute_result(graph = graph, format="PNG",output_file = "eurac_test.png")

job_id = create_job(con = eurac,
                    graph = graph,
                    title = "UC1 Graph from R client")

start_job(con = eurac, job = job_id)

describe_job(con = eurac, job = job_id)

list_results(con = eurac, job=job_id)

download_results(con = eurac,job = job_id,folder = "./eurac_test/")

Create a process graph calculating maximum NDVI but download as GTiff

graph2 = process_graph_builder(con = eurac)

data2 = graph2$load_collection(id = graph2$data$openEO_S2_32632_10m_L2A, 
                              spatial_extent = list(west = 11.2792, 
                                                    south = 46.4643, 
                                                    east = 11.4072, 
                                                    north = 46.5182), 
                              temporal_extent = c("2018-06-04T00:00:00Z","2018-06-23T00:00:00Z"))


ndvi = graph2$ndvi(data = data2)
max_t = graph2$max_time(data = ndvi)
final = graph2$save_result(data = max_t,format = "GTiff")

graph2$setFinalNode(node = final)


compute_result(con = eurac,graph=graph2, format="GTiff",output_file = "eurac_test.tif")

Min EVI example

library(openeo)
library(tibble)

# enter valid credentials!
user = ""
pwd = ""

host_url = "https://openeo.eurac.edu"

eurac = connect(host = host_url, version="0.4.2", user = user, password = pwd, login_type = "basic")

coll = describe_collection(id = "openEO_S2_32632_10m_L2A")
names(dimensions(coll))

p = processes()

# creating the graph
data = p$load_collection(id = p$data$openEO_S2_32632_10m_L2A, 
                              spatial_extent = list(west = 11.2792, 
                                                    south = 46.4643, 
                                                    east = 11.4072, 
                                                    north = 46.5182), 
                              temporal_extent = c("2018-06-04T00:00:00Z","2018-06-23T00:00:00Z"),
                              bands = c("B08","B02","B04"))

spectral_reduce = p$reduce(data=data, dimension = "spectral",reducer = function(x) {
  B08 = x[1] / 10000
  B02 = x[2] / 10000
  B04 = x[3] / 10000
  (2.5 * (B08 - B04)) / sum(B08, 6 * B04, -7.5 * B02,1)
})

temporal_reduce = p$reduce(data=spectral_reduce, dimension = "DATE", reducer = function(x) {
  min(x,na.rm=TRUE)
})

result = p$save_result(data = temporal_reduce,format="GTiff")


# print as JSON
graph = as(result,"Graph")

# client side graph validation
graph$validate()

compute_result(graph = graph, format = "GTiff",output_file = "min_evi.tif")