JeroenVerstraelen

import shapely
from shapely.geometry import Polygon, MultiPolygon
import openeo
from openeo.rest.job import JobResults
from openeo.rest.mlmodel import MlModel
from openeo.rest.job import BatchJob, JobResults
from openeo.rest.mlmodel import MlModel

c = openeo.connect("https://openeo.dev.warsaw.openeo.dataspace.copernicus.eu/openeo/").authenticate_oidc()
signed_url = "https://openeo.dataspace.copernicus.eu/openeo/1.2/jobs/j-24050486ed974fdc80e73eff53974d89/results/items/ZTNlOGRjZjMtYWM3Yy00NmMzLThiZGQtMDM0YmYyMmRjMzQy/322cfe4d8974a52f84ff8a30475568d4/ml_model_metadata.json?expires=1715585149"

JeroenVerstraelen

import shapely
from shapely.geometry import Polygon, MultiPolygon
import openeo
from openeo.rest.job import JobResults
from openeo.rest.mlmodel import MlModel
from openeo.rest.job import BatchJob

feature_collection = {"type": "FeatureCollection", "features": [
    {
        "type": "Feature",

JeroenVerstraelen

import openeo
connection = openeo.connect(url="openeo.dataspace.copernicus.eu")
connection.authenticate_oidc()


cube = connection.load_collection(
   "COPERNICUS_VEGETATION_INDICES",
   temporal_extent=["2020-12-30", "2021-01-01"],
   spatial_extent={
       'west': 3.39,

JeroenVerstraelen

import xarray
from openeo.udf import XarrayDataCube
from openeo.udf.debug import inspect
from openeo.metadata import CollectionMetadata, SpatialDimension
import numpy as np


def apply_metadata(metadata: CollectionMetadata,
                   context: dict) -> CollectionMetadata:
    """

JeroenVerstraelen

  /**
   * Iterate over every spacetimekey/tile in layer, convert it into a (bands,y,x) datacube, and run the UDF.
   *
   * @return The resulting MultibandTileLayerRDD.
  */
  def runUserCode(code: String, layer: MultibandTileLayerRDD[SpaceTimeKey],
                  bandNames: util.ArrayList[String], context: util.HashMap[String, Any]): MultibandTileLayerRDD[SpaceTimeKey] = {
    // TODO: Implement apply_timeseries, apply_hypercube.
    // Map a python function to every tile of the RDD.
    // Map will serialize + send partitions to worker nodes

JeroenVerstraelen

import openeo
import pandas as pd
import json
from pathlib import Path
import datetime
from shapely.geometry import box
from openeo.udf import execute_local_udf
from pathlib import Path
import xarray

JeroenVerstraelen

import openeo

DEPENDENCIES_URL = 'https://artifactory.vgt.vito.be:443/auxdata-public/openeo/mmlab_dependencies.zip'
MODEL_URL = 'https://artifactory.vgt.vito.be:443/auxdata-public/openeo/windmill_test_model.zip'

connection = openeo.connect("openeo.dataspace.copernicus.eu").authenticate_oidc()

# Load a specific dataset.
datacube = connection.load_collection(collection_id="SENTINEL2_L1C", bands=['B01'])
datacube = datacube.filter_bbox(west=11.279182434082033, south=46.464349400461145,

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# Job id: j-fe4997522f9748ed97ab074322a6d2f6
connection = openeo.connect("https://openeo-staging.dataspace.copernicus.eu/openeo").authenticate_oidc()
temporal_extent = ["2021-01-01", "2021-12-31"]
s1 = connection.load_collection("SENTINEL1_GRD", spatial_extent = {"west": 4220000, "south": 2980000, "east": 4240000, "north": 3000000, "crs": "EPSG:3035"}, temporal_extent = temporal_extent, bands = ["VH", "VV"], properties = {'sat:orbit_state': lambda x: x=='DESCENDING'})
s1 = s1.sar_backscatter(coefficient = "sigma0-ellipsoid", contributing_area = False, elevation_model = "COPERNICUS_30", ellipsoid_incidence_angle = False, local_incidence_angle = False, mask = False, noise_removal = True, options = {"debug": False, "elev_geoid": "/opt/openeo-vito-aux-data/egm96.tif", "implementation_version": "2", "otb_memory": 1024, "tile_size": 256})
s1_resampled = s1.resample_spatial(resolution = 10.0, projection = "EPSG:3035", method = "near")
s1_aggregated = s1_resampled.aggregate_temporal_period(period = "dekad", reducer =

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STAGING

kubectl exec -n spark-jobs-staging openeo-geotrellis-1-driver -i -t -- bash ls . # Shows layercatalog.json cd /opt/openeo Description: Attach to driver.

kubectl get sparkapplication -n spark-jobs-staging | grep 'job-' | grep 'COMPLETED' Description: All batch job that are completed.

JeroenVerstraelen

from openeo.rest.job import BatchJob
import openeo

geometries = {
    "type": "FeatureCollection",
    "name": "Daft Logic Google Maps Area Calculator Tool",
    "crs": {
        "type": "name",
        "properties": {
            "name": "urn:ogc:def:crs:OGC:1.3:CRS84"