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//VERSION=3
function setup (){
  return {
    input: [
      {datasource: "s1grd", bands:["VV", "VH"]},
      {datasource: "s2l2a", bands:["B04", "B08", "CLM"], units:"REFLECTANCE"}],
    output: [
      {id: "default", bands: 3, sampleType: SampleType.AUTO}
    ]
  }

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curl --location --request POST 'https://services.sentinel-hub.com/api/v1/process' \
--header 'Authorization: Bearer <your-token-here>' \
--form 'request={
  "input": {
    "bounds": {
      "bbox": [
        2.1421623229980464,
        41.37796785471600,
        2.2084236145019527,
        41.40784461738553

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def skyview(horz_data, slp, asp):
    """ Compute the sky view factor.
    Compute the sky view factor for every pixel, i.e. the ratio between diffuse
    irradiance received to that on unobstructed surface according to its slope,
    aspect and horizon. Algorithm by Dozier et al. 1981, 1990.
    Args:
        horz_data (ndarray): horizon elevation data computed from the DEM
        slp (ndarray): slope calculated from the DEM
        asp (ndarray): aspect calculated from the DEM
    Returns:

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//VERSION=3

// Calculate number of bands needed for all intervals
// Initialise dates and interval
// Beware: in JS months are 0 indexed
var start_date = new Date(2019, 3, 1, 0, 0, 0);
var end_date = new Date(2019, 9, 31, 0, 0, 0);
var sampled_dates = sample_timestamps(start_date, end_date, 15, 'day').map(d => withoutTime(d));
var nb_bands = sampled_dates.length;
var n_valid = 0;

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// Evaluate pixels in the bands
function evaluatePixel(samples, scenes) {
  
  // Initialise arrays
  var valid_samples = {'B02':[], 'B03':[], 'B04':[], 'B08':[], 'B11':[], 'B12':[]}; 
  
  var valid_dates = []
  // Loop over samples. 
  for (var i = samples.length-1; i >= 0; i--){
      if (is_valid(samples[i])) {

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# Make class to convert Batch to EOPatches
batch2eolearn = ImportFromAWS(aws_access_key_id, aws_secret_access_key, aws_bucket_name,
                              batch_request_id, band_names, timestamps, is_data=is_data_band, 
                              out_path=save_folder)
                              
# Convert Batch results in the Amazon Bucket to EOPatches
batch2eolearn.convert_to_EOPatches()

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order = {
  "processRequest": {
    "input": {
      "bounds": {
        "geometry": {
          "type": "Polygon",
          "coordinates": AOI_bounds
        },
        "properties": {
          "crs": "http://www.opengis.net/def/crs/EPSG/0/32632"

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//VERSION=3

// Calculate number of bands needed for all intervals
// Initialise dates and interval
// Beware: in JS months are 0 indexed
var start_date = new Date(2019, 3, 1, 0, 0, 0);
var end_date = new Date(2019, 9, 31, 0, 0, 0);
var sampled_dates = sample_timestamps(start_date, end_date, 15, 'day').map(d => withoutTime(d));
var nb_bands = sampled_dates.length;
var n_valid = 0;

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# For requests
import requests
from oauthlib.oauth2 import BackendApplicationClient
from requests_oauthlib import OAuth2Session
from sentinelhub import SHConfig

config = SHConfig()

# Set up credentials for use with batch
client = BackendApplicationClient(client_id=config.sh_client_id)

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