for the below posted request and evalscript, when i try to run it in the request builder, i receive the followinig error:
Data 1 not used in evalscript's setup function
what does error mean and it can be fixed please?
request:
{
"input": {
"bounds": {
"bbox": [
12.44693,
41.870072,
12.541001,
41.917096
]
},
"data": [
{
"dataFilter": {
"timeRange": {
"from": "2024-05-04T00:00:00Z",
"to": "2024-06-04T23:59:59Z"
}
},
"type": "sentinel-2-l2a"
},
{
"dataFilter": {
"timeRange": {
"from": "2024-05-04T00:00:00Z",
"to": "2024-06-04T00:00:00Z"
}
},
"type": "sentinel-2-l2a"
}
]
},
"output": {
"width": 512,
"height": 343.697,
"responses": [
{
"identifier": "averages",
"format": {
"type": "image/tiff"
}
},
{
"identifier": "numOfCloudFreePixels",
"format": {
"type": "image/tiff"
}
}
]
},
"evalscript": "//VERSION=3\n // Script to extract a time series of NDVI values using \n // Sentinel 2 Level 2A data and metadata file.\n \n function setup() {\n return {\n input: [{\n bands: [\"B04\", \"B08\", \"CLD\"],\n units: \"DN\"\n }],\n output: [\n {\n id: \"averages\",\n bands: 1,\n sampleType: \"FLOAT32\",\n nodataValue: NaN,\n },\n {\n id: \"numOfCloudFreePixels\",\n bands: 1,\n sampleType: \"FLOAT32\",\n nodataValue: NaN,\n },\n ],\n mosaicking: Mosaicking.ORBIT\n }\n }\n\n function updateOutput(output, collection) {\n output.default.bands = collection.scenes.length\n }\n\n const cloudFreeSamples = [];\n const cloudySamples = [];\n\n function samplesParser(samples) {\n samples.forEach((sample) => {\n if (sample.CLD > 0) {\n cloudySamples.push(sample);\n } else {\n cloudFreeSamples.push(sample);\n }\n });\n return [cloudySamples, cloudFreeSamples];\n }\n\n function calcAverage(samples) {\n let sum = 0;\n for(let i = 0;i < samples.length; i++) {\n sum += samples[i];\n }\n let avg = sum / samples.length;\n return [avg];\n }\n function calcNDVIForCloudFreeSamples(samples) {\n const ndvis = new Array(samples.length).fill(NaN);\n samples.forEach((sample, index) => {\n ndvis[index] = (sample.B08 - sample.B04) / (sample.B08 + sample.B04) ;\n });\n return ndvis;\n }\n function calcPercentage(total, incidences) {\n return ((incidence / total) * 100);\n }\n function evaluatePixel(samples) {\n if (samples.length < 1) return [NaN];\n const parsedSamples = samplesParser(samples);\n const ndvis = calcNDVIForCloudFreeSamples(parsedSamples[1]);\n const averages = calcAverage(ndvis);\n const numOfCloudyPixels = parsedSamples[0].length;\n const numOfCloudFreePixels = parsedSamples[1].length;\n const totalNumOfPixels = numOfCloudyPixels + numOfCloudFreePixels;\n const percentageOfCloudy = calcPercentage(totalNumOfPixels, numOfCloudyPixels);\n const percentageOfCloudFree = calcPercentage(totalNumOfPixels, numOfCloudFreePixels);\n\n // return [averages, numOfCloudFreePixels, numOfCloudyPixels, percentageOfCloudFree, percentageOfCloudy];\n return {\n averages: averages,\n numOfCloudFreePixels: numOfCloudFreePixels,\n }\n }"
}
evalscript:
//VERSION=3
// Script to extract a time series of NDVI values using
// Sentinel 2 Level 2A data and metadata file.
function setup() {
return {
input: [{
bands: ["B04", "B08", "CLD"],
units: "DN"
}],
output: [
{
id: "averages",
bands: 1,
sampleType: "FLOAT32",
nodataValue: NaN,
},
{
id: "numOfCloudFreePixels",
bands: 1,
sampleType: "FLOAT32",
nodataValue: NaN,
},
],
mosaicking: Mosaicking.ORBIT
}
}
function updateOutput(output, collection) {
output.default.bands = collection.scenes.length
}
const cloudFreeSamples = [];
const cloudySamples = [];
function samplesParser(samples) {
samples.forEach((sample) => {
if (sample.CLD > 0) {
cloudySamples.push(sample);
} else {
cloudFreeSamples.push(sample);
}
});
return [cloudySamples, cloudFreeSamples];
}
function calcAverage(samples) {
let sum = 0;
for(let i = 0;i < samples.length; i++) {
sum += samples[i];
}
let avg = sum / samples.length;
return [avg];
}
function calcNDVIForCloudFreeSamples(samples) {
const ndvis = new Array(samples.length).fill(NaN);
samples.forEach((sample, index) => {
ndvis[index] = (sample.B08 - sample.B04) / (sample.B08 + sample.B04) ;
});
return ndvis;
}
function calcPercentage(total, incidences) {
return ((incidence / total) * 100);
}
function evaluatePixel(samples) {
if (samples.length < 1) return [NaN];
const parsedSamples = samplesParser(samples);
const ndvis = calcNDVIForCloudFreeSamples(parsedSamples[1]);
const averages = calcAverage(ndvis);
const numOfCloudyPixels = parsedSamples[0].length;
const numOfCloudFreePixels = parsedSamples[1].length;
const totalNumOfPixels = numOfCloudyPixels + numOfCloudFreePixels;
const percentageOfCloudy = calcPercentage(totalNumOfPixels, numOfCloudyPixels);
const percentageOfCloudFree = calcPercentage(totalNumOfPixels, numOfCloudFreePixels);
return {
averages: averages,
numOfCloudFreePixels: numOfCloudFreePixels,
}
}