Hello,

I am investigating the Statistical API to pull data for specific bands. The only values I am looking for are mean reflectance values for each of the bands. I have been able to successfully pull data from the API, but the processing units are higher than I expected. Can someone look at our API request and help me optimize it to lower our processing units? See below code for my API request:

from sentinelhub import SentinelHubStatistical, DataCollection, CRS, BBox, bbox_to_dimensions, \
    Geometry, SHConfig, parse_time, parse_time_interval, SentinelHubStatisticalDownloadClient
'''

Example Polygon in fields_gpd:
POLYGON ((-98.3142984128739 46.7284272171651, -98.313747 46.727692, -98.3121418637797 46.727692, -98.312192 46.728987, -98.3142984128739 46.7284272171651))

'''

#Sentinelhub API call
evalscript = """
    //VERSION=3
    function setup() {
        return {
            input: [{
            bands: ["B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B11", "B12", "CLM", "CLP", "dataMask"],
            units: "DN"
            }],
            output: [
            {
                id: "bands",
                bands: ["B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B11", "B12"],
                sampleType: "UINT16"
            },
            {
                id: "masks",
                bands: ["CLM"],
                sampleType: "UINT16"
            },
            {
                id: "indices",
                bands: ["CLP"],
                sampleType: "UINT16"
            },
            {
                id: "dataMask",
                bands: 1
            }]
        }
        }

        function evaluatePixel(samples) {
            // cloud probability normalized to interval [0, 1]
            let CLP = samples.CLP / 255.0;

            // masking cloudy pixels
            let combinedMask = samples.dataMask
            if (samples.CLM > 0) {
                combinedMask = 0;
            }

            const f = 5000;
            return {
                bands: [samples.B01, samples.B02, samples.B03, samples.B04, samples.B05,samples.B06,
                        samples.B07, samples.B08, samples.B8A, samples.B09, samples.B1972, samples.B12],
                masks: [samples.CLM],
                indices: [toUINT(CLP, f)],
                dataMask: [combinedMask]
            };
        }

        function toUINT(product, constant){
        // Clamp the output to [-1, 10] and convert it to a UNIT16
        // value that can be converted back to float later.
        if (product < -1) {
            product = -1;
        } else if (product > 10) {
            product = 10;
        }
        return Math.round(product * constant) + constant;
        }

    """

ndvi_requests = []

#see example polygon for the geometry values in fields_gpd
for field in fields_gpd.geometry.values:

    request = SentinelHubStatistical(
    aggregation=SentinelHubStatistical.aggregation(
        evalscript=evalscript,
        time_interval=('2022-09-01', '2022-09-30'),
        aggregation_interval='P1D',
    ),
    input_data = [SentinelHubStatistical.input_data(
    DataCollection.SENTINEL2_L2A,
    
    )],
    geometry=Geometry(field, crs=CRS.WGS84),
    config=config,
    )
    ndvi_requests.append(request)

download_requests = [band_request.download_list[0] for band_request in ndvi_requests]
client = SentinelHubStatisticalDownloadClient(config=config)
band_stats = client.download(download_requests)