42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
import new_import_ODC
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importlib = __import__('importlib')
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importlib.reload(new_import_ODC)
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from new_import_ODC import *
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import numpy as np
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date_range = ("2022-09-01", "2022-10-01")
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longtitude_range = (105.86, 105.94)
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latitude_range = (9.65, 9.69)
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coordinates = (longtitude_range, latitude_range)
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print("Loading S2...")
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data = load_data(None, date_range, longtitude_range, latitude_range)
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result = mask_clean(data)
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ds1 = calculate_indices(result, index="NDVI", satellite_mission="s2")
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ndvi = ds1["NDVI"]
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time_split = [
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slice("2022-09-01", "2023-01-01"),
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slice("2023-01-01", "2023-05-01"),
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slice("2023-05-01", "2023-07-01"),
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slice("2023-07-01", "2022-10-01"),
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]
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fill_nan_ndvi = fill_nan(ndvi, time_split)
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average_ndvi = fill_nan_ndvi.resample(time="1M").mean().compute()
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print("Loading S1...")
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dsvh, dsvv = load_data_sen1(None, date_range, coordinates)
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average_vv = calculate_average(dsvv, time_pattern='1M')
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average_vh = calculate_average(dsvh, time_pattern='1M')
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train = load_train_data("train/ST_training_data_updated_1130points_new.shp")
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point = train.iloc[0]
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ndvi_val = average_ndvi.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
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vh_val = average_vh.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
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vv_val = average_vv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
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print("NDVI shape:", ndvi_val.shape, "ndim:", ndvi_val.ndim)
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print("VH shape:", vh_val.shape, "ndim:", vh_val.ndim)
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print("VV shape:", vv_val.shape, "ndim:", vv_val.ndim)
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