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  • 2023 default protected
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eb_hooks.py

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    • hls.py 3.12 KiB
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    import os
import pandas as pd
import requests
import rasterio as rio
import matplotlib.pyplot as plt
from datetime import datetime
from shapely.geometry import Polygon
import xml.etree.ElementTree as ET
from all_functions import parse_kml_to_tiles
from all_functions import search_hls_granules_by_tile
from all_functions import acquisition_download_links_to_csv
from all_functions import plot_union_polygon




kml_file_path = './hls_tiles.kml'
output_file_name = "amazon-download-links.csv"
date_from = "2020-01-01"
date_to = "2024-12-31"
cloud_coverage_step = 10
ifPlot = False
tile_id_list = list(pd.read_csv("amazon-dlr-tile-ids.csv", header=None)[0])
total_number_of_tiles = len(tile_id_list)
# tile_id_list = ["T17LRJ"]
# tile_id_list = ["T21MYS"]
tile_index = 0
if os.path.isfile(output_file_name): os.remove(output_file_name)
for tile_id in tile_id_list:
    ifComplete = False
    tile_index += 1
    counter_message = f" ({tile_index}/{total_number_of_tiles})."
    polygon_surface_relative_diff_percent = 100
    tile_polygon, tile_coords = parse_kml_to_tiles(kml_file_path, target_tile_id=tile_id)
    # granules = search_hls_granules_by_tile(["HLSL30"], tile_id, date_from, date_to, cloud_coverage_threshold)
    needed_number_of_acquisitions = 0
    for cloud_coverage_threshold in range(0,101,cloud_coverage_step):
        granules = search_hls_granules_by_tile(["HLSL30", "HLSS30"], tile_id, date_from, date_to, cloud_coverage_threshold, cloud_coverage_step)
        union_polygon = Polygon([])
        for acquisition in granules:
            needed_number_of_acquisitions += 1
            # print(acquisition['title'][15:-5])
            coord_tmp  = acquisition['polygons'][0][0].split()
            acquisition_coords = [[float(coord_tmp[i+1]),float(coord_tmp[i])] for i in range(0,int(len(coord_tmp)),2)]
            acquisition_polygon = Polygon(acquisition_coords)
            union_polygon = union_polygon.union(acquisition_polygon)
            union_polygon = tile_polygon.intersection(union_polygon)
            polygon_surface_relative_diff_percent = (tile_polygon.area - union_polygon.area) / tile_polygon.area*100 
            if polygon_surface_relative_diff_percent < 1e-1:
                ifComplete = True
#                 plot_union_polygon(union_polygon, tile_polygon)
                break
        if polygon_surface_relative_diff_percent < 1e-1:
            status_message = f"{tile_id}, cloud coverage: {cloud_coverage_threshold}, complete in {needed_number_of_acquisitions} acquisitions."
        else:
            status_message = f"{tile_id}, cloud coverage: {cloud_coverage_threshold}, incomplete in {needed_number_of_acquisitions} acquisitions."
        print(status_message, counter_message, end="\r")
        # if polygon_surface_relative_diff_percent > 1e-1:
        #     status = f"{tile_id} is incomplete, surface diff: {polygon_surface_relative_diff_percent}%. Number of acquisitions: {len(granules)}."
        #     if ifPlot:
        
        acquisition_download_links_to_csv(tile_id, output_file_name, acquisitions=granules[0:needed_number_of_acquisitions], ifComplete=ifComplete)
        if ifComplete: 
            break
    # break