diff --git a/amazon-tile_id.csv b/amazon-tile_id.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6217f336c84826ead6d02179647a5c827e34d9a5
--- /dev/null
+++ b/amazon-tile_id.csv
@@ -0,0 +1,954 @@
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diff --git a/hls.py b/hls.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0b7826a477bead60e20ce947e3a3ccb587be50c
--- /dev/null
+++ b/hls.py
@@ -0,0 +1,70 @@
+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
+
+
+
+
+