43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
from call_efast import run_efast, prepare_s2, prepare_s3
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from ndvi import (
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generate_ndvi_raw,
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create_ndvi_timeseries_raw,
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generate_ndvi_prepared,
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create_ndvi_timeseries_prepared,
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)
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from download_s2 import download_s2
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from download_s3 import download_s3
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from clouds import detect_clouds
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def run_pipeline(season, site_position, site_name):
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try:
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# print(f"Downloading data for {site_name}, {season}")
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# download_s2(season, site_position, site_name)
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# download_s3(season, site_position, site_name)
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# print(f"Generating NDVI for raw data: {site_name}, {season}")
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# generate_ndvi_raw(season, site_position, site_name)
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# create_ndvi_timeseries_raw(season, site_position, site_name)
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# print(f"Detecting clouds for {site_name}, {season}")
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# detect_clouds(season, site_name)
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print(f"Preparing data for EFAST fusion for {site_name}, {season}")
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# prepare_s2(season, site_position, site_name)
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# prepare_s3(season, site_position, site_name)
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# print(f"Running EFAST fusion for {site_name}, {season}")
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# run_efast(season, site_position, site_name)
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# print(f"Generating NDVI for prepared outputs: {site_name}, {season}")
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generate_ndvi_prepared(season, site_position, site_name)
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create_ndvi_timeseries_prepared(season, site_position, site_name)
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except Exception as e:
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print(f"Error: {e}")
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raise
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if __name__ == "__main__":
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run_pipeline(2024, (47.116171, 11.320308), "innsbruck")
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