80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
from modules.nimrod import Nimrod
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import os
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from pathlib import Path
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import logging
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import concurrent.futures
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class BatchNimrod:
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def __init__(self, config) -> None:
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self.config = config
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def _process_single_file(self, in_file):
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"""Process a single Nimrod DAT file.
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Args:
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in_file (str): Filename of the DAT file.
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Returns:
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bool: True if successful, False otherwise.
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"""
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in_file_full = Path(self.config.DAT_TOP_FOLDER, in_file)
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try:
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# We need to open the file here, inside the thread
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with open(in_file_full, "rb") as f:
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image = Nimrod(f)
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out_file_name = f"{image.get_validity_time()}.asc"
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out_file_path = Path(self.config.ASC_TOP_FOLDER, out_file_name)
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with open(out_file_path, "w") as outfile:
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image.extract_asc(outfile)
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if self.config.delete_dat_after_processing:
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os.remove(in_file_full)
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logging.debug(f"Successfully processed: {in_file_full}")
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return True
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except Nimrod.HeaderReadError as e:
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logging.error(f"Failed to read file {in_file_full}, is it corrupt?")
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logging.error(e)
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return False
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except Nimrod.PayloadReadError as e:
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logging.error(f"Failed to load the raster data in {in_file_full}")
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logging.error(e)
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return False
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except Exception as e:
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logging.error(f"Unexpected error processing {in_file_full}: {e}")
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return False
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def process_nimrod_files(self) -> None:
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"""
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Process all Nimrod files in the input directory concurrently, applying bounding box clipping
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and exporting to ASC format.
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This function reads all files from DAT_TOP_FOLDER, applies the appropriate bounding
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box for each area, and exports clipped raster data to OUT_TOP_FOLDER.
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"""
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# Read all file names in the folder
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files_to_process = [f for f in os.listdir(Path(self.config.DAT_TOP_FOLDER)) if not f.startswith('.')]
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total_files = len(files_to_process)
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logging.info(f"Processing {total_files} files concurrently...")
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with concurrent.futures.ThreadPoolExecutor() as executor:
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# Submit all tasks
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future_to_file = {
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executor.submit(self._process_single_file, in_file): in_file
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for in_file in files_to_process
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}
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completed_count = 0
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for future in concurrent.futures.as_completed(future_to_file):
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completed_count += 1
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if completed_count % 10 == 0:
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logging.info(f'processed {completed_count} out of {total_files} files')
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