research-rainfallradar/aimodel/src/parse_args.py

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import argparse
import sys
import re
import importlib
# import pysnooper
# @pysnooper.snoop()
def parse_args():
"""Defines and parses the CLI arguments."""
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if len(sys.argv) < 2 or sys.argv[1] == "--help":
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sys.stderr.write("""
This program trains, manipulates, visualises, and manages a contrastive learning based rainfall radar water depth prediction model.
It functions by first finding relationships between the rainfall radar data and the water depth + heightmap data (the 'pretrain' subcommand). After this, a decoder model to predict water depth (modelled as an image segmentation task), can then be trained.
Available subcommands:
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pretrain Pretrain a contrastive learning model as an encoder.
pretrain-predict Make predictions using a trained contrastive learning encoder.
pretrain-plot Plot using embeddings predicted using pretrain-predict.
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For more information, do src/index.py <subcommand> --help.
""")
exit(0)
subcommand = re.sub(r'[^a-z0-9-]', '', sys.argv[1])
subcommand_argparser = importlib.import_module(f"subcommands.{subcommand}").parse_args
parser = subcommand_argparser()
# sys.stderr.write(f"Error: Unknown subcommand '{subcommand} (try --help).\n")
# exit(1)
if parser == None:
sys.stderr.write(f"Error: The subcommand '{subcommand}' did not return an argument parser. This is a bug.\n")
exit(1)
parser.add_argument("--only-gpu",
help="If the GPU is not available, exit with an error (useful on shared HPC systems to avoid running out of memory & affecting other users)", action="store_true")
return subcommand, parser.parse_args(args=sys.argv[2:])