42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import argparse
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import configparser
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import logging
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import pathlib
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import numpy as np
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parser = argparse.ArgumentParser(description="Pre-process time-series")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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parser.add_argument("-c", "--config", default="config.ini")
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args = parser.parse_args()
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logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
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log = logging.getLogger("bathy")
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log.info("Starting time-series pre-processing")
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config = configparser.ConfigParser()
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config.read(args.config)
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inp_root = pathlib.Path(config.get("inp", "root"))
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out_root = pathlib.Path(config.get("out", "root"))
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inp_ts = inp_root.joinpath(config.get("inp", "raw_ts"))
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out_ts = out_root.joinpath("ts.dat")
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raw_ts = np.loadtxt(
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inp_ts,
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dtype=[("state", int), ("z", float), ("y", float), ("x", float)],
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delimiter=",",
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max_rows=2304,
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)
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log.debug(f"{raw_ts=}")
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if (errs := np.count_nonzero(raw_ts["state"])) != 0:
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log.warning(f"{errs} transmission errors!")
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log.debug(f"{dict(zip(*np.unique(raw_ts['state'], return_counts=True)))}")
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t = np.linspace(0, 30 * 60, 2305)[:-1]
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log.debug(f"{t=}")
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log.info(f"Saving timeseries to '{out_ts}'")
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np.savetxt(out_ts, np.stack((t, raw_ts["z"]), axis=1))
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