internship/data/processing/ts.py

import argparse
import configparser
import logging
import pathlib

import matplotlib.pyplot as plt
import numpy as np

parser = argparse.ArgumentParser(description="Pre-process time-series")
parser.add_argument("-v", "--verbose", action="count", default=0)
parser.add_argument("-c", "--config", default="config.ini")
args = parser.parse_args()

logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
log = logging.getLogger("bathy")

log.info("Starting time-series pre-processing")
config = configparser.ConfigParser()
config.read(args.config)

inp_root = pathlib.Path(config.get("inp", "root"))
out_root = pathlib.Path(config.get("out", "root"))

inp_ts = inp_root.joinpath(config.get("inp", "raw_ts"))
out_ts = out_root.joinpath("ts.dat")

raw_ts = np.loadtxt(
    inp_ts,
    dtype=[("state", int), ("z", float), ("y", float), ("x", float)],
    delimiter=",",
    max_rows=2304,
)
log.debug(f"{raw_ts=}")

if (errs := np.count_nonzero(raw_ts["state"])) != 0:
    log.warning(f"{errs} transmission errors!")
    log.debug(f"{dict(zip(*np.unique(raw_ts['state'], return_counts=True)))}")

t = np.linspace(0, 30 * 60, 2305)[:-1]
log.debug(f"{t=}")

log.info(f"Saving timeseries to '{out_ts}'")
np.savetxt(out_ts, np.stack((t, raw_ts["z"]/100), axis=1))

fig, ax = plt.subplots()
ax.plot(t, raw_ts["z"])
ax.set(xlabel="t (s)", ylabel="z (cm)")
fig.savefig(out_root.joinpath("ts.pdf"))