Time-series pre-processing

data/config.ini

@@ -5,6 +5,7 @@ hires=bathyhires.dat
 hstru=Hstru.dat
 poro=Poro.dat
 psize=Psize.dat
+raw_ts=201702281700.raw
 hires_step=0.5
 
 [out]

data/data/.gitignore

@@ -1 +1,2 @@
 *.xyz
+*.raw

data/processing/ts.py

@@ -0,0 +1,42 @@
+import argparse
+import configparser
+import logging
+import pathlib
+
+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"]), axis=1))