Add swash compatible spectrum generation

data/config.ini

@@ -6,6 +6,7 @@ hstru=Hstru.dat
 poro=Poro.dat
 psize=Psize.dat
 raw_ts=cerema/raw/201702281700.raw,cerema/raw/201702281730.raw
+raw_spec=cerema/spt/201702281715.spt
 hires_step=0.5
 
 [out]

data/processing/spec.py

@@ -0,0 +1,42 @@
+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_spec = inp_root.joinpath(config.get("inp", "raw_spec"))
+out_spec = out_root.joinpath("spec.dat")
+
+inp = np.loadtxt(
+    inp_spec,
+    dtype=[("f", float), ("S", float)],
+    delimiter=",",
+    skiprows=12,
+    usecols=(0, 6),
+    max_rows=64,
+)
+
+with out_spec.open("w") as out:
+    out.write("SPEC1D\n")
+    np.savetxt(out, inp)
+
+df = np.diff(inp['f']).min()
+log.info(f"Minimum frequency delta: {df:.4f}Hz")
+log.info(f"Maximum modelled time: {1/df:.0f}s")