66 lines
1.6 KiB
Python
66 lines
1.6 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 matplotlib.pyplot as plt
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import numpy as np
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from scipy.interpolate import griddata
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parser = argparse.ArgumentParser(description="Pre-process time-series")
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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 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_spec = inp_root.joinpath(config.get("inp", "raw_spec"))
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out_spec = out_root.joinpath("spec.dat")
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Sm = np.loadtxt(
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inp_spec,
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skiprows=3,
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max_rows=1,
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)
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inp = np.loadtxt(
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inp_spec,
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dtype=[("f", float), ("S", float)],
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delimiter=",",
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skiprows=12,
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usecols=(0, 1),
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max_rows=64,
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)
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cycle = config.getfloat("inp", "cycle", fallback=None)
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if cycle is None:
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f = inp["f"]
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S = inp["S"] * Sm
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else:
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f = np.arange(inp["f"].min(), inp["f"].max() + 1 / cycle, 1 / cycle)
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S = griddata(inp["f"], inp["S"] * Sm, f)
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with out_spec.open("w") as out:
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out.write("SPEC1D\n")
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np.savetxt(out, np.stack((f, S), axis=1))
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df = np.diff(f).min()
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log.info(f"Minimum frequency delta: {df:.4f}Hz")
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log.info(f"Maximum modelled time: {1/df:.0f}s")
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fig, ax = plt.subplots()
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ax.plot(f, S, c="k", lw=1)
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ax.autoscale(True, "x", tight=True)
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ax.grid()
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ax.set(xlim=0, ylim=0, xlabel="f (Hz)", ylabel="S (m^2/Hz)")
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fig.savefig(out_root.joinpath("spec.pdf"))
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plt.show()
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