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")

Sm = np.loadtxt(
    inp_spec,
    skiprows=3,
    max_rows=1,
)

inp = np.loadtxt(
    inp_spec,
    dtype=[("f", float), ("S", float)],
    delimiter=",",
    skiprows=12,
    usecols=(0, 1),
    max_rows=64,
)

with out_spec.open("w") as out:
    out.write("SPEC1D\n")
    np.savetxt(out, np.stack((inp["f"], inp["S"] * Sm), axis=1))

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")

fig, ax = plt.subplots()
ax.plot(inp["f"], inp["S"]*Sm, c="k", lw=1)
ax.autoscale(True, "x", tight=True)
ax.grid()
ax.set(xlim=0, ylim=0, xlabel="f (Hz)", ylabel="S (m^2/Hz)")

fig.savefig(out_root.joinpath("spec.pdf"))
plt.show()