internship/swash/processing/zero_cross.py

import argparse
import configparser
import logging
import pathlib

import matplotlib.pyplot as plt
import numpy as np

parser = argparse.ArgumentParser(description="Post-process swash output")
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("post")

log.info("Starting post-processing")
config = configparser.ConfigParser()
config.read(args.config)

inp = pathlib.Path(config.get("post", "inp"))
inp_comp = pathlib.Path(config.get("post", "compare"))
root = pathlib.Path(config.get("swash", "out"))

log.info(f"Reading data from '{inp}'")
x = np.load(inp.joinpath("x.npy"))
t = np.load(inp.joinpath("t.npy"))

watl = np.load(inp.joinpath("watl.npy"))
watl_comp = np.load(inp_comp.joinpath("watl.npy"))

# Cospectral calculations
x0 = config.getint("post", "x0")
arg_x0 = np.abs(x - x0).argmin()

w0 = watl[:, arg_x0]
w0_comp = watl_comp[:, arg_x0]
cr0 = np.where(np.diff(np.sign(w0)))[0]
cr0_comp = np.where(np.diff(np.sign(w0_comp)))[0]

log.info(f"1: {cr0.size} waves")
log.info(f"2: {cr0_comp.size} waves")

wave = np.fromiter(
    (
        np.abs(
            np.max(np.abs(w0[cr0[i - 1] : cr0[i]]))
            + np.max(np.abs(w0[cr0[i] : cr0[i + 1]]))
        )
        for i in range(1, len(cr0) - 1)
    ),
    dtype=np.single,
)
wave_comp = np.fromiter(
    (
        np.abs(
            np.max(np.abs(w0_comp[cr0_comp[i - 1] : cr0_comp[i]]))
            + np.max(np.abs(w0_comp[cr0_comp[i] : cr0_comp[i + 1]]))
        )
        for i in range(1, len(cr0) - 1)
    ),
    dtype=np.single,
)

i0 = np.argmax(wave)

out = pathlib.Path(config.get("post", "out")).joinpath(f"x{x0}")
log.info(f"Saving plots in '{out}'")
out.mkdir(parents=True, exist_ok=True)

fig, ax = plt.subplots()
ax.plot(t[cr0[1:-1]] * 1e-3, wave)
ax.set(xlabel="t (s)", ylabel="z (m)")
ax.autoscale(True, "x", True)
ax.grid()
fig.savefig(out.joinpath("wsize.pdf"))

fig2, ax2 = plt.subplots(
    figsize=(10 / 2.54, 2 / 3 * 10 / 2.54), constrained_layout=True
)
ax2.plot(
    t[cr0[i0 - 5] : cr0[i0 + 7]] * 1e-3,
    w0[cr0[i0 - 5] : cr0[i0 + 7]],
    color="k",
    label="Cas 1",
)
ax2.plot(
    t[cr0[i0 - 5] : cr0[i0 + 7]] * 1e-3,
    w0_comp[cr0[i0 - 5] : cr0[i0 + 7]],
    color="k",
    ls="-.",
    label="Cas 2",
)
ax2.set(xlabel="t (s)", ylabel="z (m)")
ax2.autoscale(True, "x", True)
ax2.grid()
ax2.legend()
fig2.savefig(out.joinpath("maxw.pdf"))
fig2.savefig(out.joinpath("maxw.jpg"), dpi=200)

log.info(
    f"RMS difference: {np.sqrt(np.mean((w0_comp-w0)**2))}m ; {np.sqrt(np.mean((w0_comp-w0)**2))/(w0.max()-w0.min()):%}"
)
log.info(f"Bias: {np.mean(w0_comp-w0)}m")
log.info(f"Maximum wave size: {wave.max()}m ; {wave_comp.max()}m")
log.info(
    f"Maximum wave size difference: {abs(wave_comp.max()-wave.max())/wave.max():%}"
)