internship/swash/processing/post.py

import argparse
import configparser
import logging
import pathlib

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.signal as sgl

from .read_swash import *

parser = argparse.ArgumentParser(description="Post-process swash output")
parser.add_argument("-v", "--verbose", action="count", default=0)
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("config.ini")

cache = pathlib.Path(config.get("data", "out"))
root = pathlib.Path(config.get("swash", "out"))

log.info(f"Reading bathymetry from '{cache}'")
bathy = pd.read_hdf(cache.joinpath("bathy.h5"), "bathy")
data = np.load(pathlib.Path(config.get("post", "inp")).joinpath("sws.npz"))
x, t = data["x"], data["t"]

# Cospectral calculations
x0 = config.getint("post", "x0")
arg_x0 = (x - x0).abs().argmin()
t0 = config.getfloat("post", "t0")
arg_t0 = (t - t0).abs().argmin()
dt = config.getfloat("post", "dt")
f = 1 / dt
log.info(f"Computing reflection coefficient at x={x0}")

eta = data["watl"][t > t0, arg_x0]
u = data["vel"][t > t0, 0, arg_x0]

phi_eta = np.abs(sgl.csd(eta, eta, f))
phi_u = np.abs(sgl.csd(u, u, f))
phi_eta_u = np.abs(sgl.csd(eta, u, f))

R = np.sqrt(
    (phi_eta[1] + phi_u[1] - 2 * phi_eta_u[1])
    / (phi_eta[1] + phi_u[1] + 2 * phi_eta_u[1])
)

# Plotting
log.info("Plotting results")
fig, (ax_watl, ax_vel) = plt.subplots(2)

ax_watl.plot(t, data["watl"][:, arg_x0], label="watl")
ax_watl.set(xlabel="t (s)", ylabel="z (m)")
ax_watl.autoscale(axis="x", tight=True)
ax_watl.grid()
ax_watl.axvline(t0, c="k", alpha=0.2)

ax_vel.plot(t, data["vel"][:, 0, arg_x0], label="vel")
ax_vel.set(xlabel="t (s)", ylabel="U (m/s)")
ax_vel.autoscale(axis="x", tight=True)
ax_vel.grid()
ax_vel.axvline(t0, c="k", alpha=0.2)

fig.tight_layout()

fig_r, ax_r = plt.subplots()

ax_r.plot(phi_eta[0], R)
ax_r.autoscale(axis="x", tight=True)
ax_r.set(ylim=(0, 1), xlabel="f (Hz)", ylabel="R")
ax_r.grid()

fig_x, ax_x = plt.subplots()
ax_x.plot(data["x"], -data["botl"], color="k")
ax_x.plot(data["x"], np.maximum(data["watl"][arg_t0, :], -data["botl"]))
ax_x.axvline(x0, c="k", alpha=0.2)
ax_x.set(xlabel="x (m)", ylabel="z (m)")
ax_x.autoscale(axis="x", tight=True)
ax_x.grid()

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

fig.savefig(out.joinpath(f"t{x0}.png"))
fig_r.savefig(out.joinpath(f"R{x0}.png"))
fig_x.savefig(out.joinpath(f"x{t0}.png"))

log.info("Finished post-processing")