internship/swash/processing/post.py

import configparser
import pathlib
import argparse
import logging

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

from .read_swash import *


parser = argparse.ArgumentParser(description="Pre-process bathymetry")
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")
n = bathy.index.size

log.info(f"Reading swash output from '{root}'")
botl = read_nohead_scalar(root.joinpath("botl.dat"), n)
dep = np.maximum(0, read_nohead_scalar(root.joinpath("dep.dat"), n))
vel = read_nohead_vect(root.joinpath("vel.dat"), n)

n_t = botl.shape[0]

# Cospectral calculations
pos_x = config.getint("post", "x0")
f = 1 / config.getfloat("post", "dt")
log.info(f"Computing reflection coefficient at x={pos_x}")

eta = (dep - botl)[n_t // 2 :, pos_x]
u = vel[n_t // 2 :, 0, pos_x]

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_dep, ax_vel) = plt.subplots(2)

ax_dep.plot((dep - botl)[:, pos_x], label="dep", color="#0066ff")
ax_dep.set(xlabel="t (s)", ylabel="z (m)")
ax_dep.autoscale(axis="x", tight=True)
ax_dep.grid()

ax_vel.plot(vel[:, 0, pos_x], label="vel")
ax_vel.set(xlabel="t (s)", ylabel="U (m/s)")
ax_vel.autoscale(axis="x", tight=True)
ax_vel.grid()

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()

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

fig.savefig(out.joinpath(f"{pos_x}.png"))
fig_r.savefig(out.joinpath(f"R{pos_x}.png"))

log.info("Finished post-processing")