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="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") t0 = config.getfloat("post", "t0") dt = config.getfloat("post", "dt") f = 1 / dt log.info(f"Computing reflection coefficient at x={x0}") eta = data["dep"][t > t0, x0] - data["botl"][x0] u = data["vel"][t > t0, 0, 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_dep, ax_vel) = plt.subplots(2) ax_dep.plot(t, data["dep"][:, x0] - data["botl"][x0], label="dep") ax_dep.set(xlabel="t (s)", ylabel="z (m)") ax_dep.autoscale(axis="x", tight=True) ax_dep.grid() ax_dep.axvline(t0, c="k", alpha=0.2) ax_vel.plot(t, data["vel"][:, 0, 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["botl"], color="k") ax_x.plot(data["dep"][np.argmin(np.abs(t - 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")