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 import scipy.fft as fft 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") inp = pathlib.Path(config.get("post", "inp")) root = pathlib.Path(config.get("swash", "out")) log.info(f"Reading bathymetry from '{inp}'") data = np.load(inp.joinpath(config.get("post", "case"))) x, t = data["x"], data["t"] # Cospectral calculations x0 = config.getint("post", "x0") arg_x0 = np.abs(x - x0).argmin() t0 = config.getfloat("post", "t0") arg_t0 = np.abs(t - t0).argmin() dt = config.getfloat("post", "dt") f = 1 / dt nperseg = config.getint("post", "nperseg", fallback=None) 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.welch(eta, f, nperseg=nperseg)) phi_u = np.abs(sgl.welch(u, f, nperseg=nperseg)) phi_eta_u = np.abs(sgl.csd(eta, u, f, nperseg=nperseg)) 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], lw=1, 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], lw=1, 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_fft = ax_r.twinx() ax_fft.plot( *sgl.welch(eta, 1/dt, nperseg=nperseg), lw=1, c="k", alpha=0.2, label="PSD ($\\eta$)", ) ax_r.plot(phi_eta[0], R, marker="+", label="R") ax_r.set(xlim=(0, 0.3), ylim=(0, 1), xlabel="f (Hz)", ylabel="R") ax_fft.set(ylim=0, ylabel="PSD (m²/Hz)") ax_r.grid() ax_r.legend(loc="upper left") ax_fft.legend(loc="upper right") fig_r.tight_layout() fig_x, ax_x = plt.subplots(figsize=(10, 1)) ax_x.plot(data["x"], -data["botl"], color="k") ax_x.fill_between( data["x"], -data["botl"], 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.set(aspect="equal") fig_x.tight_layout() out = pathlib.Path(config.get("post", "out")).joinpath(f"t{t0}x{x0}") log.info(f"Saving plots in '{out}'") out.mkdir(parents=True, exist_ok=True) fig.savefig(out.joinpath("t.png")) fig_r.savefig(out.joinpath("R.png")) fig_x.savefig(out.joinpath("x.png")) log.info("Finished post-processing")