95 lines
2.6 KiB
Python
95 lines
2.6 KiB
Python
import argparse
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import configparser
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import logging
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import pathlib
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import scipy.signal as sgl
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from .read_swash import *
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parser = argparse.ArgumentParser(description="Post-process swash output")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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args = parser.parse_args()
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logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
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log = logging.getLogger("post")
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log.info("Starting post-processing")
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config = configparser.ConfigParser()
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config.read("config.ini")
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cache = pathlib.Path(config.get("data", "out"))
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root = pathlib.Path(config.get("swash", "out"))
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log.info(f"Reading bathymetry from '{cache}'")
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bathy = pd.read_hdf(cache.joinpath("bathy.h5"), "bathy")
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data = np.load(pathlib.Path(config.get("post", "inp")).joinpath("sws.npz"))
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x, t = data["x"], data["t"]
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# Cospectral calculations
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x0 = config.getint("post", "x0")
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arg_x0 = np.abs(x - x0).argmin()
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t0 = config.getfloat("post", "t0")
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arg_t0 = np.abs(t - t0).argmin()
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dt = config.getfloat("post", "dt")
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f = 1 / dt
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log.info(f"Computing reflection coefficient at x={x0}")
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eta = data["watl"][t > t0, arg_x0]
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u = data["vel"][t > t0, 0, arg_x0]
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phi_eta = np.abs(sgl.csd(eta, eta, f))
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phi_u = np.abs(sgl.csd(u, u, f))
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phi_eta_u = np.abs(sgl.csd(eta, u, f))
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R = np.sqrt(
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(phi_eta[1] + phi_u[1] - 2 * phi_eta_u[1])
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/ (phi_eta[1] + phi_u[1] + 2 * phi_eta_u[1])
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)
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# Plotting
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log.info("Plotting results")
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fig, (ax_watl, ax_vel) = plt.subplots(2)
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ax_watl.plot(t, data["watl"][:, arg_x0], label="watl")
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ax_watl.set(xlabel="t (s)", ylabel="z (m)")
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ax_watl.autoscale(axis="x", tight=True)
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ax_watl.grid()
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ax_watl.axvline(t0, c="k", alpha=0.2)
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ax_vel.plot(t, data["vel"][:, 0, arg_x0], label="vel")
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ax_vel.set(xlabel="t (s)", ylabel="U (m/s)")
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ax_vel.autoscale(axis="x", tight=True)
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ax_vel.grid()
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ax_vel.axvline(t0, c="k", alpha=0.2)
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fig.tight_layout()
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fig_r, ax_r = plt.subplots()
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ax_r.plot(phi_eta[0], R)
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ax_r.autoscale(axis="x", tight=True)
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ax_r.set(ylim=(0, 1), xlabel="f (Hz)", ylabel="R")
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ax_r.grid()
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fig_x, ax_x = plt.subplots()
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ax_x.plot(data["x"], -data["botl"], color="k")
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ax_x.plot(data["x"], np.maximum(data["watl"][arg_t0, :], -data["botl"]))
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ax_x.axvline(x0, c="k", alpha=0.2)
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ax_x.set(xlabel="x (m)", ylabel="z (m)")
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ax_x.autoscale(axis="x", tight=True)
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ax_x.grid()
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out = pathlib.Path(config.get("post", "out")).joinpath(f"t{t0}x{x0}")
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log.info(f"Saving plots in '{out}'")
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out.mkdir(parents=True, exist_ok=True)
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fig.savefig(out.joinpath(f"t.png"))
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fig_r.savefig(out.joinpath(f"R.png"))
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fig_x.savefig(out.joinpath(f"x.png"))
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log.info("Finished post-processing")
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