170 lines
4.7 KiB
Python
170 lines
4.7 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 scipy.signal as sgl
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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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parser.add_argument("-c", "--config", default="config.ini")
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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(args.config)
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inp = pathlib.Path(config.get("post", "inp"))
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root = pathlib.Path(config.get("swash", "out"))
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log.info(f"Reading data from '{inp}'")
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x = np.load(inp.joinpath("x.npy"))
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t = np.load(inp.joinpath("t.npy"))
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botl = np.load(inp.joinpath("botl.npy"))
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watl = np.load(inp.joinpath("watl.npy"))
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#vel = np.load(inp.joinpath("vel_x.npy"))
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vel = np.load(inp.joinpath("vel.npy"))[0]
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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 = int(config.getfloat("post", "t0") * 1e3)
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arg_t0 = np.abs(t - t0).argmin()
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dt = np.diff(t).mean() * 1e-3
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f = 1 / dt
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nperseg = config.getint("post", "nperseg", fallback=None)
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log.info(f"Computing reflection coefficient at x={x0}")
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eta = watl[t > t0, arg_x0]
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u = vel[t > t0, arg_x0]
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phi_eta = sgl.welch(eta, f, nperseg=nperseg)
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phi_u = sgl.welch(u, f, nperseg=nperseg)
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phi_eta_u = sgl.csd(eta, u, f, nperseg=nperseg)
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H = np.sqrt(np.abs(phi_eta[1]))
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U = np.sqrt(np.abs(phi_u[1]))
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G = H / U
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th_eta_u = np.angle(phi_eta_u[1])
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R = np.sqrt(
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(np.abs(phi_eta[1]) + np.abs(phi_u[1]) - 2 * phi_eta_u[1].real)
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/ (np.abs(phi_eta[1]) + np.abs(phi_u[1]) + 2 * phi_eta_u[1].real)
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)
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# R = np.sqrt(
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# (1 + G**2 - 2 * G * np.cos(th_eta_u))
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# / (1 + G**2 + 2 * G * np.cos(th_eta_u))
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# )
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if config.has_option("post", "compare"):
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inp_comp = pathlib.Path(config.get("post", "compare"))
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x_ = np.load(inp_comp.joinpath("x.npy"))
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t_ = np.load(inp_comp.joinpath("t.npy"))
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botl_ = np.load(inp_comp.joinpath("botl.npy"))
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watl_ = np.load(inp_comp.joinpath("watl.npy"))
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vel_ = np.load(inp_comp.joinpath("vel_x.npy"))
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# Cospectral calculations
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arg_x0_ = np.abs(x_ - x0).argmin()
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arg_t0_ = np.abs(t_ - t0).argmin()
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dt_ = np.diff(t_).mean() * 1e-3
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f_ = 1 / dt_
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eta_ = watl_[t_ > t0, arg_x0_]
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u_ = vel_[t_ > t0, arg_x0_]
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phi_eta_ = sgl.welch(eta_, f_, nperseg=nperseg)
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phi_u_ = sgl.welch(u_, f_, nperseg=nperseg)
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phi_eta_u_ = sgl.csd(eta_, u_, f_, nperseg=nperseg)
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H_ = np.sqrt(np.abs(phi_eta_[1]))
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U_ = np.sqrt(np.abs(phi_u_[1]))
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G_ = H_ / U_
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th_eta_u_ = np.angle(phi_eta_u_[1])
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R_ = np.sqrt(
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(np.abs(phi_eta_[1]) + np.abs(phi_u_[1]) - 2 * phi_eta_u_[1].real)
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/ (np.abs(phi_eta_[1]) + np.abs(phi_u_[1]) + 2 * phi_eta_u_[1].real)
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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 * 1e-3, watl[:, arg_x0], lw=1, 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 * 1e-3, c="k", alpha=0.2)
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ax_vel.plot(t * 1e-3, vel[:, arg_x0], lw=1, 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 * 1e-3, 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_fft = ax_r.twinx()
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ax_fft.plot(
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*sgl.welch(eta, 1 / dt, nperseg=nperseg),
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lw=1,
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c="k",
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alpha=0.2,
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label="PSD ($\\eta$, cas 1)",
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)
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ax_r.plot(phi_eta[0], R, marker="+", label="R (cas 1)")
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if config.has_option("post", "compare"):
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ax_fft.plot(
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*sgl.welch(eta_, 1 / dt, nperseg=nperseg),
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lw=1,
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c="k",
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alpha=0.2,
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label="PSD ($\\eta$, cas 2)",
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)
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ax_r.plot(phi_eta[0], R_, marker="+", label="R (cas 2)")
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ax_r.set(xlim=(0, 0.3), ylim=(0, 1), xlabel="f (Hz)", ylabel="R")
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ax_fft.set(ylim=0, ylabel="PSD (m²/Hz)")
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ax_r.grid()
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ax_r.legend(loc="upper left")
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ax_fft.legend(loc="upper right")
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fig_r.tight_layout()
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fig_x, ax_x = plt.subplots()
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ax_x.plot(x, -botl, color="k")
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ax_x.plot(
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x,
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np.maximum(watl[arg_t0, :], -botl),
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)
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if config.has_option("post", "compare"):
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ax_x.plot(x, -botl_, color="k", ls="-.")
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ax_x.plot(
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x,
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np.maximum(watl_[arg_t0, :], -botl_),
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ls="-.",
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)
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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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fig_x.tight_layout()
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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("t.pdf"))
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fig_r.savefig(out.joinpath("R.pdf"))
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fig_x.savefig(out.joinpath("x.pdf"))
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log.info("Finished post-processing")
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