70 lines
1.6 KiB
Python
70 lines
1.6 KiB
Python
import configparser
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import pathlib
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import scipy.signal as sgl
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from .read_swash import *
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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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bathy = pd.read_hdf(cache.joinpath("bathy.h5"), "bathy")
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n = bathy.index.size
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botl = read_nohead_scalar(root.joinpath("botl.dat"), n)
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dep = np.maximum(0, read_nohead_scalar(root.joinpath("dep.dat"), n))
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vel = read_nohead_vect(root.joinpath("vel.dat"), n)
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n_t = botl.shape[0]
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# Cospectral calculations
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pos_x = n // 10
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f = 1 / 0.25
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eta = (dep - botl)[n_t // 2 :, pos_x]
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u = vel[n_t // 2 :, 0, pos_x]
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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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fig, (ax_dep, ax_vel) = plt.subplots(2)
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ax_dep.plot((dep - botl)[:, pos_x], label="dep", color="#0066ff")
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ax_dep.set(xlabel="t (s)", ylabel="z (m)")
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ax_dep.autoscale(axis="x", tight=True)
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ax_dep.grid()
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ax_vel.plot(vel[:, 0, pos_x], 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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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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out = pathlib.Path(config.get("post", "out"))
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out.mkdir(exist_ok=True)
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fig.savefig(out.joinpath(f"{pos_x}.png"))
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fig_r.savefig(out.joinpath(f"R{pos_x}.png"))
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