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