internship/swash/processing/post.py

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

eta = (dep - botl)[n_t//2:, pos_x]
u = vel[n_t//2:, 0, pos_x]

phi_eta = np.abs(sgl.csd(eta, eta)[1])
phi_u = np.abs(sgl.csd(u, u)[1])
phi_eta_u = np.abs(sgl.csd(eta, u)[1])

R = np.sqrt((phi_eta + phi_u - 2*phi_eta_u)/(phi_eta + phi_u + 2*phi_eta_u))

# 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_vel.plot(vel[:, 0, pos_x], label="vel")
ax_vel.set(xlabel="t (s)", ylabel="U (m/s)")

fig.tight_layout()

fig_r, ax_r = plt.subplots()

ax_r.plot(R)
ax_r.set(ylim=(0,1))
ax_r.grid()

plt.show(block=True)