internship/swash/processing/animate.py

import argparse
import configparser
import logging
import pathlib

import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

parser = argparse.ArgumentParser(description="Animate swash output")
parser.add_argument("-v", "--verbose", action="count", default=0)
args = parser.parse_args()

logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
log = logging.getLogger("post")

log.info("Starting post-processing")
config = configparser.ConfigParser()
config.read("config.ini")

inp = pathlib.Path(config.get("post", "inp"))
root = pathlib.Path(config.get("swash", "out"))

bathy = pd.read_hdf(
    pathlib.Path(config.get("data", "out")).joinpath("bathy.h5"), "bathy"
)


def data(var):
    return np.load(inp.joinpath(f"{var}.npy"))


x = data("xp")
t = data("tsec")

watl = data("watl")
botl = data("botl")

wl = np.maximum(watl, -botl)
# print(x.size, -np.arange(0, 1 * bathy.hstru.size, 1)[::-1].size)

fig, ax = plt.subplots()
ax.plot(x, -botl, c="k")
# ax.fill_between(
#    x, -botl, -data["botl"] + bathy.hstru, color="k", alpha=0.2
# )
(line,) = ax.plot(x, wl[0])


def animate(i):
    line.set_ydata(wl[i])
    return (line,)


ani = animation.FuncAnimation(
    fig, animate, frames=wl[:, 0].size, interval=20, blit=True
)

plt.show(block=True)