70 lines
1.5 KiB
Python
70 lines
1.5 KiB
Python
|
import argparse
|
||
|
import configparser
|
||
|
import logging
|
||
|
import pathlib
|
||
|
|
||
|
import matplotlib.pyplot as plt
|
||
|
import matplotlib.animation as animation
|
||
|
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")
|
||
|
zk = data("zk")
|
||
|
velk = data("velk")
|
||
|
|
||
|
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
|
||
|
# )
|
||
|
lines = ax.plot(x, zk[0].T)
|
||
|
|
||
|
print(velk.shape)
|
||
|
velk = velk.reshape((6001, 10, 2, 1251))
|
||
|
vk = np.sqrt((velk[1000] ** 2).sum(axis=1))
|
||
|
print(vk.shape)
|
||
|
plt.imshow(vk)
|
||
|
plt.colorbar()
|
||
|
|
||
|
# def animate(i):
|
||
|
# for line, z in zip(lines, zk[i]):
|
||
|
# line.set_ydata(z)
|
||
|
# return lines
|
||
|
#
|
||
|
# ani = animation.FuncAnimation(
|
||
|
# fig, animate, frames=wl[:, 0].size, interval=20, blit=True
|
||
|
# )
|
||
|
|
||
|
plt.show(block=True)
|