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internship/swash/processing/layers.py

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2022-03-28 10:15:36 +02:00
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")
zk = data("zk")
velk = data("velk")
vz = data("vz")
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
# )
n = 0
vk = np.sqrt((velk[n] ** 2).sum(axis=1))
# print(vk.shape)
# plt.imshow(vk)
# plt.colorbar()
lines = ax.plot(x, zk[n].T, c="#0066cc")
quiv = []
for i in range(10):
quiv.append(
ax.quiver(
x[::50],
(zk[n, i, ::50] + zk[n, i + 1, ::50]) / 2,
velk[n, i, 0, ::50],
vz[n, i, ::50],
units="dots",
width=2,
scale=0.05,
)
)
ax.autoscale(True, "w", True)
ax.set_ylim(top=15)
def animate(k):
for i, q in enumerate(quiv):
q.set_UVC(
velk[k, i, 0, ::50],
vz[k, i, ::50],
)
for i, l in enumerate(lines):
l.set_ydata(zk[k, i])
return *quiv, *lines
ani = animation.FuncAnimation(
fig, animate, frames=wl[:, 0].size, interval=20, blit=True
)
plt.show(block=True)