2022-03-28 10:15:36 +02:00
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import argparse
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import configparser
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import logging
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import pathlib
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import matplotlib.animation as animation
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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parser = argparse.ArgumentParser(description="Animate swash output")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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args = parser.parse_args()
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logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
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log = logging.getLogger("post")
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log.info("Starting post-processing")
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config = configparser.ConfigParser()
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config.read("config.ini")
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inp = pathlib.Path(config.get("post", "inp"))
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root = pathlib.Path(config.get("swash", "out"))
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bathy = pd.read_hdf(
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pathlib.Path(config.get("data", "out")).joinpath("bathy.h5"), "bathy"
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)
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def data(var):
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return np.load(inp.joinpath(f"{var}.npy"))
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x = data("xp")
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t = data("tsec")
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watl = data("watl")
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botl = data("botl")
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zk = data("zk")
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velk = data("velk")
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vz = data("vz")
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wl = np.maximum(watl, -botl)
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# print(x.size, -np.arange(0, 1 * bathy.hstru.size, 1)[::-1].size)
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fig, ax = plt.subplots()
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# ax.plot(x, -botl, c="k")
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# ax.fill_between(
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# x, -botl, -data["botl"] + bathy.hstru, color="k", alpha=0.2
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# )
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n = 0
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vk = np.sqrt((velk[n] ** 2).sum(axis=1))
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# print(vk.shape)
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# plt.imshow(vk)
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# plt.colorbar()
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lines = ax.plot(x, zk[n].T, c="#0066cc")
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quiv = []
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for i in range(10):
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quiv.append(
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ax.quiver(
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x[::50],
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(zk[n, i, ::50] + zk[n, i + 1, ::50]) / 2,
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velk[n, i, 0, ::50],
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vz[n, i, ::50],
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units="dots",
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width=2,
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scale=0.05,
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)
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)
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ax.autoscale(True, "w", True)
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ax.set_ylim(top=15)
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def animate(k):
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for i, q in enumerate(quiv):
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q.set_UVC(
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velk[k, i, 0, ::50],
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vz[k, i, ::50],
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)
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for i, l in enumerate(lines):
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l.set_ydata(zk[k, i])
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return *quiv, *lines
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ani = animation.FuncAnimation(
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fig, animate, frames=wl[:, 0].size, interval=20, blit=True
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)
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plt.show(block=True)
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