Black+isort, layers, vz in sws_npz
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parent
12ad3b5c59
commit
8fc6885ef0
5 changed files with 49 additions and 25 deletions
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@ -19,9 +19,9 @@ out=out_sws
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mpi=4
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[post]
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inp=inp_post
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case=sws_spec_buoy.npz
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compare=sws_spec_buoy_nb.npz
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inp=inp_post_test
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#case=sws_spec_buoy.npz
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#compare=sws_spec_buoy_nb.npz
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out=out_post
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#nperseg=1024
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dt=0.25
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@ -3,12 +3,11 @@ import configparser
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import logging
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import pathlib
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import matplotlib.pyplot as plt
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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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@ -3,12 +3,11 @@ import configparser
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import logging
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import pathlib
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import matplotlib.pyplot as plt
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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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@ -39,31 +38,55 @@ 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.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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lines = ax.plot(x, zk[0].T)
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print(velk.shape)
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n = 0
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velk = velk.reshape((6001, 10, 2, 1251))
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vk = np.sqrt((velk[1000] ** 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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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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# def animate(i):
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# for line, z in zip(lines, zk[i]):
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# line.set_ydata(z)
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# return lines
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#
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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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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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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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@ -5,8 +5,8 @@ import pathlib
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import matplotlib.pyplot as plt
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import numpy as np
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import scipy.signal as sgl
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import scipy.fft as fft
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import scipy.signal as sgl
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from .read_swash import *
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@ -1,8 +1,8 @@
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import argparse
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import configparser
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import logging
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from multiprocessing.pool import ThreadPool
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import pathlib
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from multiprocessing.pool import ThreadPool
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import numpy as np
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import pandas as pd
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@ -36,6 +36,7 @@ var = {
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"press": rsws.read_scalar,
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"zk": rsws.read_scalar_lay,
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"velk": rsws.read_vector_lay,
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"vz": rsws.read_scalar_lay,
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}
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inp.mkdir(exist_ok=True)
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@ -43,7 +44,8 @@ with ThreadPool() as pool:
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log.info("Converting all data")
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pool.map(
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lambda x: np.save(
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inp.joinpath(x[0]), x[1](sws_out.joinpath(x[0]).with_suffix(".dat"))
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inp.joinpath(x[0]),
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x[1](sws_out.joinpath(x[0]).with_suffix(".dat")),
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),
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var.items(),
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)
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