Switched to new storage convention for swash output in sws_ola
This commit is contained in:
parent
b557712372
commit
71049d49ea
GPG key ID:
9833D3C5A25BD227
63 changed files with 174 additions and 5717 deletions
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@ -1,60 +1,3 @@
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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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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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(line,) = ax.plot(x, wl[0])
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def animate(i):
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line.set_ydata(wl[i])
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return (line,)
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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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version https://git-lfs.github.com/spec/v1
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oid sha256:4ab57e0a813e6ea3086045717e251b2c44e00891a858d9f6365e3e604fefee03
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size 1316
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@ -1,93 +1,3 @@
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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 sys
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import numpy as np
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import pandas as pd
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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plt = None
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parser = argparse.ArgumentParser(description="Pre-process bathymetry")
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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("bathy")
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log.info("Starting bathymetry pre-processing")
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config = configparser.ConfigParser()
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config.read("config.ini")
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root = pathlib.Path(config.get("data", "root"))
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log.info(f"Reading input data from '{root}'")
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bathy_hires = np.loadtxt(root.joinpath(config.get("data", "hires")))
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bathy_lores = np.loadtxt(root.joinpath(config.get("data", "bathy")))
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hstru = np.loadtxt(root.joinpath(config.get("data", "hstru")))
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poro = np.loadtxt(root.joinpath(config.get("data", "poro")))
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psize = np.loadtxt(root.joinpath(config.get("data", "psize")))
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log.info("Generating grid")
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x_hires = -np.arange(0, 0.5 * bathy_hires.size, 0.5)[::-1]
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x_lores = -np.arange(0, 1 * bathy_lores.size, 1)[::-1]
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x_hstru = -np.arange(0, 0.5 * hstru.size, 0.5)[::-1]
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log.info("Generating output data")
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bathy_hires_pd = pd.Series(bathy_hires.copy(), index=x_hires)
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bathy_lores_pd = pd.Series(bathy_lores.copy(), index=x_lores)
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bathy = pd.DataFrame(
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index=bathy_lores_pd.index.union(bathy_hires_pd.index),
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columns=("z", "hstru", "poro", "psize"),
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data=0,
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)
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bathy.z[bathy_lores_pd.index] = bathy_lores_pd
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bathy.z[bathy_hires_pd.index] = bathy_hires_pd
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bathy.loc[x_hstru, ("hstru", "poro", "psize")] = np.array(
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(hstru, poro, psize)
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).T
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bathy = bathy.reindex(bathy_lores_pd.index)
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log.debug(f"Bathymetry:\n{bathy}")
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log.info(
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f"xmin: {bathy.index.min()}, "
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f"xmax: {bathy.index.max()}, "
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f"n: {bathy.index.size}"
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)
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if config.has_option("data", "out"):
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out = pathlib.Path(config.get("data", "out"))
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log.info(f"Writing output data to '{out}'")
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out.mkdir(exist_ok=True)
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np.savetxt(out.joinpath("bathy.dat"), bathy.z, newline=" ")
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np.savetxt(out.joinpath("hstru.dat"), bathy.hstru, newline=" ")
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np.savetxt(out.joinpath("poro.dat"), bathy.poro, newline=" ")
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np.savetxt(out.joinpath("psize.dat"), bathy.psize, newline=" ")
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bathy.to_hdf(out.joinpath("bathy.h5"), "bathy", mode="w")
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if config.getboolean("proc", "plot", fallback=False):
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if plt is None:
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log.error("Could not import PyPlot")
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sys.exit(1)
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log.info("Plotting data")
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fig, ax = plt.subplots()
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ax.plot(x_hires, bathy_hires, label="High-res")
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ax.plot(x_lores, bathy_lores, label="Low-res")
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ax.plot(bathy.index, bathy.z, ls="-.", c="k", label="Combined")
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ax.plot(bathy.index, bathy.z + bathy.hstru, label="Hstru")
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ax.grid()
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ax.legend()
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plt.show(block=True)
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log.info("Processing finished")
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version https://git-lfs.github.com/spec/v1
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oid sha256:93e058c82e05d4d81cb1bb68459b978780f6c32fd0ba6c5d3662212542a0d118
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size 2877
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@ -1,94 +1,3 @@
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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 sys
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import numpy as np
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import pandas as pd
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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plt = None
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parser = argparse.ArgumentParser(description="Pre-process bathymetry")
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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("bathy")
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log.info("Starting bathymetry pre-processing")
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config = configparser.ConfigParser()
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config.read("config.ini")
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root = pathlib.Path(config.get("data", "root"))
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log.info(f"Reading input data from '{root}'")
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bathy_hires = np.loadtxt(root.joinpath(config.get("data", "hires")))
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bathy_lores = np.loadtxt(root.joinpath(config.get("data", "bathy")))
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hstru = np.loadtxt(root.joinpath(config.get("data", "hstru")))
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poro = np.loadtxt(root.joinpath(config.get("data", "poro")))
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psize = np.loadtxt(root.joinpath(config.get("data", "psize")))
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log.info("Generating grid")
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x_hires = -np.arange(0, 0.5 * bathy_hires.size, 0.5)[::-1]
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x_lores = -np.arange(0, 1 * bathy_lores.size, 1)[::-1]
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x_hstru = -np.arange(0, 0.5 * hstru.size, 0.5)[::-1]
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log.info("Generating output data")
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bathy_hires_pd = pd.Series(bathy_hires.copy(), index=x_hires)
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bathy_lores_pd = pd.Series(bathy_lores.copy(), index=x_lores)
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bathy = pd.DataFrame(
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index=bathy_lores_pd.index.union(bathy_hires_pd.index),
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columns=("z", "hstru", "poro", "psize"),
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data=0,
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)
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bathy.z[bathy_lores_pd.index] = bathy_lores_pd
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bathy.z[bathy_hires_pd.index] = bathy_hires_pd
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bathy.z = np.minimum(bathy.z, -15)
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# bathy.loc[x_hstru, ("hstru", "poro", "psize")] = np.array(
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# (hstru, poro, psize)
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# ).T
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bathy = bathy.reindex(bathy_lores_pd.index)
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log.debug(f"Bathymetry:\n{bathy}")
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log.info(
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f"xmin: {bathy.index.min()}, "
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f"xmax: {bathy.index.max()}, "
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f"n: {bathy.index.size}"
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)
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if config.has_option("data", "out_nb"):
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out = pathlib.Path(config.get("data", "out_nb"))
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log.info(f"Writing output data to '{out}'")
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out.mkdir(exist_ok=True)
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np.savetxt(out.joinpath("bathy.dat"), bathy.z, newline=" ")
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np.savetxt(out.joinpath("hstru.dat"), bathy.hstru, newline=" ")
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np.savetxt(out.joinpath("poro.dat"), bathy.poro, newline=" ")
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np.savetxt(out.joinpath("psize.dat"), bathy.psize, newline=" ")
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bathy.to_hdf(out.joinpath("bathy.h5"), "bathy", mode="w")
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if config.getboolean("proc", "plot", fallback=False):
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if plt is None:
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log.error("Could not import PyPlot")
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sys.exit(1)
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log.info("Plotting data")
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fig, ax = plt.subplots()
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ax.plot(x_hires, bathy_hires, label="High-res")
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ax.plot(x_lores, bathy_lores, label="Low-res")
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ax.plot(bathy.index, bathy.z, ls="-.", c="k", label="Combined")
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ax.plot(bathy.index, bathy.z + bathy.hstru, label="Hstru")
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ax.grid()
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ax.legend()
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plt.show(block=True)
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log.info("Processing finished")
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e3a75a559d7bf52e3e1703c5e45c8e473cd813aa99a95169bd9a500285f5a2d
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size 2923
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@ -1,92 +1,3 @@
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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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version https://git-lfs.github.com/spec/v1
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oid sha256:60aeefb03fe5f6ec669d64cd781e7254d7aeace3a2faab75098a4734343385a0
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size 1992
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@ -1,169 +1,3 @@
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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.pyplot as plt
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import numpy as np
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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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parser = argparse.ArgumentParser(description="Post-process 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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log.info(f"Reading data from '{inp}'")
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x = np.load(inp.joinpath("xp.npy"))
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t = np.load(inp.joinpath("tsec.npy"))
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botl = np.load(inp.joinpath("botl.npy"))
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watl = np.load(inp.joinpath("watl.npy"))
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vel = np.load(inp.joinpath("vel.npy"))
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# Cospectral calculations
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x0 = config.getint("post", "x0")
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arg_x0 = np.abs(x - x0).argmin()
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t0 = config.getfloat("post", "t0")
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arg_t0 = np.abs(t - t0).argmin()
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dt = config.getfloat("post", "dt")
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f = 1 / dt
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nperseg = config.getint("post", "nperseg", fallback=None)
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log.info(f"Computing reflection coefficient at x={x0}")
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eta = watl[t > t0, arg_x0]
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u = vel[t > t0, 0, arg_x0]
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phi_eta = sgl.welch(eta, f, nperseg=nperseg)
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phi_u = sgl.welch(u, f, nperseg=nperseg)
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phi_eta_u = sgl.csd(eta, u, f, nperseg=nperseg)
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H = np.sqrt(np.abs(phi_eta[1]))
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U = np.sqrt(np.abs(phi_u[1]))
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G = H / U
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th_eta_u = np.angle(phi_eta_u[1])
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# R1 = np.sqrt(
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# (np.abs(phi_eta[1]) + np.abs(phi_u[1]) - 2 * np.abs(phi_eta_u[1]))
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# / (np.abs(phi_eta[1]) + np.abs(phi_u[1]) + 2 * np.abs(phi_eta_u[1]))
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# )
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R = np.sqrt(
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(1 + G**2 - 2 * G * np.cos(th_eta_u))
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/ (1 + G**2 + 2 * G * np.cos(th_eta_u))
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)
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if config.has_option("post", "compare"):
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inp_comp = pathlib.Path(config.get("post", "compare"))
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x_ = np.load(inp_comp.joinpath("xp.npy"))
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t_ = np.load(inp_comp.joinpath("tsec.npy"))
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botl_ = np.load(inp_comp.joinpath("botl.npy"))
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watl_ = np.load(inp_comp.joinpath("watl.npy"))
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||||
vel_ = np.load(inp_comp.joinpath("vel.npy"))
|
||||
|
||||
arg_x0_ = np.abs(x_ - x0).argmin()
|
||||
arg_t0_ = np.abs(t_ - t0).argmin()
|
||||
|
||||
eta_ = watl_[t_ > t0, arg_x0_]
|
||||
u_ = vel_[t_ > t0, 0, arg_x0_]
|
||||
|
||||
phi_eta_ = sgl.welch(eta_, f, nperseg=nperseg)
|
||||
phi_u_ = sgl.welch(u_, f, nperseg=nperseg)
|
||||
phi_eta_u_ = sgl.csd(eta_, u_, f, nperseg=nperseg)
|
||||
|
||||
H_ = np.sqrt(np.abs(phi_eta_[1]))
|
||||
U_ = np.sqrt(np.abs(phi_u_[1]))
|
||||
G_ = H_ / U_
|
||||
th_eta_u_ = np.angle(phi_eta_u_[1])
|
||||
|
||||
R_ = np.sqrt(
|
||||
(1 + G_**2 - 2 * G_ * np.cos(th_eta_u_))
|
||||
/ (1 + G_**2 + 2 * G_ * np.cos(th_eta_u_))
|
||||
)
|
||||
|
||||
|
||||
# Plotting
|
||||
log.info("Plotting results")
|
||||
fig, (ax_watl, ax_vel) = plt.subplots(2)
|
||||
|
||||
ax_watl.plot(t, watl[:, arg_x0], lw=1, label="watl")
|
||||
ax_watl.set(xlabel="t (s)", ylabel="z (m)")
|
||||
ax_watl.autoscale(axis="x", tight=True)
|
||||
ax_watl.grid()
|
||||
ax_watl.axvline(t0, c="k", alpha=0.2)
|
||||
|
||||
ax_vel.plot(t, vel[:, 0, arg_x0], lw=1, label="vel")
|
||||
ax_vel.set(xlabel="t (s)", ylabel="U (m/s)")
|
||||
ax_vel.autoscale(axis="x", tight=True)
|
||||
ax_vel.grid()
|
||||
ax_vel.axvline(t0, c="k", alpha=0.2)
|
||||
|
||||
fig.tight_layout()
|
||||
|
||||
fig_r, ax_r = plt.subplots()
|
||||
ax_fft = ax_r.twinx()
|
||||
|
||||
ax_fft.plot(
|
||||
*sgl.welch(eta, 1 / dt, nperseg=nperseg),
|
||||
lw=1,
|
||||
c="k",
|
||||
alpha=0.2,
|
||||
label="PSD ($\\eta$, cas 1)",
|
||||
)
|
||||
ax_r.plot(phi_eta[0], R, marker="+", label="R (cas 1)")
|
||||
if config.has_option("post", "compare"):
|
||||
ax_fft.plot(
|
||||
*sgl.welch(eta_, 1 / dt, nperseg=nperseg),
|
||||
lw=1,
|
||||
c="k",
|
||||
alpha=0.2,
|
||||
label="PSD ($\\eta$, cas 2)",
|
||||
)
|
||||
ax_r.plot(phi_eta[0], R_, marker="+", label="R (cas 2)")
|
||||
ax_r.set(xlim=(0, 0.3), ylim=(0, 1), xlabel="f (Hz)", ylabel="R")
|
||||
ax_fft.set(ylim=0, ylabel="PSD (m²/Hz)")
|
||||
ax_r.grid()
|
||||
ax_r.legend(loc="upper left")
|
||||
ax_fft.legend(loc="upper right")
|
||||
fig_r.tight_layout()
|
||||
|
||||
fig_x, ax_x = plt.subplots(figsize=(10, 1))
|
||||
ax_x.plot(x, -botl, color="k")
|
||||
ax_x.plot(
|
||||
x,
|
||||
np.maximum(watl[arg_t0, :], -botl),
|
||||
)
|
||||
if config.has_option("post", "compare"):
|
||||
ax_x.plot(x, -botl_, color="k", ls="-.")
|
||||
ax_x.plot(
|
||||
x,
|
||||
np.maximum(watl_[arg_t0, :], -botl_),
|
||||
ls="-.",
|
||||
)
|
||||
ax_x.axvline(x0, c="k", alpha=0.2)
|
||||
ax_x.set(xlabel="x (m)", ylabel="z (m)")
|
||||
ax_x.autoscale(axis="x", tight=True)
|
||||
ax_x.set(aspect="equal")
|
||||
fig_x.tight_layout()
|
||||
|
||||
out = pathlib.Path(config.get("post", "out")).joinpath(f"t{t0}x{x0}")
|
||||
log.info(f"Saving plots in '{out}'")
|
||||
out.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
fig.savefig(out.joinpath("t.png"))
|
||||
fig_r.savefig(out.joinpath("R.png"))
|
||||
fig_x.savefig(out.joinpath("x.png"))
|
||||
plt.show(block=True)
|
||||
|
||||
log.info("Finished post-processing")
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c8fefac1023d769a2a43c86f159cf33155f5462aed2dcd4ac968d782f79760d4
|
||||
size 4665
|
||||
|
|
|
|||
|
|
@ -1,52 +1,3 @@
|
|||
import subprocess
|
||||
import tempfile
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class ReadSwash:
|
||||
def __init__(self):
|
||||
self._n_x = None
|
||||
self._n_t = None
|
||||
self._t = None
|
||||
self._x = None
|
||||
|
||||
@classmethod
|
||||
def read_nohead(cls, path):
|
||||
subprocess.run(("sed", "-i", r"s/ /\n/g", path))
|
||||
return np.loadtxt(path)
|
||||
|
||||
def read_time(self, path):
|
||||
self._t = np.unique(self.read_nohead(path))
|
||||
self._n_t = self._t.size
|
||||
return self.t
|
||||
|
||||
def read_x(self, path):
|
||||
self._x = np.unique(self.read_nohead(path))
|
||||
self._n_x = self._x.size
|
||||
return self.x
|
||||
|
||||
def read_scalar(self, path, const=False):
|
||||
if const:
|
||||
return self.read_nohead(path).reshape((self._n_t, self._n_x))[0, :]
|
||||
return self.read_nohead(path).reshape((self._n_t, self._n_x))
|
||||
|
||||
def read_const(self, path):
|
||||
return self.read_scalar(path, const=True)
|
||||
|
||||
def read_vector(self, path):
|
||||
return self.read_nohead(path).reshape((self._n_t, 2, self._n_x))
|
||||
|
||||
def read_scalar_lay(self, path):
|
||||
return self.read_nohead(path).reshape((self._n_t, -1, self._n_x))
|
||||
|
||||
def read_vector_lay(self, path):
|
||||
return self.read_nohead(path).reshape((self._n_t, -1, 2, self._n_x))
|
||||
|
||||
@property
|
||||
def t(self):
|
||||
return self._t
|
||||
|
||||
@property
|
||||
def x(self):
|
||||
return self._x
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:81ddc1baff1a727e68dc2819fe3f39ca657c488ab8cbeb83c2d260ae7c9c13fd
|
||||
size 1349
|
||||
|
|
|
|||
|
|
@ -1,67 +1,3 @@
|
|||
import argparse
|
||||
import configparser
|
||||
import logging
|
||||
import os
|
||||
import pathlib
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
|
||||
parser = argparse.ArgumentParser(description="Run swash model")
|
||||
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("swash")
|
||||
|
||||
log.info("Starting swash model")
|
||||
config = configparser.ConfigParser()
|
||||
config.read("config.ini")
|
||||
|
||||
inp = pathlib.Path(config.get("swash", "input"))
|
||||
out = pathlib.Path(config.get("swash", "out"))
|
||||
if out.exists():
|
||||
log.error(f"Swash output '{out}' already exists")
|
||||
sys.exit(1)
|
||||
|
||||
with tempfile.TemporaryDirectory(prefix="swash_", dir=".") as tmp_raw:
|
||||
tmpdir = pathlib.Path(tmp_raw)
|
||||
|
||||
log.info(f"Copying files to '{tmpdir}'")
|
||||
shutil.copy2(inp, tmpdir)
|
||||
if config.getboolean("swash", "nb", fallback=False):
|
||||
path = pathlib.Path(config.get("data", "out_nb"))
|
||||
else:
|
||||
path = pathlib.Path(config.get("data", "out"))
|
||||
shutil.copytree(path, tmpdir, dirs_exist_ok=True)
|
||||
|
||||
if config.has_option("swash", "mpi"):
|
||||
mpi = ("-mpi", config.get("swash", "mpi"))
|
||||
log.info(f"Using mpi with {mpi}")
|
||||
else:
|
||||
mpi = ()
|
||||
|
||||
with open(tmpdir.joinpath("sws.log"), "w") as logfile:
|
||||
log.info(f"Runing swash in '{tmpdir}'")
|
||||
path = pathlib.Path(config.get("swash", "path"))
|
||||
|
||||
cmd = (path.joinpath("swashrun"), "-input", inp.name, *mpi)
|
||||
log.info(f"Running {cmd}")
|
||||
|
||||
swash_run = subprocess.Popen(
|
||||
cmd,
|
||||
cwd=tmpdir,
|
||||
stdout=logfile,
|
||||
stderr=logfile,
|
||||
env={"PATH": f"{path}:{os.environ['PATH']}"},
|
||||
)
|
||||
|
||||
code = swash_run.wait()
|
||||
if code != 0:
|
||||
log.error(f"Swash returned error code {code}")
|
||||
|
||||
log.info(f"Moving swash output to '{out}'")
|
||||
shutil.move(tmpdir, out)
|
||||
|
||||
log.info(f"Swash model finished successfully")
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b17bd29f45896e07ef9e48ab27c905158de21bdedc7b9ecbf1a7943e4af7ae70
|
||||
size 1962
|
||||
|
|
|
|||
|
|
@ -1,51 +1,3 @@
|
|||
import argparse
|
||||
import configparser
|
||||
import logging
|
||||
import pathlib
|
||||
from multiprocessing.pool import ThreadPool
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from .read_swash import ReadSwash
|
||||
|
||||
parser = argparse.ArgumentParser(description="Convert swash output to numpy")
|
||||
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("sws_npz")
|
||||
|
||||
log.info("Starting sws -> npz converter")
|
||||
config = configparser.ConfigParser()
|
||||
config.read("config.ini")
|
||||
|
||||
sws_out = pathlib.Path(config.get("swash", "out"))
|
||||
inp = pathlib.Path(config.get("post", "inp"))
|
||||
|
||||
log.info(f"Reading swash output from '{sws_out}'")
|
||||
rsws = ReadSwash()
|
||||
np.save(inp.joinpath("tsec"), rsws.read_time(sws_out.joinpath("tsec.dat")))
|
||||
np.save(inp.joinpath("xp"), rsws.read_x(sws_out.joinpath("xp.dat")))
|
||||
|
||||
var = {
|
||||
"dep": rsws.read_scalar,
|
||||
"botl": rsws.read_const,
|
||||
"watl": rsws.read_scalar,
|
||||
"vel": rsws.read_vector,
|
||||
"press": rsws.read_scalar,
|
||||
"zk": rsws.read_scalar_lay,
|
||||
"velk": rsws.read_vector_lay,
|
||||
"vz": rsws.read_scalar_lay,
|
||||
}
|
||||
|
||||
inp.mkdir(exist_ok=True)
|
||||
with ThreadPool() as pool:
|
||||
log.info("Converting all data")
|
||||
pool.map(
|
||||
lambda x: np.save(
|
||||
inp.joinpath(x[0]),
|
||||
x[1](sws_out.joinpath(x[0]).with_suffix(".dat")),
|
||||
),
|
||||
var.items(),
|
||||
)
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f3b186f594089b65b3739d2f03855c691f45e0b2744e37196e07cdf60c896cfc
|
||||
size 1393
|
||||
|
|
|
|||
Reference in a new issue