68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
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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from scipy import interpolate
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from scipy import fft
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from .olaflow import OFModel
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parser = argparse.ArgumentParser(description="Convert swash output to olaFlow input")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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parser.add_argument("-c", "--config", default="config.ini")
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parser.add_argument("-o", "--output", type=pathlib.Path)
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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("sws_ola")
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log.info("Starting sws -> olaFlow converter")
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config = configparser.ConfigParser()
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config.read(args.config)
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level = config.getfloat("bathy", "level", fallback=0.0)
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sws_out = pathlib.Path(config.get("swash", "np_out"))
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def data(var):
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return np.load(sws_out.joinpath(f"{var}.npy"))
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x = data("x")
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t_ = data("t")
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vel = data("vel")
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watl = data("watl")
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x0 = config.getfloat("olaflow", "x0")
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arg_x0 = np.argmin(np.abs(x - x0))
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t0 = config.getfloat("olaflow", "t0")
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tf = config.getfloat("olaflow", "tf")
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arg_t0 = -np.argmax(t_[::-1] < (t0 * 1e3))
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arg_tf = np.argmax(t_ > (tf * 1e3)) + 1
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t = t_[arg_t0:arg_tf] * 1e-3
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t = t - t.min()
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wl = watl[arg_t0:arg_tf, arg_x0]
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v = vel[0, arg_t0:arg_tf, arg_x0]
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olaflow_root = args.output
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org = olaflow_root.joinpath("constant", "waveDict_paddle.org").read_text()
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org = org.replace("{n}", str(t.size))
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org = org.replace("{t}", "\n".join(t.astype(np.str_)))
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org = org.replace("{v}", "\n".join(v.astype(np.str_)))
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org = org.replace("{eta}", "\n".join(wl.astype(np.str_)))
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olaflow_root.joinpath("constant", "waveDict").write_text(org)
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fig, (ax, ax2) = plt.subplots(2)
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ax.plot(t, wl)
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ax.autoscale(True, "x", tight=True)
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ax.set(xlabel="t (s)", ylabel="z (m)")
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ax2.plot(t, v)
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ax2.autoscale(True, "x", tight=True)
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ax2.set(xlabel="t (s)", ylabel="U (m/s)")
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fig.savefig(olaflow_root.joinpath("constant", "wave.pdf"))
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