61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
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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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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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parser.add_argument("-c", "--config", default="config.ini")
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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(args.config)
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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("x.npy"))
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t = np.load(inp.joinpath("t.npy"))
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watl = np.load(inp.joinpath("watl.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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w0 = watl[:, arg_x0]
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cr0 = np.where(np.diff(np.sign(w0)))[0]
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wave = np.fromiter(
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(
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np.abs(
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np.max(np.abs(w0[cr0[i - 1] : cr0[i]]))
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+ np.max(np.abs(w0[cr0[i] : cr0[i + 1]]))
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)
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for i in range(1, len(cr0) - 1)
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),
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dtype=np.single,
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)
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i0 = np.argmax(wave)
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out = pathlib.Path(config.get("post", "out")).joinpath(f"x{x0}")
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log.info(f"Saving plots in '{out}'")
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out.mkdir(parents=True, exist_ok=True)
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fig, ax = plt.subplots()
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ax.plot(t[cr0[1:-1]] * 1e-3, wave)
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fig.savefig(out.joinpath("wsize.pdf"))
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fig2, ax2 = plt.subplots()
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ax2.plot(t[cr0[i0 - 5] : cr0[i0 + 7]], w0[cr0[i0 - 5] : cr0[i0 + 7]])
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fig2.savefig(out.joinpath("maxw.pdf"))
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