Custom implementation of reflex3s (fft, working, with plotting)
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@ -0,0 +1,68 @@
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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.signal as sgl
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from .reflex3s import reflex3s
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from .pa.reflex3S import reflex3S
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from .pa.PUV import PUV_dam
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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("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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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 = np.diff(t).mean()
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f = 1 / dt
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idx = [arg_x0 - 5, arg_x0, arg_x0 + 7]
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wl = watl[arg_t0:, idx]
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res = reflex3S(*wl.T, *x[idx], botl[idx].mean(), f, 0.02, 0.2)
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f_, ai_, ar_ = reflex3s(wl.T, x[idx], botl[idx].mean(), f)
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ai, ar, _, _, _, _, fre = res
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out = pathlib.Path(config.get("post", "out")).joinpath(f"t{t0}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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plt.plot(fre, np.asarray(ar) / np.asarray(ai))
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plt.xlim((0, 0.3))
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plt.ylim((0, 1))
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plt.savefig(out.joinpath("reflex3s_pa.pdf"))
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plt.clf()
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plt.plot(f_, ar_ / ai_)
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plt.xlim((0, 0.3))
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plt.ylim((0, 1))
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plt.savefig(out.joinpath("reflex3s.pdf"))
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# pressk = np.load(inp.joinpath("pressk.npy"))
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# press = pressk[:, 1, :]
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# res = PUV_dam(vel[arg_t0:, 0, 500], press[arg_t0:, 500], botl[500], f, botl[500]/2, botl[500]/2)
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# print(res)
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@ -5,43 +5,41 @@ from scipy import optimize as opti
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def reflex3s(eta, x, h, f):
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#_welch = [sgl.welch(z, f) for z in eta]
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#eta_psd = np.stack([_w[1] for _w in _welch])
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#f_psd = _welch[0][0]
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eta_psd = np.stack([fft.rfft(z) for z in eta])
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f_psd = fft.rfftfreq(eta.shape[1])
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f_psd = fft.rfftfreq(eta.shape[1], 1 / f)
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eta_amp = np.abs(eta_psd) / eta_psd.shape[1]
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eta_amp = np.abs(eta_psd) / (f_psd.size - 1)
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eta_phase = np.angle(eta_psd)
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g = 9.81
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k = np.asarray([opti.root_scalar(
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k = np.asarray(
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[
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opti.root_scalar(
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f=lambda k: k * np.tanh(k) - (2 * np.pi * f) ** 2 / g * h,
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fprime=lambda k: np.tanh(k) + k * (1 - np.tanh(k)) ** 2,
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x0=0.2,
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).root for f in f_psd])
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dx = np.roll(x, 1) - x
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dphi = np.roll(eta_phase, 1, axis=0) - eta_phase
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ai = np.sqrt(
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eta_amp**2
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+ np.roll(eta_amp, 1, axis=0)**2
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- 2
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* eta_amp
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* np.roll(eta_amp, 1, axis=0)
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* np.cos(dphi - k * dx[:, None])
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/ (2 * np.abs(np.sin(k * dx[:, None])))
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)
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ar = np.sqrt(
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eta_amp**2
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+ np.roll(eta_amp, 1, axis=0)**2
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- 2
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* eta_amp
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* np.roll(eta_amp, 1, axis=0)
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* np.cos(dphi + k * dx[:, None])
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/ (2 * np.abs(np.sin(k * dx[:, None])))
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fprime=lambda k: np.tanh(k) + k * (1 - np.tanh(k) ** 2),
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x0=0.5,
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).root
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/ h
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for f in f_psd
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]
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)
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return f_psd, ar / ai
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s1 = 1 + np.exp(2j * k * (x[1] - x[0])) + np.exp(2j * k * (x[2] - x[0]))
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s2 = 1 + np.exp(-2j * k * (x[1] - x[0])) + np.exp(-2j * k * (x[2] - x[0]))
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s12 = 3
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s3 = (
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eta_amp[0] * np.exp(-1j * (eta_phase[0]))
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+ eta_amp[1] * np.exp(-1j * (eta_phase[1] - k * (x[1] - x[0])))
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+ eta_amp[2] * np.exp(-1j * (eta_phase[2] - k * (x[2] - x[0])))
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)
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s4 = (
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eta_amp[0] * np.exp(-1j * (eta_phase[0]))
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+ eta_amp[1] * np.exp(-1j * (eta_phase[1] + k * (x[1] - x[0])))
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+ eta_amp[2] * np.exp(-1j * (eta_phase[2] + k * (x[2] - x[0])))
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)
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s5 = s1 * s2 - s12**2
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ai = np.abs((s2 * s3 - s12 * s4) / s5)
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ar = np.abs((s1 * s4 - s12 * s3) / s5)
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return f_psd, ai, ar
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