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Custom implementation of reflex3s (fft, working, with plotting)

This commit is contained in:
Edgar P. Burkhart 2022-04-01 15:50:18 +02:00
parent 0cb87c5032
commit 4f7780c6ff
Signed by: edpibu
GPG key ID: 9833D3C5A25BD227
2 changed files with 99 additions and 33 deletions

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@ -0,0 +1,68 @@
import argparse
import configparser
import logging
import pathlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as sgl
from .reflex3s import reflex3s
from .pa.reflex3S import reflex3S
from .pa.PUV import PUV_dam
parser = argparse.ArgumentParser(description="Post-process swash output")
parser.add_argument("-v", "--verbose", action="count", default=0)
parser.add_argument("-c", "--config", default="config.ini")
args = parser.parse_args()
logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
log = logging.getLogger("post")
log.info("Starting post-processing")
config = configparser.ConfigParser()
config.read(args.config)
inp = pathlib.Path(config.get("post", "inp"))
root = pathlib.Path(config.get("swash", "out"))
log.info(f"Reading data from '{inp}'")
x = np.load(inp.joinpath("xp.npy"))
t = np.load(inp.joinpath("tsec.npy"))
botl = np.load(inp.joinpath("botl.npy"))
watl = np.load(inp.joinpath("watl.npy"))
vel = np.load(inp.joinpath("vel.npy"))
x0 = config.getint("post", "x0")
arg_x0 = np.abs(x - x0).argmin()
t0 = config.getfloat("post", "t0")
arg_t0 = np.abs(t - t0).argmin()
dt = np.diff(t).mean()
f = 1 / dt
idx = [arg_x0 - 5, arg_x0, arg_x0 + 7]
wl = watl[arg_t0:, idx]
res = reflex3S(*wl.T, *x[idx], botl[idx].mean(), f, 0.02, 0.2)
f_, ai_, ar_ = reflex3s(wl.T, x[idx], botl[idx].mean(), f)
ai, ar, _, _, _, _, fre = res
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)
plt.plot(fre, np.asarray(ar) / np.asarray(ai))
plt.xlim((0, 0.3))
plt.ylim((0, 1))
plt.savefig(out.joinpath("reflex3s_pa.pdf"))
plt.clf()
plt.plot(f_, ar_ / ai_)
plt.xlim((0, 0.3))
plt.ylim((0, 1))
plt.savefig(out.joinpath("reflex3s.pdf"))
# pressk = np.load(inp.joinpath("pressk.npy"))
# press = pressk[:, 1, :]
# res = PUV_dam(vel[arg_t0:, 0, 500], press[arg_t0:, 500], botl[500], f, botl[500]/2, botl[500]/2)
# print(res)

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@ -5,43 +5,41 @@ from scipy import optimize as opti
def reflex3s(eta, x, h, f): def reflex3s(eta, x, h, f):
#_welch = [sgl.welch(z, f) for z in eta]
#eta_psd = np.stack([_w[1] for _w in _welch])
#f_psd = _welch[0][0]
eta_psd = np.stack([fft.rfft(z) for z in eta]) eta_psd = np.stack([fft.rfft(z) for z in eta])
f_psd = fft.rfftfreq(eta.shape[1]) f_psd = fft.rfftfreq(eta.shape[1], 1 / f)
eta_amp = np.abs(eta_psd) / eta_psd.shape[1] eta_amp = np.abs(eta_psd) / (f_psd.size - 1)
eta_phase = np.angle(eta_psd) eta_phase = np.angle(eta_psd)
g = 9.81 g = 9.81
k = np.asarray([opti.root_scalar( k = np.asarray(
[
opti.root_scalar(
f=lambda k: k * np.tanh(k) - (2 * np.pi * f) ** 2 / g * h, f=lambda k: k * np.tanh(k) - (2 * np.pi * f) ** 2 / g * h,
fprime=lambda k: np.tanh(k) + k * (1 - np.tanh(k)) ** 2, fprime=lambda k: np.tanh(k) + k * (1 - np.tanh(k) ** 2),
x0=0.2, x0=0.5,
).root for f in f_psd]) ).root
/ h
dx = np.roll(x, 1) - x for f in f_psd
dphi = np.roll(eta_phase, 1, axis=0) - eta_phase ]
ai = np.sqrt(
eta_amp**2
+ np.roll(eta_amp, 1, axis=0)**2
- 2
* eta_amp
* np.roll(eta_amp, 1, axis=0)
* np.cos(dphi - k * dx[:, None])
/ (2 * np.abs(np.sin(k * dx[:, None])))
)
ar = np.sqrt(
eta_amp**2
+ np.roll(eta_amp, 1, axis=0)**2
- 2
* eta_amp
* np.roll(eta_amp, 1, axis=0)
* np.cos(dphi + k * dx[:, None])
/ (2 * np.abs(np.sin(k * dx[:, None])))
) )
return f_psd, ar / ai s1 = 1 + np.exp(2j * k * (x[1] - x[0])) + np.exp(2j * k * (x[2] - x[0]))
s2 = 1 + np.exp(-2j * k * (x[1] - x[0])) + np.exp(-2j * k * (x[2] - x[0]))
s12 = 3
s3 = (
eta_amp[0] * np.exp(-1j * (eta_phase[0]))
+ eta_amp[1] * np.exp(-1j * (eta_phase[1] - k * (x[1] - x[0])))
+ eta_amp[2] * np.exp(-1j * (eta_phase[2] - k * (x[2] - x[0])))
)
s4 = (
eta_amp[0] * np.exp(-1j * (eta_phase[0]))
+ eta_amp[1] * np.exp(-1j * (eta_phase[1] + k * (x[1] - x[0])))
+ eta_amp[2] * np.exp(-1j * (eta_phase[2] + k * (x[2] - x[0])))
)
s5 = s1 * s2 - s12**2
ai = np.abs((s2 * s3 - s12 * s4) / s5)
ar = np.abs((s1 * s4 - s12 * s3) / s5)
return f_psd, ai, ar