Use high res smoothed bathymetry for breakwater
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7 changed files with 38 additions and 1 deletions
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@ -1,5 +1,7 @@
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[bathy]
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inp=data/Database_20220224.xyz
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hires=data/bathyhires.dat
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hires_step=0.5
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sub=out/bathy_sub.npy
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out=out/bathy.npy
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step=1
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0
data/data/Hstru.dat
Executable file → Normal file
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data/data/Hstru.dat
Executable file → Normal file
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data/data/Poro.dat
Executable file → Normal file
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data/data/Poro.dat
Executable file → Normal file
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data/data/Psize.dat
Executable file → Normal file
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data/data/Psize.dat
Executable file → Normal file
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data/data/bathyhires.dat
Executable file → Normal file
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data/data/bathyhires.dat
Executable file → Normal file
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data/data/buoyarthabathy.dat
Executable file → Normal file
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data/data/buoyarthabathy.dat
Executable file → Normal file
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@ -21,6 +21,7 @@ config = configparser.ConfigParser()
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config.read("config.ini")
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bathy_inp = pathlib.Path(config.get("bathy", "sub"))
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hires_inp = pathlib.Path(config.get("bathy", "hires"))
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bathy_out = pathlib.Path(config.get("bathy", "out"))
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log.info(f"Loading bathymetry from {bathy_inp}")
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@ -58,6 +59,40 @@ coords = artha + (x * np.stack((np.cos(theta), np.sin(theta)))).T
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log.info("Interpolating bathymetry in 1D")
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z = interpolate.griddata(bathy[:, :2], bathy[:, 2], coords)
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log.debug(z)
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log.debug(f"z: {z}")
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_hires = np.genfromtxt(hires_inp)[::-1]
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bathy_hires = np.stack(
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(
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np.linspace(
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0,
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(_hires.size - 1) * config.getfloat("bathy", "hires_step"),
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_hires.size,
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),
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_hires,
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),
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axis=1,
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)
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del _hires
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log.debug(f"Bathy hires: {bathy_hires}")
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z_cr = 5
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hires_crossing = np.diff(np.signbit(bathy_hires[:, 1] - z_cr)).nonzero()[0][-1]
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log.debug(f"Hires crossing: {hires_crossing}")
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z_crossing = np.diff(np.signbit(z - z_cr)).nonzero()[0][-1]
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log.debug(f"Z crossing: {z_crossing}")
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x_min_hires = x[z_crossing] + (
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bathy_hires[:, 0].min() - bathy_hires[hires_crossing, 0]
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)
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x_max_hires = x[z_crossing] + (
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bathy_hires[:, 0].max() - bathy_hires[hires_crossing, 0]
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)
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log.debug(f"Replacing range: [{x_min_hires},{x_max_hires}]")
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flt_x = (x > x_min_hires) & (x < x_max_hires)
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z[flt_x] = interpolate.griddata(
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(bathy_hires[:, 0],),
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bathy_hires[:, 1],
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(x[flt_x] - x[z_crossing] + bathy_hires[hires_crossing, 0]),
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)
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