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LFS wtf

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This commit is contained in:
Edgar P. Burkhart 2022-03-28 10:15:36 +02:00
parent 71049d49ea
commit ba675fb763
Signed by: edpibu
GPG key ID: 9833D3C5A25BD227
68 changed files with 5727 additions and 204 deletions
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4
data/.gitignore vendored
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oid sha256:17bc194f27793ae12ccd604a1f667d03cc6f9b07222f4e67d633b7296cc97e24
size 5
/out

29
data/config.ini
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version https://git-lfs.github.com/spec/v1
oid sha256:13605228ca2c48f7bc869db92b329f5395122662ced9a7ce930241b821a11f7a
size 300
[inp]
root=data
base=Database_20220224.xyz
hires=bathyhires.dat
hstru=Hstru.dat
poro=Poro.dat
psize=Psize.dat
hires_step=0.5
[out]
no_breakwater=True
root=out
sub=bathy_sub.npy
out=bathy.npy
step=1
left=0
right=150
#plot=True
[artha]
lat=43.398450
lon=-1.673097
[buoy]
lat=43.408333
lon=-1.681667

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data/data/Hstru.dat
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data/data/Poro.dat
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data/data/Psize.dat
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data/data/bathyhires.dat
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4
data/data/buoyarthabathy.dat
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103
data/processing/lambert.py
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version https://git-lfs.github.com/spec/v1
oid sha256:418acad81c40d8d5dcde803335a86309b51121bab736b553a60c0735ff98cc19
size 2601
import numpy as np
class Lambert:
def __init__(
self,
X0=1.7e6,
Y0=2.2e6,
lambda0=3,
phi0=43,
phi1=42.25,
phi2=43.75,
a=6378137,
e=0.081819190842622,
):
self._X0 = X0
self._Y0 = Y0
self._lambda0 = np.radians(lambda0)
self._phi0 = np.radians(phi0)
self._phi1 = np.radians(phi1)
self._phi2 = np.radians(phi2)
self._a = a
self._e = e
self._set_n()
self._set_rho0()
def cartesian(self, lam, phi):
lam = np.radians(lam)
phi = np.radians(phi)
theta = self._n * (lam - self._lambda0)
rho = self._rho(phi)
rho0 = self._rho(self._phi0)
X = self._X0 + rho * np.sin(theta)
Y = self._Y0 + rho0 - rho * np.cos(theta)
return X, Y
def _set_n(self):
self._n = (
np.log(np.cos(self._phi2) / np.cos(self._phi1))
+ (
np.log(
(1 - (self._e * np.sin(self._phi1) ** 2))
/ (1 - (self._e * np.sin(self._phi2) ** 2))
)
/ 2
)
) / (
np.log(
(
np.tan(self._phi1 / 2 + np.pi / 4)
* (
(1 - self._e * np.sin(self._phi2))
* (1 + self._e * np.sin(self._phi1))
)
** (self._e / 2)
)
/ (
np.tan(self._phi2 / 2 + np.pi / 4)
* (
(1 + self._e * np.sin(self._phi2))
* (1 - self._e * np.sin(self._phi1))
)
** (self._e / 2)
)
)
)
def _set_rho0(self):
self._rho0 = (
self._a
* np.cos(self._phi1)
/ (self._n * np.sqrt(1 - self._e**2 * np.sin(self._phi1) ** 2))
* (
np.tan(self._phi1 / 2 + np.pi / 4)
* (
(1 - self._e * np.sin(self._phi1))
/ (1 + self._e * np.sin(self._phi1))
)
** (self._e / 2)
)
** self._n
)
def _rho(self, phi):
return (
self._rho0
* (
1
/ np.tan(phi / 2 + np.pi / 4)
* ((1 + self._e * np.sin(phi)) / (1 - self._e * np.sin(phi)))
** (self._e / 2)
)
** self._n
)

147
data/processing/projection.py
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@ -1,3 +1,144 @@
version https://git-lfs.github.com/spec/v1
oid sha256:45448faf5475a6aac61efe9b3f69e20a7e44bdcb7e493d7e8d7c8d1cb7eac5fd
size 4370
import argparse
import configparser
import logging
import pathlib
import numpy as np
from scipy import interpolate
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
from .lambert import Lambert
parser = argparse.ArgumentParser(description="Pre-process bathymetry")
parser.add_argument("-v", "--verbose", action="count", default=0)
args = parser.parse_args()
logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
log = logging.getLogger("bathy")
log.info("Starting bathymetry pre-processing")
config = configparser.ConfigParser()
config.read("config.ini")
inp_root = pathlib.Path(config.get("inp", "root"))
out_root = pathlib.Path(config.get("out", "root"))
bathy_inp = out_root.joinpath(config.get("out", "sub"))
hires_inp = inp_root.joinpath(config.get("inp", "hires"))
hstru_inp = inp_root.joinpath(config.get("inp", "hstru"))
poro_inp = inp_root.joinpath(config.get("inp", "poro"))
psize_inp = inp_root.joinpath(config.get("inp", "psize"))
bathy_out = inp_root.joinpath(config.get("out", "out"))
log.info(f"Loading bathymetry from {bathy_inp}")
bathy_curvi = np.load(bathy_inp)
projection = Lambert()
bathy = np.stack(
(
*projection.cartesian(bathy_curvi[:, 0], bathy_curvi[:, 1]),
bathy_curvi[:, 2],
),
axis=1,
)
log.debug(f"Cartesian bathy: {bathy}")
artha_curvi = np.array(
(config.getfloat("artha", "lon"), config.getfloat("artha", "lat"))
)
buoy_curvi = np.array(
(config.getfloat("buoy", "lon"), config.getfloat("buoy", "lat"))
)
artha = np.asarray(projection.cartesian(*artha_curvi))
buoy = np.asarray(projection.cartesian(*buoy_curvi))
D = np.diff(np.stack((artha, buoy)), axis=0)
x = np.arange(
config.getfloat("out", "left", fallback=0),
np.sqrt((D**2).sum()) + config.getfloat("out", "right", fallback=0),
config.getfloat("out", "step", fallback=1),
)
theta = np.angle(D.dot((1, 1j)))
coords = artha + (x * np.stack((np.cos(theta), np.sin(theta)))).T
log.info("Interpolating bathymetry in 1D")
z = interpolate.griddata(bathy[:, :2], bathy[:, 2], coords)
log.debug(f"z: {z}")
_hires = np.loadtxt(hires_inp)[::-1]
bathy_hires = np.stack(
(
np.linspace(
0,
(_hires.size - 1) * config.getfloat("inp", "hires_step"),
_hires.size,
),
_hires,
),
axis=1,
)
del _hires
log.debug(f"Bathy hires: {bathy_hires}")
z_cr = 5
hires_crossing = np.diff(np.signbit(bathy_hires[:, 1] - z_cr)).nonzero()[0][-1]
log.debug(f"Hires crossing: {hires_crossing}")
z_crossing = np.diff(np.signbit(z - z_cr)).nonzero()[0][-1]
log.debug(f"Z crossing: {z_crossing}")
x_min_hires = x[z_crossing] + (
bathy_hires[:, 0].min() - bathy_hires[hires_crossing, 0]
)
x_max_hires = x[z_crossing] + (
bathy_hires[:, 0].max() - bathy_hires[hires_crossing, 0]
)
log.debug(f"Replacing range: [{x_min_hires},{x_max_hires}]")
flt_x = (x > x_min_hires) & (x < x_max_hires)
hstru = np.zeros(z.shape)
poro = np.zeros(z.shape)
psize = np.zeros(z.shape)
if config.getboolean("out", "no_breakwater", fallback=False):
z[flt_x] = z[flt_x][-1]
else:
z[flt_x] = interpolate.griddata(
(bathy_hires[:, 0],),
bathy_hires[:, 1],
(x[flt_x] - x[z_crossing] + bathy_hires[hires_crossing, 0]),
)
hstru_in = np.loadtxt(hstru_inp)[::-1]
hstru[flt_x] = interpolate.griddata(
(bathy_hires[:,0],),
hstru_in,
(x[flt_x] - x[z_crossing] + bathy_hires[hires_crossing, 0]),
)
poro_in = np.loadtxt(poro_inp)[::-1]
poro[flt_x] = interpolate.griddata(
(bathy_hires[:,0],),
poro_in,
(x[flt_x] - x[z_crossing] + bathy_hires[hires_crossing, 0]),
)
psize_in = np.loadtxt(psize_inp)[::-1]
psize[flt_x] = interpolate.griddata(
(bathy_hires[:,0],),
psize_in,
(x[flt_x] - x[z_crossing] + bathy_hires[hires_crossing, 0]),
)
np.savetxt(out_root.joinpath("bathy.dat"), z[::-1], newline=" ")
np.savetxt(out_root.joinpath("hstru.dat"), hstru[::-1], newline=" ")
np.savetxt(out_root.joinpath("poro.dat"), poro[::-1], newline=" ")
np.savetxt(out_root.joinpath("psize.dat"), psize[::-1], newline=" ")
if plt is not None and config.getboolean("out", "plot", fallback=False):
fig, ax = plt.subplots()
ax.plot(-x, z, color="k")
ax.fill_between(-x, z+hstru, z, color="k", alpha=.2)
plt.show(block=True)

56
data/processing/subdomain.py
View file

@ -1,3 +1,53 @@
version https://git-lfs.github.com/spec/v1
oid sha256:cee15595a8dac4e684da41c7b1cb9b80bd091a6879407ad516114d46addd0af0
size 1435
import argparse
import configparser
import logging
import pathlib
import numpy as np
parser = argparse.ArgumentParser(description="Pre-process bathymetry")
parser.add_argument("-v", "--verbose", action="count", default=0)
args = parser.parse_args()
logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
log = logging.getLogger("bathy")
log.info("Starting bathymetry pre-processing")
config = configparser.ConfigParser()
config.read("config.ini")
artha = np.array(
(config.getfloat("artha", "lon"), config.getfloat("artha", "lat"))
)
buoy = np.array(
(config.getfloat("buoy", "lon"), config.getfloat("buoy", "lat"))
)
log.debug(f"artha: {artha}")
log.debug(f"buoy: {buoy}")
domain = np.stack((artha, buoy))
domain.sort(axis=0)
log.debug(f"domain: {domain}")
domain[0] -= 0.002
domain[1] += 0.002
log.debug(f"domain: {domain}")
inp_root = pathlib.Path(config.get("inp", "root"))
out_root = pathlib.Path(config.get("out", "root"))
bathy_inp = inp_root.joinpath(config.get("inp", "base"))
bathy_out = out_root.joinpath(config.get("out", "sub"))
log.info(f"Reading bathymetry from '{bathy_inp}'")
raw_bathy = np.loadtxt(bathy_inp)
log.debug(f"Initial size: {raw_bathy.shape}")
bathy = raw_bathy[
((raw_bathy[:, :2] > domain[0]) & (raw_bathy[:, :2] < domain[1])).all(
axis=1
)
]
del raw_bathy
log.debug(f"Final size: {bathy.shape}")
log.info(f"Saving subdomain to 'bathy'")
np.save(bathy_out, bathy)