Merge branch 'master' of ssh://git.edgarpierre.fr:39529/m2cce/internship
This commit is contained in:
commit
07378a3127
5 changed files with 150 additions and 3 deletions
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@ -5,7 +5,7 @@ hires=bathyhires.dat
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hstru=Hstru.dat
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poro=Poro.dat
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psize=Psize.dat
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raw_ts=201702281700.raw,201702281730.raw
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raw_ts=cerema/raw/201702281700.raw,cerema/raw/201702281730.raw
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hires_step=0.5
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[out]
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1
data/data/.gitignore
vendored
1
data/data/.gitignore
vendored
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@ -1,2 +1,3 @@
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*.xyz
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*.raw
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cerema
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97
data/processing/orbitals.py
Normal file
97
data/processing/orbitals.py
Normal file
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@ -0,0 +1,97 @@
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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="Plot orbitals")
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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("bathy")
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log.info("Starting time-series pre-processing")
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config = configparser.ConfigParser()
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config.read(args.config)
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inp_root = pathlib.Path(config.get("inp", "root"))
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out_root = pathlib.Path(config.get("out", "root"))
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out_ts = out_root.joinpath("ts.dat")
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raw_ts = []
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for tsi in config.get("inp", "raw_ts").split(","):
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raw_ts.append(
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np.loadtxt(
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inp_root.joinpath(tsi),
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dtype=[("state", int), ("z", float), ("y", float), ("x", float)],
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delimiter=",",
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max_rows=2304,
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)
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)
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n = len(raw_ts)
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raw_ts = np.concatenate(raw_ts)
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log.debug(f"{raw_ts=}")
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if (errs := np.count_nonzero(raw_ts["state"])) != 0:
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log.warning(f"{errs} transmission errors!")
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log.debug(f"{dict(zip(*np.unique(raw_ts['state'], return_counts=True)))}")
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t = np.linspace(0, 30 * 60 * n, 2304 * n + 1)[:-1]
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log.debug(f"{t=}")
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flt = (t > 1370) & (t < 1405)
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figt, axt = plt.subplots(3)
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axt[0].plot(t, raw_ts["x"])
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axt[1].plot(t, raw_ts["y"])
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axt[2].plot(t, raw_ts["z"])
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for ax in axt:
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ax.axvline(t[flt].min(), c="k")
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ax.axvline(t[flt].max(), c="k")
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ax.grid()
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ax.set(xlim=(t.min(), t.max()))
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ts_flt = raw_ts[flt]
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z0 = ts_flt["z"]
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figtz, axtz = plt.subplots(3)
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axtz[0].plot(t[flt], ts_flt["x"])
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axtz[1].plot(t[flt], ts_flt["y"])
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axtz[2].plot(t[flt], z0)
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for ax in axtz:
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ax.grid()
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ax.set(xlim=(t[flt].min(), t[flt].max()))
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fig3d = plt.figure()
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ax3d = fig3d.add_subplot(projection="3d")
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ax3d.plot(ts_flt["x"], ts_flt["y"], z0, c="#0066ff")
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ax3d.quiver3D(
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ts_flt["x"][:-1],
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ts_flt["y"][:-1],
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z0[:-1],
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np.diff(ts_flt["x"])[:],
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np.diff(ts_flt["y"])[:],
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np.diff(z0)[:],
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color="#0066ff",
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)
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ax3d.set(xlabel="x (cm)", ylabel="y (cm)", zlabel="z (cm)")
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theta = np.angle(raw_ts["x"] + 1j * raw_ts["y"]).mean()
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fig2dv, ax2dv = plt.subplots()
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x0 = ts_flt["x"] * np.cos(theta) + ts_flt["y"] * np.sin(theta)
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ax2dv.plot(x0, z0, c="#0066ff", lw=1)
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ax2dv.quiver(
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x0[:-1],
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z0[:-1],
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np.diff(x0)[:],
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np.diff(z0)[:],
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color="#0066ff",
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)
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ax2dv.grid()
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ax2dv.set(aspect="equal")
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plt.show()
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49
swash/processing/r_test.py
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49
swash/processing/r_test.py
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@ -0,0 +1,49 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from numpy import random
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import scipy.signal as sgl
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yi = random.normal(size=2**20)
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yr = np.roll(yi, -(2**10))
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figy, axy = plt.subplots()
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axy.plot(np.arange(2**10, 2**11), yi[2**10 : 2**11])
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axy.plot(np.arange(2**10, 2**11), yr[2**10 : 2**11])
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figf, axf = plt.subplots()
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axf.plot(*sgl.welch(yi))
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axf.plot(*sgl.welch(yr))
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eta = lambda r: yi + r * yr
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u = lambda r: -yi + r * yr
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def puv(eta, u):
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f, phi_eta = sgl.welch(eta)
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phi_u = sgl.welch(u)[1]
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phi_eta_u = np.abs(sgl.csd(eta, u)[1].real)
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return f, np.sqrt(
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(phi_eta + phi_u - 2 * phi_eta_u) / (phi_eta + phi_u + 2 * phi_eta_u)
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)
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figr, axr = plt.subplots()
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for r in np.arange(0, 1.1, 0.1):
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axr.plot(*puv(eta(r), u(r)), c="k")
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Rn = puv(
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eta(r) + 0.4 * random.normal(size=2**20),
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u(r) + 0.4 * random.normal(size=2**20),
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)
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axr.plot(
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*Rn,
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c="#ff6600",
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)
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axr.annotate(f"{r=:.1f}", (Rn[0][0], Rn[1][0]), bbox={"boxstyle": "square", "facecolor": "w"})
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axr.grid()
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axr.autoscale(True, "x", tight=True)
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axr.set(ylim=(0, 1), ylabel="R", xlabel="f")
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axr.legend(("No noise", "40% noise"), loc="lower left")
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plt.show()
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4
tasks.md
4
tasks.md
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@ -1,10 +1,10 @@
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Vérification méthodes calcul réflection avec données forcées en Python
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* Vérification méthodes calcul réflection avec données forcées en Python
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Vérification vague incidente possible avec digue et sans digue sur temps long (4h) avec spectre Jonswap
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Tracer trajectoires bouées (3d, 2d) autour de vague 15m
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* Tracer trajectoires bouées (3d, 2d) autour de vague 15m
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Swash output into binary matlab files -> input with scipy.io.matlab
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