Outputs for article

data/processing/plot.py

@@ -0,0 +1,76 @@
+import argparse
+import configparser
+import logging
+import pathlib
+
+import numpy as np
+from scipy import interpolate
+
+import matplotlib.pyplot as plt
+
+from .lambert import Lambert
+
+parser = argparse.ArgumentParser(description="Pre-process bathymetry")
+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("bathy")
+
+log.info("Starting bathymetry pre-processing")
+config = configparser.ConfigParser()
+config.read(args.config)
+
+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))
+
+bathy[:, :2] = bathy[:, :2] - artha
+
+fig, ax = plt.subplots(figsize=(6 / 2.54, 5 / 2.54), constrained_layout=True, dpi=200)
+
+c = ax.tricontourf(
+    bathy[:, 0],
+    bathy[:, 1],
+    bathy[:, 2],
+    cmap="plasma",
+    levels=np.arange(-30, 10, 5),
+    extend="both",
+)
+ax.plot(*(np.stack((artha, buoy)) - artha).T, lw=1, ls="-.", c="k", marker="x")
+ax.set(xlim=(bathy[np.argmax(bathy[:, 1]), 0], bathy[np.argmin(bathy[:, 1]), 0]))
+ax.set(ylim=(bathy[np.argmin(bathy[:, 0]), 1], bathy[np.argmax(bathy[:, 0]), 1]))
+ax.set(xlabel="x (m)", ylabel="y (m)")
+fig.colorbar(c, label="z (m)")
+ax.set_aspect("equal")
+ax.set_rasterization_zorder(1.5)
+
+fig.savefig("bathy2d.pdf")
+plt.show()

data/processing/projection.py

@@ -135,7 +135,7 @@ fig, ax = plt.subplots(figsize=(16 / 2.54, 2 / 3 * 10 / 2.54), constrained_layou
 ax.plot(-x, z, color="k")
 ax.fill_between(-x, z + hstru, z, color="k", alpha=0.2)
 #ax.set_title(f"N={z.size-1}, x=[{-x.max()};{-x.min()}]")
-ax.set(ylim=(-30, 15))
+ax.set(ylim=(-40, 15))
 ax.set(xlabel="x (m)", ylabel="z (m)")
 ax.autoscale(True, "x", True)
 ax.grid()

data/processing/zero_cross.py

@@ -121,10 +121,10 @@ if bigw.size > 32:
     log.warning(f"Number of large waves: {bigw.size}")
     sys.exit()
 
-for w in bigw:
-    fig, (ax2, ax) = plt.subplots(2, figsize=(15/2.54, 2/3*10/2.54), constrained_layout=True)
-    i0 = cr0[w] - int(1200 / dt)
-    i1 = cr0[w + 2] + int(1200 / dt)
+fig, ax_ = plt.subplots(2 * (bigw.size // 2), 2, figsize=(15/2.54, 4/3*10/2.54), constrained_layout=True)
+for w, ax2, ax in zip(bigw, ax_[::2].flatten(), ax_[1::2].flatten()):
+    i0 = cr0[w] - int(400 / dt)
+    i1 = cr0[w + 2] + int(400 / dt)
     # a = [t0[i0], t0[i1], *Mlims]
     # c = ax2.imshow(M[:, i0:i1], extent=a, aspect="auto", cmap="Spectral", vmin=-v, vmax=+v)
     ws = np.ptp(raw_ts["z"][cr0[w]:cr0[w+2]]) * 1e-2
@@ -143,13 +143,15 @@ for w in bigw:
     #ax.plot(t[i0:i1], z[i0:i1], c="k", lw=1, alpha=0.2, ls="-.")
     # ax.vlines(t[raw_ts["state"] != 0], -20, 20, colors=c[np.where(st != 777, st, 0)])
     ax.set(xlim=(t[i0], t[i1 - 1]), ylim=(-ym, ym))
+    ax2.set(ylim=(2, 200))
     ax2.set(ylabel="T (s)")
     ax2.grid(c="k", alpha=0.2)
     ax2.semilogy()
     ax.grid(c="k", alpha=.2)
     #ax.axhline(0, c="k", alpha=0.2, lw=1, ls="-.")
     #ax.set(zorder=1, frame_on=False)
-    ax.set(xlabel="t (s)", ylabel="z (m)")
+    ax.set_xlabel("t (s)", loc="left")
+    ax.set_ylabel("z (m)")
     ax.axvspan(t[cr0[w]], t[cr0[w+2]], color="k", alpha=.1)
 
     locator = mdates.AutoDateLocator(minticks=3, maxticks=7)
@@ -162,8 +164,8 @@ for w in bigw:
 
     ax2.set_rasterization_zorder(1.5)
 
-    fig.savefig(out_root.joinpath(f"wavelet{w}.pdf"), dpi=300)
-    fig.savefig(out_root.joinpath(f"wavelet{w}.png"), dpi=200)
+fig.savefig(out_root.joinpath(f"wavelet.pdf"), dpi=300)
+fig.savefig(out_root.joinpath(f"wavelet.png"), dpi=200)
 
 #fig, ax = plt.subplots(constrained_layout=True)
 ## ax.plot(fft.rfftfreq(raw_ts["z"].size, dt), np.abs(fft.rfft(raw_ts["z"])), c="k", alpha=.2, lw=1)