171 lines
5.3 KiB
Python
171 lines
5.3 KiB
Python
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 sys
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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import scipy.signal as sgl
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from scipy import fft
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parser = argparse.ArgumentParser(description="Pre-process time-series")
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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()
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log = logging.getLogger("bathy")
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log.setLevel(max((10, 20 - 10 * args.verbose)))
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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"), "cerema/raw")
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out_root = pathlib.Path(config.get("out", "root"))
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out_root.mkdir(exist_ok=True)
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raw_ts = []
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#for tsi in sorted(inp_root.glob("2017022817*.raw")):
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for tsi in sorted(inp_root.glob("*.raw")):
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raw_ts.append(
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np.loadtxt(
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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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log.debug(f"Loading <{tsi}>")
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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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t0 = np.linspace(0, 30 * 60 * n, 2304 * n, endpoint=False)
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t = (t0 * 1e3).astype("timedelta64[ms]") + np.datetime64("2017-02-28T00:00")
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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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# log.debug(f"{t[raw_ts['state'] != 0]}")
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sos = sgl.butter(1, 0.2, btype="lowpass", fs=2305 / (30 * 60), output="sos")
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z = sgl.sosfiltfilt(sos, raw_ts["z"]*1e-2)
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cr0 = np.where(np.diff(np.sign(z)))[0]
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wave = np.fromiter(
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(
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np.max(np.abs(z[cr0[i - 1] : cr0[i]])) + np.max(np.abs(z[cr0[i] : cr0[i + 1]]))
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for i in range(1, len(cr0) - 1)
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),
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dtype=np.single,
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)
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log.debug(f"{wave=}")
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log.debug(f"{t=}")
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# plt.plot(t[cr0[1:-1]], wave)
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dt = 30 * 60 / 2304
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# Mlims = (int(5 / dt), int(30 / dt))
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N = t.size // 24
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s0 = 2 * dt
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dj = 0.5
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J = 1 / dj * np.log2(N * dt / s0)
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j = np.arange(0, J)
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sj = s0 * 2 ** (j * dj)
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# sj = s0 * np.arange(1, 2 ** (J * dj))
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Mw = sj / dt
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Mlims = sj[[0, -1]]
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M = (np.abs(sgl.cwt(raw_ts["z"]*1e-2, sgl.morlet2, Mw))/np.var(raw_ts["z"]*1e-2))**2
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# M = np.abs(sgl.cwt(z, sgl.morlet, Mw))
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v = np.max(np.abs(M))
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fig, ax = plt.subplots()
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# ax2 = ax.twinx()
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# ax.plot(t0, raw_ts["z"], lw=.5, c="k", alpha=.2)
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# ax.plot(t0, z, ls="-.", lw=.25, alpha=.2, c="k")
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st = raw_ts["state"][raw_ts["state"] != 0]
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c = np.asarray(["g", "b", "r"])
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# ax.vlines(t0[raw_ts["state"] != 0], -20, 20, colors=c[np.where(st != 777, st, 0)])
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# ax.set(xlabel="t (s)", ylabel="z (cm)")
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# ax.set(xlim=(17 * 3600 + 20 * 60, 17 * 3600 + 30 * 60))
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ax.grid(c="k", alpha=0.2)
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ax.set(zorder=1, frame_on=False)
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ax.semilogy()
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a = [t0[0], t0[-1], *Mlims]
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# c = ax.imshow(M, extent=a, aspect="auto", cmap="plasma", vmin=0)
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c = ax.contourf(t, sj, M, cmap="Greys", vmin=0, vmax=v)
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fig.colorbar(c)
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H13 = np.quantile(wave, 2 / 3)
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th = 10
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log.info(f"Threshold: {th}m")
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bigw = np.where(wave > th)[0]
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ym = 1.1 * np.max(np.abs(z))
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nw = wave.size / 2
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nlw = bigw.size
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log.info(f"Number of waves: {nw}")
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log.info(f"Number of waves >m: {nlw}")
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log.info(f"Proportion: {nlw/nw:e}")
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log.info(f"H1/3: {H13}m")
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if bigw.size > 32:
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log.warning(f"Number of large waves: {bigw.size}")
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sys.exit()
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for w in bigw:
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fig, (ax2, ax) = plt.subplots(2, figsize=(15/2.54, 2/3*10/2.54), constrained_layout=True)
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i0 = cr0[w] - int(1200 / dt)
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i1 = cr0[w + 2] + int(1200 / dt)
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# a = [t0[i0], t0[i1], *Mlims]
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# c = ax2.imshow(M[:, i0:i1], extent=a, aspect="auto", cmap="Spectral", vmin=-v, vmax=+v)
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c = ax2.contourf(
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t[i0:i1],
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sj,
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M[:, i0:i1],
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cmap="Greys",
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vmin=0,
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levels=[1, 2.5, 5, 10, 20, 40],
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extend="both",
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)
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fig.colorbar(c, ax=ax2, label="NWPS")
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ax.plot(t[i0:i1], (raw_ts["z"]*1e-2)[i0:i1], c="k", lw=1)
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#ax.plot(t[i0:i1], z[i0:i1], c="k", lw=1, alpha=0.2, ls="-.")
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# ax.vlines(t[raw_ts["state"] != 0], -20, 20, colors=c[np.where(st != 777, st, 0)])
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ax.set(xlim=(t[i0], t[i1 - 1]), ylim=(-ym, ym))
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ax2.set(ylabel="T (s)")
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ax2.grid(c="k", alpha=0.2)
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ax2.semilogy()
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ax.grid(c="k", alpha=.2)
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#ax.axhline(0, c="k", alpha=0.2, lw=1, ls="-.")
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#ax.set(zorder=1, frame_on=False)
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ax.set(xlabel="t (s)", ylabel="z (m)")
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ax.axvspan(t[cr0[w]], t[cr0[w+2]], color="k", alpha=.1)
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locator = mdates.AutoDateLocator(minticks=3, maxticks=7)
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formatter = mdates.ConciseDateFormatter(locator)
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ax.xaxis.set_major_locator(locator)
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ax.xaxis.set_major_formatter(formatter)
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ax2.xaxis.set_major_locator(locator)
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ax2.xaxis.set_major_formatter(formatter)
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ax2.axes.set_xticklabels([])
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ax2.set_rasterization_zorder(1.5)
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fig.savefig(out_root.joinpath(f"wavelet{w}.pdf"), dpi=300)
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fig.savefig(out_root.joinpath(f"wavelet{w}.png"), dpi=200)
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#fig, ax = plt.subplots(constrained_layout=True)
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## ax.plot(fft.rfftfreq(raw_ts["z"].size, dt), np.abs(fft.rfft(raw_ts["z"])), c="k", alpha=.2, lw=1)
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#ax.plot(*sgl.welch(raw_ts["z"], 1 / dt), c="k", alpha=0.2, label="PSD")
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#ax.plot(1 / sj, N * np.mean(M, axis=1), c="k", label="CWT")
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## ax.grid(color="k", alpha=.2)
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#ax.set(xlabel="T (s)", ylabel="PSD")
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## ax2.set(ylabel="Average Wavelet Transform")
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#ax.set(xlim=1 / Mlims)
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#ax.legend()
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plt.show()
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