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3 commits
c443947e3c
...
b2c4227f83
Author | SHA1 | Date | |
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b2c4227f83 | |||
14ef85246a | |||
34fcb4e879 |
6 changed files with 234 additions and 5 deletions
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@ -60,6 +60,12 @@ x = np.arange(
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)
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)
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theta = np.angle(D.dot((1, 1j)))
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theta = np.angle(D.dot((1, 1j)))
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log.info(f"N points: {bathy.size:e}")
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S = bathy[:, 0].ptp() * bathy[:, 1].ptp()
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log.info(f"Surface: {S*1e-6:.2f}km^2")
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res = np.sqrt(S / bathy.size)
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log.info(f"Resolution: {res:.2f}m")
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coords = artha + (x * np.stack((np.cos(theta), np.sin(theta)))).T
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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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log.info("Interpolating bathymetry in 1D")
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@ -78,6 +78,7 @@ dj = 0.5
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J = 1 / dj * np.log2(N * dt / s0)
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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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j = np.arange(0, J)
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sj = s0 * 2 ** (j * dj)
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sj = s0 * 2 ** (j * dj)
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Tj = 2 * sj * np.pi / 5
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# sj = s0 * np.arange(1, 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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Mw = sj / dt
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Mlims = sj[[0, -1]]
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Mlims = sj[[0, -1]]
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@ -99,7 +100,7 @@ ax.set(zorder=1, frame_on=False)
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ax.semilogy()
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ax.semilogy()
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a = [t0[0], t0[-1], *Mlims]
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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.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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c = ax.contourf(t, Tj, M, cmap="Greys", vmin=0, vmax=v)
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fig.colorbar(c)
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fig.colorbar(c)
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H13 = np.quantile(wave, 2 / 3)
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H13 = np.quantile(wave, 2 / 3)
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@ -131,7 +132,7 @@ for w, ax2, ax in zip(bigw, ax_[::2].flatten(), ax_[1::2].flatten()):
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log.info(f"Wave [{w}] size: {ws:.2f}m")
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log.info(f"Wave [{w}] size: {ws:.2f}m")
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c = ax2.contourf(
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c = ax2.contourf(
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t[i0:i1],
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t[i0:i1],
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sj,
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Tj,
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M[:, i0:i1],
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M[:, i0:i1],
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cmap="Greys",
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cmap="Greys",
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vmin=0,
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vmin=0,
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137
olaflow/processing/flow_velocity.py
Normal file
137
olaflow/processing/flow_velocity.py
Normal file
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@ -0,0 +1,137 @@
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import argparse
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import gzip
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from itertools import starmap
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import logging
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from multiprocessing import pool
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import pathlib
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import pickle
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import sys
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from cycler import cycler
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import matplotlib.pyplot as plt
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from matplotlib.ticker import MultipleLocator
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import numpy as np
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from scipy import interpolate
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from .olaflow import OFModel
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parser = argparse.ArgumentParser(description="Post-process olaflow results")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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parser.add_argument(
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"-o",
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"--output",
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type=pathlib.Path,
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help="Output directory for pickled data",
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required=True,
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)
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parser.add_argument(
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"-f",
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"--func",
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type=str,
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help="Post-process function to compare",
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default="graphUniform",
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choices=("graphUniform", "graphUniform2"),
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)
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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("ola_post")
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log.info("Plotting comparison of model output")
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def get_pickle(out):
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with (
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path.open("rb")
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if (path := out.joinpath("pickle")).exists()
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else gzip.open(path.with_suffix(".gz"), "rb")
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) as f:
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return pickle.load(f)
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model = get_pickle(args.output)
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figsize = 15 / 2.54, 6 / 2.54
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fig, ax_ = plt.subplots(
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2,
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figsize=figsize,
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dpi=200,
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constrained_layout=True,
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)
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_ax = ax_[0]
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v = np.nanmax(np.abs(np.where(
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model.post_fields[args.func]["alpha.water"] > 0.5,
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#np.linalg.norm(model.post_fields[args.func]["U"], axis=2),
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model.post_fields[args.func]["U"][..., 0],
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np.nan,
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)))
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v150 = np.nanmax(np.abs(np.where(
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(model.post_fields[args.func]["alpha.water"] > 0.5) & (model.t[:, None] > 170) & (model.t[:, None] < 200),
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#np.linalg.norm(model.post_fields[args.func]["U"], axis=2),
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model.post_fields[args.func]["U"][..., 0],
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np.nan,
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)))
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_data = model.post_fields[args.func]["U"][..., 0].T
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#_c = _ax.contourf(
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# model.t,
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# model.post_fields[args.func]["x_U"],
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# _data,
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# cmap="PiYG",
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# #levels=[-15, -10, -5, -2, -1, 0, 1, 2, 5, 10, 15],
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# vmin=-np.nanmax(np.abs(_data)),
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# vmax=np.nanmax(np.abs(_data)),
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# extend="both",
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#)
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_c = _ax.imshow(
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_data[::-1],
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cmap="PiYG",
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alpha=np.clip(model.post_fields[args.func]["alpha.water"], 0, 1).T[::-1],
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extent=(
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model.t.min(),
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model.t.max(),
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model.post_fields[args.func]["x_U"].min(),
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model.post_fields[args.func]["x_U"].max(),
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),
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vmin=-v150,
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vmax=v150,
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aspect="auto",
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)
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_ax.set(xlim=(0, 400))
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_ax.set(facecolor="k")
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_ax.xaxis.set_minor_locator(MultipleLocator(10))
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_ax.yaxis.set_minor_locator(MultipleLocator(1))
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_ax.set(ylabel="z (m)")
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_ax.axes.set_xticklabels([])
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fig.colorbar(_c, label=f"U (m/s)", ax=_ax)
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log.info(f"Vitesse max: {v}m/s")
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log.info(f"Vitesse max [170,200]: {v150}m/s")
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log.info(f"Écart: {abs(np.nanmax(_data)-17.7)/17.7:%}")
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x = model.post_fields[args.func]["x_U"]
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i0 = np.argmin(np.abs(x - 5))
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_data = model.post_fields[args.func]["U"][..., i0, 0]
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_alpha = model.post_fields[args.func]["alpha.water"][..., i0]
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ax = ax_[1]
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ax.fill_between(model.t, np.where(_alpha > 0.5, _data, 0), lw=1, color="#898989", edgecolor="k")
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#ax.autoscale(True, "x", True)
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ax.set(xlim=(0, 400))
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ax.set(xlabel="t (s)", ylabel="U (m/s)")
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ax.grid(c="k", alpha=.2)
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ax.xaxis.set_minor_locator(MultipleLocator(10))
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ax.yaxis.set_minor_locator(MultipleLocator(2))
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fig.savefig(
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args.output.joinpath(
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f"U_{args.func}.pdf"
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)
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)
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fig.savefig(
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args.output.joinpath(
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f"U_{args.func}.jpg"
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)
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)
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79
olaflow/processing/snap.py
Normal file
79
olaflow/processing/snap.py
Normal file
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@ -0,0 +1,79 @@
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import argparse
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import gzip
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import logging
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import multiprocessing as mp
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import pathlib
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import pickle
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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from matplotlib.gridspec import GridSpec
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from matplotlib.ticker import MultipleLocator
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import numpy as np
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from scipy import interpolate
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from .olaflow import OFModel
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parser = argparse.ArgumentParser(description="Post-process olaflow results")
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parser.add_argument("-v", "--verbose", action="count", default=0)
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parser.add_argument(
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"-o",
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"--output",
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type=pathlib.Path,
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help="Output directory for pickled data",
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required=True,
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)
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parser.add_argument(
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"-m",
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"--max",
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help="Only compute maximum rather than animation",
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action="store_true",
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)
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parser.add_argument(
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"-i",
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"--initial",
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help="Only compute initial domain",
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action="store_true",
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)
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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("ola_post")
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log.info("Animating olaFlow output")
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out = args.output
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out.mkdir(parents=True, exist_ok=True)
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with (
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path.open("rb")
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if (path := out.joinpath("pickle")).exists()
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else gzip.open(path.with_suffix(".gz"), "rb")
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) as f:
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model = pickle.load(f)
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x0, idx0 = np.unique(model.x.astype(np.half), return_inverse=True)
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z0, idz0 = np.unique(model.z.astype(np.half), return_inverse=True)
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ix0 = np.argsort(x0)
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iz0 = np.argsort(z0)[::-1]
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X, Z = np.meshgrid(x0, z0)
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P = np.full((model.t.size, *X.shape), np.nan)
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P[:, iz0[idz0], ix0[idx0]] = model.fields["porosity"]
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AW = np.full((model.t.size, *X.shape), np.nan)
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AW[:, iz0[idz0], ix0[idx0]] = model.fields["alpha.water"]
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#U = np.full((model.t.size, *X.shape), np.nan)
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#U[:, iz0[idz0], ix0[idx0]] = np.linalg.norm(model.fields["U"], axis=1)
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i0 = np.argmin(np.abs(model.t[:, None] - np.asarray((102, 118, 144.5, 176.5))[None, :]), axis=0)
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fig, ax_ = plt.subplots(
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2, 2, figsize=(15 / 2.54, 4 / 2.54), dpi=200, constrained_layout=True
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)
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for ax, i in zip(ax_.flatten(), i0):
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ax.imshow(AW[i], cmap="Blues", vmin=0, vmax=1)
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fig.savefig(out.joinpath("snap.pdf"))
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@ -4,6 +4,7 @@ import logging
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import pathlib
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import pathlib
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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from matplotlib.ticker import MultipleLocator, LogLocator, NullFormatter
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import numpy as np
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import numpy as np
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import scipy.signal as sgl
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import scipy.signal as sgl
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@ -49,12 +50,13 @@ J = 1 / dj * np.log2(N * dt / s0)
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j = np.arange(0, J)
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j = np.arange(0, J)
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sj = s0 * 2 ** (j * dj)
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sj = s0 * 2 ** (j * dj)
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Mw = sj / dt
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Mw = sj / dt
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sig = np.var(watl[:, i0])
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sig = np.std(watl[:, i0])
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M = np.stack([(np.abs(sgl.cwt(watl[:, i], sgl.morlet2, Mw))/sig)**2 for i in i0])
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M = np.stack([(np.abs(sgl.cwt(watl[:, i], sgl.morlet2, Mw))/sig)**2 for i in i0])
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v = np.max(M)
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v = np.max(M)
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T = 2 * sj * np.pi / 5
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for ax_x, M_, x_ in zip(ax.reshape(-1), M, x[i0]):
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for ax_x, M_, x_ in zip(ax.reshape(-1), M, x[i0]):
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c = ax_x.contourf(t, sj, M_, cmap="Greys", vmin=0, levels=[1, 2.5, 5, 10, 20, 40], extend="both")
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c = ax_x.contourf(t, T, M_, cmap="Greys", vmin=0, levels=[1, 2.5, 5, 10, 20, 40], extend="both")
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fig_x.colorbar(c, ax=ax_x, label="NWPS")
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fig_x.colorbar(c, ax=ax_x, label="NWPS")
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ax_x.grid(color="k", alpha=0.2)
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ax_x.grid(color="k", alpha=0.2)
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ax_x.text(
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ax_x.text(
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@ -71,6 +73,10 @@ for ax_x, M_, x_ in zip(ax.reshape(-1), M, x[i0]):
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ax_x.set_rasterization_zorder(1.5)
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ax_x.set_rasterization_zorder(1.5)
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ax_x.set(ylabel="T (s)", ylim=(sj[0], sj[-1]))
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ax_x.set(ylabel="T (s)", ylim=(sj[0], sj[-1]))
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ax_x.xaxis.set_minor_locator(MultipleLocator(100))
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ax_x.yaxis.set_major_locator(LogLocator(10, numticks=2**10))
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ax_x.yaxis.set_minor_locator(LogLocator(10, subs=np.arange(10), numticks=2**10))
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ax_x.yaxis.set_minor_formatter(NullFormatter())
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if ax_x != ax.reshape(-1)[-1]:
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if ax_x != ax.reshape(-1)[-1]:
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ax_x.axes.set_xticklabels([])
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ax_x.axes.set_xticklabels([])
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else:
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else:
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@ -76,7 +76,7 @@ ax.grid()
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fig.savefig(out.joinpath("wsize.pdf"))
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fig.savefig(out.joinpath("wsize.pdf"))
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fig2, ax2 = plt.subplots(
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fig2, ax2 = plt.subplots(
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figsize=(10 / 2.54, 2 / 3 * 10 / 2.54), constrained_layout=True
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figsize=(10 / 2.54, 4 / 2.54), constrained_layout=True
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)
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)
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ax2.plot(
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ax2.plot(
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t[cr0[i0 - 5] : cr0[i0 + 7]] * 1e-3,
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t[cr0[i0 - 5] : cr0[i0 + 7]] * 1e-3,
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||||||
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