105 lines
2.8 KiB
Python
105 lines
2.8 KiB
Python
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import argparse
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import configparser
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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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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("-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("ola_post")
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log.info("Animating olaFlow output")
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config = configparser.ConfigParser()
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config.read(args.config)
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out = pathlib.Path(config.get("post", "out"))
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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 = config.getfloat("post", "x")
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z0 = config.getfloat("post", "z")
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flt = np.where((model.x >= -60) & (model.x <= -20) & (model.z >= 0) & (model.z <= 10))[
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0
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]
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x0, idx0 = np.unique(model.x[flt].astype(np.half), return_inverse=True)
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z0, idz0 = np.unique(model.z[flt].astype(np.half), return_inverse=True)
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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[:, idz0, idx0] = model.fields["porosity"][:, flt]
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AW = np.full((model.t.size, *X.shape), np.nan)
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AW[:, idz0, idx0] = model.fields["alpha.water"][:, flt]
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watl = z0[np.argmax((AW > 0.5)[:, ::-1, :], axis=1)]
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U = np.full((model.t.size, 2, *X.shape), np.nan)
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UU = np.full((model.t.size, *X.shape), np.nan)
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U[..., idz0, idx0] = model.fields["U"][..., flt][:, (0, 2)]
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UU[..., idz0, idx0] = np.linalg.norm(model.fields["U"][..., flt], axis=1)
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figU = plt.figure(figsize=(19.2, 10.8), dpi=100)
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gsU = GridSpec(2, 1, figure=figU, height_ratios=[1, 0.05])
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axU = figU.add_subplot(gsU[0])
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caxu1 = figU.add_subplot(gsU[1])
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# caxu2 = figU.add_subplot(gsU[2])
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alp = np.clip(np.nan_to_num(AW), 0, 1)
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axU.pcolormesh(X, Z, P[1], vmin=0, vmax=1, cmap="Greys_r")
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u_m = axU.quiver(
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X,
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Z,
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*U[0],
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UU[0],
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alpha=alp[0],
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cmap="spring",
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clim=(0, np.nanquantile(UU, 0.99)),
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)
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# (wat_p,) = axU.plot(x0, watl[0])
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axU.set(xlabel="x (m)", ylabel="z (m)", aspect="equal", facecolor="#bebebe")
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axU.grid(c="k", alpha=0.2)
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titU = axU.text(
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0.5,
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0.95,
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f"t={model.t[0]}s",
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horizontalalignment="center",
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verticalalignment="top",
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transform=axU.transAxes,
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)
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figU.colorbar(u_m, label=r"$U$", cax=caxu1, shrink=0.6, orientation="horizontal")
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def animU(i):
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titU.set_text(f"t={model.t[i]}s")
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u_m.set_UVC(*U[i], UU[i])
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u_m.set_alpha(alp[i])
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# wat_p.set_ydata(watl[i])
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aniU = animation.FuncAnimation(figU, animU, frames=model.t.size, interval=1 / 24)
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aniU.save(out.joinpath("animUzoom.mp4"), fps=24)
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