81 lines
2.1 KiB
Python
81 lines
2.1 KiB
Python
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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 pickle
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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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("Starting sws -> olaFlow converter")
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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", "pickle"))
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out.parent.mkdir(parents=True, exist_ok=True)
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with out.open("rb") 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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i0 = np.argmin(np.abs((model.x - x0) + 1j * (model.z - z0)))
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X, Z = np.meshgrid(np.unique(model.x), np.unique(model.z))
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C = np.where(
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(model.x[:, None, None].astype(np.single) == X[None, :, :].astype(np.single))
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& (model.z[:, None, None].astype(np.single) == Z[None, :, :].astype(np.single))
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)
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P = np.full((model.t.size, *X.shape), np.nan)
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P[:, C[1], C[2]] = model.fields["porosity"][:, C[0]]
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AW = np.full((model.t.size, *X.shape), np.nan)
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AW[:, C[1], C[2]] = model.fields["alpha.water"][:, C[0]]
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fig, ax = plt.subplots()
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tit = ax.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=ax.transAxes,
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)
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aw_m = ax.pcolormesh(X, Z, AW[0], vmin=0, vmax=1, cmap="Blues", zorder=1)
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ax.pcolormesh(
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X,
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Z,
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P[1],
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vmin=0,
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vmax=1,
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cmap="Greys_r",
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alpha=np.nan_to_num(1 - P[1])/2,
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zorder=1.1,
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)
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ax.axhline(4.5, ls="-.", lw=1, c="k", alpha=0.2, zorder=1.2)
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def anim(i):
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tit.set_text(f"t={i[0]}s")
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aw_m.set_array(i[1])
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return (aw_m,)
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fig.colorbar(aw_m)
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ax.set(xlabel="x (m)", ylabel="z (m)", aspect="equal", facecolor="#bebebe")
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ax.grid(c="k", alpha=0.2)
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ani = animation.FuncAnimation(fig, anim, frames=zip(model.t, AW), interval=1 / 25)
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plt.show()
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