89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
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import argparse
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import configparser
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import gzip
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from itertools import starmap
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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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from cycler import cycler
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as 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 gzip.open(gzf, "rb") if (
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gzf := out.joinpath("pickle.gz")
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).exists() else gzf.with_suffix("").open("rb") as f:
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model = pickle.load(f)
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x0_conf = config.getfloat("post", "x")
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x0_val = model.x[np.argmin(np.abs(model.x - x0_conf))]
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# z0 = config.getfloat("post", "z")
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# z0 = np.linspace(-5, 5, 16)
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c0_ = ((model.x == x0_val)[None, :] & (model.fields["alpha.water"] > 0.95)).any(axis=0)
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c0 = model.coords[c0_][:: (c0_.sum() // 8 + 1)]
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i0 = np.argmin(
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np.linalg.norm(model.coords[..., None] - c0.T[None, ...], axis=1),
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axis=0,
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)
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aw = model.fields["alpha.water"][:, i0]
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U = np.where(aw > 0.95, np.linalg.norm(model.fields["U"][..., i0], axis=1), np.nan)
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P = np.where(aw > 0.95, model.fields["p"][..., i0], np.nan)
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P_rgh = np.where(aw > 0.95, model.fields["p_rgh"][..., i0], np.nan)
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with plt.rc_context(
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{
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"axes.prop_cycle": cycler(
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color=np.linspace(0, 1, i0.size + 1)[:-1].astype("U")
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),
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"axes.grid": True,
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"axes.xmargin": 0,
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}
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):
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fig, ax = plt.subplots(3, constrained_layout=True)
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ax1, ax2, ax3 = ax
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ha = ax1.plot(model.t, U, lw=1)
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ax1.set(xlabel="t (s)", ylabel="U (m/s)")
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ax2.plot(model.t, P*1e-3, lw=1)
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ax2.set(xlabel="t (s)", ylabel="P (kPa)")
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ax3.plot(model.t, P_rgh*1e-3, lw=1)
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ax3.set(xlabel="t (s)", ylabel="P_rgh (kPa)")
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for a in ax:
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a.set(ylim=0)
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ax2.legend(
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ha,
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list(
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starmap(lambda x, z: f"x={x:8}m; z={z:8}m", zip(model.x[i0], model.z[i0]))
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),
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bbox_to_anchor=(1.05, .5),
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loc="center left"
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)
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plt.show()
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