Update README, sws_ola
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3 changed files with 22 additions and 169 deletions
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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 (
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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_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, 0.5),
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loc="center left",
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)
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fig.savefig(out.joinpath("fig.pdf"))
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@ -1,79 +0,0 @@
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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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