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Update README, sws_ola

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Edgar P. Burkhart 2022-07-06 09:32:20 +02:00
parent 65c97cf8d1
commit 89a05e191f
Signed by: edpibu
GPG key ID: 9833D3C5A25BD227
3 changed files with 22 additions and 169 deletions
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22
olaflow/README.md
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@ -67,3 +67,25 @@ d'enregistrer cet objet pour une utilisation efficace avec Python.
* `-i INPUT` : dossier de sortie d'Olaflow
* `-o OUTPUT` : dossier de sortie à utiliser
* `-z` : activer la compression gzip (déconseillé)
### STL
`stl.py` définit une fonction permettant de convertir un tableau de bathymétrie en fichier STL. Nécessite Openscad.
### SWS Olaflow
`sws_ola.py` permet de convertir les données de sortie d'un modèle Swash en données d'entrée d'un modèle Olaflow.
```python -m processing.sws_ola -o OUTPUT [-c CONFIG]```
* `-o OUTPUT` : dossier de sortie à utiliser
* `-c CONFIG` : choix d'un fichier de configuration
```
[swash]
np_out : dossier de sortie swash
[olaflow]
t0 : instant initial du modèle Olaflow
level : niveau d'eau dans SWASH
```

90
olaflow/processing/post.py
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@ -1,90 +0,0 @@
import argparse
import configparser
import gzip
from itertools import starmap
import logging
import multiprocessing as mp
import pathlib
import pickle
from cycler import cycler
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
from scipy import interpolate
from .olaflow import OFModel
parser = argparse.ArgumentParser(description="Post-process olaflow results")
parser.add_argument("-v", "--verbose", action="count", default=0)
parser.add_argument("-c", "--config", default="config.ini")
args = parser.parse_args()
logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
log = logging.getLogger("ola_post")
log.info("Animating olaFlow output")
config = configparser.ConfigParser()
config.read(args.config)
out = pathlib.Path(config.get("post", "out"))
out.mkdir(parents=True, exist_ok=True)
with (
path.open("rb")
if (path := out.joinpath("pickle")).exists()
else gzip.open(path.with_suffix(".gz"), "rb")
) as f:
model = pickle.load(f)
x0_conf = config.getfloat("post", "x")
x0_val = model.x[np.argmin(np.abs(model.x - x0_conf))]
# z0 = config.getfloat("post", "z")
# z0 = np.linspace(-5, 5, 16)
c0_ = ((model.x == x0_val)[None, :] & (model.fields["alpha.water"] > 0.95)).any(axis=0)
c0 = model.coords[c0_][:: (c0_.sum() // 8 + 1)]
i0 = np.argmin(
np.linalg.norm(model.coords[..., None] - c0.T[None, ...], axis=1),
axis=0,
)
aw = model.fields["alpha.water"][:, i0]
U = np.where(aw > 0.95, np.linalg.norm(model.fields["U"][..., i0], axis=1), np.nan)
P = np.where(aw > 0.95, model.fields["p"][..., i0], np.nan)
P_rgh = np.where(aw > 0.95, model.fields["p_rgh"][..., i0], np.nan)
with plt.rc_context(
{
"axes.prop_cycle": cycler(
color=np.linspace(0, 1, i0.size + 1)[:-1].astype("U")
),
"axes.grid": True,
"axes.xmargin": 0,
}
):
fig, ax = plt.subplots(3, constrained_layout=True)
ax1, ax2, ax3 = ax
ha = ax1.plot(model.t, U, lw=1)
ax1.set(xlabel="t (s)", ylabel="U (m/s)")
ax2.plot(model.t, P * 1e-3, lw=1)
ax2.set(xlabel="t (s)", ylabel="P (kPa)")
ax3.plot(model.t, P_rgh * 1e-3, lw=1)
ax3.set(xlabel="t (s)", ylabel="P_rgh (kPa)")
for a in ax:
a.set(ylim=0)
ax2.legend(
ha,
list(
starmap(lambda x, z: f"x={x:8}m; z={z:8}m", zip(model.x[i0], model.z[i0]))
),
bbox_to_anchor=(1.05, 0.5),
loc="center left",
)
fig.savefig(out.joinpath("fig.pdf"))

79
olaflow/processing/snap.py
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@ -1,79 +0,0 @@
import argparse
import gzip
import logging
import multiprocessing as mp
import pathlib
import pickle
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.gridspec import GridSpec
from matplotlib.ticker import MultipleLocator
import numpy as np
from scipy import interpolate
from .olaflow import OFModel
parser = argparse.ArgumentParser(description="Post-process olaflow results")
parser.add_argument("-v", "--verbose", action="count", default=0)
parser.add_argument(
"-o",
"--output",
type=pathlib.Path,
help="Output directory for pickled data",
required=True,
)
parser.add_argument(
"-m",
"--max",
help="Only compute maximum rather than animation",
action="store_true",
)
parser.add_argument(
"-i",
"--initial",
help="Only compute initial domain",
action="store_true",
)
args = parser.parse_args()
logging.basicConfig(level=max((10, 20 - 10 * args.verbose)))
log = logging.getLogger("ola_post")
log.info("Animating olaFlow output")
out = args.output
out.mkdir(parents=True, exist_ok=True)
with (
path.open("rb")
if (path := out.joinpath("pickle")).exists()
else gzip.open(path.with_suffix(".gz"), "rb")
) as f:
model = pickle.load(f)
x0, idx0 = np.unique(model.x.astype(np.half), return_inverse=True)
z0, idz0 = np.unique(model.z.astype(np.half), return_inverse=True)
ix0 = np.argsort(x0)
iz0 = np.argsort(z0)[::-1]
X, Z = np.meshgrid(x0, z0)
P = np.full((model.t.size, *X.shape), np.nan)
P[:, iz0[idz0], ix0[idx0]] = model.fields["porosity"]
AW = np.full((model.t.size, *X.shape), np.nan)
AW[:, iz0[idz0], ix0[idx0]] = model.fields["alpha.water"]
#U = np.full((model.t.size, *X.shape), np.nan)
#U[:, iz0[idz0], ix0[idx0]] = np.linalg.norm(model.fields["U"], axis=1)
i0 = np.argmin(np.abs(model.t[:, None] - np.asarray((102, 118, 144.5, 176.5))[None, :]), axis=0)
fig, ax_ = plt.subplots(
2, 2, figsize=(15 / 2.54, 4 / 2.54), dpi=200, constrained_layout=True
)
for ax, i in zip(ax_.flatten(), i0):
ax.imshow(AW[i], cmap="Blues", vmin=0, vmax=1)
fig.savefig(out.joinpath("snap.pdf"))