Merge branch 'master' of ssh://git.edgarpierre.fr:39529/m2cce/internship
This commit is contained in:
commit
166331cd31
GPG Key ID:
9833D3C5A25BD227
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@ -5,6 +5,16 @@ données.
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## Scripts
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### Run Olaflow
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`run_ola.sh` permet de lancer les scripts Python et openfoam nécessaires au bon lancement du modèle Olaflow.
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```./run_ola.sh CASE```
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* `CASE` : choix du modèle Olaflow à utiliser
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Le cas de base utilisé est dans le dossier `of`. Les fichiers modifiés sont dans le dossier `of_$CASE`. Les sorties
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sont dans `out_of_$CASE` puis `out_post_$CASE`.
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### Animate
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`animate.py` permet d'obtenir une animation de `alpha.water` et `U` en sortie du modèle Olaflow.
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@ -67,3 +77,55 @@ d'enregistrer cet objet pour une utilisation efficace avec Python.
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* `-i INPUT` : dossier de sortie d'Olaflow
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* `-o OUTPUT` : dossier de sortie à utiliser
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* `-z` : activer la compression gzip (déconseillé)
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### STL
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`stl.py` définit une fonction permettant de convertir un tableau de bathymétrie en fichier STL. Nécessite Openscad.
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### SWS Olaflow
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`sws_ola.py` permet de convertir les données de sortie d'un modèle Swash en condition initiale d'un modèle Olaflow.
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```python -m processing.sws_ola -o OUTPUT [-c CONFIG]```
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* `-o OUTPUT` : dossier de sortie à utiliser
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* `-c CONFIG` : choix d'un fichier de configuration
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```
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[swash]
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np_out : dossier de sortie swash
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[bathy]
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level : niveau d'eau dans SWASH
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[olaflow]
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t0 : instant initial du modèle Olaflow
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```
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### SWS Wavedict Irregular
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`sws_wavedict_irregular.py` est une tentative de convertir les données de sortie d'un modèle SWASH en condition limite
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irrégulière de spectre d'un modèle Olaflow. Ne fonctionne pas.
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### SWS Wavedict Paddle
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`sws_wavedict_paddle.py` permet de convertir les données de sortie d'un modèle SWASH en condition limite en hauteur
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d'eau et vitesse d'un modèle Olaflow.
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```python -m processing.sws_wavedict_paddle -o OUTPUT [-c CONFIG]```
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* `-o OUTPUT` : dossier de sortie à utiliser
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* `-c CONFIG` : choix d'un fichier de configuration
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```
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[swash]
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np_out : dossier de sortie swash
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[bathy]
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level : niveau d'eau dans SWASH
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[olaflow]
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t0 : instant initial du modèle Olaflow
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tf : instant final du modèle Olaflow
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x0 : position de la limite du modèle Olaflow
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```
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@ -1,90 +0,0 @@
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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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@ -0,0 +1,36 @@
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# SWASH
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Ce dossier regroupe l'ensemble des scripts nécessaires à l'éxécution du modèle SWASH ainsi qu'au post-traitement des
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données.
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## Scripts
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### Animate
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`animate.py` permet d'obtenir une animation des résultats de SWASH.
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```python -m processing.animate [-c CONFIG]```
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* `-c CONFIG` : choix du fichier de configuration
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```
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[post]
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inp : dossier contenant les données d'entrée
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out : dossier de sortie
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```
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### Mat Npz
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`mat_npz` permet de convertir les données de sortie de SWASH en données Numpy plus facile à exploiter en Python.
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```python -m processing.mat_npz [-c CONFIG]```
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* `-c CONFIG` : choix du fichier de configuration
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```
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[swash]
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out : dossier de sortie de swash
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[post]
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inp : dossier de sortie pour les données numpy
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```
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config.read(args.config)
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inp = pathlib.Path(config.get("post", "inp"))
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root = pathlib.Path(config.get("swash", "out"))
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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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@ -1,90 +0,0 @@
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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 matplotlib.animation as animation
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import matplotlib.pyplot as plt
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import numpy as np
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parser = argparse.ArgumentParser(description="Animate swash output")
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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("post")
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log.info("Starting post-processing")
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config = configparser.ConfigParser()
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config.read(args.config)
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inp = pathlib.Path(config.get("post", "inp"))
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root = pathlib.Path(config.get("swash", "out"))
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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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def data(var):
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return np.load(inp.joinpath(f"{var}.npy"))
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x = data("xp")
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t = data("tsec")
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watl = data("watl")
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botl = data("botl")
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zk = data("zk")
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velk = data("velk")
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vz = data("vz")
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wl = np.maximum(watl, -botl)
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# print(x.size, -np.arange(0, 1 * bathy.hstru.size, 1)[::-1].size)
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fig, ax = plt.subplots()
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# ax.plot(x, -botl, c="k")
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# ax.fill_between(
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# x, -botl, -data["botl"] + bathy.hstru, color="k", alpha=0.2
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# )
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n = 0
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vk = np.sqrt((velk[n] ** 2).sum(axis=1))
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# print(vk.shape)
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# plt.imshow(vk)
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# plt.colorbar()
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lines = ax.plot(x, zk[n].T, c="#0066cc")
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quiv = []
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for i in range(len(lines) - 1):
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quiv.append(
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ax.quiver(
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x[::50],
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(zk[n, i, ::50] + zk[n, i + 1, ::50]) / 2,
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velk[n, i, 0, ::50],
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vz[n, i, ::50],
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units="dots",
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width=2,
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scale=0.05,
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)
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)
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ax.autoscale(True, "w", True)
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ax.set_ylim(top=15)
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def animate(k):
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for i, q in enumerate(quiv):
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q.set_UVC(
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velk[k, i, 0, ::50],
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vz[k, i, ::50],
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)
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for i, l in enumerate(lines):
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l.set_ydata(zk[k, i])
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return *quiv, *lines
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ani = animation.FuncAnimation(
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fig, animate, frames=wl[:, 0].size, interval=20, blit=True
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)
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ani.save(out.joinpath("layers.mp4"))
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