diff --git a/olaflow/README.md b/olaflow/README.md
index a76ba46..9f4ad32 100644
--- a/olaflow/README.md
+++ b/olaflow/README.md
@@ -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
+```
diff --git a/olaflow/processing/post.py b/olaflow/processing/post.py
deleted file mode 100644
index 36e4078..0000000
--- a/olaflow/processing/post.py
+++ /dev/null
@@ -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"))
diff --git a/olaflow/processing/snap.py b/olaflow/processing/snap.py
deleted file mode 100644
index 5628eed..0000000
--- a/olaflow/processing/snap.py
+++ /dev/null
@@ -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"))