From 0bdd73da194d7db151ac39ceddaeebfa87a495ee Mon Sep 17 00:00:00 2001
From: "Edgar P. Burkhart" <git@edgarpierre.fr>
Date: Fri, 4 Mar 2022 11:01:01 +0100
Subject: [PATCH] Add real x support to post

---
 swash/config.ini         |  2 +-
 swash/processing/post.py | 14 ++++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/swash/config.ini b/swash/config.ini
index a39acf8..08df32a 100644
--- a/swash/config.ini
+++ b/swash/config.ini
@@ -20,5 +20,5 @@ mpi=4
 inp=inp_post
 out=out_post
 dt=0.25
-x0=125
+x0=-1000
 t0=180
diff --git a/swash/processing/post.py b/swash/processing/post.py
index f3ed9e9..9032879 100644
--- a/swash/processing/post.py
+++ b/swash/processing/post.py
@@ -31,13 +31,15 @@ x, t = data["x"], data["t"]
 
 # Cospectral calculations
 x0 = config.getint("post", "x0")
+arg_x0 = (x - x0).abs().argmin()
 t0 = config.getfloat("post", "t0")
+arg_t0 = (t - t0).abs().argmin()
 dt = config.getfloat("post", "dt")
 f = 1 / dt
 log.info(f"Computing reflection coefficient at x={x0}")
 
-eta = data["watl"][t > t0, x0]
-u = data["vel"][t > t0, 0, x0]
+eta = data["watl"][t > t0, arg_x0]
+u = data["vel"][t > t0, 0, arg_x0]
 
 phi_eta = np.abs(sgl.csd(eta, eta, f))
 phi_u = np.abs(sgl.csd(u, u, f))
@@ -52,13 +54,13 @@ R = np.sqrt(
 log.info("Plotting results")
 fig, (ax_watl, ax_vel) = plt.subplots(2)
 
-ax_watl.plot(t, data["watl"][:, x0], label="watl")
+ax_watl.plot(t, data["watl"][:, arg_x0], label="watl")
 ax_watl.set(xlabel="t (s)", ylabel="z (m)")
 ax_watl.autoscale(axis="x", tight=True)
 ax_watl.grid()
 ax_watl.axvline(t0, c="k", alpha=0.2)
 
-ax_vel.plot(t, data["vel"][:, 0, x0], label="vel")
+ax_vel.plot(t, data["vel"][:, 0, arg_x0], label="vel")
 ax_vel.set(xlabel="t (s)", ylabel="U (m/s)")
 ax_vel.autoscale(axis="x", tight=True)
 ax_vel.grid()
@@ -74,8 +76,8 @@ ax_r.set(ylim=(0, 1), xlabel="f (Hz)", ylabel="R")
 ax_r.grid()
 
 fig_x, ax_x = plt.subplots()
-ax_x.plot(-data["botl"], color="k")
-ax_x.plot(data["watl"][np.argmin(np.abs(t - t0)), :])
+ax_x.plot(data["x"], -data["botl"], color="k")
+ax_x.plot(data["x"], np.maximum(data["watl"][arg_t0, :], -data["botl"]))
 ax_x.axvline(x0, c="k", alpha=0.2)
 ax_x.set(xlabel="x (m)", ylabel="z (m)")
 ax_x.autoscale(axis="x", tight=True)