From fd90a4d45d97188483d4ecbac9310d4b5959748f Mon Sep 17 00:00:00 2001
From: Technici4n <13494793+Technici4n@users.noreply.github.com>
Date: Wed, 26 May 2021 22:37:38 +0200
Subject: [PATCH] Tweak a little for the report
---
methodes-spectrales/main.py | 9 ++++++---
1 file changed, 6 insertions(+), 3 deletions(-)
diff --git a/methodes-spectrales/main.py b/methodes-spectrales/main.py
index f95e367..058037a 100644
--- a/methodes-spectrales/main.py
+++ b/methodes-spectrales/main.py
@@ -34,13 +34,13 @@ def run_simulation(L, u0, dt, timesteps, nu=1):
def wrap_simulation(L, N, dt=0.05, tmax=200, nu=1):
x = linspace(0, L, N)
- time_values = linspace(0, 200, int(tmax / dt)+1)
+ time_values = linspace(0, tmax, int(tmax / dt)+1)
s = run_simulation(L, cos(2*pi/L * x) + 0.1 * cos(4*pi/L * x), dt, len(time_values)-1, nu=nu)
return x, time_values, s
# define a few constants
-x, time_values, s = wrap_simulation(100, 1024)
+x, time_values, s = wrap_simulation(100, 1024, nu=1)
# pick a nice color for the plot
colormap = plt.get_cmap("jet")
# print the values
@@ -71,10 +71,13 @@ def test_multiple_Ls(nu=1):
# Plot A as a function of L for nu = 1
Ls, As, critical_L = test_multiple_Ls(1)
plt.plot(Ls, As)
+plt.xlabel("L")
+plt.ylabel("A")
plt.show()
# Now find the critical value for multiple nus
nus_exp = linspace(-0.5, 1.75, 20)
+# nu between e^-0.5 and e^1.75
nus = exp(nus_exp) # use a log scale
critical_Ls = zeros(nus.shape)
for i, nu in enumerate(nus):
@@ -89,7 +92,7 @@ from sklearn.linear_model import LinearRegression
reg = LinearRegression().fit(log(nus).reshape(-1, 1), log(critical_Ls)) # we fit the logs
# because we fitted the logs, we have the predict with the log, then take the exp()
plt.plot(nus, exp(reg.predict(log(nus).reshape(-1, 1))), label="Linear Regression (slope = %f)" % reg.coef_)
-plt.xlabel("$nu$")
+plt.xlabel("$\\nu$")
plt.ylabel("Critical L")
plt.xscale("log")
plt.yscale("log")
--
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