removed GE4 for clarity

Author: thomas-risse

python/plot_convergence_JAES.py

@@ -6,7 +6,7 @@
 import numpy as np
 from helper_plots import set_size
 
-#%% Settings
+# %% Settings
 result_folder = "results/conv_full_2"
 
 with h5py.File(join(result_folder, "settings.h5"), "r") as f:
@@ -17,19 +17,22 @@
     modes = json.loads(f.attrs["modes"])
     A0s = json.loads(f.attrs["A0s"])
 
+modes = ["Linear", "KC", "GE4"]
+
 lambda0s = lambda0s[-1:]
 print(lambda0s)
-#%% Init plot
-Nrows = 2
-Ncols = 2
+# %% Init plot
+Nrows = 3
+Ncols = 1
 
-fig, axs = plt.subplots(Nrows, Ncols, sharex=True, sharey=False, figsize = set_size("JAES", fraction=1, height_ratio=0.6))
+fig, axs = plt.subplots(Nrows, Ncols, sharex=True, sharey=False,
+                        figsize=set_size("JAES", fraction=1, height_ratio=0.7))
 colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
 markers = ['*', '+', '.', '1', 'o', 'v']
 
 for k, mode in enumerate(modes):
     for l, A0 in enumerate(A0s):
-        #%% Retrieve reference data at midpoint
+        # %% Retrieve reference data at midpoint
         with h5py.File(join(result_folder, f"{mode}/{A0}/ref.h5"), "r") as f:
             q_ref = f["q"][...]
             model_setting = json.loads(f.attrs['model_dict'])
@@ -54,35 +57,38 @@ def compute_error(q, qref):
                         errors[i, j] = compute_error(q, q_ref)
                     else:
                         errors[i, j] = np.nan()
-        
-        #%% Plot
-        ax = axs[k//2, k%2]
+
+        # %% Plot
+        ax = axs[k]
         ax.set_xscale("log")
         ax.set_yscale("log")
         for i, lambda0 in enumerate(lambda0s):
-            ax.plot(hs, errors[:, i], label=r"$Q_0 = $" + f"{A0} m", ls="dotted", marker = markers[l], markersize = 10, color = colors[l])
+            ax.plot(hs, errors[:, i], label=r"$Q_0 = $" +
+                    f"{A0} m", ls="dotted", marker=markers[l], markersize=10, color=colors[l])
         if (k == 3):
             # legend
-            ax.legend(bbox_to_anchor=(1, 0.5), loc = "lower left", frameon = True)
+            ax.legend(bbox_to_anchor=(1, 0.5), loc="lower left", frameon=True)
 
             # ax.legend(frameon = True) #, ncol=3, bbox_to_anchor=(0.5, 1.05), loc = "upper center")
         # ax.set_ylim([np.min(errors) * 0.5, np.max(errors) * 2])
 
-        ax.plot([hs[-1], hs[0]], [errors[-1, 0], errors[-1, 0]/ (hs[-1] / hs[0])**2], linestyle = "--", color="gray")
+        ax.plot([hs[-1], hs[0]], [errors[-1, 0], errors[-1, 0] /
+                (hs[-1] / hs[0])**2], linestyle="--", color="gray")
 
-    ax.text(0.1, 0.8, mode, transform = ax.transAxes)
+    ax.text(0.1, 0.7, mode, transform=ax.transAxes)
     # Set x-axis ticks to align with data points
     stride = 2
-    if (k//2 == 1):
+    if (k == 2):
         ax.xaxis.set_minor_formatter(NullFormatter())
         ax.xaxis.set_tick_params(which='minor', size=0)
-        ax.xaxis.set_tick_params(which='minor', width=0) 
+        ax.xaxis.set_tick_params(which='minor', width=0)
         ax.set_xticks(hs[::stride])
         ax.set_xticklabels([f"{x:.2e}" for x in hs[::stride]])
-    else:
+    elif (k == 0):
         # Add a secondary x axis with sampling frequency
         def forward(x):
             return x
+
         def backward(x):
             return x
         secax = ax.secondary_xaxis('top', functions=(forward, backward))
@@ -94,10 +100,10 @@ def backward(x):
         secax.xaxis.set_tick_params(which='minor', width=0)
     # plt.xlabel("h (m)")
     # plt.ylabel("Relative error e")
-fig.text(0.47, 0.01, 'h (m)', ha='center')
-fig.text(0.47, 0.95, r'$f_{\rm s}$ (Hz)', ha='center')
+fig.text(0.52, 0.01, 'h (m)', ha='center')
+fig.text(0.52, 0.95, r'$f_{\rm s}$ (Hz)', ha='center')
 fig.text(0.01, 0.5, 'Relative error e', va='center', rotation='vertical')
 plt.tight_layout()
-fig.subplots_adjust(left = 0.1, hspace=0.05, wspace=0.2)
+fig.subplots_adjust(left=0.1, hspace=0.05, wspace=0.2)
 
-plt.show(block = True)
+plt.show(block=True)

python/plot_drift_JAES.py

@@ -47,7 +47,7 @@
 
 model = FD_string_model(44100, **StringParams)
 
-modes = ["KC", "GE", "GE4"]  # Nonlinear modes
+modes = ["KC", "GE4"]  # Nonlinear modes
 
 # %%
 # Simulation parameters
@@ -81,7 +81,7 @@ def Fext(t):
 
 # %% Run simulations and plot results
 fig, axs = plt.subplots(1 + 2*len(lambda0s), 1,
-                        figsize=set_size("JAES", height_ratio=0.6), sharex=True)
+                        figsize=set_size("JAES", height_ratio=0.7), sharex=True)
 linestyles = [":", "--", "-."]
 for i, lambda0 in enumerate(lambda0s):
 
@@ -122,10 +122,15 @@ def Fext(t):
     axs[2*i+2].set_ylim([1e-4, 1.5e3])
     axs[2*i+2].set_yticks([1e-4, 1e-1, 1e2])
     axs[2*i+2].set_yticklabels([1e-4, 1e-1, 1e2])
-    axs[2*i+1].text(0.8, 0.6, fr"$\lambda_0 = {lambda0} s^{-1}$", transform=axs[2*i+1].transAxes,
-                    color="red", bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'))
-    axs[2*i+2].text(0.8, 0.6, fr"$\lambda_0 = {lambda0} s^{-1}$", transform=axs[2*i+2].transAxes,
-                    color="red", bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'))
+axs[1].text(1.05, 0, "Original \n algorithm", transform=axs[1].transAxes,
+            color="red", bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'), horizontalalignment="center", verticalalignment="center", rotation=90)
+# axs[2].text(0.95, 0.6, fr"Original algorithm", transform=axs[2].transAxes,
+#             color="red", bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'), horizontalalignment="right")
+
+axs[3].text(1.035, 0, fr"$\tau_0 = 0.001$ s", transform=axs[3].transAxes,
+            color="red", bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'), horizontalalignment="center", verticalalignment="center", rotation=90)
+# axs[4].text(0.95, 0.6, fr"$\tau_0 = 0.001 s$", transform=axs[4].transAxes,
+#             color="red", bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'), horizontalalignment="right")
 
 axs[1].legend(loc="lower center", frameon=True, fancybox=True,
               bbox_to_anchor=(0.5, 2.1), ncol=3)