New plot

Author: brownbaerchen

pySDC/implementations/convergence_controller_classes/estimate_polynomial_error.py

@@ -148,8 +148,7 @@ def post_iteration_processing(self, controller, S, **kwargs):
                 L.status.order_embedded_estimate = coll.num_nodes * 1
 
             if self.comm:
-                # buf = np.array(abs(u_inter - high_order_sol) if self.comm.rank == rank else 0.0)
-                buf = np.array(np.max([abs(u_inter[i] - high_order_sol[i]) for i in range(u_inter.shape[0]-1)]) if self.comm.rank == rank else 0.0)
+                buf = np.array(abs(u_inter - high_order_sol) if self.comm.rank == rank else 0.0)
                 self.comm.Bcast(buf, root=rank)
                 L.status.error_embedded_estimate = float(buf)
             else:

pySDC/projects/GPU/analysis_scripts/plot_RBC_matrix.py

@@ -1,25 +1,66 @@
 from pySDC.implementations.problem_classes.RayleighBenard import RayleighBenard
 import numpy as np
 import matplotlib.pyplot as plt
-from pySDC.helpers.plot_helper import figsize_by_journal
+from pySDC.helpers.plot_helper import figsize_by_journal, setup_mpl
 
 
-P = RayleighBenard(nx=3, nz=5, Dirichlet_recombination=True, left_preconditioner=True)
-dt = 1.0
+def plot_preconditioners():  # pragma: no cover
+    P = RayleighBenard(nx=3, nz=5, Dirichlet_recombination=True, left_preconditioner=True)
+    dt = 1.0
 
+    A = P.M + dt * P.L
+    A_b = P.put_BCs_in_matrix(P.M + dt * P.L)
+    A_r = P.spectral.put_BCs_in_matrix(A) @ P.Pr
+    A_l = P.Pl @ P.spectral.put_BCs_in_matrix(A) @ P.Pr
 
-A = P.M + dt * P.L
-A_b = P.put_BCs_in_matrix(P.M + dt * P.L)
-A_r = P.spectral.put_BCs_in_matrix(A) @ P.Pr
-A_l = P.Pl @ P.spectral.put_BCs_in_matrix(A) @ P.Pr
+    fig, axs = plt.subplots(1, 4, figsize=figsize_by_journal('TUHH_thesis', 1, 0.4), sharex=True, sharey=True)
 
-fig, axs = plt.subplots(1, 4, figsize=figsize_by_journal('TUHH_thesis', 1, 0.4), sharex=True, sharey=True)
+    for M, ax in zip([A, A_b, A_r, A_l], axs):
+        ax.imshow((M / abs(M)).real + (M / abs(M)).imag, rasterized=False, cmap='Spectral')
 
-for M, ax in zip([A, A_b, A_r, A_l], axs):
-    ax.imshow((M / abs(M)).real + (M / abs(M)).imag, rasterized=False, cmap='Spectral')
+    for ax in axs:
+        ax.set_xticks([])
+        ax.set_yticks([])
+    fig.savefig('plots/RBC_matrix.pdf', bbox_inches='tight', dpi=300)
+    plt.show()
 
-for ax in axs:
-    ax.set_xticks([])
-    ax.set_yticks([])
-fig.savefig('plots/RBC_matrix.pdf', bbox_inches='tight', dpi=300)
-plt.show()
+
+def plot_ultraspherical():
+    from pySDC.helpers.spectral_helper import ChebychevHelper, UltrasphericalHelper
+
+    N = 16
+    cheby = ChebychevHelper(N=N)
+    ultra = UltrasphericalHelper(N=N)
+
+    D_cheby = cheby.get_differentiation_matrix()
+    I_cheby = cheby.get_Id()
+
+    fig, axs = plt.subplots(2, 3, figsize=figsize_by_journal('TUHH_thesis', 0.9, 0.65), sharex=True, sharey=True)
+
+    axs[0, 0].imshow(D_cheby / abs(D_cheby))
+    axs[1, 0].imshow(I_cheby / abs(I_cheby))
+
+    for i in range(2):
+        D_ultra = ultra.get_differentiation_matrix(p=i + 1)
+        I_ultra = ultra.get_basis_change_matrix(0, i + 1)
+        axs[0, i + 1].imshow(D_ultra / abs(D_ultra))
+        axs[1, i + 1].imshow(I_ultra / abs(I_ultra))
+        axs[1, i + 1].set_xlabel(rf'$T \rightarrow C^{{({{{i+1}}})}}$')
+    for ax in axs.flatten():
+        ax.set_xticks([])
+        ax.set_yticks([])
+
+    axs[0, 0].set_ylabel('Differentiation')
+    axs[1, 0].set_ylabel('Left preconditioner')
+    axs[1, 0].set_xlabel(r'$T \rightarrow T$')
+
+    axs[0, 0].set_title('first derivative')
+    axs[0, 1].set_title('first derivative')
+    axs[0, 2].set_title('second derivative')
+    fig.savefig('plots/ultraspherical_matrix.pdf', bbox_inches='tight', dpi=300)
+    plt.show()
+
+
+if __name__ == '__main__':
+    setup_mpl()
+    plot_ultraspherical()

pySDC/projects/GPU/analysis_scripts/plot_large_simulations.py

@@ -282,7 +282,7 @@ def plot_series(self, test=False):
             indices = [0, 1, 2, 3, 4, 5]
             process = 0
         else:
-            indices = [0, 10, 20, 30, 40, 90]
+            indices = [0, 10, 20, 30, 40, 91]
             process = 96
 
         from mpl_toolkits.axes_grid1 import make_axes_locatable