Merge 00a607e into 40a7249

examples/advection_2d_annulus/advection_annulus.py

@@ -18,7 +18,7 @@
 """
 import numpy as np
 
-def mapc2p_annulus(grid,c_centers):
+def mapc2p_annulus(xc, yc):
     """
     Specifies the mapping to curvilinear coordinates.
 
@@ -31,8 +31,8 @@ def mapc2p_annulus(grid,c_centers):
     p_centers = []
 
     # Polar coordinates (first coordinate = radius,  second coordinate = theta)
-    p_centers.append(c_centers[0][:]*np.cos(c_centers[1][:]))
-    p_centers.append(c_centers[0][:]*np.sin(c_centers[1][:]))
+    p_centers.append(xc[:]*np.cos(yc[:]))
+    p_centers.append(xc[:]*np.sin(yc[:]))
 
     return p_centers
 
@@ -63,13 +63,10 @@ def ghost_velocities_upper(state,dim,t,qbc,auxbc,num_ghost):
     Set the velocities for the ghost cells outside the outer radius of the annulus.
     In the computational domain, these are the cells at the top of the grid.
     """
-    from mapc2p import mapc2p
-
     grid=state.grid
     if dim == grid.dimensions[0]:
         dx, dy = grid.delta
-        X_edges, Y_edges = grid.c_edges_with_ghost(num_ghost=2)
-        R_edges,Theta_edges = mapc2p(X_edges,Y_edges)  # Compute edge coordinates in physical domain
+        R_edges,Theta_edges = grid.p_edges_with_ghost(num_ghost=2)
 
         auxbc[:,-num_ghost:,:] = edge_velocities_and_area(R_edges[-num_ghost-1:,:],Theta_edges[-num_ghost-1:,:],dx,dy)
 
@@ -82,13 +79,10 @@ def ghost_velocities_lower(state,dim,t,qbc,auxbc,num_ghost):
     Set the velocities for the ghost cells outside the inner radius of the annulus.
     In the computational domain, these are the cells at the bottom of the grid.
     """
-    from mapc2p import mapc2p
-
     grid=state.grid
     if dim == grid.dimensions[0]:
         dx, dy = grid.delta
-        X_edges, Y_edges = grid.c_edges_with_ghost(num_ghost=2)
-        R_edges,Theta_edges = mapc2p(X_edges,Y_edges)
+        R_edges,Theta_edges = grid.p_edges_with_ghost(num_ghost=2)
 
         auxbc[:,0:num_ghost,:] = edge_velocities_and_area(R_edges[0:num_ghost+1,:],Theta_edges[0:num_ghost+1,:],dx,dy)
 
@@ -193,6 +187,7 @@ def setup(use_petsc=False,outdir='./_output',solver_type='classic'):
     theta = pyclaw.Dimension('theta',theta_lower,theta_upper,m_theta)
     domain = pyclaw.Domain([r,theta])
     domain.grid.mapc2p = mapc2p_annulus
+    domain.grid.num_ghost = solver.num_ghost
 
     num_eqn = 1
     state = pyclaw.State(domain,num_eqn)

examples/shallow_sphere/Rossby_wave.py

@@ -69,35 +69,27 @@ def fortran_src_wrapper(solver,state,dt):
     state.q = problem.src2(mx,my,num_ghost,xlower,ylower,dx,dy,q,aux,t,dt,Rsphere)
 
 
-def mapc2p_sphere_nonvectorized(grid,mC):
+def mapc2p_sphere_nonvectorized(X,Y):
     """
     Maps to points on a sphere of radius Rsphere. Nonvectorized version (slow).
     
-    Takes as input: array_list made by x_coordinates, y_ccordinates in the map 
-                    space.
+    Inputs: x-coordinates, y-coordinates in the computational space.
 
-    Returns as output: array_list made by x_coordinates, y_ccordinates in the 
-                       physical space.
-
-    Inputs: mC = list composed by two arrays
-                 [array ([xc1, xc2, ...]), array([yc1, yc2, ...])]
-
-    Output: pC = list composed by three arrays
-                 [array ([xp1, xp2, ...]), array([yp1, yp2, ...]), array([zp1, zp2, ...])]
+    Output: list of x-, y- and z-coordinates in the physical space.
 
     NOTE: this function is not used in the standard script.
     """
 
     # Get number of cells in both directions
-    mx, my = grid.num_cells[0], grid.num_cells[1]
+    mx, my = X.shape
 
     # Define new list of numpy array, pC = physical coordinates
     pC = []
 
     for i in range(mx):
         for j in range(my):
-            xc = mC[0][i][j]
-            yc = mC[1][i][j]
+            xc = X[i][j]
+            yc = Y[i][j]
 
             # Ghost cell values outside of [-3,1]x[-1,1] get mapped to other
             # hemisphere:
@@ -150,34 +142,26 @@ def mapc2p_sphere_nonvectorized(grid,mC):
     return pC
 
 
-def mapc2p_sphere_vectorized(grid,mC):
+def mapc2p_sphere_vectorized(X,Y):
     """
     Maps to points on a sphere of radius Rsphere. Vectorized version (fast).  
 
-    Takes as input: array_list made by x_coordinates, y_ccordinates in the map 
-                    space.
+    Inputs: x-coordinates, y-coordinates in the computational space.
 
-    Returns as output: array_list made by x_coordinates, y_ccordinates in the 
-                       physical space.
-
-    Inputs: mC = list composed by two arrays
-                 [array ([xc1, xc2, ...]), array([yc1, yc2, ...])]
-
-    Output: pC = list composed by three arrays
-                 [array ([xp1, xp2, ...]), array([yp1, yp2, ...]), array([zp1, zp2, ...])]
+    Output: list of x-, y- and z-coordinates in the physical space.
 
     NOTE: this function is used in the standard script.
     """
 
     # Get number of cells in both directions
-    mx, my = grid.num_cells[0], grid.num_cells[1]
+    mx, my = X.shape
 
     # 2D array useful for the vectorization of the function
     sgnz = np.ones((mx,my))
 
     # 2D coordinates in the computational domain
-    xc = mC[0][:][:]
-    yc = mC[1][:][:]
+    xc = X[:][:]
+    yc = Y[:][:]
 
     # Compute 3D coordinates in the physical domain
     # =============================================