Update to be compatible with newer versions of dolfinx and scipy

Author: gouwens

src/neuron_morphology/transforms/streamline.py

@@ -31,7 +31,9 @@ def solve_laplace_2d(V: fem.FunctionSpace,
     a = ufl.dot(ufl.grad(u), ufl.grad(v)) * ufl.dx
     L = f * v * ufl.dx
 
-    problem = LinearProblem(a, L, bcs=bcs, petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
+    problem = LinearProblem(a, L, bcs=bcs,
+        petsc_options_prefix="laplace_2d_linear_problem",
+        petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
     uh = problem.solve()
 
     return uh
@@ -48,7 +50,9 @@ def compute_gradient(uh, W, bcs=[]):
     a = ufl.inner(u, v) * dx
     L = ufl.inner(f, v) * dx
 
-    problem = LinearProblem(a, L, bcs=bcs, petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
+    problem = LinearProblem(a, L, bcs=bcs,
+        petsc_options_prefix="compute_gradient_linear_problem",
+        petsc_options={"ksp_type": "preonly", "pc_type": "lu"})
     grad_uh = problem.solve()
 
     return grad_uh
@@ -132,8 +136,9 @@ def generate_laplace_field(top_line: List[Tuple],
     facet_markers = mesh_data.facet_tags
 
     # Create variational space
-    V = fem.FunctionSpace(domain, ("CG", 1))
-    W = fem.VectorFunctionSpace(domain, ("CG", 1))
+    V = fem.functionspace(domain, ("CG", 1))
+    gdim = domain.geometry.dim
+    W = fem.functionspace(domain, ("CG", 1, (gdim, )))
 
     # Create boundary conditions
     top_ls = geo.LineString(top_line)

src/neuron_morphology/transforms/upright_angle/compute_angle.py

@@ -4,7 +4,7 @@
 import copy as cp
 
 import numpy as np
-from scipy.interpolate import interp2d
+from scipy.interpolate import RectBivariateSpline
 import xarray as xr
 
 from argschema.argschema_parser import ArgSchemaParser
@@ -53,14 +53,16 @@ def get_upright_angle(gradient: xr.DataArray,
              ny_idx - n_win:ny_idx + n_win,
              :]
 
-    # Also transpose because if x=m,y=n, interp2d requires z=(n,m)
-    f_dx = interp2d(x_win, y_win, values_win[:, :, 0].T)
-    f_dy = interp2d(x_win, y_win, values_win[:, :, 1].T)
+    r_x = RectBivariateSpline(x_win, y_win, values_win[:, :, 0])
+    f_dx = lambda xnew, ynew: r_x(xnew, ynew).T
+
+    r_y = RectBivariateSpline(x_win, y_win, values_win[:, :, 1])
+    f_dy = lambda xnew, ynew: r_y(xnew, ynew).T
 
     dx = f_dx(x_point, y_point)
     dy = f_dy(x_point, y_point)
 
-    return np.pi / 2 - np.arctan2(dy[0], dx[0])
+    return np.pi / 2 - np.arctan2(dy[0, 0], dx[0, 0])
 
 def calculate_transform(gradient_field: xr.DataArray,
          morph: Morphology,