Resolve issues and comments

README.md

@@ -37,7 +37,7 @@ TL;DR
 ```bash
 conda create --name voxenv python=3.12
 conda activate voxenv
-pip install evoxels[torch,jax,dev,notebooks]
+pip install "evoxels[torch,jax,dev,notebooks]"
 pip install --upgrade "jax[cuda12]"
 ```
 
@@ -59,7 +59,7 @@ Navigate to the evoxels folder, then
 ```
 pip install -e .[torch] # install with torch backend
 pip install -e .[jax]   # install with jax backend
-pip install -e .[dev, notebooks] # install testing and notebooks
+pip install -e .[dev,notebooks] # install testing and notebooks
 ```
 Note that the default `[jax]` installation is only CPU compatible. To install the corresponding CUDA libraries check your CUDA version with
 ```bash
@@ -71,7 +71,7 @@ pip install -U "jax[cuda12]"
 ```
 To install both backends within one environment it is important to install torch first and then upgrade the `jax` installation e.g.
 ```bash
-pip install evoxels[torch, jax, dev, notebooks]
+pip install "evoxels[torch,jax,dev,notebooks]"
 pip install --upgrade "jax[cuda12]"
 ```
 To work with the example notebooks install Jupyter and all notebook related dependencies via

evoxels/problem_definition.py

@@ -120,6 +120,8 @@ def __post_init__(self):
             bc_fun = self.vg.bc.pad_zero_flux_periodic
             self.pad_boundary = lambda field, bc0, bc1: bc_fun(field)
             k_squared = self.vg.fft_k_squared_nonperiodic()
+        else:
+            raise ValueError(f"Unsupported BC type: {self.BC_type}")
 
         self._fourier_symbol = -self.D * self.A * k_squared
 

evoxels/utils.py

@@ -215,6 +215,8 @@ def mms_convergence_test(
             vf = evo.VoxelFields((2**p, 2**p, 2**p), (1, 1, 1), convention=convention)
         elif convention == 'staggered_x':
             vf = evo.VoxelFields((2**p + 1, 2**p, 2**p), (1, 1, 1), convention=convention)
+        else:
+            raise ValueError("Chosen convention must be cell_center or staggered_x.")
         vf.precision = dtype
         dx[i] = vf.spacing[0]
 
@@ -231,6 +233,8 @@ def mms_convergence_test(
         ODE = ODE_class(vg, **problem_kwargs)
         rhs_orig = ODE.rhs
         grid = vg.meshgrid()
+        if convention == 'staggered_x':
+            grid = (grid[0][1:-1,:,:], grid[1][1:-1,:,:], grid[2][1:-1,:,:])
 
         # Construct new rhs including forcing term from MMS
         if mode == 'temporal':
@@ -246,7 +250,6 @@ def mms_rhs(t, u):
                     u_ex_list.append(vg.expand_dim(u_list[j](t_, *grid), 0))
                     rhs = vg.set(rhs, j, rhs[j] + u_t_list[j](t_, *grid))
                 u_ex = vg.concatenate(u_ex_list, 0)
-                u_ex = vg.bc.trim_boundary_nodes(u_ex)
                 rhs -= rhs_orig(t, u_ex)
                 return rhs
 
@@ -322,7 +325,10 @@ def mms_rhs(t, u):
             for j, func in enumerate(test_functions):
                 exact = vf.fields[f'u{j}_final']
                 diff = vf.fields[f'u{j}'] - exact
-                errors[j, k, i] = np.linalg.norm(diff) / np.linalg.norm(exact)
+                if convention == 'staggered_x':
+                    errors[j, k, i] = np.linalg.norm(diff[1:-1,:,:]) / np.linalg.norm(exact[1:-1,:,:])
+                else:
+                    errors[j, k, i] = np.linalg.norm(diff) / np.linalg.norm(exact)
 
     # Fit slope after loop
     def calc_slope(x, y):

pyproject.toml

@@ -53,7 +53,8 @@ dev = [
 notebooks = [
     "ipywidgets",
     "ipympl",
-    "notebook"
+    "notebook",
+    "taufactor"
 ]
 
 [tool.setuptools.packages.find]