Update all tutorials with new API and new plotting

.github/workflows/build-publish.yml

@@ -20,7 +20,7 @@ jobs:
   build:
     # The type of runner that the job will run on
     runs-on: ubuntu-latest
-    container: dokken92/dolfinx_custom:26122021
+    container: dokken92/dolfinx_custom:07012022
 
     env:
       HDF5_MPI: "ON"
@@ -35,12 +35,11 @@ jobs:
       - uses: actions/checkout@v2
 
       - name: Install dependencies
-        run: pip3 install notebook nbconvert "jupyter-book<6" --upgrade
+        run: pip3 install notebook nbconvert jupyter-book --upgrade
       - name: Test notebooks in parallel
         run: |
           cd chapter1
           jupyter-nbconvert --to python fundamentals_code.ipynb
-          python3 -c "from pyvista import start_xvfb; start_xvfb(0.1)"
           mpirun -n 2 python3 fundamentals_code.py
           jupyter-nbconvert --to python membrane_code.ipynb
           mpirun -n 2 python3 membrane_code.py

.github/workflows/docker-image.yml

@@ -37,4 +37,4 @@ jobs:
         run: echo ${{ secrets.DOCKERHUB_TOKEN }} | docker login -u ${{ secrets.DOCKERHUB_USERNAME }} --password-stdin
       - name: Push to the DockerHub registry
         run: |
-          docker push dokken92/dolfinx_custom:26122021
+          docker push dokken92/dolfinx_custom:07012022

.github/workflows/main-test.yml

@@ -36,22 +36,22 @@ jobs:
 
       - name: Install dependencies
         run: |
-          wget -qO - https://deb.nodesource.com/setup_17.x | bash
+          CC=mpicc HDF_MPI="ON" HDF5_DIR="/usr/lib/x86_64-linux-gnu/hdf5/mpich/" pip3 install --no-cache-dir --no-binary=h5py h5py meshio
           apt-get -qq update
           apt-get install -y libgl1-mesa-dev xvfb nodejs
           apt-get clean
           rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-          CC=mpicc HDF_MPI="ON" HDF5_DIR="/usr/lib/x86_64-linux-gnu/hdf5/mpich/" pip3 install --no-cache-dir --no-binary=h5py h5py meshio
           pip3 install --no-cache-dir tqdm pandas seaborn
           pip3 install notebook nbconvert jupyter-book myst_parser pyvista jupyterlab
-          pip3 install --no-cache-dir matplotlib itkwidgets ipywidgets setuptools --upgrade
-          jupyter labextension install jupyter-matplotlib jupyterlab-datawidgets && \
+          pip3 install ipygany pythreejs
+          pip3 install --no-cache-dir matplotlib setuptools --upgrade
+          jupyter nbextension enable --py --sys-prefix ipygany
           rm -rf /usr/local/share/.cache/*
       - name: Test notebooks in parallel
         run: |
           cd chapter1
-          jupyter-nbconvert --to python fundamentals_code.ipynb
           python3 -c "from pyvista import start_xvfb; start_xvfb(0.1)"
+          jupyter-nbconvert --to python fundamentals_code.ipynb
           mpirun -n 2 python3 fundamentals_code.py
           jupyter-nbconvert --to python nitsche.ipynb
           mpirun -n 2 python3 nitsche.py

Changelog.md

@@ -1,6 +1,8 @@
 # Changelog
 
 ## Dev 
+- API updates wrt. DOLFINx. `Form`->`form`, `DirichletBC`->`dirichletbc`.
+- Switch plotting backend to `ipygany` and `pythreejs`
 - Updates on error computations in [Error control: Computing convergence rates](chapter4/convergence).
 - Added tutorial on interpolation of `ufl.Expression` in [Deflection of a membrane](chapter1/membrane_code).
 - Added tutorial on how to apply constant-valued Dirichet conditions in [Deflection of a membrane](chapter1/membrane_code).

Dockerfile

@@ -1,4 +1,4 @@
-FROM dokken92/dolfinx_custom:26122021
+FROM dokken92/dolfinx_custom:07012022
 
 # create user with a home directory
 ARG NB_USER

chapter1/fundamentals_code.ipynb

@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,13 +176,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from dolfinx.fem import locate_dofs_topological, DirichletBC\n",
+    "from dolfinx.fem import locate_dofs_topological, dirichletbc\n",
     "boundary_dofs = locate_dofs_topological(V, fdim, boundary_facets)\n",
-    "bc = DirichletBC(uD, boundary_dofs)"
+    "bc = dirichletbc(uD, boundary_dofs)"
    ]
   },
   {
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,12 +316,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from dolfinx.fem import assemble_scalar\n",
-    "L2_error = ufl.inner(uh - uex, uh - uex) * ufl.dx\n",
+    "from dolfinx.fem import assemble_scalar, form\n",
+    "L2_error = form(ufl.inner(uh - uex, uh - uex) * ufl.dx)\n",
     "error_local = assemble_scalar(L2_error)\n",
     "error_L2 = numpy.sqrt(mesh.comm.allreduce(error_local, op=MPI.SUM))"
    ]
@@ -340,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -374,37 +374,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
     "from dolfinx.plot import create_vtk_topology\n",
     "topology, cell_types = create_vtk_topology(mesh, mesh.topology.dim)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are several backends that can be used with pyvista, and they have different benefits and drawbacks. See the [pyvista documentation](https://docs.pyvista.org/user-guide/jupyter/index.html) for more information and installation details. In this example and the rest of the tutorial we will use [ipygany](https://github.com/QuantStack/ipygany) and [pythreejs](https://github.com/jupyter-widgets/pythreejs)."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pyvista\n",
-    "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)"
+    "pyvista.set_jupyter_backend(\"pythreejs\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "We start by creating a pyvista grid the `vtk_topology` and the `mesh.geometry`.\n",
     "Next, we attach data from our solution `uh` by computing the values of the function at each vertex."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
+    "grid = pyvista.UnstructuredGrid(topology, cell_types, mesh.geometry.x)\n",
     "grid.point_data[\"u\"] = uh.compute_point_values().real\n",
     "grid.set_active_scalars(\"u\")"
    ]
@@ -414,65 +423,62 @@
    "metadata": {},
    "source": [
     "We can now use the `pyvista.Plotter` to visualize the solution. We visualize it by showing it in 2D and warped in 3D.\n",
-    "In the jupyter notebook environment, we use the default setting of `pyvista.OFF_SCREEN=False`, which will render plots directly in the notebook. . PYVITo render in the online notebooks or in docker containers without X-forwarding, we need to start a virtual framebuffer.\n",
-    "```{admonition} Rendering with pyvista using Docker-containers\n",
-    "To make the plots render, we have to call `pyvista.start_xvfb` to start a virtual framebuffer if using docker-containers or jupyter notebooks on Linux.\n",
-    "```"
+    "In the jupyter notebook environment, we use the default setting of `pyvista.OFF_SCREEN=False`, which will render plots directly in the notebook."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "52f926f800b84003bd2eda5e14ba1871",
+       "model_id": "026dd4bdc8354af88ecd23be302ee96c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "ViewInteractiveWidget(height=768, layout=Layout(height='auto', width='100%'), width=1024)"
+       "Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(color='#fefefe', inten…"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "pyvista.start_xvfb(wait=0.05)\n",
     "plotter = pyvista.Plotter()\n",
-    "plotter.add_mesh(grid, show_edges=True, show_scalar_bar=True)\n",
+    "plotter.add_mesh(grid, show_edges=True)\n",
     "plotter.view_xy()\n",
     "if not pyvista.OFF_SCREEN:\n",
     "    plotter.show()\n",
     "else:\n",
+    "    pyvista.start_xvfb()\n",
     "    figure = plotter.screenshot(\"fundamentals.png\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Alternative plotting\n",
-    "To create a more interactive plot using pyvista in a Jupyter notebook we us `pyvista.ITKPlotter` which uses `itkwidgets`. To modify the visualization, click on the three bars in the top left corner."
+    "## ipygany\n",
+    "We change plotting from `pythreejs` to `ipygany` by initializing another `Plotter` with `jupyter_backend=\"ipygany\"`. We also warp the mesh by scalar to make use of the 3D plotting."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b86e726693d42f3b675cea1633f9136",
+       "model_id": "2b14ac59f2cf4d129b2843ce038c4b90",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Viewer(geometries=[{'vtkClass': 'vtkPolyData', 'points': {'vtkClass': 'vtkPoints', 'name': '_points', 'numberO…"
+       "AppLayout(children=(VBox(children=(HTML(value='<h3>u</h3>'), Dropdown(description='Colormap:', options={'BrBG'…"
       ]
      },
      "metadata": {},
@@ -481,10 +487,10 @@
    ],
    "source": [
     "if not pyvista.OFF_SCREEN:\n",
-    "    plotter_itk = pyvista.PlotterITK()\n",
-    "    warped = grid.warp_by_scalar(\"u\", factor=0.5)\n",
-    "    plotter_itk.add_mesh(warped)\n",
-    "    plotter_itk.show(True)"
+    "    warped = grid.warp_by_scalar()\n",
+    "    plotter2 = pyvista.Plotter()\n",
+    "    plotter2.add_mesh(warped, show_edges=True, show_scalar_bar=True)\n",
+    "    plotter2.show(jupyter_backend=\"ipygany\")"
    ]
   },
   {
@@ -497,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [