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Update version to 0.4.0 (#75)
* Update version to 0.4.0 * Update underlying docker images
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‎.github/workflows/build-publish.yml

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build:
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# The type of runner that the job will run on
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runs-on: ubuntu-latest
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container: dokken92/dolfinx_custom:17032022
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container: dokken92/dolfinx_custom:02052022
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env:
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HDF5_MPI: "ON"

‎.github/workflows/docker-image.yml

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run: echo ${{ secrets.DOCKERHUB_TOKEN }} | docker login -u ${{ secrets.DOCKERHUB_USERNAME }} --password-stdin
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- name: Push to the DockerHub registry
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run: |
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docker push dokken92/dolfinx_custom:17032022
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docker push dokken92/dolfinx_custom:02052022

‎Changelog.md

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# Changelog
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## Dev
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## Dev
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- No changes
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## 0.4.0 (05.02.2021)
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- All `pyvista` plotting has been rewritten to use `ipygany` and `pythreejs` as well as using a cleaner interface.
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- `dolfinx.plot.create_vtk_topology` has been renamed to `dolfinx.plot.create_vtk_mesh` and can now be directly used as input to `pyvista.UnstructuredGrid`.
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- `dolfinx.fem.Function.compute_point_values` has been deprecated. Interpolation into a CG-1 is now the way of getting vertex values.

‎Dockerfile

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FROM dokken92/dolfinx_custom:17032022
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FROM dokken92/dolfinx_custom:02052022
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# create user with a home directory
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ARG NB_USER

‎README.md

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```bash
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docker run --rm -ti -v $(pwd):/root/shared -w /root/shared --init -p 8888:8888 local_lab_env
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```
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from the main directory
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from the main directory.

‎chapter1/fundamentals_code.ipynb

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"We now have the boundary data (and in this case the solution of \n",
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"the finite element problem) represented in the discrete function space.\n",
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"Next we would like to apply the boundary values to all degrees of freedom that are on the boundary of the discrete domain. We start by identifying the facets (line-segments) representing the outer boundary, using `dolfinx.mesh.compute_boundary_facets`.\n",
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"This function returns an array of booleans of the same size as the number of facets on this processor, where `True` indicates that the local facet $i$ is on the boundary. To reduce this to only the indices that are `True`, we use [`numpy.flatnonzero`](https://numpy.org/doc/stable/reference/generated/numpy.flatnonzero.html)."
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"This function returns an array of integers of the same size as the number of facets on this processor, where `1` indicates that the local facet $i$ is on the boundary. To reduce this to only the indices that are equal to `1`, we use [`numpy.flatnonzero`](https://numpy.org/doc/stable/reference/generated/numpy.flatnonzero.html)."
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]
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},
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{
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"output_type": "stream",
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"text": [
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"Error_L2 : 8.24e-03\n",
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"Error_max : 2.22e-15\n"
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"Error_max : 2.00e-15\n"
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]
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}
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],
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c0fbae39627443d095ae8e138ed6eb5b",
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"model_id": "be85adc5c1eb4871a7d86de3509c438e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(color='#fefefe', inten…"
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"Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(intensity=0.25, positi…"
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"metadata": {},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8865425a451440cbbab2398e17448c41",
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"model_id": "12ac381a9d92468b88ac86a9348fb316",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(color='#fefefe', inten…"
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"Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(intensity=0.25, positi…"
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"metadata": {},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "291ccff816554752956b35a46b1ccc07",
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"model_id": "1089f8c8d3d648c5be3284509702bf97",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [

‎chapter1/membrane_code.ipynb

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"text": [
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"Info : Meshing 1D...\n",
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"Info : Meshing curve 1 (Ellipse)\n",
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"Info : Done meshing 1D (Wall 0.000709738s, CPU 0.000866s)\n",
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"Info : Done meshing 1D (Wall 0.000912963s, CPU 0.000868s)\n",
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"Info : Meshing 2D...\n",
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"Info : Meshing surface 1 (Plane, Frontal-Delaunay)\n",
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"Info : Done meshing 2D (Wall 0.0881007s, CPU 0.086016s)\n",
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"Info : Done meshing 2D (Wall 0.0879933s, CPU 0.085647s)\n",
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"Info : 1550 nodes 3099 elements\n"
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}
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b488188eb55746359cfb4e887ee24fbb",
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"model_id": "2c73502e2eb841e0836fcec2f73df258",
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"version_major": 2,
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"version_minor": 0
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"text/plain": [
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"Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(color='#fefefe', inten…"
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "cbb80d941c1d49b18b2aa767a4ecab4f",
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"model_id": "005557f7534c452fb96cd6f8b2c71fa4",
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"version_major": 2,
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"version_minor": 0
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‎chapter1/nitsche.ipynb

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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8034af5a19554feab775b613a7877a0d",
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"model_id": "0a2508a473d04c9ba002bd095af29434",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(color='#fefefe', inten…"
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"Renderer(camera=PerspectiveCamera(aspect=1.3333333333333333, children=(DirectionalLight(intensity=0.25, positi…"
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"metadata": {},

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