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Release version compatible with 0.5.0 (#92)
* Bump version * Try fixing weird formatting * Try fixing pyvista ci
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.github/workflows/build-publish.yml

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build-publish:
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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:10082022
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container: dokken92/dolfinx_custom:v0.5.0
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env:
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HDF5_MPI: "ON"

.github/workflows/nightly.yml

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pip3 install --upgrade pip setuptools
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CC=mpicc HDF_MPI="ON" HDF5_DIR="/usr/local/" pip3 install --no-cache-dir -r docker/requirements.txt
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pip3 uninstall pyvista -y
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pip3 install pyvista --upgrade
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pip3 install pyvista --upgrade --user
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apt-get -qq update
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apt-get install -y libgl1-mesa-dev xvfb nodejs
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Changelog.md

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# Changelog
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## Dev
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- No Changes
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## v0.5.0
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- Using new GMSH interface in DOLFINx (`dolfinx.io.gmshio`) in all demos using GMSH
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- Added a section on custom Newton-solvers, see [chapter4/newton-solver].
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- Various minor DOLFINx API updates. `dolfinx.mesh.compute_boundary_facets` -> `dolfinx.mesh.exterior_facet_indices` with slightly different functionality. Use `dolfinx.mesh.MeshTagsMetaClass.find` instead of `mt.indices[mt.values==value]`.

Dockerfile

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FROM dokken92/dolfinx_custom:10082022
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FROM dokken92/dolfinx_custom:v0.5.0
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# create user with a home directory
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ARG NB_USER

chapter3/neumann_dirichlet_code.ipynb

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"\n",
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"Let's return to the Poisson problem from the [Fundamentals chapter](./../chapter1/fundamentals.md) and see how to extend the mathematics and the implementation to handle Dirichlet condition in combination with a Neumann condition.\n",
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"The domain is still the unit square, but now we set the Dirichlet condition $u=u_D$ at the left and right sides, while the Neumann condition \n",
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"\n",
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"$$\n",
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"-\\frac{\\partial u}{\\partial n}=g\n",
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"$$\n",
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"\n",
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"is applied to the remaining sides $y=0$ and $y=1$.\n",
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"\n",
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"## The PDE problem\n",

chapter3/neumann_dirichlet_code.py

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#
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# Let's return to the Poisson problem from the [Fundamentals chapter](./../chapter1/fundamentals.md) and see how to extend the mathematics and the implementation to handle Dirichlet condition in combination with a Neumann condition.
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# The domain is still the unit square, but now we set the Dirichlet condition $u=u_D$ at the left and right sides, while the Neumann condition
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#
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# $$
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# -\frac{\partial u}{\partial n}=g
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# $$
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#
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# is applied to the remaining sides $y=0$ and $y=1$.
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#
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# ## The PDE problem

docker/Dockerfile

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FROM dolfinx/lab:nightly
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FROM dolfinx/lab:v0.5.0
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WORKDIR /tmp/
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docker/LocalDockerfile

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# Can be set to "notebook"
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ARG JUPYTER_TYPE="lab"
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# Reinstall HDF5
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RUN export HDF5_SERIES=1.12 && \
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export HDF5_PATCH=2 && \
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wget -nc --quiet https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-${HDF5_SERIES}/hdf5-${HDF5_SERIES}.${HDF5_PATCH}/src/hdf5-${HDF5_SERIES}.${HDF5_PATCH}.tar.gz && \
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tar xfz hdf5-${HDF5_SERIES}.${HDF5_PATCH}.tar.gz && \
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cmake -G Ninja -DCMAKE_INSTALL_PREFIX=/usr/local -DCMAKE_BUILD_TYPE=Release -DHDF5_ENABLE_PARALLEL=on -DHDF5_ENABLE_Z_LIB_SUPPORT=on -B build-dir -S hdf5-${HDF5_SERIES}.${HDF5_PATCH} && \
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cmake --build build-dir && \
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cmake --install build-dir
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# Install python dependencies
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RUN pip3 install --upgrade pip

fem.md

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## Obtaining the software
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If you would like to work with DOLFINx outside of the binder-notebooks, you need to install the FEniCS software.
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The recommended way of installing DOLFINx for new usesers is by using Docker.
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The recommended way of installing DOLFINx for new users is by using Docker.
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Docker is a software that uses *containers* to supply software across different kinds of operating systems (Linux, Mac, Windows). The first step is to install docker, following the instructions at their [webpage](https://docs.docker.com/get-started/).
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All notebooks can be converted to python files using [nbconvert](https://nbconvert.readthedocs.io/en/latest/).
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### Tutorial compatible docker images
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The tutorial uses several dependencies for meshing, plotting and timings. A compatible `JupyterLab` image is available at [DockerHub/dokken92/dolfinx_custom:10082022](https://hub.docker.com/r/dokken92/dolfinx_custom/tags)
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The tutorial uses several dependencies for meshing, plotting and timings. A compatible `JupyterLab` image is available at [DockerHub/dokken92/dolfinx_custom:v0.5.0](https://hub.docker.com/r/dokken92/dolfinx_custom/tags)
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To use the notebookes in this tutorial with DOLFINx on your own computer, you should use the docker image using the following command
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To use the notebooks in this tutorial with DOLFINx on your own computer, you should use the docker image using the following command
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```bash
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docker run --init -p 8888:8888 -v "$(pwd)":/root/shared dokken92/dolfinx_custom:v0.5.0
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```
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docker run --init -p 8888:8888 -v "$(pwd)":/root/shared dokken92/dolfinx_custom:10082022
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This image can also be used as a normal docker container by adding:
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```bash
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docker run --ti -v "$(pwd)":/root/shared --entrypoint="/bin/bash" dokken92/dolfinx_custom:v0.5.0
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```
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The tutorials can also be exported as a notebook or PDF by clicking the ![Download](save.png)-symbol in the top right corner of the relevant tutorialThe notebook can in turn be used with a Python kernel which has DOLFINx.
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### Official images
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The [Dockerfile](https://github.com/FEniCS/dolfinx/blob/main/docker/Dockerfile)
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provides a definitive build recipe. As the DOLFINx docker images are hosted at Docker-hub, one can directly access the image
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```
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docker run dolfinx/dolfinx
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docker run dolfinx/dolfinx:v0.5.0
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```
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There are several ways of customizing a docker container, such as mounting volumes/sharing folder, setting a working directory, sharing graphical interfaces etc. See `docker run --help` for an extensive list.
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