Use GitHub notebook preview instead of nbviewer (#4513)

* Use GitHub notebook preview instead of nbviewer

nbviewer is slow and occasionally fails the linkcheck.

Author: connorjward

docs/source/notebooks.rst

@@ -28,16 +28,15 @@ Colab <https://fem-on-colab.github.io/index.html>`__ by `Francesco
 Ballarin <https://www.francescoballarin.it>`__, you can run the notebooks on
 Google Colab through your web browser, without installing Firedrake.
 
-We also provide links to non-interactive renderings of the notebooks using
-`Jupyter nbviewer <https://nbviewer.jupyter.org>`__.
+Non-interactive renderings of the notebooks can also be seen on `GitHub
+<https://github.com/firedrakeproject/firedrake/tree/release/docs/notebooks>`__.
 
 
 A first example
 ===============
 In this notebook, we solve the symmetric positive definite "Helmholtz"
 equation, and learn about meshes and function spaces.  A rendered
-version of this notebook is available `here
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/01-spd-helmholtz.ipynb>`__
+version of this notebook is available `here <https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/01-spd-helmholtz.ipynb>`__
 and there is a version `on Colab <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/01-spd-helmholtz.ipynb>`__
 
 
@@ -47,7 +46,7 @@ Incorporating strong boundary conditions
 Next, we modify the problem slightly and solve the Poisson equation.
 We introduce strong (Dirichlet) boundary conditions and `how to use
 them
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/02-poisson.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/02-poisson.ipynb>`__.
 You can run this notebook yourself `on Colab <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/02-poisson.ipynb>`__
 
 
@@ -58,7 +57,7 @@ Moving on from scalar problems, we look at our first vector-valued problem,
 namely the equations of linear elasticity.  In this notebook, we learn about
 some of UFL's support for tensor algebra, and start looking at `configuring
 linear solvers
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/03-elasticity.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/03-elasticity.ipynb>`__.
 You can run this notebook yourself `on Colab
 <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/03-elasticity.ipynb>`__
 
@@ -70,7 +69,7 @@ This notebook looks at a simple nonlinear problem, the viscous
 Burgers' equation, and also treats simple timestepping schemes.  We
 learn about formulating nonlinear, as opposed to linear problems, and
 also a little bit about how to write `efficient Firedrake code
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/04-burgers.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/04-burgers.ipynb>`__.
 You can run this notebook yourself `on Colab
 <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/04-burgers.ipynb>`__
 
@@ -79,7 +78,7 @@ A mixed formulation of the Poisson equation
 ===========================================
 
 `In this notebook
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/05-mixed-poisson.ipynb>`__.,
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/05-mixed-poisson.ipynb>`__.,
 we look at our first mixed finite element problem.  A dual formulation of the
 Poisson equation.  This equation also appears in the context of flow in porous
 media, as Darcy flow.  We introduce mixed function spaces and how to work with
@@ -95,7 +94,7 @@ PDE-constrained optimisation with `dolfin-adjoint <http://www.dolfin-adjoint.org
 
 Now that we've learnt how to solve some PDEs, we might want to consider
 optimisation subject to PDE constraints.  `This notebook
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/06-pde-constrained-optimisation.ipynb>`__
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/06-pde-constrained-optimisation.ipynb>`__
 introduces the use of `dolfin-adjoint <http://www.dolfin-adjoint.org/>`__ to
 solve PDE constrained optimisation problems.  We solve the Stokes equations and
 minimise energy loss due to heat, controlling inflow/outflow in a pipe. You can
@@ -109,7 +108,7 @@ Geometric multigrid
 The next notebook looks a little bit at the support Firedrake has for
 geometric multigrid, and how you can configure complex multilevel
 solvers purely using `PETSc options
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/07-geometric-multigrid.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/07-geometric-multigrid.ipynb>`__.
 You can run this notebook yourself `on Colab
 <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/07-geometric-multigrid.ipynb>`__
 
@@ -121,7 +120,7 @@ We next dive a little deeper into the advanced ways in which Firedrake
 and PETSc enable solvers and preconditioners to be composed in
 arbitrarily complex ways to `create an optimal solution strategy for a
 particular problem
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/08-composable-solvers.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/08-composable-solvers.ipynb>`__.
 You can run this notebook yourself `on Colab
 <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/08-composable-solvers.ipynb>`__
 
@@ -132,7 +131,7 @@ Hybridisation
 Building on the theme of composable solvers, we now explore
 `Firedrake's capabilities in the area of static condensation and
 hybridisation
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/09-hybridisation.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/09-hybridisation.ipynb>`__.
 You can run this notebook yourself `on Colab
 <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/09-hybridisation.ipynb>`__
 
@@ -143,7 +142,7 @@ Sum Factorisation
 In this notebook, we take a look under the hood at the sorts of performance
 optimisation that Firedrake's compilers can generate. In this case, we focus on
 `sum factorisation for tensor product elements
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/10-sum-factorisation.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/10-sum-factorisation.ipynb>`__.
 You can run this notebook yourself `on Colab
 <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/10-sum-factorisation.ipynb>`__
 
@@ -154,7 +153,7 @@ Solving adjoint problems
 In some cases, it can be useful to extract adjoint solution data which is
 written to tape by `dolfin-adjoint`. This notebook shows how to do that:
 `solving adjoint equations using firedrake-adjoint
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/11-extract-adjoint-solutions.ipynb>`__.
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/11-extract-adjoint-solutions.ipynb>`__.
 You can run this notebook yourself `on Colab
 <https://colab.research.google.com/github/firedrakeproject/notebooks/blob/main/11-extract-adjoint-solutions.ipynb>`__
 
@@ -165,7 +164,7 @@ Running on HPC
 When it comes to running Firedrake on a high performance computer
 there are a range of different techniques to get the best performance
 from your code. The `HPC demonstration
-<https://nbviewer.jupyter.org/github/firedrakeproject/firedrake/blob/release/docs/notebooks/12-HPC_demo.ipynb>`__
+<https://github.com/firedrakeproject/firedrake/blob/release/docs/notebooks/12-HPC_demo.ipynb>`__
 notebook builds up a multigrid solver for an elliptic problem
 specifically designed for solving very large problems using Firedrake on
 HPC. You can run this notebook yourself `on Colab