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# # Poisson equation
#
# This demo illustrates how to:
#
# - Create a {py:class}`function space <dolfinx.fem.FunctionSpace>`
# - Solve a linear partial differential equation
#
# ```{admonition} Download sources
# :class: download
# * {download}`Python script <./demo_poisson.py>`
# * {download}`Jupyter notebook <./demo_poisson.ipynb>`
# ```
# ## Equation and problem definition
#
# For a domain $\Omega \subset \mathbb{R}^n$ with boundary $\partial
# \Omega = \Gamma_{D} \cup \Gamma_{N}$, the Poisson equation with
# particular boundary conditions reads:
#
# $$
# \begin{align}
#   - \nabla^{2} u &= f \quad {\rm in} \ \Omega, \\
#   u &= 0 \quad {\rm on} \ \Gamma_{D}, \\
#   \nabla u \cdot n &= g \quad {\rm on} \ \Gamma_{N}. \\
# \end{align}
# $$
#
# where $f$ and $g$ are input data and $n$ denotes the outward directed
# boundary normal. The variational problem reads: find $u \in V$ such
# that
#
# $$
# a(u, v) = L(v) \quad \forall \ v \in V,
# $$
#
# where $V$ is a suitable function space and
#
# $$
# \begin{align}
#   a(u, v) &:= \int_{\Omega} \nabla u \cdot \nabla v \, {\rm d} x, \\
#   L(v)    &:= \int_{\Omega} f v \, {\rm d} x + \int_{\Gamma_{N}} g v \,
#               {\rm d} s.
# \end{align}
# $$
#
# The expression $a(u, v)$ is the bilinear form and $L(v)$
# is the linear form. It is assumed that all functions in $V$
# satisfy the Dirichlet boundary conditions ($u = 0 \ {\rm on} \
# \Gamma_{D}$).
#
# In this demo we consider:
#
# - $\Omega = [0,2] \times [0,1]$ (a rectangle)
# - $\Gamma_{D} = \{(0, y) \cup (2, y) \subset \partial \Omega\}$
# - $\Gamma_{N} = \{(x, 0) \cup (x, 1) \subset \partial \Omega\}$
# - $g = \sin(5x)$
# - $f = 10\exp(-((x - 0.5)^2 + (y - 0.5)^2) / 0.02)$
#
# ## Implementation
#
# The modules that will be used are imported:

# +
from pathlib import Path

from mpi4py import MPI
from petsc4py.PETSc import ScalarType  # type: ignore

import numpy as np

import ufl
from dolfinx import fem, io, mesh, plot
from dolfinx.fem.petsc import LinearProblem

# -

# Note that it is important to first `from mpi4py import MPI` to
# ensure that MPI is correctly initialised.

# We create a rectangular {py:class}`Mesh <dolfinx.mesh.Mesh>` using
# {py:func}`create_rectangle <dolfinx.mesh.create_rectangle>`, and
# create a finite element {py:class}`function space
# <dolfinx.fem.FunctionSpace>` $V$ on the mesh.

# +
msh = mesh.create_rectangle(
    comm=MPI.COMM_WORLD,
    points=((0.0, 0.0), (2.0, 1.0)),
    n=(32, 16),
    cell_type=mesh.CellType.triangle,
)
V = fem.functionspace(msh, ("Lagrange", 1))
# -

# The second argument to {py:func}`functionspace
# <dolfinx.fem.functionspace>` is a tuple `(family, degree)`, where
# `family` is the finite element family, and `degree` specifies the
# polynomial degree. In this case `V` is a space of continuous Lagrange
# finite elements of degree 1. For further details of how one can specify
# finite elements as tuples, see {py:class}`ElementMetaData
# <dolfinx.fem.ElementMetaData>`.
#
# To apply the Dirichlet boundary conditions, we find the mesh facets
# (entities of topological co-dimension 1) that lie on the boundary
# $\Gamma_D$ using {py:func}`locate_entities_boundary
# <dolfinx.mesh.locate_entities_boundary>`. The function is provided
# with a 'marker' function that returns `True` for points `x` on the
# boundary and `False` otherwise.

facets = mesh.locate_entities_boundary(
    msh,
    dim=(msh.topology.dim - 1),
    marker=lambda x: np.isclose(x[0], 0.0) | np.isclose(x[0], 2.0),
)

# We now find the degrees-of-freedom that are associated with the
# boundary facets using {py:func}`locate_dofs_topological
# <dolfinx.fem.locate_dofs_topological>`:

dofs = fem.locate_dofs_topological(V=V, entity_dim=1, entities=facets)

# and use {py:func}`dirichletbc <dolfinx.fem.dirichletbc>` to create a
# {py:class}`DirichletBC <dolfinx.fem.DirichletBC>` class that
# represents the boundary condition:

bc = fem.dirichletbc(value=ScalarType(0), dofs=dofs, V=V)

# Next, the variational problem is defined:

# +
u = ufl.TrialFunction(V)
v = ufl.TestFunction(V)
x = ufl.SpatialCoordinate(msh)
f = 10 * ufl.exp(-((x[0] - 0.5) ** 2 + (x[1] - 0.5) ** 2) / 0.02)
g = ufl.sin(5 * x[0])
a = ufl.inner(ufl.grad(u), ufl.grad(v)) * ufl.dx
L = ufl.inner(f, v) * ufl.dx + ufl.inner(g, v) * ufl.ds
# -

# A {py:class}`LinearProblem <dolfinx.fem.petsc.LinearProblem>` object is
# created that brings together the variational problem, the Dirichlet
# boundary condition, and which specifies the linear solver. In this
# case an LU solver is used, and we ask that PETSc throws an error
# if the solver does not converge. The {py:func}`solve
# <dolfinx.fem.petsc.LinearProblem.solve>` computes the solution.

# +
problem = LinearProblem(
    a,
    L,
    bcs=[bc],
    petsc_options_prefix="demo_poisson_",
    petsc_options={"ksp_type": "preonly", "pc_type": "lu", "ksp_error_if_not_converged": True},
)
uh = problem.solve()
assert isinstance(uh, fem.Function)
# -

# The solution can be written to a {py:class}`XDMFFile
# <dolfinx.io.XDMFFile>` file visualization with [ParaView](https://www.paraview.org/)
# or [VisIt](https://visit-dav.github.io/visit-website/):

# +
out_folder = Path("out_poisson")
out_folder.mkdir(parents=True, exist_ok=True)
with io.XDMFFile(msh.comm, out_folder / "poisson.xdmf", "w") as file:
    file.write_mesh(msh)
    file.write_function(uh)
# -

# and displayed using [pyvista](https://docs.pyvista.org/).

# +
try:
    import pyvista

    cells, types, x = plot.vtk_mesh(V)
    grid = pyvista.UnstructuredGrid(cells, types, x)
    grid.point_data["u"] = uh.x.array.real
    grid.set_active_scalars("u")
    plotter = pyvista.Plotter()
    plotter.add_mesh(grid, show_edges=True)
    warped = grid.warp_by_scalar()
    plotter.add_mesh(warped)
    if pyvista.OFF_SCREEN:
        plotter.screenshot(out_folder / "uh_poisson.png")
    else:
        plotter.show()
except ModuleNotFoundError:
    print("'pyvista' is required to visualise the solution.")
    print("To install pyvista with pip: 'python3 -m pip install pyvista'.")
# -
