xref: /petsc/src/binding/petsc4py/demo/legacy/ode/heat.py (revision 226f8a8a5081bc6ad7227cd631662400f0d6e2a0)

# Solves Heat equation on a periodic domain, using raw VecScatter
import sys
import petsc4py

petsc4py.init(sys.argv)

from petsc4py import PETSc
from mpi4py import MPI
import numpy


class Heat:
    def __init__(self, comm, N):
        self.comm = comm
        self.N = N  # global problem size
        self.h = 1 / N  # grid spacing on unit interval
        self.n = N // comm.size + int(
            comm.rank < (N % comm.size)
        )  # owned part of global problem
        self.start = comm.exscan(self.n)
        if comm.rank == 0:
            self.start = 0
        gindices = (
            numpy.arange(self.start - 1, self.start + self.n + 1, dtype=PETSc.IntType)
            % N
        )  # periodic
        self.mat = PETSc.Mat().create(comm=comm)
        size = (self.n, self.N)  # local and global sizes
        self.mat.setSizes((size, size))
        self.mat.setFromOptions()
        self.mat.setPreallocationNNZ(
            (3, 1)
        )  # Conservative preallocation for 3 "local" columns and one non-local

        # Allow matrix insertion using local indices [0:n+2]
        lgmap = PETSc.LGMap().create(list(gindices), comm=comm)
        self.mat.setLGMap(lgmap, lgmap)

        # Global and local vectors
        self.gvec = self.mat.createVecRight()
        self.lvec = PETSc.Vec().create(comm=PETSc.COMM_SELF)
        self.lvec.setSizes(self.n + 2)
        self.lvec.setUp()
        # Configure scatter from global to local
        isg = PETSc.IS().createGeneral(list(gindices), comm=comm)
        self.g2l = PETSc.Scatter().create(self.gvec, isg, self.lvec, None)

        self.tozero, self.zvec = PETSc.Scatter.toZero(self.gvec)
        self.history = []

        if False:  # Print some diagnostics
            print(
                '[%d] local size %d, global size %d, starting offset %d'
                % (comm.rank, self.n, self.N, self.start)
            )
            self.gvec.setArray(numpy.arange(self.start, self.start + self.n))
            self.gvec.view()
            self.g2l.scatter(self.gvec, self.lvec, PETSc.InsertMode.INSERT)
            for rank in range(comm.size):
                if rank == comm.rank:
                    print('Contents of local Vec on rank %d' % rank)
                    self.lvec.view()
                comm.barrier()

    def evalSolution(self, t, x):
        if t != 0.0:
            raise ValueError('Only for t=0')
        coord = numpy.arange(self.start, self.start + self.n) / self.N
        x.setArray((numpy.abs(coord - 0.5) < 0.1) * 1.0)

    def evalFunction(self, ts, t, x, xdot, f):
        self.g2l.scatter(x, self.lvec, PETSc.InsertMode.INSERT)  # lvec is a work vector
        h = self.h
        with self.lvec as u, xdot as udot:
            f.setArray(
                udot * h + 2 * u[1:-1] / h - u[:-2] / h - u[2:] / h
            )  # Scale equation by volume element

    def evalJacobian(self, ts, t, x, xdot, a, A, B):
        h = self.h
        for i in range(self.n):
            lidx = i + 1
            B.setValuesLocal(
                [lidx], [lidx - 1, lidx, lidx + 1], [-1 / h, a * h + 2 / h, -1 / h]
            )
        B.assemble()
        if A != B:
            A.assemble()  # If operator is different from matrix used to construct the preconditioner

    def monitor(self, ts, i, t, x):
        if self.history:
            lasti, lastt, lastx = self.history[-1]
            if i < lasti + 4 or t < lastt + 1e-4:
                return
        self.tozero.scatter(x, self.zvec, PETSc.InsertMode.INSERT)
        xx = self.zvec[:].tolist()
        self.history.append((i, t, xx))

    def plotHistory(self):
        try:
            from matplotlib import pylab, rcParams
        except ImportError:
            return
        rcParams.update({'text.usetex': True, 'figure.figsize': (10, 6)})
        # rc('figure', figsize=(600,400))
        pylab.title('Heat: TS \\texttt{%s}' % ts.getType())
        x = numpy.arange(self.N) / self.N
        for i, t, u in self.history:
            pylab.plot(x, u, label='step=%d t=%8.2g' % (i, t))
        pylab.xlabel('$x$')
        pylab.ylabel('$u$')
        pylab.legend(loc='upper right')
        pylab.savefig('heat-history.png')
        # pylab.show()


OptDB = PETSc.Options()
ode = Heat(MPI.COMM_WORLD, OptDB.getInt('n', 100))

x = ode.gvec.duplicate()
f = ode.gvec.duplicate()

ts = PETSc.TS().create(comm=ode.comm)
ts.setType(ts.Type.ROSW)  # Rosenbrock-W. ARKIMEX is a nonlinearly implicit alternative.

ts.setIFunction(ode.evalFunction, ode.gvec)
ts.setIJacobian(ode.evalJacobian, ode.mat)

ts.setMonitor(ode.monitor)

ts.setTime(0.0)
ts.setTimeStep(ode.h**2)
ts.setMaxTime(1)
ts.setMaxSteps(100)
ts.setExactFinalTime(PETSc.TS.ExactFinalTime.INTERPOLATE)
ts.setMaxSNESFailures(
    -1
)  # allow an unlimited number of failures (step will be rejected and retried)

snes = ts.getSNES()  # Nonlinear solver
snes.setTolerances(
    max_it=10
)  # Stop nonlinear solve after 10 iterations (TS will retry with shorter step)
ksp = snes.getKSP()  # Linear solver
ksp.setType(ksp.Type.CG)  # Conjugate gradients
pc = ksp.getPC()  # Preconditioner
if False:  # Configure algebraic multigrid, could use run-time options instead
    pc.setType(
        pc.Type.GAMG
    )  # PETSc's native AMG implementation, mostly based on smoothed aggregation
    OptDB['mg_coarse_pc_type'] = 'svd'  # more specific multigrid options
    OptDB['mg_levels_pc_type'] = 'sor'

ts.setFromOptions()  # Apply run-time options, e.g. -ts_adapt_monitor -ts_type arkimex -snes_converged_reason
ode.evalSolution(0.0, x)
ts.solve(x)
if ode.comm.rank == 0:
    print(
        'steps %d (%d rejected, %d SNES fails), nonlinear its %d, linear its %d'
        % (
            ts.getStepNumber(),
            ts.getStepRejections(),
            ts.getSNESFailures(),
            ts.getSNESIterations(),
            ts.getKSPIterations(),
        )
    )

if OptDB.getBool('plot_history', True) and ode.comm.rank == 0:
    ode.plotHistory()