python-ufl 1.4.0-1 / usr / lib / python2.7 / dist-packages / ufl / algorithms / formtransformations.py

"""This module defines utilities for transforming
complete Forms into new related Forms."""

# Copyright (C) 2008-2014 Martin Sandve Alnes
#
# This file is part of UFL.
#
# UFL is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# UFL is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with UFL. If not, see <http://www.gnu.org/licenses/>.
#
# Modified by Anders Logg, 2008-2009.
# Modified by Garth N. Wells, 2010.
# Modified by Marie E. Rognes, 2010.

from ufl.common import product
from ufl.log import error, warning, debug
from ufl.assertions import ufl_assert

# All classes:
from ufl.argument import Argument
from ufl.coefficient import Coefficient
from ufl.constantvalue import Zero
from ufl.algebra import Sum

# Other algorithms:
from ufl.algorithms.analysis import extract_arguments, expr_has_terminal_types
from ufl.algorithms.transformer import Transformer, transform_integrands
from ufl.algorithms.replace import replace

def zero(e):
    return Zero(e.shape(), e.free_indices(), e.index_dimensions())

class PartExtracter(Transformer):
    """
    PartExtracter extracts those parts of a form that contain the
    given argument(s).
    """

    def __init__(self, arguments):
        Transformer.__init__(self)
        self._want = set(arguments)

    def expr(self, x):
        """The default is a nonlinear operator not accepting any
        Arguments among its children."""
        if expr_has_terminal_types(x, Argument):
            error("Found Argument in %s, this is an invalid expression." % repr(x))
        return (x, set())

    # Terminals that are not Variables or Arguments behave as default
    # expr-s.
    terminal = expr

    def variable(self, x):
        "Return relevant parts of this variable."

        # Extract parts/provides from this variable's expression
        expression, label = x.operands()
        part, provides = self.visit(expression)

        # If the extracted part is zero or we provide more than we
        # want, return zero
        if isinstance(part, Zero) or (provides - self._want):
            return (zero(x), set())

        # Reuse varible if possible (or reconstruct from part)
        x = self.reuse_if_possible(x, part, label)

        return (x, provides)

    def argument(self, x):
        "Return itself unless itself provides too much."

        # An argument provides itself
        provides = set((x,))

        # If we provide more than we want, return zero
        if provides - self._want:
            return (zero(x), set())

        return (x, provides)

    def sum(self, x):
        """
        Return the terms that might eventually yield the correct
        parts(!)

        The logic required for sums is a bit elaborate:

        A sum may contain terms providing different arguments. We
        should return (a sum of) a suitable subset of these
        terms. Those should all provide the same arguments.

        For each term in a sum, there are 2 simple possibilities:

        1a) The relevant part of the term is zero -> skip.
        1b) The term provides more arguments than we want -> skip

        2) If all terms fall into the above category, we can just
        return zero.

        Any remaining terms may provide exactly the arguments we want,
        or fewer. This is where things start getting interesting.

        3) Bottom-line: if there are terms with providing different
        arguments -- provide terms that contain the most arguments. If
        there are terms providing different sets of same size -> throw
        error (e.g. Argument(-1) + Argument(-2))
        """

        parts_that_provide = {}

        # 1. Skip terms that provide too much
        original_terms = x.operands()
        assert len(original_terms) == 2
        for term in original_terms:

            # Visit this term in the sum
            part, term_provides = self.visit(term)

            # If this part is zero or it provides more than we want,
            # skip it
            if isinstance(part, Zero) or (term_provides - self._want):
                continue

            # Add the contributions from this part to temporary list
            term_provides = frozenset(term_provides)
            if term_provides in parts_that_provide:
                parts_that_provide[term_provides] += [part]
            else:
                parts_that_provide[term_provides] = [part]

        # 2. If there are no remaining terms, return zero
        if not parts_that_provide:
            return (zero(x), set())

        # 3. Return the terms that provide the biggest set
        most_provided = frozenset()
        for (provideds, parts) in parts_that_provide.iteritems(): # TODO: Just sort instead?

            # Throw error if size of sets are equal (and not zero)
            if len(provideds) == len(most_provided) and len(most_provided):
                error("Don't know what to do with sums with different Arguments.")

            if provideds > most_provided:
                most_provided = provideds

        terms = parts_that_provide[most_provided]
        if len(terms) == 2:
            x = self.reuse_if_possible(x, *terms)
        else:
            x, = terms

        return (x, most_provided)

    def product(self, x, *ops):
        """ Note: Product is a visit-children-first handler. ops are
        the visited factors."""

        provides = set()
        factors = []

        for factor, factor_provides in ops:

            # If any factor is zero, return
            if isinstance(factor, Zero):
                return (zero(x), set())

            # Add factor to factors and extend provides
            factors.append(factor)
            provides = provides | factor_provides

            # If we provide more than we want, return zero
            if provides - self._want:
                return (zero(x), provides)

        # Reuse product if possible (or reconstruct from factors)
        x = self.reuse_if_possible(x, *factors)

        return (x, provides)

    # inner, outer and dot all behave as product
    inner = product
    outer = product
    dot = product

    def division(self, x):
        "Return parts_of_numerator/denominator."

        # Get numerator and denominator
        numerator, denominator = x.operands()

        # Check for Arguments in the denominator
        if expr_has_terminal_types(denominator, Argument):
            error("Found Argument in denominator of %s , this is an invalid expression." % repr(x))

        # Visit numerator
        numerator_parts, provides = self.visit(numerator)

        # If numerator is zero, return zero. (No need to check whether
        # it provides too much, already checked by visit.)
        if isinstance(numerator_parts, Zero):
            return (zero(x), set())

        # Reuse x if possible, otherwise reconstruct from (parts of)
        # numerator and denominator
        x = self.reuse_if_possible(x, numerator_parts, denominator)

        return (x, provides)

    def linear_operator(self, x, arg):
        """A linear operator with a single operand accepting arity > 0,
        providing whatever Argument its operand does."""

        # linear_operator is a visit-children-first handler. Handled
        # arguments are in arg.
        part, provides = arg

        # Discard if part is zero. (No need to check whether we
        # provide too much, already checked by children.)
        if isinstance(part, Zero):
            return (zero(x), set())

        x = self.reuse_if_possible(x, part)

        return (x, provides)

    # Positive and negative restrictions behave as linear operators
    positive_restricted = linear_operator
    negative_restricted = linear_operator

    # Cell and facet average are linear operators
    cell_avg = linear_operator
    facet_avg = linear_operator

    # Grad is a linear operator
    grad = linear_operator

    def linear_indexed_type(self, x):
        """Return parts of expression belonging to this indexed
        expression."""

        expression, index = x.operands()
        part, provides = self.visit(expression)

        # Return zero if extracted part is zero. (The expression
        # should already have checked if it provides too much.)
        if isinstance(part, Zero):
            return (zero(x), set())

        # Reuse x if possible (or reconstruct by indexing part)
        x = self.reuse_if_possible(x, part, index)

        return (x, provides)

    # All of these indexed thingies behave as a linear_indexed_type
    indexed = linear_indexed_type
    index_sum = linear_indexed_type
    component_tensor = linear_indexed_type
    spatial_derivative = linear_indexed_type

    def list_tensor(self, x, *ops):
        # list_tensor is a visit-children-first handler. ops contains
        # the visited operands with their provides. (It follows that
        # none of the visited operands provide more than wanted.)

        # Extract the most arguments provided by any of the components
        most_provides = ops[0][1]
        for (component, provides) in ops:
            if (provides - most_provides):
                most_provides = provides

        # Check that all components either provide the same arguments
        # or vanish. (This check is here b/c it is not obvious what to
        # return if the components provide different arguments, at
        # least with the current transformer design.)
        for (component, provides) in ops:
            if (provides != most_provides and not isinstance(component, Zero)):
                error("PartExtracter does not know how to handle list_tensors with non-zero components providing fewer arguments")

        # Return components
        components = [op[0] for op in ops]
        x = self.reuse_if_possible(x, *components)

        return (x, most_provides)

def compute_form_with_arity(form, arity, arguments=None):
    """Compute parts of form of given arity."""

    # Extract all arguments in form
    if arguments is None:
        arguments = extract_arguments(form)

    parts = [arg.part() for arg in arguments]
    if set(parts) - set((None,)):
        error("compute_form_with_arity cannot handle parts.")

    if len(arguments) < arity:
        warning("Form has no parts with arity %d." % arity)
        return 0*form

    # Assuming that the form is not a sum of terms
    # that depend on different arguments, e.g. (u+v)*dx
    # would result in just v*dx. But that doesn't make
    # any sense anyway.
    sub_arguments = set(arguments[:arity])
    pe = PartExtracter(sub_arguments)
    def _transform(e):
        e, provides = pe.visit(e)
        if provides == sub_arguments:
            return e
        return Zero()
    res = transform_integrands(form, _transform)
    return res

def compute_form_arities(form):
    """Return set of arities of terms present in form."""
    #ufl_assert(form.is_preprocessed(), "Assuming a preprocessed form.")

    # Extract all arguments present in form
    arguments = extract_arguments(form)

    parts = [arg.part() for arg in arguments]
    if set(parts) - set((None,)):
        error("compute_form_arities cannot handle parts.")

    arities = set()
    for arity in range(len(arguments)+1):

        # Compute parts with arity "arity"
        parts = compute_form_with_arity(form, arity, arguments)

        # Register arity if "parts" does not vanish
        if parts and parts.integrals():
            arities.add(arity)

    return arities

def compute_form_lhs(form):
    """Compute the left hand side of a form.

    Example:

        a = u*v*dx + f*v*dx
        a = lhs(a) -> u*v*dx
    """
    return compute_form_with_arity(form, 2)

def compute_form_rhs(form):
    """Compute the right hand side of a form.

    Example:

        a = u*v*dx + f*v*dx
        L = rhs(a) -> -f*v*dx
    """
    return -compute_form_with_arity(form, 1)

def compute_form_functional(form):
    """Compute the functional part of a form, that
    is the terms independent of Arguments.

    (Used for testing, not sure if it's useful for anything?)"""
    return compute_form_with_arity(form, 0)

def compute_form_action(form, coefficient):
    """Compute the action of a form on a Coefficient.

    This works simply by replacing the last Argument
    with a Coefficient on the same function space (element).
    The form returned will thus have one Argument less
    and one additional Coefficient at the end if no
    Coefficient has been provided.
    """
    # TODO: Check whatever makes sense for coefficient

    # Extract all arguments
    arguments = extract_arguments(form)

    parts = [arg.part() for arg in arguments]
    if set(parts) - set((None,)):
        error("compute_form_action cannot handle parts.")

    # Pick last argument (will be replaced)
    u = arguments[-1]

    e = u.element()
    if coefficient is None:
        coefficient = Coefficient(e)
    else:
        #ufl_assert(coefficient.element() == e, \
        if coefficient.element() != e:
            debug("Computing action of form on a coefficient in a different element space.")
    return replace(form, { u: coefficient })

def compute_energy_norm(form, coefficient):
    """Compute the a-norm of a Coefficient given a form a.

    This works simply by replacing the two Arguments
    with a Coefficient on the same function space (element).
    The Form returned will thus be a functional with no
    Arguments, and one additional Coefficient at the
    end if no coefficient has been provided.
    """
    arguments = extract_arguments(form)

    parts = [arg.part() for arg in arguments]
    if set(parts) - set((None,)):
        error("compute_energy_norm cannot handle parts.")

    ufl_assert(len(arguments) == 2, "Expecting bilinear form.")
    v, u = arguments
    e = u.element()
    e2 = v.element()
    ufl_assert(e == e2, "Expecting equal finite elements for test and trial functions, got '%s' and '%s'." % (str(e), str(e2)))
    if coefficient is None:
        coefficient = Coefficient(e)
    else:
        ufl_assert(coefficient.element() == e, \
            "Trying to compute action of form on a "\
            "coefficient in an incompatible element space.")
    return replace(form, { u: coefficient, v: coefficient })

def compute_form_adjoint(form, reordered_arguments=None):
    """Compute the adjoint of a bilinear form.

    This works simply by swapping the number and part of the two arguments,
    but keeping their elements and places in the integrand expressions.
    """
    arguments = extract_arguments(form)

    parts = [arg.part() for arg in arguments]
    if set(parts) - set((None,)):
        error("compute_form_adjoint cannot handle parts.")

    ufl_assert(len(arguments) == 2, "Expecting bilinear form.")

    v, u = arguments
    ufl_assert(v.number() < u.number(), "Mistaken assumption in code!")

    if reordered_arguments is None:
        reordered_u = u.reconstruct(number=v.number(), part=v.part())
        reordered_v = v.reconstruct(number=u.number(), part=u.part())
    else:
        reordered_u, reordered_v = reordered_arguments

    ufl_assert(reordered_u.number() < reordered_v.number(),
               "Ordering of new arguments is the same as the old arguments!")

    ufl_assert(reordered_u.part() == v.part(),
               "Ordering of new arguments is the same as the old arguments!")
    ufl_assert(reordered_v.part() == u.part(),
               "Ordering of new arguments is the same as the old arguments!")

    ufl_assert(reordered_u.element() == u.element(),
               "Element mismatch between new and old arguments (trial functions).")
    ufl_assert(reordered_v.element() == v.element(),
               "Element mismatch between new and old arguments (test functions).")

    return replace(form, {v: reordered_v, u: reordered_u})