# -*- coding: utf-8 -*-
"This modules provides additional functionality for users of FFC."
# Copyright (C) 2010 Anders Logg
#
# This file is part of FFC.
#
# FFC 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.
#
# FFC 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 FFC. If not, see <http://www.gnu.org/licenses/>.
#
# First added: 2010-09-03
# Last changed: 2010-09-06
__all__ = ["compute_tensor_representation"]
# Python modules
from time import time
# FFC modules
from ffc.compiler import _print_timing
from ffc.parameters import default_parameters
from ffc.analysis import analyze_forms
from ffc.representation import compute_ir
from ffc.optimization import optimize_ir
from ffc.codegeneration import generate_code
[docs]def compute_tensor_representation(form):
"""Compute tensor representation for given form. This function may
be useful for those (Hi Matt!) that want to access the FFC tensor
representation from outside FFC."""
# Set parameters
parameters = default_parameters()
parameters["representation"] = "tensor"
# parameters["optimize"] = "optimize"
# The below steps basically duplicate the compiler process but
# skip the code formatting step. Instead, we extract the relevant
# portions for tabulate_tensor.
# Stage 1: analysis
cpu_time = time()
analysis = analyze_forms([form], {}, parameters)
_print_timing(1, time() - cpu_time)
# Stage 2: intermediate representation
cpu_time = time()
ir = compute_ir(analysis, parameters)
_print_timing(2, time() - cpu_time)
# Stage 3: optimization
cpu_time = time()
oir = optimize_ir(ir, parameters)
_print_timing(3, time() - cpu_time)
# Stage 4: code generation
cpu_time = time()
code = generate_code(oir, "foo", parameters)
_print_timing(4, time() - cpu_time)
# Extract representations
ir_elements, ir_dofmaps, ir_integrals, ir_forms = ir
# Extract entries in reference tensor
reference_tensors = []
for i in ir_integrals:
if i["integral_type"] == "cell":
t = [A0.A0 for (A0, GK, dummy) in i["AK"]]
if len(t) == 1:
t = t[0]
elif i["integral_type"] == "exterior_facet":
t = [A0.A0 for j in i["AK"] for (A0, GK, dummy) in j]
if len(t) == 1:
t = t[0]
elif i["integral_type"] == "interior_facet":
t = [A0.A0 for j in i["AK"] for k in j for (A0, GK, dummy) in k]
if len(t) == 1:
t = t[0]
else:
raise RuntimeError("Unhandled domain type: %s" % str(i["integral_type"]))
reference_tensors.append(t)
# Extract code
code_elements, code_dofmaps, code_integrals, code_forms = code
# Extract code for computing the geometry tensor
geometry_tensor_codes = [c["tabulate_tensor"].split("// Compute element tensor")[0] for c in code_integrals]
# Simplify return values when there is just one term
if len(reference_tensors) == 1:
reference_tensors = reference_tensors[0]
if len(geometry_tensor_codes) == 1:
geometry_tensor_codes = geometry_tensor_codes[0]
return reference_tensors, geometry_tensor_codes