/usr/lib/python2.7/dist-packages/ffc/quadrature/tabulate_basis.py is in python-ffc 2016.2.0-1.
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# Copyright (C) 2009-2014 Kristian B. Oelgaard
#
# 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/>.
#
# Modified by Anders Logg, 2009, 2015
# Modified by Martin Sandve Alnæs, 2013-2014
"Quadrature representation class."
import numpy
import itertools
# UFL modules
from ufl.cell import num_cell_entities
from ufl.classes import ReferenceGrad, Grad, CellAvg, FacetAvg
from ufl.algorithms import extract_unique_elements, extract_type, extract_elements
from ufl import custom_integral_types
# FFC modules
from ffc.log import error
from ffc.utils import product, insert_nested_dict
from ffc.fiatinterface import create_element
from ffc.fiatinterface import map_facet_points, reference_cell_vertices
from ffc.representationutils import create_quadrature_points_and_weights
from ffc.representationutils import integral_type_to_entity_dim
def _find_element_derivatives(expr, elements, element_replace_map):
"Find the highest derivatives of given elements in expression."
# TODO: This is most likely not the best way to get the highest
# derivative of an element, but it works!
# Initialise dictionary of elements and the number of derivatives.
# (Note that elements are already mapped through the
# element_replace_map)
num_derivatives = dict((e, 0) for e in elements)
# Extract the derivatives from the integral.
derivatives = set(extract_type(expr, Grad)) | set(extract_type(expr,
ReferenceGrad))
# Loop derivatives and extract multiple derivatives.
for d in list(derivatives):
# After UFL has evaluated derivatives, only one element can be
# found inside any single Grad expression
elem, = extract_elements(d.ufl_operands[0])
elem = element_replace_map[elem]
# Set the number of derivatives to the highest value
# encountered so far.
num_derivatives[elem] = max(num_derivatives[elem],
len(extract_type(d, Grad)),
len(extract_type(d, ReferenceGrad)))
return num_derivatives
def _tabulate_empty_psi_table(tdim, deriv_order, element):
"Tabulate psi table when there are no points (custom integrals)."
# All combinations of partial derivatives up to given order
gdim = tdim # hack, consider passing gdim variable here
derivs = [d for d in itertools.product(*(gdim*[list(range(0, deriv_order + 1))]))]
derivs = [d for d in derivs if sum(d) <= deriv_order]
# Return empty table
table = {}
for d in derivs:
value_shape = element.value_shape()
if value_shape == ():
table[d] = [[]]
else:
value_size = product(value_shape)
table[d] = [[[] for c in range(value_size)]]
# Let entity be 0 even for non-cells, this is for
# custom integrals where we don't need tables to
# contain multiple entitites
entity = 0
return {entity: table}
def _map_entity_points(cellname, tdim, points, entity_dim, entity):
# Not sure if this is useful anywhere else than in _tabulate_psi_table!
if entity_dim == tdim:
assert entity == 0
return points
elif entity_dim == tdim-1:
return map_facet_points(points, entity)
elif entity_dim == 0:
return (reference_cell_vertices(cellname)[entity],)
def _tabulate_psi_table(integral_type, cellname, tdim,
element, deriv_order, points):
"Tabulate psi table for different integral types."
# Handle case when list of points is empty
if points is None:
return _tabulate_empty_psi_table(tdim, deriv_order, element)
# Otherwise, call FIAT to tabulate
entity_dim = integral_type_to_entity_dim(integral_type, tdim)
num_entities = num_cell_entities[cellname][entity_dim]
psi_table = {}
for entity in range(num_entities):
entity_points = _map_entity_points(cellname, tdim, points, entity_dim, entity)
psi_table[entity] = element.tabulate(deriv_order, entity_points)
return psi_table
# MSA: This function is in serious need for some refactoring and
# splitting up. Or perhaps I should just add a new
# implementation for uflacs, but I'd rather not have two versions
# to maintain.
def tabulate_basis(sorted_integrals, form_data, itg_data):
"Tabulate the basisfunctions and derivatives."
# MER: Note to newbies: this code assumes that each integral in
# the dictionary of sorted_integrals that enters here, has a
# unique number of quadrature points ...
# Initialise return values.
quadrature_rules = {}
psi_tables = {}
integrals = {}
avg_elements = {"cell": [], "facet": []}
# Get some useful variables in short form
integral_type = itg_data.integral_type
cell = itg_data.domain.ufl_cell()
cellname = cell.cellname()
tdim = itg_data.domain.topological_dimension()
entity_dim = integral_type_to_entity_dim(integral_type, tdim)
num_entities = num_cell_entities[cellname][entity_dim]
# Create canonical ordering of quadrature rules
rules = sorted(sorted_integrals.keys())
# Loop the quadrature points and tabulate the basis values.
for rule in rules:
scheme, degree = rule
# --------- Creating quadrature rule
# Make quadrature rule and get points and weights.
(points, weights) = create_quadrature_points_and_weights(integral_type,
cell, degree,
scheme)
# The TOTAL number of weights/points
num_points = None if weights is None else len(weights)
# Add points and rules to dictionary
if num_points in quadrature_rules:
error("This number of points is already present in the weight table: " + repr(quadrature_rules))
quadrature_rules[num_points] = (weights, points)
# --------- Store integral
# Add the integral with the number of points as a key to the
# return integrals.
integral = sorted_integrals[rule]
if num_points in integrals:
error("This number of points is already present in the integrals: " + repr(integrals))
integrals[num_points] = integral
# --------- Analyse UFL elements in integral
# Get all unique elements in integral.
ufl_elements = [form_data.element_replace_map[e]
for e in extract_unique_elements(integral)]
# Insert elements for x and J
domain = integral.ufl_domain() # FIXME: For all domains to be sure? Better to rewrite though.
x_element = domain.ufl_coordinate_element()
if x_element not in ufl_elements:
if integral_type in custom_integral_types:
# FIXME: Not yet implemented, in progress
# warning("Vector elements not yet supported in custom integrals so element for coordinate function x will not be generated.")
pass
else:
ufl_elements.append(x_element)
# Find all CellAvg and FacetAvg in integrals and extract
# elements
for avg, AvgType in (("cell", CellAvg), ("facet", FacetAvg)):
expressions = extract_type(integral, AvgType)
avg_elements[avg] = [form_data.element_replace_map[e]
for expr in expressions
for e in extract_unique_elements(expr)]
# Find the highest number of derivatives needed for each element
num_derivatives = _find_element_derivatives(integral.integrand(),
ufl_elements,
form_data.element_replace_map)
# Need at least 1 for the Jacobian
num_derivatives[x_element] = max(num_derivatives.get(x_element, 0), 1)
# --------- Evaluate FIAT elements in quadrature points and
# --------- store in tables
# Add the number of points to the psi tables dictionary
if num_points in psi_tables:
error("This number of points is already present in the psi table: " + repr(psi_tables))
psi_tables[num_points] = {}
# Loop FIAT elements and tabulate basis as usual.
for ufl_element in ufl_elements:
fiat_element = create_element(ufl_element)
# Tabulate table of basis functions and derivatives in
# points
psi_table = _tabulate_psi_table(integral_type, cellname, tdim,
fiat_element,
num_derivatives[ufl_element],
points)
# Insert table into dictionary based on UFL elements
# (None=not averaged)
avg = None
psi_tables[num_points][ufl_element] = { avg: psi_table }
# Loop over elements found in CellAvg and tabulate basis averages
num_points = 1
for avg in ("cell", "facet"):
# Doesn't matter if it's exterior or interior
if avg == "cell":
avg_integral_type = "cell"
elif avg == "facet":
avg_integral_type = "exterior_facet"
for element in avg_elements[avg]:
fiat_element = create_element(element)
# Make quadrature rule and get points and weights.
(points, weights) = create_quadrature_points_and_weights(avg_integral_type, cell, element.degree(), "default")
wsum = sum(weights)
# Tabulate table of basis functions and derivatives in
# points
entity_psi_tables = _tabulate_psi_table(avg_integral_type,
cellname, tdim,
fiat_element, 0, points)
rank = len(element.value_shape())
# Hack, duplicating table with per-cell values for each
# facet in the case of cell_avg(f) in a facet integral
if num_entities > len(entity_psi_tables):
assert len(entity_psi_tables) == 1
assert avg_integral_type == "cell"
assert "facet" in integral_type
v, = sorted(entity_psi_tables.values())
entity_psi_tables = dict((e, v) for e in range(num_entities))
for entity, deriv_table in sorted(entity_psi_tables.items()):
deriv, = sorted(deriv_table.keys()) # Not expecting derivatives of averages
psi_table = deriv_table[deriv]
if rank:
# Compute numeric integral
num_dofs, num_components, num_points = psi_table.shape
if num_points != len(weights):
error("Weights and table shape does not match.")
avg_psi_table = numpy.asarray([[[numpy.dot(psi_table[j, k, :], weights) / wsum]
for k in range(num_components)]
for j in range(num_dofs)])
else:
# Compute numeric integral
num_dofs, num_points = psi_table.shape
if num_points != len(weights):
error("Weights and table shape does not match.")
avg_psi_table = numpy.asarray([[numpy.dot(psi_table[j, :],
weights) / wsum] for j in range(num_dofs)])
# Insert table into dictionary based on UFL elements
insert_nested_dict(psi_tables, (num_points, element, avg,
entity, deriv), avg_psi_table)
return (integrals, psi_tables, quadrature_rules)
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