/usr/lib/python2.7/dist-packages/ffc/quadrature/quadraturetransformer.py is in python-ffc 1.4.0-1.
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# Copyright (C) 2009-2011 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 Peter Brune 2009
# Modified by Anders Logg 2009, 2013
#
# First added: 2009-02-09
# Last changed: 2014-04-23
# Python modules.
from numpy import shape
# UFL common.
from ufl.common import product, StackDict, Stack
# UFL Classes.
from ufl.classes import FixedIndex
from ufl.classes import IntValue
from ufl.classes import FloatValue
from ufl.classes import Coefficient
from ufl.classes import Operator
# UFL Algorithms.
from ufl.algorithms.printing import tree_format
# FFC modules.
from ffc.log import info, debug, error, ffc_assert
from ffc.cpp import format
# Utility and optimisation functions for quadraturegenerator.
from quadraturetransformerbase import QuadratureTransformerBase
from quadratureutils import create_permutations
from reduce_operations import operation_count
from symbolics import IP
class QuadratureTransformer(QuadratureTransformerBase):
"Transform UFL representation to quadrature code."
def __init__(self, *args):
# Initialise base class.
QuadratureTransformerBase.__init__(self, *args)
# -------------------------------------------------------------------------
# Start handling UFL classes.
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# AlgebraOperators (algebra.py).
# -------------------------------------------------------------------------
def sum(self, o, *operands):
#print("Visiting Sum: " + "\noperands: \n" + "\n".join(map(repr, operands)))
# Prefetch formats to speed up code generation.
f_group = format["grouping"]
f_add = format["add"]
f_mult = format["multiply"]
f_float = format["floating point"]
code = {}
# Loop operands that has to be summed and sort according to map (j,k).
for op in operands:
# If entries does already exist we can add the code, otherwise just
# dump them in the element tensor.
for key, val in op.items():
if key in code:
code[key].append(val)
else:
code[key] = [val]
# Add sums and group if necessary.
for key, val in code.items():
# Exclude all zero valued terms from sum
value = [v for v in val if not v is None]
if len(value) > 1:
# NOTE: Since we no longer call expand_indices, the following
# is needed to prevent the code from exploding for forms like
# HyperElasticity
duplications = {}
for val in value:
if val in duplications:
duplications[val] += 1
continue
duplications[val] = 1
# Add a product for each term that has duplicate code
expressions = []
for expr, num_occur in duplications.items():
if num_occur > 1:
# Pre-multiply expression with number of occurrences
expressions.append(f_mult([f_float(num_occur), expr]))
continue
# Just add expression if there is only one
expressions.append(expr)
ffc_assert(expressions, "Where did the expressions go?")
if len(expressions) > 1:
code[key] = f_group(f_add(expressions))
continue
code[key] = expressions[0]
else:
# Check for zero valued sum and delete from code
# This might result in returning an empty dict, but that should
# be interpreted as zero by other handlers.
if not value:
del code[key]
continue
code[key] = value[0]
return code
def product(self, o, *operands):
#print("Visiting Product with operands: \n" + "\n".join(map(repr,operands)))
# Prefetch formats to speed up code generation.
f_mult = format["multiply"]
permute = []
not_permute = []
# Sort operands in objects that needs permutation and objects that does not.
for op in operands:
# If we get an empty dict, something was zero and so is the product.
if not op:
return {}
if len(op) > 1 or (op and op.keys()[0] != ()):
permute.append(op)
elif op and op.keys()[0] == ():
not_permute.append(op[()])
# Create permutations.
#print("\npermute: " + repr(permute))
#print("\nnot_permute: " + repr(not_permute))
permutations = create_permutations(permute)
#print("\npermutations: " + repr(permutations))
# Create code.
code ={}
if permutations:
for key, val in permutations.items():
# Sort key in order to create a unique key.
l = list(key)
l.sort()
# Loop products, don't multiply by '1' and if we encounter a None the product is zero.
# TODO: Need to find a way to remove and J_inv00 terms that might
# disappear as a consequence of eliminating a zero valued term
value = []
zero = False
for v in val + not_permute:
if v is None:
ffc_assert(tuple(l) not in code, "This key should not be in the code.")
code[tuple(l)] = None
zero = True
break
elif not v:
print "v: '%s'" % repr(v)
error("should not happen")
elif v == "1":
pass
else:
value.append(v)
if not value:
value = ["1"]
if zero:
code[tuple(l)] = None
else:
code[tuple(l)] = f_mult(value)
else:
# Loop products, don't multiply by '1' and if we encounter a None the product is zero.
# TODO: Need to find a way to remove terms from 'used sets' that might
# disappear as a consequence of eliminating a zero valued term
value = []
for v in not_permute:
if v is None:
code[()] = None
return code
elif not v:
print "v: '%s'" % repr(v)
error("should not happen")
elif v == "1":
pass
else:
value.append(v)
# We did have values, but they might have been all ones.
if value == [] and not_permute != []:
code[()] = f_mult(["1"])
else:
code[()] = f_mult(value)
return code
def division(self, o, *operands):
#print("Visiting Division with operands: \n" + "\n".join(map(repr,operands)))
# Prefetch formats to speed up code generation.
f_div = format["div"]
f_grouping = format["grouping"]
ffc_assert(len(operands) == 2, \
"Expected exactly two operands (numerator and denominator): " + repr(operands))
# Get the code from the operands.
numerator_code, denominator_code = operands
# TODO: Are these safety checks needed? Need to check for None?
ffc_assert(() in denominator_code and len(denominator_code) == 1, \
"Only support function type denominator: " + repr(denominator_code))
code = {}
# Get denominator and create new values for the numerator.
denominator = denominator_code[()]
ffc_assert(denominator is not None, "Division by zero!")
for key, val in numerator_code.items():
# If numerator is None the fraction is also None
if val is None:
code[key] = None
# If denominator is '1', just return numerator
elif denominator == "1":
code[key] = val
# Create fraction and add to code
else:
code[key] = f_div(val, f_grouping(denominator))
return code
def power(self, o):
#print("\n\nVisiting Power: " + repr(o))
# Get base and exponent.
base, expo = o.operands()
# Visit base to get base code.
base_code = self.visit(base)
# TODO: Are these safety checks needed? Need to check for None?
ffc_assert(() in base_code and len(base_code) == 1, "Only support function type base: " + repr(base_code))
# Get the base code.
val = base_code[()]
# Handle different exponents
if isinstance(expo, IntValue):
return {(): format["power"](val, expo.value())}
elif isinstance(expo, FloatValue):
return {(): format["std power"](val, format["floating point"](expo.value()))}
elif isinstance(expo, (Coefficient, Operator)):
exp = self.visit(expo)
return {(): format["std power"](val, exp[()])}
else:
error("power does not support this exponent: " + repr(expo))
def abs(self, o, *operands):
#print("\n\nVisiting Abs: " + repr(o) + "with operands: " + "\n".join(map(repr,operands)))
# Prefetch formats to speed up code generation.
f_abs = format["absolute value"]
# TODO: Are these safety checks needed? Need to check for None?
ffc_assert(len(operands) == 1 and () in operands[0] and len(operands[0]) == 1, \
"Abs expects one operand of function type: " + repr(operands))
# Take absolute value of operand.
return {():f_abs(operands[0][()])}
# -------------------------------------------------------------------------
# Condition, Conditional (conditional.py).
# -------------------------------------------------------------------------
def not_condition(self, o, *operands):
# This is a Condition but not a BinaryCondition, and the operand will be another Condition
# Get condition expression and do safety checks.
# Might be a bit too strict?
cond, = operands
ffc_assert(len(cond) == 1 and cond.keys()[0] == (),\
"Condition for NotCondition should only be one function: " + repr(cond))
return {(): format["not"](cond[()])}
def binary_condition(self, o, *operands):
# Get LHS and RHS expressions and do safety checks.
# Might be a bit too strict?
lhs, rhs = operands
ffc_assert(len(lhs) == 1 and lhs.keys()[0] == (),\
"LHS of Condition should only be one function: " + repr(lhs))
ffc_assert(len(rhs) == 1 and rhs.keys()[0] == (),\
"RHS of Condition should only be one function: " + repr(rhs))
# Map names from UFL to cpp.py.
name_map = {"==":"is equal", "!=":"not equal",\
"<":"less than", ">":"greater than",\
"<=":"less equal", ">=":"greater equal",\
"&&":"and", "||": "or"}
# Get values and test for None
l_val = lhs[()]
r_val = rhs[()]
if l_val is None:
l_val = format["float"](0.0)
if r_val is None:
r_val = format["float"](0.0)
return {(): format["grouping"](l_val + format[name_map[o._name]] + r_val)}
def conditional(self, o, *operands):
# Get condition and return values; and do safety check.
cond, true, false = operands
ffc_assert(len(cond) == 1 and cond.keys()[0] == (),\
"Condtion should only be one function: " + repr(cond))
ffc_assert(len(true) == 1 and true.keys()[0] == (),\
"True value of Condtional should only be one function: " + repr(true))
ffc_assert(len(false) == 1 and false.keys()[0] == (),\
"False value of Condtional should only be one function: " + repr(false))
# Get values and test for None
t_val = true[()]
f_val = false[()]
if t_val is None:
t_val = format["float"](0.0)
if f_val is None:
f_val = format["float"](0.0)
# Create expression for conditional
expr = format["evaluate conditional"](cond[()], t_val, f_val)
num = len(self.conditionals)
name = format["conditional"](num)
if not expr in self.conditionals:
self.conditionals[expr] = (IP, operation_count(expr, format), num)
else:
num = self.conditionals[expr][2]
name = format["conditional"](num)
return {():name}
# -------------------------------------------------------------------------
# FacetNormal, CellVolume, Circumradius, FacetArea (geometry.py).
# -------------------------------------------------------------------------
def cell_coordinate(self, o): # FIXME
error("This object should be implemented by the child class.")
def facet_coordinate(self, o): # FIXME
error("This object should be implemented by the child class.")
def cell_origin(self, o): # FIXME
error("This object should be implemented by the child class.")
def facet_origin(self, o): # FIXME
error("This object should be implemented by the child class.")
def cell_facet_origin(self, o): # FIXME
error("This object should be implemented by the child class.")
def jacobian(self, o): # FIXME
error("This object should be implemented by the child class.")
def jacobian_determinant(self, o): # FIXME
error("This object should be implemented by the child class.")
def jacobian_inverse(self, o): # FIXME
error("This object should be implemented by the child class.")
def facet_jacobian(self, o): # FIXME
error("This object should be implemented by the child class.")
def facet_jacobian_determinant(self, o): # FIXME
error("This object should be implemented by the child class.")
def facet_jacobian_inverse(self, o): # FIXME
error("This object should be implemented by the child class.")
def cell_facet_jacobian(self, o): # FIXME
error("This object should be implemented by the child class.")
def cell_facet_jacobian_determinant(self, o): # FIXME
error("This object should be implemented by the child class.")
def cell_facet_jacobian_inverse(self, o): # FIXME
error("This object should be implemented by the child class.")
def facet_normal(self, o):
#print("Visiting FacetNormal:")
# Get the component
components = self.component()
# Safety check.
ffc_assert(len(components) == 1, "FacetNormal expects 1 component index: " + repr(components))
# Handle 1D as a special case.
# FIXME: KBO: This has to change for mD elements in R^n : m < n
if self.gdim == 1: # FIXME: MSA: UFL uses shape (1,) now, can we remove the special case here then?
normal_component = format["normal component"](self.restriction, "")
else:
normal_component = format["normal component"](self.restriction, components[0])
self.trans_set.add(normal_component)
return {():normal_component}
def cell_normal(self, o): # FIXME
error("This object should be implemented by the child class.")
def cell_volume(self, o):
# FIXME: KBO: This has to change for higher order elements
volume = format["cell volume"](self.restriction)
self.trans_set.add(volume)
return {():volume}
def circumradius(self, o):
# FIXME: KBO: This has to change for higher order elements
circumradius = format["circumradius"](self.restriction)
self.trans_set.add(circumradius)
return {():circumradius}
def facet_area(self, o):
# FIXME: KBO: This has to change for higher order elements
# NOTE: Omitting restriction because the area of a facet is the same
# on both sides.
# FIXME: Since we use the scale factor, facet area has no meaning
# for cell integrals. (Need check in FFC or UFL).
area = format["facet area"]
self.trans_set.add(area)
return {():area}
def min_facet_edge_length(self, o):
# FIXME: this has no meaning for cell integrals. (Need check in FFC or UFL).
tdim = self.tdim # FIXME: o.domain().topological_dimension()
if tdim < 3:
return self.facet_area(o)
edgelen = format["min facet edge length"](self.restriction)
self.trans_set.add(edgelen)
return {():edgelen}
def max_facet_edge_length(self, o):
# FIXME: this has no meaning for cell integrals. (Need check in FFC or UFL).
tdim = self.tdim # FIXME: o.domain().topological_dimension()
if tdim < 3:
return self.facet_area(o)
edgelen = format["max facet edge length"](self.restriction)
self.trans_set.add(edgelen)
return {():edgelen}
def cell_orientation(self, o): # FIXME
error("This object should be implemented by the child class.")
def quadrature_weight(self, o): # FIXME
error("This object should be implemented by the child class.")
# -------------------------------------------------------------------------
def create_argument(self, ufl_argument, derivatives, component, local_comp,
local_offset, ffc_element, transformation, multiindices,
tdim, gdim, avg):
"Create code for basis functions, and update relevant tables of used basis."
# Prefetch formats to speed up code generation.
f_group = format["grouping"]
f_add = format["add"]
f_mult = format["multiply"]
f_transform = format["transform"]
f_detJ = format["det(J)"]
f_inv = format["inverse"]
# Reset code
code = {}
# Handle affine mappings.
if transformation == "affine":
# Loop derivatives and get multi indices.
for multi in multiindices:
deriv = [multi.count(i) for i in range(tdim)]
if not any(deriv):
deriv = []
# Create mapping and basis name.
#print "component = ", component
mapping, basis = self._create_mapping_basis(component, deriv, avg, ufl_argument, ffc_element)
if not mapping in code:
code[mapping] = []
if basis is not None:
# Add transformation
code[mapping].append(self.__apply_transform(basis, derivatives, multi, tdim, gdim))
# Handle non-affine mappings.
else:
ffc_assert(avg is None, "Taking average is not supported for non-affine mappings.")
# Loop derivatives and get multi indices.
for multi in multiindices:
deriv = [multi.count(i) for i in range(tdim)]
if not any(deriv):
deriv = []
for c in range(tdim):
# Create mapping and basis name.
mapping, basis = self._create_mapping_basis(c + local_offset, deriv, avg, ufl_argument, ffc_element)
if not mapping in code:
code[mapping] = []
if basis is not None:
# Multiply basis by appropriate transform.
if transformation == "covariant piola":
dxdX = f_transform("JINV", c, local_comp, tdim, gdim, self.restriction)
self.trans_set.add(dxdX)
basis = f_mult([dxdX, basis])
elif transformation == "contravariant piola":
self.trans_set.add(f_detJ(self.restriction))
detJ = f_inv(f_detJ(self.restriction))
dXdx = f_transform("J", local_comp, c, gdim, tdim, self.restriction)
self.trans_set.add(dXdx)
basis = f_mult([detJ, dXdx, basis])
else:
error("Transformation is not supported: " + repr(transformation))
# Add transformation if needed.
code[mapping].append(self.__apply_transform(basis, derivatives, multi, tdim, gdim))
# Add sums and group if necessary.
for key, val in code.items():
if len(val) > 1:
code[key] = f_group(f_add(val))
elif val:
code[key] = val[0]
else:
# Return a None (zero) because val == []
code[key] = None
return code
def create_function(self, ufl_function, derivatives, component, local_comp,
local_offset, ffc_element, is_quad_element,
transformation, multiindices, tdim, gdim, avg):
"Create code for basis functions, and update relevant tables of used basis."
ffc_assert(ufl_function in self._function_replace_values, "Expecting ufl_function to have been mapped prior to this call.")
# Prefetch formats to speed up code generation.
f_mult = format["multiply"]
f_transform = format["transform"]
f_detJ = format["det(J)"]
f_inv = format["inverse"]
# Reset code
code = []
# Handle affine mappings.
if transformation == "affine":
# Loop derivatives and get multi indices.
for multi in multiindices:
deriv = [multi.count(i) for i in range(tdim)]
if not any(deriv):
deriv = []
# Create function name.
function_name = self._create_function_name(component, deriv, avg, is_quad_element, ufl_function, ffc_element)
if function_name:
# Add transformation if needed.
code.append(self.__apply_transform(function_name, derivatives, multi, tdim, gdim))
# Handle non-affine mappings.
else:
ffc_assert(avg is None, "Taking average is not supported for non-affine mappings.")
# Loop derivatives and get multi indices.
for multi in multiindices:
deriv = [multi.count(i) for i in range(tdim)]
if not any(deriv):
deriv = []
for c in range(tdim):
function_name = self._create_function_name(c + local_offset, deriv, avg, is_quad_element, ufl_function, ffc_element)
if function_name:
# Multiply basis by appropriate transform.
if transformation == "covariant piola":
dxdX = f_transform("JINV", c, local_comp, tdim, gdim, self.restriction)
self.trans_set.add(dxdX)
function_name = f_mult([dxdX, function_name])
elif transformation == "contravariant piola":
self.trans_set.add(f_detJ(self.restriction))
detJ = f_inv(f_detJ(self.restriction))
dXdx = f_transform("J", local_comp, c, gdim, tdim, self.restriction)
self.trans_set.add(dXdx)
function_name = f_mult([detJ, dXdx, function_name])
else:
error("Transformation is not supported: ", repr(transformation))
# Add transformation if needed.
code.append(self.__apply_transform(function_name, derivatives, multi, tdim, gdim))
if not code:
return None
elif len(code) > 1:
code = format["grouping"](format["add"](code))
else:
code = code[0]
return code
# -------------------------------------------------------------------------
# Helper functions for Argument and Coefficient
# -------------------------------------------------------------------------
def __apply_transform(self, function, derivatives, multi, tdim, gdim): # XXX UFLACS REUSE
"Apply transformation (from derivatives) to basis or function."
f_mult = format["multiply"]
f_transform = format["transform"]
# Add transformation if needed.
transforms = []
if not self.integral_type == "custom":
for i, direction in enumerate(derivatives):
ref = multi[i]
t = f_transform("JINV", ref, direction, tdim, gdim, self.restriction)
self.trans_set.add(t)
transforms.append(t)
# Only multiply by basis if it is present.
if function:
prods = transforms + [function]
else:
prods = transforms
return format["multiply"](prods)
# -------------------------------------------------------------------------
# Helper functions for transformation of UFL objects in base class
# -------------------------------------------------------------------------
def _create_symbol(self, symbol, domain):
return {():symbol}
def _create_product(self, symbols):
return format["multiply"](symbols)
def _format_scalar_value(self, value):
#print("format_scalar_value: %d" % value)
if value is None:
return {():None}
# TODO: Handle value < 0 better such that we don't have + -2 in the code.
return {():format["floating point"](value)}
def _math_function(self, operands, format_function):
# TODO: Are these safety checks needed?
ffc_assert(len(operands) == 1 and () in operands[0] and len(operands[0]) == 1, \
"MathFunctions expect one operand of function type: " + repr(operands))
# Use format function on value of operand.
new_operand = {}
operand = operands[0]
for key, val in operand.items():
new_operand[key] = format_function(val)
return new_operand
def _atan_2_function(self, operands, format_function):
x1,x2 = operands
x1,x2 = x1.values()[0],x2.values()[0]
if x1 is None:
x1 = format["floating point"](0.0)
if x2 is None:
x2 = format["floating point"](0.0)
return {():format_function(x1, x2)}
def _bessel_function(self, operands, format_function):
# TODO: Are these safety checks needed?
ffc_assert(len(operands) == 2,\
"BesselFunctions expect two operands of function type: " + repr(operands))
nu, x = operands
ffc_assert(len(nu) == 1 and () in nu,\
"Expecting one operand of function type as first argument to BesselFunction : " + repr(nu))
ffc_assert(len(x) == 1 and () in x,\
"Expecting one operand of function type as second argument to BesselFunction : " + repr(x))
nu = nu[()]
x = x[()]
if nu is None:
nu = format["floating point"](0.0)
if x is None:
x = format["floating point"](0.0)
# Use format function on arguments.
# NOTE: Order of nu and x is reversed compared to the UFL and C++
# function calls because of how Symbol treats exponents.
# this will change once quadrature optimisations has been cleaned up.
return {():format_function(x, nu)}
# -------------------------------------------------------------------------
# Helper functions for code_generation()
# -------------------------------------------------------------------------
def _count_operations(self, expression):
return operation_count(expression, format)
def _create_entry_data(self, val, integral_type):
# Multiply value by weight and determinant
# Create weight and scale factor.
weight = format["weight"](self.points)
if self.points is None or self.points > 1:
weight += format["component"]("", format["integration points"])
# Update sets of used variables.
if integral_type in ("point", "custom"):
trans_set = set()
value = format["mul"]([val, weight])
else:
f_scale_factor = format["scale factor"]
trans_set = set([f_scale_factor])
value = format["mul"]([val, weight, f_scale_factor])
trans_set.update(self.trans_set)
used_points = set([self.points])
ops = self._count_operations(value)
used_psi_tables = set([v for k, v in self.psi_tables_map.items()])
return (value, ops, [trans_set, used_points, used_psi_tables])
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