def tensorDandDD(self, d_graph, dd_graph):
"""Computes the type D structure D1 * CFAA(Id) * DD2, where D1 is a
type D structure with graph d_graph, CFAA(Id) is represented by this
graph, and DD2 is a type DD structure with graph dd_graph.
"""
assert dd_graph.tensor_side == 1
assert d_graph.algebra.opp() == self.pmc_alg
assert dd_graph.algebra1 == self.pmc_alg
dstr = SimpleDStructure(F2, dd_graph.algebra2)
# Generators of the type D structure:
for node1 in d_graph.getNodes():
for ddgen, node2 in dd_graph.ddgen_node.items():
if node1.idem == node2.idem1.opp().comp():
cur_gen = ATensorDDGenerator(dstr, node1.dgen, ddgen)
dstr.addGenerator(cur_gen)
# Search the graphs for type D operations
for gen_start in dstr.getGenerators():
dgen, ddgen = gen_start
d1_pos = d_graph.graph_node[dgen]
d2_pos = dd_graph.ddgen_node[ddgen]
aa_pos = self.homology_node[dgen.idem.opp()]
pos = [(d1_pos, d2_pos, aa_pos)]
end_states = self._searchDoubleD(d_graph, dd_graph, pos)[0]
for d1_end, d2_end, aa_end in end_states:
gen_end = ATensorDDGenerator(dstr, d1_end.dgen, d2_end.ddgen)
dstr.addDelta(gen_start, gen_end, d2_end.sd, 1)
return dstr
def tensorD(self, dstr):
"""Compute the box tensor product DA * D of this bimodule with the given
type D structure. Returns the resulting type D structure. Uses delta()
and deltaPrefix() functions of this type DA structure.
"""
dstr_result = SimpleDStructure(F2, self.algebra1)
# Compute list of generators in the box tensor product
for gen_left in self.getGenerators():
for gen_right in dstr.getGenerators():
if gen_left.idem2 == gen_right.idem:
dstr_result.addGenerator(DATensorDGenerator(
dstr_result, gen_left, gen_right))
def search(start_gen, cur_dgen, cur_coeffs_a):
"""Searching for an arrow in the box tensor product.
- start_gen: starting generator in the box tensor product. The
resulting arrow will start from here.
- cur_dgen: current location in the type D structure.
- cur_coeffs_a: current list of A-side inputs to the type DA
structure (or alternatively, list of algebra outputs produced by
the existing path through the type D structure).
"""
start_dagen, start_dgen = start_gen
cur_delta = self.delta(start_dagen, cur_coeffs_a)
for (coeff_d, gen_to), ring_coeff in cur_delta.items():
dstr_result.addDelta(start_gen, DATensorDGenerator(
dstr_result, gen_to, cur_dgen), coeff_d, 1)
if self.deltaPrefix(start_dagen, cur_coeffs_a):
for (coeff_out, dgen_to), ring_coeff in \
dstr.delta(cur_dgen).items():
search(start_gen, dgen_to, cur_coeffs_a + (coeff_out,))
for x in dstr_result.getGenerators():
dagen, dgen = x
search(x, dgen, ())
# Add arrows coming from idempotent output on the D-side
for (coeff_out, dgen_to), ring_coeff in dstr.delta(dgen).items():
if coeff_out.isIdempotent():
dstr_result.addDelta(
x, DATensorDGenerator(dstr_result, dagen, dgen_to),
dagen.idem1.toAlgElt(self.algebra1), 1)
# Find grading set if available on both components
def tensorGradingSet():
"""Find the grading set of the new type D structure."""
return GeneralGradingSet([self.gr_set, dstr.gr_set])
def tensorGrading(gr_set, dagen, dgen):
"""Find the grading of the generator (x, y) in the tensor type D
structure. The grading set need to be provided as gr_set.
"""
return GeneralGradingSetElement(
gr_set, [self.grading[dagen], dstr.grading[dgen]])
if hasattr(self, "gr_set") and hasattr(dstr, "gr_set"):
dstr_result.gr_set = tensorGradingSet()
dstr_result.grading = dict()
for x in dstr_result.getGenerators():
dagen, dgen = x
dstr_result.grading[x] = tensorGrading(
dstr_result.gr_set, dagen, dgen)
return dstr_result
def tensorD(self, dstr):
"""Compute the box tensor product DA * D of this bimodule with the given
type D structure. Returns the resulting type D structure. Uses delta()
and deltaPrefix() functions of this type DA structure.
"""
dstr_result = SimpleDStructure(F2, self.algebra1)
# Compute list of generators in the box tensor product
for gen_left in self.getGenerators():
for gen_right in dstr.getGenerators():
if gen_left.idem2 == gen_right.idem:
dstr_result.addGenerator(DATensorDGenerator(
dstr_result, gen_left, gen_right))
def search(start_gen, cur_dgen, algs, last_assign, algs_local,
last_prod_d):
"""Searching for an arrow in the box tensor product.
- start_gen: starting generator in the box tensor product. The
resulting arrow will start from here.
- cur_dgen: current location in the type D structure.
- algs: current list of A-side inputs to the type DA structure (or
alternatively, list of algebra outputs produced by the existing
path through the type D structure).
- algs_local: current list of local restrictions of algs.
- last_assign: a list of length self.num_singles. For each split
idempotent, specify the single assignments at the last algebra
input.
- prod_d: product of the outer restrictions, except for the last
algebra input.
"""
start_dagen, start_dgen = start_gen
local_MGen = start_dagen.local_gen
# Preliminary tests
if len(algs) > 0:
assert algs[0].left_idem == start_dagen.idem2
for i in range(len(algs)-1):
assert algs[i].right_idem == algs[i+1].left_idem
if any(alg.isIdempotent() for alg in algs):
return
# First, adjust local module generator, and check for delta.
if len(algs_local) > 0:
local_MGen = self.adjustLocalMGen(local_MGen, algs_local[0])
if local_MGen is None:
return
local_delta = self.local_da.delta(local_MGen, tuple(algs_local))
has_delta = (local_delta != E0)
# Second, check for delta prefix.
has_delta_prefix = False
if len(algs) == 0:
has_delta_prefix = True
else:
dbls = [self.single_idems2[i] for i in range(self.num_singles)
if last_assign[i] == self.DOUBLE]
for to_remove in subset(dbls):
if len(to_remove) != 0:
cur_algs_local = tuple([alg.removeSingleHor(to_remove)
for alg in algs_local])
else:
cur_algs_local = algs_local
if self.testPrefix(local_MGen, cur_algs_local):
has_delta_prefix = True
break
if (not has_delta) and (not has_delta_prefix):
return
# Now, compute new prod_d.
if len(algs) > 0:
prod_d = self.getNewProdD(last_assign, algs[-1], last_prod_d)
else:
prod_d = last_prod_d
if prod_d is None:
return
# If has_delta is True, add to delta
for (local_d, local_y), ring_coeff in local_delta.items():
alg_d, y = self.joinOutput(local_d, local_y, prod_d)
if alg_d is not None:
dstr_result.addDelta(start_gen, DATensorDGenerator(
dstr_result, y, cur_dgen), alg_d, 1)
if not has_delta_prefix:
return
for (new_alg, dgen_to), ring_coeff in dstr.delta(cur_dgen).items():
new_assign, new_local, last_prod_d = self.extendRestrictions(
last_assign, algs_local, prod_d, new_alg)
if new_assign is not None:
search(start_gen, dgen_to, algs + [new_alg],
new_assign, new_local, last_prod_d)
# Perform search for each generator in dstr_result.
for x in dstr_result.getGenerators():
dagen, dgen = x
prod_d = \
self.splitting2.restrictIdempotentOuter(dagen.idem2).toAlgElt()
prod_d = prod_d.removeSingleHor() # always goes to LOCAL
search(x, dgen, [], [self.DOUBLE] * self.num_singles, [], prod_d)
# Add arrows coming from idempotent output on the D-side
for (coeff_out, dgen_to), ring_coeff in dstr.delta(dgen).items():
if coeff_out.isIdempotent():
dstr_result.addDelta(
x, DATensorDGenerator(dstr_result, dagen, dgen_to),
dagen.idem1.toAlgElt(self.algebra1), 1)
# Find grading set if available on both components
def tensorGradingSet():
"""Find the grading set of the new type D structure."""
return GeneralGradingSet([self.gr_set, dstr.gr_set])
def tensorGrading(gr_set, dagen, dgen):
"""Find the grading of the generator (x, y) in the tensor type D
structure. The grading set need to be provided as gr_set.
"""
return GeneralGradingSetElement(
gr_set, [self.grading[dagen], dstr.grading[dgen]])
if hasattr(self, "gr_set") and hasattr(dstr, "gr_set"):
dstr_result.gr_set = tensorGradingSet()
dstr_result.grading = dict()
for x in dstr_result.getGenerators():
dagen, dgen = x
dstr_result.grading[x] = tensorGrading(
dstr_result.gr_set, dagen, dgen)
return dstr_result
