From 109b39dad95a507b726f8fc96c0f07f98792d9f9 Mon Sep 17 00:00:00 2001 From: David Ham Date: Tue, 17 Dec 2024 14:56:46 +0000 Subject: [PATCH 001/222] test for correct annotation of cofunction assign --- tests/firedrake/regression/test_adjoint_operators.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index 8d4c0ab796..d0abec9a0b 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -1002,7 +1002,6 @@ def test_cofunction_assign_functional(): f.assign(2.0) assert np.allclose(Jhat(f), 2.0) - @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done def test_bdy_control(): # Test for the case the boundary condition is a control for a From aa010bfde6759ada4d0f5b44c8164175878c4870 Mon Sep 17 00:00:00 2001 From: David Ham Date: Mon, 8 Jul 2024 16:06:06 +0100 Subject: [PATCH 002/222] DROP BEFORE MERGE --- .github/workflows/build.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 909db6990a..acebd51af8 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -83,6 +83,7 @@ jobs: --install defcon \ --install gadopt \ --install asQ \ + --package-branch ufl dham/cofunction_is_terminal \ || (cat firedrake-install.log && /bin/false) - name: Install test dependencies run: | From e56f5ffc3b5b78314d7900c020f2e2316b96f19d Mon Sep 17 00:00:00 2001 From: David Ham Date: Mon, 1 Jul 2024 13:27:24 +0100 Subject: [PATCH 003/222] beginnings of doing inner product right --- firedrake/adjoint_utils/function.py | 11 ++++++++++- firedrake/cofunction.py | 16 ++++++++-------- 2 files changed, 18 insertions(+), 9 deletions(-) diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index 5e87751d36..af44b05990 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -316,7 +316,10 @@ def _ad_add(self, other): return r def _ad_dot(self, other, options=None): - from firedrake import assemble + from firedrake import assemble, action, Cofunction + + if isinstance(other, Cofunction): + return assemble(action(other, self)) options = {} if options is None else options riesz_representation = options.get("riesz_representation", "L2") @@ -406,3 +409,9 @@ def _ad_to_petsc(self, vec=None): def __deepcopy__(self, memodict={}): return self.copy(deepcopy=True) + + +class CofunctionMixin(FunctionMixin): + + def _ad_dot(self, other): + return assemble(firedrake.action(self, other)) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index 4878e6da59..bca08c8213 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -8,13 +8,13 @@ import firedrake.functionspaceimpl as functionspaceimpl from firedrake import utils, vector, ufl_expr from firedrake.utils import ScalarType -from firedrake.adjoint_utils.function import FunctionMixin +from firedrake.adjoint_utils.function import CofunctionMixin from firedrake.adjoint_utils.checkpointing import DelegatedFunctionCheckpoint from firedrake.adjoint_utils.blocks.function import CofunctionAssignBlock from firedrake.petsc import PETSc -class Cofunction(ufl.Cofunction, FunctionMixin): +class Cofunction(ufl.Cofunction, CofunctionMixin): r"""A :class:`Cofunction` represents a function on a dual space. Like Functions, cofunctions are represented as sums of basis functions: @@ -33,7 +33,7 @@ class Cofunction(ufl.Cofunction, FunctionMixin): """ @PETSc.Log.EventDecorator() - @FunctionMixin._ad_annotate_init + @CofunctionMixin._ad_annotate_init def __init__(self, function_space, val=None, name=None, dtype=ScalarType, count=None): r""" @@ -105,13 +105,13 @@ def _analyze_form_arguments(self): self._coefficients = (self,) @utils.cached_property - @FunctionMixin._ad_annotate_subfunctions + @CofunctionMixin._ad_annotate_subfunctions def subfunctions(self): r"""Extract any sub :class:`Cofunction`\s defined on the component spaces of this this :class:`Cofunction`'s :class:`.FunctionSpace`.""" return tuple(type(self)(fs, dat) for fs, dat in zip(self.function_space(), self.dat)) - @FunctionMixin._ad_annotate_subfunctions + @CofunctionMixin._ad_annotate_subfunctions def split(self): import warnings warnings.warn("The .split() method is deprecated, please use the .subfunctions property instead", category=FutureWarning) @@ -260,7 +260,7 @@ def riesz_representation(self, riesz_map='L2', **solver_options): "riesz_representation": riesz_map, "solver_options": solver_options}) - @FunctionMixin._ad_annotate_iadd + @CofunctionMixin._ad_annotate_iadd @utils.known_pyop2_safe def __iadd__(self, expr): @@ -276,7 +276,7 @@ def __iadd__(self, expr): # Let Python hit `BaseForm.__add__` which relies on ufl.FormSum. return NotImplemented - @FunctionMixin._ad_annotate_isub + @CofunctionMixin._ad_annotate_isub @utils.known_pyop2_safe def __isub__(self, expr): @@ -293,7 +293,7 @@ def __isub__(self, expr): # Let Python hit `BaseForm.__sub__` which relies on ufl.FormSum. return NotImplemented - @FunctionMixin._ad_annotate_imul + @CofunctionMixin._ad_annotate_imul def __imul__(self, expr): if np.isscalar(expr): From 6fc8cdbc5798025876a33c8bd58d6bde795f2c8b Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Mon, 1 Jul 2024 17:51:02 +0100 Subject: [PATCH 004/222] rieszmap --- firedrake/adjoint_utils/blocks/solving.py | 8 +- firedrake/adjoint_utils/function.py | 136 ++++++++++++++++------ firedrake/cofunction.py | 4 +- firedrake/ufl_expr.py | 6 + 4 files changed, 115 insertions(+), 39 deletions(-) diff --git a/firedrake/adjoint_utils/blocks/solving.py b/firedrake/adjoint_utils/blocks/solving.py index e4664665b0..2028bc2684 100644 --- a/firedrake/adjoint_utils/blocks/solving.py +++ b/firedrake/adjoint_utils/blocks/solving.py @@ -756,7 +756,7 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx, c = block_variable.output c_rep = block_variable.saved_output - if isinstance(c, firedrake.Function): + if isinstance(c, (firedrake.Function, firedrake.Cofunction)): trial_function = firedrake.TrialFunction(c.function_space()) elif isinstance(c, firedrake.Constant): mesh = F_form.ufl_domain() @@ -793,7 +793,11 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx, replace_map[self.func] = self.get_outputs()[0].saved_output dFdm = replace(dFdm, replace_map) - dFdm = dFdm * adj_sol + if isinstance(dFdm, firedrake.Argument): + # Corner case. Should be fixed more permanenty upstream in UFL. + dFdm = ufl.Action(dFdm, adj_sol) + else: + dFdm = dFdm * adj_sol dFdm = firedrake.assemble(dFdm, **self.assemble_kwargs) return dFdm diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index af44b05990..8ef0fa6696 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -1,4 +1,4 @@ -from functools import wraps +from functools import wraps, cached_property import ufl from ufl.domain import extract_unique_domain from pyadjoint.overloaded_type import create_overloaded_object, FloatingType @@ -226,7 +226,6 @@ def _ad_convert_riesz(self, value, options=None): options = {} if options is None else options riesz_representation = options.get("riesz_representation", "L2") solver_options = options.get("solver_options", {}) - V = options.get("function_space", self.function_space()) if value == 0.: # In adjoint-based differentiation, value == 0. arises only when # the functional is independent on the control variable. @@ -235,41 +234,11 @@ def _ad_convert_riesz(self, value, options=None): if not isinstance(value, (Cofunction, Function)): raise TypeError("Expected a Cofunction or a Function") - if riesz_representation == "l2": - return Function(V, val=value.dat) - - elif riesz_representation in ("L2", "H1"): - if not isinstance(value, Cofunction): - raise TypeError("Expected a Cofunction") - - ret = Function(V) - a = self._define_riesz_map_form(riesz_representation, V) - firedrake.solve(a == value, ret, **solver_options) - return ret - - elif callable(riesz_representation): + if callable(riesz_representation): return riesz_representation(value) - else: - raise ValueError( - "Unknown Riesz representation %s" % riesz_representation) - - def _define_riesz_map_form(self, riesz_representation, V): - from firedrake import TrialFunction, TestFunction - - u = TrialFunction(V) - v = TestFunction(V) - if riesz_representation == "L2": - a = firedrake.inner(u, v)*firedrake.dx - - elif riesz_representation == "H1": - a = firedrake.inner(u, v)*firedrake.dx \ - + firedrake.inner(firedrake.grad(u), firedrake.grad(v))*firedrake.dx - - else: - raise NotImplementedError( - "Unknown Riesz representation %s" % riesz_representation) - return a + return RieszMap(self, riesz_representation, + solver_options=solver_options)(value) @no_annotations def _ad_convert_type(self, value, options=None): @@ -411,7 +380,104 @@ def __deepcopy__(self, memodict={}): return self.copy(deepcopy=True) +<<<<<<< Updated upstream class CofunctionMixin(FunctionMixin): def _ad_dot(self, other): return assemble(firedrake.action(self, other)) +======= +class RieszMap: + """Return a map from a dual to a primal function space. + + A `RieszMap` can be called on a `Cofunction` in the appropriate space + to yield the `Function` which is the Riesz representer under the. + + Parameters + ---------- + function_space_or_inner_product: FunctionSpace or DualSpace or Function or Cofunction or ufl.Form + The space from which to map, or a bilinear form defining an + inner product. + sobolev_space: String or ufl.sobolevspace.SobolevSpace. + Used to determine the inner product. + bcs: DirichletBC or list of DirichletBC + Boundary conditions to apply to the Riesz map. + solver_options: dict + A dictionary of PETSc options to be passed to the solver. + """ + + def __init__(self, function_space_or_inner_product=None, + sobolev_space=ufl.L2, *, bcs=None, solver_options=None): + if isinstance(function_space_or_inner_product, ufl.Form): + args = ufl.algorithms.extract_arguments( + function_space_or_inner_product + ) + if len(args) != 2: + raise ValueError(f"inner_product has arity {len(args)}, " + "should be 2.") + function_space = args[0].function_space() + inner_product = function_space_or_inner_product + else: + function_space = function_space_or_inner_product + if hasattr(function_space, "function_space"): + function_space = function_space.function_space() + if ufl.duals.is_dual(function_space): + function_space = function_space.dual() + + if str(sobolev_space) == "l2": + inner_product = "l2" + else: + from firedrake import TrialFunction, TestFunction + u = TrialFunction(function_space) + v = TestFunction(function_space) + inner_product = RieszMap._inner_product_form( + sobolev_space, u, v + ) + + self._function_space = function_space + self._inner_product = inner_product + self._bcs = bcs + self._solver_options = solver_options + + @staticmethod + def _inner_product_form(sobolev_space, u, v): + from firedrake import inner, dx, grad + inner_products = { + "L2": lambda u, v: inner(u, v)*dx, + "H1": lambda u, v: inner(u, v)*dx + inner(grad(u), grad(v))*dx + } + try: + return inner_products[str(sobolev_space)](u, v) + except KeyError: + raise ValueError("No inner product defined for Sobolev space " + f"{sobolev_space}.") + + @cached_property + def _solver(self): + from firedrake import (LinearVariationalSolver, + LinearVariationalProblem, Function, Cofunction) + rhs = Cofunction(self._function_space.dual()) + soln = Function(self._function_space) + lvp = LinearVariationalProblem(self._inner_product, rhs, soln, + bcs=self._bcs) + solver = LinearVariationalSolver( + lvp, solver_parameters=self._solver_options + ) + return solver.solve, rhs, soln + + def __call__(self, cofunction): + """Return the Riesz representer of a Cofunction.""" + from firedrake import Function + if cofunction.function_space().dual() != self._function_space: + raise ValueError("Function space mismatch in RieszMap.") + output = Function(self._function_space) + + if self._inner_product == "l2": + output.dat.data[:] = cofunction.dat.data[:] + else: + solve, rhs, soln = self._solver + rhs.assign(cofunction) + solve() + output = Function(self._function_space) + output.assign(soln) + return output +>>>>>>> Stashed changes diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index bca08c8213..d85b869add 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -16,8 +16,8 @@ class Cofunction(ufl.Cofunction, CofunctionMixin): r"""A :class:`Cofunction` represents a function on a dual space. - Like Functions, cofunctions are - represented as sums of basis functions: + + Like Functions, cofunctions are represented as sums of basis functions: .. math:: diff --git a/firedrake/ufl_expr.py b/firedrake/ufl_expr.py index 1f9a66df75..f7108a9cc7 100644 --- a/firedrake/ufl_expr.py +++ b/firedrake/ufl_expr.py @@ -45,6 +45,12 @@ def __init__(self, function_space, number, part=None): number, part=part) self._function_space = function_space + def arguments(self): + return (self,) + + def coefficients(self): + return () + @utils.cached_property def cell_node_map(self): return self.function_space().cell_node_map From c373c3120f76d0c840499c405ce51e57799ce90e Mon Sep 17 00:00:00 2001 From: David Ham Date: Mon, 1 Jul 2024 17:51:31 +0100 Subject: [PATCH 005/222] missing import --- firedrake/adjoint_utils/function.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index 8ef0fa6696..f1a6d86738 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -380,12 +380,12 @@ def __deepcopy__(self, memodict={}): return self.copy(deepcopy=True) -<<<<<<< Updated upstream class CofunctionMixin(FunctionMixin): def _ad_dot(self, other): - return assemble(firedrake.action(self, other)) -======= + return firedrake.assemble(firedrake.action(self, other)) + + class RieszMap: """Return a map from a dual to a primal function space. @@ -480,4 +480,3 @@ def __call__(self, cofunction): output = Function(self._function_space) output.assign(soln) return output ->>>>>>> Stashed changes From 90cd52bf0a5f7122d6d26c020f06417edb4dd336 Mon Sep 17 00:00:00 2001 From: David Ham Date: Thu, 4 Jul 2024 11:27:20 +0100 Subject: [PATCH 006/222] fix l2 for mixed spaces --- firedrake/adjoint_utils/function.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index f1a6d86738..c17fbc28de 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -472,7 +472,8 @@ def __call__(self, cofunction): output = Function(self._function_space) if self._inner_product == "l2": - output.dat.data[:] = cofunction.dat.data[:] + for o, c in zip(output.subfunctions, cofunction.subfunctions): + o.dat.data[:] = c.dat.data[:] else: solve, rhs, soln = self._solver rhs.assign(cofunction) From fc45c48bb8c6397e90e56e57eaf996ad548fda44 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Thu, 4 Jul 2024 13:35:03 +0100 Subject: [PATCH 007/222] fix reisz map for primal case --- firedrake/adjoint_utils/function.py | 57 +++++++++++++++++++---------- firedrake/function.py | 9 ++--- 2 files changed, 41 insertions(+), 25 deletions(-) diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index c17fbc28de..7bdb5cbc94 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -229,7 +229,7 @@ def _ad_convert_riesz(self, value, options=None): if value == 0.: # In adjoint-based differentiation, value == 0. arises only when # the functional is independent on the control variable. - return Function(V) + return Function(self.function_space()) if not isinstance(value, (Cofunction, Function)): raise TypeError("Expected a Cofunction or a Function") @@ -263,9 +263,7 @@ def _ad_restore_at_checkpoint(self, checkpoint): return checkpoint def _ad_will_add_as_dependency(self): - """Method called when the object is added as a Block dependency. - - """ + """Method called when the object is added as a Block dependency.""" with checkpoint_init_data(): super()._ad_will_add_as_dependency() @@ -273,7 +271,8 @@ def _ad_mul(self, other): from firedrake import Function r = Function(self.function_space()) - # `self` can be a Cofunction in which case only left multiplication with a scalar is allowed. + # `self` can be a Cofunction in which case only left multiplication + # with a scalar is allowed. r.assign(other * self) return r @@ -406,7 +405,7 @@ class RieszMap: """ def __init__(self, function_space_or_inner_product=None, - sobolev_space=ufl.L2, *, bcs=None, solver_options=None): + sobolev_space=ufl.L2, *, bcs=None, solver_options=None): if isinstance(function_space_or_inner_product, ufl.Form): args = ufl.algorithms.extract_arguments( function_space_or_inner_product @@ -464,20 +463,38 @@ def _solver(self): ) return solver.solve, rhs, soln - def __call__(self, cofunction): - """Return the Riesz representer of a Cofunction.""" - from firedrake import Function - if cofunction.function_space().dual() != self._function_space: - raise ValueError("Function space mismatch in RieszMap.") - output = Function(self._function_space) + def __call__(self, value): + """Return the Riesz representer of a Function or Cofunction.""" + from firedrake import Function, Cofunction - if self._inner_product == "l2": - for o, c in zip(output.subfunctions, cofunction.subfunctions): - o.dat.data[:] = c.dat.data[:] - else: - solve, rhs, soln = self._solver - rhs.assign(cofunction) - solve() + if ufl.duals.is_dual(value): + if value.function_space().dual() != self._function_space: + raise ValueError("Function space mismatch in RieszMap.") output = Function(self._function_space) - output.assign(soln) + + if self._inner_product == "l2": + for o, c in zip(output.subfunctions, value.subfunctions): + o.dat.data[:] = c.dat.data[:] + else: + solve, rhs, soln = self._solver + rhs.assign(value) + solve() + output = Function(self._function_space) + output.assign(soln) + elif ufl.duals.is_primal(value): + if value.function_space().dual() != self._function_space: + raise ValueError("Function space mismatch in RieszMap.") + + if self._inner_product == "l2": + output = Cofunction(self._function_space.dual()) + for o, c in zip(output.subfunctions, value.subfunctions): + o.dat.data[:] = c.dat.data[:] + else: + output = firedrake.assemble( + firedrake.action(self._inner_product, value) + ) + else: + raise ValueError( + f"Unable to ascertain if {value} is primal or dual." + ) return output diff --git a/firedrake/function.py b/firedrake/function.py index da4d264971..18c83539f9 100644 --- a/firedrake/function.py +++ b/firedrake/function.py @@ -21,7 +21,7 @@ from firedrake.cofunction import Cofunction from firedrake import utils from firedrake import vector -from firedrake.adjoint_utils import FunctionMixin +from firedrake.adjoint_utils import FunctionMixin, RieszMap from firedrake.petsc import PETSc @@ -502,11 +502,10 @@ def riesz_representation(self, riesz_map='L2'): if riesz_map == "l2": return Cofunction(V.dual(), val=self.dat) - elif riesz_map in ("L2", "H1"): - a = self._define_riesz_map_form(riesz_map, V) - return assemble(action(a, self)) + if riesz_map in ("L2", "H1"): + riesz_map = RieszMap(V, riesz_map) - elif callable(riesz_map): + if callable(riesz_map): return riesz_map(self) else: From 1c16bb5e579d3355b57e4ac23e7f3d84f9f51e4a Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Thu, 4 Jul 2024 15:11:07 +0100 Subject: [PATCH 008/222] riesz_representation --- firedrake/cofunction.py | 45 ++++++++++++++++++++--------------------- firedrake/function.py | 43 ++++++++++++++++++--------------------- 2 files changed, 42 insertions(+), 46 deletions(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index d85b869add..f58888274c 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -8,7 +8,7 @@ import firedrake.functionspaceimpl as functionspaceimpl from firedrake import utils, vector, ufl_expr from firedrake.utils import ScalarType -from firedrake.adjoint_utils.function import CofunctionMixin +from firedrake.adjoint_utils.function import CofunctionMixin, RieszMap from firedrake.adjoint_utils.checkpointing import DelegatedFunctionCheckpoint from firedrake.adjoint_utils.blocks.function import CofunctionAssignBlock from firedrake.petsc import PETSc @@ -228,37 +228,36 @@ def assign(self, expr, subset=None, expr_from_assemble=False): raise ValueError('Cannot assign %s' % expr) - def riesz_representation(self, riesz_map='L2', **solver_options): - """Return the Riesz representation of this :class:`Cofunction` with respect to the given Riesz map. + def riesz_representation(self, riesz_map='L2', *, bcs=None, + solver_options=None): + """Return the Riesz representation of this :class:`Cofunction`. - Example: For a L2 Riesz map, the Riesz representation is obtained by solving - the linear system ``Mx = self``, where M is the L2 mass matrix, i.e. M = - with u and v trial and test functions, respectively. + Example: For a L2 Riesz map, the Riesz representation is obtained by + solving the linear system ``Mx = self``, where M is the L2 mass matrix, + i.e. M = with u and v trial and test functions, respectively. Parameters ---------- - riesz_map : str or collections.abc.Callable - The Riesz map to use (`l2`, `L2`, or `H1`). This can also be a callable. - solver_options : dict - Solver options to pass to the linear solver: - - solver_parameters: optional solver parameters. - - nullspace: an optional :class:`.VectorSpaceBasis` (or :class:`.MixedVectorSpaceBasis`) - spanning the null space of the operator. - - transpose_nullspace: as for the nullspace, but used to make the right hand side consistent. - - near_nullspace: as for the nullspace, but used to add the near nullspace. - - options_prefix: an optional prefix used to distinguish PETSc options. - If not provided a unique prefix will be created. - Use this option if you want to pass options to the solver from the command line - in addition to through the ``solver_parameters`` dict. + riesz_map : str or ufl.sobolevspace.SobolevSpace or + collections.abc.Callable + The Riesz map to use (`l2`, `L2`, or `H1`). This can also be a + callable. + bcs: DirichletBC or list of DirichletBC + Boundary conditions to apply to the Riesz map. + solver_options: dict + A dictionary of PETSc options to be passed to the solver. Returns ------- firedrake.function.Function - Riesz representation of this :class:`Cofunction` with respect to the given Riesz map. + Riesz representation of this :class:`Cofunction` with respect to + the given Riesz map. """ - return self._ad_convert_riesz(self, options={"function_space": self.function_space().dual(), - "riesz_representation": riesz_map, - "solver_options": solver_options}) + if not callable(riesz_map): + riesz_map = RieszMap(self.function_space(), riesz_map, bcs=bcs, + solver_options=solver_options) + + return riesz_map(self) @CofunctionMixin._ad_annotate_iadd @utils.known_pyop2_safe diff --git a/firedrake/function.py b/firedrake/function.py index 18c83539f9..94ebdc7cb3 100644 --- a/firedrake/function.py +++ b/firedrake/function.py @@ -478,39 +478,36 @@ def assign(self, expr, subset=None): Assigner(self, expr, subset).assign() return self - def riesz_representation(self, riesz_map='L2'): - """Return the Riesz representation of this :class:`Function` with respect to the given Riesz map. + def riesz_representation(self, riesz_map='L2', bcs=None, + solver_options=None): + """Return the Riesz representation of this :class:`Function`. - Example: For a L2 Riesz map, the Riesz representation is obtained by taking the action - of ``M`` on ``self``, where M is the L2 mass matrix, i.e. M = - with u and v trial and test functions, respectively. + Example: For a L2 Riesz map, the Riesz representation is obtained by + taking the action of ``M`` on ``self``, where M is the L2 mass matrix, + i.e. M = with u and v trial and test functions, respectively. Parameters ---------- - riesz_map : str or collections.abc.Callable - The Riesz map to use (`l2`, `L2`, or `H1`). This can also be a callable. + riesz_map : str or ufl.sobolevspace.SobolevSpace or + collections.abc.Callable + The Riesz map to use (`l2`, `L2`, or `H1`). This can also be a + callable which applies the Riesz map. + bcs: DirichletBC or list of DirichletBC + Boundary conditions to apply to the Riesz map. + solver_options: dict + A dictionary of PETSc options to be passed to the solver. Returns ------- firedrake.cofunction.Cofunction - Riesz representation of this :class:`Function` with respect to the given Riesz map. + Riesz representation of this :class:`Function` with respect to the + given Riesz map. """ - from firedrake.ufl_expr import action - from firedrake.assemble import assemble + if not callable(riesz_map): + riesz_map = RieszMap(self.function_space(), riesz_map, bcs=bcs, + solver_options=solver_options) - V = self.function_space() - if riesz_map == "l2": - return Cofunction(V.dual(), val=self.dat) - - if riesz_map in ("L2", "H1"): - riesz_map = RieszMap(V, riesz_map) - - if callable(riesz_map): - return riesz_map(self) - - else: - raise NotImplementedError( - "Unknown Riesz representation %s" % riesz_map) + return riesz_map(self) @FunctionMixin._ad_annotate_iadd def __iadd__(self, expr): From 42f974cbe941161489cc6f3217588cafce6b77c0 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Thu, 4 Jul 2024 15:18:29 +0100 Subject: [PATCH 009/222] Update firedrake/adjoint_utils/blocks/solving.py --- firedrake/adjoint_utils/blocks/solving.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/adjoint_utils/blocks/solving.py b/firedrake/adjoint_utils/blocks/solving.py index 2028bc2684..6412f5b2e5 100644 --- a/firedrake/adjoint_utils/blocks/solving.py +++ b/firedrake/adjoint_utils/blocks/solving.py @@ -794,7 +794,7 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx, dFdm = replace(dFdm, replace_map) if isinstance(dFdm, firedrake.Argument): - # Corner case. Should be fixed more permanenty upstream in UFL. + # Corner case. Should be fixed more permanently upstream in UFL. dFdm = ufl.Action(dFdm, adj_sol) else: dFdm = dFdm * adj_sol From 8b93fe4aba6e37035262bf2c49c126548e1d99a6 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Thu, 4 Jul 2024 16:02:58 +0100 Subject: [PATCH 010/222] rearrange to pass documentation --- firedrake/adjoint_utils/function.py | 129 +--------------------------- firedrake/cofunction.py | 118 ++++++++++++++++++++++++- firedrake/function.py | 4 +- 3 files changed, 123 insertions(+), 128 deletions(-) diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index 7bdb5cbc94..1e3301e63d 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -1,4 +1,4 @@ -from functools import wraps, cached_property +from functools import wraps import ufl from ufl.domain import extract_unique_domain from pyadjoint.overloaded_type import create_overloaded_object, FloatingType @@ -221,7 +221,7 @@ def _ad_create_checkpoint(self): return self.copy(deepcopy=True) def _ad_convert_riesz(self, value, options=None): - from firedrake import Function, Cofunction + from firedrake import Function options = {} if options is None else options riesz_representation = options.get("riesz_representation", "L2") @@ -231,14 +231,8 @@ def _ad_convert_riesz(self, value, options=None): # the functional is independent on the control variable. return Function(self.function_space()) - if not isinstance(value, (Cofunction, Function)): - raise TypeError("Expected a Cofunction or a Function") - - if callable(riesz_representation): - return riesz_representation(value) - - return RieszMap(self, riesz_representation, - solver_options=solver_options)(value) + return value.riesz_representation(riesz_map=riesz_representation, + solver_options=solver_options) @no_annotations def _ad_convert_type(self, value, options=None): @@ -383,118 +377,3 @@ class CofunctionMixin(FunctionMixin): def _ad_dot(self, other): return firedrake.assemble(firedrake.action(self, other)) - - -class RieszMap: - """Return a map from a dual to a primal function space. - - A `RieszMap` can be called on a `Cofunction` in the appropriate space - to yield the `Function` which is the Riesz representer under the. - - Parameters - ---------- - function_space_or_inner_product: FunctionSpace or DualSpace or Function or Cofunction or ufl.Form - The space from which to map, or a bilinear form defining an - inner product. - sobolev_space: String or ufl.sobolevspace.SobolevSpace. - Used to determine the inner product. - bcs: DirichletBC or list of DirichletBC - Boundary conditions to apply to the Riesz map. - solver_options: dict - A dictionary of PETSc options to be passed to the solver. - """ - - def __init__(self, function_space_or_inner_product=None, - sobolev_space=ufl.L2, *, bcs=None, solver_options=None): - if isinstance(function_space_or_inner_product, ufl.Form): - args = ufl.algorithms.extract_arguments( - function_space_or_inner_product - ) - if len(args) != 2: - raise ValueError(f"inner_product has arity {len(args)}, " - "should be 2.") - function_space = args[0].function_space() - inner_product = function_space_or_inner_product - else: - function_space = function_space_or_inner_product - if hasattr(function_space, "function_space"): - function_space = function_space.function_space() - if ufl.duals.is_dual(function_space): - function_space = function_space.dual() - - if str(sobolev_space) == "l2": - inner_product = "l2" - else: - from firedrake import TrialFunction, TestFunction - u = TrialFunction(function_space) - v = TestFunction(function_space) - inner_product = RieszMap._inner_product_form( - sobolev_space, u, v - ) - - self._function_space = function_space - self._inner_product = inner_product - self._bcs = bcs - self._solver_options = solver_options - - @staticmethod - def _inner_product_form(sobolev_space, u, v): - from firedrake import inner, dx, grad - inner_products = { - "L2": lambda u, v: inner(u, v)*dx, - "H1": lambda u, v: inner(u, v)*dx + inner(grad(u), grad(v))*dx - } - try: - return inner_products[str(sobolev_space)](u, v) - except KeyError: - raise ValueError("No inner product defined for Sobolev space " - f"{sobolev_space}.") - - @cached_property - def _solver(self): - from firedrake import (LinearVariationalSolver, - LinearVariationalProblem, Function, Cofunction) - rhs = Cofunction(self._function_space.dual()) - soln = Function(self._function_space) - lvp = LinearVariationalProblem(self._inner_product, rhs, soln, - bcs=self._bcs) - solver = LinearVariationalSolver( - lvp, solver_parameters=self._solver_options - ) - return solver.solve, rhs, soln - - def __call__(self, value): - """Return the Riesz representer of a Function or Cofunction.""" - from firedrake import Function, Cofunction - - if ufl.duals.is_dual(value): - if value.function_space().dual() != self._function_space: - raise ValueError("Function space mismatch in RieszMap.") - output = Function(self._function_space) - - if self._inner_product == "l2": - for o, c in zip(output.subfunctions, value.subfunctions): - o.dat.data[:] = c.dat.data[:] - else: - solve, rhs, soln = self._solver - rhs.assign(value) - solve() - output = Function(self._function_space) - output.assign(soln) - elif ufl.duals.is_primal(value): - if value.function_space().dual() != self._function_space: - raise ValueError("Function space mismatch in RieszMap.") - - if self._inner_product == "l2": - output = Cofunction(self._function_space.dual()) - for o, c in zip(output.subfunctions, value.subfunctions): - o.dat.data[:] = c.dat.data[:] - else: - output = firedrake.assemble( - firedrake.action(self._inner_product, value) - ) - else: - raise ValueError( - f"Unable to ascertain if {value} is primal or dual." - ) - return output diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index f58888274c..cd4b150218 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -1,3 +1,4 @@ +from functools import cached_property import numpy as np import ufl @@ -8,7 +9,7 @@ import firedrake.functionspaceimpl as functionspaceimpl from firedrake import utils, vector, ufl_expr from firedrake.utils import ScalarType -from firedrake.adjoint_utils.function import CofunctionMixin, RieszMap +from firedrake.adjoint_utils.function import CofunctionMixin from firedrake.adjoint_utils.checkpointing import DelegatedFunctionCheckpoint from firedrake.adjoint_utils.blocks.function import CofunctionAssignBlock from firedrake.petsc import PETSc @@ -359,3 +360,118 @@ def __str__(self): def cell_node_map(self): return self.function_space().cell_node_map() + + +class RieszMap: + """Return a map from a dual to a primal function space. + + A `RieszMap` can be called on a `Cofunction` in the appropriate space + to yield the `Function` which is the Riesz representer under the. + + Parameters + ---------- + function_space_or_inner_product: FunctionSpace or Function or Cofunction or ufl.Form + The space from which to map, or a bilinear form defining an + inner product. + sobolev_space: str or ufl.sobolevspace.SobolevSpace. + Used to determine the inner product. + bcs: DirichletBC or list of DirichletBC + Boundary conditions to apply to the Riesz map. + solver_options: dict + A dictionary of PETSc options to be passed to the solver. + """ + + def __init__(self, function_space_or_inner_product=None, + sobolev_space=ufl.L2, *, bcs=None, solver_options=None): + if isinstance(function_space_or_inner_product, ufl.Form): + args = ufl.algorithms.extract_arguments( + function_space_or_inner_product + ) + if len(args) != 2: + raise ValueError(f"inner_product has arity {len(args)}, " + "should be 2.") + function_space = args[0].function_space() + inner_product = function_space_or_inner_product + else: + function_space = function_space_or_inner_product + if hasattr(function_space, "function_space"): + function_space = function_space.function_space() + if ufl.duals.is_dual(function_space): + function_space = function_space.dual() + + if str(sobolev_space) == "l2": + inner_product = "l2" + else: + from firedrake import TrialFunction, TestFunction + u = TrialFunction(function_space) + v = TestFunction(function_space) + inner_product = RieszMap._inner_product_form( + sobolev_space, u, v + ) + + self._function_space = function_space + self._inner_product = inner_product + self._bcs = bcs + self._solver_options = solver_options + + @staticmethod + def _inner_product_form(sobolev_space, u, v): + from firedrake import inner, dx, grad + inner_products = { + "L2": lambda u, v: inner(u, v)*dx, + "H1": lambda u, v: inner(u, v)*dx + inner(grad(u), grad(v))*dx + } + try: + return inner_products[str(sobolev_space)](u, v) + except KeyError: + raise ValueError("No inner product defined for Sobolev space " + f"{sobolev_space}.") + + @cached_property + def _solver(self): + from firedrake import (LinearVariationalSolver, + LinearVariationalProblem, Function, Cofunction) + rhs = Cofunction(self._function_space.dual()) + soln = Function(self._function_space) + lvp = LinearVariationalProblem(self._inner_product, rhs, soln, + bcs=self._bcs) + solver = LinearVariationalSolver( + lvp, solver_parameters=self._solver_options + ) + return solver.solve, rhs, soln + + def __call__(self, value): + """Return the Riesz representer of a Function or Cofunction.""" + from firedrake import Function, Cofunction + + if ufl.duals.is_dual(value): + if value.function_space().dual() != self._function_space: + raise ValueError("Function space mismatch in RieszMap.") + output = Function(self._function_space) + + if self._inner_product == "l2": + for o, c in zip(output.subfunctions, value.subfunctions): + o.dat.data[:] = c.dat.data[:] + else: + solve, rhs, soln = self._solver + rhs.assign(value) + solve() + output = Function(self._function_space) + output.assign(soln) + elif ufl.duals.is_primal(value): + if value.function_space().dual() != self._function_space: + raise ValueError("Function space mismatch in RieszMap.") + + if self._inner_product == "l2": + output = Cofunction(self._function_space.dual()) + for o, c in zip(output.subfunctions, value.subfunctions): + o.dat.data[:] = c.dat.data[:] + else: + output = firedrake.assemble( + firedrake.action(self._inner_product, value) + ) + else: + raise ValueError( + f"Unable to ascertain if {value} is primal or dual." + ) + return output diff --git a/firedrake/function.py b/firedrake/function.py index 94ebdc7cb3..fad5502e7f 100644 --- a/firedrake/function.py +++ b/firedrake/function.py @@ -18,10 +18,10 @@ from firedrake.utils import ScalarType, IntType, as_ctypes from firedrake import functionspaceimpl -from firedrake.cofunction import Cofunction +from firedrake.cofunction import Cofunction, RieszMap from firedrake import utils from firedrake import vector -from firedrake.adjoint_utils import FunctionMixin, RieszMap +from firedrake.adjoint_utils import FunctionMixin from firedrake.petsc import PETSc From 85a8267047ad3f6113658222be56f26ecb6f91e9 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Thu, 4 Jul 2024 16:18:41 +0100 Subject: [PATCH 011/222] fix solver options --- firedrake/cofunction.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index cd4b150218..c7dbb80549 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -412,7 +412,7 @@ def __init__(self, function_space_or_inner_product=None, self._function_space = function_space self._inner_product = inner_product self._bcs = bcs - self._solver_options = solver_options + self._solver_options = solver_options or {} @staticmethod def _inner_product_form(sobolev_space, u, v): From 6768c9ffe0e9a309ea808808690f714c786564ad Mon Sep 17 00:00:00 2001 From: David Ham Date: Sat, 6 Jul 2024 20:54:39 +0100 Subject: [PATCH 012/222] make solve string more robust --- firedrake/adjoint_utils/blocks/solving.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/firedrake/adjoint_utils/blocks/solving.py b/firedrake/adjoint_utils/blocks/solving.py index 6412f5b2e5..4c4020a249 100644 --- a/firedrake/adjoint_utils/blocks/solving.py +++ b/firedrake/adjoint_utils/blocks/solving.py @@ -86,8 +86,15 @@ def _init_solver_parameters(self, args, kwargs): self.assemble_kwargs = {} def __str__(self): - return "solve({} = {})".format(ufl2unicode(self.lhs), - ufl2unicode(self.rhs)) + try: + lhs_string = ufl2unicode(self.lhs) + except AttributeError: + lhs_string = str(self.lhs) + try: + rhs_string = ufl2unicode(self.rhs) + except AttributeError: + rhs_string = str(self.rhs) + return "solve({} = {})".format(lhs_string, rhs_string) def _create_F_form(self): # Process the equation forms, replacing values with checkpoints, From f98e837743190282742480624a153d83d0a41872 Mon Sep 17 00:00:00 2001 From: David Ham Date: Mon, 8 Jul 2024 17:36:01 +0100 Subject: [PATCH 013/222] docstrings --- firedrake/cofunction.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index c7dbb80549..689269c0aa 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -363,16 +363,18 @@ def cell_node_map(self): class RieszMap: - """Return a map from a dual to a primal function space. + """Return a map between dual and primal function spaces. - A `RieszMap` can be called on a `Cofunction` in the appropriate space - to yield the `Function` which is the Riesz representer under the. + A `RieszMap` can be called on a `Cofunction` in the appropriate space to + yield the `Function` which is the Riesz representer under the given inner + product. Conversely, it can be called on a `Function` to apply the given + inner product and return a `Cofunction`. Parameters ---------- - function_space_or_inner_product: FunctionSpace or Function or Cofunction or ufl.Form - The space from which to map, or a bilinear form defining an - inner product. + function_space_or_inner_product: FunctionSpace or ufl.Form + The space from which to map, or a bilinear form defining an inner + product. sobolev_space: str or ufl.sobolevspace.SobolevSpace. Used to determine the inner product. bcs: DirichletBC or list of DirichletBC From 1d8e58a6cf1828c91c14a14a2d224ebcba99c366 Mon Sep 17 00:00:00 2001 From: David Ham Date: Tue, 17 Dec 2024 15:14:59 +0000 Subject: [PATCH 014/222] remove merged branch reference --- .github/workflows/build.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index acebd51af8..909db6990a 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -83,7 +83,6 @@ jobs: --install defcon \ --install gadopt \ --install asQ \ - --package-branch ufl dham/cofunction_is_terminal \ || (cat firedrake-install.log && /bin/false) - name: Install test dependencies run: | From cc402aa47b978d47d566af55b8c81e56104cb6ff Mon Sep 17 00:00:00 2001 From: David Ham Date: Tue, 17 Dec 2024 15:20:25 +0000 Subject: [PATCH 015/222] lint --- tests/firedrake/regression/test_adjoint_operators.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index d0abec9a0b..8d4c0ab796 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -1002,6 +1002,7 @@ def test_cofunction_assign_functional(): f.assign(2.0) assert np.allclose(Jhat(f), 2.0) + @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done def test_bdy_control(): # Test for the case the boundary condition is a control for a From cde10edd7112d05d63f1ca17cf3487d534906359 Mon Sep 17 00:00:00 2001 From: David Ham Date: Tue, 17 Dec 2024 16:20:00 +0000 Subject: [PATCH 016/222] Remove unused solver options --- firedrake/cofunction.py | 33 +++++++++++++++++++++++---------- firedrake/function.py | 8 ++------ 2 files changed, 25 insertions(+), 16 deletions(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index 689269c0aa..f3981b7b4c 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -230,7 +230,8 @@ def assign(self, expr, subset=None, expr_from_assemble=False): raise ValueError('Cannot assign %s' % expr) def riesz_representation(self, riesz_map='L2', *, bcs=None, - solver_options=None): + solver_parameters=None, + form_compiler_parameters=None): """Return the Riesz representation of this :class:`Cofunction`. Example: For a L2 Riesz map, the Riesz representation is obtained by @@ -245,8 +246,11 @@ def riesz_representation(self, riesz_map='L2', *, bcs=None, callable. bcs: DirichletBC or list of DirichletBC Boundary conditions to apply to the Riesz map. - solver_options: dict + solver_parameters: dict A dictionary of PETSc options to be passed to the solver. + form_compiler_parameters: dict + A dictionary of form compiler parameters to be passed to the + variational problem that solves for the Riesz map. Returns ------- @@ -255,8 +259,11 @@ def riesz_representation(self, riesz_map='L2', *, bcs=None, the given Riesz map. """ if not callable(riesz_map): - riesz_map = RieszMap(self.function_space(), riesz_map, bcs=bcs, - solver_options=solver_options) + riesz_map = RieszMap( + self.function_space(), riesz_map, bcs=bcs, + solver_parameters=solver_parameters, + form_compiler_parameters=form_compiler_parameters + ) return riesz_map(self) @@ -379,12 +386,16 @@ class RieszMap: Used to determine the inner product. bcs: DirichletBC or list of DirichletBC Boundary conditions to apply to the Riesz map. - solver_options: dict + solver_parameters: dict A dictionary of PETSc options to be passed to the solver. + form_compiler_parameters: dict + A dictionary of form compiler parameters to be passed to the + variational problem that solves for the Riesz map. """ def __init__(self, function_space_or_inner_product=None, - sobolev_space=ufl.L2, *, bcs=None, solver_options=None): + sobolev_space=ufl.L2, *, bcs=None, solver_parameters=None, + form_compiler_parameters=None): if isinstance(function_space_or_inner_product, ufl.Form): args = ufl.algorithms.extract_arguments( function_space_or_inner_product @@ -414,7 +425,8 @@ def __init__(self, function_space_or_inner_product=None, self._function_space = function_space self._inner_product = inner_product self._bcs = bcs - self._solver_options = solver_options or {} + self._solver_parameters = solver_parameters or {} + self._form_compiler_parameters = form_compiler_parameters or {} @staticmethod def _inner_product_form(sobolev_space, u, v): @@ -435,10 +447,11 @@ def _solver(self): LinearVariationalProblem, Function, Cofunction) rhs = Cofunction(self._function_space.dual()) soln = Function(self._function_space) - lvp = LinearVariationalProblem(self._inner_product, rhs, soln, - bcs=self._bcs) + lvp = LinearVariationalProblem( + self._inner_product, rhs, soln, bcs=self._bcs, restrict=True, + form_compiler_parameters=self._form_compiler_parameters) solver = LinearVariationalSolver( - lvp, solver_parameters=self._solver_options + lvp, solver_parameters=self._solver_parameters ) return solver.solve, rhs, soln diff --git a/firedrake/function.py b/firedrake/function.py index fad5502e7f..d296a464f6 100644 --- a/firedrake/function.py +++ b/firedrake/function.py @@ -478,8 +478,7 @@ def assign(self, expr, subset=None): Assigner(self, expr, subset).assign() return self - def riesz_representation(self, riesz_map='L2', bcs=None, - solver_options=None): + def riesz_representation(self, riesz_map='L2', bcs=None): """Return the Riesz representation of this :class:`Function`. Example: For a L2 Riesz map, the Riesz representation is obtained by @@ -494,8 +493,6 @@ def riesz_representation(self, riesz_map='L2', bcs=None, callable which applies the Riesz map. bcs: DirichletBC or list of DirichletBC Boundary conditions to apply to the Riesz map. - solver_options: dict - A dictionary of PETSc options to be passed to the solver. Returns ------- @@ -504,8 +501,7 @@ def riesz_representation(self, riesz_map='L2', bcs=None, given Riesz map. """ if not callable(riesz_map): - riesz_map = RieszMap(self.function_space(), riesz_map, bcs=bcs, - solver_options=solver_options) + riesz_map = RieszMap(self.function_space(), riesz_map, bcs=bcs) return riesz_map(self) From 8f86232b0c7548cbd6a0c6a8504661de3f3da0da Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 18 Dec 2024 10:20:53 +0000 Subject: [PATCH 017/222] wind back API changes --- firedrake/cofunction.py | 6 +++--- firedrake/function.py | 6 ++---- 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index f3981b7b4c..6e8bebe8bd 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -230,7 +230,7 @@ def assign(self, expr, subset=None, expr_from_assemble=False): raise ValueError('Cannot assign %s' % expr) def riesz_representation(self, riesz_map='L2', *, bcs=None, - solver_parameters=None, + solver_options=None, form_compiler_parameters=None): """Return the Riesz representation of this :class:`Cofunction`. @@ -246,7 +246,7 @@ def riesz_representation(self, riesz_map='L2', *, bcs=None, callable. bcs: DirichletBC or list of DirichletBC Boundary conditions to apply to the Riesz map. - solver_parameters: dict + solver_options: dict A dictionary of PETSc options to be passed to the solver. form_compiler_parameters: dict A dictionary of form compiler parameters to be passed to the @@ -261,7 +261,7 @@ def riesz_representation(self, riesz_map='L2', *, bcs=None, if not callable(riesz_map): riesz_map = RieszMap( self.function_space(), riesz_map, bcs=bcs, - solver_parameters=solver_parameters, + solver_parameters=solver_options, form_compiler_parameters=form_compiler_parameters ) diff --git a/firedrake/function.py b/firedrake/function.py index d296a464f6..5aae0fe561 100644 --- a/firedrake/function.py +++ b/firedrake/function.py @@ -478,7 +478,7 @@ def assign(self, expr, subset=None): Assigner(self, expr, subset).assign() return self - def riesz_representation(self, riesz_map='L2', bcs=None): + def riesz_representation(self, riesz_map='L2'): """Return the Riesz representation of this :class:`Function`. Example: For a L2 Riesz map, the Riesz representation is obtained by @@ -491,8 +491,6 @@ def riesz_representation(self, riesz_map='L2', bcs=None): collections.abc.Callable The Riesz map to use (`l2`, `L2`, or `H1`). This can also be a callable which applies the Riesz map. - bcs: DirichletBC or list of DirichletBC - Boundary conditions to apply to the Riesz map. Returns ------- @@ -501,7 +499,7 @@ def riesz_representation(self, riesz_map='L2', bcs=None): given Riesz map. """ if not callable(riesz_map): - riesz_map = RieszMap(self.function_space(), riesz_map, bcs=bcs) + riesz_map = RieszMap(self.function_space(), riesz_map) return riesz_map(self) From 720ad1dfdab1bfe3e37a4abf96236945545f50d9 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 18 Dec 2024 12:11:02 +0000 Subject: [PATCH 018/222] test for correct annotation of cofunction assign --- .../regression/test_adjoint_operators.py | 36 +++++++++++-------- 1 file changed, 21 insertions(+), 15 deletions(-) diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index 8d4c0ab796..479fd18a92 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -932,21 +932,27 @@ def test_riesz_representation_for_adjoints(): dJdu_function_L2 = Function(space) solve(a == dJdu_cofunction, dJdu_function_L2) - dJdu_none = rf.derivative(options={"riesz_representation": None}) - dJdu_l2 = rf.derivative(options={"riesz_representation": "l2"}) - dJdu_H1 = rf.derivative(options={"riesz_representation": "H1"}) - dJdu_L2 = rf.derivative(options={"riesz_representation": "L2"}) - dJdu_default_L2 = rf.derivative() - assert ( - isinstance(dJdu_none, Cofunction) and isinstance(dJdu_function_l2, Function) - and isinstance(dJdu_H1, Function) and isinstance(dJdu_default_L2, Function) - and isinstance(dJdu_L2, Function) - and np.allclose(dJdu_none.dat.data, dJdu_cofunction.dat.data) - and np.allclose(dJdu_l2.dat.data, dJdu_function_l2.dat.data) - and np.allclose(dJdu_H1.dat.data, dJdu_function_H1.dat.data) - and np.allclose(dJdu_default_L2.dat.data, dJdu_function_L2.dat.data) - and np.allclose(dJdu_L2.dat.data, dJdu_function_L2.dat.data) + dJdu_none = ReducedFunctional(J, Control(f)).derivative() + dJdu_l2 = ReducedFunctional(J, Control(f, riesz_map="l2")).derivative( + apply_riesz=True ) + dJdu_H1 = ReducedFunctional(J, Control(f, riesz_map="H1")).derivative( + apply_riesz=True + ) + dJdu_L2 = ReducedFunctional(J, Control(f, riesz_map="L2")).derivative( + apply_riesz=True + ) + dJdu_default_L2 = ReducedFunctional(J, Control(f)).derivative( + apply_riesz=True + ) + assert isinstance(dJdu_none, Cofunction) and isinstance(dJdu_function_l2, Function) + assert isinstance(dJdu_H1, Function) and isinstance(dJdu_default_L2, Function) + assert isinstance(dJdu_L2, Function) + assert np.allclose(dJdu_none.dat.data, dJdu_cofunction.dat.data) + assert np.allclose(dJdu_l2.dat.data, dJdu_function_l2.dat.data) + assert np.allclose(dJdu_H1.dat.data, dJdu_function_H1.dat.data) + assert np.allclose(dJdu_default_L2.dat.data, dJdu_function_L2.dat.data) + assert np.allclose(dJdu_L2.dat.data, dJdu_function_L2.dat.data) @pytest.mark.skipcomplex @@ -998,7 +1004,7 @@ def test_cofunction_assign_functional(): cof2.assign(cof) # Test is checking that this is taped. J = assemble(action(cof2, f2)) Jhat = ReducedFunctional(J, Control(f)) - assert np.allclose(float(Jhat.derivative()), 1.0) + assert np.allclose(float(Jhat.derivative(apply_riesz=True)), 1.0) f.assign(2.0) assert np.allclose(Jhat(f), 2.0) From 674e4bb882cc5e905c01221827fa958ef9b6d239 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 18 Dec 2024 12:16:22 +0000 Subject: [PATCH 019/222] overloadedtype interface changes --- firedrake/adjoint_utils/function.py | 34 ++++--------------- .../regression/test_adjoint_operators.py | 8 ++--- 2 files changed, 11 insertions(+), 31 deletions(-) diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index 1e3301e63d..6b78163ed7 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -220,35 +220,15 @@ def _ad_create_checkpoint(self): else: return self.copy(deepcopy=True) - def _ad_convert_riesz(self, value, options=None): - from firedrake import Function + def _ad_convert_riesz(self, value, riesz_map=None): + return value.riesz_representation(riesz_map=riesz_map or "L2") - options = {} if options is None else options - riesz_representation = options.get("riesz_representation", "L2") - solver_options = options.get("solver_options", {}) - if value == 0.: - # In adjoint-based differentiation, value == 0. arises only when - # the functional is independent on the control variable. - return Function(self.function_space()) - - return value.riesz_representation(riesz_map=riesz_representation, - solver_options=solver_options) - - @no_annotations - def _ad_convert_type(self, value, options=None): - # `_ad_convert_type` is not annotated, unlike `_ad_convert_riesz` - options = {} if options is None else options.copy() - options.setdefault("riesz_representation", "L2") - if options["riesz_representation"] is None: - if value == 0.: - # In adjoint-based differentiation, value == 0. arises only when - # the functional is independent on the control variable. - V = options.get("function_space", self.function_space()) - return firedrake.Cofunction(V.dual()) - else: - return value + def _ad_init_zero(self, dual=False): + from firedrake import Function, Cofunction + if dual: + return Cofunction(self.function_space().dual()) else: - return self._ad_convert_riesz(value, options=options) + return Function(self.function_space()) def _ad_restore_at_checkpoint(self, checkpoint): if isinstance(checkpoint, CheckpointBase): diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index 479fd18a92..de1252be19 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -864,10 +864,10 @@ def test_assign_zero_cofunction(): J = assemble(((sol + Constant(1.0)) ** 2) * dx) # The zero assignment should break the tape and hence cause a zero # gradient. - grad_l2 = compute_gradient(J, Control(k), options={"riesz_representation": "l2"}) - grad_none = compute_gradient(J, Control(k), options={"riesz_representation": None}) - grad_h1 = compute_gradient(J, Control(k), options={"riesz_representation": "H1"}) - grad_L2 = compute_gradient(J, Control(k), options={"riesz_representation": "L2"}) + grad_l2 = compute_gradient(J, Control(k, riesz_map="l2"), apply_riesz=True) + grad_none = compute_gradient(J, Control(k)) + grad_h1 = compute_gradient(J, Control(k, riesz_map="H1"), apply_riesz=True) + grad_L2 = compute_gradient(J, Control(k, riesz_map="L2"), apply_riesz=True) assert isinstance(grad_l2, Function) and isinstance(grad_L2, Function) \ and isinstance(grad_h1, Function) assert isinstance(grad_none, Cofunction) From de14bf34ff7eef6e965459e60e8fdc2ef47e2119 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 18 Dec 2024 12:17:09 +0000 Subject: [PATCH 020/222] interface update in test --- tests/firedrake/regression/test_adjoint_operators.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index de1252be19..540932913b 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -913,7 +913,6 @@ def test_riesz_representation_for_adjoints(): space = FunctionSpace(mesh, "Lagrange", 1) f = Function(space).interpolate(SpatialCoordinate(mesh)[0]) J = assemble((f ** 2) * dx) - rf = ReducedFunctional(J, Control(f)) with stop_annotating(): v = TestFunction(space) u = TrialFunction(space) From cf57ae05b067de1774aecec9ab47f8a1209c4fbc Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 18 Dec 2024 14:04:54 +0000 Subject: [PATCH 021/222] fix GlobalDataSet._apply_local_global_filter --- pyop2/types/dataset.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pyop2/types/dataset.py b/pyop2/types/dataset.py index 087d9b091d..4a26cfc6d1 100644 --- a/pyop2/types/dataset.py +++ b/pyop2/types/dataset.py @@ -211,6 +211,7 @@ def __init__(self, global_): self._globalset = GlobalSet(comm=self.comm) self._name = "gdset_#x%x" % id(self) self._initialized = True + self._apply_local_global_filter = False @classmethod def _cache_key(cls, *args): From 55e11940c50dc98446ae995dfcfdd52021106753 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 18 Dec 2024 16:21:41 +0000 Subject: [PATCH 022/222] DROP BEFORE MERGE --- .github/workflows/build.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 909db6990a..aaf4239556 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -83,6 +83,7 @@ jobs: --install defcon \ --install gadopt \ --install asQ \ + --package-branch pyadjoint dham/abstract_reduced_functional || (cat firedrake-install.log && /bin/false) - name: Install test dependencies run: | From 112750e8d69548a348fbe46eff241738f4649453 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 18 Dec 2024 16:24:33 +0000 Subject: [PATCH 023/222] lint --- firedrake/adjoint_utils/function.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/adjoint_utils/function.py b/firedrake/adjoint_utils/function.py index 6b78163ed7..f593bf605d 100644 --- a/firedrake/adjoint_utils/function.py +++ b/firedrake/adjoint_utils/function.py @@ -2,7 +2,7 @@ import ufl from ufl.domain import extract_unique_domain from pyadjoint.overloaded_type import create_overloaded_object, FloatingType -from pyadjoint.tape import annotate_tape, stop_annotating, get_working_tape, no_annotations +from pyadjoint.tape import annotate_tape, stop_annotating, get_working_tape from firedrake.adjoint_utils.blocks import FunctionAssignBlock, ProjectBlock, SubfunctionBlock, FunctionMergeBlock, SupermeshProjectBlock import firedrake from .checkpointing import disk_checkpointing, CheckpointFunction, \ From c408ee3c1a9cbe3c3bcee2515bd04f8edf33f700 Mon Sep 17 00:00:00 2001 From: David Ham Date: Sun, 29 Dec 2024 21:13:51 +0000 Subject: [PATCH 024/222] typo --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index aaf4239556..a9537b4e7d 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -83,7 +83,7 @@ jobs: --install defcon \ --install gadopt \ --install asQ \ - --package-branch pyadjoint dham/abstract_reduced_functional + --package-branch pyadjoint dham/abstract_reduced_functional \ || (cat firedrake-install.log && /bin/false) - name: Install test dependencies run: | From da4793894a08bcf94e9f38d1113e2e4300c84a24 Mon Sep 17 00:00:00 2001 From: David Ham Date: Thu, 2 Jan 2025 16:20:16 +0000 Subject: [PATCH 025/222] fix interface in more tests --- tests/firedrake/regression/test_adjoint_operators.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index 540932913b..6d5b915b72 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -976,13 +976,13 @@ def test_lvs_constant_jacobian(constant_jacobian): J_hat = ReducedFunctional(J, Control(u)) - dJ = J_hat.derivative(options={"riesz_representation": None}) + dJ = J_hat.derivative() assert np.allclose(dJ.dat.data_ro, 2 * assemble(inner(u_ref, test) * dx).dat.data_ro) u_ref = Function(space, name="u").interpolate(X[0] - 0.1) J_hat(u_ref) - dJ = J_hat.derivative(options={"riesz_representation": None}) + dJ = J_hat.derivative() assert np.allclose(dJ.dat.data_ro, 2 * assemble(inner(u_ref, test) * dx).dat.data_ro) @@ -1036,6 +1036,6 @@ def test_bdy_control(): der_analytical1 = assemble(derivative((u_analytical**2) * dx, b)) J = assemble(sol * sol * dx) J_hat = ReducedFunctional(J, [Control(a), Control(b)]) - adj_derivatives = J_hat.derivative(options={"riesz_representation": "l2"}) + adj_derivatives = J_hat.derivative() assert np.allclose(adj_derivatives[0].dat.data_ro, der_analytical0.dat.data_ro) assert np.allclose(adj_derivatives[1].dat.data_ro, der_analytical1.dat.data_ro) From 7d03656f4fc0b90d0f9d7c5239c171a4ba2980ae Mon Sep 17 00:00:00 2001 From: David Ham Date: Thu, 2 Jan 2025 16:34:44 +0000 Subject: [PATCH 026/222] fix pytorch interface --- firedrake/ml/pytorch/fem_operator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/ml/pytorch/fem_operator.py b/firedrake/ml/pytorch/fem_operator.py index 7eda1d07ed..59a1482587 100644 --- a/firedrake/ml/pytorch/fem_operator.py +++ b/firedrake/ml/pytorch/fem_operator.py @@ -83,7 +83,7 @@ def backward(ctx, grad_output): adj_input = float(adj_input) # Compute adjoint model of `F`: delegated to pyadjoint.ReducedFunctional - adj_output = F.derivative(adj_input=adj_input, options={"riesz_representation": "l2"}) + adj_output = F.derivative(adj_input=adj_input) # Tuplify adjoint output adj_output = (adj_output,) if not isinstance(adj_output, collections.abc.Sequence) else adj_output From 63215762de4655787eb2fbd572560008516ff5f1 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Mon, 3 Feb 2025 16:57:12 +0000 Subject: [PATCH 027/222] fdvrf - CovarianceNormRF to keep track of B,R,Q for saddle point pc --- .../adjoint/fourdvar_reduced_functional.py | 107 +++++++++++------- .../test_4dvar_reduced_functional.py | 48 ++++---- 2 files changed, 93 insertions(+), 62 deletions(-) diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index 82effbf514..c32442046b 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -3,11 +3,11 @@ from pyadjoint.enlisting import Enlist from firedrake.function import Function from firedrake.ensemblefunction import EnsembleFunction, EnsembleCofunction +from firedrake import assemble, inner, dx, Constant from firedrake.adjoint.composite_reduced_functional import ( CompositeReducedFunctional, tlm, hessian, intermediate_options) - -from functools import wraps, cached_property -from typing import Callable, Optional +from functools import wraps, cached_property, partial +from typing import Callable, Optional, Collection, Union from types import SimpleNamespace from contextlib import contextmanager from mpi4py import MPI @@ -91,7 +91,7 @@ class FourDVarReducedFunctional(ReducedFunctional): The :class:`.EnsembleFunction` for the control x_{i} at the initial condition and at the end of each observation stage. - background_iprod + background_covariance The inner product to calculate the background error functional from the background error :math:`x_{0} - x_{b}`. Can include the error covariance matrix. Only used on ensemble rank 0. @@ -101,18 +101,18 @@ class FourDVarReducedFunctional(ReducedFunctional): If not provided, the value of the first subfunction on the first ensemble member of the control :class:`.EnsembleFunction` will be used. - observation_err + observation_error Given a state :math:`x`, returns the observations error :math:`y_{0} - \\mathcal{H}_{0}(x)` where :math:`y_{0}` are the observations at the initial time and :math:`\\mathcal{H}_{0}` is the observation operator for the initial time. Only used on ensemble rank 0. Optional. - observation_iprod + observation_covariance The inner product to calculate the observation error functional from the observation error :math:`y_{0} - \\mathcal{H}_{0}(x)`. Can include the error covariance matrix. Must be provided if - observation_err is provided. Only used on ensemble rank 0 + observation_error is provided. Only used on ensemble rank 0 weak_constraint Whether to use the weak or strong constraint 4DVar formulation. @@ -123,10 +123,10 @@ class FourDVarReducedFunctional(ReducedFunctional): """ def __init__(self, control: Control, - background_iprod: Optional[Callable[[OverloadedType], AdjFloat]], + background_covariance: Union[Constant, tuple], background: Optional[OverloadedType] = None, - observation_err: Optional[Callable[[OverloadedType], OverloadedType]] = None, - observation_iprod: Optional[Callable[[OverloadedType], AdjFloat]] = None, + observation_error: Optional[Callable[[OverloadedType], OverloadedType]] = None, + observation_covariance: Optional[Callable[[OverloadedType], AdjFloat]] = None, weak_constraint: bool = True, tape: Optional[Tape] = None, _annotate_accumulation: bool = False): @@ -134,7 +134,7 @@ def __init__(self, control: Control, self.tape = get_working_tape() if tape is None else tape self.weak_constraint = weak_constraint - self.initial_observations = observation_err is not None + self.initial_observations = observation_error is not None if self.weak_constraint: self._annotate_accumulation = _annotate_accumulation @@ -184,9 +184,9 @@ def __init__(self, control: Control, control_name="Control_0_bkg_copy") # RF to recalculate inner product |x_0 - x_b|_B - self.background_norm = isolated_rf( - operation=background_iprod, - control=self.background_error.functional, + self.background_norm = CovarianceNormReducedFunctional( + self.background_error.functional, + background_covariance, control_name="bkg_err_vec_copy") # compose background reduced functionals to evaluate both together @@ -197,16 +197,16 @@ def __init__(self, control: Control, # RF to recalculate error vector (H(x_0) - y_0) self.initial_observation_error = isolated_rf( - operation=observation_err, + operation=observation_error, control=_x[0], functional_name="obs_err_vec_0", control_name="Control_0_obs_copy") # RF to recalculate inner product |H(x_0) - y_0|_R - self.initial_observation_norm = isolated_rf( - operation=observation_iprod, - control=self.initial_observation_error.functional, - functional_name="obs_err_vec_0_copy") + self.initial_observation_norm = CovarianceNormReducedFunctional( + self.initial_observation_error.functional, + observation_covariance, + control_name="obs_err_vec_0_copy") # compose initial observation reduced functionals to evaluate both together self.initial_observation_rf = CompositeReducedFunctional( @@ -246,12 +246,14 @@ def __init__(self, control: Control, # penalty for straying from prior self._accumulate_functional( - background_iprod(control.control - self.background)) + covariance_norm(control.control - self.background, + background_covariance)) # penalty for not hitting observations at initial time if self.initial_observations: self._accumulate_functional( - observation_iprod(observation_err(control.control))) + covariance_norm(observation_error(control.control), + observation_covariance)) @cached_property def strong_reduced_functional(self): @@ -745,8 +747,8 @@ def __init__(self, control: OverloadedType, self.observation_index = observation_index def set_observation(self, state: OverloadedType, - observation_err: Callable[[OverloadedType], OverloadedType], - observation_iprod: Callable[[OverloadedType], AdjFloat]): + observation_error: Callable[[OverloadedType], OverloadedType], + observation_covariance: Callable[[OverloadedType], AdjFloat]): """ Record an observation at the time of `state`. @@ -756,14 +758,14 @@ def set_observation(self, state: OverloadedType, state The state at the current observation time. - observation_err + observation_error Given a state :math:`x`, returns the observations error :math:`y_{i} - \\mathcal{H}_{i}(x)` where :math:`y_{i}` are the observations at the current observation time and :math:`\\mathcal{H}_{i}` is the observation operator for the current observation time. - observation_iprod + observation_covariance The inner product to calculate the observation error functional from the observation error :math:`y_{i} - \\mathcal{H}_{i}(x)`. Can include the error covariance matrix. @@ -772,7 +774,9 @@ def set_observation(self, state: OverloadedType, raise ValueError("Cannot add observations once strong" " constraint ReducedFunctional instantiated") self.aaorf._accumulate_functional( - observation_iprod(observation_err(state))) + covariance_norm(observation_error(state), + observation_covariance)) + # save the user's state to hand back for beginning of next stage self.state = state @@ -819,9 +823,9 @@ def __init__(self, control: Control, self._stage_tape = get_working_tape() def set_observation(self, state: OverloadedType, - observation_err: Callable[[OverloadedType], OverloadedType], - observation_iprod: Callable[[OverloadedType], AdjFloat], - forward_model_iprod: Callable[[OverloadedType], AdjFloat]): + observation_error: Callable[[OverloadedType], OverloadedType], + observation_covariance: Callable[[OverloadedType], AdjFloat], + forward_model_covariance: Callable[[OverloadedType], AdjFloat]): """ Record an observation at the time of `state`. @@ -831,19 +835,19 @@ def set_observation(self, state: OverloadedType, state The state at the current observation time. - observation_err + observation_error Given a state :math:`x`, returns the observations error :math:`y_{i} - \\mathcal{H}_{i}(x)` where :math:`y_{i}` are the observations at the current observation time and :math:`\\mathcal{H}_{i}` is the observation operator for the current observation time. - observation_iprod + observation_covariance The inner product to calculate the observation error functional from the observation error :math:`y_{i} - \\mathcal{H}_{i}(x)`. Can include the error covariance matrix. - forward_model_iprod + forward_model_covariance The inner product to calculate the model error functional from the model error :math:`x_{i} - \\mathcal{M}_{i}(x_{i-1})`. Can include the error covariance matrix. @@ -889,9 +893,9 @@ def set_observation(self, state: OverloadedType, 'control_name': f"model_err_vec_{self.global_index}_copy" } if self.global_index else {} - self.model_norm = isolated_rf( - operation=forward_model_iprod, - control=self.model_error.functional, + self.model_norm = CovarianceNormReducedFunctional( + self.model_error.functional, + forward_model_covariance, **names) # compose model error reduced functionals to evaluate both together @@ -907,17 +911,17 @@ def set_observation(self, state: OverloadedType, } if self.global_index else {} self.observation_error = isolated_rf( - operation=observation_err, + operation=observation_error, control=self.controls[-1], **names) # RF to recalculate inner product |H(x_i) - y_i|_R names = { - 'functional_name': "obs_err_vec_{self.global_index}_copy" + 'control_name': "obs_err_vec_{self.global_index}_copy" } if self.global_index else {} - self.observation_norm = isolated_rf( - operation=observation_iprod, - control=self.observation_error.functional, + self.observation_norm = CovarianceNormReducedFunctional( + self.observation_error.functional, + observation_covariance, **names) # compose observation reduced functionals to evaluate both together @@ -1106,3 +1110,26 @@ def _model_hessian(self, m_dot, options): model_hessian._ad_convert_type(error_hessian[1], options=options) ] + + +def covariance_norm(x, covariance): + if isinstance(covariance, Collection): + covariance, power = covariance + else: + power = 1 + weight = Constant(1/covariance) + return assemble(inner(x, weight*x)*dx)**power + + +class CovarianceNormReducedFunctional(ReducedFunctional): + def __init__(self, x, covariance, + functional_name=None, + control_name=None): + self.covariance = covariance + cov_norm = partial(covariance_norm, covariance=covariance) + rf = isolated_rf(cov_norm, x, + functional_name=functional_name, + control_name=control_name) + super().__init__(rf.functional, + rf.controls.delist(rf.controls), + tape=rf.tape) diff --git a/tests/firedrake/regression/test_4dvar_reduced_functional.py b/tests/firedrake/regression/test_4dvar_reduced_functional.py index 67fde266b7..858fdd0260 100644 --- a/tests/firedrake/regression/test_4dvar_reduced_functional.py +++ b/tests/firedrake/regression/test_4dvar_reduced_functional.py @@ -50,16 +50,20 @@ def tendency(q, phi): return qn, qn1, stepper -def prod2(w): - """generate weighted inner products to pass to FourDVarReducedFunctional""" +def covariance_norm(covariance): + """generate weighted inner products to pass to FourDVarReducedFunctional. + Use the quadratic norm so Hessian is not linear.""" + cov, power = covariance + weight = fd.Constant(1/cov) + def n2(x): - return fd.assemble(fd.inner(x, fd.Constant(w)*x)*fd.dx)**2 + return fd.assemble(fd.inner(x, weight*x)*fd.dx)**power return n2 -prodB = prod2(0.1) # background error -prodR = prod2(10.) # observation error -prodQ = prod2(1.0) # model error +B = (fd.Constant(10.), 2) # background error covariance +R = (fd.Constant(0.1), 2) # observation error covariance +Q = (fd.Constant(0.5), 2) # model error covariance """Advecting velocity""" @@ -182,10 +186,10 @@ def strong_fdvar_pyadjoint(V): set_working_tape() # background functional - J = prodB(control - bkg) + J = covariance_norm(B)(control - bkg) # initial observation functional - J += prodR(obs_errors(0)(control)) + J += covariance_norm(R)(obs_errors(0)(control)) qn.assign(control) @@ -198,7 +202,7 @@ def strong_fdvar_pyadjoint(V): qn.assign(qn1) # observation functional - J += prodR(obs_errors(i)(qn)) + J += covariance_norm(R)(obs_errors(i)(qn)) pause_annotation() @@ -226,9 +230,9 @@ def strong_fdvar_firedrake(V): Jhat = FourDVarReducedFunctional( Control(control), - background_iprod=prodB, - observation_iprod=prodR, - observation_err=obs_errors(0), + background_covariance=B, + observation_covariance=R, + observation_error=obs_errors(0), weak_constraint=False) # record observation stages @@ -247,7 +251,7 @@ def strong_fdvar_firedrake(V): # take observation obs_index = stage.observation_index stage.set_observation(qn, obs_errors(obs_index), - observation_iprod=prodR) + observation_covariance=R) pause_annotation() return Jhat @@ -274,10 +278,10 @@ def weak_fdvar_pyadjoint(V): set_working_tape() # background error - J = prodB(controls[0] - bkg) + J = covariance_norm(B)(controls[0] - bkg) # initial observation error - J += prodR(obs_errors(0)(controls[0])) + J += covariance_norm(R)(obs_errors(0)(controls[0])) # record observation stages for i in range(1, len(controls)): @@ -298,10 +302,10 @@ def weak_fdvar_pyadjoint(V): controls[i].assign(qn) # model error for this stage - J += prodQ(qn - controls[i]) + J += covariance_norm(Q)(qn - controls[i]) # observation error - J += prodR(obs_errors(i)(controls[i])) + J += covariance_norm(R)(obs_errors(i)(controls[i])) pause_annotation() @@ -340,9 +344,9 @@ def weak_fdvar_firedrake(V, ensemble): Jhat = FourDVarReducedFunctional( Control(control), - background_iprod=prodB, - observation_iprod=prodR, - observation_err=obs_errors(0), + background_covariance=B, + observation_covariance=R, + observation_error=obs_errors(0), weak_constraint=True) # record observation stages @@ -362,8 +366,8 @@ def weak_fdvar_firedrake(V, ensemble): # take observation obs_err = obs_errors(stage.observation_index) stage.set_observation(qn, obs_err, - observation_iprod=prodR, - forward_model_iprod=prodQ) + observation_covariance=R, + forward_model_covariance=Q) pause_annotation() From 52e22d6a0966279a5fdae902229e76125f5c69c0 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 4 Feb 2025 10:33:59 +0000 Subject: [PATCH 028/222] ensemblefunction - bugfix for riesz map --- firedrake/ensemblefunction.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/firedrake/ensemblefunction.py b/firedrake/ensemblefunction.py index 33ed7e7b2f..23a5b0bac2 100644 --- a/firedrake/ensemblefunction.py +++ b/firedrake/ensemblefunction.py @@ -107,8 +107,8 @@ def riesz_representation(self, riesz_map="L2", **kwargs): }[type(self)] Vdual = [V.dual() for V in self.local_function_spaces] riesz = DualType(self.ensemble, Vdual) - for u in riesz.subfunctions: - u.assign(u.riesz_representation(riesz_map=riesz_map, **kwargs)) + for uself, uriesz in zip(self.subfunctions, riesz.subfunctions): + uriesz.assign(uself.riesz_representation(riesz_map=riesz_map, **kwargs)) return riesz @PETSc.Log.EventDecorator() From d698d1d860496013b90cd695014ed694e224e403 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 4 Feb 2025 10:34:44 +0000 Subject: [PATCH 029/222] fdvar - covarianceRF update --- firedrake/adjoint/fourdvar_reduced_functional.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index c32442046b..efba5b1a0c 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -1116,16 +1116,21 @@ def covariance_norm(x, covariance): if isinstance(covariance, Collection): covariance, power = covariance else: - power = 1 + power = None weight = Constant(1/covariance) - return assemble(inner(x, weight*x)*dx)**power + val = assemble(inner(x, weight*x)*dx) + return val if power is None else val**power class CovarianceNormReducedFunctional(ReducedFunctional): def __init__(self, x, covariance, functional_name=None, control_name=None): - self.covariance = covariance + if isinstance(covariance, Collection): + self.covariance, self.power = covariance + else: + self.covariance = covariance + self.power = None cov_norm = partial(covariance_norm, covariance=covariance) rf = isolated_rf(cov_norm, x, functional_name=functional_name, From 7af9880cfdc641212706753eaee30313da18c765 Mon Sep 17 00:00:00 2001 From: David Ham Date: Fri, 7 Feb 2025 15:47:27 +0000 Subject: [PATCH 030/222] fix dual spec --- firedrake/cofunction.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index 2885575209..6a9ec72bc3 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -492,7 +492,7 @@ def __call__(self, value): output = Function(self._function_space) output.assign(soln) elif ufl.duals.is_primal(value): - if value.function_space().dual() != self._function_space: + if value.function_space() != self._function_space: raise ValueError("Function space mismatch in RieszMap.") if self._inner_product == "l2": From 8e4ae06571ca3e46647d1100670a00b13fb97d56 Mon Sep 17 00:00:00 2001 From: David Ham Date: Sat, 8 Feb 2025 13:58:43 +0000 Subject: [PATCH 031/222] Update pyadjoint interface --- .../full_waveform_inversion/full_waveform_inversion.py.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/demos/full_waveform_inversion/full_waveform_inversion.py.rst b/demos/full_waveform_inversion/full_waveform_inversion.py.rst index 28794ee92d..2a7fdf0bae 100644 --- a/demos/full_waveform_inversion/full_waveform_inversion.py.rst +++ b/demos/full_waveform_inversion/full_waveform_inversion.py.rst @@ -280,7 +280,9 @@ To have the step 4, we need first to tape the forward problem. That is done by c We now instantiate :class:`~.EnsembleReducedFunctional`:: - J_hat = EnsembleReducedFunctional(J_val, Control(c_guess), my_ensemble) + J_hat = EnsembleReducedFunctional(J_val, + Control(c_guess, riesz_map="l2"), + my_ensemble) which enables us to recompute :math:`J` and its gradient :math:`\nabla_{\mathtt{c\_guess}} J`, where the :math:`J_s` and its gradients :math:`\nabla_{\mathtt{c\_guess}} J_s` are computed in parallel @@ -291,8 +293,7 @@ based on the ``my_ensemble`` configuration. is then passed as an argument to the ``minimize`` function:: c_optimised = minimize(J_hat, method="L-BFGS-B", options={"disp": True, "maxiter": 1}, - bounds=(1.5, 2.0), derivative_options={"riesz_representation": 'l2'} - ) + bounds=(1.5, 2.0)) The ``minimize`` function executes the optimisation algorithm until the stopping criterion (``maxiter``) is met. For 20 iterations, the predicted velocity model is shown in the following figure. From b92e400f599e894a79945f2a4b05bda98d7bc3c4 Mon Sep 17 00:00:00 2001 From: David Ham Date: Sat, 8 Feb 2025 14:03:55 +0000 Subject: [PATCH 032/222] apply riesz where needed --- tests/firedrake/output/test_adjoint_disk_checkpointing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/firedrake/output/test_adjoint_disk_checkpointing.py b/tests/firedrake/output/test_adjoint_disk_checkpointing.py index a492f568e9..91d353802b 100644 --- a/tests/firedrake/output/test_adjoint_disk_checkpointing.py +++ b/tests/firedrake/output/test_adjoint_disk_checkpointing.py @@ -54,7 +54,7 @@ def adjoint_example(mesh): assert np.allclose(J, Jnew) - grad_Jnew = Jhat.derivative() + grad_Jnew = Jhat.derivative(apply_riesz=True) return Jnew, grad_Jnew From 2f625bfcb50f4a8c9afd7d51448d66ee1583bcde Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Mon, 17 Feb 2025 13:07:06 +0000 Subject: [PATCH 033/222] bump ReducedFunctional.tlm upstream to pyadjoint --- .../adjoint/composite_reduced_functional.py | 191 +----------------- .../adjoint/fourdvar_reduced_functional.py | 12 +- .../adjoint/test_4dvar_reduced_functional.py | 23 ++- 3 files changed, 29 insertions(+), 197 deletions(-) diff --git a/firedrake/adjoint/composite_reduced_functional.py b/firedrake/adjoint/composite_reduced_functional.py index d09dfebe9f..358763b434 100644 --- a/firedrake/adjoint/composite_reduced_functional.py +++ b/firedrake/adjoint/composite_reduced_functional.py @@ -1,5 +1,4 @@ -from pyadjoint import stop_annotating, get_working_tape, OverloadedType, Control, Tape, ReducedFunctional -from pyadjoint.enlisting import Enlist +from pyadjoint import OverloadedType from typing import Optional @@ -28,165 +27,6 @@ def intermediate_options(options: dict): } -def compute_tlm(J: OverloadedType, - m: Control, - m_dot: OverloadedType, - options: Optional[dict] = None, - tape: Optional[Tape] = None): - """ - Compute the tangent linear model of J in a direction m_dot at the current value of m - - Parameters - ---------- - - J - The objective functional. - m - The (list of) :class:`pyadjoint.Control` for the functional. - m_dot - The direction in which to compute the Hessian. - Must be a (list of) :class:`pyadjoint.OverloadedType`. - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - tape - The tape to use. Default is the current tape. - - Returns - ------- - pyadjoint.OverloadedType - The tangent linear with respect to the control in direction m_dot. - Should be an instance of the same type as the control. - - """ - tape = tape or get_working_tape() - - # reset tlm values - tape.reset_tlm_values() - - m = Enlist(m) - m_dot = Enlist(m_dot) - - # set initial tlm values - for mi, mdi in zip(m, m_dot): - mi.tlm_value = mdi - - # evaluate tlm - with stop_annotating(): - with tape.marked_nodes(m): - tape.evaluate_tlm(markings=True) - - # return functional's tlm - return J._ad_convert_type(J.block_variable.tlm_value, - options=options or {}) - - -def compute_hessian(J: OverloadedType, - m: Control, - options: Optional[dict] = None, - tape: Optional[Tape] = None, - hessian_value: Optional[OverloadedType] = 0.): - """ - Compute the Hessian of J at the current value of m with the current tlm values on the tape. - - Parameters - ---------- - J - The objective functional. - m - The (list of) :class:`pyadjoint.Control` for the functional. - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - tape - The tape to use. Default is the current tape. - hessian_value - The initial hessian_value to start accumulating from. - - Returns - ------- - pyadjoint.OverloadedType - The second derivative with respect to the control in direction m_dot. - Should be an instance of the same type as the control. - - """ - tape = tape or get_working_tape() - - # reset hessian values - tape.reset_hessian_values() - - m = Enlist(m) - - # set initial hessian_value - J.block_variable.hessian_value = J._ad_convert_type( - hessian_value, options=intermediate_options(options)) - - # evaluate hessian - with stop_annotating(): - with tape.marked_nodes(m): - tape.evaluate_hessian(markings=True) - - # return controls' hessian values - return m.delist([v.get_hessian(options=options or {}) for v in m]) - - -def tlm(rf: ReducedFunctional, - m_dot: OverloadedType, - options: Optional[dict] = None): - """Returns the action of the tangent linear model of the functional w.r.t. the control on a vector m_dot. - - Parameters - ---------- - rf - The :class:`pyadjoint.ReducedFunctional` to evaluate the tlm of. - m_dot - The direction in which to compute the action of the tangent linear model. - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - - Returns - ------- - pyadjoint.OverloadedType - The action of the tangent linear model in the direction m_dot. - Should be an instance of the same type as the control. - - """ - return compute_tlm(rf.functional, rf.controls, m_dot, - tape=rf.tape, options=options) - - -def hessian(rf: ReducedFunctional, - options: Optional[dict] = None, - hessian_value: Optional[OverloadedType] = 0.): - """Returns the action of the Hessian of the functional w.r.t. the control. - - Using the second-order adjoint method, the action of the Hessian of the - functional with respect to the control, around the last supplied value - of the control and the last tlm values, is computed and returned. - - Parameters - ---------- - rf - The :class:`pyadjoint.ReducedFunctional` to evaluate the tlm of. - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - hessian_value - The initial hessian_value to start accumulating from. - - Returns - ------- - pyadjoint.OverloadedType - The action of the Hessian. Should be an instance of the same type as the control. - - """ - return rf.controls.delist( - compute_hessian(rf.functional, rf.controls, - tape=rf.tape, options=options, - hessian_value=hessian_value)) - - class CompositeReducedFunctional: """Class representing the composition of two reduced functionals. @@ -274,14 +114,11 @@ def tlm(self, m_dot: OverloadedType, options: Optional[dict] = None): Should be an instance of the same type as the control. """ - tlm1 = self._eval_tlm( - self.rf1, m_dot, intermediate_options(options)), - tlm2 = self._eval_tlm( - self.rf2, tlm1, options) - return tlm2 + return self.rf2.tlm(self.rf1.tlm(m_dot, intermediate_options(options)), options) def hessian(self, m_dot: OverloadedType, options: Optional[dict] = None, + hessian_input: OverloadedType = 0.0, evaluate_tlm: Optional[bool] = True): """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. @@ -313,20 +150,8 @@ def hessian(self, m_dot: OverloadedType, """ if evaluate_tlm: self.tlm(m_dot, options=intermediate_options(options)) - hess2 = self._eval_hessian( - self.rf2, 0., intermediate_options(options)) - hess1 = self._eval_hessian( - self.rf1, hess2, options or {}) - return hess1 - - def _eval_tlm(self, rf, m_dot, options): - if isinstance(rf, CompositeReducedFunctional): - return rf.tlm(m_dot, options=options) - else: - return tlm(rf, m_dot=m_dot, options=options) - - def _eval_hessian(self, rf, hessian_value, options): - if isinstance(rf, CompositeReducedFunctional): - return rf.hessian(None, options, evaluate_tlm=False) - else: - return hessian(rf, hessian_value=hessian_value, options=options) + return self.rf1.hessian( + None, options or {}, evaluate_tlm=False, + hessian_input=self.rf2.hessian( + None, options=intermediate_options(options), evaluate_tlm=False, + hessian_input=hessian_input)) diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index ceb2a2b35c..f7fbbfbcb6 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -5,7 +5,7 @@ from firedrake.ensemble import EnsembleFunction from firedrake import assemble, inner, dx, Constant from firedrake.adjoint.composite_reduced_functional import ( - CompositeReducedFunctional, tlm, hessian, intermediate_options) + CompositeReducedFunctional, intermediate_options) from ufl.duals import is_primal, is_dual from functools import wraps, cached_property, partial from typing import Callable, Optional, Collection, Union @@ -1089,8 +1089,7 @@ def _model_hessian(self, m_dot, options): iopts = intermediate_options(options) # TLM for model from mdot[0] - forward_tlm = tlm(self.forward_model, m_dot[0], - options=iopts) + forward_tlm = self.forward_model.tlm(m_dot[0], options=iopts) # combine model TLM and mdot[1] mdot_error = [forward_tlm, m_dot[1]] @@ -1100,9 +1099,10 @@ def _model_hessian(self, m_dot, options): mdot_error, options=iopts, evaluate_tlm=True) # Hessian for model - model_hessian = hessian( - self.forward_model, options=options, - hessian_value=error_hessian[0]) + model_hessian = self.forward_model.hessian( + None, options=options, + hessian_input=error_hessian[0], + evaluate_tlm=False) # combine model Hessian and converted error Hessian return [ diff --git a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py index e2c23ce9ad..4d0b56bb99 100644 --- a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py +++ b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py @@ -61,9 +61,9 @@ def n2(x): return n2 -B = (fd.Constant(10.), 2) # background error covariance -R = (fd.Constant(0.1), 2) # observation error covariance -Q = (fd.Constant(0.5), 2) # model error covariance +B = (fd.Constant(10.), 4) # background error covariance +R = (fd.Constant(0.1), 4) # observation error covariance +Q = (fd.Constant(0.5), 4) # model error covariance """Advecting velocity""" @@ -395,8 +395,12 @@ def main_test_strong_4dvar_advection(): eps = 1e-12 # Does evaluating the functional match the reference rf? - assert abs(Jhat_pyadj(mp) - Jhat_aaorf(ma)) < eps - assert abs(Jhat_pyadj(hp) - Jhat_aaorf(ha)) < eps + Jpm = Jhat_pyadj(mp) + Jph = Jhat_pyadj(hp) + Jam = Jhat_aaorf(ma) + Jah = Jhat_aaorf(ha) + assert abs((Jpm - Jam)/Jpm) < eps + assert abs((Jph - Jah)/Jph) < eps # If we match the functional, then passing the taylor tests # should mean that we match the derivative too. @@ -436,10 +440,13 @@ def main_test_weak_4dvar_advection(): ma = m(V, ensemble) ha = h(V, ensemble) - eps = 1e-10 + Jam = Jhat_aaorf(ma) + Jah = Jhat_aaorf(ha) + + eps = 1e-12 # Does evaluating the functional match the reference rf? - assert abs(Jpm - Jhat_aaorf(ma)) < eps - assert abs(Jph - Jhat_aaorf(ha)) < eps + assert abs((Jpm - Jam)/Jpm) < eps + assert abs((Jph - Jah)/Jph) < eps # If we match the functional, then passing the taylor tests # should mean that we match the derivative too. From 1327e79e222d4da4e5d4828d583eddaf029c27ee Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 18 Feb 2025 10:53:17 +0000 Subject: [PATCH 034/222] WIP: saddle point pc impl --- .../adjoint/fourdvar_reduced_functional.py | 29 +++++++++++++++---- 1 file changed, 24 insertions(+), 5 deletions(-) diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index f7fbbfbcb6..bfac84616e 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -2,7 +2,7 @@ stop_annotating, get_working_tape, set_working_tape from pyadjoint.enlisting import Enlist from firedrake.function import Function -from firedrake.ensemble import EnsembleFunction +from firedrake.ensemble import EnsembleFunction, EnsembleFunctionSpace from firedrake import assemble, inner, dx, Constant from firedrake.adjoint.composite_reduced_functional import ( CompositeReducedFunctional, intermediate_options) @@ -153,8 +153,8 @@ def __init__(self, control: Control, self.background = control.control.subfunctions[0]._ad_copy() _rename(self.background, "Background") - self.control_space = control.function_space() - ensemble = self.control_space.ensemble + self.solution_space = control.function_space() + ensemble = self.solution_space.ensemble self.ensemble = ensemble self.trank = ensemble.ensemble_comm.rank if ensemble else 0 self.nchunks = ensemble.ensemble_comm.size if ensemble else 1 @@ -175,6 +175,12 @@ def __init__(self, control: Control, self.stages = [] # The record of each observation stage + self.observation_rfs = [] + self.observation_norms = [] + + self.model_rfs = [] + self.model_norms = [] + # first rank sets up functionals for background initial observations if self.trank == 0: @@ -210,6 +216,9 @@ def __init__(self, control: Control, observation_covariance, control_name="obs_err_vec_0_copy") + self.observation_rfs.append(self.initial_observation_error) + self.observation_norms.append(self.initial_observation_norm) + # compose initial observation reduced functionals to evaluate both together self.initial_observation_rf = CompositeReducedFunctional( self.initial_observation_error, self.initial_observation_norm) @@ -392,12 +401,12 @@ def derivative(self, adj_input: float = 1.0, options: dict = {}): # create the derivative in the right primal or dual space if is_primal(sderiv0[0]): - derivative_space = self.control_space + derivative_space = self.solution_space else: if not is_dual(sderiv0[0]): raise ValueError( "Do not know how to handle stage derivative which is not primal or dual") - derivative_space = self.control_space.dual() + derivative_space = self.solution_space.dual() derivatives = EnsembleFunction(derivative_space) derivatives.zero() @@ -631,6 +640,12 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): # let the user record the local stages yield stage_sequence + for stage in self.stages: + self.observation_rfs.append(stage.observation_error) + self.observation_norms.append(stage.observation_norm) + self.model_rfs.append(stage.forward_model) + self.model_norms.append(stage.model_norm) + # send the state forward with stop_annotating(): state = self.stages[-1].controls[1].control @@ -646,6 +661,10 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): # values of the initial timeseris self.control.assign(self._cbuf) + self.observation_space = EnsembleFunctionSpace( + [Jo.functional.function_space() for Jo in self.observation_rfs], + self.ensemble) + else: # strong constraint yield ObservationStageSequence( From 0905489459ca2b86fb7100a82de4930d0b6f94ee Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Tue, 11 Mar 2025 15:29:42 +0000 Subject: [PATCH 035/222] wip --- .../adjoint/fourdvar_reduced_functional.py | 43 +++++++++++++------ 1 file changed, 31 insertions(+), 12 deletions(-) diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index bfac84616e..e64586a319 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -12,6 +12,7 @@ from types import SimpleNamespace from contextlib import contextmanager from mpi4py import MPI +from firedrake.petsc import PETSc __all__ = ['FourDVarReducedFunctional'] @@ -123,6 +124,7 @@ class FourDVarReducedFunctional(ReducedFunctional): :class:`pyadjoint.ReducedFunctional`. """ + @PETSc.Log.EventDecorator() def __init__(self, control: Control, background_covariance: Union[Constant, tuple], background: Optional[OverloadedType] = None, @@ -153,8 +155,8 @@ def __init__(self, control: Control, self.background = control.control.subfunctions[0]._ad_copy() _rename(self.background, "Background") - self.solution_space = control.function_space() - ensemble = self.solution_space.ensemble + self.control_space = control.function_space() + ensemble = self.control_space.ensemble self.ensemble = ensemble self.trank = ensemble.ensemble_comm.rank if ensemble else 0 self.nchunks = ensemble.ensemble_comm.size if ensemble else 1 @@ -168,7 +170,7 @@ def __init__(self, control: Control, self._controls = tuple(Control(xi) for xi in _x) self.control = control - self.controls = [control] + self.controls = Enlist(control) # first control on rank 0 is initial conditions, not end of observation stage self.nlocal_stages = len(_x) - (1 if self.trank == 0 else 0) @@ -291,6 +293,7 @@ def __getattr__(self, attr): @sc_passthrough @stop_annotating() + @PETSc.Log.EventDecorator() def __call__(self, values: OverloadedType): """Computes the reduced functional with supplied control value. @@ -370,6 +373,7 @@ def __call__(self, values: OverloadedType): @sc_passthrough @stop_annotating() + @PETSc.Log.EventDecorator() def derivative(self, adj_input: float = 1.0, options: dict = {}): """Returns the derivative of the functional w.r.t. the control. Using the adjoint method, the derivative of the functional with @@ -401,12 +405,12 @@ def derivative(self, adj_input: float = 1.0, options: dict = {}): # create the derivative in the right primal or dual space if is_primal(sderiv0[0]): - derivative_space = self.solution_space + derivative_space = self.control_space else: if not is_dual(sderiv0[0]): raise ValueError( "Do not know how to handle stage derivative which is not primal or dual") - derivative_space = self.solution_space.dual() + derivative_space = self.control_space.dual() derivatives = EnsembleFunction(derivative_space) derivatives.zero() @@ -468,6 +472,7 @@ def derivative(self, adj_input: float = 1.0, options: dict = {}): @sc_passthrough @stop_annotating() + @PETSc.Log.EventDecorator() def hessian(self, m_dot: OverloadedType, options: dict = {}): """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. @@ -502,6 +507,9 @@ def hessian(self, m_dot: OverloadedType, options: dict = {}): hess = self.control.copy_data() hess.zero() + if not isinstance(m_dot, list): + m_dot = [m_dot] + # set up arrays including halos if trank == 0: hs = [*hess.subfunctions] @@ -593,6 +601,7 @@ def _accumulate_functional(self, val): self._accumulation_started = True @contextmanager + @PETSc.Log.EventDecorator() def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): if not sequential: raise ValueError("Recording stages concurrently not yet implemented") @@ -657,13 +666,14 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): ectx.global_index = self.stages[-1].global_index ectx.observation_index = self.stages[-1].observation_index - # make sure that self.control now holds the - # values of the initial timeseris - self.control.assign(self._cbuf) + with stop_annotating(): + # make sure that self.control now holds the + # values of the initial timeseris + self.control.assign(self._cbuf) - self.observation_space = EnsembleFunctionSpace( - [Jo.functional.function_space() for Jo in self.observation_rfs], - self.ensemble) + self.observation_space = EnsembleFunctionSpace( + [Jo.functional.function_space() for Jo in self.observation_rfs], + self.ensemble) else: # strong constraint @@ -693,6 +703,7 @@ def __init__(self, controls: Control, def __iter__(self): return self + @PETSc.Log.EventDecorator() def __next__(self): # increment global indices. @@ -765,6 +776,7 @@ def __init__(self, control: OverloadedType, self.index = index self.observation_index = observation_index + @PETSc.Log.EventDecorator() def set_observation(self, state: OverloadedType, observation_error: Callable[[OverloadedType], OverloadedType], observation_covariance: Callable[[OverloadedType], AdjFloat]): @@ -841,6 +853,7 @@ def __init__(self, control: Control, set_working_tape() self._stage_tape = get_working_tape() + @PETSc.Log.EventDecorator() def set_observation(self, state: OverloadedType, observation_error: Callable[[OverloadedType], OverloadedType], observation_covariance: Callable[[OverloadedType], AdjFloat], @@ -954,6 +967,7 @@ def set_observation(self, state: OverloadedType, set_working_tape() @stop_annotating() + @PETSc.Log.EventDecorator() def __call__(self, values: OverloadedType, rftype: Optional[str] = None): """Computes the reduced functional with supplied control value. @@ -993,6 +1007,7 @@ def __call__(self, values: OverloadedType, return J @stop_annotating() + @PETSc.Log.EventDecorator() def derivative(self, adj_input: float = 1.0, options: dict = {}, rftype: Optional[str] = None): """Returns the derivative of the functional w.r.t. the control. @@ -1057,6 +1072,7 @@ def derivative(self, adj_input: float = 1.0, options: dict = {}, return derivatives @stop_annotating() + @PETSc.Log.EventDecorator() def hessian(self, m_dot: OverloadedType, options: dict = {}, rftype: Optional[str] = None): """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. @@ -1138,7 +1154,10 @@ def covariance_norm(x, covariance): power = None weight = Constant(1/covariance) val = assemble(inner(x, weight*x)*dx) - return val if power is None else val**power + from pyadjoint import AdjFloat + result = val if power is None else val**power + assert type(result) is AdjFloat + return result class CovarianceNormReducedFunctional(ReducedFunctional): From 4084593692207f45bde55833177a32f5ada2a8b7 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Fri, 21 Mar 2025 13:53:41 +0000 Subject: [PATCH 036/222] wip --- .../adjoint/fourdvar_reduced_functional.py | 3 +++ .../adjoint/test_4dvar_reduced_functional.py | 26 ++++++++++++++----- 2 files changed, 23 insertions(+), 6 deletions(-) diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index e64586a319..3d9de5b817 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -504,7 +504,10 @@ def hessian(self, m_dot: OverloadedType, options: dict = {}): """ trank = self.trank + # This should be hess = self.control._ad_dual_type() + # to avoid unnecessary solve in riesz_representation hess = self.control.copy_data() + hess = hess.riesz_representation() hess.zero() if not isinstance(m_dot, list): diff --git a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py index 4d0b56bb99..04990284e5 100644 --- a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py +++ b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py @@ -2,21 +2,33 @@ import firedrake as fd from firedrake.__future__ import interpolate from firedrake.adjoint import ( - continue_annotation, pause_annotation, stop_annotating, + continue_annotation, pause_annotation, stop_annotating, annotate_tape, set_working_tape, get_working_tape, Control, taylor_test, taylor_to_dict, - ReducedFunctional, FourDVarReducedFunctional) + ReducedFunctional, FourDVarReducedFunctional, AdjFloat) from numpy import mean @pytest.fixture(autouse=True) -def clear_tape_teardown(): +def handle_taping(): yield - get_working_tape().clear_tape() + tape = get_working_tape() + tape.clear_tape() + + +@pytest.fixture(autouse=True, scope="module") +def handle_annotation(): + if not annotate_tape(): + continue_annotation() + yield + # Ensure annotation is paused when we finish. + if annotate_tape(): + pause_annotation() def function_space(comm): """DG0 periodic advection""" - mesh = fd.PeriodicUnitIntervalMesh(nx, comm=comm) + mesh = fd.PeriodicUnitIntervalMesh(nx, comm=comm, + distribution_parameters={"partitioner_type": "simple"}) return fd.FunctionSpace(mesh, "DG", 0) @@ -57,7 +69,9 @@ def covariance_norm(covariance): weight = fd.Constant(1/cov) def n2(x): - return fd.assemble(fd.inner(x, weight*x)*fd.dx)**power + result = fd.assemble(fd.inner(x, weight*x)*fd.dx)**power + assert type(result) is AdjFloat + return result return n2 From c6cf8030e077b24c01b5bb8ccc7f0c6fac3beb53 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Fri, 21 Mar 2025 14:52:34 +0000 Subject: [PATCH 037/222] be more discerning about when to dual return type of 4dvar hessian --- firedrake/adjoint/fourdvar_reduced_functional.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index 3d9de5b817..66b76b9353 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -504,10 +504,12 @@ def hessian(self, m_dot: OverloadedType, options: dict = {}): """ trank = self.trank - # This should be hess = self.control._ad_dual_type() - # to avoid unnecessary solve in riesz_representation hess = self.control.copy_data() - hess = hess.riesz_representation() + + if options.get('riesz_representation', '') in (None, 'l2'): + # This should be hess = self.control._ad_dual_type() + # to avoid unnecessary solve in riesz_representation + hess = hess.riesz_representation() hess.zero() if not isinstance(m_dot, list): From e1f5d797740e65133c60df02330b124d3a955a7c Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Fri, 21 Mar 2025 15:08:45 +0000 Subject: [PATCH 038/222] ensemble test warnings and lint --- tests/firedrake/ensemble/test_ensemble.py | 29 +++++----- .../ensemble/test_ensemble_function.py | 1 + .../ensemble/test_ensemble_functionspace.py | 54 +++++++++---------- 3 files changed, 42 insertions(+), 42 deletions(-) diff --git a/tests/firedrake/ensemble/test_ensemble.py b/tests/firedrake/ensemble/test_ensemble.py index d8180dbad8..f7c3a9a893 100644 --- a/tests/firedrake/ensemble/test_ensemble.py +++ b/tests/firedrake/ensemble/test_ensemble.py @@ -1,8 +1,7 @@ from firedrake import * from pyop2.mpi import MPI -from pytest_mpi import parallel_assert import pytest -from pytest_mpi import parallel_assert +from pytest_mpi.parallel_assert import parallel_assert from operator import mul from functools import reduce @@ -105,7 +104,7 @@ def test_ensemble_allreduce(ensemble, mesh, W, urank, urank_sum, blocking): requests = ensemble.iallreduce(urank, u_reduce) MPI.Request.Waitall(requests) - parallel_assert(lambda: errornorm(urank_sum, u_reduce) < 1e-12) + parallel_assert(errornorm(urank_sum, u_reduce) < 1e-12) @pytest.mark.parallel(nprocs=2) @@ -157,7 +156,7 @@ def test_comm_manager_allreduce(blocking): f5 = Function(V5) with f4.dat.vec_ro as v4, f5.dat.vec_ro as v5: - parallel_assert(lambda: v4.getSizes() == v5.getSizes()) + parallel_assert(v4.getSizes() == v5.getSizes()) with pytest.raises(ValueError): allreduce(f4, f5) @@ -188,15 +187,15 @@ def test_ensemble_reduce(ensemble, mesh, W, urank, urank_sum, root, blocking): error = errornorm(urank_sum, u_reduce) rank = ensemble.ensemble_rank parallel_assert( - lambda: error < 1e-12, + error < 1e-12, participating=(rank == root), - msg=f"{error = :.5f}" + msg=f"{error=:.5f}" ) error = errornorm(Function(W).assign(10), u_reduce) parallel_assert( - lambda: error < 1e-12, + error < 1e-12, participating=(rank != root), - msg=f"{error = :.5f}" + msg=f"{error=:.5f}" ) # check that u_reduce dat vector is still synchronised @@ -206,7 +205,7 @@ def test_ensemble_reduce(ensemble, mesh, W, urank, urank_sum, root, blocking): with u_reduce.dat.vec as v: states[spatial_rank] = v.stateGet() ensemble.comm.Allgather(MPI.IN_PLACE, states) - parallel_assert(lambda: len(set(states)) == 1) + parallel_assert(len(set(states)) == 1) @pytest.mark.parallel(nprocs=2) @@ -257,7 +256,7 @@ def test_comm_manager_reduce(blocking): f5 = Function(V5) with f4.dat.vec_ro as v4, f5.dat.vec_ro as v5: - parallel_assert(lambda: v4.getSizes() == v5.getSizes()) + parallel_assert(v4.getSizes() == v5.getSizes()) with pytest.raises(ValueError): reduction(f4, f5) @@ -285,7 +284,7 @@ def test_ensemble_bcast(ensemble, mesh, W, urank, root, blocking): # broadcasted function u_correct = unique_function(mesh, root, W) - parallel_assert(lambda: errornorm(u_correct, urank) < 1e-12) + parallel_assert(errornorm(u_correct, urank) < 1e-12) @pytest.mark.parallel(nprocs=6) @@ -324,9 +323,9 @@ def test_send_and_recv(ensemble, mesh, W, blocking): # Test send/recv between first two spatial comms # ie: ensemble.ensemble_comm.rank == 0 and 1 parallel_assert( - lambda: error < 1e-12, + error < 1e-12, participating=ensemble.ensemble_rank in (rank0, rank1), - msg=f"{error = :.5f}" # noqa: E203, E251 + msg=f"{error=:.5f}" # noqa: E203, E251 ) @@ -352,7 +351,7 @@ def test_sendrecv(ensemble, mesh, W, urank, blocking): if not blocking: MPI.Request.Waitall(requests) - parallel_assert(lambda: errornorm(urecv, u_expect) < 1e-12) + parallel_assert(errornorm(urecv, u_expect) < 1e-12) @pytest.mark.parallel(nprocs=6) @@ -383,4 +382,4 @@ def test_ensemble_solvers(ensemble, W, urank, urank_sum): usum = Function(W) ensemble.allreduce(u_separate, usum) - parallel_assert(lambda: errornorm(u_combined, usum) < 1e-8) + parallel_assert(errornorm(u_combined, usum) < 1e-8) diff --git a/tests/firedrake/ensemble/test_ensemble_function.py b/tests/firedrake/ensemble/test_ensemble_function.py index 8051595d2a..a7a3f40a93 100644 --- a/tests/firedrake/ensemble/test_ensemble_function.py +++ b/tests/firedrake/ensemble/test_ensemble_function.py @@ -2,6 +2,7 @@ from pyop2 import Subset import pytest import numpy as np +from pytest_mpi.parallel_assert import parallel_assert def random_func(f): diff --git a/tests/firedrake/ensemble/test_ensemble_functionspace.py b/tests/firedrake/ensemble/test_ensemble_functionspace.py index f10b34ab03..e496dfa3d9 100644 --- a/tests/firedrake/ensemble/test_ensemble_functionspace.py +++ b/tests/firedrake/ensemble/test_ensemble_functionspace.py @@ -1,6 +1,6 @@ import firedrake as fd import pytest -from pytest_mpi import parallel_assert +from pytest_mpi.parallel_assert import parallel_assert @pytest.fixture @@ -119,7 +119,7 @@ def test_ensemble_space_reject_inconsistent_meshes(ensemble, space_type): elif space_type == 'dual': spaces = (FS0.dual(), FS1.dual()) else: - raise ValueError(f"Unrecognised {space_type = }") + raise ValueError(f"Unrecognised {space_type=}") with pytest.raises(ValueError): _ = fd.EnsembleFunctionSpace(spaces, ensemble) @@ -179,7 +179,7 @@ def test_ensemble_space_allows_dual_init(ensemble, mesh): efs = fd.EnsembleFunctionSpace(spaces, ensemble) - parallel_assert(lambda: isinstance(efs, fd.EnsembleDualSpace)) + parallel_assert(isinstance(efs, fd.EnsembleDualSpace)) @pytest.mark.parallel(nprocs=[1, 2, 4, 6]) @@ -196,12 +196,12 @@ def test_ensemble_local_spaces_correct(ensemblespace, Wlocal): Wlocal = [W.dual() for W in Wlocal] # Does efs have the correct number of spaces locally? - parallel_assert(lambda: len(efs.local_spaces) == len(Wlocal)) - parallel_assert(lambda: efs.nlocal_spaces == len(efs.local_spaces)) + parallel_assert(len(efs.local_spaces) == len(Wlocal)) + parallel_assert(efs.nlocal_spaces == len(efs.local_spaces)) # Correct number globally? nglobal = ensemble.ensemble_comm.allreduce(efs.nlocal_spaces) - parallel_assert(lambda: efs.nglobal_spaces == nglobal) + parallel_assert(efs.nglobal_spaces == nglobal) local_space_matches = [ V == W for V, W in zip(efs.local_spaces, Wlocal)] @@ -210,9 +210,9 @@ def test_ensemble_local_spaces_correct(ensemblespace, Wlocal): # rank-by-rank not space-by-space for root in range(ensemble.ensemble_size): parallel_assert( - lambda: all(local_space_matches), + all(local_space_matches), participating=(ensemble.ensemble_rank == root), - msg=f"{ensemble.ensemble_rank = }, {local_space_matches = }") + msg=f"{ensemble.ensemble_rank=}, {local_space_matches=}") @pytest.mark.parallel(nprocs=[1, 2, 4, 6]) @@ -231,16 +231,16 @@ def test_ensemble_dofsizes_correct(ensemblespace): nglobal_dofs = ensemble.ensemble_comm.allreduce(nlocal_comm_dofs) parallel_assert( - lambda: efs.nlocal_rank_dofs == nlocal_rank_dofs, - msg=f"{rank = }, {efs.nlocal_rank_dofs = }, {nlocal_rank_dofs = }") + efs.nlocal_rank_dofs == nlocal_rank_dofs, + msg=f"{rank=}, {efs.nlocal_rank_dofs=}, {nlocal_rank_dofs=}") parallel_assert( - lambda: efs.nlocal_comm_dofs == nlocal_comm_dofs, - msg=f"{rank = }, {efs.nlocal_comm_dofs = }, {nlocal_comm_dofs = }") + efs.nlocal_comm_dofs == nlocal_comm_dofs, + msg=f"{rank=}, {efs.nlocal_comm_dofs=}, {nlocal_comm_dofs=}") parallel_assert( - lambda: efs.nglobal_dofs == nglobal_dofs, - msg=f"{rank = }, {efs.nglobal_dofs = }, {nglobal_dofs = }") + efs.nglobal_dofs == nglobal_dofs, + msg=f"{rank=}, {efs.nglobal_dofs=}, {nglobal_dofs=}") @pytest.mark.parallel(nprocs=[1, 2, 4, 6]) @@ -258,7 +258,7 @@ def test_ensemble_space_dual(ensemblespace): dual_type = fd.EnsembleFunctionSpace dual = ensemblespace.dual() - parallel_assert(lambda: type(dual) is dual_type) + parallel_assert(type(dual) is dual_type) # are the dual subspaces correct? local_space_matches = [ @@ -269,12 +269,12 @@ def test_ensemble_space_dual(ensemblespace): ensemble_rank = ensemble.ensemble_rank for root in range(ensemble.ensemble_size): parallel_assert( - lambda: all(local_space_matches), + all(local_space_matches), participating=(ensemble.ensemble_rank == root), - msg=f"{ensemble_rank = }, {local_space_matches = }") + msg=f"{ensemble_rank=}, {local_space_matches=}") dual2 = dual.dual() - parallel_assert(lambda: type(dual.dual()) is type(ensemblespace)) + parallel_assert(type(dual.dual()) is type(ensemblespace)) # are the dual subspaces the originals? local_space_matches = [ @@ -285,9 +285,9 @@ def test_ensemble_space_dual(ensemblespace): ensemble_rank = ensemblespace.ensemble.ensemble_rank for root in range(ensemble.ensemble_size): parallel_assert( - lambda: all(local_space_matches), + all(local_space_matches), participating=(ensemble.ensemble_rank == root), - msg=f"{ensemble_rank = }, {local_space_matches = }") + msg=f"{ensemble_rank=}, {local_space_matches=}") @pytest.mark.parallel(nprocs=[1, 2, 4]) @@ -300,13 +300,13 @@ def test_ensemble_space_equality(ensemblespace): orig_spaces = ensemblespace.local_spaces # .dual().dual() should be a round trip - parallel_assert(lambda: ensemblespace == ensemblespace) - parallel_assert(lambda: ensemblespace != ensemblespace.dual()) - parallel_assert(lambda: ensemblespace == ensemblespace.dual().dual()) + parallel_assert(ensemblespace == ensemblespace) + parallel_assert(ensemblespace != ensemblespace.dual()) + parallel_assert(ensemblespace == ensemblespace.dual().dual()) # Duplicate is equal dup_fs = fd.EnsembleFunctionSpace(orig_spaces, ensemble) - parallel_assert(lambda: ensemblespace == dup_fs) + parallel_assert(ensemblespace == dup_fs) # Same length, different space is not equal if ensemble.ensemble_rank == (ensemble.ensemble_size - 1): @@ -315,7 +315,7 @@ def test_ensemble_space_equality(ensemblespace): diff_spaces = orig_spaces diff_fs = fd.EnsembleFunctionSpace(diff_spaces, ensemble) - parallel_assert(lambda: ensemble != diff_fs) + parallel_assert(ensemble != diff_fs) # Same spaces, shorter length is not equal if ensemble.ensemble_rank == 0: @@ -324,7 +324,7 @@ def test_ensemble_space_equality(ensemblespace): short_spaces = orig_spaces short_fs = fd.EnsembleFunctionSpace(short_spaces, ensemble) - parallel_assert(lambda: ensemble != short_fs) + parallel_assert(ensemble != short_fs) # Same spaces, longer length is not equal if ensemble.ensemble_rank == (ensemble.ensemble_size - 1): @@ -333,4 +333,4 @@ def test_ensemble_space_equality(ensemblespace): long_spaces = orig_spaces long_fs = fd.EnsembleFunctionSpace(long_spaces, ensemble) - parallel_assert(lambda: ensemble != long_fs) + parallel_assert(ensemble != long_fs) From be0285e60d0bf04bfe9353505c57831403406e82 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Fri, 21 Mar 2025 15:08:45 +0000 Subject: [PATCH 039/222] ensemble test warnings and lint --- tests/firedrake/ensemble/test_ensemble.py | 29 +++++----- .../ensemble/test_ensemble_function.py | 1 + .../ensemble/test_ensemble_functionspace.py | 54 +++++++++---------- 3 files changed, 42 insertions(+), 42 deletions(-) diff --git a/tests/firedrake/ensemble/test_ensemble.py b/tests/firedrake/ensemble/test_ensemble.py index d8180dbad8..f7c3a9a893 100644 --- a/tests/firedrake/ensemble/test_ensemble.py +++ b/tests/firedrake/ensemble/test_ensemble.py @@ -1,8 +1,7 @@ from firedrake import * from pyop2.mpi import MPI -from pytest_mpi import parallel_assert import pytest -from pytest_mpi import parallel_assert +from pytest_mpi.parallel_assert import parallel_assert from operator import mul from functools import reduce @@ -105,7 +104,7 @@ def test_ensemble_allreduce(ensemble, mesh, W, urank, urank_sum, blocking): requests = ensemble.iallreduce(urank, u_reduce) MPI.Request.Waitall(requests) - parallel_assert(lambda: errornorm(urank_sum, u_reduce) < 1e-12) + parallel_assert(errornorm(urank_sum, u_reduce) < 1e-12) @pytest.mark.parallel(nprocs=2) @@ -157,7 +156,7 @@ def test_comm_manager_allreduce(blocking): f5 = Function(V5) with f4.dat.vec_ro as v4, f5.dat.vec_ro as v5: - parallel_assert(lambda: v4.getSizes() == v5.getSizes()) + parallel_assert(v4.getSizes() == v5.getSizes()) with pytest.raises(ValueError): allreduce(f4, f5) @@ -188,15 +187,15 @@ def test_ensemble_reduce(ensemble, mesh, W, urank, urank_sum, root, blocking): error = errornorm(urank_sum, u_reduce) rank = ensemble.ensemble_rank parallel_assert( - lambda: error < 1e-12, + error < 1e-12, participating=(rank == root), - msg=f"{error = :.5f}" + msg=f"{error=:.5f}" ) error = errornorm(Function(W).assign(10), u_reduce) parallel_assert( - lambda: error < 1e-12, + error < 1e-12, participating=(rank != root), - msg=f"{error = :.5f}" + msg=f"{error=:.5f}" ) # check that u_reduce dat vector is still synchronised @@ -206,7 +205,7 @@ def test_ensemble_reduce(ensemble, mesh, W, urank, urank_sum, root, blocking): with u_reduce.dat.vec as v: states[spatial_rank] = v.stateGet() ensemble.comm.Allgather(MPI.IN_PLACE, states) - parallel_assert(lambda: len(set(states)) == 1) + parallel_assert(len(set(states)) == 1) @pytest.mark.parallel(nprocs=2) @@ -257,7 +256,7 @@ def test_comm_manager_reduce(blocking): f5 = Function(V5) with f4.dat.vec_ro as v4, f5.dat.vec_ro as v5: - parallel_assert(lambda: v4.getSizes() == v5.getSizes()) + parallel_assert(v4.getSizes() == v5.getSizes()) with pytest.raises(ValueError): reduction(f4, f5) @@ -285,7 +284,7 @@ def test_ensemble_bcast(ensemble, mesh, W, urank, root, blocking): # broadcasted function u_correct = unique_function(mesh, root, W) - parallel_assert(lambda: errornorm(u_correct, urank) < 1e-12) + parallel_assert(errornorm(u_correct, urank) < 1e-12) @pytest.mark.parallel(nprocs=6) @@ -324,9 +323,9 @@ def test_send_and_recv(ensemble, mesh, W, blocking): # Test send/recv between first two spatial comms # ie: ensemble.ensemble_comm.rank == 0 and 1 parallel_assert( - lambda: error < 1e-12, + error < 1e-12, participating=ensemble.ensemble_rank in (rank0, rank1), - msg=f"{error = :.5f}" # noqa: E203, E251 + msg=f"{error=:.5f}" # noqa: E203, E251 ) @@ -352,7 +351,7 @@ def test_sendrecv(ensemble, mesh, W, urank, blocking): if not blocking: MPI.Request.Waitall(requests) - parallel_assert(lambda: errornorm(urecv, u_expect) < 1e-12) + parallel_assert(errornorm(urecv, u_expect) < 1e-12) @pytest.mark.parallel(nprocs=6) @@ -383,4 +382,4 @@ def test_ensemble_solvers(ensemble, W, urank, urank_sum): usum = Function(W) ensemble.allreduce(u_separate, usum) - parallel_assert(lambda: errornorm(u_combined, usum) < 1e-8) + parallel_assert(errornorm(u_combined, usum) < 1e-8) diff --git a/tests/firedrake/ensemble/test_ensemble_function.py b/tests/firedrake/ensemble/test_ensemble_function.py index 8051595d2a..a7a3f40a93 100644 --- a/tests/firedrake/ensemble/test_ensemble_function.py +++ b/tests/firedrake/ensemble/test_ensemble_function.py @@ -2,6 +2,7 @@ from pyop2 import Subset import pytest import numpy as np +from pytest_mpi.parallel_assert import parallel_assert def random_func(f): diff --git a/tests/firedrake/ensemble/test_ensemble_functionspace.py b/tests/firedrake/ensemble/test_ensemble_functionspace.py index f10b34ab03..e496dfa3d9 100644 --- a/tests/firedrake/ensemble/test_ensemble_functionspace.py +++ b/tests/firedrake/ensemble/test_ensemble_functionspace.py @@ -1,6 +1,6 @@ import firedrake as fd import pytest -from pytest_mpi import parallel_assert +from pytest_mpi.parallel_assert import parallel_assert @pytest.fixture @@ -119,7 +119,7 @@ def test_ensemble_space_reject_inconsistent_meshes(ensemble, space_type): elif space_type == 'dual': spaces = (FS0.dual(), FS1.dual()) else: - raise ValueError(f"Unrecognised {space_type = }") + raise ValueError(f"Unrecognised {space_type=}") with pytest.raises(ValueError): _ = fd.EnsembleFunctionSpace(spaces, ensemble) @@ -179,7 +179,7 @@ def test_ensemble_space_allows_dual_init(ensemble, mesh): efs = fd.EnsembleFunctionSpace(spaces, ensemble) - parallel_assert(lambda: isinstance(efs, fd.EnsembleDualSpace)) + parallel_assert(isinstance(efs, fd.EnsembleDualSpace)) @pytest.mark.parallel(nprocs=[1, 2, 4, 6]) @@ -196,12 +196,12 @@ def test_ensemble_local_spaces_correct(ensemblespace, Wlocal): Wlocal = [W.dual() for W in Wlocal] # Does efs have the correct number of spaces locally? - parallel_assert(lambda: len(efs.local_spaces) == len(Wlocal)) - parallel_assert(lambda: efs.nlocal_spaces == len(efs.local_spaces)) + parallel_assert(len(efs.local_spaces) == len(Wlocal)) + parallel_assert(efs.nlocal_spaces == len(efs.local_spaces)) # Correct number globally? nglobal = ensemble.ensemble_comm.allreduce(efs.nlocal_spaces) - parallel_assert(lambda: efs.nglobal_spaces == nglobal) + parallel_assert(efs.nglobal_spaces == nglobal) local_space_matches = [ V == W for V, W in zip(efs.local_spaces, Wlocal)] @@ -210,9 +210,9 @@ def test_ensemble_local_spaces_correct(ensemblespace, Wlocal): # rank-by-rank not space-by-space for root in range(ensemble.ensemble_size): parallel_assert( - lambda: all(local_space_matches), + all(local_space_matches), participating=(ensemble.ensemble_rank == root), - msg=f"{ensemble.ensemble_rank = }, {local_space_matches = }") + msg=f"{ensemble.ensemble_rank=}, {local_space_matches=}") @pytest.mark.parallel(nprocs=[1, 2, 4, 6]) @@ -231,16 +231,16 @@ def test_ensemble_dofsizes_correct(ensemblespace): nglobal_dofs = ensemble.ensemble_comm.allreduce(nlocal_comm_dofs) parallel_assert( - lambda: efs.nlocal_rank_dofs == nlocal_rank_dofs, - msg=f"{rank = }, {efs.nlocal_rank_dofs = }, {nlocal_rank_dofs = }") + efs.nlocal_rank_dofs == nlocal_rank_dofs, + msg=f"{rank=}, {efs.nlocal_rank_dofs=}, {nlocal_rank_dofs=}") parallel_assert( - lambda: efs.nlocal_comm_dofs == nlocal_comm_dofs, - msg=f"{rank = }, {efs.nlocal_comm_dofs = }, {nlocal_comm_dofs = }") + efs.nlocal_comm_dofs == nlocal_comm_dofs, + msg=f"{rank=}, {efs.nlocal_comm_dofs=}, {nlocal_comm_dofs=}") parallel_assert( - lambda: efs.nglobal_dofs == nglobal_dofs, - msg=f"{rank = }, {efs.nglobal_dofs = }, {nglobal_dofs = }") + efs.nglobal_dofs == nglobal_dofs, + msg=f"{rank=}, {efs.nglobal_dofs=}, {nglobal_dofs=}") @pytest.mark.parallel(nprocs=[1, 2, 4, 6]) @@ -258,7 +258,7 @@ def test_ensemble_space_dual(ensemblespace): dual_type = fd.EnsembleFunctionSpace dual = ensemblespace.dual() - parallel_assert(lambda: type(dual) is dual_type) + parallel_assert(type(dual) is dual_type) # are the dual subspaces correct? local_space_matches = [ @@ -269,12 +269,12 @@ def test_ensemble_space_dual(ensemblespace): ensemble_rank = ensemble.ensemble_rank for root in range(ensemble.ensemble_size): parallel_assert( - lambda: all(local_space_matches), + all(local_space_matches), participating=(ensemble.ensemble_rank == root), - msg=f"{ensemble_rank = }, {local_space_matches = }") + msg=f"{ensemble_rank=}, {local_space_matches=}") dual2 = dual.dual() - parallel_assert(lambda: type(dual.dual()) is type(ensemblespace)) + parallel_assert(type(dual.dual()) is type(ensemblespace)) # are the dual subspaces the originals? local_space_matches = [ @@ -285,9 +285,9 @@ def test_ensemble_space_dual(ensemblespace): ensemble_rank = ensemblespace.ensemble.ensemble_rank for root in range(ensemble.ensemble_size): parallel_assert( - lambda: all(local_space_matches), + all(local_space_matches), participating=(ensemble.ensemble_rank == root), - msg=f"{ensemble_rank = }, {local_space_matches = }") + msg=f"{ensemble_rank=}, {local_space_matches=}") @pytest.mark.parallel(nprocs=[1, 2, 4]) @@ -300,13 +300,13 @@ def test_ensemble_space_equality(ensemblespace): orig_spaces = ensemblespace.local_spaces # .dual().dual() should be a round trip - parallel_assert(lambda: ensemblespace == ensemblespace) - parallel_assert(lambda: ensemblespace != ensemblespace.dual()) - parallel_assert(lambda: ensemblespace == ensemblespace.dual().dual()) + parallel_assert(ensemblespace == ensemblespace) + parallel_assert(ensemblespace != ensemblespace.dual()) + parallel_assert(ensemblespace == ensemblespace.dual().dual()) # Duplicate is equal dup_fs = fd.EnsembleFunctionSpace(orig_spaces, ensemble) - parallel_assert(lambda: ensemblespace == dup_fs) + parallel_assert(ensemblespace == dup_fs) # Same length, different space is not equal if ensemble.ensemble_rank == (ensemble.ensemble_size - 1): @@ -315,7 +315,7 @@ def test_ensemble_space_equality(ensemblespace): diff_spaces = orig_spaces diff_fs = fd.EnsembleFunctionSpace(diff_spaces, ensemble) - parallel_assert(lambda: ensemble != diff_fs) + parallel_assert(ensemble != diff_fs) # Same spaces, shorter length is not equal if ensemble.ensemble_rank == 0: @@ -324,7 +324,7 @@ def test_ensemble_space_equality(ensemblespace): short_spaces = orig_spaces short_fs = fd.EnsembleFunctionSpace(short_spaces, ensemble) - parallel_assert(lambda: ensemble != short_fs) + parallel_assert(ensemble != short_fs) # Same spaces, longer length is not equal if ensemble.ensemble_rank == (ensemble.ensemble_size - 1): @@ -333,4 +333,4 @@ def test_ensemble_space_equality(ensemblespace): long_spaces = orig_spaces long_fs = fd.EnsembleFunctionSpace(long_spaces, ensemble) - parallel_assert(lambda: ensemble != long_fs) + parallel_assert(ensemble != long_fs) From e3883f5059b9d5d5745f53f0f9a9a760a3f0b2c9 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Sun, 23 Mar 2025 14:13:54 +0000 Subject: [PATCH 040/222] WIP: ensemble reduced functional with ensemble function --- .../adjoint/ensemble_reduced_functional.py | 528 +++++++++++++++++- firedrake/ensemble/ensemble_functionspace.py | 2 +- .../test_ensemble_reduced_functional.py | 38 ++ 3 files changed, 560 insertions(+), 8 deletions(-) diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py index 0abf6c67af..9809131a32 100644 --- a/firedrake/adjoint/ensemble_reduced_functional.py +++ b/firedrake/adjoint/ensemble_reduced_functional.py @@ -1,11 +1,20 @@ -from pyadjoint import ReducedFunctional +from functools import partial, cached_property +from contextlib import contextmanager +from pyadjoint import ReducedFunctional, Control, set_working_tape from pyadjoint.enlisting import Enlist from pyop2.mpi import MPI +from firedrake.function import Function +from firedrake.ensemble.ensemble_function import EnsembleFunction, EnsembleFunctionSpace -import firedrake +def _maybe_ensemble_function_input(val, n): + if hasattr(val, 'subfunctions'): + return val.subfunctions + else: + return [val for _ in range(n)] -class EnsembleReducedFunctional(ReducedFunctional): + +class EnsembleReducedFunctionalOriginal(ReducedFunctional): """Enable solving simultaneously reduced functionals in parallel. Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`, @@ -107,7 +116,7 @@ def __init__(self, J, control, ensemble, scatter_control=True, def _allgather_J(self, J): if isinstance(J, float): vals = self.ensemble.ensemble_comm.allgather(J) - elif isinstance(J, firedrake.Function): + elif isinstance(J, Function): # allgather not implemented in ensemble.py vals = [] for i in range(self.ensemble.ensemble_comm.size): @@ -142,7 +151,7 @@ def __call__(self, values): # if gather_functional is None then we do a sum elif isinstance(local_functional, float): total_functional = ensemble_comm.allreduce(sendobj=local_functional, op=MPI.SUM) - elif isinstance(local_functional, firedrake.Function): + elif isinstance(local_functional, Function): total_functional = type(local_functional)(local_functional.function_space()) total_functional = self.ensemble.allreduce(local_functional, total_functional) else: @@ -182,12 +191,12 @@ def derivative(self, adj_input=1.0, options=None): dJdm_total = [] for dJdm in dJdm_local: - if not isinstance(dJdm, (firedrake.Function, float)): + if not isinstance(dJdm, (Function, float)): raise NotImplementedError("This type of gradient is not supported.") dJdm_total.append( self.ensemble.allreduce(dJdm, type(dJdm)(dJdm.function_space())) - if isinstance(dJdm, firedrake.Function) + if isinstance(dJdm, Function) else self.ensemble.ensemble_comm.allreduce(sendobj=dJdm, op=MPI.SUM) ) return dJdm_local.delist(dJdm_total) @@ -200,3 +209,508 @@ def hessian(self, m_dot, options=None): NotImplementedError: This method is not yet implemented for ensemble reduced functional. """ raise NotImplementedError("Hessian is not yet implemented for ensemble reduced functional.") + + +class EnsembleReducedFunctionalAdjFloat(ReducedFunctional): + def __init__(self, rfs, functional, controls, ensemble, reduce_functional=None): + self.rfs = rfs + self.functional = functional + self.controls = Enlist(controls) + self.ensemble = ensemble + self.reduce_functional = reduce_functional + + def __call__(self, values): + Jlocals = [rf(values) for rf in self.rfs] + + if self.reduce_functional: + J = self.reduce_functional( + self.ensemble.ensemble_comm.allgather(Jlocals)) + else: + J = self.ensemble.ensemble_comm.allreduce(sum(Jlocals)) + return J + + def tlm(self, m_dot, options=None): + intermediate_options = None + tlm_options = intermediate_options if self.reduce_functional else options + + tlm_local = [rf.tlm(m_dot, options=tlm_options) for rf in self.rfs] + + if self.reduce_functional: + tlm = self.reduce_functional.tlm( + self.ensemble.ensemble_comm.allgather(tlm_local), + options=options) + else: + tlm = self.ensemble.ensemble_comm.allreduce(sum(tlm_local)) + + return tlm + + def derivative(self, adj_input=1.0, options=None): + + if self.reduce_functional: + intermediate_options = None + adj_global = self.reduce_functional.derivative( + adj_input=adj_input, options=intermediate_options) + + adj_inputs = [adj_global[i] for i in self._local_indices] + else: + adj_inputs = [adj_input for _ in range(len(self.rfs))] + + dJ_local = [Enlist(rf.derivative(adj, options=options)) + for rf, adj in zip(self.rfs, adj_inputs)] + + dJ_global = [] + for djls in zip(*dJ_local): + dJ_global.append(self.ensemble.ensemble_comm.allreduce(sum(djls))) + + return self.controls.delist(dJ_global) + + def hessian(self, m_dot, options=None, hessian_value=0.0, evaluate_tlm=True): + intermediate_options = None + + if evaluate_tlm: + self.tlm(m_dot, options=intermediate_options) + + if self.reduce_functional: + hess_global = self.reduce_functional.hessian( + m_dot=None, evaluate_tlm=False, + hessian_value=hessian_value, + options=intermediate_options) + hessian_values = [hess_global[i] for i in self._local_indices] + else: + hessian_values = [hessian_value for _ in range(len(self.rfs))] + + hessian_local = [ + Enlist(rf.hessian(m_dot=None, evaluate_tlm=False, + hessian_value=hess, options=options)) + for rf, hess in zip(self.rfs, hessian_values)] + + hessian_global = [] + + for hls in zip(*hessian_local): + hessian_global.append(self.ensemble.ensemble_comm.allreduce(sum(hls))) + + return self.controls.delist(hessian_global) + + @cached_property + def _local_indices(self): + fs = self.functional.function_space() + offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) + return tuple(offset + i for i in range(fs.nlocal_spaces)) + + +class EnsembleReducedFunctional: + def __init__(self, rfs, functional, control, ensemble=None, + bcast_control=False, + reduce_functional=False): + self.rfs = rfs + self.controls = Enlist(control) + self.functional = functional + self.bcast_control = bcast_control + self.reduce_functional = reduce_functional + self.ensemble = ensemble + + if bcast_control: + control_spaces = ( + EnsembleFunctionSpace([c.function_space() + for _ in range(len(rfs))], ensemble) + for c in self.controls) + + ensemble_controls = ( + EnsembleFunction(space) for space in control_spaces) + + self.bcast_rfs = ( + EnsembleBcastReducedFunctional(ec, Control(c)) + for ec, c in zip(ensemble_controls, self.controls)) + else: + ensemble_controls = self.controls + + if reduce_functional: + functional_space = EnsembleFunctionSpace( + [rf.functional.function_space() for rf in rfs], ensemble) + ensemble_functional = EnsembleFunction(functional_space) + else: + ensemble_functional = functional + + self.full_rf = EnsembleReducedFunctionalImpl( + rfs, ensemble_functional, self.controls.delist(ensemble_controls)) + + if reduce_functional: + self.reduce_rf = EnsembleReduceReducedFunctional( + reduce_functional, Control(ensemble_functional._ad_copy())) + + def __call__(self, values): + """Computes the reduced functional with supplied control value. + + Parameters + ---------- + values : pyadjoint.OverloadedType + If you have multiple controls this should be a list of + new values for each control in the order you listed the controls to the constructor. + If you have a single control it can either be a list or a single object. + Each new value should have the same type as the corresponding control. + + Returns + ------- + pyadjoint.OverloadedType + The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. + + """ + if self.bcast_control: + values = Enlist(values) + values = values.delist( + [bcast(val) + for bcast, val in zip(self.bcast_rfs, values)]) + + J = self.full_rf(values) + + if self.reduce_functional: + J = self.reduce_rf(J) + + return J + + def derivative(self, adj_input=1.0, options=None): + """Compute derivatives of a functional with respect to the control parameters. + + Parameters + ---------- + adj_input : float + The adjoint input. + options : dict + Additional options for the derivative computation. + + Returns + ------- + dJdm_total : pyadjoint.OverloadedType + The result of Allreduce operations of ``dJdm_local`` into ``dJdm_total`` over the`Ensemble.ensemble_comm`. + + See Also + -------- + :meth:`~.ensemble.Ensemble.allreduce`, :meth:`pyadjoint.ReducedFunctional.derivative`. + """ + intermediate_options = None + if self.reduce_functional: + adj_input = self.reduce_rf.derivative(adj_input=adj_input, + options=intermediate_options) + + derivative_options = intermediate_options if self.bcast_control else options + + dJ = self.full_rf.derivative( + adj_input=adj_input, + options=derivative_options) + + if self.bcast_control: + dJ = Enlist(dJ) + dJ = dJ.delist( + [bcast.derivative(adj_input=dj, options=options) + for bcast, dj in zip(self.bcast_rfs, dJ)]) + + return dJ + + def tlm(self, m_dot, options=None): + """Returns the action of the tangent linear model of the functional w.r.t. the + control on a vector m_dot, around the last supplied value of the control. + + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the tangent linear model. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. + + Returns: + OverloadedType: The action of the tangent linear model in the direction m_dot. + Should be an instance of the same type as the functional. + """ + intermediate_options = None + + if self.bcast_control: + m_dot = Enlist(m_dot) + m_dot = m_dot.delist( + [bcast.tlm(md, options=intermediate_options) + for bcast, md in zip(self.bcast_rfs, m_dot)]) + + tlm_options = intermediate_options if self.reduce_functional else options + + tlm = self.full_rf.tlm(m_dot, options=tlm_options) + + if self.reduce_functional: + tlm = self.reduce_rf.tlm(tlm, options=options) + + return tlm + + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. + + Using the second-order adjoint method, the action of the Hessian of the + functional with respect to the control, around the last supplied value + of the control, is computed and returned. + + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the Hessian. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. + hessian_input (OverloadedType): The value to start the Hessian accumulation from after the TLM calculation. + evaluate_tlm (bool): Whether or not to compute the forward (tlm) part of the Hessian calculation. + If False, assumes that the tape has already been populated with required TLM values. + + Returns: + OverloadedType: The action of the Hessian in the direction m_dot. + Should be an instance of the same type as the control. + """ + intermediate_options = None + + if evaluate_tlm: + self.tlm(m_dot, options=intermediate_options) + + if self.reduce_functional: + hessian_input = self.reduce_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + options=intermediate_options) + + hessian_options = intermediate_options if self.reduce_functional else options + + hessian = self.full_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + options=hessian_options) + + if self.bcast_control: + hessian = Enlist(hessian) + hessian = hessian.delist( + [bcast.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess, options=options) + for bcast, hess in zip(self.bcast_rfs, hessian)]) + + return hessian + + +class FunctionOrFloatMPIMixin: + # Should be replaced by Ensemble passing through non-Functions to ensemble_comm + def _bcast(self, val, root=None): + if root is None: + return val + if isinstance(val, float): + val = self.ensemble.ensemble_comm.bcast(val, root=root) + elif isinstance(val, Function): + val = self.ensemble.bcast(val, root=self.root) + else: + raise NotImplementedError(f"Functionals of type {type(val).__name__} are not supported.") + return val + + def _reduce(self, vals, result_type, root=None): + vals = Enlist(vals) + for v in vals: + if not isinstance(v, (Function, float)): + raise NotImplementedError( + f"Functionals of type {type(v).__name__} are not supported.") + + if isinstance(vals[0], float): + comm = self.ensemble.ensemble_comm + local_sum = sum(vals) + if root is None: + return comm.allreduce(local_sum) + else: + return comm.reduce(local_sum, root=root) + else: + comm = self.ensemble + global_sum = Function(result_type.function_space()) + local_sum = Function(result_type.function_space()) + local_sum.assign(sum(vals)) + if root is None: + return comm.allreduce(local_sum, global_sum) + else: + return comm.reduce(local_sum, global_sum, root=root) + + def _allgather(self, vals): + pass + + +class EnsembleBcastReducedFunctional(FunctionOrFloatMPIMixin): + def __init__(self, functional, control, root=None): + self.functional = functional + self.ensemble = functional.ensemble + self.controls = Enlist(control) + self.control = Enlist(control) + self.root = root + + def __call__(self, values): + val = self._bcast(values, root=self.root) + for subfunc in self.functional.subfunctions: + subfunc.assign(val) + return self.functional._ad_copy() + + def derivative(self, adj_input=1.0, options=None): + adj_inputs = _maybe_ensemble_function_input( + adj_input, len(self.functional.subfunctions)) + return self._reduce(adj_inputs, result_type=self.control, root=self.root) + + def tlm(self, m_dot, options=None): + return self.control._ad_convert_type(self(m_dot), options=options) + + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + intermediate_options = None + self.tlm(m_dot, options=intermediate_options) + hessian_inputs = _maybe_ensemble_function_input( + hessian_input, len(self.functional.subfunctions)) + return self.derivative(hessian_inputs, options=options) + + +class EnsembleReduceReducedFunctional(FunctionOrFloatMPIMixin): + def __init__(self, functional, control): + if isinstance(functional, ReducedFunctional): + self.reduction_rf = functional + self.functional = self.reduction_rf.functional + else: + self.reduction_rf = None + self.functional = functional + self.controls = Enlist(control) + self.control = control + self.ensemble = control.ensemble + + def __call__(self, values): + if self.reduction_rf: + return self.reduction_rf(self._allgather(values)) + else: + return self._reduce(self._sum_rf(values.subfunctions), + result_type=self.functional) + + def derivative(self, adj_input=1.0, options=None): + if self.reduction_rf: + dJ_global = self.reduction_rf.derivative( + adj_input=adj_input, options=options) + dJ_local = [dJ_global[i] for i in range(self._local_indices)] + else: + dJ_local = self._sum_rf.derivative(adj_input, options=options) + + dJ_global = EnsembleReducedFunctionalImpl._new_efuncs( + self, self.control.function_space(), options=options) + + for dJg, dJl in zip(dJ_global.subfunctions, dJ_local): + dJg.assign(dJl) + + return dJ_global + + def tlm(self, m_dot, options=None): + return self.control._ad_convert_type(self(m_dot), options=options) + + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + intermediate_options = None + self.tlm(m_dot, options=intermediate_options) + return self.derivative(hessian_input, options=options) + + @cached_property + def _sum_rf(self): + controls = self.control._ad_copy().subfunctions + with set_working_tape() as tape: + J = Function(self.functional.function_space()) + J.assign(sum(controls)) + return ReducedFunctional( + J, [Control(c) for c in controls], tape=tape) + + @cached_property + def _local_indices(self): + fs = self.functional.function_space() + offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) + return tuple(offset + i for i in range(fs.nlocal_spaces)) + + +class EnsembleReducedFunctionalImpl: + def __init__(self, rfs, functional, controls): + self.rfs = rfs + self.controls = Enlist(controls) + self.ensemble = self.controls[0].ensemble + + self.functional = functional + + self.functional_space = functional.function_space() + self.control_spaces = [c.control.function_space() + for c in self.controls] + + def __call__(self, values): + J = self.functional + + with self._local_data(data=values, output=J) as (local_vals, output): + output([rf(rf.controls.delist(vals)) + for rf, vals in zip(self.rfs, local_vals)]) + return J._ad_copy() + + def tlm(self, m_dot, options=None): + tlm = self._new_efuncs(self.functional_space, options) + + with self._local_data(data=m_dot, output=tlm) as (local_mdot, output): + output([rf.tlm(rf.controls.delist(md), options=options) + for rf, md in zip(self.rfs, local_mdot)]) + return tlm + + def derivative(self, adj_input=1.0, options=None): + # adj_input may need to be bcast to a list + adj_inputs = _maybe_ensemble_function_input( + adj_input, len(self.functional.subfunctions)) + + dJ = self._new_efuncs(self.control_spaces, options) + + with self._local_data(data=adj_inputs, output=dJ) as (local_adjs, output): + output([rf.derivative(adj, options=options) + for rf, adj in zip(self.rfs, local_adjs)]) + return dJ + + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + intermediate_options = None + self.tlm(m_dot, options=intermediate_options) + + # hessian_input may need to be bcast to a list + hessian_inputs = _maybe_ensemble_function_input( + hessian_input, len(self.functional.subfunctions)) + + hessian = self._new_efuncs(self.control_spaces, options) + + with self._local_data(data=hessian_inputs, output=hessian) as (local_hessian, output): + output([rf.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess, options=options) + for rf, hess in zip(self.rfs, local_hessian)]) + return hessian + + @contextmanager + def _local_data(self, data, output): + local_data = self._global_to_local_data(data) + output_transform = partial( + self._local_to_global_data, global_data=output) + yield local_data, output_transform + + def _local_to_global_data(self, local_data, global_data): + local_data = Enlist(local_data) + global_data = Enlist(global_data) + + global_groups = [*zip(*(gdata.subfunctions + for gdata in global_data))] + + for new_group, dst_group in zip(local_data, global_groups): + for new, dst in zip(new_group, dst_group): + dst.assign(new) + + return global_data.delist() + + def _global_to_local_data(self, global_data, local_data=None): + local_data = Enlist(local_data) + global_data = Enlist(global_data) + + global_groups = [*zip(*(gdata.subfunctions + for gdata in Enlist(global_data)))] + + if local_data: + for new_group, dst_group in zip(global_groups, local_data): + for new, dst in zip(new_group, dst_group): + dst.assign(new) + global_groups = local_data + return global_data.delist(global_groups) + + def _new_efuncs(self, spaces, options=None): + options = options or {} + dual = (options.get("riesz_representation", "") in (None, "l2")) + spaces = Enlist(spaces) + return spaces.delist([EnsembleFunction(space.dual() if dual else space) + for space in spaces]) diff --git a/firedrake/ensemble/ensemble_functionspace.py b/firedrake/ensemble/ensemble_functionspace.py index fca1efa1cf..6f3956d394 100644 --- a/firedrake/ensemble/ensemble_functionspace.py +++ b/firedrake/ensemble/ensemble_functionspace.py @@ -150,7 +150,7 @@ def dual(self): @cached_property def nlocal_spaces(self): - """The total number of subspaces across all ensemble ranks. + """Number of subspaces on this ensemble rank. """ return len(self.local_spaces) diff --git a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py index 6d5285a687..bea897a24d 100644 --- a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py +++ b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py @@ -91,6 +91,44 @@ def test_verification_gather_functional_Function(): assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 +@pytest.mark.parallel(nprocs=4) +@pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done +def test_verification_gather_functional_Function(): + ensemble = Ensemble(COMM_WORLD, 2) + + mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) + R = FunctionSpace(mesh, "R", 0) + nlocal_spaces = 2//ensemble.ensemble_size + espace = EnsembleFunctionSpace([R for _ in nlocal_spaces], ensemble) + efunc = EnsembleFunction(espace) + + rank = ensemble.ensemble_rank + offset = rank*nlocal_spaces + rfs = [] + # each term in J sum + for i, x in enumerate(efunc.subfunctions): + with set_working_tape() as tape: + x = function.Function(R, val=offset+i+1) + J = Function(R).assign(x**2) + rfs.append(ReducedFunctional(J, x, tape=tape)) + + # reduction operator + with set_working_tape() as tape: + a = Function(R) + b = Function(R) + Jr = assemble((a**2 + b**2)*dx) + controls = [Control(a), Control(b)] + Jred = ReducedFunctional(Jr, controls, tape=tape) + + erf = EnsembleReducedFunctional(rfs, Control(efunc), + bcast=False, allreduce=Jred) + ensemble_J = rf(x) + dJdm = rf.derivative() + assert_allclose(ensemble_J, 1.0**4+2.0**4, rtol=1e-12) + assert_allclose(dJdm.dat.data_ro, 4*(rank+1)**3, rtol=1e-12) + assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 + + @pytest.mark.parallel(nprocs=6) @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done def test_minimise(): From a96ee52a8efb1d96f7f7dec2903ca362dffd1155 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 25 Mar 2025 10:08:26 +0000 Subject: [PATCH 041/222] fix pyadjoint branch install command --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 2bafb5ada8..aea14d1821 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -92,7 +92,7 @@ jobs: --no-binary h5py \ --extra-index-url https://download.pytorch.org/whl/cpu \ './firedrake-repo[ci]' - pip install "pyadjoint-ad @ https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional" + pip install "pyadjoint-ad @ git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional" firedrake-clean pip list From a40f4202ffd841384b3982879807fdc04f3f8d35 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 25 Mar 2025 11:48:42 +0000 Subject: [PATCH 042/222] AbstractReducedFunctional updates for EnsembleReducedFunctional --- .../full_waveform_inversion.py.rst | 10 ++- .../adjoint/ensemble_reduced_functional.py | 66 ++++++++++++------- .../test_ensemble_reduced_functional.py | 66 +++++++++++++++---- 3 files changed, 102 insertions(+), 40 deletions(-) diff --git a/demos/full_waveform_inversion/full_waveform_inversion.py.rst b/demos/full_waveform_inversion/full_waveform_inversion.py.rst index e96bc2ca83..c25588b577 100644 --- a/demos/full_waveform_inversion/full_waveform_inversion.py.rst +++ b/demos/full_waveform_inversion/full_waveform_inversion.py.rst @@ -290,9 +290,15 @@ based on the ``my_ensemble`` configuration. **Steps 4-6**: The instance of the :class:`~.EnsembleReducedFunctional`, named ``J_hat``, -is then passed as an argument to the ``minimize`` function:: +is then passed as an argument to the ``minimize`` function. The default ``minimize`` function +uses ``scipy.minimize``, and wraps the ``ReducedFunctional`` in a ``ReducedFunctionalNumPy`` +that handles transferring data between Firedrake and numpy data structures. However, because +we have a custom ``ReducedFunctional``, we need to do this ourselves:: - c_optimised = minimize(J_hat, method="L-BFGS-B", options={"disp": True, "maxiter": 1}, + from pyadjoint.reduced_functional_numpy import ReducedFunctionalNumPy + Jnumpy = ReducedFunctionalNumPy(J_hat) + + c_optimised = minimize(Jnumpy, method="L-BFGS-B", options={"disp": True, "maxiter": 1}, bounds=(1.5, 2.0)) The ``minimize`` function executes the optimisation algorithm until the stopping criterion (``maxiter``) is met. diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py index 0abf6c67af..6a2eaccd7f 100644 --- a/firedrake/adjoint/ensemble_reduced_functional.py +++ b/firedrake/adjoint/ensemble_reduced_functional.py @@ -1,11 +1,12 @@ -from pyadjoint import ReducedFunctional +from pyadjoint.reduced_functional import AbstractReducedFunctional, ReducedFunctional from pyadjoint.enlisting import Enlist from pyop2.mpi import MPI -import firedrake +from firedrake.function import Function +from firedrake.cofunction import Cofunction -class EnsembleReducedFunctional(ReducedFunctional): +class EnsembleReducedFunctional(AbstractReducedFunctional): """Enable solving simultaneously reduced functionals in parallel. Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`, @@ -34,7 +35,7 @@ class EnsembleReducedFunctional(ReducedFunctional): Parameters ---------- - J : pyadjoint.OverloadedType + functional : pyadjoint.OverloadedType An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`. This should be the functional that we want to reduce. control : pyadjoint.Control or list of pyadjoint.Control @@ -86,28 +87,40 @@ class EnsembleReducedFunctional(ReducedFunctional): works, please refer to the `Firedrake manual `_. """ - def __init__(self, J, control, ensemble, scatter_control=True, - gather_functional=None, derivative_components=None, - scale=1.0, tape=None, eval_cb_pre=lambda *args: None, + def __init__(self, functional, control, ensemble, scatter_control=True, + gather_functional=None, + derivative_components=None, + scale=1.0, tape=None, + eval_cb_pre=lambda *args: None, eval_cb_post=lambda *args: None, derivative_cb_pre=lambda controls: controls, derivative_cb_post=lambda checkpoint, derivative_components, controls: derivative_components, - hessian_cb_pre=lambda *args: None, hessian_cb_post=lambda *args: None): - super(EnsembleReducedFunctional, self).__init__( - J, control, derivative_components=derivative_components, - scale=scale, tape=tape, eval_cb_pre=eval_cb_pre, - eval_cb_post=eval_cb_post, derivative_cb_pre=derivative_cb_pre, + hessian_cb_pre=lambda *args: None, + hessian_cb_post=lambda *args: None): + self.local_reduced_functional = ReducedFunctional( + functional, control, + derivative_components=derivative_components, + scale=scale, tape=tape, + eval_cb_pre=eval_cb_pre, + eval_cb_post=eval_cb_post, + derivative_cb_pre=derivative_cb_pre, derivative_cb_post=derivative_cb_post, - hessian_cb_pre=hessian_cb_pre, hessian_cb_post=hessian_cb_post) + hessian_cb_pre=hessian_cb_pre, + hessian_cb_post=hessian_cb_post + ) self.ensemble = ensemble self.scatter_control = scatter_control self.gather_functional = gather_functional + @property + def controls(self): + return self.local_reduced_functional.controls + def _allgather_J(self, J): if isinstance(J, float): vals = self.ensemble.ensemble_comm.allgather(J) - elif isinstance(J, firedrake.Function): + elif isinstance(J, Function): # allgather not implemented in ensemble.py vals = [] for i in range(self.ensemble.ensemble_comm.size): @@ -134,7 +147,7 @@ def __call__(self, values): The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. """ - local_functional = super(EnsembleReducedFunctional, self).__call__(values) + local_functional = self.local_reduced_functional(values) ensemble_comm = self.ensemble.ensemble_comm if self.gather_functional: controls_g = self._allgather_J(local_functional) @@ -142,22 +155,23 @@ def __call__(self, values): # if gather_functional is None then we do a sum elif isinstance(local_functional, float): total_functional = ensemble_comm.allreduce(sendobj=local_functional, op=MPI.SUM) - elif isinstance(local_functional, firedrake.Function): + elif isinstance(local_functional, Function): total_functional = type(local_functional)(local_functional.function_space()) total_functional = self.ensemble.allreduce(local_functional, total_functional) else: raise NotImplementedError("This type of functional is not supported.") return total_functional - def derivative(self, adj_input=1.0, options=None): + def derivative(self, adj_input=1.0, apply_riesz=False): """Compute derivatives of a functional with respect to the control parameters. Parameters ---------- adj_input : float The adjoint input. - options : dict - Additional options for the derivative computation. + apply_riesz: bool + If True, apply the Riesz map of each control in order to return + a primal gradient rather than a derivative in the dual space. Returns ------- @@ -171,29 +185,31 @@ def derivative(self, adj_input=1.0, options=None): if self.gather_functional: dJg_dmg = self.gather_functional.derivative(adj_input=adj_input, - options=options) + apply_riesz=False) i = self.ensemble.ensemble_comm.rank adj_input = dJg_dmg[i] - dJdm_local = super(EnsembleReducedFunctional, self).derivative(adj_input=adj_input, options=options) + dJdm_local = self.local_reduced_functional.derivative(adj_input=adj_input, + apply_riesz=apply_riesz) if self.scatter_control: dJdm_local = Enlist(dJdm_local) dJdm_total = [] for dJdm in dJdm_local: - if not isinstance(dJdm, (firedrake.Function, float)): - raise NotImplementedError("This type of gradient is not supported.") + if not isinstance(dJdm, (Cofunction, Function, float)): + raise NotImplementedError( + f"Gradients of type {type(dJdm).__name__} are not supported.") dJdm_total.append( self.ensemble.allreduce(dJdm, type(dJdm)(dJdm.function_space())) - if isinstance(dJdm, firedrake.Function) + if isinstance(dJdm, (Cofunction, Function)) else self.ensemble.ensemble_comm.allreduce(sendobj=dJdm, op=MPI.SUM) ) return dJdm_local.delist(dJdm_total) return dJdm_local - def hessian(self, m_dot, options=None): + def hessian(self, m_dot, apply_riesz=False): """The Hessian is not yet implemented for ensemble reduced functional. Raises: diff --git a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py index 6d5285a687..b9b21d1ad2 100644 --- a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py +++ b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py @@ -1,5 +1,6 @@ from firedrake import * from firedrake.adjoint import * +from pyadjoint.reduced_functional_numpy import ReducedFunctionalNumPy import pytest from numpy.testing import assert_allclose @@ -32,18 +33,15 @@ def test_verification(): J = assemble(x * x * dx(domain=mesh)) rf = EnsembleReducedFunctional(J, Control(x), ensemble) ensemble_J = rf(x) - dJdm = rf.derivative() assert_allclose(ensemble_J, size, rtol=1e-12) + dJdm = rf.derivative() assert_allclose(dJdm.dat.data_ro, 2.0 * size, rtol=1e-12) assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 @pytest.mark.parallel(nprocs=4) @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done -@pytest.mark.xfail(reason="Taylor's test fails because the inner product \ - between the perturbation and gradient is not allreduced \ - for `scatter_control=False`.") -def test_verification_gather_functional_adjfloat(): +def test_verification_gather_functional_adjfloat_evaluation(): ensemble = Ensemble(COMM_WORLD, 2) rank = ensemble.ensemble_comm.rank mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) @@ -61,7 +59,6 @@ def test_verification_gather_functional_adjfloat(): dJdm = rf.derivative() assert_allclose(ensemble_J, 1.0**4+2.0**4, rtol=1e-12) assert_allclose(dJdm.dat.data_ro, 4*(rank+1)**3, rtol=1e-12) - assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 @pytest.mark.parallel(nprocs=4) @@ -69,7 +66,26 @@ def test_verification_gather_functional_adjfloat(): @pytest.mark.xfail(reason="Taylor's test fails because the inner product \ between the perturbation and gradient is not allreduced \ for `scatter_control=False`.") -def test_verification_gather_functional_Function(): +def test_verification_gather_functional_adjfloat_taylor(): + ensemble = Ensemble(COMM_WORLD, 2) + rank = ensemble.ensemble_comm.rank + mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) + R = FunctionSpace(mesh, "R", 0) + x = function.Function(R, val=rank+1) + J = assemble(x * x * dx(domain=mesh)) + a = AdjFloat(1.0) + b = AdjFloat(1.0) + Jg_m = [Control(a), Control(b)] + Jg = ReducedFunctional(a**2 + b**2, Jg_m) + rf = EnsembleReducedFunctional(J, Control(x), ensemble, + scatter_control=False, + gather_functional=Jg) + assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 + + +@pytest.mark.parallel(nprocs=4) +@pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done +def test_verification_gather_functional_Function_evaluation(): ensemble = Ensemble(COMM_WORLD, 2) rank = ensemble.ensemble_comm.rank mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) @@ -88,17 +104,39 @@ def test_verification_gather_functional_Function(): dJdm = rf.derivative() assert_allclose(ensemble_J, 1.0**4+2.0**4, rtol=1e-12) assert_allclose(dJdm.dat.data_ro, 4*(rank+1)**3, rtol=1e-12) + + +@pytest.mark.parallel(nprocs=4) +@pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done +@pytest.mark.xfail(reason="Taylor's test fails because the inner product \ + between the perturbation and gradient is not allreduced \ + for `scatter_control=False`.") +def test_verification_gather_functional_Function_taylor(): + ensemble = Ensemble(COMM_WORLD, 2) + rank = ensemble.ensemble_comm.rank + mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) + R = FunctionSpace(mesh, "R", 0) + x = function.Function(R, val=rank+1) + J = Function(R).assign(x**2) + a = Function(R).assign(1.0) + b = Function(R).assign(1.0) + Jg_m = [Control(a), Control(b)] + Jg = assemble((a**2 + b**2)*dx) + Jghat = ReducedFunctional(Jg, Jg_m) + rf = EnsembleReducedFunctional(J, Control(x), ensemble, + scatter_control=False, + gather_functional=Jghat) assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 -@pytest.mark.parallel(nprocs=6) +@pytest.mark.parallel(nprocs=1) @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done def test_minimise(): # Optimisation test using a list of controls. # This test is equivalent to the one found at: # https://github.com/dolfin-adjoint/pyadjoint/blob/master/tests/firedrake_adjoint/test_optimisation.py#L9. # In this test, the functional is the result of an ensemble allreduce operation. - ensemble = Ensemble(COMM_WORLD, 2) + ensemble = Ensemble(COMM_WORLD, 1) mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) R = FunctionSpace(mesh, "R", 0) n = 2 @@ -106,8 +144,10 @@ def test_minimise(): c = [Control(xi) for xi in x] # Rosenbrock function https://en.wikipedia.org/wiki/Rosenbrock_function # with minimum at x = (1, 1, 1, ...) - f = 100*(x[1] - x[0]**2)**2 + (1 - x[0])**2 - J = assemble(f * dx(domain=mesh)) - rf = EnsembleReducedFunctional(J, c, ensemble) - result = minimize(rf) + with set_working_tape(): + f = 3*100*(x[1] - x[0]**2)**2 + (1 - x[0])**2 + J = assemble(f * dx(domain=mesh)) + rf = EnsembleReducedFunctional(J, c, ensemble) + rf_np = ReducedFunctionalNumPy(rf) + result = minimize(rf_np) assert_allclose([float(xi) for xi in result], 1., rtol=1e-8) From b5afacaa09353acc3a63292c33697e2109b70597 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 25 Mar 2025 14:24:17 +0000 Subject: [PATCH 043/222] fix pyadjoint pip install command?? --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index aea14d1821..9b0d0a2a28 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -92,7 +92,7 @@ jobs: --no-binary h5py \ --extra-index-url https://download.pytorch.org/whl/cpu \ './firedrake-repo[ci]' - pip install "pyadjoint-ad @ git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional" + pip install --verbose --editable git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional#egg=pyadjoint-ad firedrake-clean pip list From da694b0c9d15bc489790863e7fb852278d1e5a24 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 25 Mar 2025 15:58:33 +0000 Subject: [PATCH 044/222] change test derivative to old primal return value --- tests/firedrake/adjoint/test_disk_checkpointing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/firedrake/adjoint/test_disk_checkpointing.py b/tests/firedrake/adjoint/test_disk_checkpointing.py index 1dd68d0d5e..7e8de86ffe 100644 --- a/tests/firedrake/adjoint/test_disk_checkpointing.py +++ b/tests/firedrake/adjoint/test_disk_checkpointing.py @@ -73,7 +73,7 @@ def adjoint_example(fine, coarse=None): assert np.allclose(J, Jnew) - grad_Jnew = Jhat.derivative() + grad_Jnew = Jhat.derivative(apply_riesz=True) return Jnew, grad_Jnew From 5adabb32b537c7f5448c69bb8bc70c604a9f4085 Mon Sep 17 00:00:00 2001 From: David Ham Date: Tue, 25 Mar 2025 16:47:41 +0000 Subject: [PATCH 045/222] apply Riesz when needed --- tests/firedrake/adjoint/test_assignment.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/firedrake/adjoint/test_assignment.py b/tests/firedrake/adjoint/test_assignment.py index c4b9fcd467..89d988df5c 100644 --- a/tests/firedrake/adjoint/test_assignment.py +++ b/tests/firedrake/adjoint/test_assignment.py @@ -179,11 +179,11 @@ def test_assign_with_constant(): J = assemble(u ** 2 * dx) rf = ReducedFunctional(J, Control(c)) - dJdc = rf.derivative() + dJdc = rf.derivative(apply_riesz=True) assert_approx_equal(float(dJdc), 10.) rf = ReducedFunctional(J, Control(d)) - dJdd = rf.derivative() + dJdd = rf.derivative(apply_riesz=True) assert_approx_equal(float(dJdd), 228.) From 78c2ab3d8ff0b9d6701721e34b6d2a4a16648254 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 25 Mar 2025 17:21:29 +0000 Subject: [PATCH 046/222] demo doc typo --- demos/full_waveform_inversion/full_waveform_inversion.py.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demos/full_waveform_inversion/full_waveform_inversion.py.rst b/demos/full_waveform_inversion/full_waveform_inversion.py.rst index c25588b577..614e6edf0b 100644 --- a/demos/full_waveform_inversion/full_waveform_inversion.py.rst +++ b/demos/full_waveform_inversion/full_waveform_inversion.py.rst @@ -289,7 +289,7 @@ where the :math:`J_s` and its gradients :math:`\nabla_{\mathtt{c\_guess}} J_s` a based on the ``my_ensemble`` configuration. -**Steps 4-6**: The instance of the :class:`~.EnsembleReducedFunctional`, named ``J_hat``, +**Steps 4-6**: The instance of the :class:`~.adjoint.EnsembleReducedFunctional`, named ``J_hat``, is then passed as an argument to the ``minimize`` function. The default ``minimize`` function uses ``scipy.minimize``, and wraps the ``ReducedFunctional`` in a ``ReducedFunctionalNumPy`` that handles transferring data between Firedrake and numpy data structures. However, because From 75247c6bd0881d748f61d64c656c095b5350be46 Mon Sep 17 00:00:00 2001 From: David Ham Date: Tue, 25 Mar 2025 17:50:25 +0000 Subject: [PATCH 047/222] don't restrict without BCs --- firedrake/variational_solver.py | 2 +- pyop2/types/dataset.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/firedrake/variational_solver.py b/firedrake/variational_solver.py index 8a26220765..d92bb58ee5 100644 --- a/firedrake/variational_solver.py +++ b/firedrake/variational_solver.py @@ -89,7 +89,7 @@ def __init__(self, F, u, bcs=None, J=None, bcs = J.bcs if bcs and any(isinstance(bc, EquationBC) for bc in bcs): restrict = False - self.restrict = restrict + self.restrict = restrict and bcs if restrict and bcs: V_res = restricted_function_space(V, extract_subdomain_ids(bcs)) diff --git a/pyop2/types/dataset.py b/pyop2/types/dataset.py index 4a26cfc6d1..087d9b091d 100644 --- a/pyop2/types/dataset.py +++ b/pyop2/types/dataset.py @@ -211,7 +211,6 @@ def __init__(self, global_): self._globalset = GlobalSet(comm=self.comm) self._name = "gdset_#x%x" % id(self) self._initialized = True - self._apply_local_global_filter = False @classmethod def _cache_key(cls, *args): From 73f50fcb7d17c884b47823e4af3b8bc4a4417e57 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 25 Mar 2025 17:52:09 +0000 Subject: [PATCH 048/222] remove old ad methods --- firedrake/adjoint_utils/constant.py | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/firedrake/adjoint_utils/constant.py b/firedrake/adjoint_utils/constant.py index fed5978a6d..5f0c83f98b 100644 --- a/firedrake/adjoint_utils/constant.py +++ b/firedrake/adjoint_utils/constant.py @@ -2,7 +2,6 @@ from pyadjoint.adjfloat import AdjFloat from pyadjoint.tape import get_working_tape, annotate_tape from pyadjoint.overloaded_type import OverloadedType, create_overloaded_object -from pyadjoint.reduced_functional_numpy import gather from firedrake.functionspace import FunctionSpace from firedrake.adjoint_utils.blocks import ConstantAssignBlock @@ -58,15 +57,8 @@ def wrapper(self, *args, **kwargs): return wrapper - def get_derivative(self, options={}): - return self._ad_convert_type(self.adj_value, options=options) - - def _ad_convert_type(self, value, options={}): - if value is None: - # TODO: Should the default be 0 constant here or return just None? - return type(self)(numpy.zeros(self.ufl_shape)) - value = gather(value) - return self._constant_from_values(value) + def _ad_init_zero(self, dual=False): + return type(self)(numpy.zeros(self.ufl_shape)) def _ad_function_space(self, mesh): element = self.ufl_element() From b2fee4272f2f92d62baf6abd2fcd6e7dc57580dc Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 25 Mar 2025 17:52:41 +0000 Subject: [PATCH 049/222] apply_riesz in adjoint tests --- tests/firedrake/adjoint/test_assemble.py | 4 ++-- tests/firedrake/adjoint/test_hessian.py | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tests/firedrake/adjoint/test_assemble.py b/tests/firedrake/adjoint/test_assemble.py index 3d99770901..40937ee9eb 100644 --- a/tests/firedrake/adjoint/test_assemble.py +++ b/tests/firedrake/adjoint/test_assemble.py @@ -38,7 +38,7 @@ def test_assemble_0_forms(): # where stored as a Function instead of Vector() s = a1 + a2 + 2.0 * a3 rf = ReducedFunctional(s, Control(u)) - dJdm = rf.derivative() + dJdm = rf.derivative(apply_riesz=True) assert_allclose(dJdm.dat.data_ro, 1. + 2. * 4. + 6. * 16.) @@ -58,7 +58,7 @@ def test_assemble_0_forms_mixed(): s -= a3 # this is done deliberately to end up with an adj_input of 0.0 for the a3 AssembleBlock rf = ReducedFunctional(s, Control(u)) # derivative is: (1+4*u)*dx - summing is equivalent to testing with 1 - dJdm = rf.derivative() + dJdm = rf.derivative(apply_riesz=True) assert_allclose(dJdm.dat.data_ro[0], 1. + 4. * 7) assert_allclose(dJdm.dat.data_ro[1], 0.0) diff --git a/tests/firedrake/adjoint/test_hessian.py b/tests/firedrake/adjoint/test_hessian.py index ba0f0cad1e..8db08620df 100644 --- a/tests/firedrake/adjoint/test_hessian.py +++ b/tests/firedrake/adjoint/test_hessian.py @@ -60,8 +60,8 @@ def test_simple_solve(): tape.evaluate_adj() m = f.copy(deepcopy=True) - dJdm = assemble(inner(Jhat.derivative(), h)*dx) - Hm = assemble(inner(Jhat.hessian(h), h)*dx) + dJdm = assemble(inner(Jhat.derivative(apply_riesz=True), h)*dx) + Hm = assemble(inner(Jhat.hessian(h, apply_riesz=True), h)*dx) assert taylor_test(Jhat, m, h, dJdm=dJdm, Hm=Hm) > 2.9 @@ -140,7 +140,7 @@ def test_function(): J = assemble(c ** 2 * u ** 2 * dx) Jhat = ReducedFunctional(J, [control_c, control_f]) - dJdc, dJdf = compute_gradient(J, [control_c, control_f]) + dJdc, dJdf = compute_gradient(J, [control_c, control_f], apply_riesz=True) # Step direction for derivatives and convergence test h_c = Function(R, val=1.0) @@ -148,11 +148,11 @@ def test_function(): h_f.vector()[:] = 10*rng.random(V.dim()) # Total derivative - dJdc, dJdf = compute_gradient(J, [control_c, control_f]) + dJdc, dJdf = compute_gradient(J, [control_c, control_f], apply_riesz=True) dJdm = assemble(dJdc * h_c * dx + dJdf * h_f * dx) # Hessian - Hcc, Hff = compute_hessian(J, [control_c, control_f], [h_c, h_f]) + Hcc, Hff = compute_hessian(J, [control_c, control_f], [h_c, h_f], apply_riesz=True) Hm = assemble(Hcc * h_c * dx + Hff * h_f * dx) assert taylor_test(Jhat, [c, f], [h_c, h_f], dJdm=dJdm, Hm=Hm) > 2.9 From a19b2802f584fcf0ef8fd3d85f87cdaa685e1a8f Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 26 Mar 2025 08:57:56 +0000 Subject: [PATCH 050/222] set test back to parallel --- tests/firedrake/adjoint/test_ensemble_reduced_functional.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py index b9b21d1ad2..2ca2ed7d33 100644 --- a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py +++ b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py @@ -129,7 +129,7 @@ def test_verification_gather_functional_Function_taylor(): assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 -@pytest.mark.parallel(nprocs=1) +@pytest.mark.parallel(nprocs=3) @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done def test_minimise(): # Optimisation test using a list of controls. @@ -145,7 +145,7 @@ def test_minimise(): # Rosenbrock function https://en.wikipedia.org/wiki/Rosenbrock_function # with minimum at x = (1, 1, 1, ...) with set_working_tape(): - f = 3*100*(x[1] - x[0]**2)**2 + (1 - x[0])**2 + f = 100*(x[1] - x[0]**2)**2 + (1 - x[0])**2 J = assemble(f * dx(domain=mesh)) rf = EnsembleReducedFunctional(J, c, ensemble) rf_np = ReducedFunctionalNumPy(rf) From 565b9bab55e2cddfcdc435d04c451617d0ba2202 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Wed, 26 Mar 2025 09:16:39 +0000 Subject: [PATCH 051/222] fix fwi demo class reference --- demos/full_waveform_inversion/full_waveform_inversion.py.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demos/full_waveform_inversion/full_waveform_inversion.py.rst b/demos/full_waveform_inversion/full_waveform_inversion.py.rst index 614e6edf0b..c25588b577 100644 --- a/demos/full_waveform_inversion/full_waveform_inversion.py.rst +++ b/demos/full_waveform_inversion/full_waveform_inversion.py.rst @@ -289,7 +289,7 @@ where the :math:`J_s` and its gradients :math:`\nabla_{\mathtt{c\_guess}} J_s` a based on the ``my_ensemble`` configuration. -**Steps 4-6**: The instance of the :class:`~.adjoint.EnsembleReducedFunctional`, named ``J_hat``, +**Steps 4-6**: The instance of the :class:`~.EnsembleReducedFunctional`, named ``J_hat``, is then passed as an argument to the ``minimize`` function. The default ``minimize`` function uses ``scipy.minimize``, and wraps the ``ReducedFunctional`` in a ``ReducedFunctionalNumPy`` that handles transferring data between Firedrake and numpy data structures. However, because From 6ea43a44274cb438cbd3709ece320792119871f4 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Wed, 26 Mar 2025 09:35:00 +0000 Subject: [PATCH 052/222] more apply_riesz in adjoint tests --- tests/firedrake/adjoint/test_optimisation.py | 7 ------- tests/firedrake/adjoint/test_shape_derivatives.py | 15 +++++++-------- 2 files changed, 7 insertions(+), 15 deletions(-) diff --git a/tests/firedrake/adjoint/test_optimisation.py b/tests/firedrake/adjoint/test_optimisation.py index 8fdac40ad1..895e63e028 100644 --- a/tests/firedrake/adjoint/test_optimisation.py +++ b/tests/firedrake/adjoint/test_optimisation.py @@ -139,13 +139,6 @@ def test_simple_inversion(): x = minimize(rf) assert_allclose(x.dat.data, source_ref.dat.data, rtol=1e-2) - rf(source) - x = minimize(rf, derivative_options={"riesz_representation": "l2"}) - assert_allclose(x.dat.data, source_ref.dat.data, rtol=1e-2) - rf(source) - x = minimize(rf, derivative_options={"riesz_representation": "H1"}) - # Assert that the optimisation does not converge for H1 representation - assert not np.allclose(x.dat.data, source_ref.dat.data, rtol=1e-2) @pytest.mark.parametrize("minimize", [minimize_tao_lmvm, diff --git a/tests/firedrake/adjoint/test_shape_derivatives.py b/tests/firedrake/adjoint/test_shape_derivatives.py index 88cee34f78..084a37f374 100644 --- a/tests/firedrake/adjoint/test_shape_derivatives.py +++ b/tests/firedrake/adjoint/test_shape_derivatives.py @@ -40,8 +40,7 @@ def test_sin_weak_spatial(): V = TestFunction(S) # Derivative (Cofunction) dJV = assemble(div(V)*sin(x[0])*dx + V[0]*cos(x[0])*dx) - # Apply L2 riesz representation to obtain the gradient. - actual = dJV.riesz_representation().vector().get_local() + actual = dJV.vector().get_local() assert np.allclose(computed, actual, rtol=1e-14) @@ -96,8 +95,8 @@ def test_shape_hessian(): h.interpolate(as_vector((cos(x[2]), A*cos(x[1]), A*x[1]))) # Second order taylor - dJdm = assemble(inner(Jhat.derivative(), h)*dx) - Hm = assemble(inner(compute_hessian(J, c, h), h)*dx) + dJdm = assemble(inner(Jhat.derivative(apply_riesz=True), h)*dx) + Hm = assemble(inner(compute_hessian(J, c, h, apply_riesz=True), h)*dx) r2 = taylor_test(Jhat, s, h, dJdm=dJdm, Hm=Hm) print(r2) assert (r2 > 2.9) @@ -151,8 +150,8 @@ def test_PDE_hessian_neumann(): Jhat(s) # # Second order taylor - dJdm = assemble(inner(Jhat.derivative(), h)*dx) - Hm = assemble(inner(compute_hessian(J, c, h), h)*dx) + dJdm = assemble(inner(Jhat.derivative(apply_riesz=True), h)*dx) + Hm = assemble(inner(compute_hessian(J, c, h, apply_riesz=True), h)*dx) r2 = taylor_test(Jhat, s, h, dJdm=dJdm, Hm=Hm) assert (r2 > 2.95) @@ -204,8 +203,8 @@ def test_PDE_hessian_dirichlet(): Jhat(s) # # Second order taylor - dJdm = assemble(inner(Jhat.derivative(), h)*dx) - Hm = assemble(inner(compute_hessian(J, c, h), h)*dx) + dJdm = assemble(inner(Jhat.derivative(apply_riesz=True), h)*dx) + Hm = assemble(inner(compute_hessian(J, c, h, apply_riesz=True), h)*dx) r2 = taylor_test(Jhat, s, h, dJdm=dJdm, Hm=Hm) assert (r2 > 2.95) From 2dab55a3d11a45aff21a37a744f2514900128287 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 26 Mar 2025 10:01:07 +0000 Subject: [PATCH 053/222] move riesz map to control --- tests/firedrake/adjoint/test_optimisation.py | 26 ++++++++------------ 1 file changed, 10 insertions(+), 16 deletions(-) diff --git a/tests/firedrake/adjoint/test_optimisation.py b/tests/firedrake/adjoint/test_optimisation.py index 895e63e028..609d266445 100644 --- a/tests/firedrake/adjoint/test_optimisation.py +++ b/tests/firedrake/adjoint/test_optimisation.py @@ -65,23 +65,21 @@ def test_petsc_roundtrip_multiple(): assert (u_2.dat.data_ro == u_2_test.dat.data_ro).all() -def minimize_tao_lmvm(rf, *, convert_options=None): +def minimize_tao_lmvm(rf): problem = MinimizationProblem(rf) solver = TAOSolver(problem, {"tao_type": "lmvm", "tao_gatol": 1.0e-7, "tao_grtol": 0.0, - "tao_gttol": 0.0}, - convert_options=convert_options) + "tao_gttol": 0.0}) return solver.solve() -def minimize_tao_nls(rf, *, convert_options=None): +def minimize_tao_nls(rf): problem = MinimizationProblem(rf) solver = TAOSolver(problem, {"tao_type": "nls", "tao_gatol": 1.0e-7, "tao_grtol": 0.0, - "tao_gttol": 0.0}, - convert_options=convert_options) + "tao_gttol": 0.0}) return solver.solve() @@ -159,14 +157,13 @@ def test_tao_simple_inversion(minimize, riesz_representation): # now rerun annotated model with zero source source = Function(V) - c = Control(source) + c = Control(source, riesz_map=riesz_representation) u = _simple_helmholz_model(V, source) J = assemble(1e6 * (u - u_ref)**2*dx) rf = ReducedFunctional(J, c) - x = minimize(rf, convert_options=(None if riesz_representation is None - else {"riesz_representation": riesz_representation})) + x = minimize(rf) assert_allclose(x.dat.data, source_ref.dat.data, rtol=1e-2) @@ -288,7 +285,7 @@ def test_simple_inversion_riesz_representation(tao_type): u_ref = _simple_helmholz_model(V, source_ref) def forward(source): - c = Control(source) + c = Control(source, riesz_map=riesz_representation) u = _simple_helmholz_model(V, source) J = assemble(1e6 * (u - u_ref)**2*dx) @@ -299,9 +296,7 @@ def forward(source): source = Function(V) rf = forward(source) with stop_annotating(): - solver = TAOSolver( - MinimizationProblem(rf), tao_parameters, - convert_options={"riesz_representation": riesz_representation}) + solver = TAOSolver(MinimizationProblem(rf)) x = solver.solve() assert_allclose(x.dat.data, source_ref.dat.data, rtol=1e-2) @@ -311,7 +306,7 @@ def forward(source): mfn_parameters=mfn_parameters) def forward_transform(source): - c = Control(source) + c = Control(source, riesz_map="l2") source = transform(source, TransformType.PRIMAL, riesz_representation, mfn_parameters=mfn_parameters) @@ -324,8 +319,7 @@ def forward_transform(source): with stop_annotating(): solver_transform = TAOSolver( - MinimizationProblem(rf_transform), tao_parameters, - convert_options={"riesz_representation": "l2"}) + MinimizationProblem(rf_transform), tao_parameters) x_transform = transform(solver_transform.solve(), TransformType.PRIMAL, riesz_representation, mfn_parameters=mfn_parameters) From 4b027e8b1f5680ca22d4fa4f06408b4ec80b2879 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Wed, 26 Mar 2025 10:41:15 +0000 Subject: [PATCH 054/222] ensemble rf docs fix? --- .github/workflows/docs.yml | 1 + firedrake/adjoint/__init__.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 281c30e038..f65c8b9bfb 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -33,6 +33,7 @@ jobs: . venv/bin/activate pip uninstall -y firedrake pip install --verbose './firedrake-repo[docs]' + pip list - name: Check bibtex run: | diff --git a/firedrake/adjoint/__init__.py b/firedrake/adjoint/__init__.py index c48b990420..0c1681f0fd 100644 --- a/firedrake/adjoint/__init__.py +++ b/firedrake/adjoint/__init__.py @@ -37,7 +37,7 @@ from firedrake.adjoint.ufl_constraints import UFLInequalityConstraint, \ UFLEqualityConstraint # noqa F401 -from firedrake.adjoint.ensemble_reduced_functional import EnsembleReducedFunctional # noqa F401 +from .ensemble_reduced_functional import EnsembleReducedFunctional # noqa F401 import numpy_adjoint # noqa F401 import firedrake.ufl_expr import types From f10c113f44f04cf083e7536c98e918336c6825dd Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Wed, 26 Mar 2025 10:58:36 +0000 Subject: [PATCH 055/222] rf riesz options in Jax compat --- firedrake/ml/jax/fem_operator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/ml/jax/fem_operator.py b/firedrake/ml/jax/fem_operator.py index b9e1914846..24c14a96b3 100644 --- a/firedrake/ml/jax/fem_operator.py +++ b/firedrake/ml/jax/fem_operator.py @@ -92,7 +92,7 @@ def bwd(self, _, grad_output: "jax.Array") -> "jax.Array": adj_input = float(adj_input) # Compute adjoint model of `F`: delegated to pyadjoint.ReducedFunctional - adj_output = self.F.derivative(adj_input=adj_input, options={'riesz_representation': None}) + adj_output = self.F.derivative(adj_input=adj_input, apply_riesz=False) # Tuplify adjoint output adj_output = (adj_output,) if not isinstance(adj_output, collections.abc.Sequence) else adj_output From 506bc4ff8aafbde895064d34ce7d6b3880109ab8 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Wed, 26 Mar 2025 11:05:22 +0000 Subject: [PATCH 056/222] restore riesz map comparison to optimisation test --- tests/firedrake/adjoint/test_optimisation.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/tests/firedrake/adjoint/test_optimisation.py b/tests/firedrake/adjoint/test_optimisation.py index 609d266445..80fc476f51 100644 --- a/tests/firedrake/adjoint/test_optimisation.py +++ b/tests/firedrake/adjoint/test_optimisation.py @@ -114,8 +114,13 @@ def _simple_helmholz_model(V, source): return u +@pytest.mark.parametrize( + "riesz_representation", + [None, + "l2", + pytest.param("H1", marks=pytest.mark.xfail(reason="H1 is the wrong norm for this problem"))]) @pytest.mark.skipcomplex -def test_simple_inversion(): +def test_simple_inversion(riesz_representation): """Test inversion of source term in helmholze eqn.""" mesh = UnitIntervalMesh(10) V = FunctionSpace(mesh, "CG", 1) @@ -129,7 +134,7 @@ def test_simple_inversion(): # now rerun annotated model with zero source source = Function(V) - c = Control(source) + c = Control(source, riesz_map=riesz_representation) u = _simple_helmholz_model(V, source) J = assemble(1e6 * (u - u_ref)**2*dx) @@ -296,7 +301,7 @@ def forward(source): source = Function(V) rf = forward(source) with stop_annotating(): - solver = TAOSolver(MinimizationProblem(rf)) + solver = TAOSolver(MinimizationProblem(rf), tao_parameters) x = solver.solve() assert_allclose(x.dat.data, source_ref.dat.data, rtol=1e-2) From 83dacc4eb806acbd43b9ef9919b8139089029717 Mon Sep 17 00:00:00 2001 From: Pablo Brubeck Date: Wed, 26 Mar 2025 12:03:38 +0000 Subject: [PATCH 057/222] Fix bcs --- firedrake/assemble.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/assemble.py b/firedrake/assemble.py index 50287cc2f2..64acf9ce1a 100644 --- a/firedrake/assemble.py +++ b/firedrake/assemble.py @@ -1216,7 +1216,7 @@ def _apply_bc(self, tensor, bc, u=None): else: # The residual belongs to a mixed space that is dual on the boundary nodes # and primal on the interior nodes. Therefore, this is a type-safe operation. - r = tensor.riesz_representation("l2") + r = firedrake.Function(tensor.function_space().dual(), val=tensor.dat) bc.apply(r, u=u) elif isinstance(bc, EquationBCSplit): bc.zero(tensor) From 0ad26baea1d98e4e12ce4bec7e62fae201150ea0 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 26 Mar 2025 11:16:38 +0000 Subject: [PATCH 058/222] remove spurious split method --- firedrake/cofunction.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index b2ff483606..f3fa8cee08 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -113,11 +113,6 @@ def subfunctions(self): of this this :class:`Cofunction`'s :class:`.FunctionSpace`.""" return tuple(type(self)(fs, dat) for fs, dat in zip(self.function_space(), self.dat)) - def split(self): - import warnings - warnings.warn("The .split() method is deprecated, please use the .subfunctions property instead", category=FutureWarning) - return self.subfunctions - @utils.cached_property def _components(self): if self.function_space().rank == 0: From 12df6ac4093692e212f8d5c7b789cd94f388f119 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 26 Mar 2025 11:20:58 +0000 Subject: [PATCH 059/222] make restrict a parameter to RieszMap --- firedrake/cofunction.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py index f3fa8cee08..3a54ba910d 100644 --- a/firedrake/cofunction.py +++ b/firedrake/cofunction.py @@ -403,11 +403,13 @@ class RieszMap: form_compiler_parameters: dict A dictionary of form compiler parameters to be passed to the variational problem that solves for the Riesz map. + restrict: bool + If `True`, use restricted function spaces in the Riesz map solver. """ def __init__(self, function_space_or_inner_product=None, sobolev_space=ufl.L2, *, bcs=None, solver_parameters=None, - form_compiler_parameters=None): + form_compiler_parameters=None, restrict=True): if isinstance(function_space_or_inner_product, ufl.Form): args = ufl.algorithms.extract_arguments( function_space_or_inner_product @@ -439,6 +441,7 @@ def __init__(self, function_space_or_inner_product=None, self._bcs = bcs self._solver_parameters = solver_parameters or {} self._form_compiler_parameters = form_compiler_parameters or {} + self._restrict = restrict @staticmethod def _inner_product_form(sobolev_space, u, v): @@ -460,7 +463,8 @@ def _solver(self): rhs = Cofunction(self._function_space.dual()) soln = Function(self._function_space) lvp = LinearVariationalProblem( - self._inner_product, rhs, soln, bcs=self._bcs, restrict=True, + self._inner_product, rhs, soln, bcs=self._bcs, + restrict=self._restrict, form_compiler_parameters=self._form_compiler_parameters) solver = LinearVariationalSolver( lvp, solver_parameters=self._solver_parameters From 7bbf2cc0779dd2f7f4df6eaa0b620d819916250f Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 26 Mar 2025 11:37:32 +0000 Subject: [PATCH 060/222] DROP BEFORE MERGE --- .github/workflows/docs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index f65c8b9bfb..1cedf5008b 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -34,6 +34,7 @@ jobs: pip uninstall -y firedrake pip install --verbose './firedrake-repo[docs]' pip list + pip install --verbose git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional#egg=pyadjoint-ad' - name: Check bibtex run: | From 2483ef415849ef2afc53ebf0bf81b262e10d753c Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Wed, 26 Mar 2025 12:37:35 +0000 Subject: [PATCH 061/222] Update tests/firedrake/adjoint/test_ensemble_reduced_functional.py Co-authored-by: Daiane Iglesia Dolci <63597005+Ig-dolci@users.noreply.github.com> --- tests/firedrake/adjoint/test_ensemble_reduced_functional.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py index 2ca2ed7d33..a9fe89d132 100644 --- a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py +++ b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py @@ -134,7 +134,7 @@ def test_verification_gather_functional_Function_taylor(): def test_minimise(): # Optimisation test using a list of controls. # This test is equivalent to the one found at: - # https://github.com/dolfin-adjoint/pyadjoint/blob/master/tests/firedrake_adjoint/test_optimisation.py#L9. + # https://github.com/firedrakeproject/firedrake/blob/master/tests/firedrake/adjoint/test_optimisation.py#L92 # In this test, the functional is the result of an ensemble allreduce operation. ensemble = Ensemble(COMM_WORLD, 1) mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) From c626576cfe0798d6aeeadb40ad328ebf1d76bca7 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Wed, 26 Mar 2025 12:37:48 +0000 Subject: [PATCH 062/222] Update .github/workflows/docs.yml Co-authored-by: Josh Hope-Collins --- .github/workflows/docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 1cedf5008b..3e80bbe4f6 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -34,7 +34,7 @@ jobs: pip uninstall -y firedrake pip install --verbose './firedrake-repo[docs]' pip list - pip install --verbose git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional#egg=pyadjoint-ad' + pip install --verbose git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional#egg=pyadjoint-ad - name: Check bibtex run: | From 0f8d13c533098c1d7cdeb0532f97019acd63e2b7 Mon Sep 17 00:00:00 2001 From: Pablo Brubeck Date: Wed, 26 Mar 2025 12:38:10 +0000 Subject: [PATCH 063/222] Test pre_apply_bcs in adjoint --- .../adjoint/test_reduced_functional.py | 38 +++++++++---------- tests/firedrake/adjoint/test_solving.py | 5 ++- 2 files changed, 22 insertions(+), 21 deletions(-) diff --git a/tests/firedrake/adjoint/test_reduced_functional.py b/tests/firedrake/adjoint/test_reduced_functional.py index fd802d3cb4..1574625010 100644 --- a/tests/firedrake/adjoint/test_reduced_functional.py +++ b/tests/firedrake/adjoint/test_reduced_functional.py @@ -31,7 +31,7 @@ def test_constant(): c = Function(R, val=1) f = Function(V) - f.vector()[:] = 1 + f.assign(1) u = Function(V) v = TestFunction(V) @@ -52,7 +52,7 @@ def test_function(): c = Constant(1) f = Function(V) - f.vector()[:] = 1 + f.assign(1) u = Function(V) v = TestFunction(V) @@ -65,19 +65,18 @@ def test_function(): Jhat = ReducedFunctional(J, Control(f)) h = Function(V) - h.vector()[:] = rand(V.dof_dset.size) + h.dat.data[:] = rand(V.dof_dset.size) assert taylor_test(Jhat, f, h) > 1.9 @pytest.mark.skipcomplex -@pytest.mark.parametrize("control", ["dirichlet", "neumann"]) +@pytest.mark.parametrize("control", ["dirichlet-pre", "dirichlet-post", "neumann"]) def test_wrt_function_dirichlet_boundary(control): mesh = UnitSquareMesh(10, 10) V = FunctionSpace(mesh, "CG", 1) R = FunctionSpace(mesh, "R", 0) - u = TrialFunction(V) - u_ = Function(V) + u = Function(V) v = TestFunction(V) x, y = SpatialCoordinate(mesh) @@ -89,20 +88,21 @@ def test_wrt_function_dirichlet_boundary(control): g1 = Function(R, val=2) g2 = Function(R, val=1) f = Function(V) - f.vector()[:] = 10 + f.assign(10) - a = inner(grad(u), grad(v))*dx + a = inner(grad(u), grad(v))*dx + u**2*v*dx L = inner(f, v)*dx + inner(g1, v)*ds(4) + inner(g2, v)*ds(3) - solve(a == L, u_, bc) + pre_apply_bcs = (control == "dirichlet-post") + solve(a - L == 0, u, bc, pre_apply_bcs=pre_apply_bcs) - J = assemble(u_**2*dx) + J = assemble(u**2*dx) - if control == "dirichlet": + if control.startswith("dirichlet"): Jhat = ReducedFunctional(J, Control(bc_func)) g = bc_func h = Function(V) - h.vector()[:] = 1 + h.assign(1) else: Jhat = ReducedFunctional(J, Control(g1)) g = g1 @@ -131,10 +131,10 @@ def test_time_dependent(): T = 0.5 dt = 0.1 f = Function(V) - f.vector()[:] = 1 + f.assign(1) u_1 = Function(V) - u_1.vector()[:] = 1 + u_1.assign(1) control = Control(u_1) a = u_1*u*v*dx + dt*f*inner(grad(u), grad(v))*dx @@ -152,7 +152,7 @@ def test_time_dependent(): Jhat = ReducedFunctional(J, control) h = Function(V) - h.vector()[:] = 1 + h.assign(1) assert taylor_test(Jhat, control.tape_value(), h) > 1.9 @@ -172,7 +172,7 @@ def test_mixed_boundary(): g1 = Constant(2) g2 = Constant(1) f = Function(V) - f.vector()[:] = 10 + f.assign(10) a = f*inner(grad(u), grad(v))*dx L = inner(f, v)*dx + inner(g1, v)*ds(4) + inner(g2, v)*ds(3) @@ -183,7 +183,7 @@ def test_mixed_boundary(): Jhat = ReducedFunctional(J, Control(f)) h = Function(V) - h.vector()[:] = 1 + h.assign(1) assert taylor_test(Jhat, f, h) > 1.9 @@ -194,11 +194,11 @@ def test_assemble_recompute(): R = FunctionSpace(mesh, "R", 0) f = Function(V) - f.vector()[:] = 2 + f.assign(2) expr = Function(R).assign(assemble(f**2*dx)) J = assemble(expr**2*dx(domain=mesh)) Jhat = ReducedFunctional(J, Control(f)) h = Function(V) - h.vector()[:] = 1 + h.assign(1) assert taylor_test(Jhat, f, h) > 1.9 diff --git a/tests/firedrake/adjoint/test_solving.py b/tests/firedrake/adjoint/test_solving.py index d6e5a162c9..bfbf7b348c 100644 --- a/tests/firedrake/adjoint/test_solving.py +++ b/tests/firedrake/adjoint/test_solving.py @@ -76,7 +76,8 @@ def J(f): @pytest.mark.skipcomplex -def test_nonlinear_problem(): +@pytest.mark.parametrize("pre_apply_bcs", (True, False)) +def test_nonlinear_problem(pre_apply_bcs): """This tests whether nullspace and solver_parameters are passed on in adjoint solves""" mesh = IntervalMesh(10, 0, 1) V = FunctionSpace(mesh, "Lagrange", 1) @@ -91,7 +92,7 @@ def test_nonlinear_problem(): def J(f): a = f*inner(grad(u), grad(v))*dx + u**2*v*dx - f*v*dx L = 0 - solve(a == L, u, bc) + solve(a == L, u, bc, pre_apply_bcs=pre_apply_bcs) return assemble(u**2*dx) _test_adjoint(J, f) From 22a3f9b901595e2b48c95c2feaaf291470247898 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Sun, 23 Mar 2025 14:13:54 +0000 Subject: [PATCH 064/222] WIP: ensemble reduced functional with ensemble function --- firedrake/adjoint/__init__.py | 2 +- firedrake/adjoint/ensemble_adjvec.py | 66 ++ .../adjoint/ensemble_reduced_functional.py | 653 +++++++++++--- .../ensemble_reduced_functional_prevec.py | 673 ++++++++++++++ firedrake/adjoint_utils/ensemble_function.py | 47 +- firedrake/ensemble/ensemble_functionspace.py | 2 +- .../test_ensemble_reduced_functional.py | 826 +++++++++++++++++- tests/firedrake/ensemble/test_ensemble.py | 20 +- .../ensemble/test_ensemble_function.py | 1 - .../ensemble/test_ensemble_functionspace.py | 38 +- 10 files changed, 2162 insertions(+), 166 deletions(-) create mode 100644 firedrake/adjoint/ensemble_adjvec.py create mode 100644 firedrake/adjoint/ensemble_reduced_functional_prevec.py diff --git a/firedrake/adjoint/__init__.py b/firedrake/adjoint/__init__.py index c48b990420..b78d0ce188 100644 --- a/firedrake/adjoint/__init__.py +++ b/firedrake/adjoint/__init__.py @@ -37,7 +37,7 @@ from firedrake.adjoint.ufl_constraints import UFLInequalityConstraint, \ UFLEqualityConstraint # noqa F401 -from firedrake.adjoint.ensemble_reduced_functional import EnsembleReducedFunctional # noqa F401 +from firedrake.adjoint.ensemble_reduced_functional import * # noqa F401 import numpy_adjoint # noqa F401 import firedrake.ufl_expr import types diff --git a/firedrake/adjoint/ensemble_adjvec.py b/firedrake/adjoint/ensemble_adjvec.py new file mode 100644 index 0000000000..55d5d5ba22 --- /dev/null +++ b/firedrake/adjoint/ensemble_adjvec.py @@ -0,0 +1,66 @@ +from pyadjoint.overloaded_type import OverloadedType +from pyadjoint.tape import no_annotations +from pyadjoint.adjfloat import AdjFloat +from firedrake.ensemble import Ensemble + + +class EnsembleAdjVec(OverloadedType): + """ + Basic functionality for a list of AdjFloats distributed + across multiple ensemble ranks to be an OverloadedType. + """ + + def __init__(self, subvec, ensemble): + if not isinstance(ensemble, Ensemble): + raise TypeError( + f"EnsembleAdjVec needs an Ensemble, not a {type(ensemble).__name__}") + if not all(isinstance(v, AdjFloat) for v in subvec): + raise TypeError( + f"EnsembleAdjVec must be instantiated with a list of AdjFloats, not {subvec}") + self.subvec = subvec + self.ensemble = ensemble + OverloadedType.__init__(self) + + def _ad_dot(self, other, options=None): + local_dot = sum(s._ad_dot(o) + for s, o in zip(self.subvec, other.subvec)), + return self.ensemble.ensemble_comm.allreduce(local_dot) + + def _ad_add(self, other): + return EnsembleAdjVec( + [s._ad_add(o) for s, o in zip(self.subvec, other.subvec)], + ensemble=self.ensemble) + + def _ad_mul(self, other): + return EnsembleAdjVec( + [s._ad_mul(o) for s, o in zip(self.subvec, + self._maybe_scalar(other))], + ensemble=self.ensemble) + + def _ad_iadd(self, other): + for s, o in zip(self.subvec, other.subvec): + s._ad_iadd(o) + return self + + def _ad_imul(self, other): + for s, o in zip(self.subvec, self._maybe_scalar(other)): + s._ad_imul(o) + return self + + def _maybe_scalar(self, val): + if isinstance(val, EnsembleAdjVec): + return val.subvec + else: + return [val for _ in range(len(self.subvec))] + + def _ad_copy(self): + return EnsembleAdjVec( + [v._ad_copy() for v in self.subvec], + ensemble=self.ensemble) + + @no_annotations + def _ad_convert_type(self, value, options=None): + return EnsembleAdjVec( + [s._ad_convert_type(v, options=options) + for s, v in zip(self.subvec, self._maybe_scalar(value))], + ensemble=self.ensemble) diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py index 0abf6c67af..c4b40db549 100644 --- a/firedrake/adjoint/ensemble_reduced_functional.py +++ b/firedrake/adjoint/ensemble_reduced_functional.py @@ -1,8 +1,47 @@ -from pyadjoint import ReducedFunctional +from functools import partial, cached_property +from contextlib import contextmanager +from pyadjoint import ReducedFunctional, Control, set_working_tape, AdjFloat, no_annotations from pyadjoint.enlisting import Enlist -from pyop2.mpi import MPI +from firedrake.function import Function +from firedrake.cofunction import Cofunction +from .ensemble_adjvec import EnsembleAdjVec +from firedrake.ensemble.ensemble_function import ( + EnsembleFunctionSpace, EnsembleFunction, EnsembleFunctionBase) -import firedrake +__all__ = ( + "EnsembleReducedFunctional", + "EnsembleBcastReducedFunctional", + "EnsembleReduceReducedFunctional", + "EnsembleTransformReducedFunctional", +) + + +def _intermediate_options(options): + iopts = {} if options is None else options.copy() + iopts["riesz_representation"] = None + return iopts + + +def _local_subs(val): + if isinstance(val, EnsembleFunctionBase): + return val.subfunctions + elif isinstance(val, EnsembleAdjVec): + return val.subvec + elif isinstance(val, (list, tuple)): + return val + else: + raise TypeError( + f"Cannot use {type(val).__name__} as an ensemble overloaded type.") + + +def _set_local_subs(dst, src): + dst = _local_subs(dst) + for i, s in enumerate(src): + if hasattr(dst[i], 'assign'): + dst[i].assign(s) + else: + dst[i] = s + return dst class EnsembleReducedFunctional(ReducedFunctional): @@ -34,46 +73,30 @@ class EnsembleReducedFunctional(ReducedFunctional): Parameters ---------- - J : pyadjoint.OverloadedType + rfs : list[pyadjoint.ReducedFunctional] + The reduced functionals for each term Ji. + functional : pyadjoint.OverloadedType An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`. - This should be the functional that we want to reduce. + This should be the functional that we want to calculate. control : pyadjoint.Control or list of pyadjoint.Control A single or a list of Control instances, which you want to map to the functional. ensemble : Ensemble An instance of the :class:`~.ensemble.Ensemble`. It is used to communicate the functionals and their derivatives between the ensemble members. - scatter_control : bool - Whether scattering a control (or a list of controls) over the ensemble communicator - ``Ensemble.ensemble comm``. - gather_functional : An instance of the :class:`pyadjoint.ReducedFunctional`. - that takes in all of the Js. - derivative_components : list of int - The indices of the controls that the derivative should be computed with respect to. - If present, it overwrites ``derivative_cb_pre`` and ``derivative_cb_post``. - scale : float - A scaling factor applied to the functional and its gradient(with respect to the control). - tape : pyadjoint.Tape - A tape object that the reduced functional will use to evaluate the functional and - its gradients (or derivatives). - eval_cb_pre : :func: - Callback function before evaluating the functional. Input is a list of Controls. - eval_cb_pos : :func: - Callback function after evaluating the functional. Inputs are the functional value - and a list of Controls. - derivative_cb_pre : :func: - Callback function before evaluating gradients (or derivatives). Input is a list of - gradients (or derivatives). Should return a list of Controls (usually the same list as - the input) to be passed to :func:`pyadjoint.compute_gradient`. - derivative_cb_post : :func: - Callback function after evaluating derivatives. Inputs are the functional, a list of - gradients (or derivatives), and controls. All of them are the checkpointed versions. - Should return a list of gradients (or derivatives) (usually the same list as the input) - to be returned from ``self.derivative``. - hessian_cb_pre : :func: - Callback function before evaluating the Hessian. Input is a list of Controls. - hessian_cb_post : :func: - Callback function after evaluating the Hessian. Inputs are the functional, a list of - Hessian, and controls. + bcast_control : bool + Whether the control (or a list of controls) is broadcast over the ensemble + communicator ``Ensemble.ensemble_comm`` before evaluating the reduced functionals. + If True, then the control must be a :class:`Function` or :class:`pyadjoint.AdjFloat` + defined on ``Ensemble.comm``, and is logically identical across all ensemble members. + If False, then the control must be a :class:`EnsembleFunction` defined on the ensemble, + and is logically collective over all ensemble members. + reduce_functional : bool + Whether the functional is reduced over the ensemble communicator + ``Ensemble.ensemble_comm`` after evaluating the reduced functionals. + If True, then the functional must be a :class:`Function` or :class:`pyadjoint.AdjFloat` + defined on ``Ensemble.comm``, and is logically identical across all ensemble members. + If False, then the functional must be a :class:`EnsembleFunction` defined on the + ensemble, and is logically collective over all ensemble members. See Also -------- @@ -86,36 +109,76 @@ class EnsembleReducedFunctional(ReducedFunctional): works, please refer to the `Firedrake manual `_. """ - def __init__(self, J, control, ensemble, scatter_control=True, - gather_functional=None, derivative_components=None, - scale=1.0, tape=None, eval_cb_pre=lambda *args: None, - eval_cb_post=lambda *args: None, - derivative_cb_pre=lambda controls: controls, - derivative_cb_post=lambda checkpoint, derivative_components, controls: derivative_components, - hessian_cb_pre=lambda *args: None, hessian_cb_post=lambda *args: None): - super(EnsembleReducedFunctional, self).__init__( - J, control, derivative_components=derivative_components, - scale=scale, tape=tape, eval_cb_pre=eval_cb_pre, - eval_cb_post=eval_cb_post, derivative_cb_pre=derivative_cb_pre, - derivative_cb_post=derivative_cb_post, - hessian_cb_pre=hessian_cb_pre, hessian_cb_post=hessian_cb_post) - + def __init__(self, rfs, functional, control, ensemble=None, + bcast_control=True, reduce_functional=True): + self.rfs = rfs + self.controls = Enlist(control) + self.functional = functional + self.bcast_control = bcast_control + self.reduce_functional = reduce_functional self.ensemble = ensemble - self.scatter_control = scatter_control - self.gather_functional = gather_functional - - def _allgather_J(self, J): - if isinstance(J, float): - vals = self.ensemble.ensemble_comm.allgather(J) - elif isinstance(J, firedrake.Function): - # allgather not implemented in ensemble.py - vals = [] - for i in range(self.ensemble.ensemble_comm.size): - J0 = J.copy(deepcopy=True) - vals.append(self.ensemble.bcast(J0, root=i)) + + if bcast_control: + # controls are Functions, so need EnsembleFunctions for Transform input + ensemble_controls = [] + for cntrl in self.controls: + if isinstance(cntrl.control, float): + ensemble_controls.append( + EnsembleAdjVec( + [AdjFloat(0.) for _ in range(len(rfs))], + ensemble)) + + elif isinstance(cntrl.control, (Function, Cofunction)): + control_space = EnsembleFunctionSpace( + [cntrl.control.function_space() for _ in range(len(rfs))], + ensemble) + ensemble_controls.append( + EnsembleFunction(control_space)) + + else: + TypeError( + f"Don't know how to broadcast controls of type {type(cntrl.control).__name__}") + + self.bcast_rfs = [ + EnsembleBcastReducedFunctional( + ec, Control(c.control._ad_copy()), ensemble=ensemble) + for ec, c in zip(ensemble_controls, self.controls)] + + ensemble_controls = [Control(ec) for ec in ensemble_controls] + else: - raise NotImplementedError(f"Functionals of type {type(J).__name__} are not supported.") - return vals + ensemble_controls = self.controls + + if reduce_functional: + # functional is Function or AdjFloat, so need EnsembleFunction for Transform output + if isinstance(functional, float): + ensemble_functional = EnsembleAdjVec( + [functional._ad_copy() for _ in range(len(rfs))], + ensemble=ensemble) + reduce_control = Control(ensemble_functional) + + elif isinstance(functional, Function): + ensemble_functional_space = EnsembleFunctionSpace( + [rf.functional.function_space() for rf in rfs], ensemble) + + ensemble_functional = EnsembleFunction(ensemble_functional_space) + reduce_control = Control(ensemble_functional) + + else: + raise TypeError( + f"Do not know how to handle a {type(functional).__name__}" + f" control for {type(self).__name__}") + + self.reduce_rf = EnsembleReduceReducedFunctional( + functional._ad_copy(), reduce_control, ensemble=ensemble) + + else: + ensemble_functional = functional + + self.transform_rf = EnsembleTransformReducedFunctional( + rfs, ensemble_functional, + self.controls.delist(ensemble_controls), + ensemble=ensemble) def __call__(self, values): """Computes the reduced functional with supplied control value. @@ -134,20 +197,16 @@ def __call__(self, values): The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. """ - local_functional = super(EnsembleReducedFunctional, self).__call__(values) - ensemble_comm = self.ensemble.ensemble_comm - if self.gather_functional: - controls_g = self._allgather_J(local_functional) - total_functional = self.gather_functional(controls_g) - # if gather_functional is None then we do a sum - elif isinstance(local_functional, float): - total_functional = ensemble_comm.allreduce(sendobj=local_functional, op=MPI.SUM) - elif isinstance(local_functional, firedrake.Function): - total_functional = type(local_functional)(local_functional.function_space()) - total_functional = self.ensemble.allreduce(local_functional, total_functional) - else: - raise NotImplementedError("This type of functional is not supported.") - return total_functional + if self.bcast_control: + values = self.controls.delist( + [bcast(val) for bcast, val in zip(self.bcast_rfs, Enlist(values))]) + + J = self.transform_rf(values) + + if self.reduce_functional: + J = self.reduce_rf(J) + + return J def derivative(self, adj_input=1.0, options=None): """Compute derivatives of a functional with respect to the control parameters. @@ -168,35 +227,423 @@ def derivative(self, adj_input=1.0, options=None): -------- :meth:`~.ensemble.Ensemble.allreduce`, :meth:`pyadjoint.ReducedFunctional.derivative`. """ + iopts = _intermediate_options(options) + if self.reduce_functional: + adj_input = self.reduce_rf.derivative(adj_input=adj_input, + options=iopts) + + transform_options = iopts if self.bcast_control else options + + dJ = self.transform_rf.derivative(adj_input=adj_input, + options=transform_options) + + if self.bcast_control: + dJ = self.controls.delist( + [bcast.derivative(adj_input=dj, options=options) + for bcast, dj in zip(self.bcast_rfs, Enlist(dJ))]) + + return dJ + + @no_annotations + def tlm(self, m_dot, options=None): + """Returns the action of the tangent linear model of the functional w.r.t. the + control on a vector m_dot, around the last supplied value of the control. + + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the tangent linear model. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. + + Returns: + OverloadedType: The action of the tangent linear model in the direction m_dot. + Should be an instance of the same type as the functional. + """ + iopts = _intermediate_options(options) + + if self.bcast_control: + m_dot = self.controls.delist( + [bcast.tlm(md, options=iopts) + for bcast, md in zip(self.bcast_rfs, Enlist(m_dot))]) - if self.gather_functional: - dJg_dmg = self.gather_functional.derivative(adj_input=adj_input, - options=options) - i = self.ensemble.ensemble_comm.rank - adj_input = dJg_dmg[i] + tlm_options = iopts if self.reduce_functional else options - dJdm_local = super(EnsembleReducedFunctional, self).derivative(adj_input=adj_input, options=options) + tlm = self.transform_rf.tlm(m_dot, options=tlm_options) - if self.scatter_control: - dJdm_local = Enlist(dJdm_local) - dJdm_total = [] + if self.reduce_functional: + tlm = self.reduce_rf.tlm(tlm, options=options) - for dJdm in dJdm_local: - if not isinstance(dJdm, (firedrake.Function, float)): - raise NotImplementedError("This type of gradient is not supported.") + return tlm - dJdm_total.append( - self.ensemble.allreduce(dJdm, type(dJdm)(dJdm.function_space())) - if isinstance(dJdm, firedrake.Function) - else self.ensemble.ensemble_comm.allreduce(sendobj=dJdm, op=MPI.SUM) - ) - return dJdm_local.delist(dJdm_total) - return dJdm_local + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. - def hessian(self, m_dot, options=None): - """The Hessian is not yet implemented for ensemble reduced functional. + Using the second-order adjoint method, the action of the Hessian of the + functional with respect to the control, around the last supplied value + of the control, is computed and returned. - Raises: - NotImplementedError: This method is not yet implemented for ensemble reduced functional. + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the Hessian. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. + hessian_input (OverloadedType): The value to start the Hessian accumulation from after the TLM calculation. + evaluate_tlm (bool): Whether or not to compute the forward (tlm) part of the Hessian calculation. + If False, assumes that the tape has already been populated with required TLM values. + + Returns: + OverloadedType: The action of the Hessian in the direction m_dot. + Should be an instance of the same type as the control. """ - raise NotImplementedError("Hessian is not yet implemented for ensemble reduced functional.") + iopts = _intermediate_options(options) + + if evaluate_tlm: + self.tlm(m_dot, options=iopts) + + if self.reduce_functional: + hessian_input = self.reduce_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + options=iopts) + + hessian_options = iopts if self.bcast_control else options + + hessian = self.transform_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + options=hessian_options) + + if self.bcast_control: + hessian = self.controls.delist( + [bcast.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess, options=options) + for bcast, hess in zip(self.bcast_rfs, Enlist(hessian))]) + + return hessian + + +class FunctionOrFloatMPIMixin: + # Should be replaced by Ensemble passing through non-Functions to ensemble_comm + def _bcast(self, val, root=None): + if root is None: + return val + if isinstance(val, float): + val = self.ensemble.ensemble_comm.bcast(val, root=root) + elif isinstance(val, Function): + val = self.ensemble.bcast(val, root=self.root) + else: + raise NotImplementedError(f"Functionals of type {type(val).__name__} are not supported.") + return val + + def _reduce(self, vals, root=None): + vals = Enlist(vals) + for v in vals: + if not isinstance(v, (Function, Cofunction, float)): + raise NotImplementedError( + f"Functionals of type {type(v).__name__} are not supported.") + + if isinstance(vals[0], float): + comm = self.ensemble.ensemble_comm + local_sum = sum(vals) + if root is None: + return comm.allreduce(local_sum) + else: + return comm.reduce(local_sum, root=root) + else: + comm = self.ensemble + global_sum = vals[0]._ad_convert_type(0) + local_sum = vals[0]._ad_convert_type(0) + local_sum.assign(sum(vals)) + if root is None: + return comm.allreduce(local_sum, global_sum) + else: + return comm.reduce(local_sum, global_sum, root=root) + + def _allgather(self, vals): + pass + + +class EnsembleBcastReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): + def __init__(self, functional, control, root=None, ensemble=None): + self.functional = functional + self.controls = Enlist(control) + self.control = control + self.root = root + + if isinstance(control.control, AdjFloat): + if not isinstance(functional, EnsembleAdjVec): + raise TypeError( + f"Functional for {type(self).__name__} must be an EnsembleAdjVec" + " if using an AdjFloat control.") + if ensemble is not None and ensemble is not functional.ensemble: + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the functional.") + self.ensemble = functional.ensemble + self.nlocal_outputs = len(functional.subvec) + + elif isinstance(control.control, Function): + if not isinstance(functional, EnsembleFunction): + raise TypeError( + f"Functional for {type(self).__name__} must be an" + " EnsembleFunction if using a Function control.") + if not all([f.function_space() == control.function_space() + for f in functional.subfunctions]): + raise ValueError( + f"All subfunctions of the {type(self).__name__} functional" + " must have the same function space as the control.") + if (ensemble is not None and ensemble is not functional.function_space().ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the functional.") + self.ensemble = functional.function_space().ensemble + self.nlocal_outputs = len(functional.subfunctions) + + else: + raise ValueError( + f"Do not know how to handle a {type(control).__name__}" + f" control for {type(self).__name__}.") + + def __call__(self, values): + val = self._bcast(values, root=self.root) + J = self.functional._ad_convert_type(0) + _set_local_subs(J, [val for _ in range(self.nlocal_outputs)]) + return J + + def derivative(self, adj_input=1.0, options=None): + dJ = self._reduce(_local_subs(adj_input), + root=self.root) + + return self.control._ad_convert_type(dJ, options=options) + + @no_annotations + def tlm(self, m_dot, options=None): + return self.functional._ad_convert_type(self(m_dot), options=options) + + @no_annotations + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + iopts = _intermediate_options(options) + self.tlm(m_dot, options=iopts) + return self.derivative(hessian_input, options=options) + + +class EnsembleReduceReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): + def __init__(self, functional, control, ensemble=None): + if isinstance(functional, ReducedFunctional): + self.reduction_rf = functional + self.functional = self.reduction_rf.functional + else: + self.reduction_rf = None + self.functional = functional + + self.controls = Enlist(control) + self.control = control + + if isinstance(functional, AdjFloat): + if not isinstance(control.control, EnsembleAdjVec): + raise TypeError( + f"Control for {type(self).__name__} must be an EnsembleAdjVec" + " if using an AdjFloat functional.") + if (ensemble is not None and ensemble is not control.control.ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the control.") + self.ensemble = ensemble + self.nlocal_inputs = len(control.subvec) + + elif isinstance(functional, Function): + if not isinstance(control.control, EnsembleFunction): + raise TypeError( + f"Control for {type(self).__name__} must be an" + " EnsembleFunction if using a Function functional.") + if not all([c.function_space() == functional.function_space() + for c in control.subfunctions]): + raise ValueError( + f"All subfunctions of the {type(self).__name__} control" + " must have the same function space as the functional.") + if (ensemble is not None and ensemble is not control.function_space().ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the control.") + self.ensemble = control.function_space().ensemble + self.nlocal_inputs = len(control.subfunctions) + + else: + raise ValueError( + f"Do not know how to handle a {type(functional).__name__}" + f" control for {type(self).__name__}.") + + _ = self._sum_rf + + @no_annotations + def __call__(self, values): + if self.reduction_rf: + return self.reduction_rf(self._allgather(_local_subs(values))) + else: + return self._reduce( + self._sum_rf(_local_subs(values))) + + def derivative(self, adj_input=1.0, options=None): + if self.reduction_rf: + dJ_global = self.reduction_rf.derivative( + adj_input=adj_input, options=options) + dJ_local = [dJ_global[i] for i in range(self._local_indices)] + else: + dJ_local = self.functional._ad_convert_type(adj_input, options=options) + dJ_local = [dJ_local for _ in range(self.nlocal_inputs)] + + dJ_global = self.control._ad_convert_type(0, options=options) + _set_local_subs(dJ_global, dJ_local) + return dJ_global + + @no_annotations + def tlm(self, m_dot, options=None): + if self.reduction_rf: + return self.reduction_rf.tlm( + self._allgather(_local_subs(m_dot)), options=options) + else: + return self._reduce( + self._sum_rf.tlm(_local_subs(m_dot), options=options)) + + @no_annotations + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + iopts = _intermediate_options(options) + self.tlm(m_dot, options=iopts) + return self.derivative(hessian_input, options=options) + + @cached_property + def _sum_rf(self): + controls = [c._ad_convert_type(0.) + for c in _local_subs(self.controls[0].control)] + J = self.functional._ad_convert_type(0.) + with set_working_tape() as tape: + if isinstance(J, float): + J = sum(controls) + else: + for c in controls: + J = J._ad_add(c) + rf = ReducedFunctional( + J, [Control(c) for c in controls], tape=tape) + return rf + + @cached_property + def _local_indices(self): + fs = self.functional.function_space() + offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) + return tuple(offset + i for i in range(fs.nlocal_spaces)) + + +class EnsembleTransformReducedFunctional(ReducedFunctional): + def __init__(self, rfs, functional, control, ensemble=None): + self.rfs = rfs + self.functional = functional + self.controls = Enlist(control) + + if isinstance(functional, EnsembleFunction): + if not all(isinstance(c.control, EnsembleFunction) + for c in self.controls): + raise TypeError( + f"Controls for {type(self).__name__} must be EnsembleFunctions") + if ensemble is not None and ensemble is not functional.function_space().ensemble: + raise ValueError( + f"Ensemble provided to {type(self).__name__} must" + " match the ensemble of the functional.") + + self.ensemble = functional.function_space().ensemble + self.nlocal_outputs = len(functional.subfunctions) + + elif isinstance(functional, EnsembleAdjVec): + if ensemble is not None and ensemble is not functional.ensemble: + raise ValueError( + f"Ensemble provided to {type(self).__name__} must" + " match the ensemble of the functional.") + + self.ensemble = ensemble + self.nlocal_outputs = len(functional.subvec) + + else: + raise TypeError( + f"Functional for {type(self).__name__} must be either" + " an EnsembleFunction or EnsembleAdjVec") + + @no_annotations + def __call__(self, values): + J = self.functional._ad_convert_type(0) + + with self._local_data(values, output=J) as (local_vals, output): + + output([rf(rf.controls.delist(vals)) + for rf, vals in zip(self.rfs, local_vals)]) + + return J + + @no_annotations + def tlm(self, m_dot, options=None): + tlm = self.functional._ad_convert_type(0) + + with self._local_data(m_dot, output=tlm) as (local_mdots, output): + + output([rf.tlm(rf.controls.delist(md), options=options) + for rf, md in zip(self.rfs, local_mdots)]) + + return tlm + + def derivative(self, adj_input=1.0, options=None): + dJ = self.controls.delist( + [c.control._ad_convert_type(0, options=options) + for c in self.controls]) + + with self._local_data(adj_input, output=dJ) as (local_adjs, output): + + output([rf.derivative(adj_input=adj[0], options=options) + for rf, adj in zip(self.rfs, local_adjs)]) + + return dJ + + @no_annotations + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + iopts = _intermediate_options(options) + self.tlm(m_dot, options=iopts) + + hessian = self.controls.delist( + [c.control._ad_convert_type(0, options=options) + for c in self.controls]) + + with self._local_data(hessian_input, output=hessian) as (local_hessians, output): + + output([rf.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess[0], options=options) + for rf, hess in zip(self.rfs, local_hessians)]) + + return hessian + + @contextmanager + def _local_data(self, data, output): + local_data = self._global_to_local_data(data) + output_transform = partial( + self._local_to_global_data, global_data=output) + yield local_data, output_transform + + def _local_to_global_data(self, local_data, global_data): + # N local lists of length n -> n global lists of length N + # [(1,), (2,), (3,)]-> [(1, 2, 3)] + # [(1, 11), (2, 12), (3, 13)] -> [(1, 2, 3), (11, 12, 13)] + + for j, global_group in enumerate(Enlist(global_data)): + local_group = [Enlist(local_group)[j] + for local_group in local_data] + _set_local_subs(global_group, local_group) + + return global_data + + def _global_to_local_data(self, global_data): + # n global lists of length N -> N local lists of length n + # [(1, 2, 3)] -> [(1,), (2,), (3,)] + # [(1, 2, 3), (11, 12, 13)] -> [(1, 11), (2, 12), (3, 13)] + + local_groups = [ + ld for ld in zip(*map(_local_subs, Enlist(global_data)))] + return local_groups diff --git a/firedrake/adjoint/ensemble_reduced_functional_prevec.py b/firedrake/adjoint/ensemble_reduced_functional_prevec.py new file mode 100644 index 0000000000..f018b20dfc --- /dev/null +++ b/firedrake/adjoint/ensemble_reduced_functional_prevec.py @@ -0,0 +1,673 @@ +from functools import partial, cached_property +from contextlib import contextmanager +from pyadjoint import ReducedFunctional, Control, set_working_tape, AdjFloat, no_annotations +from pyadjoint.enlisting import Enlist +from firedrake.function import Function +from firedrake.cofunction import Cofunction +from firedrake.ensemble.ensemble_function import ( + EnsembleFunctionSpace, EnsembleFunction, EnsembleCofunction, EnsembleFunctionBase) + +__all__ = ( + "EnsembleReducedFunctional", + "EnsembleBcastReducedFunctional", + "EnsembleReduceReducedFunctional", + "EnsembleTransformReducedFunctional", +) + + +def _intermediate_options(options): + iopts = {} if options is None else options.copy() + iopts["riesz_representation"] = None + return iopts + + +def _local_subs(val): + return val.subfunctions if isinstance(val, EnsembleFunctionBase) else val + + +def _set_local_subs(dst, src): + dst = _local_subs(dst) + for i, s in enumerate(src): + if hasattr(dst[i], 'assign'): + dst[i].assign(s) + else: + dst[i] = s + return dst + + +class EnsembleReducedFunctional(ReducedFunctional): + """Enable solving simultaneously reduced functionals in parallel. + + Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`, + where :math:`m` is the control parameter. Let us assume that :math:`J` is the sum of + :math:`N` functionals :math:`J_i(m)`, i.e., + + .. math:: + + J = \\sum_{i=1}^{N} J_i(m). + + The gradient over a summation is a linear operation. Therefore, we can write the gradient + :math:`\\dfrac{dJ}{dm}` as + + .. math:: + + \\frac{dJ}{dm} = \\sum_{i=1}^{N} \\frac{dJ_i}{dm}, + + The :class:`EnsembleReducedFunctional` allows simultaneous evaluation of :math:`J_i` and + :math:`\\dfrac{dJ_i}{dm}`. After that, the allreduce :class:`~.ensemble.Ensemble` + operation is employed to sum the functionals and their gradients over an ensemble + communicator. + + If gather_functional is present, then all the values of J are communicated to all ensemble + ranks, and passed in a list to gather_functional, which is a reduced functional that expects + a list of that size of the relevant types. + + Parameters + ---------- + rfs : list[pyadjoint.ReducedFunctional] + The reduced functionals for each term Ji. + functional : pyadjoint.OverloadedType + An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`. + This should be the functional that we want to calculate. + control : pyadjoint.Control or list of pyadjoint.Control + A single or a list of Control instances, which you want to map to the functional. + ensemble : Ensemble + An instance of the :class:`~.ensemble.Ensemble`. It is used to communicate the + functionals and their derivatives between the ensemble members. + bcast_control : bool + Whether the control (or a list of controls) is broadcast over the ensemble + communicator ``Ensemble.ensemble_comm`` before evaluating the reduced functionals. + If True, then the control must be a :class:`Function` or :class:`pyadjoint.AdjFloat` + defined on ``Ensemble.comm``, and is logically identical across all ensemble members. + If False, then the control must be a :class:`EnsembleFunction` defined on the ensemble, + and is logically collective over all ensemble members. + reduce_functional : bool + Whether the functional is reduced over the ensemble communicator + ``Ensemble.ensemble_comm`` after evaluating the reduced functionals. + If True, then the functional must be a :class:`Function` or :class:`pyadjoint.AdjFloat` + defined on ``Ensemble.comm``, and is logically identical across all ensemble members. + If False, then the functional must be a :class:`EnsembleFunction` defined on the + ensemble, and is logically collective over all ensemble members. + + See Also + -------- + :class:`~.ensemble.Ensemble`, :class:`pyadjoint.ReducedFunctional`. + + Notes + ----- + The functionals :math:`J_i` and the control must be defined over a common + `ensemble.comm` communicator. To understand more about how ensemble parallelism + works, please refer to the `Firedrake manual + `_. + """ + def __init__(self, rfs, functional, control, ensemble=None, + bcast_control=True, reduce_functional=True): + self.rfs = rfs + self.controls = Enlist(control) + self.functional = functional + self.bcast_control = bcast_control + self.reduce_functional = reduce_functional + self.ensemble = ensemble + + if bcast_control: + # controls are Functions, so need EnsembleFunctions for Transform input + ensemble_control_spaces = [ + EnsembleFunctionSpace([c.control.function_space() + for _ in range(len(rfs))], ensemble) + for c in self.controls] + + ensemble_controls = [ + Control(EnsembleFunction(space)) for space in ensemble_control_spaces] + + self.bcast_rfs = [ + EnsembleBcastReducedFunctional( + ec.control, Control(c.control._ad_copy()), ensemble=ensemble) + for ec, c in zip(ensemble_controls, self.controls)] + + else: + ensemble_controls = self.controls + + if reduce_functional: + # functional is Function or AdjFloat, so need EnsembleFunction for Transform output + if isinstance(functional, float): + ensemble_functional = [functional._ad_copy() + for _ in range(len(rfs))] + reduce_control = [Control(f) for f in ensemble_functional] + + elif isinstance(functional, Function): + ensemble_functional_space = EnsembleFunctionSpace( + [rf.functional.function_space() for rf in rfs], ensemble) + + ensemble_functional = EnsembleFunction(ensemble_functional_space) + reduce_control = Control(ensemble_functional) + + else: + raise TypeError( + f"Do not know how to handle a {type(functional).__name__}" + f" control for {type(self).__name__}") + + self.reduce_rf = EnsembleReduceReducedFunctional( + functional._ad_copy(), reduce_control, ensemble=ensemble) + + else: + ensemble_functional = functional + + self.transform_rf = EnsembleTransformReducedFunctional( + rfs, ensemble_functional, + self.controls.delist(ensemble_controls), + ensemble=ensemble) + + def __call__(self, values): + """Computes the reduced functional with supplied control value. + + Parameters + ---------- + values : pyadjoint.OverloadedType + If you have multiple controls this should be a list of + new values for each control in the order you listed the controls to the constructor. + If you have a single control it can either be a list or a single object. + Each new value should have the same type as the corresponding control. + + Returns + ------- + pyadjoint.OverloadedType + The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. + + """ + if self.bcast_control: + values = self.controls.delist( + [bcast(val) for bcast, val in zip(self.bcast_rfs, Enlist(values))]) + + J = self.transform_rf(values) + + if self.reduce_functional: + J = self.reduce_rf(J) + + return J + + def derivative(self, adj_input=1.0, options=None): + """Compute derivatives of a functional with respect to the control parameters. + + Parameters + ---------- + adj_input : float + The adjoint input. + options : dict + Additional options for the derivative computation. + + Returns + ------- + dJdm_total : pyadjoint.OverloadedType + The result of Allreduce operations of ``dJdm_local`` into ``dJdm_total`` over the`Ensemble.ensemble_comm`. + + See Also + -------- + :meth:`~.ensemble.Ensemble.allreduce`, :meth:`pyadjoint.ReducedFunctional.derivative`. + """ + iopts = _intermediate_options(options) + if self.reduce_functional: + adj_input = self.reduce_rf.derivative(adj_input=adj_input, + options=iopts) + + transform_options = iopts if self.bcast_control else options + + dJ = self.transform_rf.derivative(adj_input=adj_input, + options=transform_options) + + if self.bcast_control: + dJ = Enlist(dJ) + dJ = dJ.delist( + [bcast.derivative(adj_input=dj, options=options) + for bcast, dj in zip(self.bcast_rfs, dJ)]) + + return dJ + + @no_annotations + def tlm(self, m_dot, options=None): + """Returns the action of the tangent linear model of the functional w.r.t. the + control on a vector m_dot, around the last supplied value of the control. + + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the tangent linear model. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. + + Returns: + OverloadedType: The action of the tangent linear model in the direction m_dot. + Should be an instance of the same type as the functional. + """ + iopts = _intermediate_options(options) + + if self.bcast_control: + m_dot = Enlist(m_dot) + m_dot = m_dot.delist( + [bcast.tlm(md, options=iopts) + for bcast, md in zip(self.bcast_rfs, m_dot)]) + + tlm_options = iopts if self.reduce_functional else options + + tlm = self.transform_rf.tlm(m_dot, options=tlm_options) + + if self.reduce_functional: + tlm = self.reduce_rf.tlm(tlm, options=options) + + return tlm + + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. + + Using the second-order adjoint method, the action of the Hessian of the + functional with respect to the control, around the last supplied value + of the control, is computed and returned. + + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the Hessian. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. + hessian_input (OverloadedType): The value to start the Hessian accumulation from after the TLM calculation. + evaluate_tlm (bool): Whether or not to compute the forward (tlm) part of the Hessian calculation. + If False, assumes that the tape has already been populated with required TLM values. + + Returns: + OverloadedType: The action of the Hessian in the direction m_dot. + Should be an instance of the same type as the control. + """ + iopts = _intermediate_options(options) + + if evaluate_tlm: + self.tlm(m_dot, options=iopts) + + if self.reduce_functional: + hessian_input = self.reduce_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + options=iopts) + + hessian_options = iopts if self.bcast_control else options + + hessian = self.transform_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + options=hessian_options) + + if self.bcast_control: + hessian = Enlist(hessian) + hessian = hessian.delist( + [bcast.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess, options=options) + for bcast, hess in zip(self.bcast_rfs, hessian)]) + + return hessian + + +class FunctionOrFloatMPIMixin: + # Should be replaced by Ensemble passing through non-Functions to ensemble_comm + def _bcast(self, val, root=None): + if root is None: + return val + if isinstance(val, float): + val = self.ensemble.ensemble_comm.bcast(val, root=root) + elif isinstance(val, Function): + val = self.ensemble.bcast(val, root=self.root) + else: + raise NotImplementedError(f"Functionals of type {type(val).__name__} are not supported.") + return val + + def _reduce(self, vals, root=None): + vals = Enlist(vals) + for v in vals: + if not isinstance(v, (Function, Cofunction, float)): + raise NotImplementedError( + f"Functionals of type {type(v).__name__} are not supported.") + + if isinstance(vals[0], float): + comm = self.ensemble.ensemble_comm + local_sum = sum(vals) + if root is None: + return comm.allreduce(local_sum) + else: + return comm.reduce(local_sum, root=root) + else: + comm = self.ensemble + global_sum = vals[0]._ad_convert_type(0) + local_sum = vals[0]._ad_convert_type(0) + local_sum.assign(sum(vals)) + if root is None: + return comm.allreduce(local_sum, global_sum) + else: + return comm.reduce(local_sum, global_sum, root=root) + + def _allgather(self, vals): + pass + + +class EnsembleBcastReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): + def __init__(self, functional, control, root=None, ensemble=None): + self.functional = functional + self.controls = Enlist(control) + self.control = control + self.root = root + + # for AdjFloats functional is a list + self._functionals = Enlist(functional) + + if isinstance(control.control, AdjFloat): + if not all([isinstance(f, AdjFloat) for f in functional]): + raise TypeError( + f"Functional for {type(self).__name__} must be a list of" + " AdjFloats if using an AdjFloat control.") + if ensemble is None: + raise ValueError( + f"Must provide {type(self).__name__} an Ensemble" + " if using an AdjFloat control.") + self.ensemble = ensemble + self.nlocal_outputs = len(functional) + + elif isinstance(control.control, Function): + if not isinstance(functional, EnsembleFunction): + raise TypeError( + f"Functional for {type(self).__name__} must be an" + " EnsembleFunction if using a Function control.") + if not all([f.function_space() == control.function_space() + for f in functional.subfunctions]): + raise ValueError( + f"All subfunctions of the {type(self).__name__} functional" + " must have the same function space as the control.") + if (ensemble is not None and ensemble is not functional.function_space().ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the functional.") + self.ensemble = functional.function_space().ensemble + self.nlocal_outputs = len(self.functional.subfunctions) + + else: + raise ValueError( + f"Do not know how to handle a {type(control).__name__}" + f" control for {type(self).__name__}.") + + def __call__(self, values): + val = self._bcast(values, root=self.root) + + # "bcast" val into all elements of J (0+val = val) + J = self._functionals.delist([ + functional._ad_convert_type(0) + for functional in self._functionals + ]) + for Ji in _local_subs(J): + Ji._ad_iadd(val) + + _set_local_subs(J, [val for _ in range(self.nlocal_outputs)]) + + return J + + def derivative(self, adj_input=1.0, options=None): + if isinstance(self.control.control, Function): + if not isinstance(adj_input, EnsembleCofunction): + raise TypeError( + f"{type(self).__name__} needs an EnsembleCofunction" + f" adj_input, not a {type(adj_input).__name__}") + else: + if not all([isinstance(adj, AdjFloat) for adj in adj_input]): + raise TypeError( + f"{type(self).__name__} needs a list of AdjFloats" + f" adj_input, not a {type(adj_input).__name__}") + + dJ = self._reduce(_local_subs(adj_input), + root=self.root) + + return self.control._ad_convert_type(dJ, options=options) + + @no_annotations + def tlm(self, m_dot, options=None): + return self.functional._ad_convert_type(self(m_dot), options=options) + + @no_annotations + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + iopts = _intermediate_options(options) + self.tlm(m_dot, options=iopts) + return self.derivative(hessian_input, options=options) + + +class EnsembleReduceReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): + def __init__(self, functional, control, ensemble=None): + if isinstance(functional, ReducedFunctional): + self.reduction_rf = functional + self.functional = self.reduction_rf.functional + else: + self.reduction_rf = None + self.functional = functional + + self.controls = Enlist(control) + self.control = control + + if isinstance(functional, AdjFloat): + if not all([isinstance(c.control, AdjFloat) for c in control]): + raise TypeError( + f"Control for {type(self).__name__} must be a list of" + " AdjFloats if using an AdjFloat functional.") + if ensemble is None: + raise ValueError( + f"Must provide {type(self).__name__} an Ensemble" + " if using an AdjFloat functional.") + self.ensemble = ensemble + self.nlocal_inputs = len(control) + + elif isinstance(functional, Function): + if not isinstance(control.control, EnsembleFunction): + raise TypeError( + f"Control for {type(self).__name__} must be an" + " EnsembleFunction if using a Function functional.") + if not all([c.function_space() == functional.function_space() + for c in control.subfunctions]): + raise ValueError( + f"All subfunctions of the {type(self).__name__} control" + " must have the same function space as the functional.") + if (ensemble is not None and ensemble is not control.function_space().ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the control.") + self.ensemble = control.function_space().ensemble + self.nlocal_inputs = len(self.control.subfunctions) + + else: + raise ValueError( + f"Do not know how to handle a {type(functional).__name__}" + f" control for {type(self).__name__}.") + + _ = self._sum_rf + + @no_annotations + def __call__(self, values): + if self.reduction_rf: + return self.reduction_rf(self._allgather(_local_subs(values))) + else: + return self._reduce( + self._sum_rf(_local_subs(values))) + + def derivative(self, adj_input=1.0, options=None): + if self.reduction_rf: + dJ_global = self.reduction_rf.derivative( + adj_input=adj_input, options=options) + dJ_local = [dJ_global[i] for i in range(self._local_indices)] + else: + dJ_local = self.functional._ad_convert_type(adj_input, options=options) + dJ_local = [dJ_local for _ in range(self.nlocal_inputs)] + + dJ_global = self.controls.delist( + [c.control._ad_convert_type(0, options=options) + for c in self.controls]) + + _set_local_subs(dJ_global, dJ_local) + + return dJ_global + + @no_annotations + def tlm(self, m_dot, options=None): + if self.reduction_rf: + return self.reduction_rf.tlm( + self._allgather(_local_subs(m_dot)), options=options) + else: + return self._reduce( + self._sum_rf.tlm(_local_subs(m_dot), options=options)) + + @no_annotations + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + iopts = _intermediate_options(options) + self.tlm(m_dot, options=iopts) + return self.derivative(hessian_input, options=options) + + @cached_property + def _sum_rf(self): + subs = self.controls.delist( + [_local_subs(c.control) for c in self.controls]) + controls = [c._ad_convert_type(0.) for c in subs] + J = self.functional._ad_convert_type(0.) + with set_working_tape() as tape: + if isinstance(J, float): + J = sum(controls) + else: + for c in controls: + J = J._ad_add(c) + rf = ReducedFunctional( + J, [Control(c) for c in controls], tape=tape) + return rf + + @cached_property + def _local_indices(self): + fs = self.functional.function_space() + offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) + return tuple(offset + i for i in range(fs.nlocal_spaces)) + + +class EnsembleTransformReducedFunctional(ReducedFunctional): + def __init__(self, rfs, functional, control, ensemble=None): + self.rfs = rfs + self.functional = functional + self.controls = Enlist(control) + + # AdjFloat functional is a list but need to treat as a single object + self._flist = Enlist(functional) + + # list[EFunction] -> EFunction + if isinstance(functional, EnsembleFunction): + if not all(isinstance(c.control, EnsembleFunction) + for c in self.controls): + raise TypeError( + f"Controls for {type(self).__name__} must be EnsembleFunctions") + if ensemble is not None: + ensembles = [c.control.function_space().ensemble + for c in self.controls] + ensembles.append(functional.function_space().ensemble) + if not all(e is ensemble for e in ensembles): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must" + " match the ensemble of the controls and functional.") + + self.ensemble = functional.function_space().ensemble + self.nlocal_outputs = len(functional.subfunctions) + + # list[EFunction] -> list[AdjFloat] + elif isinstance(functional, list): + if not all(isinstance(f, AdjFloat) for f in functional): + raise TypeError( + f"Functional for {type(self).__name__} must be either" + " an EnsembleFunction or a list of AdjFloats") + if ensemble is None: + raise ValueError( + f"Must provide {type(self).__name__} an Ensemble" + " if using an AdjFloat functional.") + + self.ensemble = ensemble + self.nlocal_outputs = len(functional) + + else: + raise TypeError( + f"Functional for {type(self).__name__} must be either" + " an EnsembleFunction or a list of AdjFloats") + + @no_annotations + def __call__(self, values): + # For AdjFloat output, a single functional is a list of AdjFloats + J = [f._ad_convert_type(0) for f in self._flist] + if self._flist.listed: + J = [J] + + with self._local_data(data=Enlist(values), output=J) as (local_vals, output): + + output([rf(rf.controls.delist(vals)) + for rf, vals in zip(self.rfs, local_vals)]) + + return J[0] + + @no_annotations + def tlm(self, m_dot, options=None): + tlm = [f._ad_convert_type(0) for f in self._flist] + if self._flist.listed: + tlm = [tlm] + + with self._local_data(data=Enlist(m_dot), output=tlm) as (local_mdots, output): + + output([rf.tlm(rf.controls.delist(md), options=options) + for rf, md in zip(self.rfs, local_mdots)]) + + return tlm[0] + + def derivative(self, adj_input=1.0, options=None): + dJ = [c.control._ad_convert_type(0, options=options) + for c in self.controls] + + with self._local_data(data=[adj_input], output=dJ) as (local_adjs, output): + output([rf.derivative(adj_input=adj[0], options=options) + for rf, adj in zip(self.rfs, local_adjs)]) + + return self.controls.delist(dJ) + + @no_annotations + def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): + if evaluate_tlm: + iopts = _intermediate_options(options) + self.tlm(m_dot, options=iopts) + + hessian = [ + c.control._ad_convert_type(0, options=options) + for c in self.controls] + + with self._local_data(data=[hessian_input], output=hessian) as (local_hessians, output): + output([rf.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess[0], options=options) + for rf, hess in zip(self.rfs, local_hessians)]) + return hessian + + @contextmanager + def _local_data(self, data, output): + local_data = self._global_to_local_data(data) + output_transform = partial( + self._local_to_global_data, global_data=output) + yield local_data, output_transform + + def _local_to_global_data(self, local_data, global_data): + # N local lists of length n -> n global lists of length N + # [(1,), (2,), (3,)]-> [(1, 2, 3)] + # [(1, 11), (2, 12), (3, 13)] -> [(1, 2, 3), (11, 12, 13)] + + for j, global_group in enumerate(global_data): + local_group = [Enlist(local_group)[j] + for local_group in local_data] + _set_local_subs(global_group, local_group) + + return global_data + + def _global_to_local_data(self, global_data): + # n global lists of length N -> N local lists of length n + # [(1, 2, 3)] -> [(1,), (2,), (3,)] + # [(1, 2, 3), (11, 12, 13)] -> [(1, 11), (2, 12), (3, 13)] + + global_groups = [ + ld for ld in zip(*map(_local_subs, global_data))] + + local_groups = global_groups + return local_groups diff --git a/firedrake/adjoint_utils/ensemble_function.py b/firedrake/adjoint_utils/ensemble_function.py index ac5b9c3413..8b42083fd3 100644 --- a/firedrake/adjoint_utils/ensemble_function.py +++ b/firedrake/adjoint_utils/ensemble_function.py @@ -1,5 +1,6 @@ from pyadjoint.overloaded_type import OverloadedType -from firedrake.petsc import PETSc +from pyadjoint.tape import no_annotations +import firedrake from .checkpointing import disk_checkpointing from functools import wraps @@ -13,7 +14,7 @@ class EnsembleFunctionMixin(OverloadedType): Enables EnsembleFunction to do the following: - Be a Control for a NumpyReducedFunctional (_ad_to_list and _ad_assign_numpy) - Be used with pyadjoint TAO solver (_ad_{to,from}_petsc) - - Be used as a Control for Taylor tests (_ad_dot) + - Be used as a Control for Taylor tests (_ad_dot, _ad_add, _ad_mul) """ @staticmethod @@ -31,6 +32,7 @@ def wrapper(self, *args, **kwargs): @staticmethod def _ad_to_list(m): + PETSc = firedrake.PETSc with m.vec_ro() as gvec: lvec = PETSc.Vec().createSeq(gvec.size, comm=PETSc.COMM_SELF) @@ -50,10 +52,47 @@ def _ad_dot(self, other, options=None): local_dot = sum(uself._ad_dot(uother, options=options) for uself, uother in zip(self.subfunctions, other.subfunctions)) - return self.ensemble.ensemble_comm.allreduce(local_dot) + return self.function_space().ensemble.ensemble_comm.allreduce(local_dot) + + @no_annotations + def _ad_convert_type(self, value, options=None): + options = {} if options is None else options.copy() + options.setdefault("riesz_representation", "L2") + if options["riesz_representation"] is None: + if value == 0.: + # In adjoint-based differentiation, value == 0. arises only when + # the functional is independent on the control variable. + from firedrake import EnsembleCofunction + V = options.get("function_space", self.function_space()) + return EnsembleCofunction(V.dual()) + else: + return value + else: + return self._ad_convert_riesz(value, options=options) def _ad_convert_riesz(self, value, options=None): - raise NotImplementedError + from firedrake import EnsembleFunction, EnsembleCofunction + + options = {} if options is None else options + riesz_representation = options.get("riesz_representation", "L2") + V = options.get("function_space", self.function_space()) + if value == 0.: + # In adjoint-based differentiation, value == 0. arises only when + # the functional is independent on the control variable. + return EnsembleFunction(V) + + if not isinstance(value, (EnsembleCofunction, EnsembleFunction)): + raise TypeError( + "Expected an EnsembleCofunction or an EnsembleFunction" + f" not a {type(value).__name__}") + + else: + if isinstance(value, EnsembleCofunction): + kwargs = options.get("solver_options", {}) + else: + kwargs = {} + return value.riesz_representation( + riesz_representation, **kwargs) def _ad_create_checkpoint(self): if disk_checkpointing(): diff --git a/firedrake/ensemble/ensemble_functionspace.py b/firedrake/ensemble/ensemble_functionspace.py index fca1efa1cf..6f3956d394 100644 --- a/firedrake/ensemble/ensemble_functionspace.py +++ b/firedrake/ensemble/ensemble_functionspace.py @@ -150,7 +150,7 @@ def dual(self): @cached_property def nlocal_spaces(self): - """The total number of subspaces across all ensemble ranks. + """Number of subspaces on this ensemble rank. """ return len(self.local_spaces) diff --git a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py index 6d5285a687..224527b230 100644 --- a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py +++ b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py @@ -1,7 +1,10 @@ from firedrake import * from firedrake.adjoint import * +from firedrake.adjoint.ensemble_adjvec import EnsembleAdjVec import pytest from numpy.testing import assert_allclose +from numpy import mean +from pytest_mpi.parallel_assert import parallel_assert @pytest.fixture(autouse=True) @@ -21,21 +24,768 @@ def handle_annotation(): pause_annotation() -@pytest.mark.parallel(nprocs=4) +@pytest.mark.parallel(nprocs=[1, 2, 3, 6]) +@pytest.mark.skipcomplex +def test_ensemble_bcast_float(): + ensemble = Ensemble(COMM_WORLD, 1) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_floats = 6 + nlocal_floats = nglobal_floats // size + + c = AdjFloat(0.0) + J = EnsembleAdjVec( + [AdjFloat(0.0) for _ in range(nlocal_floats)], ensemble) + + Jhat = EnsembleBcastReducedFunctional(J, Control(c), ensemble=ensemble) + + # check the control is broadcast to all ranks + eps = 1e-12 + + x = AdjFloat(3.0) + Jx = Jhat(x) + + expect = x + match_local = all((Ji - expect) < eps for Ji in Jx.subvec) + + parallel_assert( + lambda: match_local, + msg=f"Broadcast AdjFloats {Jx.subvec} do not match expected value {expect}." + ) + + # Check the adjoint is reduced back to all ranks. + # Because the functional is an array we need to + # pass an adj_input of an array. + + offset = rank*nlocal_floats + adj_input = EnsembleAdjVec( + [AdjFloat(offset + i + 1.0) for i in range(nlocal_floats)], + ensemble=ensemble) + + expect = AdjFloat(sum(i+1.0 for i in range(nglobal_floats))) + + dJ = Jhat.derivative(adj_input=adj_input) + + match_local = dJ - expect < eps + + parallel_assert( + lambda: match_local, + msg=f"Broadcast derivative {dJ} does not match" + f" expected value {expect}." + ) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) +@pytest.mark.skipcomplex +def test_ensemble_bcast_function(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_funcs = 6 + nlocal_funcs = nglobal_funcs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) + + R = FunctionSpace(mesh, "R", 0) + Re = EnsembleFunctionSpace( + [R for _ in range(nlocal_funcs)], ensemble) + + c = Function(R).assign(1.0) + J = EnsembleFunction(Re) + + Jhat = EnsembleBcastReducedFunctional(J, Control(c)) + + # check the control is broadcast to all ranks + eps = 1e-12 + + x = Function(R).assign(3.0) + Jx = Jhat(x) + + expect = x + match_local = all(errornorm(Ji, expect) < eps + for Ji in Jx.subfunctions) + + parallel_assert( + lambda: match_local, + msg=f"Broadcast Functions do not match on rank {rank}" + ) + + # Check the adjoint is reduced back to all ranks. + # Because the functional is an EnsembleFunction we + # need to pass an adj_input of an EnsembleCofunction. + + adj_input = EnsembleFunction(Re) + offset = rank*nlocal_funcs + for i, adji in enumerate(adj_input.subfunctions): + adji.assign(offset + i + 1.0) + adj_input = adj_input.riesz_representation() + + expect = Function(R).assign(sum(i+1.0 for i in range(nglobal_funcs))) + + dJ = Jhat.derivative(adj_input=adj_input) + + match_local = errornorm(dJ, expect) < eps + + parallel_assert( + lambda: match_local, + msg=f"Broadcast derivative {dJ.dat.data[:]} does not match" + f" expected value {expect.dat.data[:]}." + ) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 6]) +@pytest.mark.skipcomplex +def test_ensemble_reduction_float(): + ensemble = Ensemble(COMM_WORLD, 1) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_floats = 6 + nlocal_floats = nglobal_floats // size + + control = EnsembleAdjVec( + [AdjFloat(0.0) for _ in range(nlocal_floats)], + ensemble=ensemble) + J = AdjFloat(0.0) + + Jhat = EnsembleReduceReducedFunctional( + J, Control(control), ensemble=ensemble) + + # check the control is reduced to all ranks + eps = 1e-12 + + offset = rank*nlocal_floats + x = EnsembleAdjVec( + [AdjFloat(offset + i + 1.0) for i in range(nlocal_floats)], + ensemble=ensemble) + + Jx = Jhat(x) + + expect = AdjFloat(sum(i+1.0 for i in range(nglobal_floats))) + match_local = Jx - expect < eps + + parallel_assert( + lambda: match_local, + msg=f"Reduced AdjFloat {Jx} does not match" + f" expected value {expect}" + ) + + # Check the adjoint is broadcast back to all ranks. + # Because the functional is a Function we need to + # pass an adj_input of an Cofunction. + + adj_value = 20.0 + adj_input = AdjFloat(adj_value) + + expect = AdjFloat(adj_value) + dJ = Jhat.derivative(adj_input=adj_input) + + match_local = all((dJi - expect) < eps for dJi in dJ.subvec) + + parallel_assert( + lambda: match_local, + msg=f"Reduced derivatives {dJ} do not match expected value {expect}." + ) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) +@pytest.mark.skipcomplex +def test_ensemble_reduction_function(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_funcs = 6 + nlocal_funcs = nglobal_funcs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) + + R = FunctionSpace(mesh, "R", 0) + Re = EnsembleFunctionSpace( + [R for _ in range(nlocal_funcs)], ensemble) + + c = EnsembleFunction(Re) + J = Function(R) + + Jhat = EnsembleReduceReducedFunctional(J, Control(c)) + + # check the control is reduced to all ranks + eps = 1e-12 + + x = EnsembleFunction(Re) + + offset = rank*nlocal_funcs + for i, xi in enumerate(x.subfunctions): + xi.assign(offset + i + 1.0) + + Jx = Jhat(x) + + expect = Function(R).assign(sum(i+1.0 for i in range(nglobal_funcs))) + match_local = errornorm(Jx, expect) < eps + + parallel_assert( + lambda: match_local, + msg=f"Reduced Function {Jx.dat.data[:]} does not match" + f" expected value {expect.dat.data[:]}" + ) + + # Check the adjoint is broadcast back to all ranks. + # Because the functional is a Function we need to + # pass an adj_input of an Cofunction. + + adj_value = 20.0 + adj_input = (Function(R).assign(adj_value)).riesz_representation() + + expect = Function(R).assign(adj_value) + dJ = Jhat.derivative(adj_input=adj_input) + + match_local = all(errornorm(dJi, expect) < eps + for dJi in dJ.subfunctions) + + parallel_assert( + lambda: match_local, + msg=f"Reduced derivatives {[dJi.dat.data[:] for dJi in dJ.subfunctions]}" + f" do not match expected value {expect.dat.data[:]}." + ) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) +@pytest.mark.skipcomplex +def test_ensemble_transform_float(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_funcs = 6 + nlocal_funcs = nglobal_funcs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) + + R = FunctionSpace(mesh, "R", 0) + Re = EnsembleFunctionSpace( + [R for _ in range(nlocal_funcs)], ensemble) + + c = EnsembleFunction(Re) + + rfs = [] + J = [] + offset = rank*nlocal_funcs + for ci in c.subfunctions: + with set_working_tape() as tape: + Ji = assemble(ci*ci*dx) + J.append(Ji) + rfs.append(ReducedFunctional(Ji, Control(ci), tape=tape)) + + J = EnsembleAdjVec(J, ensemble) + + Jhat = EnsembleTransformReducedFunctional( + rfs, J, Control(c), ensemble=ensemble) + + # check the control is reduced to all ranks + eps = 1e-12 + + x = EnsembleFunction(Re) + + for i, xi in enumerate(x.subfunctions): + xi.assign(offset + i + 1.0) + + # check + Jx = Jhat(x) + + expect = [rf(xi) for rf, xi in zip(rfs, x.subfunctions)] + + match_local = all((Ji - ei) < eps for Ji, ei in zip(Jx.subvec, expect)) + + parallel_assert( + lambda: match_local, + msg=f"Transformed results {Jx} do not match expected values {expect}" + ) + + # Check the adjoint matches on all slots. + # Because the functional is a list[AdjFloat] we need to + # pass an adj_input of a list[AdjFloat]. + + adj_input = EnsembleAdjVec( + [AdjFloat(offset + i + 1.0) + for i in range(nlocal_funcs)], + ensemble=ensemble) + + expect = EnsembleFunction(Re) + for rf, adji, ei in zip(rfs, adj_input.subvec, expect.subfunctions): + ei.assign(rf.derivative(adj_input=adji)) + + dJ = Jhat.derivative(adj_input=adj_input) + + match_local = all( + errornorm(dJi, ei) < eps + for dJi, ei in zip(dJ.subfunctions, expect.subfunctions)) + + parallel_assert( + lambda: match_local, + msg=f"Reduced derivatives {[dJi.dat.data[:] for dJi in dJ.subfunctions]}" + f" do not match expected value {ei.dat.data[:] for ei in expect.subfunctions}." + ) + + _ = Jhat.tlm(x) + _ = Jhat.hessian(x, hessian_input=adj_input) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) +@pytest.mark.skipcomplex +def test_ensemble_transform_float_two_controls(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_funcs = 6 + nlocal_funcs = nglobal_funcs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) + + R = FunctionSpace(mesh, "R", 0) + Re = EnsembleFunctionSpace( + [R for _ in range(nlocal_funcs)], ensemble) + + c0 = EnsembleFunction(Re) + c1 = EnsembleFunction(Re) + + rfs = [] + J = [] + offset = rank*nlocal_funcs + for c0i, c1i in zip(c0.subfunctions, c1.subfunctions): + with set_working_tape() as tape: + Ji = assemble((c0i*c0i + c1i*c1i)*dx) + J.append(Ji) + rfs.append(ReducedFunctional( + Ji, [Control(c0i), Control(c1i)], tape=tape)) + + J = EnsembleAdjVec(J, ensemble) + + Jhat = EnsembleTransformReducedFunctional( + rfs, J, [Control(c0), Control(c1)], ensemble=ensemble) + + # check the control is reduced to all ranks + eps = 1e-12 + + x0 = EnsembleFunction(Re) + x1 = EnsembleFunction(Re) + + for i, (x0i, x1i) in enumerate(zip(x0.subfunctions, x1.subfunctions)): + x0i.assign(offset + i + 1.0) + x1i.assign(2*(offset + i + 1.0)) + + # check + Jx = Jhat([x0, x1]) + + expect = [rf([x0i, x1i]) + for rf, x0i, x1i in zip(rfs, x0.subfunctions, x1.subfunctions)] + + match_local = all((Ji - ei) < eps for Ji, ei in zip(Jx.subvec, expect)) + + parallel_assert( + lambda: match_local, + msg=f"Transformed results {Jx} do not match expected values {expect}" + ) + + # Check the adjoint matches on all slots. + # Because the functional is a AdjFloat we need to + # pass an adj_input of a list[AdjFloat]. + + adj_input = EnsembleAdjVec( + [AdjFloat(offset + i + 1.0) + for i in range(nlocal_funcs)], + ensemble=ensemble) + + expect0 = EnsembleFunction(Re) + expect1 = EnsembleFunction(Re) + for rf, adji, e0i, e1i in zip(rfs, adj_input.subvec, + expect0.subfunctions, + expect1.subfunctions): + e0, e1 = rf.derivative(adj_input=adji) + e0i.assign(e0) + e1i.assign(e1) + + dJ0, dJ1 = Jhat.derivative(adj_input=adj_input) + + match_local0 = all( + errornorm(dJi, ei) < eps + for dJi, ei in zip(dJ0.subfunctions, expect0.subfunctions)) + + match_local1 = all( + errornorm(dJi, ei) < eps + for dJi, ei in zip(dJ1.subfunctions, expect1.subfunctions)) + + parallel_assert( + lambda: match_local0, + msg=f"Reduced derivatives {[dJ0i.dat.data[:] for dJ0i in dJ0.subfunctions]}" + f" do not match expected value {e0i.dat.data[:] for e0i in expect0.subfunctions}." + ) + + parallel_assert( + lambda: match_local1, + msg=f"Reduced derivatives {[dJ1i.dat.data[:] for dJ1i in dJ1.subfunctions]}" + f" do not match expected value {e1i.dat.data[:] for e1i in expect1.subfunctions}." + ) + + _ = Jhat.tlm([x0, x1]) + _ = Jhat.hessian([x0, x1], hessian_input=adj_input) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) +@pytest.mark.skipcomplex +def test_ensemble_transform_function(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_funcs = 6 + nlocal_funcs = nglobal_funcs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) + + R = FunctionSpace(mesh, "R", 0) + Re = EnsembleFunctionSpace( + [R for _ in range(nlocal_funcs)], ensemble) + + c = EnsembleFunction(Re) + J = EnsembleFunction(Re) + + rfs = [] + offset = rank*nlocal_funcs + for i, (Ji, ci) in enumerate(zip(J.subfunctions, c.subfunctions)): + with set_working_tape() as tape: + Ji.assign(ci) + Ji += 2*(offset + i + 1.0) + rfs.append(ReducedFunctional(Ji, Control(ci), tape=tape)) + + Jhat = EnsembleTransformReducedFunctional(rfs, J, Control(c)) + + # check the control is reduced to all ranks + eps = 1e-12 + + x = EnsembleFunction(Re) + + for i, xi in enumerate(x.subfunctions): + xi.assign(offset + i + 1.0) + + # check + Jx = Jhat(x) + + expect = EnsembleFunction(Re) + for rf, xi, ei in zip(rfs, x.subfunctions, + expect.subfunctions): + ei.assign(rf(xi)) + + match_local = all( + errornorm(Ji, ei) < eps + for Ji, ei in zip(Jx.subfunctions, expect.subfunctions)) + + parallel_assert( + lambda: match_local, + msg=f"Transformed Functions {[Ji.dat.data[:] for Ji in Jx.subfunctions]}" + f" do not match expected value {[ei.dat.data[:] for ei in expect.subfunctions]}" + ) + + # Check the adjoint matches on all slots. + # Because the functional is a Function we need to + # pass an adj_input of an Cofunction. + + adj_input = EnsembleFunction(Re) + for i, adj in enumerate(adj_input.subfunctions): + adj.assign(offset + i + 1.0) + + adj_input = adj_input.riesz_representation() + + expect = EnsembleFunction(Re) + for rf, adji, ei in zip(rfs, adj_input.subfunctions, + expect.subfunctions): + ei.assign(rf.derivative(adj_input=adji)) + + dJ = Jhat.derivative(adj_input=adj_input) + + match_local = all( + errornorm(dJi, ei) < eps + for dJi, ei in zip(dJ.subfunctions, expect.subfunctions)) + + parallel_assert( + lambda: match_local, + msg=f"Reduced derivatives {[dJi.dat.data[:] for dJi in dJ.subfunctions]}" + f" do not match expected value {ei.dat.data[:] for ei in expect.subfunctions}." + ) + + _ = Jhat.tlm(x) + _ = Jhat.hessian(x, hessian_input=adj_input) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) +@pytest.mark.skipcomplex +def test_ensemble_transform_function_two_controls(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_funcs = 6 + nlocal_funcs = nglobal_funcs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) + + R = FunctionSpace(mesh, "R", 0) + Re = EnsembleFunctionSpace( + [R for _ in range(nlocal_funcs)], ensemble) + + c0 = EnsembleFunction(Re) + c1 = EnsembleFunction(Re) + J = EnsembleFunction(Re) + + rfs = [] + offset = rank*nlocal_funcs + for i, (Ji, c0i, c1i) in enumerate(zip(J.subfunctions, + c0.subfunctions, + c1.subfunctions)): + with set_working_tape() as tape: + Ji.assign(c0i + c1i) + rfs.append(ReducedFunctional( + Ji, [Control(c0i), Control(c1i)], tape=tape)) + + Jhat = EnsembleTransformReducedFunctional(rfs, J, [Control(c0), Control(c1)]) + + # check the control is reduced to all ranks + eps = 1e-12 + + x0 = EnsembleFunction(Re) + x1 = EnsembleFunction(Re) + + for i, (x0i, x1i) in enumerate(zip(x0.subfunctions, + x1.subfunctions)): + x0i.assign(offset + i + 1.0) + x1i.assign(2*(offset + i + 1.0)) + + Jx = Jhat([x0, x1]) + + expect = EnsembleFunction(Re) + for rf, x0i, x1i, ei in zip(rfs, x0.subfunctions, + x1.subfunctions, + expect.subfunctions): + ei.assign(rf([x0i, x1i])) + + match_local = all( + errornorm(Ji, ei) < eps + for Ji, ei in zip(Jx.subfunctions, + expect.subfunctions)) + + parallel_assert( + lambda: match_local, + msg=f"Transformed Functions {[Ji.dat.data[:] for Ji in Jx.subfunctions]}" + f" do not match expected value {[ei.dat.data[:] for ei in expect.subfunctions]}" + ) + + # Check the adjoint matches on all slots. + # Because the functional is a Function we need to + # pass an adj_input of an Cofunction. + + adj_input = EnsembleFunction(Re) + for i, adj in enumerate(adj_input.subfunctions): + adj.assign(offset + i + 1.0) + + adj_input = adj_input.riesz_representation() + + expect0 = EnsembleFunction(Re) + expect1 = EnsembleFunction(Re) + for rf, adji, e0i, e1i in zip(rfs, adj_input.subfunctions, + expect0.subfunctions, + expect1.subfunctions): + e0, e1 = rf.derivative(adj_input=adji) + e0i.assign(e0) + e1i.assign(e1) + + dJ0, dJ1 = Jhat.derivative(adj_input=adj_input) + + match_local0 = all( + errornorm(dJi, ei) < eps + for dJi, ei in zip(dJ0.subfunctions, expect0.subfunctions)) + + match_local1 = all( + errornorm(dJi, ei) < eps + for dJi, ei in zip(dJ1.subfunctions, expect1.subfunctions)) + + parallel_assert( + lambda: match_local0, + msg=f"Reduced derivatives {[dJ0i.dat.data[:] for dJ0i in dJ0.subfunctions]}" + f" do not match expected value {e0i.dat.data[:] for e0i in expect0.subfunctions}." + ) + + parallel_assert( + lambda: match_local1, + msg=f"Reduced derivatives {[dJ1i.dat.data[:] for dJ1i in dJ1.subfunctions]}" + f" do not match expected value {e1i.dat.data[:] for e1i in expect1.subfunctions}." + ) + + _ = Jhat.tlm([x0, x1]) + _ = Jhat.hessian([x0, x1], hessian_input=adj_input) + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done -def test_verification(): - ensemble = Ensemble(COMM_WORLD, 2) - size = ensemble.ensemble_comm.size - mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) +def test_ensemble_rf_function_to_float(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_rfs = 6 + nlocal_rfs = nglobal_rfs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) R = FunctionSpace(mesh, "R", 0) - x = function.Function(R, val=1.0) - J = assemble(x * x * dx(domain=mesh)) - rf = EnsembleReducedFunctional(J, Control(x), ensemble) - ensemble_J = rf(x) - dJdm = rf.derivative() - assert_allclose(ensemble_J, size, rtol=1e-12) - assert_allclose(dJdm.dat.data_ro, 2.0 * size, rtol=1e-12) - assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 + + control = Control(Function(R)) + J = AdjFloat(0.) + + rfs = [] + offset = rank*nlocal_rfs + for i in range(nlocal_rfs): + c = Function(R) + weight = (offset + i + 1.0) + with set_working_tape() as tape: + Ji = weight*assemble((c**4)*dx) + rfs.append( + ReducedFunctional(Ji, Control(c), tape=tape)) + + Jhat = EnsembleReducedFunctional( + rfs, J, control, ensemble=ensemble) + + sum_weights = sum((i + 1.0) for i in range(nglobal_rfs)) + + xval = 3.0 + Jexpect = (xval**4)*sum_weights + + x = Function(R).assign(xval) + J = Jhat(x) + assert_allclose(J, Jexpect, rtol=1e-12) + + adj_input = AdjFloat(4.0) + edJ = Jhat.derivative(adj_input=adj_input) + + assert_allclose(edJ.dat.data_ro, adj_input*(4*xval**3)*sum_weights, rtol=1e-12) + + dy = Function(R, val=0.1) + assert taylor_test(Jhat, x, dy) > 1.95 + + _ = Jhat.tlm(x) + _ = Jhat.hessian(x) + + taylor = taylor_to_dict(Jhat, x, dy) + + R0 = mean(taylor['R0']['Rate']) + R1 = mean(taylor['R1']['Rate']) + R2 = mean(taylor['R2']['Rate']) + + assert R0 > 0.95 + assert R1 > 1.95 + assert R2 > 2.95 + + +@pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) +@pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done +def test_ensemble_rf_efunction_to_float(): + nspatial_ranks = 2 if COMM_WORLD.size == 4 else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + rank = ensemble.ensemble_rank + size = ensemble.ensemble_size + + nglobal_funcs = 6 + nlocal_funcs = nglobal_funcs // size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) + R = FunctionSpace(mesh, "R", 0) + Re = EnsembleFunctionSpace( + [R for _ in range(nlocal_funcs)], ensemble) + + control = Control(EnsembleFunction(Re)) + J = AdjFloat(0.) + + rfs = [] + for i in range(nlocal_funcs): + ci = Function(R) + with set_working_tape() as tape: + Ji = assemble((ci**4)*dx) + rfs.append( + ReducedFunctional(Ji, Control(ci), tape=tape)) + + Jhat = EnsembleReducedFunctional( + rfs, J, control, bcast_control=False, ensemble=ensemble) + + x = EnsembleFunction(Re) + + offset = rank*nlocal_funcs + for i, xi in enumerate(x.subfunctions): + xi.assign(offset + i + 1.0) + + eps = 1e-12 + + J = Jhat(x) + + expect = sum(w**4 for w in range(1, nglobal_funcs+1)) + + parallel_assert( + lambda: (J - expect) < eps, + msg=f"Functional {J} does not match expected value {expect}." + ) + + adj_input = 3.0 + dJ = Jhat.derivative(adj_input=adj_input) + + expect = EnsembleFunction(Re) + for i, ei in enumerate(expect.subfunctions): + ei.assign(adj_input*4*(offset + i + 1.0)**3) + + match_local = all( + errornorm(dJi, ei) < eps + for dJi, ei in zip(dJ.subfunctions, expect.subfunctions)) + + parallel_assert( + lambda: match_local, + msg=f"Derivatives {[dJi.dat.data[:] for dJi in dJ.subfunctions]}" + f" do not match expected values {ei.dat.data[:] for ei in expect.subfunctions}." + ) + + dy = EnsembleFunction(Re) + + for i, dyi in enumerate(dy.subfunctions): + dyi.assign(0.1*(-0.5*offset - (i + 1.0))) + + assert taylor_test(Jhat, x, dy) > 1.95 + + _ = Jhat.tlm(x) + _ = Jhat.hessian(x) + + taylor = taylor_to_dict(Jhat, x, dy) + + R0 = mean(taylor['R0']['Rate']) + R1 = mean(taylor['R1']['Rate']) + R2 = mean(taylor['R2']['Rate']) + + assert R0 > 0.95 + assert R1 > 1.95 + assert R2 > 2.95 @pytest.mark.parallel(nprocs=4) @@ -52,16 +802,20 @@ def test_verification_gather_functional_adjfloat(): J = assemble(x * x * dx(domain=mesh)) a = AdjFloat(1.0) b = AdjFloat(1.0) + Jg_m = [Control(a), Control(b)] Jg = ReducedFunctional(a**2 + b**2, Jg_m) + rf = EnsembleReducedFunctional(J, Control(x), ensemble, scatter_control=False, gather_functional=Jg) + ensemble_J = rf(x) dJdm = rf.derivative() assert_allclose(ensemble_J, 1.0**4+2.0**4, rtol=1e-12) assert_allclose(dJdm.dat.data_ro, 4*(rank+1)**3, rtol=1e-12) - assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 + + assert taylor_test(rf, x, Function(R, val=0.1)) > 1.95 @pytest.mark.parallel(nprocs=4) @@ -88,26 +842,44 @@ def test_verification_gather_functional_Function(): dJdm = rf.derivative() assert_allclose(ensemble_J, 1.0**4+2.0**4, rtol=1e-12) assert_allclose(dJdm.dat.data_ro, 4*(rank+1)**3, rtol=1e-12) - assert taylor_test(rf, x, Function(R, val=0.1)) > 1.9 + assert taylor_test(rf, x, Function(R, val=0.1)) > 1.95 -@pytest.mark.parallel(nprocs=6) +@pytest.mark.parallel(nprocs=[1, 2, 3, 6]) @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done -def test_minimise(): +def test_ensemble_rf_minimise(): # Optimisation test using a list of controls. # This test is equivalent to the one found at: # https://github.com/dolfin-adjoint/pyadjoint/blob/master/tests/firedrake_adjoint/test_optimisation.py#L9. # In this test, the functional is the result of an ensemble allreduce operation. - ensemble = Ensemble(COMM_WORLD, 2) - mesh = UnitSquareMesh(4, 4, comm=ensemble.comm) + nspatial_ranks = 2 if COMM_WORLD.size in (2, 6) else 1 + ensemble = Ensemble(COMM_WORLD, nspatial_ranks) + + size = ensemble.ensemble_size + + mesh = UnitIntervalMesh(12, comm=ensemble.comm) R = FunctionSpace(mesh, "R", 0) + + nglobal_rfs = 3 + nlocal_rfs = nglobal_rfs//size n = 2 - x = [Function(R) for i in range(n)] - c = [Control(xi) for xi in x] - # Rosenbrock function https://en.wikipedia.org/wiki/Rosenbrock_function - # with minimum at x = (1, 1, 1, ...) - f = 100*(x[1] - x[0]**2)**2 + (1 - x[0])**2 - J = assemble(f * dx(domain=mesh)) - rf = EnsembleReducedFunctional(J, c, ensemble) - result = minimize(rf) + + rfs = [] + for _ in range(nlocal_rfs): + x = [Function(R) for _ in range(n)] + controls = [Control(xi) for xi in x] + with set_working_tape() as tape: + # Rosenbrock function https://en.wikipedia.org/wiki/Rosenbrock_function + # with minimum at x = (1, 1, 1, ...) + f = 100*(x[1] - x[0]**2)**2 + (1 - x[0])**2 + J = assemble(f * dx(domain=mesh)) + rfs.append( + ReducedFunctional(J, controls, tape=tape)) + + J = AdjFloat(0.) + controls = [Control(Function(R)) for _ in range(n)] + + Jhat = EnsembleReducedFunctional(rfs, J, controls, ensemble=ensemble) + + result = minimize(Jhat) assert_allclose([float(xi) for xi in result], 1., rtol=1e-8) diff --git a/tests/firedrake/ensemble/test_ensemble.py b/tests/firedrake/ensemble/test_ensemble.py index f7c3a9a893..528cbf30f4 100644 --- a/tests/firedrake/ensemble/test_ensemble.py +++ b/tests/firedrake/ensemble/test_ensemble.py @@ -104,7 +104,7 @@ def test_ensemble_allreduce(ensemble, mesh, W, urank, urank_sum, blocking): requests = ensemble.iallreduce(urank, u_reduce) MPI.Request.Waitall(requests) - parallel_assert(errornorm(urank_sum, u_reduce) < 1e-12) + parallel_assert(lambda: errornorm(urank_sum, u_reduce) < 1e-12) @pytest.mark.parallel(nprocs=2) @@ -156,7 +156,7 @@ def test_comm_manager_allreduce(blocking): f5 = Function(V5) with f4.dat.vec_ro as v4, f5.dat.vec_ro as v5: - parallel_assert(v4.getSizes() == v5.getSizes()) + parallel_assert(lambda: v4.getSizes() == v5.getSizes()) with pytest.raises(ValueError): allreduce(f4, f5) @@ -187,13 +187,13 @@ def test_ensemble_reduce(ensemble, mesh, W, urank, urank_sum, root, blocking): error = errornorm(urank_sum, u_reduce) rank = ensemble.ensemble_rank parallel_assert( - error < 1e-12, + lambda: error < 1e-12, participating=(rank == root), msg=f"{error=:.5f}" ) error = errornorm(Function(W).assign(10), u_reduce) parallel_assert( - error < 1e-12, + lambda: error < 1e-12, participating=(rank != root), msg=f"{error=:.5f}" ) @@ -205,7 +205,7 @@ def test_ensemble_reduce(ensemble, mesh, W, urank, urank_sum, root, blocking): with u_reduce.dat.vec as v: states[spatial_rank] = v.stateGet() ensemble.comm.Allgather(MPI.IN_PLACE, states) - parallel_assert(len(set(states)) == 1) + parallel_assert(lambda: len(set(states)) == 1) @pytest.mark.parallel(nprocs=2) @@ -256,7 +256,7 @@ def test_comm_manager_reduce(blocking): f5 = Function(V5) with f4.dat.vec_ro as v4, f5.dat.vec_ro as v5: - parallel_assert(v4.getSizes() == v5.getSizes()) + parallel_assert(lambda: v4.getSizes() == v5.getSizes()) with pytest.raises(ValueError): reduction(f4, f5) @@ -284,7 +284,7 @@ def test_ensemble_bcast(ensemble, mesh, W, urank, root, blocking): # broadcasted function u_correct = unique_function(mesh, root, W) - parallel_assert(errornorm(u_correct, urank) < 1e-12) + parallel_assert(lambda: errornorm(u_correct, urank) < 1e-12) @pytest.mark.parallel(nprocs=6) @@ -323,7 +323,7 @@ def test_send_and_recv(ensemble, mesh, W, blocking): # Test send/recv between first two spatial comms # ie: ensemble.ensemble_comm.rank == 0 and 1 parallel_assert( - error < 1e-12, + lambda: error < 1e-12, participating=ensemble.ensemble_rank in (rank0, rank1), msg=f"{error=:.5f}" # noqa: E203, E251 ) @@ -351,7 +351,7 @@ def test_sendrecv(ensemble, mesh, W, urank, blocking): if not blocking: MPI.Request.Waitall(requests) - parallel_assert(errornorm(urecv, u_expect) < 1e-12) + parallel_assert(lambda: errornorm(urecv, u_expect) < 1e-12) @pytest.mark.parallel(nprocs=6) @@ -382,4 +382,4 @@ def test_ensemble_solvers(ensemble, W, urank, urank_sum): usum = Function(W) ensemble.allreduce(u_separate, usum) - parallel_assert(errornorm(u_combined, usum) < 1e-8) + parallel_assert(lambda: errornorm(u_combined, usum) < 1e-8) diff --git a/tests/firedrake/ensemble/test_ensemble_function.py b/tests/firedrake/ensemble/test_ensemble_function.py index a7a3f40a93..8051595d2a 100644 --- a/tests/firedrake/ensemble/test_ensemble_function.py +++ b/tests/firedrake/ensemble/test_ensemble_function.py @@ -2,7 +2,6 @@ from pyop2 import Subset import pytest import numpy as np -from pytest_mpi.parallel_assert import parallel_assert def random_func(f): diff --git a/tests/firedrake/ensemble/test_ensemble_functionspace.py b/tests/firedrake/ensemble/test_ensemble_functionspace.py index e496dfa3d9..cc71e6bc0d 100644 --- a/tests/firedrake/ensemble/test_ensemble_functionspace.py +++ b/tests/firedrake/ensemble/test_ensemble_functionspace.py @@ -179,7 +179,7 @@ def test_ensemble_space_allows_dual_init(ensemble, mesh): efs = fd.EnsembleFunctionSpace(spaces, ensemble) - parallel_assert(isinstance(efs, fd.EnsembleDualSpace)) + parallel_assert(lambda: isinstance(efs, fd.EnsembleDualSpace)) @pytest.mark.parallel(nprocs=[1, 2, 4, 6]) @@ -196,12 +196,12 @@ def test_ensemble_local_spaces_correct(ensemblespace, Wlocal): Wlocal = [W.dual() for W in Wlocal] # Does efs have the correct number of spaces locally? - parallel_assert(len(efs.local_spaces) == len(Wlocal)) - parallel_assert(efs.nlocal_spaces == len(efs.local_spaces)) + parallel_assert(lambda: len(efs.local_spaces) == len(Wlocal)) + parallel_assert(lambda: efs.nlocal_spaces == len(efs.local_spaces)) # Correct number globally? nglobal = ensemble.ensemble_comm.allreduce(efs.nlocal_spaces) - parallel_assert(efs.nglobal_spaces == nglobal) + parallel_assert(lambda: efs.nglobal_spaces == nglobal) local_space_matches = [ V == W for V, W in zip(efs.local_spaces, Wlocal)] @@ -210,7 +210,7 @@ def test_ensemble_local_spaces_correct(ensemblespace, Wlocal): # rank-by-rank not space-by-space for root in range(ensemble.ensemble_size): parallel_assert( - all(local_space_matches), + lambda: all(local_space_matches), participating=(ensemble.ensemble_rank == root), msg=f"{ensemble.ensemble_rank=}, {local_space_matches=}") @@ -231,15 +231,15 @@ def test_ensemble_dofsizes_correct(ensemblespace): nglobal_dofs = ensemble.ensemble_comm.allreduce(nlocal_comm_dofs) parallel_assert( - efs.nlocal_rank_dofs == nlocal_rank_dofs, + lambda: efs.nlocal_rank_dofs == nlocal_rank_dofs, msg=f"{rank=}, {efs.nlocal_rank_dofs=}, {nlocal_rank_dofs=}") parallel_assert( - efs.nlocal_comm_dofs == nlocal_comm_dofs, + lambda: efs.nlocal_comm_dofs == nlocal_comm_dofs, msg=f"{rank=}, {efs.nlocal_comm_dofs=}, {nlocal_comm_dofs=}") parallel_assert( - efs.nglobal_dofs == nglobal_dofs, + lambda: efs.nglobal_dofs == nglobal_dofs, msg=f"{rank=}, {efs.nglobal_dofs=}, {nglobal_dofs=}") @@ -258,7 +258,7 @@ def test_ensemble_space_dual(ensemblespace): dual_type = fd.EnsembleFunctionSpace dual = ensemblespace.dual() - parallel_assert(type(dual) is dual_type) + parallel_assert(lambda: type(dual) is dual_type) # are the dual subspaces correct? local_space_matches = [ @@ -269,12 +269,12 @@ def test_ensemble_space_dual(ensemblespace): ensemble_rank = ensemble.ensemble_rank for root in range(ensemble.ensemble_size): parallel_assert( - all(local_space_matches), + lambda: all(local_space_matches), participating=(ensemble.ensemble_rank == root), msg=f"{ensemble_rank=}, {local_space_matches=}") dual2 = dual.dual() - parallel_assert(type(dual.dual()) is type(ensemblespace)) + parallel_assert(lambda: type(dual.dual()) is type(ensemblespace)) # are the dual subspaces the originals? local_space_matches = [ @@ -285,7 +285,7 @@ def test_ensemble_space_dual(ensemblespace): ensemble_rank = ensemblespace.ensemble.ensemble_rank for root in range(ensemble.ensemble_size): parallel_assert( - all(local_space_matches), + lambda: all(local_space_matches), participating=(ensemble.ensemble_rank == root), msg=f"{ensemble_rank=}, {local_space_matches=}") @@ -300,13 +300,13 @@ def test_ensemble_space_equality(ensemblespace): orig_spaces = ensemblespace.local_spaces # .dual().dual() should be a round trip - parallel_assert(ensemblespace == ensemblespace) - parallel_assert(ensemblespace != ensemblespace.dual()) - parallel_assert(ensemblespace == ensemblespace.dual().dual()) + parallel_assert(lambda: ensemblespace == ensemblespace) + parallel_assert(lambda: ensemblespace != ensemblespace.dual()) + parallel_assert(lambda: ensemblespace == ensemblespace.dual().dual()) # Duplicate is equal dup_fs = fd.EnsembleFunctionSpace(orig_spaces, ensemble) - parallel_assert(ensemblespace == dup_fs) + parallel_assert(lambda: ensemblespace == dup_fs) # Same length, different space is not equal if ensemble.ensemble_rank == (ensemble.ensemble_size - 1): @@ -315,7 +315,7 @@ def test_ensemble_space_equality(ensemblespace): diff_spaces = orig_spaces diff_fs = fd.EnsembleFunctionSpace(diff_spaces, ensemble) - parallel_assert(ensemble != diff_fs) + parallel_assert(lambda: ensemble != diff_fs) # Same spaces, shorter length is not equal if ensemble.ensemble_rank == 0: @@ -324,7 +324,7 @@ def test_ensemble_space_equality(ensemblespace): short_spaces = orig_spaces short_fs = fd.EnsembleFunctionSpace(short_spaces, ensemble) - parallel_assert(ensemble != short_fs) + parallel_assert(lambda: ensemble != short_fs) # Same spaces, longer length is not equal if ensemble.ensemble_rank == (ensemble.ensemble_size - 1): @@ -333,4 +333,4 @@ def test_ensemble_space_equality(ensemblespace): long_spaces = orig_spaces long_fs = fd.EnsembleFunctionSpace(long_spaces, ensemble) - parallel_assert(ensemble != long_fs) + parallel_assert(lambda: ensemble != long_fs) From 6ffee3262fd65fdfbd504dbd3c2d6225ae8f833f Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Wed, 26 Mar 2025 14:25:27 +0000 Subject: [PATCH 065/222] compute_gradient -> compute_derivative --- firedrake/adjoint/__init__.py | 2 +- tests/firedrake/regression/test_adjoint_operators.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/firedrake/adjoint/__init__.py b/firedrake/adjoint/__init__.py index 0c1681f0fd..bcfa0b6758 100644 --- a/firedrake/adjoint/__init__.py +++ b/firedrake/adjoint/__init__.py @@ -29,7 +29,7 @@ from firedrake.adjoint_utils import get_solve_blocks # noqa F401 from pyadjoint.verification import taylor_test, taylor_to_dict # noqa F401 -from pyadjoint.drivers import compute_gradient, compute_hessian # noqa F401 +from pyadjoint.drivers import compute_gradient, compute_derivative, compute_hessian # noqa F401 from pyadjoint.adjfloat import AdjFloat # noqa F401 from pyadjoint.control import Control # noqa F401 from pyadjoint import IPOPTSolver, ROLSolver, MinimizationProblem, \ diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index 6d5b915b72..0276852ce4 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -864,10 +864,10 @@ def test_assign_zero_cofunction(): J = assemble(((sol + Constant(1.0)) ** 2) * dx) # The zero assignment should break the tape and hence cause a zero # gradient. - grad_l2 = compute_gradient(J, Control(k, riesz_map="l2"), apply_riesz=True) - grad_none = compute_gradient(J, Control(k)) - grad_h1 = compute_gradient(J, Control(k, riesz_map="H1"), apply_riesz=True) - grad_L2 = compute_gradient(J, Control(k, riesz_map="L2"), apply_riesz=True) + grad_l2 = compute_derivative(J, Control(k, riesz_map="l2"), apply_riesz=True) + grad_none = compute_derivative(J, Control(k), apply_riesz=False) + grad_h1 = compute_derivative(J, Control(k, riesz_map="H1"), apply_riesz=True) + grad_L2 = compute_derivative(J, Control(k, riesz_map="L2"), apply_riesz=True) assert isinstance(grad_l2, Function) and isinstance(grad_L2, Function) \ and isinstance(grad_h1, Function) assert isinstance(grad_none, Cofunction) From 112892e9a7a9577ef41b86cc96f58bbb1b75f056 Mon Sep 17 00:00:00 2001 From: David Ham Date: Wed, 26 Mar 2025 17:24:32 +0000 Subject: [PATCH 066/222] random echo issue --- docs/Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/Makefile b/docs/Makefile index 07d3230375..2e922b109a 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -86,7 +86,7 @@ source/team.rst: source/team2.py source/firedrake_citations.rst: sphinx-apidoc -f -T -o source/ ../firedrake_citations - echo -e ":orphan:\n\n" | cat - source/firedrake_citations.rst > source/firedrake_citations.tmp + echo ":orphan:\n\n" | cat - source/firedrake_citations.rst > source/firedrake_citations.tmp mv source/firedrake_citations.tmp source/firedrake_citations.rst source/apt_deps.txt: From 59940d84022d0822e4c5b4be6511ff640000d16a Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Wed, 26 Mar 2025 17:26:09 +0000 Subject: [PATCH 067/222] Update tests/firedrake/output/test_adjoint_disk_checkpointing.py Co-authored-by: Daiane Iglesia Dolci <63597005+Ig-dolci@users.noreply.github.com> --- tests/firedrake/output/test_adjoint_disk_checkpointing.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/firedrake/output/test_adjoint_disk_checkpointing.py b/tests/firedrake/output/test_adjoint_disk_checkpointing.py index 91d353802b..37747e36e1 100644 --- a/tests/firedrake/output/test_adjoint_disk_checkpointing.py +++ b/tests/firedrake/output/test_adjoint_disk_checkpointing.py @@ -60,7 +60,6 @@ def adjoint_example(mesh): @pytest.mark.skipcomplex -# A serial version of this test is included in the pyadjoint tests. @pytest.mark.parallel(nprocs=3) def test_disk_checkpointing(): from firedrake.adjoint import enable_disk_checkpointing, \ From 4a3c02ee4c5a421f3bae770c6e698faa736f15d8 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Wed, 26 Mar 2025 17:26:46 +0000 Subject: [PATCH 068/222] Update firedrake/adjoint/__init__.py Co-authored-by: Josh Hope-Collins --- firedrake/adjoint/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/firedrake/adjoint/__init__.py b/firedrake/adjoint/__init__.py index bcfa0b6758..d3d28e6129 100644 --- a/firedrake/adjoint/__init__.py +++ b/firedrake/adjoint/__init__.py @@ -37,7 +37,7 @@ from firedrake.adjoint.ufl_constraints import UFLInequalityConstraint, \ UFLEqualityConstraint # noqa F401 -from .ensemble_reduced_functional import EnsembleReducedFunctional # noqa F401 +from firedrake.adjoint.ensemble_reduced_functional import EnsembleReducedFunctional # noqa F401 import numpy_adjoint # noqa F401 import firedrake.ufl_expr import types From 3959ba2041f33d5d3e342a4eb017e6e9d85c5111 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Thu, 27 Mar 2025 10:01:11 +0000 Subject: [PATCH 069/222] wip --- .../adjoint/test_4dvar_reduced_functional.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py index 04990284e5..8bde5681dd 100644 --- a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py +++ b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py @@ -75,9 +75,13 @@ def n2(x): return n2 -B = (fd.Constant(10.), 4) # background error covariance -R = (fd.Constant(0.1), 4) # observation error covariance -Q = (fd.Constant(0.5), 4) # model error covariance +# B = (fd.Constant(10.), 4) # background error covariance +# R = (fd.Constant(0.1), 4) # observation error covariance +# Q = (fd.Constant(0.5), 4) # model error covariance + +B = (fd.Constant(1e10), 4) # background error covariance +R = (fd.Constant(1e10), 4) # observation error covariance +Q = (fd.Constant(1e0), 4) # model error covariance """Advecting velocity""" @@ -426,7 +430,9 @@ def main_test_strong_4dvar_advection(): def main_test_weak_4dvar_advection(): global_comm = fd.COMM_WORLD - if global_comm.size in (1, 2): # time serial + if global_comm.size == 1: # time serial + nspace = global_comm.size + if global_comm.size == 2: # space parallel nspace = global_comm.size elif global_comm.size == 3: # time parallel nspace = 1 From 70064df4a78f6d88a065e349efe1b79d17f19213 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Thu, 27 Mar 2025 10:01:34 +0000 Subject: [PATCH 070/222] wip --- .../adjoint/ensemble_reduced_functional.py | 14 +- .../adjoint/fourdvar_reduced_functional.py | 377 +++-- .../fourdvar_reduced_functional_bak.py | 1258 +++++++++++++++++ 3 files changed, 1506 insertions(+), 143 deletions(-) create mode 100644 firedrake/adjoint/fourdvar_reduced_functional_bak.py diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py index c4b40db549..e4a96b5500 100644 --- a/firedrake/adjoint/ensemble_reduced_functional.py +++ b/firedrake/adjoint/ensemble_reduced_functional.py @@ -1,6 +1,8 @@ from functools import partial, cached_property from contextlib import contextmanager -from pyadjoint import ReducedFunctional, Control, set_working_tape, AdjFloat, no_annotations +from pyadjoint import ( + ReducedFunctional, Control, set_working_tape, AdjFloat, + no_annotations, annotate_tape, pause_annotation, continue_annotation) from pyadjoint.enlisting import Enlist from firedrake.function import Function from firedrake.cofunction import Cofunction @@ -449,7 +451,7 @@ def __init__(self, functional, control, ensemble=None): raise ValueError( f"Ensemble provided to {type(self).__name__} must match" " the ensemble of the control.") - self.ensemble = ensemble + self.ensemble = control.control.ensemble self.nlocal_inputs = len(control.subvec) elif isinstance(functional, Function): @@ -474,13 +476,13 @@ def __init__(self, functional, control, ensemble=None): f"Do not know how to handle a {type(functional).__name__}" f" control for {type(self).__name__}.") - _ = self._sum_rf - @no_annotations def __call__(self, values): if self.reduction_rf: return self.reduction_rf(self._allgather(_local_subs(values))) else: + local_sum = self._sum_rf(_local_subs(values)) + global_sum = self._reduce(local_sum) return self._reduce( self._sum_rf(_local_subs(values))) @@ -518,6 +520,8 @@ def _sum_rf(self): controls = [c._ad_convert_type(0.) for c in _local_subs(self.controls[0].control)] J = self.functional._ad_convert_type(0.) + annotating = annotate_tape() + continue_annotation() with set_working_tape() as tape: if isinstance(J, float): J = sum(controls) @@ -526,6 +530,8 @@ def _sum_rf(self): J = J._ad_add(c) rf = ReducedFunctional( J, [Control(c) for c in controls], tape=tape) + if not annotating: + pause_annotation() return rf @cached_property diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index 66b76b9353..e56d8ae149 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -4,8 +4,12 @@ from firedrake.function import Function from firedrake.ensemble import EnsembleFunction, EnsembleFunctionSpace from firedrake import assemble, inner, dx, Constant -from firedrake.adjoint.composite_reduced_functional import ( +from .composite_reduced_functional import ( CompositeReducedFunctional, intermediate_options) +from .ensemble_reduced_functional import ( + EnsembleReduceReducedFunctional, EnsembleBcastReducedFunctional, + EnsembleTransformReducedFunctional, EnsembleReducedFunctional) +from .ensemble_adjvec import EnsembleAdjVec from ufl.duals import is_primal, is_dual from functools import wraps, cached_property, partial from typing import Callable, Optional, Collection, Union @@ -175,6 +179,11 @@ def __init__(self, control: Control, # first control on rank 0 is initial conditions, not end of observation stage self.nlocal_stages = len(_x) - (1 if self.trank == 0 else 0) + if self.trank == 0 and self.initial_observations: + self.nlocal_observations = self.nlocal_stages + 1 + else: + self.nlocal_observations = self.nlocal_stages + self.stages = [] # The record of each observation stage self.observation_rfs = [] @@ -326,49 +335,34 @@ def __call__(self, values: OverloadedType): # first "control" for later ranks is the halo if self.ensemble and trank != 0: x = [self.xprev, *self._x] + of = 1 else: x = [*self._x] + of = 0 - # post messages for control of time propagator on next chunk - if self.ensemble: - src = trank - 1 - dst = trank + 1 - - if trank != self.nchunks - 1: - self.ensemble.isend( - x[-1], dest=dst, tag=dst) + # halos for propagation + xim1 = self.JM.functional.copy() + xi = self.JM.functional.copy() - if trank != 0: - recv_reqs = self.ensemble.irecv( - self.xprev, source=src, tag=trank) + xi.zero() + xim1.zero() - # Initial condition functionals - if trank == 0: - Jlocal = self.background_rf(x[0]) + xprev = None if trank == 0 else self.xprev - # observations at time 0 - if self.initial_observations: - Jlocal += self.initial_observation_rf(x[0]) - else: - Jlocal = 0. + # post messages for control of time propagator on next chunk + self._forward_halos(value, xi, xim1, xprev) - # evaluate all stages on chunk except first - for i in range(1, len(self.stages)): - Jlocal += self.stages[i](x[i:i+2]) + # Model functionals + Jm = self.Jmodel([self.JM(xim1), xi]) - # wait for halo swap to finish - if trank != 0: - MPI.Request.Waitall(recv_reqs) + # Observation functional + Jo = self.Jobservations(value) - # evaluate first stage model on chunk now we have data - Jlocal += self.stages[0](x[0:2]) - - # sum all stages - if self.ensemble: - J = self.ensemble.ensemble_comm.allreduce(Jlocal) - else: - J = Jlocal + # Background functional + Jb = self.ensemble.ensemble_comm.bcast( + self.background_rf(value.subfunctions[0]) if trank == 0 else None, root=0) + J = Jb + Jo + Jm return J @sc_passthrough @@ -398,75 +392,38 @@ def derivative(self, adj_input: float = 1.0, options: dict = {}): # chaining ReducedFunctionals means we need to pass Cofunctions not Functions options = options or {} + ioptions = intermediate_options(options) - # evaluate first time propagator, which contributes to previous chunk - sderiv0 = self.stages[0].derivative( + # observation derivatives + dJ_do = self.Jobservations.derivative( adj_input=adj_input, options=options) - # create the derivative in the right primal or dual space - if is_primal(sderiv0[0]): - derivative_space = self.control_space - else: - if not is_dual(sderiv0[0]): - raise ValueError( - "Do not know how to handle stage derivative which is not primal or dual") - derivative_space = self.control_space.dual() - derivatives = EnsembleFunction(derivative_space) - - derivatives.zero() + derivatives = dJ_do + dJ = dJ_do.subfunctions - if self.ensemble: - with stop_annotating(): - xprev = derivatives.subfunctions[0]._ad_copy() - xnext = derivatives.subfunctions[0]._ad_copy() - xprev.zero() - xnext.zero() - if trank != 0: - derivs = [xprev, *derivatives.subfunctions] - else: - derivs = [*derivatives.subfunctions] - - # start accumulating the complete derivative - derivs[0] += sderiv0[0] - derivs[1] += sderiv0[1] - - # post the derivative halo exchange - if self.ensemble: - # halos sent backward in time - src = trank + 1 - dst = trank - 1 - - if trank != 0: - self.ensemble.isend( - derivs[0], dest=dst, tag=dst) - - if trank != self.nchunks - 1: - recv_reqs = self.ensemble.irecv( - xnext, source=src, tag=trank) - - # initial condition derivatives + # background derivatives if trank == 0: - derivs[0] += self.background_rf.derivative( + dJ_x0 = dJ[0] + dJ_x0 += self.background_rf.derivative( adj_input=adj_input, options=options) - # observations at time 0 - if self.initial_observations: - derivs[0] += self.initial_observation_rf.derivative( - adj_input=adj_input, options=options) + # evaluate model derivatives + dj_mx, dj_x = self.Jmodel.derivative( + adj_input=adj_input, options=ioptions) - # # evaluate all time stages on chunk except first while halo in flight - for i in range(1, len(self.stages)): - sderiv = self.stages[i].derivative( - adj_input=adj_input, options=options) + # derivative from propagation + dj_mx = self.JM.derivative( + adj_input=dj_mx, options=options) - derivs[i] += sderiv[0] - derivs[i+1] += sderiv[1] + dj_x = dj_mx._ad_convert_type(dj_x) - # finish the derivative halo exchange - if self.ensemble: - if trank != self.nchunks - 1: - MPI.Request.Waitall(recv_reqs) - derivs[-1] += xnext + dJ_x = derivatives._ad_copy().zero() + dJ_mx = derivatives._ad_copy().zero() + + self._backward_halos(dj_x, dj_mx, dJ_x, dJ_mx) + + derivatives += dJ_x + derivatives += dJ_mx return derivatives @@ -504,29 +461,32 @@ def hessian(self, m_dot: OverloadedType, options: dict = {}): """ trank = self.trank - hess = self.control.copy_data() + if isinstance(m_dot, list): + m_dot = m_dot[0] + + hessians = self.Jobservations.hessian( + m_dot=m_dot, options=options) - if options.get('riesz_representation', '') in (None, 'l2'): - # This should be hess = self.control._ad_dual_type() - # to avoid unnecessary solve in riesz_representation - hess = hess.riesz_representation() - hess.zero() + hess = hessians.subfunctions - if not isinstance(m_dot, list): - m_dot = [m_dot] + # hessian actions at the initial condition + if trank == 0: + hess_x0 = hess[0] + hess_x0 += self.background_rf.hessian( + m_dot.subfunctions[0], options=options) # set up arrays including halos if trank == 0: - hs = [*hess.subfunctions] - mdot = [*m_dot[0].subfunctions] + hs = [*hess] + mdot = [*m_dot.subfunctions] else: - hprev = hess.subfunctions[0].copy(deepcopy=True) - mprev = m_dot[0].subfunctions[0].copy(deepcopy=True) - hs = [hprev, *hess.subfunctions] - mdot = [mprev, *m_dot[0].subfunctions] + hprev = hess[0].copy(deepcopy=True).zero() + mprev = m_dot.subfunctions[0].copy(deepcopy=True).zero() + hs = [hprev, *hess] + mdot = [mprev, *m_dot.subfunctions] if trank != self.nchunks - 1: - hnext = hess.subfunctions[0].copy(deepcopy=True) + hnext = hess[0].copy(deepcopy=True).zero() # send m_dot halo forward if self.ensemble: @@ -535,40 +495,21 @@ def hessian(self, m_dot: OverloadedType, options: dict = {}): if trank != self.nchunks - 1: self.ensemble.isend( - mdot[-1], dest=dst, tag=dst) + m_dot.subfunctions[-1], dest=dst, tag=dst) if trank != 0: recv_reqs = self.ensemble.irecv( - mdot[0], source=src, tag=trank) - - # hessian actions at the initial condition - if trank == 0: - hs[0] += self.background_rf.hessian( - mdot[0], options=options) - - if self.initial_observations: - hs[0] += self.initial_observation_rf.hessian( - mdot[0], options=options) + mprev, source=src, tag=trank) + MPI.Request.Waitall(recv_reqs) # evaluate all stages on chunk except first - for i in range(1, len(self.stages)): + for i in range(len(self.stages)): hms = self.stages[i].hessian( - mdot[i:i+2], options=options) + mdot[i:i+2], options=options, rftype='model') hs[i] += hms[0] hs[i+1] += hms[1] - # wait for halo swap to finish - if trank != 0: - MPI.Request.Waitall(recv_reqs) - - # evaluate first stage on chunk now we have the halo - hms = self.stages[0].hessian( - mdot[:2], options=options) - - hs[0] += hms[0] - hs[1] += hms[1] - # send result halo backward if self.ensemble: src = trank + 1 @@ -581,13 +522,11 @@ def hessian(self, m_dot: OverloadedType, options: dict = {}): if trank != self.nchunks - 1: recv_reqs = self.ensemble.irecv( hnext, source=src, tag=trank) + MPI.Request.Waitall(recv_reqs) + hess_xm = hess[-1] + hess_xm += hnext - # finish the result halo - if trank != self.nchunks - 1: - MPI.Request.Waitall(recv_reqs) - hs[-1] += hnext - - return hess + return hessians @stop_annotating() def hessian_matrix(self): @@ -676,9 +615,100 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): # values of the initial timeseris self.control.assign(self._cbuf) + # # # # # # + # Create observation ReducedFunctionals + # # # # # # + self.observation_space = EnsembleFunctionSpace( [Jo.functional.function_space() for Jo in self.observation_rfs], - self.ensemble) + ensemble=self.ensemble) + + self._observation_float_vec = EnsembleAdjVec( + [AdjFloat(0.) for _ in range(self.nlocal_observations)], + ensemble=self.ensemble) + + # (JH : V^n -> U^n) = (x -> y - H(x)) + observation_control = EnsembleFunction(self.control_space) + observation_functional = EnsembleFunction(self.observation_space) + + self.JH = EnsembleTransformReducedFunctional( + self.observation_rfs, + observation_functional, + Control(observation_control)) + + # (Jr : U^n -> R^n) = (x -> x^T R^{-1} x) + observation_control = EnsembleFunction(self.observation_space) + observation_functional = self._observation_float_vec._ad_convert_type(0.) + + self.JR = EnsembleTransformReducedFunctional( + self.observation_norms, + observation_functional, + Control(observation_control)) + + # (Jsum : R^n -> R) = (x -> \sum_{i} x_{i}) + obs_sum_control = self._observation_float_vec._ad_convert_type(0.) + obs_sum_functional = AdjFloat(0.) + + self.JOsum = EnsembleReduceReducedFunctional( + obs_sum_functional, + Control(obs_sum_control)) + + self.Jobservations = CompositeReducedFunctional( + CompositeReducedFunctional(self.JH, self.JR), self.JOsum) + + # # # # # # + # Create model propagator ReducedFunctionals + # # # # # # + + self._state_float_vec = EnsembleAdjVec( + [AdjFloat(0.) for _ in range(self.nlocal_stages)], + ensemble=self.ensemble) + + # contains the halo and propagated states (one less than full series) + stage_state_space = EnsembleFunctionSpace( + [Jm.functional.function_space() for Jm in self.model_rfs], + ensemble=self.ensemble) + + # (Jm : V^{n-1} -> V^{n-1}) = (x -> M(x)) + propagator_control = EnsembleFunction(stage_state_space) + propagator_functional = EnsembleFunction(stage_state_space) + + self.JM = EnsembleTransformReducedFunctional( + self.model_rfs, + propagator_functional, + Control(propagator_control)) + + # (JQerr : [V^{n-1}, V^{n-1}] -> V^{n-1}) = ([x, y] -> x - y) + propagator_controls = [ + Control(EnsembleFunction(stage_state_space)), + Control(EnsembleFunction(stage_state_space))] + propagator_functional = EnsembleFunction(stage_state_space) + + self.JQerr = EnsembleTransformReducedFunctional( + [stage.model_error for state in self.stages], + propagator_functional, + propagator_controls + ) + + # (JQnorm : V^{n-1} -> R^{n-1}) = (x -> x^T Q^{-1} x) + propagator_control = EnsembleFunction(stage_state_space) + propagator_functional = self._state_float_vec._ad_convert_type(0.) + + self.JQnorm = EnsembleTransformReducedFunctional( + self.model_norms, + propagator_functional, + Control(propagator_control)) + + # (JMsum : R^n -> R) = (x -> \sum_{i} x_{i}) + prop_sum_control = self._state_float_vec._ad_convert_type(0.) + prop_sum_functional = AdjFloat(0.) + + self.JMsum = EnsembleReduceReducedFunctional( + prop_sum_functional, + Control(prop_sum_control)) + + self.Jmodel = CompositeReducedFunctional( + CompositeReducedFunctional(self.JQerr, self.JQnorm), self.JMsum) else: # strong constraint @@ -687,6 +717,75 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): observation_index=0 if self.initial_observations else -1, stage_kwargs=stage_kwargs, nstages=nstages) + def _forward_halos(self, x, xi, xim1, xprev): + # extract x into xi and xim1 + for i in range(self.nlocal_stages): + if self.trank == 0: + xi.subfunctions[i].assign(x.subfunctions[i+1]) + xim1.subfunctions[i].assign(x.subfunctions[i]) + else: + xi.subfunctions[i].assign(x.subfunctions[i]) + xim1.subfunctions[i].assign(x.subfunctions[i-1]) + + # post messages + src = self.trank - 1 + dst = self.trank + 1 + + # # send forward xi + if self.trank != self.nchunks - 1: + self.ensemble.isend( + xi.subfunctions[-1], dest=dst, tag=dst) + + # # recv back xprev + if self.trank != 0: + recv_reqs = self.ensemble.irecv( + xprev, source=src, tag=self.trank) + MPI.Request.Waitall(recv_reqs) + + # insert into xim1 + xim1.subfunctions[0].assign(xprev) + + return + + def _backward_halos(self, dj_x, dj_mx, dJ_x, dJ_mx): + + # accumulate diagonal component + for i in range(self.nlocal_stages): + if self.trank == 0: + dJ_x.subfunctions[i+1].assign(dj_x.subfunctions[i]) + else: + dJ_x.subfunctions[i].assign(dj_x.subfunctions[i]) + + # accumulate propagation derivative + for i in range(self.nlocal_stages-1): + if self.trank == 0: + print(f"\n{i = }") + dJ_mx.subfunctions[i+1].assign(dj_mx.subfunctions[i+1]) + else: + dJ_mx.subfunctions[i].assign(dj_mx.subfunctions[i+1]) + + djnext = dj_mx.subfunctions[0]._ad_copy().zero() + + # no halo on rank 0 + if self.trank == 0: + dJ_mx.subfunctions[0].assign(dj_mx.subfunctions[0]) + + # halos sent backward in time + src = self.trank + 1 + dst = self.trank - 1 + + if self.trank != 0: + self.ensemble.isend( + dj_mx.subfunctions[0], dest=dst, tag=dst) + + if self.trank != self.nchunks - 1: + recv_reqs = self.ensemble.irecv( + djnext, source=src, tag=self.trank) + MPI.Request.Waitall(recv_reqs) + dJ_mx.subfunctions[-1].assign(djnext) + + return djnext + class ObservationStageSequence: def __init__(self, controls: Control, diff --git a/firedrake/adjoint/fourdvar_reduced_functional_bak.py b/firedrake/adjoint/fourdvar_reduced_functional_bak.py new file mode 100644 index 0000000000..9458bf2933 --- /dev/null +++ b/firedrake/adjoint/fourdvar_reduced_functional_bak.py @@ -0,0 +1,1258 @@ +from pyadjoint import ReducedFunctional, OverloadedType, Control, Tape, AdjFloat, \ + stop_annotating, get_working_tape, set_working_tape +from pyadjoint.enlisting import Enlist +from firedrake.function import Function +from firedrake.ensemble import EnsembleFunction, EnsembleFunctionSpace +from firedrake import assemble, inner, dx, Constant +from .composite_reduced_functional import ( + CompositeReducedFunctional, intermediate_options) +from .ensemble_reduced_functional import ( + EnsembleReduceReducedFunctional, EnsembleBcastReducedFunctional, + EnsembleTransformReducedFunctional, EnsembleReducedFunctional) +from .ensemble_adjvec import EnsembleAdjVec +from ufl.duals import is_primal, is_dual +from functools import wraps, cached_property, partial +from typing import Callable, Optional, Collection, Union +from types import SimpleNamespace +from contextlib import contextmanager +from mpi4py import MPI +from firedrake.petsc import PETSc + +__all__ = ['FourDVarReducedFunctional'] + + +# @set_working_tape() # ends up using old_tape = None because evaluates when imported - need separate decorator +def isolated_rf(operation, control, + functional_name=None, + control_name=None): + """ + Return a ReducedFunctional where the functional is `operation` applied + to a copy of `control`, and the tape contains only `operation`. + """ + with stop_annotating(): + controls = Enlist(control) + control_copies = [control._ad_copy() for control in controls] + + if control_name: + for control, name in zip(control_copies, Enlist(control_name)): + _rename(control, name) + + with set_working_tape(): + functional = operation(controls.delist(control_copies)) + + if functional_name: + _rename(functional, functional_name) + + control = controls.delist([Control(control_copy) + for control_copy in control_copies]) + + return ReducedFunctional( + functional, control) + + +def sc_passthrough(func): + """ + Wraps standard ReducedFunctional methods to differentiate strong or + weak constraint implementations. + + If using strong constraint, makes sure strong_reduced_functional + is instantiated then passes args/kwargs through to the + corresponding strong_reduced_functional method. + + If using weak constraint, returns the FourDVarReducedFunctional + method definition. + """ + @wraps(func) + def wrapper(self, *args, **kwargs): + if self.weak_constraint: + return func(self, *args, **kwargs) + else: + sc_func = getattr(self.strong_reduced_functional, func.__name__) + return sc_func(*args, **kwargs) + return wrapper + + +def _rename(obj, name): + if hasattr(obj, "rename"): + obj.rename(name) + + +def _ad_sub(left, right): + result = right._ad_copy() + result._ad_imul(-1) + result._ad_iadd(left) + return result + + +class FourDVarReducedFunctional(ReducedFunctional): + """ReducedFunctional for 4DVar data assimilation. + + Creates either the strong constraint or weak constraint system + by logging observations through the initial time propagator run. + + Parameters + ---------- + + control + The :class:`.EnsembleFunction` for the control x_{i} at the initial condition + and at the end of each observation stage. + + background_covariance + The inner product to calculate the background error functional + from the background error :math:`x_{0} - x_{b}`. Can include the + error covariance matrix. Only used on ensemble rank 0. + + background + The background (prior) data for the initial condition :math:`x_{b}`. + If not provided, the value of the first subfunction on the first ensemble + member of the control :class:`.EnsembleFunction` will be used. + + observation_error + Given a state :math:`x`, returns the observations error + :math:`y_{0} - \\mathcal{H}_{0}(x)` where :math:`y_{0}` are the + observations at the initial time and :math:`\\mathcal{H}_{0}` is + the observation operator for the initial time. Only used on + ensemble rank 0. Optional. + + observation_covariance + The inner product to calculate the observation error functional + from the observation error :math:`y_{0} - \\mathcal{H}_{0}(x)`. + Can include the error covariance matrix. Must be provided if + observation_error is provided. Only used on ensemble rank 0 + + weak_constraint + Whether to use the weak or strong constraint 4DVar formulation. + + See Also + -------- + :class:`pyadjoint.ReducedFunctional`. + """ + + @PETSc.Log.EventDecorator() + def __init__(self, control: Control, + background_covariance: Union[Constant, tuple], + background: Optional[OverloadedType] = None, + observation_error: Optional[Callable[[OverloadedType], OverloadedType]] = None, + observation_covariance: Optional[Callable[[OverloadedType], AdjFloat]] = None, + weak_constraint: bool = True, + tape: Optional[Tape] = None, + _annotate_accumulation: bool = False): + + self.tape = get_working_tape() if tape is None else tape + + self.weak_constraint = weak_constraint + self.initial_observations = observation_error is not None + + if self.weak_constraint: + self._annotate_accumulation = _annotate_accumulation + self._accumulation_started = False + + if not isinstance(control.control, EnsembleFunction): + raise TypeError( + "Control for weak constraint 4DVar must be an EnsembleFunction" + ) + + with stop_annotating(): + if background: + self.background = background._ad_copy() + else: + self.background = control.control.subfunctions[0]._ad_copy() + _rename(self.background, "Background") + + self.control_space = control.function_space() + ensemble = self.control_space.ensemble + self.ensemble = ensemble + self.trank = ensemble.ensemble_comm.rank if ensemble else 0 + self.nchunks = ensemble.ensemble_comm.size if ensemble else 1 + + # because we need to manually evaluate the different bits + # of the functional, we need an internal set of controls + # to use for the stage ReducedFunctionals + self._cbuf = control.copy_data() + _x = self._cbuf.subfunctions + self._x = _x + self._controls = tuple(Control(xi) for xi in _x) + + self.control = control + self.controls = Enlist(control) + + # first control on rank 0 is initial conditions, not end of observation stage + self.nlocal_stages = len(_x) - (1 if self.trank == 0 else 0) + + if self.trank == 0 and self.initial_observations: + self.nlocal_observations = self.nlocal_stages + 1 + else: + self.nlocal_observations = self.nlocal_stages + + self.stages = [] # The record of each observation stage + + self.observation_rfs = [] + self.observation_norms = [] + + self.model_rfs = [] + self.model_norms = [] + + # first rank sets up functionals for background initial observations + if self.trank == 0: + + # RF to recalculate error vector (x_0 - x_b) + self.background_error = isolated_rf( + operation=lambda x0: _ad_sub(x0, self.background), + control=_x[0], + functional_name="bkg_err_vec", + control_name="Control_0_bkg_copy") + + # RF to recalculate inner product |x_0 - x_b|_B + self.background_norm = CovarianceNormReducedFunctional( + self.background_error.functional, + background_covariance, + control_name="bkg_err_vec_copy") + + # compose background reduced functionals to evaluate both together + self.background_rf = CompositeReducedFunctional( + self.background_error, self.background_norm) + + if self.initial_observations: + + # RF to recalculate error vector (H(x_0) - y_0) + self.initial_observation_error = isolated_rf( + operation=observation_error, + control=_x[0], + functional_name="obs_err_vec_0", + control_name="Control_0_obs_copy") + + # RF to recalculate inner product |H(x_0) - y_0|_R + self.initial_observation_norm = CovarianceNormReducedFunctional( + self.initial_observation_error.functional, + observation_covariance, + control_name="obs_err_vec_0_copy") + + self.observation_rfs.append(self.initial_observation_error) + self.observation_norms.append(self.initial_observation_norm) + + # compose initial observation reduced functionals to evaluate both together + self.initial_observation_rf = CompositeReducedFunctional( + self.initial_observation_error, self.initial_observation_norm) + + # create halo for previous state + if self.ensemble and self.trank != 0: + with stop_annotating(): + self.xprev = _x[0]._ad_copy() + self._control_prev = Control(self.xprev) + + # halo for the derivative from the next chunk + if self.ensemble and self.trank != self.nchunks - 1: + with stop_annotating(): + self.xnext = _x[0]._ad_copy() + + else: + self._annotate_accumulation = True + self._accumulation_started = False + + if not isinstance(control.control, Function): + raise TypeError( + "Control for strong constraint 4DVar must be a Function" + ) + + with stop_annotating(): + if background: + self.background = background._ad_copy() + else: + self.background = control.control._ad_copy() + _rename(self.background, "Background") + + # initial conditions guess to be updated + self.controls = Enlist(control) + + # Strong constraint functional to be converted to ReducedFunctional later + + # penalty for straying from prior + self._accumulate_functional( + covariance_norm(control.control - self.background, + background_covariance)) + + # penalty for not hitting observations at initial time + if self.initial_observations: + self._accumulate_functional( + covariance_norm(observation_error(control.control), + observation_covariance)) + + @cached_property + def strong_reduced_functional(self): + """A :class:`pyadjoint.ReducedFunctional` for the strong constraint 4DVar system. + + Only instantiated if using the strong constraint formulation, and cannot be used + before all observations are recorded. + """ + if self.weak_constraint: + msg = "Strong constraint ReducedFunctional cannot be instantiated for weak constraint 4DVar" + raise AttributeError(msg) + self._strong_reduced_functional = ReducedFunctional( + self._total_functional, self.controls.delist(), tape=self.tape) + return self._strong_reduced_functional + + def __getattr__(self, attr): + """ + If using strong constraint then grab attributes from self.strong_reduced_functional. + """ + # hasattr calls getattr, so check self.__dir__ directly here to avoid recursion + if self.weak_constraint or "_strong_reduced_functional" not in dir(self): + raise AttributeError(f"'{type(self)}' object has no attribute '{attr}'") + return getattr(self.strong_reduced_functional, attr) + + @sc_passthrough + @stop_annotating() + @PETSc.Log.EventDecorator() + def __call__(self, values: OverloadedType): + """Computes the reduced functional with supplied control value. + + Parameters + ---------- + + values + If you have multiple controls this should be a list of new values + for each control in the order you listed the controls to the constructor. + If you have a single control it can either be a list or a single object. + Each new value should have the same type as the corresponding control. + + Returns + ------- + pyadjoint.OverloadedType + The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. + + """ + value = values[0] if isinstance(values, list) else values + + if not isinstance(value, type(self.control.control)): + raise ValueError(f"Value must be of type {type(self.control.control)} not type {type(value)}") + + self.control.update(value) + # put the new value into our internal set of controls to pass to each stage + self._cbuf.assign(value) + + trank = self.trank + + xi = self._cbuf.copy() + + # halos for propagation + xim1 = self.JM.functional.copy() + xim1.zero() + + for i in range(self.nlocal_stages - 1): + xim1.subfunctions[i].assign( + xi.subfunctions[i-1]) + + # post messages for control of time propagator on next chunk + if self.ensemble: + src = trank - 1 + dst = trank + 1 + + reqs = [] + if trank != self.nchunks - 1: + reqs.extend(self.ensemble.isend( + xi.subfunctions[-1], dest=dst, tag=dst)) + + if trank != 0: + reqs.extend(self.ensemble.irecv( + xim1.subfunctions[0], source=src, tag=trank)) + + MPI.Request.Waitall(reqs) + + # evaluate stages: + Jm = 0. + for i, stage in enumerate(self.stages): + Jm += stage([xim1.subfunctions[i], + xi.subfunctions[i]]) + + # Model functionals + # Jq = self.Jmodel([self.JM(xim1), xi]) + # Mx = self.JM(xim1) + # qerr = self.JQerr([Mx, xi]) + # qnorm = self.JQnorm(qerr) + # Jq = self.JMsum(qnorm) + + # Observation functional + Jo = self.Jobservations(xi) + + # Background functional + if trank == 0: + Jb = self.background_rf(xi.subfunctions[0]) if trank == 0 else None + Jb = self.ensemble.ensemble_comm.bcast(Jb, root=0) + + J = Jb + Jo + Jm + print(f"\n{Jb = } | {Jo = } | {Jm = } | {J = }") + return J + + @sc_passthrough + @stop_annotating() + @PETSc.Log.EventDecorator() + def derivative(self, adj_input: float = 1.0, options: dict = {}): + """Returns the derivative of the functional w.r.t. the control. + Using the adjoint method, the derivative of the functional with + respect to the control, around the last supplied value of the + control, is computed and returned. + + Parameters + ---------- + adj_input + The adjoint input. + + options + Additional options for the derivative computation. + + Returns + ------- + pyadjoint.OverloadedType + The derivative with respect to the control. + Should be an instance of the same type as the control. + """ + trank = self.trank + + # chaining ReducedFunctionals means we need to pass Cofunctions not Functions + options = options or {} + ioptions = intermediate_options(options) + + derivatives = self.Jobservations.derivative( + adj_input=adj_input, options=options) + + # dJ_model = self.Jmodel.derivative( + # adj_input=adj_input, options=ioptions) + + if self.ensemble: + with stop_annotating(): + xprev = derivatives.subfunctions[0]._ad_copy().zero() + xnext = derivatives.subfunctions[0]._ad_copy().zero() + if trank != 0: + derivs = [xprev, *derivatives.subfunctions] + else: + derivs = [*derivatives.subfunctions] + + # initial condition derivatives + if trank == 0: + derivs[0] += self.background_rf.derivative( + adj_input=adj_input, options=options) + + # # evaluate all time stages on chunk except first while halo in flight + for i in range(len(self.stages)): + sderiv = self.stages[i].derivative( + adj_input=adj_input, options=options, + rftype='model') + derivs[i] += sderiv[0] + derivs[i+1] += sderiv[1] + + # post the derivative halo exchange + if self.ensemble: + # halos sent backward in time + src = trank + 1 + dst = trank - 1 + + if trank != 0: + self.ensemble.isend( + derivs[0], dest=dst, tag=dst) + + if trank != self.nchunks - 1: + recv_reqs = self.ensemble.irecv( + xnext, source=src, tag=trank) + + # finish the derivative halo exchange + if self.ensemble: + if trank != self.nchunks - 1: + MPI.Request.Waitall(recv_reqs) + derivs[-1] += xnext + + return derivatives + + @sc_passthrough + @stop_annotating() + @PETSc.Log.EventDecorator() + def hessian(self, m_dot: OverloadedType, options: dict = {}): + """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. + + Using the second-order adjoint method, the action of the Hessian of the + functional with respect to the control, around the last supplied value + of the control, is computed and returned. + + Parameters + ---------- + + m_dot + The direction in which to compute the action of the Hessian. + + options + A dictionary of options. To find a list of available options + have a look at the specific control type. + + rtype: + Whether to evaluate: + - the model error ('model'), + - the observation error ('obs'), + - both model and observation errors (None). + + Returns + ------- + pyadjoint.OverloadedType + The action of the Hessian in the direction m_dot. + Should be an instance of the same type as the control. + """ + trank = self.trank + + if isinstance(m_dot, list): + m_dot = m_dot[0] + + hess = self.Jobservations.hessian( + m_dot=m_dot, options=options) + + # set up arrays including halos + if trank == 0: + hs = [*hess.subfunctions] + mdot = [*m_dot.subfunctions] + else: + hprev = hess.subfunctions[0].copy(deepcopy=True).zero() + mprev = m_dot.subfunctions[0].copy(deepcopy=True).zero() + hs = [hprev, *hess.subfunctions] + mdot = [mprev, *m_dot.subfunctions] + + if trank != self.nchunks - 1: + hnext = hess.subfunctions[0].copy(deepcopy=True).zero() + + # send m_dot halo forward + if self.ensemble: + src = trank - 1 + dst = trank + 1 + + if trank != self.nchunks - 1: + self.ensemble.isend( + mdot[-1], dest=dst, tag=dst) + + if trank != 0: + recv_reqs = self.ensemble.irecv( + mdot[0], source=src, tag=trank) + + # hessian actions at the initial condition + if trank == 0: + hs[0] += self.background_rf.hessian( + mdot[0], options=options) + + # evaluate all stages on chunk except first + for i in range(1, len(self.stages)): + hms = self.stages[i].hessian( + mdot[i:i+2], options=options, rftype='model') + + hs[i] += hms[0] + hs[i+1] += hms[1] + + # wait for halo swap to finish + if trank != 0: + MPI.Request.Waitall(recv_reqs) + + # evaluate first stage on chunk now we have the halo + hms = self.stages[0].hessian( + mdot[:2], options=options, rftype='model') + + hs[0] += hms[0] + hs[1] += hms[1] + + # send result halo backward + if self.ensemble: + src = trank + 1 + dst = trank - 1 + + if trank != 0: + self.ensemble.isend( + hs[0], dest=dst, tag=dst) + + if trank != self.nchunks - 1: + recv_reqs = self.ensemble.irecv( + hnext, source=src, tag=trank) + + # finish the result halo + MPI.COMM_WORLD.Barrier() + if trank != self.nchunks - 1: + MPI.Request.Waitall(recv_reqs) + hs[-1] += hnext + + return hess + + @stop_annotating() + def hessian_matrix(self): + # Other reduced functionals don't have this. + if not self.weak_constraint: + raise AttributeError("Strong constraint 4DVar does not form a Hessian matrix") + raise NotImplementedError + + def _accumulate_functional(self, val): + if not self._annotate_accumulation: + return + if self._accumulation_started: + self._total_functional += val + else: + self._total_functional = val + self._accumulation_started = True + + @contextmanager + @PETSc.Log.EventDecorator() + def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): + if not sequential: + raise ValueError("Recording stages concurrently not yet implemented") + + # record over ensemble + if self.weak_constraint: + + trank = self.trank + + # index of "previous" stage and observation in global series + global_index = -1 + observation_index = 0 if self.initial_observations else -1 + with stop_annotating(): + xhalo = self._x[0]._ad_copy() + + # add our data onto the user's context data + ekwargs = {k: v for k, v in stage_kwargs.items()} + ekwargs['global_index'] = global_index + ekwargs['observation_index'] = observation_index + + ekwargs['xhalo'] = xhalo + + # proceed one ensemble rank at a time + with self.ensemble.sequential(**ekwargs) as ectx: + + # later ranks start from halo + if trank == 0: + controls = self._controls + else: + controls = [self._control_prev, *self._controls] + with stop_annotating(): + controls[0].assign(ectx.xhalo) + + # grab the user's data from the ensemble context + local_stage_kwargs = { + k: getattr(ectx, k) for k in stage_kwargs.keys() + } + + # initialise iterator for local stages + stage_sequence = ObservationStageSequence( + controls, self, ectx.global_index, + ectx.observation_index, + local_stage_kwargs, sequential) + + # let the user record the local stages + yield stage_sequence + + for stage in self.stages: + self.observation_rfs.append(stage.observation_error) + self.observation_norms.append(stage.observation_norm) + self.model_rfs.append(stage.forward_model) + self.model_norms.append(stage.model_norm) + + # send the state forward + with stop_annotating(): + state = self.stages[-1].controls[1].control + ectx.xhalo.assign(state) + # grab the user's information to send forward + for k in local_stage_kwargs.keys(): + setattr(ectx, k, getattr(stage_sequence.ctx, k)) + # increment the global indices for the last local stage + ectx.global_index = self.stages[-1].global_index + ectx.observation_index = self.stages[-1].observation_index + + with stop_annotating(): + # make sure that self.control now holds the + # values of the initial timeseris + self.control.assign(self._cbuf) + + # # # # # # + # Create observation ReducedFunctionals + # # # # # # + + self.observation_space = EnsembleFunctionSpace( + [Jo.functional.function_space() for Jo in self.observation_rfs], + ensemble=self.ensemble) + + self._observation_float_vec = EnsembleAdjVec( + [AdjFloat(0.) for _ in range(self.nlocal_observations)], + ensemble=self.ensemble) + + # (JH : V^n -> U^n) = (x -> y - H(x)) + observation_control = EnsembleFunction(self.control_space) + observation_functional = EnsembleFunction(self.observation_space) + + self.JH = EnsembleTransformReducedFunctional( + self.observation_rfs, + observation_functional, + Control(observation_control)) + + # (Jr : U^n -> R^n) = (x -> x^T R^{-1} x) + observation_control = EnsembleFunction(self.observation_space) + observation_functional = self._observation_float_vec._ad_convert_type(0.) + + self.JR = EnsembleTransformReducedFunctional( + self.observation_norms, + observation_functional, + Control(observation_control)) + + # (Jsum : R^n -> R) = (x -> \sum_{i} x_{i}) + obs_sum_control = self._observation_float_vec._ad_convert_type(0.) + obs_sum_functional = AdjFloat(0.) + + self.JOsum = EnsembleReduceReducedFunctional( + obs_sum_functional, + Control(obs_sum_control)) + + self.Jobservations = CompositeReducedFunctional( + CompositeReducedFunctional(self.JH, self.JR), self.JOsum) + + # # # # # # + # Create model propagator ReducedFunctionals + # # # # # # + + self._state_float_vec = EnsembleAdjVec( + [AdjFloat(0.) for _ in range(self.nlocal_stages)], + ensemble=self.ensemble) + + # contains the halo and propagated states (one less than full series) + stage_state_space = EnsembleFunctionSpace( + [Jm.functional.function_space() for Jm in self.model_rfs], + ensemble=self.ensemble) + + # (Jm : V^{n-1} -> V^{n-1}) = (x -> M(x)) + propagator_control = EnsembleFunction(stage_state_space) + propagator_functional = EnsembleFunction(stage_state_space) + + self.JM = EnsembleTransformReducedFunctional( + self.model_rfs, + propagator_functional, + Control(propagator_control)) + + # (JQerr : [V^{n-1}, V^{n-1}] -> V^{n-1}) = ([x, y] -> x - y) + propagator_controls = [ + Control(EnsembleFunction(stage_state_space)), + Control(EnsembleFunction(stage_state_space))] + propagator_functional = EnsembleFunction(stage_state_space) + + self.JQerr = EnsembleTransformReducedFunctional( + [stage.model_error for state in self.stages], + propagator_functional, + propagator_controls + ) + + # (JQnorm : V^{n-1} -> R^{n-1}) = (x -> x^T Q^{-1} x) + propagator_control = EnsembleFunction(stage_state_space) + propagator_functional = self._state_float_vec._ad_convert_type(0.) + + self.JQnorm = EnsembleTransformReducedFunctional( + self.model_norms, + propagator_functional, + Control(propagator_control)) + + # (JMsum : R^n -> R) = (x -> \sum_{i} x_{i}) + prop_sum_control = self._state_float_vec._ad_convert_type(0.) + prop_sum_functional = AdjFloat(0.) + + self.JMsum = EnsembleReduceReducedFunctional( + prop_sum_functional, + Control(prop_sum_control)) + + self.Jmodel = CompositeReducedFunctional( + CompositeReducedFunctional(self.JQerr, self.JQnorm), self.JMsum) + + else: # strong constraint + + yield ObservationStageSequence( + self.controls, self, global_index=-1, + observation_index=0 if self.initial_observations else -1, + stage_kwargs=stage_kwargs, nstages=nstages) + + +class ObservationStageSequence: + def __init__(self, controls: Control, + aaorf: FourDVarReducedFunctional, + global_index: int, + observation_index: int, + stage_kwargs: dict = None, + nstages: Optional[int] = None): + self.controls = controls + self.aaorf = aaorf + self.ctx = SimpleNamespace(**(stage_kwargs or {})) + self.weak_constraint = aaorf.weak_constraint + self.global_index = global_index + self.observation_index = observation_index + self.local_index = -1 + self.nstages = (len(controls) - 1 if self.weak_constraint + else nstages) + + def __iter__(self): + return self + + @PETSc.Log.EventDecorator() + def __next__(self): + + # increment global indices. + self.local_index += 1 + self.global_index += 1 + self.observation_index += 1 + + if self.weak_constraint: + stages = self.aaorf.stages + + # control for the start of the next stage. + next_control = self.controls[self.local_index] + + # smuggle state forward into aaorf's next control. + if self.local_index > 0: + state = stages[-1].controls[1].control + with stop_annotating(): + next_control.control.assign(state) + + # now we know that the aaorf's controls have + # been updated from the previous stage's controls, + # we can check if we need to exit. + if self.local_index >= self.nstages: + raise StopIteration + + stage = WeakObservationStage(next_control, + local_index=self.local_index, + global_index=self.global_index, + observation_index=self.observation_index) + stages.append(stage) + + else: # strong constraint + + # stop after we've recorded all stages + if self.local_index >= self.nstages: + raise StopIteration + + # dummy control to "start" stage from + control = (self.aaorf.controls[0].control if self.local_index == 0 + else self._prev_stage.state) + + stage = StrongObservationStage( + control, self.aaorf, + index=self.local_index, + observation_index=self.observation_index) + + self._prev_stage = stage + + return stage, self.ctx + + +class StrongObservationStage: + """ + Record an observation for strong constraint 4DVar at the time of `state`. + + Parameters + ---------- + + aaorf + The strong constraint FourDVarReducedFunctional. + + """ + + def __init__(self, control: OverloadedType, + aaorf: FourDVarReducedFunctional, + index: Optional[int] = None, + observation_index: Optional[int] = None): + self.aaorf = aaorf + self.control = control + self.index = index + self.observation_index = observation_index + + @PETSc.Log.EventDecorator() + def set_observation(self, state: OverloadedType, + observation_error: Callable[[OverloadedType], OverloadedType], + observation_covariance: Callable[[OverloadedType], AdjFloat]): + """ + Record an observation at the time of `state`. + + Parameters + ---------- + + state + The state at the current observation time. + + observation_error + Given a state :math:`x`, returns the observations error + :math:`y_{i} - \\mathcal{H}_{i}(x)` where :math:`y_{i}` are + the observations at the current observation time and + :math:`\\mathcal{H}_{i}` is the observation operator for the + current observation time. + + observation_covariance + The inner product to calculate the observation error functional + from the observation error :math:`y_{i} - \\mathcal{H}_{i}(x)`. + Can include the error covariance matrix. + """ + if hasattr(self.aaorf, "_strong_reduced_functional"): + raise ValueError("Cannot add observations once strong" + " constraint ReducedFunctional instantiated") + self.aaorf._accumulate_functional( + covariance_norm(observation_error(state), + observation_covariance)) + + # save the user's state to hand back for beginning of next stage + self.state = state + + +class WeakObservationStage: + """ + A single stage for weak constraint 4DVar at the time of `state`. + + Records the time propagator from the control at the beginning + of the stage, and the model and observation errors at the end of the stage. + + Parameters + ---------- + + control + The control x_{i-1} at the beginning of the stage + + local_index + The index of this stage in the timeseries on the + local ensemble member. + + global_index + The index of this stage in the global timeseries. + + observation_index + The index of the observation at the end of this stage in + the global timeseries. May be different from global_index if + an observation is taken at the initial time. + + """ + def __init__(self, control: Control, + local_index: Optional[int] = None, + global_index: Optional[int] = None, + observation_index: Optional[int] = None): + # "control" to use as initial condition. + # Not actual `Control` for consistency with strong constraint + self.control = control.control + + self.controls = Enlist(control) + self.local_index = local_index + self.global_index = global_index + self.observation_index = observation_index + set_working_tape() + self._stage_tape = get_working_tape() + + @PETSc.Log.EventDecorator() + def set_observation(self, state: OverloadedType, + observation_error: Callable[[OverloadedType], OverloadedType], + observation_covariance: Callable[[OverloadedType], AdjFloat], + forward_model_covariance: Callable[[OverloadedType], AdjFloat]): + """ + Record an observation at the time of `state`. + + Parameters + ---------- + + state + The state at the current observation time. + + observation_error + Given a state :math:`x`, returns the observations error + :math:`y_{i} - \\mathcal{H}_{i}(x)` where :math:`y_{i}` are + the observations at the current observation time and + :math:`\\mathcal{H}_{i}` is the observation operator for the + current observation time. + + observation_covariance + The inner product to calculate the observation error functional + from the observation error :math:`y_{i} - \\mathcal{H}_{i}(x)`. + Can include the error covariance matrix. + + forward_model_covariance + The inner product to calculate the model error functional from + the model error :math:`x_{i} - \\mathcal{M}_{i}(x_{i-1})`. Can + include the error covariance matrix. + """ + # get the tape used for this stage and make sure its the right one + stage_tape = get_working_tape() + if stage_tape is not self._stage_tape: + raise ValueError( + "Working tape at the end of the observation stage" + " differs from the tape at the stage beginning." + ) + + # record forward propogation + with set_working_tape(stage_tape.copy()) as tape: + self.forward_model = ReducedFunctional( + state._ad_copy(), controls=self.controls[0], tape=tape) + + # Beginning of next time-chunk is the control for this observation + # and the state at the end of the next time-chunk. + with stop_annotating(): + # smuggle initial guess at this time into the control without the tape seeing + self.controls.append(Control(state._ad_copy())) + if self.global_index: + _rename(self.controls[-1].control, f"Control_{self.global_index}") + + # model error links time-chunks by depending on both the + # previous and current controls + + # RF to recalculate error vector (M_i - x_i) + names = { + 'functional_name': f"model_err_vec_{self.global_index}", + 'control_name': [f"state_{self.global_index}_copy", + f"Control_{self.global_index}_model_copy"] + } if self.global_index else {} + + self.model_error = isolated_rf( + operation=lambda controls: _ad_sub(*controls), + control=[state, self.controls[-1].control], + **names) + + # RF to recalculate inner product |M_i - x_i|_Q + names = { + 'control_name': f"model_err_vec_{self.global_index}_copy" + } if self.global_index else {} + + self.model_norm = CovarianceNormReducedFunctional( + self.model_error.functional, + forward_model_covariance, + **names) + + # compose model error reduced functionals to evaluate both together + self.model_error_rf = CompositeReducedFunctional( + self.model_error, self.model_norm) + + # Observations after tape cut because this is now a control, not a state + + # RF to recalculate error vector (H(x_i) - y_i) + names = { + 'functional_name': f"obs_err_vec_{self.global_index}", + 'control_name': f"Control_{self.global_index}_obs_copy" + } if self.global_index else {} + + self.observation_error = isolated_rf( + operation=observation_error, + control=self.controls[-1], + **names) + + # RF to recalculate inner product |H(x_i) - y_i|_R + names = { + 'control_name': "obs_err_vec_{self.global_index}_copy" + } if self.global_index else {} + self.observation_norm = CovarianceNormReducedFunctional( + self.observation_error.functional, + observation_covariance, + **names) + + # compose observation reduced functionals to evaluate both together + self.observation_rf = CompositeReducedFunctional( + self.observation_error, self.observation_norm) + + # remove the stage initial condition "control" now we've finished recording + delattr(self, "control") + + # stop the stage tape recording anything else + set_working_tape() + + @stop_annotating() + @PETSc.Log.EventDecorator() + def __call__(self, values: OverloadedType, + rftype: Optional[str] = None): + """Computes the reduced functional with supplied control value. + + Parameters + ---------- + + values + If you have multiple controls this should be a list of new values + for each control in the order you listed the controls to the constructor. + If you have a single control it can either be a list or a single object. + Each new value should have the same type as the corresponding control. + + rtype: + Whether to evaluate: + - the model error ('model'), + - the observation error ('obs'), + - both model and observation errors (None). + + Returns + ------- + pyadjoint.OverloadedType + The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. + + """ + J = 0.0 + + # evaluate model error + if rftype in (None, 'model'): + J += self.model_error_rf( + [self.forward_model(values[0]), values[1]]) + + # evaluate observation errors + if rftype in (None, 'obs'): + J += self.observation_rf(values[1]) + + return J + + @stop_annotating() + @PETSc.Log.EventDecorator() + def derivative(self, adj_input: float = 1.0, options: dict = {}, + rftype: Optional[str] = None): + """Returns the derivative of the functional w.r.t. the control. + Using the adjoint method, the derivative of the functional with + respect to the control, around the last supplied value of the + control, is computed and returned. + + Parameters + ---------- + adj_input + The adjoint input. + + options + Additional options for the derivative computation. + + rtype: + Whether to evaluate: + - the model error ('model'), + - the observation error ('obs'), + - both model and observation errors (None). + + Returns + ------- + pyadjoint.OverloadedType + The derivative with respect to the control. + Should be an instance of the same type as the control. + """ + # create a list of overloaded types to put derivative into + derivatives = [] + + # chaining ReducedFunctionals means we need to pass Cofunctions not Functions + options = options or {} + ioptions = intermediate_options(options) + + if rftype in (None, 'model'): + # derivative of reduction and difference + model_err_derivs = self.model_error_rf.derivative( + adj_input=adj_input, options=ioptions) + + # derivative through the time propagator wrt to xprev + model_forward_deriv = self.forward_model.derivative( + adj_input=model_err_derivs[0], options=options) + + derivatives.append(model_forward_deriv) + + # model_err_derivs is still in the dual space, so we need to convert it to the + # type that the user has requested - this will be the type of model_forward_deriv. + derivatives.append( + model_forward_deriv._ad_convert_type( + model_err_derivs[1], options)) + + if rftype in (None, 'obs'): + obs_deriv = self.observation_rf.derivative( + adj_input=adj_input, options=options) + + if len(derivatives) == 0: + derivatives.append(None) + derivatives.append(obs_deriv) + else: + derivatives[1] += obs_deriv + + return derivatives + + @stop_annotating() + @PETSc.Log.EventDecorator() + def hessian(self, m_dot: OverloadedType, options: dict = {}, + rftype: Optional[str] = None): + """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. + + Using the second-order adjoint method, the action of the Hessian of the + functional with respect to the control, around the last supplied value + of the control, is computed and returned. + + Parameters + ---------- + + m_dot + The direction in which to compute the action of the Hessian. + + options + A dictionary of options. To find a list of available options + have a look at the specific control type. + + rtype: + Whether to evaluate: + - the model error ('model'), + - the observation error ('obs'), + - both model and observation errors (None). + + Returns + ------- + pyadjoint.OverloadedType + The action of the Hessian in the direction m_dot. + Should be an instance of the same type as the control. + """ + hessian_value = [] + + if rftype in (None, 'model'): + hessian_value.extend(self._model_hessian( + m_dot, options=options)) + + if rftype in (None, 'obs'): + obs_hessian = self.observation_rf.hessian( + m_dot[1], options=options) + if len(hessian_value) == 0: + hessian_value.append(None) + hessian_value.append(obs_hessian) + else: + hessian_value[1] += obs_hessian + + return hessian_value + + def _model_hessian(self, m_dot, options): + iopts = intermediate_options(options) + + # TLM for model from mdot[0] + forward_tlm = self.forward_model.tlm(m_dot[0], options=iopts) + + # combine model TLM and mdot[1] + mdot_error = [forward_tlm, m_dot[1]] + + # Hessian (dual) for error + error_hessian = self.model_error_rf.hessian( + mdot_error, options=iopts, evaluate_tlm=True) + + # Hessian for model + model_hessian = self.forward_model.hessian( + None, options=options, + hessian_input=error_hessian[0], + evaluate_tlm=False) + + # combine model Hessian and converted error Hessian + return [ + model_hessian, + model_hessian._ad_convert_type(error_hessian[1], + options=options) + ] + + +def covariance_norm(x, covariance): + if isinstance(covariance, Collection): + covariance, power = covariance + else: + power = None + weight = Constant(1/covariance) + val = assemble(inner(x, weight*x)*dx) + from pyadjoint import AdjFloat + result = val if power is None else val**power + assert type(result) is AdjFloat + return result + + +class CovarianceNormReducedFunctional(ReducedFunctional): + def __init__(self, x, covariance, + functional_name=None, + control_name=None): + if isinstance(covariance, Collection): + self.covariance, self.power = covariance + else: + self.covariance = covariance + self.power = None + cov_norm = partial(covariance_norm, covariance=covariance) + rf = isolated_rf(cov_norm, x, + functional_name=functional_name, + control_name=control_name) + super().__init__(rf.functional, + rf.controls.delist(rf.controls), + tape=rf.tape) From e3eadd36932ac09ae08dc03a6c8cbf3a9103215c Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Thu, 27 Mar 2025 10:53:17 +0000 Subject: [PATCH 071/222] rm old files --- .../ensemble_reduced_functional_prevec.py | 673 --------- .../fourdvar_reduced_functional_bak.py | 1258 ----------------- 2 files changed, 1931 deletions(-) delete mode 100644 firedrake/adjoint/ensemble_reduced_functional_prevec.py delete mode 100644 firedrake/adjoint/fourdvar_reduced_functional_bak.py diff --git a/firedrake/adjoint/ensemble_reduced_functional_prevec.py b/firedrake/adjoint/ensemble_reduced_functional_prevec.py deleted file mode 100644 index f018b20dfc..0000000000 --- a/firedrake/adjoint/ensemble_reduced_functional_prevec.py +++ /dev/null @@ -1,673 +0,0 @@ -from functools import partial, cached_property -from contextlib import contextmanager -from pyadjoint import ReducedFunctional, Control, set_working_tape, AdjFloat, no_annotations -from pyadjoint.enlisting import Enlist -from firedrake.function import Function -from firedrake.cofunction import Cofunction -from firedrake.ensemble.ensemble_function import ( - EnsembleFunctionSpace, EnsembleFunction, EnsembleCofunction, EnsembleFunctionBase) - -__all__ = ( - "EnsembleReducedFunctional", - "EnsembleBcastReducedFunctional", - "EnsembleReduceReducedFunctional", - "EnsembleTransformReducedFunctional", -) - - -def _intermediate_options(options): - iopts = {} if options is None else options.copy() - iopts["riesz_representation"] = None - return iopts - - -def _local_subs(val): - return val.subfunctions if isinstance(val, EnsembleFunctionBase) else val - - -def _set_local_subs(dst, src): - dst = _local_subs(dst) - for i, s in enumerate(src): - if hasattr(dst[i], 'assign'): - dst[i].assign(s) - else: - dst[i] = s - return dst - - -class EnsembleReducedFunctional(ReducedFunctional): - """Enable solving simultaneously reduced functionals in parallel. - - Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`, - where :math:`m` is the control parameter. Let us assume that :math:`J` is the sum of - :math:`N` functionals :math:`J_i(m)`, i.e., - - .. math:: - - J = \\sum_{i=1}^{N} J_i(m). - - The gradient over a summation is a linear operation. Therefore, we can write the gradient - :math:`\\dfrac{dJ}{dm}` as - - .. math:: - - \\frac{dJ}{dm} = \\sum_{i=1}^{N} \\frac{dJ_i}{dm}, - - The :class:`EnsembleReducedFunctional` allows simultaneous evaluation of :math:`J_i` and - :math:`\\dfrac{dJ_i}{dm}`. After that, the allreduce :class:`~.ensemble.Ensemble` - operation is employed to sum the functionals and their gradients over an ensemble - communicator. - - If gather_functional is present, then all the values of J are communicated to all ensemble - ranks, and passed in a list to gather_functional, which is a reduced functional that expects - a list of that size of the relevant types. - - Parameters - ---------- - rfs : list[pyadjoint.ReducedFunctional] - The reduced functionals for each term Ji. - functional : pyadjoint.OverloadedType - An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`. - This should be the functional that we want to calculate. - control : pyadjoint.Control or list of pyadjoint.Control - A single or a list of Control instances, which you want to map to the functional. - ensemble : Ensemble - An instance of the :class:`~.ensemble.Ensemble`. It is used to communicate the - functionals and their derivatives between the ensemble members. - bcast_control : bool - Whether the control (or a list of controls) is broadcast over the ensemble - communicator ``Ensemble.ensemble_comm`` before evaluating the reduced functionals. - If True, then the control must be a :class:`Function` or :class:`pyadjoint.AdjFloat` - defined on ``Ensemble.comm``, and is logically identical across all ensemble members. - If False, then the control must be a :class:`EnsembleFunction` defined on the ensemble, - and is logically collective over all ensemble members. - reduce_functional : bool - Whether the functional is reduced over the ensemble communicator - ``Ensemble.ensemble_comm`` after evaluating the reduced functionals. - If True, then the functional must be a :class:`Function` or :class:`pyadjoint.AdjFloat` - defined on ``Ensemble.comm``, and is logically identical across all ensemble members. - If False, then the functional must be a :class:`EnsembleFunction` defined on the - ensemble, and is logically collective over all ensemble members. - - See Also - -------- - :class:`~.ensemble.Ensemble`, :class:`pyadjoint.ReducedFunctional`. - - Notes - ----- - The functionals :math:`J_i` and the control must be defined over a common - `ensemble.comm` communicator. To understand more about how ensemble parallelism - works, please refer to the `Firedrake manual - `_. - """ - def __init__(self, rfs, functional, control, ensemble=None, - bcast_control=True, reduce_functional=True): - self.rfs = rfs - self.controls = Enlist(control) - self.functional = functional - self.bcast_control = bcast_control - self.reduce_functional = reduce_functional - self.ensemble = ensemble - - if bcast_control: - # controls are Functions, so need EnsembleFunctions for Transform input - ensemble_control_spaces = [ - EnsembleFunctionSpace([c.control.function_space() - for _ in range(len(rfs))], ensemble) - for c in self.controls] - - ensemble_controls = [ - Control(EnsembleFunction(space)) for space in ensemble_control_spaces] - - self.bcast_rfs = [ - EnsembleBcastReducedFunctional( - ec.control, Control(c.control._ad_copy()), ensemble=ensemble) - for ec, c in zip(ensemble_controls, self.controls)] - - else: - ensemble_controls = self.controls - - if reduce_functional: - # functional is Function or AdjFloat, so need EnsembleFunction for Transform output - if isinstance(functional, float): - ensemble_functional = [functional._ad_copy() - for _ in range(len(rfs))] - reduce_control = [Control(f) for f in ensemble_functional] - - elif isinstance(functional, Function): - ensemble_functional_space = EnsembleFunctionSpace( - [rf.functional.function_space() for rf in rfs], ensemble) - - ensemble_functional = EnsembleFunction(ensemble_functional_space) - reduce_control = Control(ensemble_functional) - - else: - raise TypeError( - f"Do not know how to handle a {type(functional).__name__}" - f" control for {type(self).__name__}") - - self.reduce_rf = EnsembleReduceReducedFunctional( - functional._ad_copy(), reduce_control, ensemble=ensemble) - - else: - ensemble_functional = functional - - self.transform_rf = EnsembleTransformReducedFunctional( - rfs, ensemble_functional, - self.controls.delist(ensemble_controls), - ensemble=ensemble) - - def __call__(self, values): - """Computes the reduced functional with supplied control value. - - Parameters - ---------- - values : pyadjoint.OverloadedType - If you have multiple controls this should be a list of - new values for each control in the order you listed the controls to the constructor. - If you have a single control it can either be a list or a single object. - Each new value should have the same type as the corresponding control. - - Returns - ------- - pyadjoint.OverloadedType - The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. - - """ - if self.bcast_control: - values = self.controls.delist( - [bcast(val) for bcast, val in zip(self.bcast_rfs, Enlist(values))]) - - J = self.transform_rf(values) - - if self.reduce_functional: - J = self.reduce_rf(J) - - return J - - def derivative(self, adj_input=1.0, options=None): - """Compute derivatives of a functional with respect to the control parameters. - - Parameters - ---------- - adj_input : float - The adjoint input. - options : dict - Additional options for the derivative computation. - - Returns - ------- - dJdm_total : pyadjoint.OverloadedType - The result of Allreduce operations of ``dJdm_local`` into ``dJdm_total`` over the`Ensemble.ensemble_comm`. - - See Also - -------- - :meth:`~.ensemble.Ensemble.allreduce`, :meth:`pyadjoint.ReducedFunctional.derivative`. - """ - iopts = _intermediate_options(options) - if self.reduce_functional: - adj_input = self.reduce_rf.derivative(adj_input=adj_input, - options=iopts) - - transform_options = iopts if self.bcast_control else options - - dJ = self.transform_rf.derivative(adj_input=adj_input, - options=transform_options) - - if self.bcast_control: - dJ = Enlist(dJ) - dJ = dJ.delist( - [bcast.derivative(adj_input=dj, options=options) - for bcast, dj in zip(self.bcast_rfs, dJ)]) - - return dJ - - @no_annotations - def tlm(self, m_dot, options=None): - """Returns the action of the tangent linear model of the functional w.r.t. the - control on a vector m_dot, around the last supplied value of the control. - - Args: - m_dot ([OverloadedType]): The direction in which to compute the - action of the tangent linear model. - options (dict): A dictionary of options. To find a list of - available options have a look at the specific control type. - - Returns: - OverloadedType: The action of the tangent linear model in the direction m_dot. - Should be an instance of the same type as the functional. - """ - iopts = _intermediate_options(options) - - if self.bcast_control: - m_dot = Enlist(m_dot) - m_dot = m_dot.delist( - [bcast.tlm(md, options=iopts) - for bcast, md in zip(self.bcast_rfs, m_dot)]) - - tlm_options = iopts if self.reduce_functional else options - - tlm = self.transform_rf.tlm(m_dot, options=tlm_options) - - if self.reduce_functional: - tlm = self.reduce_rf.tlm(tlm, options=options) - - return tlm - - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. - - Using the second-order adjoint method, the action of the Hessian of the - functional with respect to the control, around the last supplied value - of the control, is computed and returned. - - Args: - m_dot ([OverloadedType]): The direction in which to compute the - action of the Hessian. - options (dict): A dictionary of options. To find a list of - available options have a look at the specific control type. - hessian_input (OverloadedType): The value to start the Hessian accumulation from after the TLM calculation. - evaluate_tlm (bool): Whether or not to compute the forward (tlm) part of the Hessian calculation. - If False, assumes that the tape has already been populated with required TLM values. - - Returns: - OverloadedType: The action of the Hessian in the direction m_dot. - Should be an instance of the same type as the control. - """ - iopts = _intermediate_options(options) - - if evaluate_tlm: - self.tlm(m_dot, options=iopts) - - if self.reduce_functional: - hessian_input = self.reduce_rf.hessian( - m_dot=None, evaluate_tlm=False, - hessian_input=hessian_input, - options=iopts) - - hessian_options = iopts if self.bcast_control else options - - hessian = self.transform_rf.hessian( - m_dot=None, evaluate_tlm=False, - hessian_input=hessian_input, - options=hessian_options) - - if self.bcast_control: - hessian = Enlist(hessian) - hessian = hessian.delist( - [bcast.hessian(m_dot=None, evaluate_tlm=False, - hessian_input=hess, options=options) - for bcast, hess in zip(self.bcast_rfs, hessian)]) - - return hessian - - -class FunctionOrFloatMPIMixin: - # Should be replaced by Ensemble passing through non-Functions to ensemble_comm - def _bcast(self, val, root=None): - if root is None: - return val - if isinstance(val, float): - val = self.ensemble.ensemble_comm.bcast(val, root=root) - elif isinstance(val, Function): - val = self.ensemble.bcast(val, root=self.root) - else: - raise NotImplementedError(f"Functionals of type {type(val).__name__} are not supported.") - return val - - def _reduce(self, vals, root=None): - vals = Enlist(vals) - for v in vals: - if not isinstance(v, (Function, Cofunction, float)): - raise NotImplementedError( - f"Functionals of type {type(v).__name__} are not supported.") - - if isinstance(vals[0], float): - comm = self.ensemble.ensemble_comm - local_sum = sum(vals) - if root is None: - return comm.allreduce(local_sum) - else: - return comm.reduce(local_sum, root=root) - else: - comm = self.ensemble - global_sum = vals[0]._ad_convert_type(0) - local_sum = vals[0]._ad_convert_type(0) - local_sum.assign(sum(vals)) - if root is None: - return comm.allreduce(local_sum, global_sum) - else: - return comm.reduce(local_sum, global_sum, root=root) - - def _allgather(self, vals): - pass - - -class EnsembleBcastReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): - def __init__(self, functional, control, root=None, ensemble=None): - self.functional = functional - self.controls = Enlist(control) - self.control = control - self.root = root - - # for AdjFloats functional is a list - self._functionals = Enlist(functional) - - if isinstance(control.control, AdjFloat): - if not all([isinstance(f, AdjFloat) for f in functional]): - raise TypeError( - f"Functional for {type(self).__name__} must be a list of" - " AdjFloats if using an AdjFloat control.") - if ensemble is None: - raise ValueError( - f"Must provide {type(self).__name__} an Ensemble" - " if using an AdjFloat control.") - self.ensemble = ensemble - self.nlocal_outputs = len(functional) - - elif isinstance(control.control, Function): - if not isinstance(functional, EnsembleFunction): - raise TypeError( - f"Functional for {type(self).__name__} must be an" - " EnsembleFunction if using a Function control.") - if not all([f.function_space() == control.function_space() - for f in functional.subfunctions]): - raise ValueError( - f"All subfunctions of the {type(self).__name__} functional" - " must have the same function space as the control.") - if (ensemble is not None and ensemble is not functional.function_space().ensemble): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the functional.") - self.ensemble = functional.function_space().ensemble - self.nlocal_outputs = len(self.functional.subfunctions) - - else: - raise ValueError( - f"Do not know how to handle a {type(control).__name__}" - f" control for {type(self).__name__}.") - - def __call__(self, values): - val = self._bcast(values, root=self.root) - - # "bcast" val into all elements of J (0+val = val) - J = self._functionals.delist([ - functional._ad_convert_type(0) - for functional in self._functionals - ]) - for Ji in _local_subs(J): - Ji._ad_iadd(val) - - _set_local_subs(J, [val for _ in range(self.nlocal_outputs)]) - - return J - - def derivative(self, adj_input=1.0, options=None): - if isinstance(self.control.control, Function): - if not isinstance(adj_input, EnsembleCofunction): - raise TypeError( - f"{type(self).__name__} needs an EnsembleCofunction" - f" adj_input, not a {type(adj_input).__name__}") - else: - if not all([isinstance(adj, AdjFloat) for adj in adj_input]): - raise TypeError( - f"{type(self).__name__} needs a list of AdjFloats" - f" adj_input, not a {type(adj_input).__name__}") - - dJ = self._reduce(_local_subs(adj_input), - root=self.root) - - return self.control._ad_convert_type(dJ, options=options) - - @no_annotations - def tlm(self, m_dot, options=None): - return self.functional._ad_convert_type(self(m_dot), options=options) - - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - return self.derivative(hessian_input, options=options) - - -class EnsembleReduceReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): - def __init__(self, functional, control, ensemble=None): - if isinstance(functional, ReducedFunctional): - self.reduction_rf = functional - self.functional = self.reduction_rf.functional - else: - self.reduction_rf = None - self.functional = functional - - self.controls = Enlist(control) - self.control = control - - if isinstance(functional, AdjFloat): - if not all([isinstance(c.control, AdjFloat) for c in control]): - raise TypeError( - f"Control for {type(self).__name__} must be a list of" - " AdjFloats if using an AdjFloat functional.") - if ensemble is None: - raise ValueError( - f"Must provide {type(self).__name__} an Ensemble" - " if using an AdjFloat functional.") - self.ensemble = ensemble - self.nlocal_inputs = len(control) - - elif isinstance(functional, Function): - if not isinstance(control.control, EnsembleFunction): - raise TypeError( - f"Control for {type(self).__name__} must be an" - " EnsembleFunction if using a Function functional.") - if not all([c.function_space() == functional.function_space() - for c in control.subfunctions]): - raise ValueError( - f"All subfunctions of the {type(self).__name__} control" - " must have the same function space as the functional.") - if (ensemble is not None and ensemble is not control.function_space().ensemble): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the control.") - self.ensemble = control.function_space().ensemble - self.nlocal_inputs = len(self.control.subfunctions) - - else: - raise ValueError( - f"Do not know how to handle a {type(functional).__name__}" - f" control for {type(self).__name__}.") - - _ = self._sum_rf - - @no_annotations - def __call__(self, values): - if self.reduction_rf: - return self.reduction_rf(self._allgather(_local_subs(values))) - else: - return self._reduce( - self._sum_rf(_local_subs(values))) - - def derivative(self, adj_input=1.0, options=None): - if self.reduction_rf: - dJ_global = self.reduction_rf.derivative( - adj_input=adj_input, options=options) - dJ_local = [dJ_global[i] for i in range(self._local_indices)] - else: - dJ_local = self.functional._ad_convert_type(adj_input, options=options) - dJ_local = [dJ_local for _ in range(self.nlocal_inputs)] - - dJ_global = self.controls.delist( - [c.control._ad_convert_type(0, options=options) - for c in self.controls]) - - _set_local_subs(dJ_global, dJ_local) - - return dJ_global - - @no_annotations - def tlm(self, m_dot, options=None): - if self.reduction_rf: - return self.reduction_rf.tlm( - self._allgather(_local_subs(m_dot)), options=options) - else: - return self._reduce( - self._sum_rf.tlm(_local_subs(m_dot), options=options)) - - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - return self.derivative(hessian_input, options=options) - - @cached_property - def _sum_rf(self): - subs = self.controls.delist( - [_local_subs(c.control) for c in self.controls]) - controls = [c._ad_convert_type(0.) for c in subs] - J = self.functional._ad_convert_type(0.) - with set_working_tape() as tape: - if isinstance(J, float): - J = sum(controls) - else: - for c in controls: - J = J._ad_add(c) - rf = ReducedFunctional( - J, [Control(c) for c in controls], tape=tape) - return rf - - @cached_property - def _local_indices(self): - fs = self.functional.function_space() - offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) - return tuple(offset + i for i in range(fs.nlocal_spaces)) - - -class EnsembleTransformReducedFunctional(ReducedFunctional): - def __init__(self, rfs, functional, control, ensemble=None): - self.rfs = rfs - self.functional = functional - self.controls = Enlist(control) - - # AdjFloat functional is a list but need to treat as a single object - self._flist = Enlist(functional) - - # list[EFunction] -> EFunction - if isinstance(functional, EnsembleFunction): - if not all(isinstance(c.control, EnsembleFunction) - for c in self.controls): - raise TypeError( - f"Controls for {type(self).__name__} must be EnsembleFunctions") - if ensemble is not None: - ensembles = [c.control.function_space().ensemble - for c in self.controls] - ensembles.append(functional.function_space().ensemble) - if not all(e is ensemble for e in ensembles): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must" - " match the ensemble of the controls and functional.") - - self.ensemble = functional.function_space().ensemble - self.nlocal_outputs = len(functional.subfunctions) - - # list[EFunction] -> list[AdjFloat] - elif isinstance(functional, list): - if not all(isinstance(f, AdjFloat) for f in functional): - raise TypeError( - f"Functional for {type(self).__name__} must be either" - " an EnsembleFunction or a list of AdjFloats") - if ensemble is None: - raise ValueError( - f"Must provide {type(self).__name__} an Ensemble" - " if using an AdjFloat functional.") - - self.ensemble = ensemble - self.nlocal_outputs = len(functional) - - else: - raise TypeError( - f"Functional for {type(self).__name__} must be either" - " an EnsembleFunction or a list of AdjFloats") - - @no_annotations - def __call__(self, values): - # For AdjFloat output, a single functional is a list of AdjFloats - J = [f._ad_convert_type(0) for f in self._flist] - if self._flist.listed: - J = [J] - - with self._local_data(data=Enlist(values), output=J) as (local_vals, output): - - output([rf(rf.controls.delist(vals)) - for rf, vals in zip(self.rfs, local_vals)]) - - return J[0] - - @no_annotations - def tlm(self, m_dot, options=None): - tlm = [f._ad_convert_type(0) for f in self._flist] - if self._flist.listed: - tlm = [tlm] - - with self._local_data(data=Enlist(m_dot), output=tlm) as (local_mdots, output): - - output([rf.tlm(rf.controls.delist(md), options=options) - for rf, md in zip(self.rfs, local_mdots)]) - - return tlm[0] - - def derivative(self, adj_input=1.0, options=None): - dJ = [c.control._ad_convert_type(0, options=options) - for c in self.controls] - - with self._local_data(data=[adj_input], output=dJ) as (local_adjs, output): - output([rf.derivative(adj_input=adj[0], options=options) - for rf, adj in zip(self.rfs, local_adjs)]) - - return self.controls.delist(dJ) - - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - - hessian = [ - c.control._ad_convert_type(0, options=options) - for c in self.controls] - - with self._local_data(data=[hessian_input], output=hessian) as (local_hessians, output): - output([rf.hessian(m_dot=None, evaluate_tlm=False, - hessian_input=hess[0], options=options) - for rf, hess in zip(self.rfs, local_hessians)]) - return hessian - - @contextmanager - def _local_data(self, data, output): - local_data = self._global_to_local_data(data) - output_transform = partial( - self._local_to_global_data, global_data=output) - yield local_data, output_transform - - def _local_to_global_data(self, local_data, global_data): - # N local lists of length n -> n global lists of length N - # [(1,), (2,), (3,)]-> [(1, 2, 3)] - # [(1, 11), (2, 12), (3, 13)] -> [(1, 2, 3), (11, 12, 13)] - - for j, global_group in enumerate(global_data): - local_group = [Enlist(local_group)[j] - for local_group in local_data] - _set_local_subs(global_group, local_group) - - return global_data - - def _global_to_local_data(self, global_data): - # n global lists of length N -> N local lists of length n - # [(1, 2, 3)] -> [(1,), (2,), (3,)] - # [(1, 2, 3), (11, 12, 13)] -> [(1, 11), (2, 12), (3, 13)] - - global_groups = [ - ld for ld in zip(*map(_local_subs, global_data))] - - local_groups = global_groups - return local_groups diff --git a/firedrake/adjoint/fourdvar_reduced_functional_bak.py b/firedrake/adjoint/fourdvar_reduced_functional_bak.py deleted file mode 100644 index 9458bf2933..0000000000 --- a/firedrake/adjoint/fourdvar_reduced_functional_bak.py +++ /dev/null @@ -1,1258 +0,0 @@ -from pyadjoint import ReducedFunctional, OverloadedType, Control, Tape, AdjFloat, \ - stop_annotating, get_working_tape, set_working_tape -from pyadjoint.enlisting import Enlist -from firedrake.function import Function -from firedrake.ensemble import EnsembleFunction, EnsembleFunctionSpace -from firedrake import assemble, inner, dx, Constant -from .composite_reduced_functional import ( - CompositeReducedFunctional, intermediate_options) -from .ensemble_reduced_functional import ( - EnsembleReduceReducedFunctional, EnsembleBcastReducedFunctional, - EnsembleTransformReducedFunctional, EnsembleReducedFunctional) -from .ensemble_adjvec import EnsembleAdjVec -from ufl.duals import is_primal, is_dual -from functools import wraps, cached_property, partial -from typing import Callable, Optional, Collection, Union -from types import SimpleNamespace -from contextlib import contextmanager -from mpi4py import MPI -from firedrake.petsc import PETSc - -__all__ = ['FourDVarReducedFunctional'] - - -# @set_working_tape() # ends up using old_tape = None because evaluates when imported - need separate decorator -def isolated_rf(operation, control, - functional_name=None, - control_name=None): - """ - Return a ReducedFunctional where the functional is `operation` applied - to a copy of `control`, and the tape contains only `operation`. - """ - with stop_annotating(): - controls = Enlist(control) - control_copies = [control._ad_copy() for control in controls] - - if control_name: - for control, name in zip(control_copies, Enlist(control_name)): - _rename(control, name) - - with set_working_tape(): - functional = operation(controls.delist(control_copies)) - - if functional_name: - _rename(functional, functional_name) - - control = controls.delist([Control(control_copy) - for control_copy in control_copies]) - - return ReducedFunctional( - functional, control) - - -def sc_passthrough(func): - """ - Wraps standard ReducedFunctional methods to differentiate strong or - weak constraint implementations. - - If using strong constraint, makes sure strong_reduced_functional - is instantiated then passes args/kwargs through to the - corresponding strong_reduced_functional method. - - If using weak constraint, returns the FourDVarReducedFunctional - method definition. - """ - @wraps(func) - def wrapper(self, *args, **kwargs): - if self.weak_constraint: - return func(self, *args, **kwargs) - else: - sc_func = getattr(self.strong_reduced_functional, func.__name__) - return sc_func(*args, **kwargs) - return wrapper - - -def _rename(obj, name): - if hasattr(obj, "rename"): - obj.rename(name) - - -def _ad_sub(left, right): - result = right._ad_copy() - result._ad_imul(-1) - result._ad_iadd(left) - return result - - -class FourDVarReducedFunctional(ReducedFunctional): - """ReducedFunctional for 4DVar data assimilation. - - Creates either the strong constraint or weak constraint system - by logging observations through the initial time propagator run. - - Parameters - ---------- - - control - The :class:`.EnsembleFunction` for the control x_{i} at the initial condition - and at the end of each observation stage. - - background_covariance - The inner product to calculate the background error functional - from the background error :math:`x_{0} - x_{b}`. Can include the - error covariance matrix. Only used on ensemble rank 0. - - background - The background (prior) data for the initial condition :math:`x_{b}`. - If not provided, the value of the first subfunction on the first ensemble - member of the control :class:`.EnsembleFunction` will be used. - - observation_error - Given a state :math:`x`, returns the observations error - :math:`y_{0} - \\mathcal{H}_{0}(x)` where :math:`y_{0}` are the - observations at the initial time and :math:`\\mathcal{H}_{0}` is - the observation operator for the initial time. Only used on - ensemble rank 0. Optional. - - observation_covariance - The inner product to calculate the observation error functional - from the observation error :math:`y_{0} - \\mathcal{H}_{0}(x)`. - Can include the error covariance matrix. Must be provided if - observation_error is provided. Only used on ensemble rank 0 - - weak_constraint - Whether to use the weak or strong constraint 4DVar formulation. - - See Also - -------- - :class:`pyadjoint.ReducedFunctional`. - """ - - @PETSc.Log.EventDecorator() - def __init__(self, control: Control, - background_covariance: Union[Constant, tuple], - background: Optional[OverloadedType] = None, - observation_error: Optional[Callable[[OverloadedType], OverloadedType]] = None, - observation_covariance: Optional[Callable[[OverloadedType], AdjFloat]] = None, - weak_constraint: bool = True, - tape: Optional[Tape] = None, - _annotate_accumulation: bool = False): - - self.tape = get_working_tape() if tape is None else tape - - self.weak_constraint = weak_constraint - self.initial_observations = observation_error is not None - - if self.weak_constraint: - self._annotate_accumulation = _annotate_accumulation - self._accumulation_started = False - - if not isinstance(control.control, EnsembleFunction): - raise TypeError( - "Control for weak constraint 4DVar must be an EnsembleFunction" - ) - - with stop_annotating(): - if background: - self.background = background._ad_copy() - else: - self.background = control.control.subfunctions[0]._ad_copy() - _rename(self.background, "Background") - - self.control_space = control.function_space() - ensemble = self.control_space.ensemble - self.ensemble = ensemble - self.trank = ensemble.ensemble_comm.rank if ensemble else 0 - self.nchunks = ensemble.ensemble_comm.size if ensemble else 1 - - # because we need to manually evaluate the different bits - # of the functional, we need an internal set of controls - # to use for the stage ReducedFunctionals - self._cbuf = control.copy_data() - _x = self._cbuf.subfunctions - self._x = _x - self._controls = tuple(Control(xi) for xi in _x) - - self.control = control - self.controls = Enlist(control) - - # first control on rank 0 is initial conditions, not end of observation stage - self.nlocal_stages = len(_x) - (1 if self.trank == 0 else 0) - - if self.trank == 0 and self.initial_observations: - self.nlocal_observations = self.nlocal_stages + 1 - else: - self.nlocal_observations = self.nlocal_stages - - self.stages = [] # The record of each observation stage - - self.observation_rfs = [] - self.observation_norms = [] - - self.model_rfs = [] - self.model_norms = [] - - # first rank sets up functionals for background initial observations - if self.trank == 0: - - # RF to recalculate error vector (x_0 - x_b) - self.background_error = isolated_rf( - operation=lambda x0: _ad_sub(x0, self.background), - control=_x[0], - functional_name="bkg_err_vec", - control_name="Control_0_bkg_copy") - - # RF to recalculate inner product |x_0 - x_b|_B - self.background_norm = CovarianceNormReducedFunctional( - self.background_error.functional, - background_covariance, - control_name="bkg_err_vec_copy") - - # compose background reduced functionals to evaluate both together - self.background_rf = CompositeReducedFunctional( - self.background_error, self.background_norm) - - if self.initial_observations: - - # RF to recalculate error vector (H(x_0) - y_0) - self.initial_observation_error = isolated_rf( - operation=observation_error, - control=_x[0], - functional_name="obs_err_vec_0", - control_name="Control_0_obs_copy") - - # RF to recalculate inner product |H(x_0) - y_0|_R - self.initial_observation_norm = CovarianceNormReducedFunctional( - self.initial_observation_error.functional, - observation_covariance, - control_name="obs_err_vec_0_copy") - - self.observation_rfs.append(self.initial_observation_error) - self.observation_norms.append(self.initial_observation_norm) - - # compose initial observation reduced functionals to evaluate both together - self.initial_observation_rf = CompositeReducedFunctional( - self.initial_observation_error, self.initial_observation_norm) - - # create halo for previous state - if self.ensemble and self.trank != 0: - with stop_annotating(): - self.xprev = _x[0]._ad_copy() - self._control_prev = Control(self.xprev) - - # halo for the derivative from the next chunk - if self.ensemble and self.trank != self.nchunks - 1: - with stop_annotating(): - self.xnext = _x[0]._ad_copy() - - else: - self._annotate_accumulation = True - self._accumulation_started = False - - if not isinstance(control.control, Function): - raise TypeError( - "Control for strong constraint 4DVar must be a Function" - ) - - with stop_annotating(): - if background: - self.background = background._ad_copy() - else: - self.background = control.control._ad_copy() - _rename(self.background, "Background") - - # initial conditions guess to be updated - self.controls = Enlist(control) - - # Strong constraint functional to be converted to ReducedFunctional later - - # penalty for straying from prior - self._accumulate_functional( - covariance_norm(control.control - self.background, - background_covariance)) - - # penalty for not hitting observations at initial time - if self.initial_observations: - self._accumulate_functional( - covariance_norm(observation_error(control.control), - observation_covariance)) - - @cached_property - def strong_reduced_functional(self): - """A :class:`pyadjoint.ReducedFunctional` for the strong constraint 4DVar system. - - Only instantiated if using the strong constraint formulation, and cannot be used - before all observations are recorded. - """ - if self.weak_constraint: - msg = "Strong constraint ReducedFunctional cannot be instantiated for weak constraint 4DVar" - raise AttributeError(msg) - self._strong_reduced_functional = ReducedFunctional( - self._total_functional, self.controls.delist(), tape=self.tape) - return self._strong_reduced_functional - - def __getattr__(self, attr): - """ - If using strong constraint then grab attributes from self.strong_reduced_functional. - """ - # hasattr calls getattr, so check self.__dir__ directly here to avoid recursion - if self.weak_constraint or "_strong_reduced_functional" not in dir(self): - raise AttributeError(f"'{type(self)}' object has no attribute '{attr}'") - return getattr(self.strong_reduced_functional, attr) - - @sc_passthrough - @stop_annotating() - @PETSc.Log.EventDecorator() - def __call__(self, values: OverloadedType): - """Computes the reduced functional with supplied control value. - - Parameters - ---------- - - values - If you have multiple controls this should be a list of new values - for each control in the order you listed the controls to the constructor. - If you have a single control it can either be a list or a single object. - Each new value should have the same type as the corresponding control. - - Returns - ------- - pyadjoint.OverloadedType - The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. - - """ - value = values[0] if isinstance(values, list) else values - - if not isinstance(value, type(self.control.control)): - raise ValueError(f"Value must be of type {type(self.control.control)} not type {type(value)}") - - self.control.update(value) - # put the new value into our internal set of controls to pass to each stage - self._cbuf.assign(value) - - trank = self.trank - - xi = self._cbuf.copy() - - # halos for propagation - xim1 = self.JM.functional.copy() - xim1.zero() - - for i in range(self.nlocal_stages - 1): - xim1.subfunctions[i].assign( - xi.subfunctions[i-1]) - - # post messages for control of time propagator on next chunk - if self.ensemble: - src = trank - 1 - dst = trank + 1 - - reqs = [] - if trank != self.nchunks - 1: - reqs.extend(self.ensemble.isend( - xi.subfunctions[-1], dest=dst, tag=dst)) - - if trank != 0: - reqs.extend(self.ensemble.irecv( - xim1.subfunctions[0], source=src, tag=trank)) - - MPI.Request.Waitall(reqs) - - # evaluate stages: - Jm = 0. - for i, stage in enumerate(self.stages): - Jm += stage([xim1.subfunctions[i], - xi.subfunctions[i]]) - - # Model functionals - # Jq = self.Jmodel([self.JM(xim1), xi]) - # Mx = self.JM(xim1) - # qerr = self.JQerr([Mx, xi]) - # qnorm = self.JQnorm(qerr) - # Jq = self.JMsum(qnorm) - - # Observation functional - Jo = self.Jobservations(xi) - - # Background functional - if trank == 0: - Jb = self.background_rf(xi.subfunctions[0]) if trank == 0 else None - Jb = self.ensemble.ensemble_comm.bcast(Jb, root=0) - - J = Jb + Jo + Jm - print(f"\n{Jb = } | {Jo = } | {Jm = } | {J = }") - return J - - @sc_passthrough - @stop_annotating() - @PETSc.Log.EventDecorator() - def derivative(self, adj_input: float = 1.0, options: dict = {}): - """Returns the derivative of the functional w.r.t. the control. - Using the adjoint method, the derivative of the functional with - respect to the control, around the last supplied value of the - control, is computed and returned. - - Parameters - ---------- - adj_input - The adjoint input. - - options - Additional options for the derivative computation. - - Returns - ------- - pyadjoint.OverloadedType - The derivative with respect to the control. - Should be an instance of the same type as the control. - """ - trank = self.trank - - # chaining ReducedFunctionals means we need to pass Cofunctions not Functions - options = options or {} - ioptions = intermediate_options(options) - - derivatives = self.Jobservations.derivative( - adj_input=adj_input, options=options) - - # dJ_model = self.Jmodel.derivative( - # adj_input=adj_input, options=ioptions) - - if self.ensemble: - with stop_annotating(): - xprev = derivatives.subfunctions[0]._ad_copy().zero() - xnext = derivatives.subfunctions[0]._ad_copy().zero() - if trank != 0: - derivs = [xprev, *derivatives.subfunctions] - else: - derivs = [*derivatives.subfunctions] - - # initial condition derivatives - if trank == 0: - derivs[0] += self.background_rf.derivative( - adj_input=adj_input, options=options) - - # # evaluate all time stages on chunk except first while halo in flight - for i in range(len(self.stages)): - sderiv = self.stages[i].derivative( - adj_input=adj_input, options=options, - rftype='model') - derivs[i] += sderiv[0] - derivs[i+1] += sderiv[1] - - # post the derivative halo exchange - if self.ensemble: - # halos sent backward in time - src = trank + 1 - dst = trank - 1 - - if trank != 0: - self.ensemble.isend( - derivs[0], dest=dst, tag=dst) - - if trank != self.nchunks - 1: - recv_reqs = self.ensemble.irecv( - xnext, source=src, tag=trank) - - # finish the derivative halo exchange - if self.ensemble: - if trank != self.nchunks - 1: - MPI.Request.Waitall(recv_reqs) - derivs[-1] += xnext - - return derivatives - - @sc_passthrough - @stop_annotating() - @PETSc.Log.EventDecorator() - def hessian(self, m_dot: OverloadedType, options: dict = {}): - """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. - - Using the second-order adjoint method, the action of the Hessian of the - functional with respect to the control, around the last supplied value - of the control, is computed and returned. - - Parameters - ---------- - - m_dot - The direction in which to compute the action of the Hessian. - - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - - Returns - ------- - pyadjoint.OverloadedType - The action of the Hessian in the direction m_dot. - Should be an instance of the same type as the control. - """ - trank = self.trank - - if isinstance(m_dot, list): - m_dot = m_dot[0] - - hess = self.Jobservations.hessian( - m_dot=m_dot, options=options) - - # set up arrays including halos - if trank == 0: - hs = [*hess.subfunctions] - mdot = [*m_dot.subfunctions] - else: - hprev = hess.subfunctions[0].copy(deepcopy=True).zero() - mprev = m_dot.subfunctions[0].copy(deepcopy=True).zero() - hs = [hprev, *hess.subfunctions] - mdot = [mprev, *m_dot.subfunctions] - - if trank != self.nchunks - 1: - hnext = hess.subfunctions[0].copy(deepcopy=True).zero() - - # send m_dot halo forward - if self.ensemble: - src = trank - 1 - dst = trank + 1 - - if trank != self.nchunks - 1: - self.ensemble.isend( - mdot[-1], dest=dst, tag=dst) - - if trank != 0: - recv_reqs = self.ensemble.irecv( - mdot[0], source=src, tag=trank) - - # hessian actions at the initial condition - if trank == 0: - hs[0] += self.background_rf.hessian( - mdot[0], options=options) - - # evaluate all stages on chunk except first - for i in range(1, len(self.stages)): - hms = self.stages[i].hessian( - mdot[i:i+2], options=options, rftype='model') - - hs[i] += hms[0] - hs[i+1] += hms[1] - - # wait for halo swap to finish - if trank != 0: - MPI.Request.Waitall(recv_reqs) - - # evaluate first stage on chunk now we have the halo - hms = self.stages[0].hessian( - mdot[:2], options=options, rftype='model') - - hs[0] += hms[0] - hs[1] += hms[1] - - # send result halo backward - if self.ensemble: - src = trank + 1 - dst = trank - 1 - - if trank != 0: - self.ensemble.isend( - hs[0], dest=dst, tag=dst) - - if trank != self.nchunks - 1: - recv_reqs = self.ensemble.irecv( - hnext, source=src, tag=trank) - - # finish the result halo - MPI.COMM_WORLD.Barrier() - if trank != self.nchunks - 1: - MPI.Request.Waitall(recv_reqs) - hs[-1] += hnext - - return hess - - @stop_annotating() - def hessian_matrix(self): - # Other reduced functionals don't have this. - if not self.weak_constraint: - raise AttributeError("Strong constraint 4DVar does not form a Hessian matrix") - raise NotImplementedError - - def _accumulate_functional(self, val): - if not self._annotate_accumulation: - return - if self._accumulation_started: - self._total_functional += val - else: - self._total_functional = val - self._accumulation_started = True - - @contextmanager - @PETSc.Log.EventDecorator() - def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): - if not sequential: - raise ValueError("Recording stages concurrently not yet implemented") - - # record over ensemble - if self.weak_constraint: - - trank = self.trank - - # index of "previous" stage and observation in global series - global_index = -1 - observation_index = 0 if self.initial_observations else -1 - with stop_annotating(): - xhalo = self._x[0]._ad_copy() - - # add our data onto the user's context data - ekwargs = {k: v for k, v in stage_kwargs.items()} - ekwargs['global_index'] = global_index - ekwargs['observation_index'] = observation_index - - ekwargs['xhalo'] = xhalo - - # proceed one ensemble rank at a time - with self.ensemble.sequential(**ekwargs) as ectx: - - # later ranks start from halo - if trank == 0: - controls = self._controls - else: - controls = [self._control_prev, *self._controls] - with stop_annotating(): - controls[0].assign(ectx.xhalo) - - # grab the user's data from the ensemble context - local_stage_kwargs = { - k: getattr(ectx, k) for k in stage_kwargs.keys() - } - - # initialise iterator for local stages - stage_sequence = ObservationStageSequence( - controls, self, ectx.global_index, - ectx.observation_index, - local_stage_kwargs, sequential) - - # let the user record the local stages - yield stage_sequence - - for stage in self.stages: - self.observation_rfs.append(stage.observation_error) - self.observation_norms.append(stage.observation_norm) - self.model_rfs.append(stage.forward_model) - self.model_norms.append(stage.model_norm) - - # send the state forward - with stop_annotating(): - state = self.stages[-1].controls[1].control - ectx.xhalo.assign(state) - # grab the user's information to send forward - for k in local_stage_kwargs.keys(): - setattr(ectx, k, getattr(stage_sequence.ctx, k)) - # increment the global indices for the last local stage - ectx.global_index = self.stages[-1].global_index - ectx.observation_index = self.stages[-1].observation_index - - with stop_annotating(): - # make sure that self.control now holds the - # values of the initial timeseris - self.control.assign(self._cbuf) - - # # # # # # - # Create observation ReducedFunctionals - # # # # # # - - self.observation_space = EnsembleFunctionSpace( - [Jo.functional.function_space() for Jo in self.observation_rfs], - ensemble=self.ensemble) - - self._observation_float_vec = EnsembleAdjVec( - [AdjFloat(0.) for _ in range(self.nlocal_observations)], - ensemble=self.ensemble) - - # (JH : V^n -> U^n) = (x -> y - H(x)) - observation_control = EnsembleFunction(self.control_space) - observation_functional = EnsembleFunction(self.observation_space) - - self.JH = EnsembleTransformReducedFunctional( - self.observation_rfs, - observation_functional, - Control(observation_control)) - - # (Jr : U^n -> R^n) = (x -> x^T R^{-1} x) - observation_control = EnsembleFunction(self.observation_space) - observation_functional = self._observation_float_vec._ad_convert_type(0.) - - self.JR = EnsembleTransformReducedFunctional( - self.observation_norms, - observation_functional, - Control(observation_control)) - - # (Jsum : R^n -> R) = (x -> \sum_{i} x_{i}) - obs_sum_control = self._observation_float_vec._ad_convert_type(0.) - obs_sum_functional = AdjFloat(0.) - - self.JOsum = EnsembleReduceReducedFunctional( - obs_sum_functional, - Control(obs_sum_control)) - - self.Jobservations = CompositeReducedFunctional( - CompositeReducedFunctional(self.JH, self.JR), self.JOsum) - - # # # # # # - # Create model propagator ReducedFunctionals - # # # # # # - - self._state_float_vec = EnsembleAdjVec( - [AdjFloat(0.) for _ in range(self.nlocal_stages)], - ensemble=self.ensemble) - - # contains the halo and propagated states (one less than full series) - stage_state_space = EnsembleFunctionSpace( - [Jm.functional.function_space() for Jm in self.model_rfs], - ensemble=self.ensemble) - - # (Jm : V^{n-1} -> V^{n-1}) = (x -> M(x)) - propagator_control = EnsembleFunction(stage_state_space) - propagator_functional = EnsembleFunction(stage_state_space) - - self.JM = EnsembleTransformReducedFunctional( - self.model_rfs, - propagator_functional, - Control(propagator_control)) - - # (JQerr : [V^{n-1}, V^{n-1}] -> V^{n-1}) = ([x, y] -> x - y) - propagator_controls = [ - Control(EnsembleFunction(stage_state_space)), - Control(EnsembleFunction(stage_state_space))] - propagator_functional = EnsembleFunction(stage_state_space) - - self.JQerr = EnsembleTransformReducedFunctional( - [stage.model_error for state in self.stages], - propagator_functional, - propagator_controls - ) - - # (JQnorm : V^{n-1} -> R^{n-1}) = (x -> x^T Q^{-1} x) - propagator_control = EnsembleFunction(stage_state_space) - propagator_functional = self._state_float_vec._ad_convert_type(0.) - - self.JQnorm = EnsembleTransformReducedFunctional( - self.model_norms, - propagator_functional, - Control(propagator_control)) - - # (JMsum : R^n -> R) = (x -> \sum_{i} x_{i}) - prop_sum_control = self._state_float_vec._ad_convert_type(0.) - prop_sum_functional = AdjFloat(0.) - - self.JMsum = EnsembleReduceReducedFunctional( - prop_sum_functional, - Control(prop_sum_control)) - - self.Jmodel = CompositeReducedFunctional( - CompositeReducedFunctional(self.JQerr, self.JQnorm), self.JMsum) - - else: # strong constraint - - yield ObservationStageSequence( - self.controls, self, global_index=-1, - observation_index=0 if self.initial_observations else -1, - stage_kwargs=stage_kwargs, nstages=nstages) - - -class ObservationStageSequence: - def __init__(self, controls: Control, - aaorf: FourDVarReducedFunctional, - global_index: int, - observation_index: int, - stage_kwargs: dict = None, - nstages: Optional[int] = None): - self.controls = controls - self.aaorf = aaorf - self.ctx = SimpleNamespace(**(stage_kwargs or {})) - self.weak_constraint = aaorf.weak_constraint - self.global_index = global_index - self.observation_index = observation_index - self.local_index = -1 - self.nstages = (len(controls) - 1 if self.weak_constraint - else nstages) - - def __iter__(self): - return self - - @PETSc.Log.EventDecorator() - def __next__(self): - - # increment global indices. - self.local_index += 1 - self.global_index += 1 - self.observation_index += 1 - - if self.weak_constraint: - stages = self.aaorf.stages - - # control for the start of the next stage. - next_control = self.controls[self.local_index] - - # smuggle state forward into aaorf's next control. - if self.local_index > 0: - state = stages[-1].controls[1].control - with stop_annotating(): - next_control.control.assign(state) - - # now we know that the aaorf's controls have - # been updated from the previous stage's controls, - # we can check if we need to exit. - if self.local_index >= self.nstages: - raise StopIteration - - stage = WeakObservationStage(next_control, - local_index=self.local_index, - global_index=self.global_index, - observation_index=self.observation_index) - stages.append(stage) - - else: # strong constraint - - # stop after we've recorded all stages - if self.local_index >= self.nstages: - raise StopIteration - - # dummy control to "start" stage from - control = (self.aaorf.controls[0].control if self.local_index == 0 - else self._prev_stage.state) - - stage = StrongObservationStage( - control, self.aaorf, - index=self.local_index, - observation_index=self.observation_index) - - self._prev_stage = stage - - return stage, self.ctx - - -class StrongObservationStage: - """ - Record an observation for strong constraint 4DVar at the time of `state`. - - Parameters - ---------- - - aaorf - The strong constraint FourDVarReducedFunctional. - - """ - - def __init__(self, control: OverloadedType, - aaorf: FourDVarReducedFunctional, - index: Optional[int] = None, - observation_index: Optional[int] = None): - self.aaorf = aaorf - self.control = control - self.index = index - self.observation_index = observation_index - - @PETSc.Log.EventDecorator() - def set_observation(self, state: OverloadedType, - observation_error: Callable[[OverloadedType], OverloadedType], - observation_covariance: Callable[[OverloadedType], AdjFloat]): - """ - Record an observation at the time of `state`. - - Parameters - ---------- - - state - The state at the current observation time. - - observation_error - Given a state :math:`x`, returns the observations error - :math:`y_{i} - \\mathcal{H}_{i}(x)` where :math:`y_{i}` are - the observations at the current observation time and - :math:`\\mathcal{H}_{i}` is the observation operator for the - current observation time. - - observation_covariance - The inner product to calculate the observation error functional - from the observation error :math:`y_{i} - \\mathcal{H}_{i}(x)`. - Can include the error covariance matrix. - """ - if hasattr(self.aaorf, "_strong_reduced_functional"): - raise ValueError("Cannot add observations once strong" - " constraint ReducedFunctional instantiated") - self.aaorf._accumulate_functional( - covariance_norm(observation_error(state), - observation_covariance)) - - # save the user's state to hand back for beginning of next stage - self.state = state - - -class WeakObservationStage: - """ - A single stage for weak constraint 4DVar at the time of `state`. - - Records the time propagator from the control at the beginning - of the stage, and the model and observation errors at the end of the stage. - - Parameters - ---------- - - control - The control x_{i-1} at the beginning of the stage - - local_index - The index of this stage in the timeseries on the - local ensemble member. - - global_index - The index of this stage in the global timeseries. - - observation_index - The index of the observation at the end of this stage in - the global timeseries. May be different from global_index if - an observation is taken at the initial time. - - """ - def __init__(self, control: Control, - local_index: Optional[int] = None, - global_index: Optional[int] = None, - observation_index: Optional[int] = None): - # "control" to use as initial condition. - # Not actual `Control` for consistency with strong constraint - self.control = control.control - - self.controls = Enlist(control) - self.local_index = local_index - self.global_index = global_index - self.observation_index = observation_index - set_working_tape() - self._stage_tape = get_working_tape() - - @PETSc.Log.EventDecorator() - def set_observation(self, state: OverloadedType, - observation_error: Callable[[OverloadedType], OverloadedType], - observation_covariance: Callable[[OverloadedType], AdjFloat], - forward_model_covariance: Callable[[OverloadedType], AdjFloat]): - """ - Record an observation at the time of `state`. - - Parameters - ---------- - - state - The state at the current observation time. - - observation_error - Given a state :math:`x`, returns the observations error - :math:`y_{i} - \\mathcal{H}_{i}(x)` where :math:`y_{i}` are - the observations at the current observation time and - :math:`\\mathcal{H}_{i}` is the observation operator for the - current observation time. - - observation_covariance - The inner product to calculate the observation error functional - from the observation error :math:`y_{i} - \\mathcal{H}_{i}(x)`. - Can include the error covariance matrix. - - forward_model_covariance - The inner product to calculate the model error functional from - the model error :math:`x_{i} - \\mathcal{M}_{i}(x_{i-1})`. Can - include the error covariance matrix. - """ - # get the tape used for this stage and make sure its the right one - stage_tape = get_working_tape() - if stage_tape is not self._stage_tape: - raise ValueError( - "Working tape at the end of the observation stage" - " differs from the tape at the stage beginning." - ) - - # record forward propogation - with set_working_tape(stage_tape.copy()) as tape: - self.forward_model = ReducedFunctional( - state._ad_copy(), controls=self.controls[0], tape=tape) - - # Beginning of next time-chunk is the control for this observation - # and the state at the end of the next time-chunk. - with stop_annotating(): - # smuggle initial guess at this time into the control without the tape seeing - self.controls.append(Control(state._ad_copy())) - if self.global_index: - _rename(self.controls[-1].control, f"Control_{self.global_index}") - - # model error links time-chunks by depending on both the - # previous and current controls - - # RF to recalculate error vector (M_i - x_i) - names = { - 'functional_name': f"model_err_vec_{self.global_index}", - 'control_name': [f"state_{self.global_index}_copy", - f"Control_{self.global_index}_model_copy"] - } if self.global_index else {} - - self.model_error = isolated_rf( - operation=lambda controls: _ad_sub(*controls), - control=[state, self.controls[-1].control], - **names) - - # RF to recalculate inner product |M_i - x_i|_Q - names = { - 'control_name': f"model_err_vec_{self.global_index}_copy" - } if self.global_index else {} - - self.model_norm = CovarianceNormReducedFunctional( - self.model_error.functional, - forward_model_covariance, - **names) - - # compose model error reduced functionals to evaluate both together - self.model_error_rf = CompositeReducedFunctional( - self.model_error, self.model_norm) - - # Observations after tape cut because this is now a control, not a state - - # RF to recalculate error vector (H(x_i) - y_i) - names = { - 'functional_name': f"obs_err_vec_{self.global_index}", - 'control_name': f"Control_{self.global_index}_obs_copy" - } if self.global_index else {} - - self.observation_error = isolated_rf( - operation=observation_error, - control=self.controls[-1], - **names) - - # RF to recalculate inner product |H(x_i) - y_i|_R - names = { - 'control_name': "obs_err_vec_{self.global_index}_copy" - } if self.global_index else {} - self.observation_norm = CovarianceNormReducedFunctional( - self.observation_error.functional, - observation_covariance, - **names) - - # compose observation reduced functionals to evaluate both together - self.observation_rf = CompositeReducedFunctional( - self.observation_error, self.observation_norm) - - # remove the stage initial condition "control" now we've finished recording - delattr(self, "control") - - # stop the stage tape recording anything else - set_working_tape() - - @stop_annotating() - @PETSc.Log.EventDecorator() - def __call__(self, values: OverloadedType, - rftype: Optional[str] = None): - """Computes the reduced functional with supplied control value. - - Parameters - ---------- - - values - If you have multiple controls this should be a list of new values - for each control in the order you listed the controls to the constructor. - If you have a single control it can either be a list or a single object. - Each new value should have the same type as the corresponding control. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - - Returns - ------- - pyadjoint.OverloadedType - The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. - - """ - J = 0.0 - - # evaluate model error - if rftype in (None, 'model'): - J += self.model_error_rf( - [self.forward_model(values[0]), values[1]]) - - # evaluate observation errors - if rftype in (None, 'obs'): - J += self.observation_rf(values[1]) - - return J - - @stop_annotating() - @PETSc.Log.EventDecorator() - def derivative(self, adj_input: float = 1.0, options: dict = {}, - rftype: Optional[str] = None): - """Returns the derivative of the functional w.r.t. the control. - Using the adjoint method, the derivative of the functional with - respect to the control, around the last supplied value of the - control, is computed and returned. - - Parameters - ---------- - adj_input - The adjoint input. - - options - Additional options for the derivative computation. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - - Returns - ------- - pyadjoint.OverloadedType - The derivative with respect to the control. - Should be an instance of the same type as the control. - """ - # create a list of overloaded types to put derivative into - derivatives = [] - - # chaining ReducedFunctionals means we need to pass Cofunctions not Functions - options = options or {} - ioptions = intermediate_options(options) - - if rftype in (None, 'model'): - # derivative of reduction and difference - model_err_derivs = self.model_error_rf.derivative( - adj_input=adj_input, options=ioptions) - - # derivative through the time propagator wrt to xprev - model_forward_deriv = self.forward_model.derivative( - adj_input=model_err_derivs[0], options=options) - - derivatives.append(model_forward_deriv) - - # model_err_derivs is still in the dual space, so we need to convert it to the - # type that the user has requested - this will be the type of model_forward_deriv. - derivatives.append( - model_forward_deriv._ad_convert_type( - model_err_derivs[1], options)) - - if rftype in (None, 'obs'): - obs_deriv = self.observation_rf.derivative( - adj_input=adj_input, options=options) - - if len(derivatives) == 0: - derivatives.append(None) - derivatives.append(obs_deriv) - else: - derivatives[1] += obs_deriv - - return derivatives - - @stop_annotating() - @PETSc.Log.EventDecorator() - def hessian(self, m_dot: OverloadedType, options: dict = {}, - rftype: Optional[str] = None): - """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. - - Using the second-order adjoint method, the action of the Hessian of the - functional with respect to the control, around the last supplied value - of the control, is computed and returned. - - Parameters - ---------- - - m_dot - The direction in which to compute the action of the Hessian. - - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - - Returns - ------- - pyadjoint.OverloadedType - The action of the Hessian in the direction m_dot. - Should be an instance of the same type as the control. - """ - hessian_value = [] - - if rftype in (None, 'model'): - hessian_value.extend(self._model_hessian( - m_dot, options=options)) - - if rftype in (None, 'obs'): - obs_hessian = self.observation_rf.hessian( - m_dot[1], options=options) - if len(hessian_value) == 0: - hessian_value.append(None) - hessian_value.append(obs_hessian) - else: - hessian_value[1] += obs_hessian - - return hessian_value - - def _model_hessian(self, m_dot, options): - iopts = intermediate_options(options) - - # TLM for model from mdot[0] - forward_tlm = self.forward_model.tlm(m_dot[0], options=iopts) - - # combine model TLM and mdot[1] - mdot_error = [forward_tlm, m_dot[1]] - - # Hessian (dual) for error - error_hessian = self.model_error_rf.hessian( - mdot_error, options=iopts, evaluate_tlm=True) - - # Hessian for model - model_hessian = self.forward_model.hessian( - None, options=options, - hessian_input=error_hessian[0], - evaluate_tlm=False) - - # combine model Hessian and converted error Hessian - return [ - model_hessian, - model_hessian._ad_convert_type(error_hessian[1], - options=options) - ] - - -def covariance_norm(x, covariance): - if isinstance(covariance, Collection): - covariance, power = covariance - else: - power = None - weight = Constant(1/covariance) - val = assemble(inner(x, weight*x)*dx) - from pyadjoint import AdjFloat - result = val if power is None else val**power - assert type(result) is AdjFloat - return result - - -class CovarianceNormReducedFunctional(ReducedFunctional): - def __init__(self, x, covariance, - functional_name=None, - control_name=None): - if isinstance(covariance, Collection): - self.covariance, self.power = covariance - else: - self.covariance = covariance - self.power = None - cov_norm = partial(covariance_norm, covariance=covariance) - rf = isolated_rf(cov_norm, x, - functional_name=functional_name, - control_name=control_name) - super().__init__(rf.functional, - rf.controls.delist(rf.controls), - tape=rf.tape) From 0208f5c930159d13483302dce7e1959258b8916c Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Thu, 17 Apr 2025 18:54:38 -0600 Subject: [PATCH 072/222] unnecessarily tight tao tolerance took ages to converge in H1 --- tests/firedrake/adjoint/test_optimisation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/firedrake/adjoint/test_optimisation.py b/tests/firedrake/adjoint/test_optimisation.py index 80fc476f51..1da01b7fc1 100644 --- a/tests/firedrake/adjoint/test_optimisation.py +++ b/tests/firedrake/adjoint/test_optimisation.py @@ -68,7 +68,7 @@ def test_petsc_roundtrip_multiple(): def minimize_tao_lmvm(rf): problem = MinimizationProblem(rf) solver = TAOSolver(problem, {"tao_type": "lmvm", - "tao_gatol": 1.0e-7, + "tao_gatol": 1.0e-4, "tao_grtol": 0.0, "tao_gttol": 0.0}) return solver.solve() From 4b7d9cf0b1ae6af6b0ede551759feed9baa7135c Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Sat, 19 Apr 2025 10:18:47 -0600 Subject: [PATCH 073/222] ensemble bcast, reduce, transform rfs --- firedrake/adjoint/ensemble_adjvec.py | 13 +- .../adjoint/ensemble_reduced_functional.py | 996 +++++++++--------- firedrake/adjoint_utils/ensemble_function.py | 47 +- .../test_ensemble_reduced_functional.py | 28 +- 4 files changed, 541 insertions(+), 543 deletions(-) diff --git a/firedrake/adjoint/ensemble_adjvec.py b/firedrake/adjoint/ensemble_adjvec.py index 55d5d5ba22..2d0a73f7d3 100644 --- a/firedrake/adjoint/ensemble_adjvec.py +++ b/firedrake/adjoint/ensemble_adjvec.py @@ -1,5 +1,4 @@ from pyadjoint.overloaded_type import OverloadedType -from pyadjoint.tape import no_annotations from pyadjoint.adjfloat import AdjFloat from firedrake.ensemble import Ensemble @@ -21,6 +20,11 @@ def __init__(self, subvec, ensemble): self.ensemble = ensemble OverloadedType.__init__(self) + def _ad_init_zero(self, dual=False): + return type(self)( + [v._ad_init_zero(dual=dual) for v in self.subvec], + self.ensemble) + def _ad_dot(self, other, options=None): local_dot = sum(s._ad_dot(o) for s, o in zip(self.subvec, other.subvec)), @@ -51,16 +55,15 @@ def _maybe_scalar(self, val): if isinstance(val, EnsembleAdjVec): return val.subvec else: - return [val for _ in range(len(self.subvec))] + return [val for _ in self.subvec] def _ad_copy(self): return EnsembleAdjVec( [v._ad_copy() for v in self.subvec], ensemble=self.ensemble) - @no_annotations - def _ad_convert_type(self, value, options=None): + def _ad_convert_riesz(self, value, riesz_map=None): return EnsembleAdjVec( - [s._ad_convert_type(v, options=options) + [s._ad_convert_riesz(v, riesz_map=riesz_map) for s, v in zip(self.subvec, self._maybe_scalar(value))], ensemble=self.ensemble) diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py index c4b40db549..5109e54ae4 100644 --- a/firedrake/adjoint/ensemble_reduced_functional.py +++ b/firedrake/adjoint/ensemble_reduced_functional.py @@ -1,6 +1,9 @@ from functools import partial, cached_property from contextlib import contextmanager -from pyadjoint import ReducedFunctional, Control, set_working_tape, AdjFloat, no_annotations +from pyadjoint.reduced_functional import AbstractReducedFunctional +from pyadjoint import ( + ReducedFunctional, Control, AdjFloat, set_working_tape, annotate_tape, + no_annotations, continue_annotation, pause_annotation) from pyadjoint.enlisting import Enlist from firedrake.function import Function from firedrake.cofunction import Cofunction @@ -16,12 +19,6 @@ ) -def _intermediate_options(options): - iopts = {} if options is None else options.copy() - iopts["riesz_representation"] = None - return iopts - - def _local_subs(val): if isinstance(val, EnsembleFunctionBase): return val.subfunctions @@ -44,606 +41,635 @@ def _set_local_subs(dst, src): return dst -class EnsembleReducedFunctional(ReducedFunctional): - """Enable solving simultaneously reduced functionals in parallel. +class FunctionOrFloatMPIMixin: + # Should be replaced by Ensemble passing through non-Functions to ensemble_comm + def _bcast(self, val, root=None): + if root is None: + return val + if isinstance(val, float): + val = self.ensemble.ensemble_comm.bcast(val, root=root) + elif isinstance(val, Function): + val = self.ensemble.bcast(val, root=self.root) + else: + raise NotImplementedError(f"Functionals of type {type(val).__name__} are not supported.") + return val - Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`, - where :math:`m` is the control parameter. Let us assume that :math:`J` is the sum of - :math:`N` functionals :math:`J_i(m)`, i.e., + def _reduce(self, vals, root=None): + vals = Enlist(vals) + for v in vals: + if not isinstance(v, (Function, Cofunction, float)): + raise NotImplementedError( + f"Functionals of type {type(v).__name__} are not supported.") - .. math:: + if isinstance(vals[0], float): + comm = self.ensemble.ensemble_comm + local_sum = sum(vals) + if root is None: + return comm.allreduce(local_sum) + else: + return comm.reduce(local_sum, root=root) + else: + comm = self.ensemble + global_sum = vals[0]._ad_init_zero() + local_sum = vals[0]._ad_init_zero() + local_sum.assign(sum(vals)) + if root is None: + return comm.allreduce(local_sum, global_sum) + else: + return comm.reduce(local_sum, global_sum, root=root) - J = \\sum_{i=1}^{N} J_i(m). + def _allgather(self, vals): + pass - The gradient over a summation is a linear operation. Therefore, we can write the gradient - :math:`\\dfrac{dJ}{dm}` as - .. math:: +class EnsembleReduceReducedFunctional(AbstractReducedFunctional, FunctionOrFloatMPIMixin): + def __init__(self, functional, control, ensemble=None): + if isinstance(functional, AbstractReducedFunctional): + self.reduction_rf = functional + self.functional = self.reduction_rf.functional + else: + self.reduction_rf = None + self.functional = functional - \\frac{dJ}{dm} = \\sum_{i=1}^{N} \\frac{dJ_i}{dm}, + self._controls = Enlist(control) + self._control = control - The :class:`EnsembleReducedFunctional` allows simultaneous evaluation of :math:`J_i` and - :math:`\\dfrac{dJ_i}{dm}`. After that, the allreduce :class:`~.ensemble.Ensemble` - operation is employed to sum the functionals and their gradients over an ensemble - communicator. + if isinstance(functional, AdjFloat): + if not isinstance(control.control, EnsembleAdjVec): + raise TypeError( + f"Control for {type(self).__name__} must be an EnsembleAdjVec" + " if using an AdjFloat functional.") + if (ensemble is not None and ensemble is not control.control.ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the control.") + self.ensemble = ensemble + self.nlocal_inputs = len(control.subvec) - If gather_functional is present, then all the values of J are communicated to all ensemble - ranks, and passed in a list to gather_functional, which is a reduced functional that expects - a list of that size of the relevant types. + elif isinstance(functional, Function): + if not isinstance(control.control, EnsembleFunction): + raise TypeError( + f"Control for {type(self).__name__} must be an" + " EnsembleFunction if using a Function functional.") + if not all([c.function_space() == functional.function_space() + for c in control.subfunctions]): + raise ValueError( + f"All subfunctions of the {type(self).__name__} control" + " must have the same function space as the functional.") + if (ensemble is not None and ensemble is not control.function_space().ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the control.") + self.ensemble = control.function_space().ensemble + self.nlocal_inputs = len(control.subfunctions) - Parameters - ---------- - rfs : list[pyadjoint.ReducedFunctional] - The reduced functionals for each term Ji. - functional : pyadjoint.OverloadedType - An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`. - This should be the functional that we want to calculate. - control : pyadjoint.Control or list of pyadjoint.Control - A single or a list of Control instances, which you want to map to the functional. - ensemble : Ensemble - An instance of the :class:`~.ensemble.Ensemble`. It is used to communicate the - functionals and their derivatives between the ensemble members. - bcast_control : bool - Whether the control (or a list of controls) is broadcast over the ensemble - communicator ``Ensemble.ensemble_comm`` before evaluating the reduced functionals. - If True, then the control must be a :class:`Function` or :class:`pyadjoint.AdjFloat` - defined on ``Ensemble.comm``, and is logically identical across all ensemble members. - If False, then the control must be a :class:`EnsembleFunction` defined on the ensemble, - and is logically collective over all ensemble members. - reduce_functional : bool - Whether the functional is reduced over the ensemble communicator - ``Ensemble.ensemble_comm`` after evaluating the reduced functionals. - If True, then the functional must be a :class:`Function` or :class:`pyadjoint.AdjFloat` - defined on ``Ensemble.comm``, and is logically identical across all ensemble members. - If False, then the functional must be a :class:`EnsembleFunction` defined on the - ensemble, and is logically collective over all ensemble members. + else: + raise ValueError( + f"Do not know how to handle a {type(functional).__name__}" + f" control for {type(self).__name__}.") - See Also - -------- - :class:`~.ensemble.Ensemble`, :class:`pyadjoint.ReducedFunctional`. + _ = self._sum_rf - Notes - ----- - The functionals :math:`J_i` and the control must be defined over a common - `ensemble.comm` communicator. To understand more about how ensemble parallelism - works, please refer to the `Firedrake manual - `_. - """ - def __init__(self, rfs, functional, control, ensemble=None, - bcast_control=True, reduce_functional=True): - self.rfs = rfs - self.controls = Enlist(control) - self.functional = functional - self.bcast_control = bcast_control - self.reduce_functional = reduce_functional - self.ensemble = ensemble + @property + def controls(self): + return self._controls - if bcast_control: - # controls are Functions, so need EnsembleFunctions for Transform input - ensemble_controls = [] - for cntrl in self.controls: - if isinstance(cntrl.control, float): - ensemble_controls.append( - EnsembleAdjVec( - [AdjFloat(0.) for _ in range(len(rfs))], - ensemble)) + @no_annotations + def __call__(self, values): + if self.reduction_rf: + return self.reduction_rf(self._allgather(_local_subs(values))) + else: + return self._reduce( + self._sum_rf(_local_subs(values))) - elif isinstance(cntrl.control, (Function, Cofunction)): - control_space = EnsembleFunctionSpace( - [cntrl.control.function_space() for _ in range(len(rfs))], - ensemble) - ensemble_controls.append( - EnsembleFunction(control_space)) + @no_annotations + def derivative(self, adj_input=1.0, apply_riesz=False): + if self.reduction_rf: + dJ_global = self.reduction_rf.derivative( + adj_input=adj_input, apply_riesz=False) + dJ_local = [dJ_global[i] for i in range(self._local_indices)] + else: + dJ_local = ( + self.functional._ad_init_zero(dual=True)._ad_init_object(adj_input)) + dJ_local = [dJ_local for _ in range(self.nlocal_inputs)] - else: - TypeError( - f"Don't know how to broadcast controls of type {type(cntrl.control).__name__}") + dJ_global = self._control._ad_init_zero(dual=True) + _set_local_subs(dJ_global, dJ_local) + if apply_riesz: + dJ_global = dJ_global._ad_convert_riesz( + dJ_global, riesz_map=self._control.riesz_map) + return dJ_global - self.bcast_rfs = [ - EnsembleBcastReducedFunctional( - ec, Control(c.control._ad_copy()), ensemble=ensemble) - for ec, c in zip(ensemble_controls, self.controls)] + @no_annotations + def tlm(self, m_dot): + if self.reduction_rf: + return self.reduction_rf.tlm( + self._allgather(_local_subs(m_dot))) + else: + return self._reduce( + self._sum_rf.tlm(_local_subs(m_dot))) - ensemble_controls = [Control(ec) for ec in ensemble_controls] + @no_annotations + def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False): + if evaluate_tlm: + self.tlm(m_dot) + hessian_input = 1.0 if hessian_input is None else hessian_input + return self.derivative(adj_input=hessian_input, apply_riesz=apply_riesz) - else: - ensemble_controls = self.controls + @cached_property + def _sum_rf(self): + controls = [c._ad_init_zero() + for c in _local_subs(self.controls[0].control)] + J = self.functional._ad_init_zero() + annotating = annotate_tape() + if not annotating: + continue_annotation() + with set_working_tape() as tape: + if isinstance(J, float): + J = sum(controls) + else: + for c in controls: + J = J._ad_add(c) + rf = ReducedFunctional( + J, [Control(c) for c in controls], tape=tape) + if not annotating: + pause_annotation() + return rf - if reduce_functional: - # functional is Function or AdjFloat, so need EnsembleFunction for Transform output - if isinstance(functional, float): - ensemble_functional = EnsembleAdjVec( - [functional._ad_copy() for _ in range(len(rfs))], - ensemble=ensemble) - reduce_control = Control(ensemble_functional) + @cached_property + def _local_indices(self): + fs = self.functional.function_space() + offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) + return tuple(offset + i for i in range(fs.nlocal_spaces)) - elif isinstance(functional, Function): - ensemble_functional_space = EnsembleFunctionSpace( - [rf.functional.function_space() for rf in rfs], ensemble) - ensemble_functional = EnsembleFunction(ensemble_functional_space) - reduce_control = Control(ensemble_functional) +class EnsembleBcastReducedFunctional(AbstractReducedFunctional, FunctionOrFloatMPIMixin): + def __init__(self, functional, control, root=None, ensemble=None): + self.functional = functional + self._controls = Enlist(control) + self._control = control + self.root = root - else: + if isinstance(control.control, AdjFloat): + if not isinstance(functional, EnsembleAdjVec): raise TypeError( - f"Do not know how to handle a {type(functional).__name__}" - f" control for {type(self).__name__}") + f"Functional for {type(self).__name__} must be an EnsembleAdjVec" + " if using an AdjFloat control.") + if ensemble is not None and ensemble is not functional.ensemble: + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the functional.") + self.ensemble = functional.ensemble + self.nlocal_outputs = len(functional.subvec) - self.reduce_rf = EnsembleReduceReducedFunctional( - functional._ad_copy(), reduce_control, ensemble=ensemble) + elif isinstance(control.control, Function): + if not isinstance(functional, EnsembleFunction): + raise TypeError( + f"Functional for {type(self).__name__} must be an" + " EnsembleFunction if using a Function control.") + if not all([f.function_space() == control.function_space() + for f in functional.subfunctions]): + raise ValueError( + f"All subfunctions of the {type(self).__name__} functional" + " must have the same function space as the control.") + if (ensemble is not None and ensemble is not functional.function_space().ensemble): + raise ValueError( + f"Ensemble provided to {type(self).__name__} must match" + " the ensemble of the functional.") + self.ensemble = functional.function_space().ensemble + self.nlocal_outputs = len(functional.subfunctions) else: - ensemble_functional = functional + raise ValueError( + f"Do not know how to handle a {type(control).__name__}" + f" control for {type(self).__name__}.") - self.transform_rf = EnsembleTransformReducedFunctional( - rfs, ensemble_functional, - self.controls.delist(ensemble_controls), - ensemble=ensemble) + @property + def controls(self): + return self._controls + @no_annotations def __call__(self, values): - """Computes the reduced functional with supplied control value. - - Parameters - ---------- - values : pyadjoint.OverloadedType - If you have multiple controls this should be a list of - new values for each control in the order you listed the controls to the constructor. - If you have a single control it can either be a list or a single object. - Each new value should have the same type as the corresponding control. - - Returns - ------- - pyadjoint.OverloadedType - The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. + val = self._bcast(values, root=self.root) + J = self.functional._ad_init_zero() + _set_local_subs(J, [val for _ in range(self.nlocal_outputs)]) + return J - """ - if self.bcast_control: - values = self.controls.delist( - [bcast(val) for bcast, val in zip(self.bcast_rfs, Enlist(values))]) + @no_annotations + def derivative(self, adj_input=1.0, apply_riesz=False): + dJ = self._reduce(_local_subs(adj_input), root=self.root) - J = self.transform_rf(values) + if apply_riesz: + return self._control._ad_convert_riesz( + dJ, riesz_map=self._control.riesz_map) + else: + return dJ - if self.reduce_functional: - J = self.reduce_rf(J) + @no_annotations + def tlm(self, m_dot): + return self(m_dot) - return J + @no_annotations + def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False): + if evaluate_tlm: + self.tlm(m_dot) + return self.derivative(hessian_input, apply_riesz=apply_riesz) - def derivative(self, adj_input=1.0, options=None): - """Compute derivatives of a functional with respect to the control parameters. - Parameters - ---------- - adj_input : float - The adjoint input. - options : dict - Additional options for the derivative computation. +class EnsembleTransformReducedFunctional(AbstractReducedFunctional): + def __init__(self, rfs, functional, control, ensemble=None): + self.rfs = rfs + self.functional = functional + self._controls = Enlist(control) - Returns - ------- - dJdm_total : pyadjoint.OverloadedType - The result of Allreduce operations of ``dJdm_local`` into ``dJdm_total`` over the`Ensemble.ensemble_comm`. + if isinstance(functional, EnsembleFunction): + if not all(isinstance(c.control, EnsembleFunction) + for c in self.controls): + raise TypeError( + f"Controls for {type(self).__name__} must be EnsembleFunctions") + if ensemble is not None and ensemble is not functional.function_space().ensemble: + raise ValueError( + f"Ensemble provided to {type(self).__name__} must" + " match the ensemble of the functional.") - See Also - -------- - :meth:`~.ensemble.Ensemble.allreduce`, :meth:`pyadjoint.ReducedFunctional.derivative`. - """ - iopts = _intermediate_options(options) - if self.reduce_functional: - adj_input = self.reduce_rf.derivative(adj_input=adj_input, - options=iopts) + self.ensemble = functional.function_space().ensemble + self.nlocal_outputs = len(functional.subfunctions) - transform_options = iopts if self.bcast_control else options + elif isinstance(functional, EnsembleAdjVec): + if ensemble is not None and ensemble is not functional.ensemble: + raise ValueError( + f"Ensemble provided to {type(self).__name__} must" + " match the ensemble of the functional.") - dJ = self.transform_rf.derivative(adj_input=adj_input, - options=transform_options) + self.ensemble = ensemble + self.nlocal_outputs = len(functional.subvec) - if self.bcast_control: - dJ = self.controls.delist( - [bcast.derivative(adj_input=dj, options=options) - for bcast, dj in zip(self.bcast_rfs, Enlist(dJ))]) + else: + raise TypeError( + f"Functional for {type(self).__name__} must be either" + " an EnsembleFunction or EnsembleAdjVec") - return dJ + @property + def controls(self): + return self._controls @no_annotations - def tlm(self, m_dot, options=None): - """Returns the action of the tangent linear model of the functional w.r.t. the - control on a vector m_dot, around the last supplied value of the control. + def __call__(self, values): + J = self.functional._ad_init_zero() - Args: - m_dot ([OverloadedType]): The direction in which to compute the - action of the tangent linear model. - options (dict): A dictionary of options. To find a list of - available options have a look at the specific control type. + with self._local_data(values, output=J) as (local_vals, output): - Returns: - OverloadedType: The action of the tangent linear model in the direction m_dot. - Should be an instance of the same type as the functional. - """ - iopts = _intermediate_options(options) + output([rf(rf.controls.delist(vals)) + for rf, vals in zip(self.rfs, local_vals)]) - if self.bcast_control: - m_dot = self.controls.delist( - [bcast.tlm(md, options=iopts) - for bcast, md in zip(self.bcast_rfs, Enlist(m_dot))]) + return J - tlm_options = iopts if self.reduce_functional else options + @no_annotations + def tlm(self, m_dot): + tlm = self.functional._ad_init_zero() - tlm = self.transform_rf.tlm(m_dot, options=tlm_options) + with self._local_data(m_dot, output=tlm) as (local_mdots, output): - if self.reduce_functional: - tlm = self.reduce_rf.tlm(tlm, options=options) + output([rf.tlm(rf.controls.delist(md)) + for rf, md in zip(self.rfs, local_mdots)]) return tlm - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. + @no_annotations + def derivative(self, adj_input=1.0, apply_riesz=False): + dJ = self.controls.delist( + [c.control._ad_init_zero(dual=not apply_riesz) + for c in self.controls]) - Using the second-order adjoint method, the action of the Hessian of the - functional with respect to the control, around the last supplied value - of the control, is computed and returned. + with self._local_data(adj_input, output=dJ) as (local_adjs, output): - Args: - m_dot ([OverloadedType]): The direction in which to compute the - action of the Hessian. - options (dict): A dictionary of options. To find a list of - available options have a look at the specific control type. - hessian_input (OverloadedType): The value to start the Hessian accumulation from after the TLM calculation. - evaluate_tlm (bool): Whether or not to compute the forward (tlm) part of the Hessian calculation. - If False, assumes that the tape has already been populated with required TLM values. + output([rf.derivative(adj_input=adj[0], + apply_riesz=apply_riesz) + for rf, adj in zip(self.rfs, local_adjs)]) - Returns: - OverloadedType: The action of the Hessian in the direction m_dot. - Should be an instance of the same type as the control. - """ - iopts = _intermediate_options(options) + return dJ + @no_annotations + def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False): if evaluate_tlm: - self.tlm(m_dot, options=iopts) - - if self.reduce_functional: - hessian_input = self.reduce_rf.hessian( - m_dot=None, evaluate_tlm=False, - hessian_input=hessian_input, - options=iopts) + self.tlm(m_dot) - hessian_options = iopts if self.bcast_control else options + hessian = self.controls.delist( + [c.control._ad_init_zero(dual=not apply_riesz) + for c in self.controls]) - hessian = self.transform_rf.hessian( - m_dot=None, evaluate_tlm=False, - hessian_input=hessian_input, - options=hessian_options) + with self._local_data(hessian_input, output=hessian) as (local_hessians, output): - if self.bcast_control: - hessian = self.controls.delist( - [bcast.hessian(m_dot=None, evaluate_tlm=False, - hessian_input=hess, options=options) - for bcast, hess in zip(self.bcast_rfs, Enlist(hessian))]) + output([rf.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess[0], + apply_riesz=apply_riesz) + for rf, hess in zip(self.rfs, local_hessians)]) return hessian + @contextmanager + def _local_data(self, data, output): + local_data = self._global_to_local_data(data) + output_transform = partial( + self._local_to_global_data, global_data=output) + yield local_data, output_transform -class FunctionOrFloatMPIMixin: - # Should be replaced by Ensemble passing through non-Functions to ensemble_comm - def _bcast(self, val, root=None): - if root is None: - return val - if isinstance(val, float): - val = self.ensemble.ensemble_comm.bcast(val, root=root) - elif isinstance(val, Function): - val = self.ensemble.bcast(val, root=self.root) - else: - raise NotImplementedError(f"Functionals of type {type(val).__name__} are not supported.") - return val - - def _reduce(self, vals, root=None): - vals = Enlist(vals) - for v in vals: - if not isinstance(v, (Function, Cofunction, float)): - raise NotImplementedError( - f"Functionals of type {type(v).__name__} are not supported.") + def _local_to_global_data(self, local_data, global_data): + # N local lists of length n -> n global lists of length N + # [(1,), (2,), (3,)]-> [(1, 2, 3)] + # [(1, 11), (2, 12), (3, 13)] -> [(1, 2, 3), (11, 12, 13)] - if isinstance(vals[0], float): - comm = self.ensemble.ensemble_comm - local_sum = sum(vals) - if root is None: - return comm.allreduce(local_sum) - else: - return comm.reduce(local_sum, root=root) - else: - comm = self.ensemble - global_sum = vals[0]._ad_convert_type(0) - local_sum = vals[0]._ad_convert_type(0) - local_sum.assign(sum(vals)) - if root is None: - return comm.allreduce(local_sum, global_sum) - else: - return comm.reduce(local_sum, global_sum, root=root) + for j, global_group in enumerate(Enlist(global_data)): + local_group = [Enlist(local_group)[j] + for local_group in local_data] + _set_local_subs(global_group, local_group) - def _allgather(self, vals): - pass + return global_data + def _global_to_local_data(self, global_data): + # n global lists of length N -> N local lists of length n + # [(1, 2, 3)] -> [(1,), (2,), (3,)] + # [(1, 2, 3), (11, 12, 13)] -> [(1, 11), (2, 12), (3, 13)] -class EnsembleBcastReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): - def __init__(self, functional, control, root=None, ensemble=None): - self.functional = functional - self.controls = Enlist(control) - self.control = control - self.root = root + local_groups = [ + ld for ld in zip(*map(_local_subs, Enlist(global_data)))] + return local_groups - if isinstance(control.control, AdjFloat): - if not isinstance(functional, EnsembleAdjVec): - raise TypeError( - f"Functional for {type(self).__name__} must be an EnsembleAdjVec" - " if using an AdjFloat control.") - if ensemble is not None and ensemble is not functional.ensemble: - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the functional.") - self.ensemble = functional.ensemble - self.nlocal_outputs = len(functional.subvec) - elif isinstance(control.control, Function): - if not isinstance(functional, EnsembleFunction): - raise TypeError( - f"Functional for {type(self).__name__} must be an" - " EnsembleFunction if using a Function control.") - if not all([f.function_space() == control.function_space() - for f in functional.subfunctions]): - raise ValueError( - f"All subfunctions of the {type(self).__name__} functional" - " must have the same function space as the control.") - if (ensemble is not None and ensemble is not functional.function_space().ensemble): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the functional.") - self.ensemble = functional.function_space().ensemble - self.nlocal_outputs = len(functional.subfunctions) +class EnsembleReducedFunctional(AbstractReducedFunctional): + """Enable solving simultaneously reduced functionals in parallel. - else: - raise ValueError( - f"Do not know how to handle a {type(control).__name__}" - f" control for {type(self).__name__}.") + Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`, + where :math:`m` is the control parameter. Let us assume that :math:`J` is the sum of + :math:`N` functionals :math:`J_i(m)`, i.e., - def __call__(self, values): - val = self._bcast(values, root=self.root) - J = self.functional._ad_convert_type(0) - _set_local_subs(J, [val for _ in range(self.nlocal_outputs)]) - return J + .. math:: - def derivative(self, adj_input=1.0, options=None): - dJ = self._reduce(_local_subs(adj_input), - root=self.root) + J = \\sum_{i=1}^{N} J_i(m). - return self.control._ad_convert_type(dJ, options=options) + The gradient over a summation is a linear operation. Therefore, we can write the gradient + :math:`\\dfrac{dJ}{dm}` as - @no_annotations - def tlm(self, m_dot, options=None): - return self.functional._ad_convert_type(self(m_dot), options=options) + .. math:: - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - return self.derivative(hessian_input, options=options) + \\frac{dJ}{dm} = \\sum_{i=1}^{N} \\frac{dJ_i}{dm}, + The :class:`EnsembleReducedFunctional` allows simultaneous evaluation of :math:`J_i` and + :math:`\\dfrac{dJ_i}{dm}`. After that, the allreduce :class:`~.ensemble.Ensemble` + operation is employed to sum the functionals and their gradients over an ensemble + communicator. -class EnsembleReduceReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): - def __init__(self, functional, control, ensemble=None): - if isinstance(functional, ReducedFunctional): - self.reduction_rf = functional - self.functional = self.reduction_rf.functional - else: - self.reduction_rf = None - self.functional = functional + If gather_functional is present, then all the values of J are communicated to all ensemble + ranks, and passed in a list to gather_functional, which is a reduced functional that expects + a list of that size of the relevant types. - self.controls = Enlist(control) - self.control = control + Parameters + ---------- + rfs : list[pyadjoint.ReducedFunctional] + The reduced functionals for each term Ji. + functional : pyadjoint.OverloadedType + An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`. + This should be the functional that we want to calculate. + control : pyadjoint.Control or list of pyadjoint.Control + A single or a list of Control instances, which you want to map to the functional. + ensemble : Ensemble + An instance of the :class:`~.ensemble.Ensemble`. It is used to communicate the + functionals and their derivatives between the ensemble members. + bcast_control : bool + Whether the control (or a list of controls) is broadcast over the ensemble + communicator ``Ensemble.ensemble_comm`` before evaluating the reduced functionals. + If True, then the control must be a :class:`Function` or :class:`pyadjoint.AdjFloat` + defined on ``Ensemble.comm``, and is logically identical across all ensemble members. + If False, then the control must be a :class:`EnsembleFunction` defined on the ensemble, + and is logically collective over all ensemble members. + reduce_functional : bool + Whether the functional is reduced over the ensemble communicator + ``Ensemble.ensemble_comm`` after evaluating the reduced functionals. + If True, then the functional must be a :class:`Function` or :class:`pyadjoint.AdjFloat` + defined on ``Ensemble.comm``, and is logically identical across all ensemble members. + If False, then the functional must be a :class:`EnsembleFunction` defined on the + ensemble, and is logically collective over all ensemble members. - if isinstance(functional, AdjFloat): - if not isinstance(control.control, EnsembleAdjVec): - raise TypeError( - f"Control for {type(self).__name__} must be an EnsembleAdjVec" - " if using an AdjFloat functional.") - if (ensemble is not None and ensemble is not control.control.ensemble): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the control.") - self.ensemble = ensemble - self.nlocal_inputs = len(control.subvec) + See Also + -------- + :class:`~.ensemble.Ensemble`, :class:`pyadjoint.ReducedFunctional`. - elif isinstance(functional, Function): - if not isinstance(control.control, EnsembleFunction): - raise TypeError( - f"Control for {type(self).__name__} must be an" - " EnsembleFunction if using a Function functional.") - if not all([c.function_space() == functional.function_space() - for c in control.subfunctions]): - raise ValueError( - f"All subfunctions of the {type(self).__name__} control" - " must have the same function space as the functional.") - if (ensemble is not None and ensemble is not control.function_space().ensemble): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the control.") - self.ensemble = control.function_space().ensemble - self.nlocal_inputs = len(control.subfunctions) + Notes + ----- + The functionals :math:`J_i` and the control must be defined over a common + `ensemble.comm` communicator. To understand more about how ensemble parallelism + works, please refer to the `Firedrake manual + `_. + """ + def __init__(self, rfs, functional, control, ensemble=None, + bcast_control=True, reduce_functional=True): + self.rfs = rfs + self._controls = Enlist(control) + self.functional = functional + self.bcast_control = bcast_control + self.reduce_functional = reduce_functional + self.ensemble = ensemble - else: - raise ValueError( - f"Do not know how to handle a {type(functional).__name__}" - f" control for {type(self).__name__}.") + if bcast_control: + # controls are Functions, so need EnsembleFunctions for Transform input + ensemble_controls = [] + for cntrl in self.controls: + if isinstance(cntrl.control, float): + ensemble_controls.append( + EnsembleAdjVec( + [AdjFloat(0.) for _ in range(len(rfs))], + ensemble)) - _ = self._sum_rf + elif isinstance(cntrl.control, (Function, Cofunction)): + control_space = EnsembleFunctionSpace( + [cntrl.control.function_space() for _ in range(len(rfs))], + ensemble) + ensemble_controls.append( + EnsembleFunction(control_space)) - @no_annotations - def __call__(self, values): - if self.reduction_rf: - return self.reduction_rf(self._allgather(_local_subs(values))) - else: - return self._reduce( - self._sum_rf(_local_subs(values))) + else: + TypeError( + f"Don't know how to broadcast controls of type {type(cntrl.control).__name__}") - def derivative(self, adj_input=1.0, options=None): - if self.reduction_rf: - dJ_global = self.reduction_rf.derivative( - adj_input=adj_input, options=options) - dJ_local = [dJ_global[i] for i in range(self._local_indices)] - else: - dJ_local = self.functional._ad_convert_type(adj_input, options=options) - dJ_local = [dJ_local for _ in range(self.nlocal_inputs)] + self.bcast_rfs = [ + EnsembleBcastReducedFunctional( + ec, Control(c.control._ad_copy()), ensemble=ensemble) + for ec, c in zip(ensemble_controls, self.controls)] - dJ_global = self.control._ad_convert_type(0, options=options) - _set_local_subs(dJ_global, dJ_local) - return dJ_global + ensemble_controls = [Control(ec) for ec in ensemble_controls] - @no_annotations - def tlm(self, m_dot, options=None): - if self.reduction_rf: - return self.reduction_rf.tlm( - self._allgather(_local_subs(m_dot)), options=options) else: - return self._reduce( - self._sum_rf.tlm(_local_subs(m_dot), options=options)) - - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - return self.derivative(hessian_input, options=options) - - @cached_property - def _sum_rf(self): - controls = [c._ad_convert_type(0.) - for c in _local_subs(self.controls[0].control)] - J = self.functional._ad_convert_type(0.) - with set_working_tape() as tape: - if isinstance(J, float): - J = sum(controls) - else: - for c in controls: - J = J._ad_add(c) - rf = ReducedFunctional( - J, [Control(c) for c in controls], tape=tape) - return rf + ensemble_controls = self.controls - @cached_property - def _local_indices(self): - fs = self.functional.function_space() - offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) - return tuple(offset + i for i in range(fs.nlocal_spaces)) + if reduce_functional: + # functional is Function or AdjFloat, so need EnsembleFunction for Transform output + if isinstance(functional, float): + ensemble_functional = EnsembleAdjVec( + [functional._ad_copy() for _ in range(len(rfs))], + ensemble=ensemble) + reduce_control = Control(ensemble_functional) + elif isinstance(functional, Function): + ensemble_functional_space = EnsembleFunctionSpace( + [rf.functional.function_space() for rf in rfs], ensemble) -class EnsembleTransformReducedFunctional(ReducedFunctional): - def __init__(self, rfs, functional, control, ensemble=None): - self.rfs = rfs - self.functional = functional - self.controls = Enlist(control) + ensemble_functional = EnsembleFunction(ensemble_functional_space) + reduce_control = Control(ensemble_functional) - if isinstance(functional, EnsembleFunction): - if not all(isinstance(c.control, EnsembleFunction) - for c in self.controls): + else: raise TypeError( - f"Controls for {type(self).__name__} must be EnsembleFunctions") - if ensemble is not None and ensemble is not functional.function_space().ensemble: - raise ValueError( - f"Ensemble provided to {type(self).__name__} must" - " match the ensemble of the functional.") + f"Do not know how to handle a {type(functional).__name__}" + f" control for {type(self).__name__}") - self.ensemble = functional.function_space().ensemble - self.nlocal_outputs = len(functional.subfunctions) + self.reduce_rf = EnsembleReduceReducedFunctional( + functional._ad_copy(), reduce_control, ensemble=ensemble) - elif isinstance(functional, EnsembleAdjVec): - if ensemble is not None and ensemble is not functional.ensemble: - raise ValueError( - f"Ensemble provided to {type(self).__name__} must" - " match the ensemble of the functional.") + else: + ensemble_functional = functional - self.ensemble = ensemble - self.nlocal_outputs = len(functional.subvec) + self.transform_rf = EnsembleTransformReducedFunctional( + rfs, ensemble_functional, + self.controls.delist(ensemble_controls), + ensemble=ensemble) - else: - raise TypeError( - f"Functional for {type(self).__name__} must be either" - " an EnsembleFunction or EnsembleAdjVec") + @property + def controls(self): + return self._controls @no_annotations def __call__(self, values): - J = self.functional._ad_convert_type(0) + """Computes the reduced functional with supplied control value. - with self._local_data(values, output=J) as (local_vals, output): + Parameters + ---------- + values : pyadjoint.OverloadedType + If you have multiple controls this should be a list of + new values for each control in the order you listed the controls to the constructor. + If you have a single control it can either be a list or a single object. + Each new value should have the same type as the corresponding control. - output([rf(rf.controls.delist(vals)) - for rf, vals in zip(self.rfs, local_vals)]) + Returns + ------- + pyadjoint.OverloadedType + The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. + + """ + if self.bcast_control: + values = self.controls.delist( + [bcast(val) for bcast, val in zip(self.bcast_rfs, Enlist(values))]) + + J = self.transform_rf(values) + + if self.reduce_functional: + J = self.reduce_rf(J) return J @no_annotations - def tlm(self, m_dot, options=None): - tlm = self.functional._ad_convert_type(0) + def derivative(self, adj_input=1.0, apply_riesz=False): + """Compute derivatives of a functional with respect to the control parameters. - with self._local_data(m_dot, output=tlm) as (local_mdots, output): + Parameters + ---------- + adj_input : float + The adjoint input. + options : dict + Additional options for the derivative computation. - output([rf.tlm(rf.controls.delist(md), options=options) - for rf, md in zip(self.rfs, local_mdots)]) + Returns + ------- + dJdm_total : pyadjoint.OverloadedType + The result of Allreduce operations of ``dJdm_local`` into ``dJdm_total`` over the`Ensemble.ensemble_comm`. - return tlm + See Also + -------- + :meth:`~.ensemble.Ensemble.allreduce`, :meth:`pyadjoint.ReducedFunctional.derivative`. + """ + if self.reduce_functional: + adj_input = self.reduce_rf.derivative( + adj_input=adj_input, apply_riesz=False) - def derivative(self, adj_input=1.0, options=None): - dJ = self.controls.delist( - [c.control._ad_convert_type(0, options=options) - for c in self.controls]) + transform_riesz = False if self.bcast_control else apply_riesz - with self._local_data(adj_input, output=dJ) as (local_adjs, output): + dJ = self.transform_rf.derivative( + adj_input=adj_input, apply_riesz=transform_riesz) - output([rf.derivative(adj_input=adj[0], options=options) - for rf, adj in zip(self.rfs, local_adjs)]) + if self.bcast_control: + dJ = self.controls.delist( + [bcast.derivative(adj_input=dj, apply_riesz=apply_riesz) + for bcast, dj in zip(self.bcast_rfs, Enlist(dJ))]) return dJ @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) + def tlm(self, m_dot): + """Returns the action of the tangent linear model of the functional w.r.t. the + control on a vector m_dot, around the last supplied value of the control. - hessian = self.controls.delist( - [c.control._ad_convert_type(0, options=options) - for c in self.controls]) + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the tangent linear model. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. - with self._local_data(hessian_input, output=hessian) as (local_hessians, output): + Returns: + OverloadedType: The action of the tangent linear model in the direction m_dot. + Should be an instance of the same type as the functional. + """ + if self.bcast_control: + m_dot = self.controls.delist( + [bcast.tlm(md) + for bcast, md in zip(self.bcast_rfs, Enlist(m_dot))]) - output([rf.hessian(m_dot=None, evaluate_tlm=False, - hessian_input=hess[0], options=options) - for rf, hess in zip(self.rfs, local_hessians)]) + tlm = self.transform_rf.tlm(m_dot) - return hessian + if self.reduce_functional: + tlm = self.reduce_rf.tlm(tlm) - @contextmanager - def _local_data(self, data, output): - local_data = self._global_to_local_data(data) - output_transform = partial( - self._local_to_global_data, global_data=output) - yield local_data, output_transform + return tlm - def _local_to_global_data(self, local_data, global_data): - # N local lists of length n -> n global lists of length N - # [(1,), (2,), (3,)]-> [(1, 2, 3)] - # [(1, 11), (2, 12), (3, 13)] -> [(1, 2, 3), (11, 12, 13)] + @no_annotations + def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False): + """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. - for j, global_group in enumerate(Enlist(global_data)): - local_group = [Enlist(local_group)[j] - for local_group in local_data] - _set_local_subs(global_group, local_group) + Using the second-order adjoint method, the action of the Hessian of the + functional with respect to the control, around the last supplied value + of the control, is computed and returned. - return global_data + Args: + m_dot ([OverloadedType]): The direction in which to compute the + action of the Hessian. + options (dict): A dictionary of options. To find a list of + available options have a look at the specific control type. + hessian_input (OverloadedType): The value to start the Hessian accumulation from after the TLM calculation. + evaluate_tlm (bool): Whether or not to compute the forward (tlm) part of the Hessian calculation. + If False, assumes that the tape has already been populated with required TLM values. - def _global_to_local_data(self, global_data): - # n global lists of length N -> N local lists of length n - # [(1, 2, 3)] -> [(1,), (2,), (3,)] - # [(1, 2, 3), (11, 12, 13)] -> [(1, 11), (2, 12), (3, 13)] + Returns: + OverloadedType: The action of the Hessian in the direction m_dot. + Should be an instance of the same type as the control. + """ + if evaluate_tlm: + self.tlm(m_dot) - local_groups = [ - ld for ld in zip(*map(_local_subs, Enlist(global_data)))] - return local_groups + if self.reduce_functional: + hessian_input = self.reduce_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + apply_riesz=False) + + transform_riesz = False if self.bcast_control else apply_riesz + + hessian = self.transform_rf.hessian( + m_dot=None, evaluate_tlm=False, + hessian_input=hessian_input, + apply_riesz=transform_riesz) + + if self.bcast_control: + hessian = self.controls.delist( + [bcast.hessian(m_dot=None, evaluate_tlm=False, + hessian_input=hess, + apply_riesz=apply_riesz) + for bcast, hess in zip(self.bcast_rfs, Enlist(hessian))]) + + return hessian diff --git a/firedrake/adjoint_utils/ensemble_function.py b/firedrake/adjoint_utils/ensemble_function.py index 8b42083fd3..0fc3c9aa34 100644 --- a/firedrake/adjoint_utils/ensemble_function.py +++ b/firedrake/adjoint_utils/ensemble_function.py @@ -1,5 +1,4 @@ from pyadjoint.overloaded_type import OverloadedType -from pyadjoint.tape import no_annotations import firedrake from .checkpointing import disk_checkpointing @@ -54,45 +53,15 @@ def _ad_dot(self, other, options=None): other.subfunctions)) return self.function_space().ensemble.ensemble_comm.allreduce(local_dot) - @no_annotations - def _ad_convert_type(self, value, options=None): - options = {} if options is None else options.copy() - options.setdefault("riesz_representation", "L2") - if options["riesz_representation"] is None: - if value == 0.: - # In adjoint-based differentiation, value == 0. arises only when - # the functional is independent on the control variable. - from firedrake import EnsembleCofunction - V = options.get("function_space", self.function_space()) - return EnsembleCofunction(V.dual()) - else: - return value - else: - return self._ad_convert_riesz(value, options=options) - - def _ad_convert_riesz(self, value, options=None): - from firedrake import EnsembleFunction, EnsembleCofunction - - options = {} if options is None else options - riesz_representation = options.get("riesz_representation", "L2") - V = options.get("function_space", self.function_space()) - if value == 0.: - # In adjoint-based differentiation, value == 0. arises only when - # the functional is independent on the control variable. - return EnsembleFunction(V) + def _ad_init_zero(self, dual=False): + from firedrake import EnsembleFunction + space = self.function_space() + if dual: + space = space.dual() + return EnsembleFunction(space) - if not isinstance(value, (EnsembleCofunction, EnsembleFunction)): - raise TypeError( - "Expected an EnsembleCofunction or an EnsembleFunction" - f" not a {type(value).__name__}") - - else: - if isinstance(value, EnsembleCofunction): - kwargs = options.get("solver_options", {}) - else: - kwargs = {} - return value.riesz_representation( - riesz_representation, **kwargs) + def _ad_convert_riesz(self, value, riesz_map=None): + return value.riesz_representation(riesz_map=riesz_map or "L2") def _ad_create_checkpoint(self): if disk_checkpointing(): diff --git a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py index 934fc2d95a..57c3033b2d 100644 --- a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py +++ b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py @@ -67,7 +67,7 @@ def test_ensemble_bcast_float(): expect = AdjFloat(sum(i+1.0 for i in range(nglobal_floats))) - dJ = Jhat.derivative(adj_input=adj_input) + dJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local = dJ - expect < eps @@ -128,7 +128,7 @@ def test_ensemble_bcast_function(): expect = Function(R).assign(sum(i+1.0 for i in range(nglobal_funcs))) - dJ = Jhat.derivative(adj_input=adj_input) + dJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local = errornorm(dJ, expect) < eps @@ -185,7 +185,7 @@ def test_ensemble_reduction_float(): adj_input = AdjFloat(adj_value) expect = AdjFloat(adj_value) - dJ = Jhat.derivative(adj_input=adj_input) + dJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local = all((dJi - expect) < eps for dJi in dJ.subvec) @@ -246,7 +246,7 @@ def test_ensemble_reduction_function(): adj_input = (Function(R).assign(adj_value)).riesz_representation() expect = Function(R).assign(adj_value) - dJ = Jhat.derivative(adj_input=adj_input) + dJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local = all(errornorm(dJi, expect) < eps for dJi in dJ.subfunctions) @@ -323,9 +323,9 @@ def test_ensemble_transform_float(): expect = EnsembleFunction(Re) for rf, adji, ei in zip(rfs, adj_input.subvec, expect.subfunctions): - ei.assign(rf.derivative(adj_input=adji)) + ei.assign(rf.derivative(adj_input=adji, apply_riesz=True)) - dJ = Jhat.derivative(adj_input=adj_input) + dJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local = all( errornorm(dJi, ei) < eps @@ -414,11 +414,11 @@ def test_ensemble_transform_float_two_controls(): for rf, adji, e0i, e1i in zip(rfs, adj_input.subvec, expect0.subfunctions, expect1.subfunctions): - e0, e1 = rf.derivative(adj_input=adji) + e0, e1 = rf.derivative(adj_input=adji, apply_riesz=True) e0i.assign(e0) e1i.assign(e1) - dJ0, dJ1 = Jhat.derivative(adj_input=adj_input) + dJ0, dJ1 = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local0 = all( errornorm(dJi, ei) < eps @@ -514,9 +514,9 @@ def test_ensemble_transform_function(): expect = EnsembleFunction(Re) for rf, adji, ei in zip(rfs, adj_input.subfunctions, expect.subfunctions): - ei.assign(rf.derivative(adj_input=adji)) + ei.assign(rf.derivative(adj_input=adji, apply_riesz=True)) - dJ = Jhat.derivative(adj_input=adj_input) + dJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local = all( errornorm(dJi, ei) < eps @@ -611,11 +611,11 @@ def test_ensemble_transform_function_two_controls(): for rf, adji, e0i, e1i in zip(rfs, adj_input.subfunctions, expect0.subfunctions, expect1.subfunctions): - e0, e1 = rf.derivative(adj_input=adji) + e0, e1 = rf.derivative(adj_input=adji, apply_riesz=True) e0i.assign(e0) e1i.assign(e1) - dJ0, dJ1 = Jhat.derivative(adj_input=adj_input) + dJ0, dJ1 = Jhat.derivative(adj_input=adj_input, apply_riesz=True) match_local0 = all( errornorm(dJi, ei) < eps @@ -682,7 +682,7 @@ def test_ensemble_rf_function_to_float(): assert_allclose(J, Jexpect, rtol=1e-12) adj_input = AdjFloat(4.0) - edJ = Jhat.derivative(adj_input=adj_input) + edJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) assert_allclose(edJ.dat.data_ro, adj_input*(4*xval**3)*sum_weights, rtol=1e-12) @@ -752,7 +752,7 @@ def test_ensemble_rf_efunction_to_float(): ) adj_input = 3.0 - dJ = Jhat.derivative(adj_input=adj_input) + dJ = Jhat.derivative(adj_input=adj_input, apply_riesz=True) expect = EnsembleFunction(Re) for i, ei in enumerate(expect.subfunctions): From 59590b7b5579d3a4a3989ff3e8414f954d095648 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Sat, 19 Apr 2025 18:04:40 +0100 Subject: [PATCH 074/222] ensemble rf and ensemble reduce hessian bugfix --- firedrake/adjoint/ensemble_adjvec.py | 5 +- .../adjoint/ensemble_reduced_functional.py | 8 +- .../ensemble_reduced_functional_prevec.py | 673 ------------------ .../test_ensemble_reduced_functional.py | 33 +- 4 files changed, 37 insertions(+), 682 deletions(-) delete mode 100644 firedrake/adjoint/ensemble_reduced_functional_prevec.py diff --git a/firedrake/adjoint/ensemble_adjvec.py b/firedrake/adjoint/ensemble_adjvec.py index 2d0a73f7d3..3a39c7e8ab 100644 --- a/firedrake/adjoint/ensemble_adjvec.py +++ b/firedrake/adjoint/ensemble_adjvec.py @@ -27,8 +27,9 @@ def _ad_init_zero(self, dual=False): def _ad_dot(self, other, options=None): local_dot = sum(s._ad_dot(o) - for s, o in zip(self.subvec, other.subvec)), - return self.ensemble.ensemble_comm.allreduce(local_dot) + for s, o in zip(self.subvec, other.subvec)) + global_dot = self.ensemble.ensemble_comm.allreduce(local_dot) + return global_dot def _ad_add(self, other): return EnsembleAdjVec( diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py index 5109e54ae4..3e83b58e4f 100644 --- a/firedrake/adjoint/ensemble_reduced_functional.py +++ b/firedrake/adjoint/ensemble_reduced_functional.py @@ -162,6 +162,12 @@ def derivative(self, adj_input=1.0, apply_riesz=False): @no_annotations def tlm(self, m_dot): + if isinstance(m_dot, (list, tuple)): + if len(m_dot) != 1: + raise ValueError( + f"m_dot for {self(type).__name__} TLM must" + f" only have one entry, not {len(m_dot)}") + m_dot = m_dot[0] if self.reduction_rf: return self.reduction_rf.tlm( self._allgather(_local_subs(m_dot))) @@ -173,7 +179,7 @@ def tlm(self, m_dot): def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False): if evaluate_tlm: self.tlm(m_dot) - hessian_input = 1.0 if hessian_input is None else hessian_input + hessian_input = 0.0 if hessian_input is None else hessian_input return self.derivative(adj_input=hessian_input, apply_riesz=apply_riesz) @cached_property diff --git a/firedrake/adjoint/ensemble_reduced_functional_prevec.py b/firedrake/adjoint/ensemble_reduced_functional_prevec.py deleted file mode 100644 index f018b20dfc..0000000000 --- a/firedrake/adjoint/ensemble_reduced_functional_prevec.py +++ /dev/null @@ -1,673 +0,0 @@ -from functools import partial, cached_property -from contextlib import contextmanager -from pyadjoint import ReducedFunctional, Control, set_working_tape, AdjFloat, no_annotations -from pyadjoint.enlisting import Enlist -from firedrake.function import Function -from firedrake.cofunction import Cofunction -from firedrake.ensemble.ensemble_function import ( - EnsembleFunctionSpace, EnsembleFunction, EnsembleCofunction, EnsembleFunctionBase) - -__all__ = ( - "EnsembleReducedFunctional", - "EnsembleBcastReducedFunctional", - "EnsembleReduceReducedFunctional", - "EnsembleTransformReducedFunctional", -) - - -def _intermediate_options(options): - iopts = {} if options is None else options.copy() - iopts["riesz_representation"] = None - return iopts - - -def _local_subs(val): - return val.subfunctions if isinstance(val, EnsembleFunctionBase) else val - - -def _set_local_subs(dst, src): - dst = _local_subs(dst) - for i, s in enumerate(src): - if hasattr(dst[i], 'assign'): - dst[i].assign(s) - else: - dst[i] = s - return dst - - -class EnsembleReducedFunctional(ReducedFunctional): - """Enable solving simultaneously reduced functionals in parallel. - - Consider a functional :math:`J` and its gradient :math:`\\dfrac{dJ}{dm}`, - where :math:`m` is the control parameter. Let us assume that :math:`J` is the sum of - :math:`N` functionals :math:`J_i(m)`, i.e., - - .. math:: - - J = \\sum_{i=1}^{N} J_i(m). - - The gradient over a summation is a linear operation. Therefore, we can write the gradient - :math:`\\dfrac{dJ}{dm}` as - - .. math:: - - \\frac{dJ}{dm} = \\sum_{i=1}^{N} \\frac{dJ_i}{dm}, - - The :class:`EnsembleReducedFunctional` allows simultaneous evaluation of :math:`J_i` and - :math:`\\dfrac{dJ_i}{dm}`. After that, the allreduce :class:`~.ensemble.Ensemble` - operation is employed to sum the functionals and their gradients over an ensemble - communicator. - - If gather_functional is present, then all the values of J are communicated to all ensemble - ranks, and passed in a list to gather_functional, which is a reduced functional that expects - a list of that size of the relevant types. - - Parameters - ---------- - rfs : list[pyadjoint.ReducedFunctional] - The reduced functionals for each term Ji. - functional : pyadjoint.OverloadedType - An instance of an OverloadedType, usually :class:`pyadjoint.AdjFloat`. - This should be the functional that we want to calculate. - control : pyadjoint.Control or list of pyadjoint.Control - A single or a list of Control instances, which you want to map to the functional. - ensemble : Ensemble - An instance of the :class:`~.ensemble.Ensemble`. It is used to communicate the - functionals and their derivatives between the ensemble members. - bcast_control : bool - Whether the control (or a list of controls) is broadcast over the ensemble - communicator ``Ensemble.ensemble_comm`` before evaluating the reduced functionals. - If True, then the control must be a :class:`Function` or :class:`pyadjoint.AdjFloat` - defined on ``Ensemble.comm``, and is logically identical across all ensemble members. - If False, then the control must be a :class:`EnsembleFunction` defined on the ensemble, - and is logically collective over all ensemble members. - reduce_functional : bool - Whether the functional is reduced over the ensemble communicator - ``Ensemble.ensemble_comm`` after evaluating the reduced functionals. - If True, then the functional must be a :class:`Function` or :class:`pyadjoint.AdjFloat` - defined on ``Ensemble.comm``, and is logically identical across all ensemble members. - If False, then the functional must be a :class:`EnsembleFunction` defined on the - ensemble, and is logically collective over all ensemble members. - - See Also - -------- - :class:`~.ensemble.Ensemble`, :class:`pyadjoint.ReducedFunctional`. - - Notes - ----- - The functionals :math:`J_i` and the control must be defined over a common - `ensemble.comm` communicator. To understand more about how ensemble parallelism - works, please refer to the `Firedrake manual - `_. - """ - def __init__(self, rfs, functional, control, ensemble=None, - bcast_control=True, reduce_functional=True): - self.rfs = rfs - self.controls = Enlist(control) - self.functional = functional - self.bcast_control = bcast_control - self.reduce_functional = reduce_functional - self.ensemble = ensemble - - if bcast_control: - # controls are Functions, so need EnsembleFunctions for Transform input - ensemble_control_spaces = [ - EnsembleFunctionSpace([c.control.function_space() - for _ in range(len(rfs))], ensemble) - for c in self.controls] - - ensemble_controls = [ - Control(EnsembleFunction(space)) for space in ensemble_control_spaces] - - self.bcast_rfs = [ - EnsembleBcastReducedFunctional( - ec.control, Control(c.control._ad_copy()), ensemble=ensemble) - for ec, c in zip(ensemble_controls, self.controls)] - - else: - ensemble_controls = self.controls - - if reduce_functional: - # functional is Function or AdjFloat, so need EnsembleFunction for Transform output - if isinstance(functional, float): - ensemble_functional = [functional._ad_copy() - for _ in range(len(rfs))] - reduce_control = [Control(f) for f in ensemble_functional] - - elif isinstance(functional, Function): - ensemble_functional_space = EnsembleFunctionSpace( - [rf.functional.function_space() for rf in rfs], ensemble) - - ensemble_functional = EnsembleFunction(ensemble_functional_space) - reduce_control = Control(ensemble_functional) - - else: - raise TypeError( - f"Do not know how to handle a {type(functional).__name__}" - f" control for {type(self).__name__}") - - self.reduce_rf = EnsembleReduceReducedFunctional( - functional._ad_copy(), reduce_control, ensemble=ensemble) - - else: - ensemble_functional = functional - - self.transform_rf = EnsembleTransformReducedFunctional( - rfs, ensemble_functional, - self.controls.delist(ensemble_controls), - ensemble=ensemble) - - def __call__(self, values): - """Computes the reduced functional with supplied control value. - - Parameters - ---------- - values : pyadjoint.OverloadedType - If you have multiple controls this should be a list of - new values for each control in the order you listed the controls to the constructor. - If you have a single control it can either be a list or a single object. - Each new value should have the same type as the corresponding control. - - Returns - ------- - pyadjoint.OverloadedType - The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. - - """ - if self.bcast_control: - values = self.controls.delist( - [bcast(val) for bcast, val in zip(self.bcast_rfs, Enlist(values))]) - - J = self.transform_rf(values) - - if self.reduce_functional: - J = self.reduce_rf(J) - - return J - - def derivative(self, adj_input=1.0, options=None): - """Compute derivatives of a functional with respect to the control parameters. - - Parameters - ---------- - adj_input : float - The adjoint input. - options : dict - Additional options for the derivative computation. - - Returns - ------- - dJdm_total : pyadjoint.OverloadedType - The result of Allreduce operations of ``dJdm_local`` into ``dJdm_total`` over the`Ensemble.ensemble_comm`. - - See Also - -------- - :meth:`~.ensemble.Ensemble.allreduce`, :meth:`pyadjoint.ReducedFunctional.derivative`. - """ - iopts = _intermediate_options(options) - if self.reduce_functional: - adj_input = self.reduce_rf.derivative(adj_input=adj_input, - options=iopts) - - transform_options = iopts if self.bcast_control else options - - dJ = self.transform_rf.derivative(adj_input=adj_input, - options=transform_options) - - if self.bcast_control: - dJ = Enlist(dJ) - dJ = dJ.delist( - [bcast.derivative(adj_input=dj, options=options) - for bcast, dj in zip(self.bcast_rfs, dJ)]) - - return dJ - - @no_annotations - def tlm(self, m_dot, options=None): - """Returns the action of the tangent linear model of the functional w.r.t. the - control on a vector m_dot, around the last supplied value of the control. - - Args: - m_dot ([OverloadedType]): The direction in which to compute the - action of the tangent linear model. - options (dict): A dictionary of options. To find a list of - available options have a look at the specific control type. - - Returns: - OverloadedType: The action of the tangent linear model in the direction m_dot. - Should be an instance of the same type as the functional. - """ - iopts = _intermediate_options(options) - - if self.bcast_control: - m_dot = Enlist(m_dot) - m_dot = m_dot.delist( - [bcast.tlm(md, options=iopts) - for bcast, md in zip(self.bcast_rfs, m_dot)]) - - tlm_options = iopts if self.reduce_functional else options - - tlm = self.transform_rf.tlm(m_dot, options=tlm_options) - - if self.reduce_functional: - tlm = self.reduce_rf.tlm(tlm, options=options) - - return tlm - - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. - - Using the second-order adjoint method, the action of the Hessian of the - functional with respect to the control, around the last supplied value - of the control, is computed and returned. - - Args: - m_dot ([OverloadedType]): The direction in which to compute the - action of the Hessian. - options (dict): A dictionary of options. To find a list of - available options have a look at the specific control type. - hessian_input (OverloadedType): The value to start the Hessian accumulation from after the TLM calculation. - evaluate_tlm (bool): Whether or not to compute the forward (tlm) part of the Hessian calculation. - If False, assumes that the tape has already been populated with required TLM values. - - Returns: - OverloadedType: The action of the Hessian in the direction m_dot. - Should be an instance of the same type as the control. - """ - iopts = _intermediate_options(options) - - if evaluate_tlm: - self.tlm(m_dot, options=iopts) - - if self.reduce_functional: - hessian_input = self.reduce_rf.hessian( - m_dot=None, evaluate_tlm=False, - hessian_input=hessian_input, - options=iopts) - - hessian_options = iopts if self.bcast_control else options - - hessian = self.transform_rf.hessian( - m_dot=None, evaluate_tlm=False, - hessian_input=hessian_input, - options=hessian_options) - - if self.bcast_control: - hessian = Enlist(hessian) - hessian = hessian.delist( - [bcast.hessian(m_dot=None, evaluate_tlm=False, - hessian_input=hess, options=options) - for bcast, hess in zip(self.bcast_rfs, hessian)]) - - return hessian - - -class FunctionOrFloatMPIMixin: - # Should be replaced by Ensemble passing through non-Functions to ensemble_comm - def _bcast(self, val, root=None): - if root is None: - return val - if isinstance(val, float): - val = self.ensemble.ensemble_comm.bcast(val, root=root) - elif isinstance(val, Function): - val = self.ensemble.bcast(val, root=self.root) - else: - raise NotImplementedError(f"Functionals of type {type(val).__name__} are not supported.") - return val - - def _reduce(self, vals, root=None): - vals = Enlist(vals) - for v in vals: - if not isinstance(v, (Function, Cofunction, float)): - raise NotImplementedError( - f"Functionals of type {type(v).__name__} are not supported.") - - if isinstance(vals[0], float): - comm = self.ensemble.ensemble_comm - local_sum = sum(vals) - if root is None: - return comm.allreduce(local_sum) - else: - return comm.reduce(local_sum, root=root) - else: - comm = self.ensemble - global_sum = vals[0]._ad_convert_type(0) - local_sum = vals[0]._ad_convert_type(0) - local_sum.assign(sum(vals)) - if root is None: - return comm.allreduce(local_sum, global_sum) - else: - return comm.reduce(local_sum, global_sum, root=root) - - def _allgather(self, vals): - pass - - -class EnsembleBcastReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): - def __init__(self, functional, control, root=None, ensemble=None): - self.functional = functional - self.controls = Enlist(control) - self.control = control - self.root = root - - # for AdjFloats functional is a list - self._functionals = Enlist(functional) - - if isinstance(control.control, AdjFloat): - if not all([isinstance(f, AdjFloat) for f in functional]): - raise TypeError( - f"Functional for {type(self).__name__} must be a list of" - " AdjFloats if using an AdjFloat control.") - if ensemble is None: - raise ValueError( - f"Must provide {type(self).__name__} an Ensemble" - " if using an AdjFloat control.") - self.ensemble = ensemble - self.nlocal_outputs = len(functional) - - elif isinstance(control.control, Function): - if not isinstance(functional, EnsembleFunction): - raise TypeError( - f"Functional for {type(self).__name__} must be an" - " EnsembleFunction if using a Function control.") - if not all([f.function_space() == control.function_space() - for f in functional.subfunctions]): - raise ValueError( - f"All subfunctions of the {type(self).__name__} functional" - " must have the same function space as the control.") - if (ensemble is not None and ensemble is not functional.function_space().ensemble): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the functional.") - self.ensemble = functional.function_space().ensemble - self.nlocal_outputs = len(self.functional.subfunctions) - - else: - raise ValueError( - f"Do not know how to handle a {type(control).__name__}" - f" control for {type(self).__name__}.") - - def __call__(self, values): - val = self._bcast(values, root=self.root) - - # "bcast" val into all elements of J (0+val = val) - J = self._functionals.delist([ - functional._ad_convert_type(0) - for functional in self._functionals - ]) - for Ji in _local_subs(J): - Ji._ad_iadd(val) - - _set_local_subs(J, [val for _ in range(self.nlocal_outputs)]) - - return J - - def derivative(self, adj_input=1.0, options=None): - if isinstance(self.control.control, Function): - if not isinstance(adj_input, EnsembleCofunction): - raise TypeError( - f"{type(self).__name__} needs an EnsembleCofunction" - f" adj_input, not a {type(adj_input).__name__}") - else: - if not all([isinstance(adj, AdjFloat) for adj in adj_input]): - raise TypeError( - f"{type(self).__name__} needs a list of AdjFloats" - f" adj_input, not a {type(adj_input).__name__}") - - dJ = self._reduce(_local_subs(adj_input), - root=self.root) - - return self.control._ad_convert_type(dJ, options=options) - - @no_annotations - def tlm(self, m_dot, options=None): - return self.functional._ad_convert_type(self(m_dot), options=options) - - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - return self.derivative(hessian_input, options=options) - - -class EnsembleReduceReducedFunctional(ReducedFunctional, FunctionOrFloatMPIMixin): - def __init__(self, functional, control, ensemble=None): - if isinstance(functional, ReducedFunctional): - self.reduction_rf = functional - self.functional = self.reduction_rf.functional - else: - self.reduction_rf = None - self.functional = functional - - self.controls = Enlist(control) - self.control = control - - if isinstance(functional, AdjFloat): - if not all([isinstance(c.control, AdjFloat) for c in control]): - raise TypeError( - f"Control for {type(self).__name__} must be a list of" - " AdjFloats if using an AdjFloat functional.") - if ensemble is None: - raise ValueError( - f"Must provide {type(self).__name__} an Ensemble" - " if using an AdjFloat functional.") - self.ensemble = ensemble - self.nlocal_inputs = len(control) - - elif isinstance(functional, Function): - if not isinstance(control.control, EnsembleFunction): - raise TypeError( - f"Control for {type(self).__name__} must be an" - " EnsembleFunction if using a Function functional.") - if not all([c.function_space() == functional.function_space() - for c in control.subfunctions]): - raise ValueError( - f"All subfunctions of the {type(self).__name__} control" - " must have the same function space as the functional.") - if (ensemble is not None and ensemble is not control.function_space().ensemble): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must match" - " the ensemble of the control.") - self.ensemble = control.function_space().ensemble - self.nlocal_inputs = len(self.control.subfunctions) - - else: - raise ValueError( - f"Do not know how to handle a {type(functional).__name__}" - f" control for {type(self).__name__}.") - - _ = self._sum_rf - - @no_annotations - def __call__(self, values): - if self.reduction_rf: - return self.reduction_rf(self._allgather(_local_subs(values))) - else: - return self._reduce( - self._sum_rf(_local_subs(values))) - - def derivative(self, adj_input=1.0, options=None): - if self.reduction_rf: - dJ_global = self.reduction_rf.derivative( - adj_input=adj_input, options=options) - dJ_local = [dJ_global[i] for i in range(self._local_indices)] - else: - dJ_local = self.functional._ad_convert_type(adj_input, options=options) - dJ_local = [dJ_local for _ in range(self.nlocal_inputs)] - - dJ_global = self.controls.delist( - [c.control._ad_convert_type(0, options=options) - for c in self.controls]) - - _set_local_subs(dJ_global, dJ_local) - - return dJ_global - - @no_annotations - def tlm(self, m_dot, options=None): - if self.reduction_rf: - return self.reduction_rf.tlm( - self._allgather(_local_subs(m_dot)), options=options) - else: - return self._reduce( - self._sum_rf.tlm(_local_subs(m_dot), options=options)) - - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - return self.derivative(hessian_input, options=options) - - @cached_property - def _sum_rf(self): - subs = self.controls.delist( - [_local_subs(c.control) for c in self.controls]) - controls = [c._ad_convert_type(0.) for c in subs] - J = self.functional._ad_convert_type(0.) - with set_working_tape() as tape: - if isinstance(J, float): - J = sum(controls) - else: - for c in controls: - J = J._ad_add(c) - rf = ReducedFunctional( - J, [Control(c) for c in controls], tape=tape) - return rf - - @cached_property - def _local_indices(self): - fs = self.functional.function_space() - offset = self.ensemble.ensemble_comm.exscan(fs.nlocal_spaces()) - return tuple(offset + i for i in range(fs.nlocal_spaces)) - - -class EnsembleTransformReducedFunctional(ReducedFunctional): - def __init__(self, rfs, functional, control, ensemble=None): - self.rfs = rfs - self.functional = functional - self.controls = Enlist(control) - - # AdjFloat functional is a list but need to treat as a single object - self._flist = Enlist(functional) - - # list[EFunction] -> EFunction - if isinstance(functional, EnsembleFunction): - if not all(isinstance(c.control, EnsembleFunction) - for c in self.controls): - raise TypeError( - f"Controls for {type(self).__name__} must be EnsembleFunctions") - if ensemble is not None: - ensembles = [c.control.function_space().ensemble - for c in self.controls] - ensembles.append(functional.function_space().ensemble) - if not all(e is ensemble for e in ensembles): - raise ValueError( - f"Ensemble provided to {type(self).__name__} must" - " match the ensemble of the controls and functional.") - - self.ensemble = functional.function_space().ensemble - self.nlocal_outputs = len(functional.subfunctions) - - # list[EFunction] -> list[AdjFloat] - elif isinstance(functional, list): - if not all(isinstance(f, AdjFloat) for f in functional): - raise TypeError( - f"Functional for {type(self).__name__} must be either" - " an EnsembleFunction or a list of AdjFloats") - if ensemble is None: - raise ValueError( - f"Must provide {type(self).__name__} an Ensemble" - " if using an AdjFloat functional.") - - self.ensemble = ensemble - self.nlocal_outputs = len(functional) - - else: - raise TypeError( - f"Functional for {type(self).__name__} must be either" - " an EnsembleFunction or a list of AdjFloats") - - @no_annotations - def __call__(self, values): - # For AdjFloat output, a single functional is a list of AdjFloats - J = [f._ad_convert_type(0) for f in self._flist] - if self._flist.listed: - J = [J] - - with self._local_data(data=Enlist(values), output=J) as (local_vals, output): - - output([rf(rf.controls.delist(vals)) - for rf, vals in zip(self.rfs, local_vals)]) - - return J[0] - - @no_annotations - def tlm(self, m_dot, options=None): - tlm = [f._ad_convert_type(0) for f in self._flist] - if self._flist.listed: - tlm = [tlm] - - with self._local_data(data=Enlist(m_dot), output=tlm) as (local_mdots, output): - - output([rf.tlm(rf.controls.delist(md), options=options) - for rf, md in zip(self.rfs, local_mdots)]) - - return tlm[0] - - def derivative(self, adj_input=1.0, options=None): - dJ = [c.control._ad_convert_type(0, options=options) - for c in self.controls] - - with self._local_data(data=[adj_input], output=dJ) as (local_adjs, output): - output([rf.derivative(adj_input=adj[0], options=options) - for rf, adj in zip(self.rfs, local_adjs)]) - - return self.controls.delist(dJ) - - @no_annotations - def hessian(self, m_dot, options=None, hessian_input=0.0, evaluate_tlm=True): - if evaluate_tlm: - iopts = _intermediate_options(options) - self.tlm(m_dot, options=iopts) - - hessian = [ - c.control._ad_convert_type(0, options=options) - for c in self.controls] - - with self._local_data(data=[hessian_input], output=hessian) as (local_hessians, output): - output([rf.hessian(m_dot=None, evaluate_tlm=False, - hessian_input=hess[0], options=options) - for rf, hess in zip(self.rfs, local_hessians)]) - return hessian - - @contextmanager - def _local_data(self, data, output): - local_data = self._global_to_local_data(data) - output_transform = partial( - self._local_to_global_data, global_data=output) - yield local_data, output_transform - - def _local_to_global_data(self, local_data, global_data): - # N local lists of length n -> n global lists of length N - # [(1,), (2,), (3,)]-> [(1, 2, 3)] - # [(1, 11), (2, 12), (3, 13)] -> [(1, 2, 3), (11, 12, 13)] - - for j, global_group in enumerate(global_data): - local_group = [Enlist(local_group)[j] - for local_group in local_data] - _set_local_subs(global_group, local_group) - - return global_data - - def _global_to_local_data(self, global_data): - # n global lists of length N -> N local lists of length n - # [(1, 2, 3)] -> [(1,), (2,), (3,)] - # [(1, 2, 3), (11, 12, 13)] -> [(1, 11), (2, 12), (3, 13)] - - global_groups = [ - ld for ld in zip(*map(_local_subs, global_data))] - - local_groups = global_groups - return local_groups diff --git a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py index 57c3033b2d..6a96b59af3 100644 --- a/tests/firedrake/adjoint/test_ensemble_reduced_functional.py +++ b/tests/firedrake/adjoint/test_ensemble_reduced_functional.py @@ -177,6 +177,17 @@ def test_ensemble_reduction_float(): f" expected value {expect}" ) + # TLM + tlmx = Jhat.tlm(x) + + match_local = tlmx - expect < eps + + parallel_assert( + lambda: match_local, + msg=f"Reduced TLM AdjFloat {tlmx} does not match" + f" expected value {expect}" + ) + # Check the adjoint is broadcast back to all ranks. # Because the functional is a Function we need to # pass an adj_input of an Cofunction. @@ -194,6 +205,16 @@ def test_ensemble_reduction_float(): msg=f"Reduced derivatives {dJ} do not match expected value {expect}." ) + for i, dj in enumerate(dJ.subvec): + dj *= (i+2)*0.3 + taylor = taylor_to_dict(Jhat, x, dJ) + print(f"{taylor = }") + + # derivative and hessian should be "exact" + assert mean(taylor['R0']['Rate']) + assert all(r < 1e-14 for r in taylor['R1']['Residual']) + assert all(r < 1e-14 for r in taylor['R2']['Residual']) + @pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) @pytest.mark.skipcomplex @@ -698,9 +719,9 @@ def test_ensemble_rf_function_to_float(): R1 = mean(taylor['R1']['Rate']) R2 = mean(taylor['R2']['Rate']) - assert R0 > 0.95 - assert R1 > 1.95 - assert R2 > 2.95 + assert R0 > 0.99 + assert R1 > 1.99 + assert R2 > 2.99 @pytest.mark.parallel(nprocs=[1, 2, 3, 4, 6]) @@ -784,9 +805,9 @@ def test_ensemble_rf_efunction_to_float(): R1 = mean(taylor['R1']['Rate']) R2 = mean(taylor['R2']['Rate']) - assert R0 > 0.95 - assert R1 > 1.95 - assert R2 > 2.95 + assert R0 > 0.99 + assert R1 > 1.99 + assert R2 > 2.99 @pytest.mark.parallel(nprocs=[1, 2, 3, 6]) From a49247839c86fe2f5d9fd49ad00272300cc5fbdc Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 24 Apr 2025 14:22:30 +0100 Subject: [PATCH 075/222] Undo pin on islpy (#4236) --------- Co-authored-by: ksagiyam <46749170+ksagiyam@users.noreply.github.com> --- pyproject.toml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index e493818175..93aab13cf9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,8 +37,7 @@ dependencies = [ "sympy", "fenics-ufl @ git+https://github.com/firedrakeproject/ufl.git", "firedrake-fiat @ git+https://github.com/firedrakeproject/fiat.git", - # https://github.com/inducer/islpy/issues/162 - "islpy<2025.1.3; sys_platform == 'darwin'", + "islpy>=2025.1.5; sys_platform == 'darwin'", ] classifiers = [ "Development Status :: 5 - Production/Stable", From c48cadfb503ddfcb9ff5a7ffa28605e2fc5dc95e Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Thu, 24 Apr 2025 14:23:51 +0100 Subject: [PATCH 076/222] Use apt for more PETSc packages on x86 (#4122) * remove vom-on-boundary test that didn't test on boundary behaviour --------- Co-authored-by: Connor Ward --- docker/Dockerfile.vanilla | 5 +- scripts/firedrake-configure | 90 +++++++++++++------ .../test_vertex_only_mesh_generation.py | 9 -- 3 files changed, 64 insertions(+), 40 deletions(-) diff --git a/docker/Dockerfile.vanilla b/docker/Dockerfile.vanilla index 09d30b6820..8fc0eaa93e 100644 --- a/docker/Dockerfile.vanilla +++ b/docker/Dockerfile.vanilla @@ -38,12 +38,11 @@ RUN apt-get update \ ENV OMPI_ALLOW_RUN_AS_ROOT=1 OMPI_ALLOW_RUN_AS_ROOT_CONFIRM=1 # Install PETSc. We set the compiler optimisation flags manually here to -# remove the default of '-march=native' which is not suitable for Docker images. -# We use 'sed' to make sure that the options are appended. +# remove the default of '-march=native -mtune=native' which is not suitable for Docker images. RUN git clone --depth 1 --branch $(python3 /opt/firedrake-configure --show-petsc-version) https://gitlab.com/petsc/petsc.git /opt/petsc \ && cd /opt/petsc \ && python3 /opt/firedrake-configure --arch $ARCH --show-petsc-configure-options | \ - sed "s/$/ --COPTFLAGS='-O3 -mtune=generic' --CXXOPTFLAGS='-O3 -mtune=generic' --FOPTFLAGS='-O3 -mtune=generic'/" | \ + sed "s/-march=native -mtune=native/-mtune=generic/g" | \ xargs -L1 ./configure --with-make-np=8 --download-slepc \ && make \ && make check \ diff --git a/scripts/firedrake-configure b/scripts/firedrake-configure index da5d3f4ea4..f8a93fdf5e 100755 --- a/scripts/firedrake-configure +++ b/scripts/firedrake-configure @@ -188,6 +188,15 @@ PETSC_EXTRAS_LINUX_APT_PACKAGES = ( "libfftw3-mpi-dev", "libhwloc-dev", "libhdf5-mpi-dev", + "libmumps-ptscotch-dev", + "libmetis-dev", + "libnetcdf-dev", + "libpnetcdf-dev", + "libptscotch-dev", + "libscalapack-openmpi-dev", + "libsuitesparse-dev", + "libsuperlu-dev", + "libsuperlu-dist-dev", ) COMMON_LINUX_APT_PACKAGES = BASE_LINUX_APT_PACKAGES + PETSC_EXTRAS_LINUX_APT_PACKAGES @@ -265,12 +274,8 @@ PETSC_EXTERNAL_PACKAGE_SPECS = { MACOS_HOMEBREW_ARM64: "/opt/homebrew", }, "hdf5": { - LINUX_APT_X86_64: ( - "/usr/include/hdf5/openmpi", ("/usr/lib/x86_64-linux-gnu/hdf5/openmpi/libhdf5.a",) - ), - LINUX_APT_AARCH64: ( - "/usr/include/hdf5/openmpi", ("/usr/lib/aarch64-linux-gnu/hdf5/openmpi/libhdf5.a",) - ), + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: "/opt/homebrew", }, "hwloc": { @@ -284,43 +289,43 @@ PETSC_EXTERNAL_PACKAGE_SPECS = { MACOS_HOMEBREW_ARM64: PETSC_DOWNLOAD, }, "metis": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: "/opt/homebrew", }, "mumps": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: PETSC_DOWNLOAD, }, "netcdf": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: PETSC_DOWNLOAD, }, "pnetcdf": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: "/opt/homebrew", }, "ptscotch": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: PETSC_DOWNLOAD, }, "scalapack": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: (None, ("-lscalapack-openmpi",)), + LINUX_APT_AARCH64: (None, ("-lscalapack-openmpi",)), MACOS_HOMEBREW_ARM64: "/opt/homebrew", }, "suitesparse": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: "/opt/homebrew", }, "superlu_dist": { - LINUX_APT_X86_64: PETSC_DOWNLOAD, - LINUX_APT_AARCH64: PETSC_DOWNLOAD, + LINUX_APT_X86_64: PETSC_AUTODETECT, + LINUX_APT_AARCH64: PETSC_AUTODETECT, MACOS_HOMEBREW_ARM64: PETSC_DOWNLOAD, }, "zlib": { @@ -373,8 +378,10 @@ def prepare_external_package_configure_options( # Package is installed but not findable, have to provide paths # to headers and libraries (include_dir, libs) = package_spec - configure_options.append(f"--with-{external_package}-include={include_dir}") - configure_options.append(f"--with-{external_package}-lib={','.join(libs)}") + if include_dir is not None: + configure_options.append(f"--with-{external_package}-include={include_dir}") + if len(libs) >= 1: + configure_options.append(f"--with-{external_package}-lib={','.join(libs)}") return tuple(configure_options) @@ -385,20 +392,47 @@ def prepare_configure_options( configure_options = list(COMMON_PETSC_CONFIGURE_OPTIONS) configure_options.append(f"PETSC_ARCH=arch-firedrake-{arch.value}") + # include/link flags + if package_manager in (LINUX_APT_X86_64, LINUX_APT_AARCH64): + incdir = "/usr/include" + if package_manager == LINUX_APT_X86_64: + libdir = "/usr/lib/x86_64-linux-gnu" + elif package_manager == LINUX_APT_AARCH64: + libdir = "/usr/lib/aarch64-linux-gnu" + + includes = ( + f"{incdir}/hdf5/openmpi", + f"{incdir}/scotch", + f"{incdir}/superlu", + f"{incdir}/superlu-dist", + ) + + libraries = ( + f"{libdir}/hdf5/openmpi", + ) + + incflags = " " + " ".join([f"-I{inc}" for inc in includes]) + libflags = " " + " ".join([f"-L{lib}" for lib in libraries]) + + else: + incflags = "" + libflags = "" + configure_options.extend([ - "--COPTFLAGS='-O3 -march=native -mtune=native'", - "--CXXOPTFLAGS='-O3 -march=native -mtune=native'", + f"--COPTFLAGS='-O3 -march=native -mtune=native{incflags}{libflags}'", + f"--CXXOPTFLAGS='-O3 -march=native -mtune=native{incflags}{libflags}'", ]) + # Fortran flags if package_manager == MACOS_HOMEBREW_ARM64: # -march=native and -mtune=native not available for FOPTFLAGS on macOS - configure_options.append("--FOPTFLAGS='-O3'") + configure_options.append(f"--FOPTFLAGS='-O3{incflags}{libflags}'") # Avoid macos + openmpi + mumps segmentation violation issue; # see https://github.com/firedrakeproject/firedrake/issues/4102 and https://github.com/firedrakeproject/firedrake/issues/4113. configure_options.append("-download-mumps-avoid-mpi-in-place") # PETSc does not support CMake 4.0 (hopefully fixed in v3.23.1) configure_options.append("--download-cmake") else: - configure_options.append("--FOPTFLAGS='-O3 -march=native -mtune=native'") + configure_options.append(f"--FOPTFLAGS='-O3 -march=native -mtune=native{incflags}{libflags}'") if arch == ARCH_COMPLEX: configure_options.append("--with-scalar-type=complex") diff --git a/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py b/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py index 8031173b53..18906ec924 100644 --- a/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py +++ b/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py @@ -429,15 +429,6 @@ def test_outside_boundary_behaviour(parentmesh): assert vm.cell_set.size == 1 -@pytest.mark.parallel -def test_on_boundary_behaviour(): - coords = np.array([[0.4, 0.2, 0.3]]) - mesh = UnitCubeMesh(10, 10, 10) - vm = VertexOnlyMesh(mesh, coords) - total_num_cells = MPI.COMM_WORLD.allreduce(len(vm.coordinates.dat.data_ro_with_halos), op=MPI.SUM) - assert total_num_cells == 1 - - @pytest.mark.parallel(nprocs=2) # nprocs == total number of mesh cells def test_partition_behaviour_2d_2procs(): test_partition_behaviour() From 9522ed86766c7d1c2d6701de49a41d2057a325a5 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Tue, 22 Apr 2025 20:14:18 +0100 Subject: [PATCH 077/222] arf wip --- firedrake/adjoint/__init__.py | 2 +- .../adjoint/allatonce_reduced_functional.py | 188 +++++ .../adjoint/composite_reduced_functional.py | 105 +-- .../adjoint/ensemble_reduced_functional.py | 18 +- .../adjoint/fourdvar_reduced_functional.py | 649 +++--------------- .../adjoint/test_4dvar_reduced_functional.py | 54 +- .../ensemble/test_ensemble_function.py | 1 - 7 files changed, 404 insertions(+), 613 deletions(-) create mode 100644 firedrake/adjoint/allatonce_reduced_functional.py diff --git a/firedrake/adjoint/__init__.py b/firedrake/adjoint/__init__.py index cc1b8c8393..0724821860 100644 --- a/firedrake/adjoint/__init__.py +++ b/firedrake/adjoint/__init__.py @@ -32,7 +32,7 @@ from pyadjoint.drivers import compute_gradient, compute_derivative, compute_hessian # noqa F401 from pyadjoint.adjfloat import AdjFloat # noqa F401 from pyadjoint.control import Control # noqa F401 -from pyadjoint import IPOPTSolver, ROLSolver, MinimizationProblem, \ +from pyadjoint import TAOSolver, IPOPTSolver, ROLSolver, MinimizationProblem, \ InequalityConstraint, minimize # noqa F401 from firedrake.adjoint.ufl_constraints import UFLInequalityConstraint, \ diff --git a/firedrake/adjoint/allatonce_reduced_functional.py b/firedrake/adjoint/allatonce_reduced_functional.py new file mode 100644 index 0000000000..2c2e99dbc7 --- /dev/null +++ b/firedrake/adjoint/allatonce_reduced_functional.py @@ -0,0 +1,188 @@ +from pyadjoint import Control, OverloadedType, stop_annotating +from pyadjoint.reduced_functional import AbstractReducedFunctional +from pyadjoint.enlisting import Enlist +from pyop2.mpi import MPI +from .ensemble_reduced_functional import EnsembleTransformReducedFunctional +from .composite_reduced_functional import isolated_rf, _ad_sub +from firedrake.petsc import PETSc + + +class AllAtOnceReducedFunctional(AbstractReducedFunctional): + def __init__(self, functional, control, propagator_rfs, background=None): + self.functional = functional + self._controls = Enlist(control) + + if background is None: + self.background = functional.subfunctions[0]._ad_init_zero() + else: + self.background = background + self.ensemble = functional.function_space().ensemble + + self.trank = self.ensemble.ensemble_rank + self.last_rank = self.ensemble.ensemble_size - 1 + + # dummy timestep to generate reference objects from + v = self.background._ad_init_zero() + + # propagator after halo swap: + # Jm: [x0, x0, x2, ...] -> [0, Mx0, Mx1, ...] + + nlocal_stages = len(propagator_rfs) + prop_rfs = [prf for prf in propagator_rfs] + if self.trank == 0: + prop_rfs.insert( + 0, isolated_rf(operation=lambda x0: x0._ad_imul(0), + control=v._ad_init_zero()) + ) + self.propagator_rfs = prop_rfs + + self.Jm = EnsembleTransformReducedFunctional( + prop_rfs, functional, control) + + # error after propagation and background + # Jerr: [[x0, x1, x2, ...], [0, Mx0, Mx1, ...]] + # -> [x0-xb, x1-Mx0, x2-Mx1, ...] + err_rfs = [ + isolated_rf(operation=lambda x_y: _ad_sub(x_y[0], x_y[1]), + control=[v._ad_init_zero(), + v._ad_init_zero()]) + for _ in range(nlocal_stages) + ] + if self.trank == 0: + # jankily pretend we depend on xb to avoid "adj_value is None" warnings + _bkg = self.background._ad_copy() + + def bkg_err(x0_xb): + x0, xb = x0_xb + r = x0._ad_init_zero() + r.assign(x0 - _bkg + xb - xb) + return r + + bkg_err_rf = isolated_rf( + operation=bkg_err, + control=[v._ad_init_zero(), + v._ad_init_zero()]) + err_rfs.insert(0, bkg_err_rf) + + self.Jerr = EnsembleTransformReducedFunctional( + err_rfs, + functional._ad_init_zero(), + [Control(self.Jm.functional._ad_init_zero()), + Control(self.Jm.functional._ad_init_zero())]) + + @property + def controls(self): + return self._controls + + @stop_annotating() + @PETSc.Log.EventDecorator() + def __call__(self, values: OverloadedType): + x = values + mx = self.Jm(self.forward_halos(x)) + return self.Jerr([x, mx]) + + @stop_annotating() + @PETSc.Log.EventDecorator() + def derivative(self, adj_input: float = 1.0, apply_riesz: bool = False): + dJx, dJmx0 = self.Jerr.derivative( + adj_input=adj_input, + apply_riesz=False) + + dJmx1 = self.backward_halos( + self.Jm.derivative( + adj_input=dJmx0, + apply_riesz=False)) + + dJ = dJx + dJmx1 + + return self._apply_riesz(dJ, apply_riesz) + + @stop_annotating() + @PETSc.Log.EventDecorator() + def tlm(self, m_dot: OverloadedType): + x = m_dot + mx = self.Jm.tlm(self.forward_halos(x)) + dx = self.Jerr.tlm([x, mx]) + return dx + + @stop_annotating() + @PETSc.Log.EventDecorator() + def hessian(self, m_dot: OverloadedType, hessian_input: OverloadedType = None, + evaluate_tlm: bool = True, apply_riesz: bool = False): + if evaluate_tlm: + self.tlm(m_dot) + hess_args = {'m_dot': None, 'evaluate_tlm': False, 'apply_riesz': False} + + hx, hmx = self.Jerr.hessian( + **hess_args, hessian_input=hessian_input) + + hmx = self.backward_halos(self.Jm.hessian( + **hess_args, hessian_input=hmx)) + + h = hx + hmx + + return self._apply_riesz(h, apply_riesz) + + def _apply_riesz(self, adj, apply_riesz=False): + if apply_riesz: + control = self.controls[0] + return control._ad_convert_riesz( + adj, riesz_map=control.riesz_map) + else: + return adj + + @PETSc.Log.EventDecorator() + def forward_halos(self, x): + xm1 = x._ad_init_zero() + xi = x.subfunctions + xim1 = xm1.subfunctions + + # local timesteps + for i in range(1, len(xi)): + xim1[i].assign(xi[i-1]) + + # post messages + src = self.trank - 1 + dst = self.trank + 1 + + # # send forward xi + if self.trank != self.last_rank: + self.ensemble.isend( + xi[-1], dest=dst, tag=dst) + + if self.trank == 0: # blank out ics + xim1[0].assign(0) + else: + recv_reqs = self.ensemble.irecv( + xim1[0], source=src, tag=self.trank) + MPI.Request.Waitall(recv_reqs) + + return xm1 + + @PETSc.Log.EventDecorator() + def backward_halos(self, x): + xp1 = x._ad_init_zero() + xi = x.subfunctions + xip1 = xp1.subfunctions + + # local timesteps + for i in range(len(xi)-1): + xip1[i].assign(xi[i+1]) + + # post messages + src = self.trank + 1 + dst = self.trank - 1 + + # # send backward xi + if self.trank != 0: + self.ensemble.isend( + xi[0], dest=dst, tag=dst) + + if self.trank == self.last_rank: # blank out final step + xip1[-1].assign(0) + else: + recv_reqs = self.ensemble.irecv( + xip1[-1], source=src, tag=self.trank) + MPI.Request.Waitall(recv_reqs) + + return xp1 diff --git a/firedrake/adjoint/composite_reduced_functional.py b/firedrake/adjoint/composite_reduced_functional.py index 358763b434..d4a9d35ba9 100644 --- a/firedrake/adjoint/composite_reduced_functional.py +++ b/firedrake/adjoint/composite_reduced_functional.py @@ -1,30 +1,62 @@ -from pyadjoint import OverloadedType -from typing import Optional +from pyadjoint import ( + OverloadedType, Control, ReducedFunctional, + stop_annotating, pause_annotation, continue_annotation, + annotate_tape, set_working_tape) +from pyadjoint.enlisting import Enlist -def intermediate_options(options: dict): - """ - Options set for the intermediate stages of a chain of ReducedFunctionals +def _rename(obj, name): + if hasattr(obj, "rename"): + obj.rename(name) - Takes all elements of the options except riesz_representation, which - is set to None to prevent returning derivatives to the primal space. - Parameters - ---------- - options - The dictionary of options provided by the user +def _ad_sub(left, right): + result = right._ad_copy() + result._ad_imul(-1) + result._ad_iadd(left) + return result - Returns - ------- - dict - The options for ReducedFunctionals at intermediate stages +# @set_working_tape() # ends up using old_tape = None because evaluates when imported - need separate decorator +def isolated_rf(operation, control, + functional_name=None, + control_name=None): + """ + Return a ReducedFunctional where the functional is `operation` applied + to a copy of `control`, and the tape contains only `operation`. """ - return { - **{k: v for k, v in (options or {}).items() - if k != 'riesz_representation'}, - 'riesz_representation': None - } + with stop_annotating(): + controls = Enlist(control) + control_copies = [control._ad_copy() for control in controls] + + if control_name: + for control, name in zip(control_copies, Enlist(control_name)): + _rename(control, name) + + annotating = annotate_tape() + if not annotating: + continue_annotation() + with set_working_tape() as tape: + functional = operation( + controls.delist(control_copies)) + + if functional_name: + _rename(functional, functional_name) + + control = controls.delist( + [Control(control_copy) + for control_copy in control_copies]) + + Jhat = ReducedFunctional( + functional, control, tape=tape) + + if not annotating: + pause_annotation() + return Jhat + + +def identity_reduced_functional(value): + return isolated_rf(lambda v: v._ad_init_zero()._ad_add(v), value) class CompositeReducedFunctional: @@ -67,7 +99,7 @@ def __call__(self, values: OverloadedType): """ return self.rf2(self.rf1(values)) - def derivative(self, adj_input: Optional[float] = 1.0, options: Optional[dict] = None): + def derivative(self, adj_input: float = 1.0, apply_riesz: bool = False): """Returns the derivative of the functional w.r.t. the control. Using the adjoint method, the derivative of the functional with respect to the control, around the last supplied value of the @@ -78,8 +110,8 @@ def derivative(self, adj_input: Optional[float] = 1.0, options: Optional[dict] = adj_input The adjoint input. - options - Additional options for the derivative computation. + apply_riesz + Whether to apply the Riesz map to the result to obtain the primal value. Returns ------- @@ -89,12 +121,12 @@ def derivative(self, adj_input: Optional[float] = 1.0, options: Optional[dict] = """ deriv2 = self.rf2.derivative( - adj_input=adj_input, options=intermediate_options(options)) + adj_input=adj_input, apply_riesz=False) deriv1 = self.rf1.derivative( - adj_input=deriv2, options=options or {}) + adj_input=deriv2, apply_riesz=apply_riesz) return deriv1 - def tlm(self, m_dot: OverloadedType, options: Optional[dict] = None): + def tlm(self, m_dot: OverloadedType): """Returns the action of the tangent linear model of the functional w.r.t. the control on a vector m_dot. Parameters @@ -103,10 +135,6 @@ def tlm(self, m_dot: OverloadedType, options: Optional[dict] = None): m_dot The direction in which to compute the action of the Hessian. - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - Returns ------- pyadjoint.OverloadedType @@ -114,12 +142,9 @@ def tlm(self, m_dot: OverloadedType, options: Optional[dict] = None): Should be an instance of the same type as the control. """ - return self.rf2.tlm(self.rf1.tlm(m_dot, intermediate_options(options)), options) + return self.rf2.tlm(self.rf1.tlm(m_dot)) - def hessian(self, m_dot: OverloadedType, - options: Optional[dict] = None, - hessian_input: OverloadedType = 0.0, - evaluate_tlm: Optional[bool] = True): + def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False): """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. Using the second-order adjoint method, the action of the Hessian of the @@ -132,10 +157,6 @@ def hessian(self, m_dot: OverloadedType, m_dot The direction in which to compute the action of the Hessian. - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - evaluate_tlm If True, the tlm values on the tape will be reset and evaluated before the Hessian action is evaluated. If False, the existing tlm values on @@ -149,9 +170,9 @@ def hessian(self, m_dot: OverloadedType, """ if evaluate_tlm: - self.tlm(m_dot, options=intermediate_options(options)) + self.tlm(m_dot) return self.rf1.hessian( - None, options or {}, evaluate_tlm=False, + None, evaluate_tlm=False, apply_riesz=apply_riesz, hessian_input=self.rf2.hessian( - None, options=intermediate_options(options), evaluate_tlm=False, + None, evaluate_tlm=False, apply_riesz=False, hessian_input=hessian_input)) diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py index 3e83b58e4f..c0b454779d 100644 --- a/firedrake/adjoint/ensemble_reduced_functional.py +++ b/firedrake/adjoint/ensemble_reduced_functional.py @@ -103,7 +103,7 @@ def __init__(self, functional, control, ensemble=None): raise ValueError( f"Ensemble provided to {type(self).__name__} must match" " the ensemble of the control.") - self.ensemble = ensemble + self.ensemble = control.control.ensemble self.nlocal_inputs = len(control.subvec) elif isinstance(functional, Function): @@ -315,6 +315,22 @@ def __init__(self, rfs, functional, control, ensemble=None): f"Functional for {type(self).__name__} must be either" " an EnsembleFunction or EnsembleAdjVec") + clens = set(len(_local_subs(c.control)) for c in self.controls) + flen = len(_local_subs(functional)) + rlen = len(rfs) + if len(clens) != 1: + raise ValueError( + f"All Controls for {type(self).__name__} must be the same length") + clen = clens.pop() + if clen != flen: + raise ValueError( + f"Control of length {clen} for {type(self).__name__} must have" + f" the same number of components as functional with length {flen}") + if clen != rlen: + raise ValueError( + f"{type(self).__name__} given {rlen} ReducedFunctionals, but" + f" needs one for each component of Control with length {clen}") + @property def controls(self): return self._controls diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index e56d8ae149..2434b010d7 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -1,55 +1,25 @@ from pyadjoint import ReducedFunctional, OverloadedType, Control, Tape, AdjFloat, \ stop_annotating, get_working_tape, set_working_tape +from pyadjoint.reduced_functional import AbstractReducedFunctional from pyadjoint.enlisting import Enlist from firedrake.function import Function from firedrake.ensemble import EnsembleFunction, EnsembleFunctionSpace from firedrake import assemble, inner, dx, Constant -from .composite_reduced_functional import ( - CompositeReducedFunctional, intermediate_options) +from .composite_reduced_functional import CompositeReducedFunctional +from .composite_reduced_functional import _rename, isolated_rf from .ensemble_reduced_functional import ( - EnsembleReduceReducedFunctional, EnsembleBcastReducedFunctional, - EnsembleTransformReducedFunctional, EnsembleReducedFunctional) + EnsembleReduceReducedFunctional, EnsembleTransformReducedFunctional) from .ensemble_adjvec import EnsembleAdjVec -from ufl.duals import is_primal, is_dual +from .allatonce_reduced_functional import AllAtOnceReducedFunctional from functools import wraps, cached_property, partial from typing import Callable, Optional, Collection, Union from types import SimpleNamespace from contextlib import contextmanager -from mpi4py import MPI from firedrake.petsc import PETSc __all__ = ['FourDVarReducedFunctional'] -# @set_working_tape() # ends up using old_tape = None because evaluates when imported - need separate decorator -def isolated_rf(operation, control, - functional_name=None, - control_name=None): - """ - Return a ReducedFunctional where the functional is `operation` applied - to a copy of `control`, and the tape contains only `operation`. - """ - with stop_annotating(): - controls = Enlist(control) - control_copies = [control._ad_copy() for control in controls] - - if control_name: - for control, name in zip(control_copies, Enlist(control_name)): - _rename(control, name) - - with set_working_tape(): - functional = operation(controls.delist(control_copies)) - - if functional_name: - _rename(functional, functional_name) - - control = controls.delist([Control(control_copy) - for control_copy in control_copies]) - - return ReducedFunctional( - functional, control) - - def sc_passthrough(func): """ Wraps standard ReducedFunctional methods to differentiate strong or @@ -72,19 +42,7 @@ def wrapper(self, *args, **kwargs): return wrapper -def _rename(obj, name): - if hasattr(obj, "rename"): - obj.rename(name) - - -def _ad_sub(left, right): - result = right._ad_copy() - result._ad_imul(-1) - result._ad_iadd(left) - return result - - -class FourDVarReducedFunctional(ReducedFunctional): +class FourDVarReducedFunctional(AbstractReducedFunctional): """ReducedFunctional for 4DVar data assimilation. Creates either the strong constraint or weak constraint system @@ -162,19 +120,19 @@ def __init__(self, control: Control, self.control_space = control.function_space() ensemble = self.control_space.ensemble self.ensemble = ensemble - self.trank = ensemble.ensemble_comm.rank if ensemble else 0 - self.nchunks = ensemble.ensemble_comm.size if ensemble else 1 + self.trank = ensemble.ensemble_rank if ensemble else 0 + self.nchunks = ensemble.ensemble_size if ensemble else 1 # because we need to manually evaluate the different bits # of the functional, we need an internal set of controls # to use for the stage ReducedFunctionals - self._cbuf = control.copy_data() + self._cbuf = control.control._ad_copy() _x = self._cbuf.subfunctions self._x = _x - self._controls = tuple(Control(xi) for xi in _x) + self._controls_ = tuple(Control(xi) for xi in _x) self.control = control - self.controls = Enlist(control) + self._controls = Enlist(control) # first control on rank 0 is initial conditions, not end of observation stage self.nlocal_stages = len(_x) - (1 if self.trank == 0 else 0) @@ -195,23 +153,12 @@ def __init__(self, control: Control, # first rank sets up functionals for background initial observations if self.trank == 0: - # RF to recalculate error vector (x_0 - x_b) - self.background_error = isolated_rf( - operation=lambda x0: _ad_sub(x0, self.background), - control=_x[0], - functional_name="bkg_err_vec", - control_name="Control_0_bkg_copy") - # RF to recalculate inner product |x_0 - x_b|_B self.background_norm = CovarianceNormReducedFunctional( - self.background_error.functional, + self.background._ad_init_zero(), background_covariance, control_name="bkg_err_vec_copy") - # compose background reduced functionals to evaluate both together - self.background_rf = CompositeReducedFunctional( - self.background_error, self.background_norm) - if self.initial_observations: # RF to recalculate error vector (H(x_0) - y_0) @@ -230,10 +177,6 @@ def __init__(self, control: Control, self.observation_rfs.append(self.initial_observation_error) self.observation_norms.append(self.initial_observation_norm) - # compose initial observation reduced functionals to evaluate both together - self.initial_observation_rf = CompositeReducedFunctional( - self.initial_observation_error, self.initial_observation_norm) - # create halo for previous state if self.ensemble and self.trank != 0: with stop_annotating(): @@ -261,15 +204,21 @@ def __init__(self, control: Control, self.background = control.control._ad_copy() _rename(self.background, "Background") + self.control_space = self.background.function_space() + # initial conditions guess to be updated - self.controls = Enlist(control) + self._controls = Enlist(control) # Strong constraint functional to be converted to ReducedFunctional later # penalty for straying from prior + self.background_norm = CovarianceNormReducedFunctional( + control.control._ad_init_zero(), background_covariance) + + bkg_err = Function(self.control_space) + bkg_err.assign(control.control - self.background) self._accumulate_functional( - covariance_norm(control.control - self.background, - background_covariance)) + covariance_norm(bkg_err, background_covariance)) # penalty for not hitting observations at initial time if self.initial_observations: @@ -277,6 +226,10 @@ def __init__(self, control: Control, covariance_norm(observation_error(control.control), observation_covariance)) + @property + def controls(self): + return self._controls + @cached_property def strong_reduced_functional(self): """A :class:`pyadjoint.ReducedFunctional` for the strong constraint 4DVar system. @@ -327,48 +280,13 @@ def __call__(self, values: OverloadedType): raise ValueError(f"Value must be of type {type(self.control.control)} not type {type(value)}") self.control.update(value) - # put the new value into our internal set of controls to pass to each stage - self._cbuf.assign(value) - - trank = self.trank - - # first "control" for later ranks is the halo - if self.ensemble and trank != 0: - x = [self.xprev, *self._x] - of = 1 - else: - x = [*self._x] - of = 0 - # halos for propagation - xim1 = self.JM.functional.copy() - xi = self.JM.functional.copy() - - xi.zero() - xim1.zero() - - xprev = None if trank == 0 else self.xprev - - # post messages for control of time propagator on next chunk - self._forward_halos(value, xi, xim1, xprev) - - # Model functionals - Jm = self.Jmodel([self.JM(xim1), xi]) - - # Observation functional - Jo = self.Jobservations(value) - - # Background functional - Jb = self.ensemble.ensemble_comm.bcast( - self.background_rf(value.subfunctions[0]) if trank == 0 else None, root=0) - - J = Jb + Jo + Jm - return J + return self.Jmodel(value) + self.Jobservations(value) @sc_passthrough @stop_annotating() @PETSc.Log.EventDecorator() - def derivative(self, adj_input: float = 1.0, options: dict = {}): + def derivative(self, adj_input: float = 1.0, apply_riesz: bool = False): """Returns the derivative of the functional w.r.t. the control. Using the adjoint method, the derivative of the functional with respect to the control, around the last supplied value of the @@ -388,49 +306,23 @@ def derivative(self, adj_input: float = 1.0, options: dict = {}): The derivative with respect to the control. Should be an instance of the same type as the control. """ - trank = self.trank - - # chaining ReducedFunctionals means we need to pass Cofunctions not Functions - options = options or {} - ioptions = intermediate_options(options) - - # observation derivatives - dJ_do = self.Jobservations.derivative( - adj_input=adj_input, options=options) - - derivatives = dJ_do - dJ = dJ_do.subfunctions - - # background derivatives - if trank == 0: - dJ_x0 = dJ[0] - dJ_x0 += self.background_rf.derivative( - adj_input=adj_input, options=options) + adj_args = {'adj_input': adj_input, 'apply_riesz': apply_riesz} + return ( + self.Jobservations.derivative(**adj_args) + + self.Jmodel.derivative(**adj_args) + ) - # evaluate model derivatives - dj_mx, dj_x = self.Jmodel.derivative( - adj_input=adj_input, options=ioptions) - - # derivative from propagation - dj_mx = self.JM.derivative( - adj_input=dj_mx, options=options) - - dj_x = dj_mx._ad_convert_type(dj_x) - - dJ_x = derivatives._ad_copy().zero() - dJ_mx = derivatives._ad_copy().zero() - - self._backward_halos(dj_x, dj_mx, dJ_x, dJ_mx) - - derivatives += dJ_x - derivatives += dJ_mx - - return derivatives + @sc_passthrough + @stop_annotating() + @PETSc.Log.EventDecorator() + def tlm(self, m_dot: OverloadedType): + return self.Jmodel.tlm(m_dot) + self.Jobservations.tlm(m_dot) @sc_passthrough @stop_annotating() @PETSc.Log.EventDecorator() - def hessian(self, m_dot: OverloadedType, options: dict = {}): + def hessian(self, m_dot: OverloadedType, hessian_input: Optional[OverloadedType] = None, + evaluate_tlm: bool = True, apply_riesz: bool = False): """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. Using the second-order adjoint method, the action of the Hessian of the @@ -443,97 +335,25 @@ def hessian(self, m_dot: OverloadedType, options: dict = {}): m_dot The direction in which to compute the action of the Hessian. - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - Returns ------- pyadjoint.OverloadedType The action of the Hessian in the direction m_dot. Should be an instance of the same type as the control. """ - trank = self.trank - if isinstance(m_dot, list): m_dot = m_dot[0] - hessians = self.Jobservations.hessian( - m_dot=m_dot, options=options) - - hess = hessians.subfunctions + if evaluate_tlm: + self.tlm(m_dot) - # hessian actions at the initial condition - if trank == 0: - hess_x0 = hess[0] - hess_x0 += self.background_rf.hessian( - m_dot.subfunctions[0], options=options) + hess_args = {'m_dot': None, 'hessian_input': hessian_input, + 'evaluate_tlm': False, 'apply_riesz': False} - # set up arrays including halos - if trank == 0: - hs = [*hess] - mdot = [*m_dot.subfunctions] - else: - hprev = hess[0].copy(deepcopy=True).zero() - mprev = m_dot.subfunctions[0].copy(deepcopy=True).zero() - hs = [hprev, *hess] - mdot = [mprev, *m_dot.subfunctions] - - if trank != self.nchunks - 1: - hnext = hess[0].copy(deepcopy=True).zero() - - # send m_dot halo forward - if self.ensemble: - src = trank - 1 - dst = trank + 1 - - if trank != self.nchunks - 1: - self.ensemble.isend( - m_dot.subfunctions[-1], dest=dst, tag=dst) - - if trank != 0: - recv_reqs = self.ensemble.irecv( - mprev, source=src, tag=trank) - MPI.Request.Waitall(recv_reqs) - - # evaluate all stages on chunk except first - for i in range(len(self.stages)): - hms = self.stages[i].hessian( - mdot[i:i+2], options=options, rftype='model') - - hs[i] += hms[0] - hs[i+1] += hms[1] - - # send result halo backward - if self.ensemble: - src = trank + 1 - dst = trank - 1 - - if trank != 0: - self.ensemble.isend( - hs[0], dest=dst, tag=dst) - - if trank != self.nchunks - 1: - recv_reqs = self.ensemble.irecv( - hnext, source=src, tag=trank) - MPI.Request.Waitall(recv_reqs) - hess_xm = hess[-1] - hess_xm += hnext - - return hessians - - @stop_annotating() - def hessian_matrix(self): - # Other reduced functionals don't have this. - if not self.weak_constraint: - raise AttributeError("Strong constraint 4DVar does not form a Hessian matrix") - raise NotImplementedError + return ( + self.Jobservations.hessian(**hess_args) + + self.Jmodel.hessian(**hess_args) + ) def _accumulate_functional(self, val): if not self._annotate_accumulation: @@ -573,9 +393,11 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): # later ranks start from halo if trank == 0: - controls = self._controls + controls = self._controls_ + with stop_annotating(): + controls[0].assign(self.background) else: - controls = [self._control_prev, *self._controls] + controls = [self._control_prev, *self._controls_] with stop_annotating(): controls[0].assign(ectx.xhalo) @@ -615,6 +437,15 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): # values of the initial timeseris self.control.assign(self._cbuf) + # AdjVec for controls + self._float_vec = EnsembleAdjVec( + [AdjFloat(0.) for _ in range(self.control_space.nlocal_spaces)], + ensemble=self.ensemble) + + # reduction rf + self.Jsum = EnsembleReduceReducedFunctional( + AdjFloat(0.), Control(self._float_vec._ad_init_zero())) + # # # # # # # Create observation ReducedFunctionals # # # # # # @@ -623,10 +454,6 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): [Jo.functional.function_space() for Jo in self.observation_rfs], ensemble=self.ensemble) - self._observation_float_vec = EnsembleAdjVec( - [AdjFloat(0.) for _ in range(self.nlocal_observations)], - ensemble=self.ensemble) - # (JH : V^n -> U^n) = (x -> y - H(x)) observation_control = EnsembleFunction(self.control_space) observation_functional = EnsembleFunction(self.observation_space) @@ -636,9 +463,9 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): observation_functional, Control(observation_control)) - # (Jr : U^n -> R^n) = (x -> x^T R^{-1} x) + # (JR : U^n -> R^n) = (x -> x^T R^{-1} x) observation_control = EnsembleFunction(self.observation_space) - observation_functional = self._observation_float_vec._ad_convert_type(0.) + observation_functional = self._float_vec._ad_init_zero() self.JR = EnsembleTransformReducedFunctional( self.observation_norms, @@ -646,146 +473,54 @@ def recording_stages(self, sequential=True, nstages=None, **stage_kwargs): Control(observation_control)) # (Jsum : R^n -> R) = (x -> \sum_{i} x_{i}) - obs_sum_control = self._observation_float_vec._ad_convert_type(0.) - obs_sum_functional = AdjFloat(0.) - - self.JOsum = EnsembleReduceReducedFunctional( - obs_sum_functional, - Control(obs_sum_control)) + JOsum = self.Jsum + # (Jobs : V^n -> R) = (x -> \sum_{i} |y - H(x)|_{R}) self.Jobservations = CompositeReducedFunctional( - CompositeReducedFunctional(self.JH, self.JR), self.JOsum) + CompositeReducedFunctional(self.JH, self.JR), JOsum) # # # # # # # Create model propagator ReducedFunctionals # # # # # # - self._state_float_vec = EnsembleAdjVec( - [AdjFloat(0.) for _ in range(self.nlocal_stages)], - ensemble=self.ensemble) + # (JL : V^n -> V^n) = (x -> x - ) + model_control = EnsembleFunction(self.control_space) + model_functional = EnsembleFunction(self.control_space) - # contains the halo and propagated states (one less than full series) - stage_state_space = EnsembleFunctionSpace( - [Jm.functional.function_space() for Jm in self.model_rfs], - ensemble=self.ensemble) + self.JL = AllAtOnceReducedFunctional( + EnsembleFunction(self.control_space), + Control(EnsembleFunction(self.control_space)), + self.model_rfs, background=self.background) - # (Jm : V^{n-1} -> V^{n-1}) = (x -> M(x)) - propagator_control = EnsembleFunction(stage_state_space) - propagator_functional = EnsembleFunction(stage_state_space) - - self.JM = EnsembleTransformReducedFunctional( - self.model_rfs, - propagator_functional, - Control(propagator_control)) - - # (JQerr : [V^{n-1}, V^{n-1}] -> V^{n-1}) = ([x, y] -> x - y) - propagator_controls = [ - Control(EnsembleFunction(stage_state_space)), - Control(EnsembleFunction(stage_state_space))] - propagator_functional = EnsembleFunction(stage_state_space) - - self.JQerr = EnsembleTransformReducedFunctional( - [stage.model_error for state in self.stages], - propagator_functional, - propagator_controls - ) + # (JDnorm : V^{n} -> R^{n}) = (x -> x^T ^{-1} x) + model_control = EnsembleFunction(self.control_space) + model_functional = self._float_vec._ad_init_zero() - # (JQnorm : V^{n-1} -> R^{n-1}) = (x -> x^T Q^{-1} x) - propagator_control = EnsembleFunction(stage_state_space) - propagator_functional = self._state_float_vec._ad_convert_type(0.) + model_norms = [norm for norm in self.model_norms] + if self.trank == 0: + model_norms.insert(0, self.background_norm) - self.JQnorm = EnsembleTransformReducedFunctional( - self.model_norms, - propagator_functional, - Control(propagator_control)) + self.JD = EnsembleTransformReducedFunctional( + model_norms, + model_functional, + Control(model_control)) # (JMsum : R^n -> R) = (x -> \sum_{i} x_{i}) - prop_sum_control = self._state_float_vec._ad_convert_type(0.) - prop_sum_functional = AdjFloat(0.) - - self.JMsum = EnsembleReduceReducedFunctional( - prop_sum_functional, - Control(prop_sum_control)) + JMsum = self.Jsum + # (Jmod : V^n -> R) = (x -> \sum_{i} |x - |_{}) self.Jmodel = CompositeReducedFunctional( - CompositeReducedFunctional(self.JQerr, self.JQnorm), self.JMsum) + CompositeReducedFunctional(self.JL, self.JD), JMsum) else: # strong constraint + with stop_annotating(): + self.controls[0].control.assign(self.background) yield ObservationStageSequence( self.controls, self, global_index=-1, observation_index=0 if self.initial_observations else -1, stage_kwargs=stage_kwargs, nstages=nstages) - def _forward_halos(self, x, xi, xim1, xprev): - # extract x into xi and xim1 - for i in range(self.nlocal_stages): - if self.trank == 0: - xi.subfunctions[i].assign(x.subfunctions[i+1]) - xim1.subfunctions[i].assign(x.subfunctions[i]) - else: - xi.subfunctions[i].assign(x.subfunctions[i]) - xim1.subfunctions[i].assign(x.subfunctions[i-1]) - - # post messages - src = self.trank - 1 - dst = self.trank + 1 - - # # send forward xi - if self.trank != self.nchunks - 1: - self.ensemble.isend( - xi.subfunctions[-1], dest=dst, tag=dst) - - # # recv back xprev - if self.trank != 0: - recv_reqs = self.ensemble.irecv( - xprev, source=src, tag=self.trank) - MPI.Request.Waitall(recv_reqs) - - # insert into xim1 - xim1.subfunctions[0].assign(xprev) - - return - - def _backward_halos(self, dj_x, dj_mx, dJ_x, dJ_mx): - - # accumulate diagonal component - for i in range(self.nlocal_stages): - if self.trank == 0: - dJ_x.subfunctions[i+1].assign(dj_x.subfunctions[i]) - else: - dJ_x.subfunctions[i].assign(dj_x.subfunctions[i]) - - # accumulate propagation derivative - for i in range(self.nlocal_stages-1): - if self.trank == 0: - print(f"\n{i = }") - dJ_mx.subfunctions[i+1].assign(dj_mx.subfunctions[i+1]) - else: - dJ_mx.subfunctions[i].assign(dj_mx.subfunctions[i+1]) - - djnext = dj_mx.subfunctions[0]._ad_copy().zero() - - # no halo on rank 0 - if self.trank == 0: - dJ_mx.subfunctions[0].assign(dj_mx.subfunctions[0]) - - # halos sent backward in time - src = self.trank + 1 - dst = self.trank - 1 - - if self.trank != 0: - self.ensemble.isend( - dj_mx.subfunctions[0], dest=dst, tag=dst) - - if self.trank != self.nchunks - 1: - recv_reqs = self.ensemble.irecv( - djnext, source=src, tag=self.trank) - MPI.Request.Waitall(recv_reqs) - dJ_mx.subfunctions[-1].assign(djnext) - - return djnext - class ObservationStageSequence: def __init__(self, controls: Control, @@ -877,7 +612,8 @@ def __init__(self, control: OverloadedType, observation_index: Optional[int] = None): self.aaorf = aaorf self.control = control - self.index = index + self.local_index = index + self.global_index = index self.observation_index = observation_index @PETSc.Log.EventDecorator() @@ -1019,25 +755,16 @@ def set_observation(self, state: OverloadedType, f"Control_{self.global_index}_model_copy"] } if self.global_index else {} - self.model_error = isolated_rf( - operation=lambda controls: _ad_sub(*controls), - control=[state, self.controls[-1].control], - **names) - # RF to recalculate inner product |M_i - x_i|_Q names = { 'control_name': f"model_err_vec_{self.global_index}_copy" } if self.global_index else {} self.model_norm = CovarianceNormReducedFunctional( - self.model_error.functional, + state._ad_init_zero(), forward_model_covariance, **names) - # compose model error reduced functionals to evaluate both together - self.model_error_rf = CompositeReducedFunctional( - self.model_error, self.model_norm) - # Observations after tape cut because this is now a control, not a state # RF to recalculate error vector (H(x_i) - y_i) @@ -1060,207 +787,29 @@ def set_observation(self, state: OverloadedType, observation_covariance, **names) - # compose observation reduced functionals to evaluate both together - self.observation_rf = CompositeReducedFunctional( - self.observation_error, self.observation_norm) - # remove the stage initial condition "control" now we've finished recording delattr(self, "control") # stop the stage tape recording anything else set_working_tape() - @stop_annotating() - @PETSc.Log.EventDecorator() - def __call__(self, values: OverloadedType, - rftype: Optional[str] = None): - """Computes the reduced functional with supplied control value. - - Parameters - ---------- - - values - If you have multiple controls this should be a list of new values - for each control in the order you listed the controls to the constructor. - If you have a single control it can either be a list or a single object. - Each new value should have the same type as the corresponding control. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - - Returns - ------- - pyadjoint.OverloadedType - The computed value. Typically of instance of :class:`pyadjoint.AdjFloat`. - - """ - J = 0.0 - - # evaluate model error - if rftype in (None, 'model'): - J += self.model_error_rf( - [self.forward_model(values[0]), values[1]]) - - # evaluate observation errors - if rftype in (None, 'obs'): - J += self.observation_rf(values[1]) - - return J - - @stop_annotating() - @PETSc.Log.EventDecorator() - def derivative(self, adj_input: float = 1.0, options: dict = {}, - rftype: Optional[str] = None): - """Returns the derivative of the functional w.r.t. the control. - Using the adjoint method, the derivative of the functional with - respect to the control, around the last supplied value of the - control, is computed and returned. - - Parameters - ---------- - adj_input - The adjoint input. - - options - Additional options for the derivative computation. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - - Returns - ------- - pyadjoint.OverloadedType - The derivative with respect to the control. - Should be an instance of the same type as the control. - """ - # create a list of overloaded types to put derivative into - derivatives = [] - - # chaining ReducedFunctionals means we need to pass Cofunctions not Functions - options = options or {} - ioptions = intermediate_options(options) - - if rftype in (None, 'model'): - # derivative of reduction and difference - model_err_derivs = self.model_error_rf.derivative( - adj_input=adj_input, options=ioptions) - - # derivative through the time propagator wrt to xprev - model_forward_deriv = self.forward_model.derivative( - adj_input=model_err_derivs[0], options=options) - - derivatives.append(model_forward_deriv) - - # model_err_derivs is still in the dual space, so we need to convert it to the - # type that the user has requested - this will be the type of model_forward_deriv. - derivatives.append( - model_forward_deriv._ad_convert_type( - model_err_derivs[1], options)) - - if rftype in (None, 'obs'): - obs_deriv = self.observation_rf.derivative( - adj_input=adj_input, options=options) - - if len(derivatives) == 0: - derivatives.append(None) - derivatives.append(obs_deriv) - else: - derivatives[1] += obs_deriv - - return derivatives - - @stop_annotating() - @PETSc.Log.EventDecorator() - def hessian(self, m_dot: OverloadedType, options: dict = {}, - rftype: Optional[str] = None): - """Returns the action of the Hessian of the functional w.r.t. the control on a vector m_dot. - - Using the second-order adjoint method, the action of the Hessian of the - functional with respect to the control, around the last supplied value - of the control, is computed and returned. - - Parameters - ---------- - - m_dot - The direction in which to compute the action of the Hessian. - - options - A dictionary of options. To find a list of available options - have a look at the specific control type. - - rtype: - Whether to evaluate: - - the model error ('model'), - - the observation error ('obs'), - - both model and observation errors (None). - - Returns - ------- - pyadjoint.OverloadedType - The action of the Hessian in the direction m_dot. - Should be an instance of the same type as the control. - """ - hessian_value = [] - - if rftype in (None, 'model'): - hessian_value.extend(self._model_hessian( - m_dot, options=options)) - - if rftype in (None, 'obs'): - obs_hessian = self.observation_rf.hessian( - m_dot[1], options=options) - if len(hessian_value) == 0: - hessian_value.append(None) - hessian_value.append(obs_hessian) - else: - hessian_value[1] += obs_hessian - - return hessian_value - - def _model_hessian(self, m_dot, options): - iopts = intermediate_options(options) - - # TLM for model from mdot[0] - forward_tlm = self.forward_model.tlm(m_dot[0], options=iopts) - - # combine model TLM and mdot[1] - mdot_error = [forward_tlm, m_dot[1]] - - # Hessian (dual) for error - error_hessian = self.model_error_rf.hessian( - mdot_error, options=iopts, evaluate_tlm=True) - - # Hessian for model - model_hessian = self.forward_model.hessian( - None, options=options, - hessian_input=error_hessian[0], - evaluate_tlm=False) - - # combine model Hessian and converted error Hessian - return [ - model_hessian, - model_hessian._ad_convert_type(error_hessian[1], - options=options) - ] - def covariance_norm(x, covariance): if isinstance(covariance, Collection): covariance, power = covariance else: power = None - weight = Constant(1/covariance) - val = assemble(inner(x, weight*x)*dx) + if isinstance(covariance, (float, int)): + weight = Constant(1/covariance) + else: + weight = 1/covariance + from firedrake import TestFunction + v = TestFunction(x.function_space()) + xB = inner(x*weight, v)*dx + val = assemble(xB(x)) from pyadjoint import AdjFloat result = val if power is None else val**power - assert type(result) is AdjFloat + assert type(result) is AdjFloat, f"{type(result).__name__ = } is not AdjFloat." return result diff --git a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py index 8bde5681dd..1c5f5ff8a5 100644 --- a/tests/firedrake/adjoint/test_4dvar_reduced_functional.py +++ b/tests/firedrake/adjoint/test_4dvar_reduced_functional.py @@ -6,6 +6,7 @@ set_working_tape, get_working_tape, Control, taylor_test, taylor_to_dict, ReducedFunctional, FourDVarReducedFunctional, AdjFloat) from numpy import mean +from pytest_mpi.parallel_assert import parallel_assert @pytest.fixture(autouse=True) @@ -27,7 +28,8 @@ def handle_annotation(): def function_space(comm): """DG0 periodic advection""" - mesh = fd.PeriodicUnitIntervalMesh(nx, comm=comm, + mesh = fd.PeriodicUnitIntervalMesh( + nx, comm=comm, distribution_parameters={"partitioner_type": "simple"}) return fd.FunctionSpace(mesh, "DG", 0) @@ -75,13 +77,9 @@ def n2(x): return n2 -# B = (fd.Constant(10.), 4) # background error covariance -# R = (fd.Constant(0.1), 4) # observation error covariance -# Q = (fd.Constant(0.5), 4) # model error covariance - -B = (fd.Constant(1e10), 4) # background error covariance -R = (fd.Constant(1e10), 4) # observation error covariance -Q = (fd.Constant(1e0), 4) # model error covariance +B = (fd.Constant(1e0), 4) # background error covariance +R = (fd.Constant(2e0), 4) # observation error covariance +Q = (fd.Constant(3e0), 4) # model error covariance """Advecting velocity""" @@ -430,7 +428,7 @@ def main_test_strong_4dvar_advection(): def main_test_weak_4dvar_advection(): global_comm = fd.COMM_WORLD - if global_comm.size == 1: # time serial + if global_comm.size == 1: # space-time serial nspace = global_comm.size if global_comm.size == 2: # space parallel nspace = global_comm.size @@ -450,7 +448,7 @@ def main_test_weak_4dvar_advection(): mp = m(V) hp = h(V) # make sure we've set up the reference rf correctly - assert taylor_test(Jhat_pyadj, mp, hp) > 1.99 + # assert taylor_test(Jhat_pyadj, mp, hp) > 1.99 Jpm = ensemble.ensemble_comm.bcast(Jhat_pyadj(mp) if erank == 0 else None) Jph = ensemble.ensemble_comm.bcast(Jhat_pyadj(hp) if erank == 0 else None) @@ -460,20 +458,40 @@ def main_test_weak_4dvar_advection(): ma = m(V, ensemble) ha = h(V, ensemble) + # Does evaluating the functional match the reference rf? + eps = 1e-12 + Jam = Jhat_aaorf(ma) + parallel_assert( + abs((Jpm - Jam)/Jpm) < eps, + msg=f"fdvrf evaluation {Jam} should match pyadjointrf evaluation {Jpm}") + Jah = Jhat_aaorf(ha) + parallel_assert( + abs((Jph - Jah)/Jph) < eps, + msg=f"fdvrf evaluation {Jah} should match pyadjointrf evaluation {Jph}") - eps = 1e-12 - # Does evaluating the functional match the reference rf? - assert abs((Jpm - Jam)/Jpm) < eps - assert abs((Jph - Jah)/Jph) < eps + conv_rate = taylor_test(Jhat_aaorf, ma, ha) + parallel_assert( + conv_rate > 1.99, + msg=f"Convergence rate for first order Taylor test should be >1.99, not {conv_rate}") # If we match the functional, then passing the taylor tests # should mean that we match the derivative too. taylor = taylor_to_dict(Jhat_aaorf, ma, ha) - assert mean(taylor['R0']['Rate']) > 0.9 - assert mean(taylor['R1']['Rate']) > 1.9 - assert mean(taylor['R2']['Rate']) > 2.9 + R0 = mean(taylor['R0']['Rate']) + R1 = mean(taylor['R1']['Rate']) + R2 = mean(taylor['R2']['Rate']) + + parallel_assert( + R0 > 0.99, + msg=f"Convergence rate for evaluation order Taylor test should be >0.99, not {R0}") + parallel_assert( + R1 > 1.99, + msg=f"Convergence rate for gradient order Taylor test should be >1.99, not {R0}") + parallel_assert( + R2 > 2.99, + msg=f"Convergence rate for hessian order Taylor test should be >2.99, not {R0}") @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done @@ -489,4 +507,4 @@ def test_weak_4dvar_advection(): if __name__ == '__main__': - main_test_strong_4dvar_advection() + main_test_weak_4dvar_advection() diff --git a/tests/firedrake/ensemble/test_ensemble_function.py b/tests/firedrake/ensemble/test_ensemble_function.py index a7a3f40a93..8051595d2a 100644 --- a/tests/firedrake/ensemble/test_ensemble_function.py +++ b/tests/firedrake/ensemble/test_ensemble_function.py @@ -2,7 +2,6 @@ from pyop2 import Subset import pytest import numpy as np -from pytest_mpi.parallel_assert import parallel_assert def random_func(f): From f6b6b1e93e3860a05d9ed654db0c523b9ebfdac6 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Mon, 28 Apr 2025 10:47:41 +0100 Subject: [PATCH 078/222] Fix netgen import in container by setting PYTHONPATH (#4234) * Fix netgen import in container by setting PYTHONPATH See https://github.com/NGSolve/netgen/issues/213 --- docker/Dockerfile.firedrake | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docker/Dockerfile.firedrake b/docker/Dockerfile.firedrake index a48b7b67ca..f9e2d4ac62 100644 --- a/docker/Dockerfile.firedrake +++ b/docker/Dockerfile.firedrake @@ -7,6 +7,10 @@ RUN pip install --verbose \ --extra-index-url https://download.pytorch.org/whl/cpu \ jax ngsPETSc torch vtk +# Set PYTHONPATH so netgen can be found +# (see https://github.com/NGSolve/netgen/issues/213) +ENV PYTHONPATH=/usr/local/lib/python3.12/site-packages:$PYTHONPATH + # Install Firedrake apps RUN pip install --verbose --src /opt \ -e git+https://github.com/firedrakeproject/asQ.git#egg=asQ \ From b850bd06e16772f03036d4a79e83a4a2b520b224 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Mon, 28 Apr 2025 11:37:01 +0100 Subject: [PATCH 079/222] Use better command to check if PETSc variables are correct (#4257) The previous instruction would work with empty variables. --- docs/source/install.rst | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/docs/source/install.rst b/docs/source/install.rst index 75bbc33f29..e1666ae250 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -257,16 +257,18 @@ If you encounter the error: FileNotFoundError: [Errno 2] No such file or directory: '/tmp/.../petsc/conf/petscvariables' when running the ``pip install`` instruction this is usually a sign that the -environment variable ``PETSC_DIR`` is not set correctly. You can check this -by making sure that you can run the following command without error:: +environment variables ``PETSC_DIR`` or ``PETSC_ARCH`` are not set correctly. +You can check this by making sure that you can run the following command +without error:: - $ ls $PETSC_DIR + $ ls $PETSC_DIR/$PETSC_ARCH/lib/petsc/conf/petscvariables -If this raises an error then you should re-``export`` the variable:: +If this raises an error then you should re-``export`` the variables necessary to +build Firedrake as described :ref:`above`:: - $ export PETSC_DIR=/path/to/petsc + $ export $(python3 firedrake-configure --show-env) -and try the ``pip install`` again. +and try the install again. Missing symbols post install ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ From 0347f68ef8c07ba65ea5ffb3e47a4dcb056b0424 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Mon, 28 Apr 2025 12:50:42 +0100 Subject: [PATCH 080/222] Remove fussy link (#4259) --- docs/source/events.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/events.rst b/docs/source/events.rst index 9209bead9a..3d29b37754 100644 --- a/docs/source/events.rst +++ b/docs/source/events.rst @@ -116,7 +116,7 @@ Firedrake tutorial at RWTH Aachen University Jun 18 2019 There was a hands-on Firedrake tutorial at the Aachen Institute for Advanced Study in Computational Engineering Science (AICES) on 18 -June 2019. For more details see `their website `_. +June 2019. Firedrake tutorial at CSE 19 From 733a3f102b6c84bc26faa94197e15576814dd52a Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 29 Apr 2025 11:23:02 +0100 Subject: [PATCH 081/222] Prepare for Firedrake release (#4193) * Amend workflow files to support release and master branches * Add PyPI publishing workflow * Amend firedrake-zenodo for versioned releases * Update installation docs for master/release branches --- .github/workflows/core.yml | 537 +++++++++++++++++++++++++++++ .github/workflows/docker.yml | 16 +- .github/workflows/docker_merge.yml | 20 +- .github/workflows/pr.yml | 13 + .github/workflows/push.yml | 16 + .github/workflows/release.yml | 23 ++ .github/workflows/test.yml | 376 -------------------- .github/workflows/test_macos.yml | 81 ----- Makefile | 2 +- docker/Dockerfile.docdeps | 7 - docs/source/conf.py | 7 + docs/source/documentation.rst | 12 +- docs/source/install.rst | 118 ++++--- docs/source/zenodo.rst | 13 +- firedrake/eigensolver.py | 2 +- firedrake/mg/mesh.py | 6 +- firedrake/mg/opencascade_mh.py | 2 +- firedrake/scripts/firedrake-zenodo | 439 ++++++++++++----------- pyproject.toml | 51 ++- requirements-build.txt | 13 + 20 files changed, 963 insertions(+), 791 deletions(-) create mode 100644 .github/workflows/core.yml create mode 100644 .github/workflows/pr.yml create mode 100644 .github/workflows/push.yml create mode 100644 .github/workflows/release.yml delete mode 100644 .github/workflows/test.yml delete mode 100644 .github/workflows/test_macos.yml delete mode 100644 docker/Dockerfile.docdeps create mode 100644 requirements-build.txt diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml new file mode 100644 index 0000000000..44a88e11d0 --- /dev/null +++ b/.github/workflows/core.yml @@ -0,0 +1,537 @@ +# Main Firedrake CI workflow + +on: + workflow_call: + inputs: + target_branch: + description: The target branch (usually 'master' or 'release') + type: string + required: true + run_tests: + description: Whether to run the test suite + type: boolean + default: true + test_macos: + description: Whether to test using macOS + type: boolean + default: false + deploy_website: + description: Whether to deploy the website + type: boolean + default: false + upload_pypi: + description: Whether to upload an sdist to PyPI + type: boolean + default: false + upload_testpypi: + description: Whether to upload an sdist to TestPyPI + type: boolean + default: false + + workflow_dispatch: + inputs: + target_branch: + description: The target branch (usually 'master' or 'release') + type: string + required: true + run_tests: + description: Whether to run the test suite + type: boolean + default: true + test_macos: + description: Whether to test using macOS + type: boolean + default: false + deploy_website: + description: Whether to deploy the website + type: boolean + default: false + upload_pypi: + description: Whether to upload an sdist to PyPI + type: boolean + default: false + upload_testpypi: + description: Whether to upload an sdist to TestPyPI + type: boolean + default: false + +concurrency: + # Cancel running jobs if new commits are pushed + group: > + ${{ github.workflow }}- + ${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + test_linux: + name: Build and test Firedrake (Linux) + strategy: + # We want to know all of the tests which fail, so don't kill real if + # complex fails and vice-versa + fail-fast: false + matrix: + arch: [default, complex] + runs-on: [self-hosted, Linux] + container: + image: ubuntu:latest + env: + OMPI_ALLOW_RUN_AS_ROOT: 1 + OMPI_ALLOW_RUN_AS_ROOT_CONFIRM: 1 + OMP_NUM_THREADS: 1 + OPENBLAS_NUM_THREADS: 1 + FIREDRAKE_CI: 1 + PYOP2_CI_TESTS: 1 + PYOP2_SPMD_STRICT: 1 + EXTRA_PYTEST_ARGS: --splitting-algorithm least_duration --timeout=600 --timeout-method=thread -o faulthandler_timeout=660 + PYTEST_MPI_MAX_NPROCS: 8 + steps: + - name: Fix HOME + # For unknown reasons GitHub actions overwrite HOME to /github/home + # which will break everything unless fixed + # (https://github.com/actions/runner/issues/863) + run: echo "HOME=/root" >> "$GITHUB_ENV" + + - name: Pre-run cleanup + # Make sure the current directory is empty + run: find . -delete + + - uses: actions/checkout@v4 + with: + path: firedrake-repo + + - name: Install system dependencies + run: | + apt-get update + apt-get -y install python3 python3-venv + apt-get -y install \ + $(python3 ./firedrake-repo/scripts/firedrake-configure --arch ${{ matrix.arch }} --show-system-packages) + : # Dependencies needed to run the test suite + apt-get -y install parallel + : # Dependencies needed to build the documentation + apt-get -y install inkscape texlive-full + + # Raise an error if any 'TODO RELEASE' comments remain + - name: Check no 'TODO RELEASE' comments (release only) + if: inputs.target_branch == 'release' + run: | + cd firedrake-repo + if [ -z "$( git grep 'TODO RELEASE' )" ]; then + exit 0 + else + exit 1 + fi + + # TODO: is there a 'blessed commit' or do we just point to 'main'? + # I prefer the latter because then failures are everyone's problem, not just mine! + - name: Install PETSc + run: | + if [ ${{ inputs.target_branch }} = 'release' ]; then + git clone --depth 1 \ + --branch $(python3 ./firedrake-repo/scripts/firedrake-configure --show-petsc-version) \ + https://gitlab.com/petsc/petsc.git + else + git clone --depth 1 https://gitlab.com/petsc/petsc.git + fi + cd petsc + python3 ../firedrake-repo/scripts/firedrake-configure \ + --arch ${{ matrix.arch }} --show-petsc-configure-options | \ + xargs -L1 ./configure --with-make-np=8 --download-slepc + make PETSC_DIR=/__w/firedrake/firedrake/petsc PETSC_ARCH=arch-firedrake-${{ matrix.arch }} + make check + { + echo "PETSC_DIR=/__w/firedrake/firedrake/petsc" + echo "PETSC_ARCH=arch-firedrake-${{ matrix.arch }}" + echo "SLEPC_DIR=/__w/firedrake/firedrake/petsc/arch-firedrake-${{ matrix.arch }}" + } >> "$GITHUB_ENV" + + - name: Install Firedrake + id: install + run: | + export $(python3 ./firedrake-repo/scripts/firedrake-configure --arch "${{ matrix.arch }}" --show-env) + python3 -m venv venv + . venv/bin/activate + + : # Force a rebuild of petsc4py as the cached one will not link to the fresh + : # install of PETSc. + pip cache remove petsc4py + pip cache remove slepc4py + + if [ ${{ inputs.target_branch }} = 'release' ]; then + EXTRA_PIP_FLAGS='' + else + : # Install build dependencies + pip install "$PETSC_DIR"/src/binding/petsc4py + pip install -r ./firedrake-repo/requirements-build.txt + + : # Install runtime dependencies that have been removed from the pyproject.toml + : # because they rely on non-PyPI versions of petsc4py. + pip install --no-build-isolation --no-deps \ + "$PETSC_DIR"/"$PETSC_ARCH"/externalpackages/git.slepc/src/binding/slepc4py + pip install --no-deps ngsPETSc netgen-mesher netgen-occt + + : # We have to pass '--no-build-isolation' to use a custom petsc4py + EXTRA_PIP_FLAGS='--no-build-isolation' + fi + + pip install --verbose $EXTRA_PIP_FLAGS \ + --no-binary h5py \ + --extra-index-url https://download.pytorch.org/whl/cpu \ + './firedrake-repo[ci,docs]' + + firedrake-clean + pip list + + - name: Run firedrake-check + run: | + . venv/bin/activate + firedrake-check + timeout-minutes: 5 + + - name: Run TSFC tests + # Run even if earlier tests failed + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + : # Use pytest-xdist here so we can have a single collated output (not possible + : # for parallel tests) + firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/tsfc + timeout-minutes: 60 + + - name: Run PyOP2 tests + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + : # Use pytest-xdist here so we can have a single collated output (not possible + : # for parallel tests) + firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 + firedrake-run-split-tests 2 4 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 + firedrake-run-split-tests 3 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 + firedrake-run-split-tests 4 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 + timeout-minutes: 15 + + + - name: Run Firedrake tests (nprocs = 1) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + : # Use pytest-xdist here so we can have a single collated output (not possible + : # for parallel tests) + firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 60 + + - name: Run tests (nprocs = 2) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + firedrake-run-split-tests 2 4 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 30 + + - name: Run tests (nprocs = 3) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + firedrake-run-split-tests 3 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 60 + + - name: Run tests (nprocs = 4) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + firedrake-run-split-tests 4 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 15 + + - name: Run tests (nprocs = 5) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + firedrake-run-split-tests 5 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 15 + + - name: Run tests (nprocs = 6) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + firedrake-run-split-tests 6 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 15 + + - name: Run tests (nprocs = 7) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + firedrake-run-split-tests 7 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 15 + + - name: Run tests (nprocs = 8) + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + firedrake-run-split-tests 8 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake + timeout-minutes: 15 + + - name: Run Gusto smoke tests + # Only test Gusto in real mode + if: | + inputs.run_tests && + (success() || steps.install.conclusion == 'success') && + matrix.arch == 'default' + run: | + . venv/bin/activate + git clone --depth 1 https://github.com/firedrakeproject/gusto.git gusto-repo + pip install --verbose ./gusto-repo + python -m pytest -n 8 --verbose \ + gusto-repo/integration-tests/balance/test_saturated_balance.py \ + gusto-repo/integration-tests/equations/test_thermal_sw.py \ + gusto-repo/integration-tests/transport/test_embedded_dg_advection.py + timeout-minutes: 10 + + - name: Run Thetis smoke tests + if: | + inputs.run_tests && + (success() || steps.install.conclusion == 'success') && + matrix.arch == 'default' + run: | + . venv/bin/activate + git clone --depth 1 https://github.com/thetisproject/thetis.git thetis-repo + pip install --verbose ./thetis-repo + python -m pytest -n 8 --verbose thetis-repo/test_adjoint/test_swe_adjoint.py + timeout-minutes: 10 + + - name: Run spyro smoke tests + if: | + inputs.run_tests && + (success() || steps.install.conclusion == 'success') && + matrix.arch == 'default' + run: | + . venv/bin/activate + git clone --depth 1 https://github.com/NDF-Poli-USP/spyro.git spyro-repo + pip install --verbose ./spyro-repo + mpiexec -n 6 python -m pytest spyro-repo/test_integration/ -m parallel[6] + timeout-minutes: 5 + + - name: Run G-ADOPT smoke tests + if: | + inputs.run_tests && + (success() || steps.install.conclusion == 'success') && + matrix.arch == 'default' + run: | + . venv/bin/activate + git clone --depth 1 https://github.com/g-adopt/g-adopt.git g-adopt-repo + pip install --verbose ./g-adopt-repo + make -C g-adopt-repo/demos/mantle_convection/base_case check + timeout-minutes: 5 + + - name: Upload log files + uses: actions/upload-artifact@v4 + if: inputs.run_tests && (success() || steps.install.conclusion == 'success') + with: + name: firedrake-logs-${{ matrix.arch }} + path: pytest_*.log + + - name: Build sdist (release only) + if: | + inputs.target_branch == 'release' && + matrix.arch == 'default' && + (success() || steps.install.conclusion == 'success') + run: | + . venv/bin/activate + pip install build + python -m build ./firedrake-repo --sdist + + - name: Upload sdist (release only) + if: | + inputs.target_branch == 'release' && + matrix.arch == 'default' && + (success() || steps.install.conclusion == 'success') + uses: actions/upload-artifact@v4 + with: + name: dist + path: firedrake-repo/dist/* + + - name: Check bibtex + if: always() && matrix.arch == 'default' + run: | + . venv/bin/activate + make -C firedrake-repo/docs validate-bibtex + + - name: Check documentation links + if: always() && matrix.arch == 'default' + run: | + . venv/bin/activate + make -C firedrake-repo/docs linkcheck + + - name: Build documentation + if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' + id: build_docs + run: | + . venv/bin/activate + cd firedrake-repo/docs + make SPHINXOPTS="-t ${{ inputs.target_branch }}" html + make latex + make latexpdf + # : Copy manual to HTML tree + cp build/latex/Firedrake.pdf build/html/_static/manual.pdf + + - name: Upload documentation + uses: actions/upload-pages-artifact@v3 + if: (success() || steps.build_docs.conclusion == 'success') && matrix.arch == 'default' + with: + name: github-pages + path: firedrake-repo/docs/build/html + retention-days: 1 + + - name: Post-run cleanup + if: always() + run: find . -delete + + test_macos: + name: Build and test Firedrake (macOS) + runs-on: [self-hosted, macOS] + if: inputs.test_macos + env: + FIREDRAKE_CI: 1 + OMP_NUM_THREADS: 1 + OPENBLAS_NUM_THREADS: 1 + steps: + - name: Add homebrew to PATH + # https://docs.github.com/en/actions/writing-workflows/choosing-what-your-workflow-does/workflow-commands-for-github-actions#adding-a-system-path + run: echo "/opt/homebrew/bin" >> "$GITHUB_PATH" + + - name: Pre-run cleanup + # Make sure the current directory is empty + run: find . -delete + + - uses: actions/checkout@v4 + with: + path: firedrake-repo + + - name: Install system dependencies + run: | + brew install $(python3 ./firedrake-repo/scripts/firedrake-configure --arch default --show-system-packages) + + - name: Install PETSc + run: | + if [ ${{ inputs.target_branch }} = 'release' ]; then + git clone --depth 1 \ + --branch $(python3 ./firedrake-repo/scripts/firedrake-configure --show-petsc-version) \ + https://gitlab.com/petsc/petsc.git + else + git clone --depth 1 https://gitlab.com/petsc/petsc.git + fi + cd petsc + python3 ../firedrake-repo/scripts/firedrake-configure \ + --arch default --show-petsc-configure-options | \ + xargs -L1 ./configure --with-make-np=4 + make + make check + { + echo "PETSC_DIR=/Users/github/actions-runner/_work/firedrake/firedrake/petsc" + echo "PETSC_ARCH=arch-firedrake-default" + echo "SLEPC_DIR=/Users/github/actions-runner/_work/firedrake/firedrake/petsc/arch-firedrake-default" + } >> "$GITHUB_ENV" + + - name: Install Firedrake + id: install + run: | + export $(python3 ./firedrake-repo/scripts/firedrake-configure --arch default --show-env) + python3 -m venv venv + . venv/bin/activate + + : # Force a rebuild of petsc4py as the cached one will not link to the fresh + : # install of PETSc. A similar trick may be needed for compiled dependencies + : # like h5py or mpi4py if changing HDF5/MPI libraries. + pip cache remove petsc4py + + if [ ${{ inputs.target_branch }} = 'release' ]; then + EXTRA_PIP_FLAGS='' + else + : # Install build dependencies + pip install "$PETSC_DIR"/src/binding/petsc4py + pip install -r ./firedrake-repo/requirements-build.txt + + : # We have to pass '--no-build-isolation' to use a custom petsc4py + EXTRA_PIP_FLAGS='--no-build-isolation' + fi + + pip install --verbose $EXTRA_PIP_FLAGS \ + --no-binary h5py \ + './firedrake-repo[check]' + + firedrake-clean + pip list + + - name: Run smoke tests + run: | + . venv/bin/activate + firedrake-check + timeout-minutes: 10 + + - name: Post-run cleanup + if: always() + run: | + find . -delete + + lint: + name: Lint codebase + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.10' + - name: Setup flake8 annotations + uses: rbialon/flake8-annotations@v1 + - name: Install linting packages + run: pip install flake8 pylint + - name: Lint codebase + run: make lint GITHUB_ACTIONS_FORMATTING=1 + + deploy: + name: Deploy GitHub pages + needs: test_linux + if: inputs.deploy_website + permissions: + pages: write + id-token: write + environment: + name: github-pages + url: http://firedrakeproject.github.io/firedrake/${{ inputs.target_branch }} + runs-on: ubuntu-latest + steps: + - name: Deploy to GitHub Pages + uses: actions/deploy-pages@v4 + + upload_pypi: + name: Upload to PyPI (optional) + if: inputs.upload_pypi && inputs.target_branch == 'release' + needs: test_linux + runs-on: ubuntu-latest + environment: + name: pypi + permissions: + id-token: write + steps: + - uses: actions/download-artifact@v4 + with: + name: dist + path: dist + - name: Push to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + + upload_testpypi: + name: Upload to TestPyPI (optional) + if: inputs.upload_testpypi && inputs.target_branch == 'release' + needs: test_linux + runs-on: ubuntu-latest + environment: + name: testpypi + permissions: + id-token: write + steps: + - uses: actions/download-artifact@v4 + with: + name: dist + path: dist + - name: Push to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 45e74f56b9..b26812f672 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -4,13 +4,7 @@ on: workflow_call: inputs: tag: - description: 'Optional tag (defaults to `latest`)' - required: false - default: 'latest' - type: string - status: - description: 'Firedrake test status' - required: true + description: Docker image tag type: string secrets: # Docker login information @@ -22,8 +16,6 @@ on: jobs: # Firedrake container (just Firedrake) docker_build_vanilla: - # Only run if Firedrake tests pass - if: inputs.status == 'success' strategy: fail-fast: false matrix: @@ -58,8 +50,6 @@ jobs: # # Firedrake container (Firedrake and friends) docker_build_firedrake: - # Only run if "Build Firedrake" succeeds - if: inputs.status == 'success' needs: docker_merge_vanilla uses: ./.github/workflows/docker_build.yml # Only build the 'firedrake' container for 'linux/amd64' because @@ -84,8 +74,6 @@ jobs: # # Firedrake container with TeX docker_build_docdeps: - # Only run if "Build Firedrake" succeeds - if: inputs.status == 'success' needs: docker_merge_vanilla uses: ./.github/workflows/docker_build.yml with: @@ -106,8 +94,6 @@ jobs: # # Firedrake container with Jupyter notebooks docker_build_jupyter: - # Only run if "Build Firedrake" succeeds - if: inputs.status == 'success' needs: docker_merge_firedrake uses: ./.github/workflows/docker_build.yml with: diff --git a/.github/workflows/docker_merge.yml b/.github/workflows/docker_merge.yml index c0b3f2c3a0..b7221fb10c 100644 --- a/.github/workflows/docker_merge.yml +++ b/.github/workflows/docker_merge.yml @@ -7,13 +7,12 @@ on: workflow_call: inputs: target: - description: 'Target docker image name to upload to' + description: Docker image name required: true type: string tag: - description: 'Optional tag (defaults to `latest`)' - required: false - default: 'latest' + description: Docker image tag + required: true type: string secrets: # Docker login information @@ -28,8 +27,7 @@ jobs: steps: - name: Pre-cleanup if: always() - run: | - rm -rf ${{ runner.temp }}/digests + run: rm -rf ${{ runner.temp }}/digests - name: Download digests uses: actions/download-artifact@v4 @@ -47,10 +45,15 @@ jobs: - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 + # NOTE: This action pushes a new image with the given tag but also updates + # the 'latest' tag. - name: Merge and push the per-platform images working-directory: ${{ runner.temp }}/digests run: | - docker buildx imagetools create -t firedrakeproject/${{ inputs.target }}:${{ inputs.tag }} $(printf 'firedrakeproject/${{ inputs.target }}@sha256:%s ' *) + docker buildx imagetools create \ + -t firedrakeproject/${{ inputs.target }}:${{ inputs.tag }} \ + -t firedrakeproject/${{ inputs.target }}:latest \ + $(printf 'firedrakeproject/${{ inputs.target }}@sha256:%s ' *) - name: Inspect image run: | @@ -58,5 +61,4 @@ jobs: - name: Post-cleanup if: always() - run: | - rm -rf ${{ runner.temp }}/digests + run: rm -rf ${{ runner.temp }}/digests diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml new file mode 100644 index 0000000000..eea463bacc --- /dev/null +++ b/.github/workflows/pr.yml @@ -0,0 +1,13 @@ +name: Test pull request + +on: + pull_request: + +jobs: + test: + uses: ./.github/workflows/core.yml + with: + target_branch: ${{ github.base_ref }} + # Only run macOS tests if the PR is labelled 'macOS' + test_macos: ${{ contains(github.event.pull_request.labels.*.name, 'macOS') }} + secrets: inherit diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml new file mode 100644 index 0000000000..06b0e316fc --- /dev/null +++ b/.github/workflows/push.yml @@ -0,0 +1,16 @@ +name: Deploy after push + +on: + push: + branches: + - master + - release + +jobs: + test: + uses: ./.github/workflows/core.yml + with: + target_branch: ${{ github.ref_name }} + test_macos: true + deploy_website: true + secrets: inherit diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000000..4fa26d2c40 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,23 @@ +name: Publish release + +on: + release: + types: [published] + +jobs: + deploy: + if: ! startsWith( github.ref_name, 'Zenodo' ) + uses: ./.github/workflows/core.yml + with: + target_branch: release + run_tests: false + upload_pypi: true + secrets: inherit + + docker: + if: ! startsWith( github.ref_name, 'Zenodo' ) + name: Build Docker containers + uses: ./.github/workflows/docker.yml + with: + tag: ${{ github.ref }} + secrets: inherit diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml deleted file mode 100644 index 02a258e593..0000000000 --- a/.github/workflows/test.yml +++ /dev/null @@ -1,376 +0,0 @@ -name: CI - -on: - push: - branches: - - master - pull_request: - schedule: - - cron: '0 0 * * 0' - - cron: '0 0 1 * *' # Monthly release - -concurrency: - # Cancel running jobs if new commits are pushed - group: > - ${{ github.workflow }}- - ${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true - -env: - RELEASE_TAG: latest - -jobs: - test: - name: Install and test Firedrake (Linux) - strategy: - # We want to know all of the tests which fail, so don't kill real if - # complex fails and vice-versa - fail-fast: false - matrix: - arch: [default, complex] - runs-on: [self-hosted, Linux] - container: - image: ubuntu:latest - env: - OMPI_ALLOW_RUN_AS_ROOT: 1 - OMPI_ALLOW_RUN_AS_ROOT_CONFIRM: 1 - OMP_NUM_THREADS: 1 - OPENBLAS_NUM_THREADS: 1 - FIREDRAKE_CI: 1 - PYOP2_CI_TESTS: 1 - PYOP2_SPMD_STRICT: 1 - EXTRA_PYTEST_ARGS: --splitting-algorithm least_duration --timeout=600 --timeout-method=thread -o faulthandler_timeout=660 - PYTEST_MPI_MAX_NPROCS: 8 - steps: - - name: Fix HOME - # For unknown reasons GitHub actions overwrite HOME to /github/home - # which will break everything unless fixed - # (https://github.com/actions/runner/issues/863) - run: echo "HOME=/root" >> "$GITHUB_ENV" - - - name: Pre-run cleanup - # Make sure the current directory is empty - run: find . -delete - - - uses: actions/checkout@v4 - with: - path: firedrake-repo - - - name: Install system dependencies - run: | - apt-get update - apt-get -y install python3 - apt-get -y install \ - $(python3 ./firedrake-repo/scripts/firedrake-configure --arch ${{ matrix.arch }} --show-system-packages) python3-venv parallel - - - name: Install PETSc - run: | - git clone --depth 1 --branch $(python3 ./firedrake-repo/scripts/firedrake-configure --show-petsc-version) https://gitlab.com/petsc/petsc.git - cd petsc - python3 ../firedrake-repo/scripts/firedrake-configure \ - --arch ${{ matrix.arch }} --show-petsc-configure-options | \ - xargs -L1 ./configure --with-make-np=8 --download-slepc - make PETSC_DIR=/__w/firedrake/firedrake/petsc PETSC_ARCH=arch-firedrake-${{ matrix.arch }} - make check - { - echo "PETSC_DIR=/__w/firedrake/firedrake/petsc" - echo "PETSC_ARCH=arch-firedrake-${{ matrix.arch }}" - echo "SLEPC_DIR=/__w/firedrake/firedrake/petsc/arch-firedrake-${{ matrix.arch }}" - } >> "$GITHUB_ENV" - - - name: Install Firedrake - id: install - run: | - export $(python3 ./firedrake-repo/scripts/firedrake-configure --arch ${{ matrix.arch }} --show-env) - python3 -m venv venv - . venv/bin/activate - : # Force a rebuild of petsc4py as the cached one will not link to the fresh - : # install of PETSc. - pip cache remove petsc4py - pip cache remove slepc4py - pip install --verbose \ - --no-binary h5py \ - --extra-index-url https://download.pytorch.org/whl/cpu \ - './firedrake-repo[ci]' - firedrake-clean - pip list - - - name: Run firedrake-check - run: | - . venv/bin/activate - firedrake-check - timeout-minutes: 5 - - - name: Run TSFC tests - # Run even if earlier tests failed - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - : # Use pytest-xdist here so we can have a single collated output (not possible - : # for parallel tests) - firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/tsfc - timeout-minutes: 60 - - - name: Run PyOP2 tests - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - : # Use pytest-xdist here so we can have a single collated output (not possible - : # for parallel tests) - firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 - firedrake-run-split-tests 2 4 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 - firedrake-run-split-tests 3 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 - firedrake-run-split-tests 4 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2 - timeout-minutes: 15 - - - - name: Run Firedrake tests (nprocs = 1) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - : # Use pytest-xdist here so we can have a single collated output (not possible - : # for parallel tests) - firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 60 - - - name: Run tests (nprocs = 2) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - firedrake-run-split-tests 2 4 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 30 - - - name: Run tests (nprocs = 3) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - firedrake-run-split-tests 3 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 60 - - - name: Run tests (nprocs = 4) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - firedrake-run-split-tests 4 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 15 - - - name: Run tests (nprocs = 5) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - firedrake-run-split-tests 5 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 15 - - - name: Run tests (nprocs = 6) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - firedrake-run-split-tests 6 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 15 - - - name: Run tests (nprocs = 7) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - firedrake-run-split-tests 7 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 15 - - - name: Run tests (nprocs = 8) - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - firedrake-run-split-tests 8 1 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake - timeout-minutes: 15 - - - name: Run Gusto smoke tests - # Only test Gusto in real mode - if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' - run: | - . venv/bin/activate - git clone --depth 1 https://github.com/firedrakeproject/gusto.git gusto-repo - pip install --verbose ./gusto-repo - python -m pytest -n 8 --verbose \ - gusto-repo/integration-tests/balance/test_saturated_balance.py \ - gusto-repo/integration-tests/equations/test_thermal_sw.py \ - gusto-repo/integration-tests/transport/test_embedded_dg_advection.py - timeout-minutes: 10 - - - name: Run Thetis smoke tests - if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' - run: | - . venv/bin/activate - git clone --depth 1 https://github.com/thetisproject/thetis.git thetis-repo - pip install --verbose ./thetis-repo - python -m pytest -n 8 --verbose thetis-repo/test_adjoint/test_swe_adjoint.py - timeout-minutes: 10 - - - name: Run spyro smoke tests - if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' - run: | - . venv/bin/activate - git clone --depth 1 https://github.com/NDF-Poli-USP/spyro.git spyro-repo - pip install --verbose ./spyro-repo - mpiexec -n 6 python -m pytest spyro-repo/test_integration/ -m parallel[6] - timeout-minutes: 5 - - - name: Run G-ADOPT smoke tests - if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' - run: | - . venv/bin/activate - git clone --depth 1 https://github.com/g-adopt/g-adopt.git g-adopt-repo - pip install --verbose ./g-adopt-repo - make -C g-adopt-repo/demos/mantle_convection/base_case check - timeout-minutes: 5 - - - name: Upload log files - uses: actions/upload-artifact@v4 - if: success() || steps.install.conclusion == 'success' - with: - name: firedrake-logs-${{ matrix.arch }} - path: pytest_*.log - - - name: Post-run cleanup - if: always() - run: find . -delete - - docker_tag: - name: "Set the Docker release tag" - runs-on: [self-hosted, Linux] - if: github.ref == 'refs/heads/master' - steps: - - name: Set release tag - # Set a release tag if triggered by monthly CRON job - if: github.event.schedule == '0 0 1 * *' - run: | - DATE_TAG="$(date +%Y-%m)" - echo "RELEASE_TAG=$DATE_TAG" >> "$GITHUB_ENV" - - name: Print release tag being used - run: | - echo The release tag is "$RELEASE_TAG" - outputs: - tag: ${{ env.RELEASE_TAG }} - - docker: - name: Build Docker containers - if: github.ref == 'refs/heads/master' - needs: [test, docker_tag] - uses: ./.github/workflows/docker.yml - with: - tag: ${{ needs.docker_tag.outputs.tag }} - status: ${{ needs.test.result }} - secrets: inherit - - lint: - name: Lint codebase - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: '3.10' - - name: Setup flake8 annotations - uses: rbialon/flake8-annotations@v1 - - name: Install linting packages - run: pip install flake8 pylint - - name: Lint codebase - run: make lint GITHUB_ACTIONS_FORMATTING=1 - - zenodo_canary: - name: Run zenodo canary - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 1 - - name: Setup Python - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - name: Install deps - run: pip install requests packaging - - name: Zenodo API canary - run: python scripts/firedrake-install --test-doi-resolution - - name: Upload log - uses: actions/upload-artifact@v4 - if: failure() - with: - name: zenodo-canary - path: firedrake-install.log - - build_docs: - name: Build documentation - runs-on: ubuntu-latest - container: - image: firedrakeproject/firedrake-docdeps:latest - outputs: - conclusion: ${{ steps.report.outputs.conclusion }} - steps: - - uses: actions/checkout@v4 - with: - path: firedrake-repo - - - name: Install Firedrake - id: install - run: | - : # Pass '--system-site-packages' so already installed packages can be found - python3 -m venv --system-site-packages venv - . venv/bin/activate - pip uninstall -y firedrake - pip install --verbose './firedrake-repo[docs]' - - - name: Check bibtex - run: | - . venv/bin/activate - make -C firedrake-repo/docs validate-bibtex - - - name: Check documentation links - run: | - . venv/bin/activate - make -C firedrake-repo/docs linkcheck - - - name: Build docs - id: build - if: success() || steps.install.conclusion == 'success' - run: | - . venv/bin/activate - cd firedrake-repo/docs - make html - make latex - make latexpdf - - - name: Copy manual to HTML tree - id: copy - if: success() || steps.build.conclusion == 'success' - run: | - cd firedrake-repo/docs - cp build/latex/Firedrake.pdf build/html/_static/manual.pdf - - - name: Upload artifact - id: upload - if: success() || steps.copy.conclusion == 'success' - uses: actions/upload-pages-artifact@v3 - with: - name: github-pages - path: /__w/firedrake/firedrake/firedrake-repo/docs/build/html - retention-days: 1 - - - name: Report status - id: report - if: success() || steps.upload.conclusion == 'success' - run: echo "conclusion=success" >> "$GITHUB_OUTPUT" - - deploy: - name: Deploy Github pages - needs: build_docs - # Always run this workflow on master, even if linkcheck fails - if: always() && github.ref == 'refs/heads/master' && needs.build_docs.outputs.conclusion == 'success' - permissions: - pages: write - id-token: write - environment: - name: github-pages - url: http://firedrakeproject.github.io/firedrake - runs-on: ubuntu-latest - steps: - - name: Deploy to GitHub Pages - uses: actions/deploy-pages@v4 diff --git a/.github/workflows/test_macos.yml b/.github/workflows/test_macos.yml deleted file mode 100644 index dd987e4a80..0000000000 --- a/.github/workflows/test_macos.yml +++ /dev/null @@ -1,81 +0,0 @@ -name: Install and test Firedrake (macOS) - -on: - push: - branches: - - master - pull_request: - # By default this workflow is run on the "opened", "synchronize" and - # "reopened" events. We add "labelled" so it will run if the PR is given a label. - types: [opened, synchronize, reopened, labeled] - -concurrency: - # Cancels jobs running if new commits are pushed - group: > - ${{ github.workflow }}- - ${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true - -jobs: - build: - name: Build Firedrake (macOS) - runs-on: [self-hosted, macOS] - # Only run this action if we are pushing to master or the PR is labelled "macOS" - if: ${{ (github.ref == 'refs/heads/master') || contains(github.event.pull_request.labels.*.name, 'macOS') }} - env: - FIREDRAKE_CI: 1 - OMP_NUM_THREADS: 1 - OPENBLAS_NUM_THREADS: 1 - steps: - - name: Add homebrew to PATH - # https://docs.github.com/en/actions/writing-workflows/choosing-what-your-workflow-does/workflow-commands-for-github-actions#adding-a-system-path - run: echo "/opt/homebrew/bin" >> "$GITHUB_PATH" - - - name: Pre-run cleanup - # Make sure the current directory is empty - run: find . -delete - - - uses: actions/checkout@v4 - with: - path: firedrake-repo - - - name: Install system dependencies - run: | - brew install $(python3 ./firedrake-repo/scripts/firedrake-configure --arch default --show-system-packages) - - - name: Install PETSc - run: | - git clone --depth 1 --branch $(python3 ./firedrake-repo/scripts/firedrake-configure --show-petsc-version) https://gitlab.com/petsc/petsc.git - cd petsc - python3 ../firedrake-repo/scripts/firedrake-configure \ - --arch default --show-petsc-configure-options | \ - xargs -L1 ./configure --with-make-np=4 - make - make check - - - name: Install Firedrake - id: install - run: | - export $(python3 ./firedrake-repo/scripts/firedrake-configure --arch default --show-env) - python3 -m venv venv - . venv/bin/activate - : # Force a rebuild of petsc4py as the cached one will not link to the fresh - : # install of PETSc. A similar trick may be needed for compiled dependencies - : # like h5py or mpi4py if changing HDF5/MPI libraries. - pip cache remove petsc4py - pip install --verbose --no-binary h5py './firedrake-repo[test]' - firedrake-clean - : # Extra test dependencies - pip install pytest-timeout - pip list - - - name: Run smoke tests - run: | - . venv/bin/activate - firedrake-check - timeout-minutes: 10 - - - name: Post-run cleanup - if: always() - run: | - find . -delete diff --git a/Makefile b/Makefile index 2d5007d4d4..ef491f3efe 100644 --- a/Makefile +++ b/Makefile @@ -39,7 +39,7 @@ actionlint: @docker pull rhysd/actionlint:latest @# Exclude SC2046 so it doesn't complain about unquoted $ characters (the @# quoting can prevent proper parsing) - @docker run -e SHELLCHECK_OPTS='--exclude=SC2046' --rm -v $$(pwd):/repo --workdir /repo rhysd/actionlint -color + @docker run -e SHELLCHECK_OPTS='--exclude=SC2046,SC2078' --rm -v $$(pwd):/repo --workdir /repo rhysd/actionlint -color .PHONY: dockerlint dockerlint: diff --git a/docker/Dockerfile.docdeps b/docker/Dockerfile.docdeps deleted file mode 100644 index 832b422b5f..0000000000 --- a/docker/Dockerfile.docdeps +++ /dev/null @@ -1,7 +0,0 @@ -# Dockerfile for Firedrake plus packages needed to build the documentation - -FROM firedrakeproject/firedrake-vanilla-default:latest - -RUN apt-get update \ - && apt-get -y install inkscape texlive-full \ - && rm -rf /var/lib/apt/lists/* diff --git a/docs/source/conf.py b/docs/source/conf.py index 29f5e979a4..695b014a64 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -244,6 +244,13 @@ # Output file base name for HTML help builder. htmlhelp_basename = 'Firedrakedoc' +# Optional header warning about docs version +rst_prolog = """ +.. only:: main + + .. warning:: + You are reading a version of the website built against the unstable ``main`` branch. This content is liable to change without notice and may be inappropriate for your use case. +""" # -- Options for LaTeX output -------------------------------------------- diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst index 2da93622b7..1a90527867 100644 --- a/docs/source/documentation.rst +++ b/docs/source/documentation.rst @@ -6,21 +6,17 @@ .. sidebar:: Current development information. - Firedrake and PyOP2 are continually tested using `GitHub actions `__. + Firedrake is continually tested using `GitHub actions `__. Latest Firedrake status: |firedrakebuild|_ - .. |firedrakebuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/build.yml/badge.svg - .. _firedrakebuild: https://github.com/firedrakeproject/firedrake/actions/workflows/build.yml + .. |firedrakebuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml/badge.svg + .. _firedrakebuild: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml Firedrake and its components are developed on `GitHub - `__ where we also maintain Firedrake-ready - versions of the `FEniCS `__ components - UFL. + `__. * `Firedrake on GitHub `__ - * `TSFC on GitHub `__ * `FIAT on GitHub `__ - * `Firedrake version of UFL on GitHub `__ Getting started =============== diff --git a/docs/source/install.rst b/docs/source/install.rst index e1666ae250..cde921b8e2 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -180,7 +180,7 @@ install Firedrake. To do this perform the following steps: #. Install Firedrake:: $ pip cache remove petsc4py - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git#[check]" + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check]" .. note:: Though not strictly necessary to install Firedrake's optional @@ -190,12 +190,6 @@ install Firedrake. To do this perform the following steps: #. Firedrake is now installed and ready for use! -.. warning:: - Until Firedrake has versioned releases (slated for April/May 2025), - :doc:`firedrake-zenodo` will only work with *editable* installations of - Firedrake and its components. To install Firedrake in editable mode you - should follow the instructions :ref:`below`. - .. _firedrake_check: @@ -225,7 +219,7 @@ Updating Firedrake involves following the same steps as above when :ref:`installing Firedrake`. First, use ``firedrake-configure`` to set the right environment variables and then run:: - $ pip install --upgrade git+https://github.com/firedrakeproject/firedrake.git + $ pip install --upgrade git+https://github.com/firedrakeproject/firedrake.git@release Updating PETSc ~~~~~~~~~~~~~~ @@ -288,36 +282,6 @@ To resolve the problem you should first remove any existing cached packages:: before re-running the instruction to install Firedrake. -.. _dev_install: - -Developer install ------------------ - -By default Firedrake is installed just like any other Python package into -your environment. If you want to be able to edit Firedrake itself then -an *editable* installation is needed. To install Firedrake in editable -mode you should follow the same -:ref:`steps as for a non-editable install` but replace the -final ``pip install`` command with:: - - $ git clone - $ pip install --no-binary h5py --editable './firedrake[dev]' - -where ```` is ``https://github.com/firedrakeproject/firedrake.git`` -or ``git@github.com:firedrakeproject/firedrake.git`` as preferred. - -The same process applies for Firedrake's dependencies. For example, to install -`FIAT `_ in editable mode you -should run:: - - $ git clone - $ pip install --editable ./fiat - -Note that editable versions of Firedrake's dependencies should be installed *after* -Firedrake is installed. Otherwise installing Firedrake will overwrite -whatever packages you just installed. - - .. _customising: Customising Firedrake @@ -362,7 +326,7 @@ To install Firedrake with SLEPc support you should: and install Firedrake with the ``slepc`` optional dependency. For example:: $ pip cache remove slepc4py - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git#[check,slepc]" + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,slepc]" VTK ~~~ @@ -370,7 +334,7 @@ VTK To install Firedrake with VTK, it should be installed using the ``vtk`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git#[check,vtk]" + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,vtk]" At present VTK wheels are not available for ARM Linux machines. Depending on your Python version you may be able to work around this by downloading and pip installing @@ -384,7 +348,7 @@ PyTorch To install Firedrake with `PyTorch `_, it should be installed using the ``torch`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git#[check,torch]" --extra-index-url https://download.pytorch.org/whl/cpu + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,torch]" --extra-index-url https://download.pytorch.org/whl/cpu Observe that, in addition to specifying ``torch``, an additional argument (``--extra-index-url``) is needed. More information on installing @@ -397,7 +361,7 @@ JAX To install Firedrake with JAX, it should be installed using the ``jax`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git#[check,jax]" + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,jax]" Netgen @@ -406,7 +370,7 @@ Netgen To install Firedrake with `Netgen `_ support, it should be installed with the ``netgen`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git#[check,netgen]" + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,netgen]" Customising PETSc @@ -503,6 +467,74 @@ Firedrake can also be used inside the brower using Jupyter notebooks and `Google Colab `_. For more information please see :doc:`here`. +.. _dev_install: + +Developer install +================= + +.. only:: release + + .. warning:: + You are currently looking at the documentation for the current stable + release of Firedrake. For the most recent developer documentation you + should follow the instructions `here `__. + +In order to install a development version of Firedrake the following steps +should be followed: + +#. Install system dependencies :ref:`as before` + +#. Clone and build the *default branch* of PETSc: + + .. code-block:: text + + $ git clone https://gitlab.com/petsc/petsc.git + $ cd petsc + $ python3 ../firedrake-configure --show-petsc-configure-options | xargs -L1 ./configure + $ make PETSC_DIR=/path/to/petsc PETSC_ARCH=arch-firedrake-default all + $ make check + $ cd .. + +#. Clone Firedrake:: + + $ git clone + + where ```` is ``https://github.com/firedrakeproject/firedrake.git`` + or ``git@github.com:firedrakeproject/firedrake.git`` as preferred. + +#. Set the necessary environment variables:: + + $ export $(python3 firedrake-configure --show-env) + +#. Install petsc4py and Firedrake's other build dependencies: + + .. code-block:: text + + $ pip cache remove petsc4py + $ pip install $PETSC_DIR/src/binding/petsc4py + $ pip install -r ./firedrake/requirements-build.txt + +#. Install Firedrake in editable mode without build isolation:: + + $ pip install --no-build-isolation --no-binary h5py --editable './firedrake[check]' + +Editing subpackages +------------------- + +Firedrake dependencies can be cloned and installed in editable mode in an +identical way to Firedrake. For example, to install +`FIAT `_ in editable mode you +should run:: + + $ git clone + $ pip install --editable ./fiat + +For most packages it should not be necessary to pass ``--no-build-isolation``. + +It is important to note that these packages **must be installed after Firedrake**. +This is because otherwise installing Firedrake will overwrite the just-installed +package. + .. _discussion: https://github.com/firedrakeproject/firedrake/discussions .. _issue: https://github.com/firedrakeproject/firedrake/issues .. _homebrew: https://brew.sh/ diff --git a/docs/source/zenodo.rst b/docs/source/zenodo.rst index 4b62b4e305..fcbd08a934 100644 --- a/docs/source/zenodo.rst +++ b/docs/source/zenodo.rst @@ -34,7 +34,7 @@ How to register DOIs for a version of Firedrake This section assumes that you have a Firedrake installation which you have used to conduct some numerical experiment and which you wish to publish or otherwise record for posterity. It is assumed that your -virtualenv is activated or that you otherwise have the firedrake +virtualenv is activated or that you otherwise have the Firedrake scripts in your path. 1. Use ``firedrake-zenodo`` to generate a JSON file containing the @@ -59,16 +59,17 @@ scripts in your path. This will create a file ``firedrake.json`` containing the required information. -2. Create an issue on the Firedrake GitHub page asking that a Zenodo - release be created. Attach the ``firedrake.json`` file to the +2. Create an issue on the `Firedrake GitHub page + `__ asking that a + Zenodo release be created. Attach the ``firedrake.json`` file to the issue. You can create the issue using the correct template `here `__. 3. The Firedrake developers will generate a bespoke Firedrake release containing exactly the set of versions your JSON file specifies, as well as creating a Zenodo record collating these. You will be - provided with a firedrake release tag of the form - ``Firedrake_YYYYMMDD.N``. + provided with a Firedrake release tag of the form + ``Zenodo_YYYYMMDD.N``. You can see an example such a collated record `here `__. @@ -77,7 +78,7 @@ scripts in your path. the DOI) for the collated "meta"-record, which in turn links to all the individual components:: - firedrake-zenodo --bibtex Firedrake_YYYYMMDD.N + firedrake-zenodo --bibtex Zenodo_YYYYMMDD.N Obviously, you substitute in your Firedrake release tag. diff --git a/firedrake/eigensolver.py b/firedrake/eigensolver.py index 4f05d00dbe..9294393a6e 100644 --- a/firedrake/eigensolver.py +++ b/firedrake/eigensolver.py @@ -57,7 +57,7 @@ def __init__(self, A, M=None, bcs=None, bc_shift=0.0, restrict=True): if not SLEPc: raise ImportError( "Unable to import SLEPc, eigenvalue computation not possible " - "(try firedrake-update --slepc)" + "(see https://www.firedrakeproject.org/install.html#slepc)" ) args = A.arguments() diff --git a/firedrake/mg/mesh.py b/firedrake/mg/mesh.py index 42ea985666..446e042b35 100644 --- a/firedrake/mg/mesh.py +++ b/firedrake/mg/mesh.py @@ -122,9 +122,9 @@ def MeshHierarchy(mesh, refinement_levels, try: from ngsPETSc import NetgenHierarchy except ImportError: - raise ImportError("Unable to import netgen and ngsPETSc. Please ensure that netgen and ngsPETSc\ - are installed and available to Firedrake. You can do this via \ - firedrake-update --netgen.") + raise ImportError("Unable to import netgen and ngsPETSc. Please ensure that netgen and ngsPETSc " + "are installed and available to Firedrake (see " + "https://www.firedrakeproject.org/install.html#netgen).") if hasattr(mesh, "netgen_mesh"): return NetgenHierarchy(mesh, refinement_levels, flags=netgen_flags) else: diff --git a/firedrake/mg/opencascade_mh.py b/firedrake/mg/opencascade_mh.py index e320877219..d1e5c6c843 100644 --- a/firedrake/mg/opencascade_mh.py +++ b/firedrake/mg/opencascade_mh.py @@ -20,7 +20,7 @@ def OpenCascadeMeshHierarchy(stepfile, element_size, levels, comm=COMM_WORLD, di from OCC.Core.STEPControl import STEPControl_Reader from OCC.Extend.TopologyUtils import TopologyExplorer except ImportError: - raise ImportError("To use OpenCascadeMeshHierarchy, you must install firedrake with the OpenCascade python bindings (firedrake-update --opencascade).") + raise ImportError("To use OpenCascadeMeshHierarchy, you must install firedrake with the OpenCascade python bindings (https://github.com/tpaviot/pythonocc-core).") if not os.path.isfile(stepfile): raise OSError("%s does not exist" % stepfile) diff --git a/firedrake/scripts/firedrake-zenodo b/firedrake/scripts/firedrake-zenodo index 82475e332b..44fdc0f42f 100755 --- a/firedrake/scripts/firedrake-zenodo +++ b/firedrake/scripts/firedrake-zenodo @@ -1,44 +1,43 @@ #! /usr/bin/env python3 + +from __future__ import annotations + +import abc +import contextlib +import dataclasses import hashlib -import re -import importlib import logging -import pathlib import sys import os import subprocess from argparse import ArgumentParser, RawDescriptionHelpFormatter -from collections import OrderedDict import json import time import requests import base64 import datetime +from importlib.metadata import Distribution # Change this to https://sandbox.zenodo.org/api for testing ZENODO_URL = "https://zenodo.org/api" -descriptions = OrderedDict([ - ("firedrake", "an automated finite element system"), - ("ufl", "The Unified Form Language"), - ("fiat", "The Finite Element Automated Tabulator"), - ("petsc", "Portable, Extensible Toolkit for Scientific Computation"), - ("loopy", "Transformation-Based Generation of High-Performance CPU/GPU Code"), - ("slepc", "Scalable Library for Eigenvalue Problem Computations"), -]) - -projects = dict([ - ("firedrake", "firedrakeproject"), - ("ufl", "firedrakeproject"), - ("fiat", "firedrakeproject"), - ("petsc", "firedrakeproject"), - ("loopy", "firedrakeproject"), - ("slepc", "firedrakeproject"), -]) - -components = list(descriptions.keys()) - -optional_components = ("slepc",) +DESCRIPTIONS = { + "firedrake": "Firedrake: an automated finite element system", + "ufl": "UFL: the Unified Form Language", + "fiat": "FIAT: the Finite Element Automated Tabulator", + "petsc": "PETSc: the Portable, Extensible Toolkit for Scientific Computation", + "loopy": "loopy: Transformation-Based Generation of High-Performance CPU/GPU Code", +} + +PYPI_PACKAGE_NAMES = { + "firedrake": "firedrake", + "ufl": "fenics-ufl", + "fiat": "firedrake-fiat", + "loopy": "loopy", + "petsc": "petsc4py", +} + +components = list(DESCRIPTIONS.keys()) parser = ArgumentParser(description="""Create Zenodo DOIs for specific versions of Firedrake components. @@ -153,6 +152,43 @@ log = logging.getLogger() cwd = os.getcwd() +class ComponentVersion(abc.ABC): + pass + + +@dataclasses.dataclass(frozen=True) +class ReleaseComponentVersion(ComponentVersion): + version: str + + +@dataclasses.dataclass(frozen=True) +class VCSComponentVersion(ComponentVersion): + commit_id: str + + +def get_component_version(component: str) -> ComponentVersion: + pypi_package_name = PYPI_PACKAGE_NAMES[component] + # This incantation returns a JSON string containing information about where the + # component is installed and whether it is editable or not + # (see https://stackoverflow.com/questions/43348746/how-to-detect-if-module-is-installed-in-editable-mode#75078002). + dist = Distribution.from_name(pypi_package_name) + direct_url_json = dist.read_text("direct_url.json") + + if direct_url_json: + direct_url = json.loads(direct_url_json) + is_editable = direct_url.get("dir_info", {}).get("editable", False) + if is_editable: + # sniff the commit info from the repository + repo = direct_url["url"].removeprefix("file://") + commit_id = get_git_commit_info(repo) + else: + commit_id = direct_url["vcs_info"]["commit_id"] + return VCSComponentVersion(commit_id) + else: + # 'direct_url_json' is 'None' if the package is installed via PyPI + return ReleaseComponentVersion(dist.version) + + def check_call(arguments): if args.log: try: @@ -172,69 +208,12 @@ def check_output(args): raise -class directory(object): - """Context manager that executes body in a given directory""" - def __init__(self, d): - self.d = os.path.abspath(d) - - def __enter__(self): - self.olddir = os.path.abspath(os.getcwd()) - os.chdir(self.d) - - def __exit__(self, *args): - os.chdir(self.olddir) - - -def collect_repo_shas(): - shas = {} - for component in components: - log.info(f"Retrieving git information for {component}") - - repo = None - # handle non-Python components separately - if component in {"petsc", "slepc"}: - repo_root_var = "PETSC_DIR"if component == "petsc" else "SLEPC_DIR" - if repo_root_var in os.environ: - repo = os.environ[repo_root_var] - elif "VIRTUAL_ENV" in os.environ: - venv_path = os.path.join(os.getenv("VIRTUAL_ENV"), "src", component) - if os.path.exists(venv_path): - repo = venv_path - else: - # handle the fact that the fiat Python package is called FIAT - if component == "fiat": - python_package_name = "FIAT" - else: - python_package_name = component - try: - package = importlib.import_module(python_package_name) - repo = pathlib.Path(package.__file__).parent.parent - except ImportError: - pass - - if repo: - try: - shas[component] = get_git_commit_info(component, repo) - except RepositoryNotFoundException: - log.warning("Cannot retrieve git information for component " - f"'{component}' so it will not be included in the " - "release. This may be because the package is not " - "installed in 'editable' mode.") - elif component in optional_components: - log.warning(f"Failed to find optional component '{component}', " - "continuing without it") - else: - log.error(f"Mandatory component '{component}' could not be found.") - sys.exit(1) - return shas - - class RepositoryNotFoundException(RuntimeError): pass -def get_git_commit_info(component, repo): - with directory(repo): +def get_git_commit_info(repo): + with contextlib.chdir(repo): try: check_call(["git", "status"]) except subprocess.CalledProcessError: @@ -243,7 +222,7 @@ def get_git_commit_info(component, repo): try: check_call(["git", "diff-index", "--quiet", "HEAD"]) except subprocess.CalledProcessError: - log.error(f"Component {component} has uncommitted changes, cannot create release") + log.error(f"Repository {repo} contains uncommitted changes, cannot create release") sys.exit(1) return check_output(["git", "rev-parse", "HEAD"]).strip() @@ -271,12 +250,6 @@ def check_github_token_scope(token): log.debug("FIREDRAKE_GITHUB_TOKEN has necessary scopes") -def check_tag(tag): - if re.match(r"Firedrake_20[0-9]{6,6}\.[0-9]+", tag) is None: - log.error("Provided tag '{}' is not a legal Firedrake Zenodo release tag".format(tag)) - sys.exit(1) - - def resolve_tag(tag, components): """Match a tag in component repositories. @@ -295,7 +268,7 @@ def resolve_tag(tag, components): token = os.getenv("FIREDRAKE_GITHUB_TOKEN") if token: check_github_token_scope(token) - gh = github3.login(token=token) + github3.login(token=token) else: log.error("""Need to provide FIREDRAKE_GITHUB_TOKEN for github to resolve tags. @@ -304,21 +277,13 @@ variable FIREDRAKE_GITHUB_TOKEN to a github personal access token.""") sys.exit(1) result = [] for component in components: - repo = gh.repository(projects[component], component) - tags = list(repo.tags()) + repo = get_component_repository(component) try: - found, = [t for t in tags if t.name == tag] + found, = [t for t in repo.tags() if t.name == tag] except ValueError: - if component in optional_components: - continue - if args.skip_missing: - log.warning("Tag '{tag}' does not exist in repository '{repo}'. Continuing".format(tag=tag, repo=repo)) - else: - log.error("Tag '{tag}' does not exist in repository '{repo}'".format( - tag=tag, repo=repo)) - log.error("To continue despite this use --skip-missing") - sys.exit(1) - matching = [t for t in tags if t.commit == found.commit] + log.error(f"Tag '{tag}' does not exist in repository '{repo}'") + sys.exit(1) + matching = [t for t in repo.tags() if t.commit == found.commit] result.append((repo, matching)) return result @@ -428,7 +393,7 @@ def create_description(records, title, doi, additional_dois=None): :returns: A HTML string suitable for zenodo upload.""" links = "\n".join('
  • {name} ({desc}): {doi}
    • '.format( name=repo.name, - desc=descriptions[repo.name], + desc=DESCRIPTIONS[repo.name], doi=record["doi"]) for repo, (_, record) in records) if additional_dois: additional_links = "\n".join('
  • {doi}
    • '.format( @@ -442,15 +407,7 @@ The Firedrake components and dependencies used were: {links}

    - -

    -You can install Firedrake using exactly this set of component versions using: -

    firedrake-install --doi {doi}
    -

    -

    -See firedrakeproject.org/install.html for more information. -

    - """.format(title=title, links=links, doi=doi) + """.format(title=title, links=links) if additional_dois: data = """{data} @@ -458,10 +415,7 @@ See firedrakeproject.org
      {additional_links}
    -

    -

    -You will have to download and install them separately in your Firedrake installation, since we cannot automate it for you. -

    """.format(data=data, additional_links=additional_links) +

    """.format(data=data, additional_links=additional_links) return data @@ -526,16 +480,12 @@ def create_metarecord(tag, title, components, info_file=None, matching_records = match(all_records, possible_tags) if len(matching_records) != len(possible_tags): missing = set(repo for repo, _ in possible_tags).difference(repo for repo, _ in matching_records) - if args.skip_missing: - log.warning("Did not find a Zenodo record for repositories: '{}'. Continuing.".format( - ", ".join(repo.full_name for repo in missing))) - else: - log.error("Did not find a Zenodo record for the following repositories") - for repo in missing: - log.error("{}".format(repo.full_name)) - log.error("") - log.error("If you want to continue anyway use --skip-missing") - sys.exit(1) + log.error("Did not find a Zenodo record for the following repositories") + for repo in missing: + log.error("{}".format(repo.full_name)) + log.error("") + sys.exit(1) + base_url = f"{ZENODO_URL}/deposit/depositions" if os.getenv("FIREDRAKE_ZENODO_TOKEN"): authentication_params = {"access_token": os.getenv("FIREDRAKE_ZENODO_TOKEN")} @@ -580,10 +530,10 @@ with deposit:write scope.""") metadata = { "metadata": { - "title": "Software used in `{}'".format(title), + "title": f"Software used in `{title}'", "upload_type": "software", "creators": [{"name": "firedrake-zenodo"}], - "version": "{}".format(tag), + "version": tag, "access_right": "open", "license": "cc-by", "related_identifiers": ([{"relation": "cites", "identifier": record["doi"]} @@ -599,11 +549,11 @@ with deposit:write scope.""") if depo.status_code >= 400: raise ValueError("Unable to add metadata to deposition {}".format(depo.json())) - components = create_json(matching_records, title, additional_dois=additional_dois) + components_json = create_json(matching_records, title, additional_dois=additional_dois) # This is where files live bucket_url = depo.json()["links"]["bucket"] upload = requests.put("{url}/{filename}".format(url=bucket_url, filename="components.json"), - data=components, + data=components_json, params=authentication_params) if upload.status_code >= 400: raise ValueError("Unable to upload file {}".format(upload.json())) @@ -617,7 +567,7 @@ with deposit:write scope.""") f"Actual: {actual}") checksum(upload.json()["checksum"], - hashlib.md5(components).hexdigest(), + hashlib.md5(components_json).hexdigest(), "components.json") if info_file is not None: @@ -672,7 +622,6 @@ def format_bibtex(record): def create_bibtex(tag): - check_tag(tag) response = requests.get(f"{ZENODO_URL}/records", params={"q": "creators.name:firedrake-zenodo AND version:%s" % tag}) if response.status_code >= 400: @@ -728,25 +677,30 @@ if args.list_meta_records: sys.exit(0) -if args.meta_release: - with open(args.meta_release, "r") as f: +def create_zenodo_meta_release(meta_release: str) -> None: + with open(meta_release, "r") as f: data = json.loads(f.read()) tag = data["tag"] - check_tag(tag) - title = data["title"] - components = data["components"] - info_file = decode_info_file(data["info_file"]) additional_dois = data["additional_dois"] new_version_of = data["new_version_of"] - record = create_metarecord(tag, title, components, info_file=info_file, - additional_dois=additional_dois, - update_record=new_version_of) + record = create_metarecord( + tag, + data["title"], + data["components"], + info_file=decode_info_file(data["info_file"]), + additional_dois=additional_dois, + update_record=new_version_of, + ) record = record.json() log.info("Created Zenodo meta-release.") - log.info("Tag is `{}`".format(tag)) - log.info("DOI is {}".format(record["doi"])) - log.info("Zenodo URL is {}".format(record["links"]["record_html"])) - log.info("BibTeX\n\n```bibtex\n{}\n```".format(format_bibtex(record))) + log.info(f"Tag is `{tag}`") + log.info(f"DOI is {record[doi]}") + log.info(f"Zenodo URL is {record['links']['record_html']}") + log.info(f"BibTeX\n\n```bibtex\n{format_bibtex(record)}\n```") + + +if args.meta_release: + create_zenodo_meta_release(args.meta_release) sys.exit(0) @@ -755,8 +709,7 @@ if args.release or not args.input_file: log.error("You must provide a title using the --title option") sys.exit(1) - # Collect hashes from the current repo. - shas = collect_repo_shas() + shas = {c: dataclasses.asdict(get_component_version(c)) for c in components} if args.info_file: shas["metarelease_info_file"] = encode_info_file(args.info_file[0]) else: @@ -783,7 +736,7 @@ if args.additional_dois: for component in components: new_sha = getattr(args, component) if new_sha: - shas[component] = new_sha[0] + shas[component] = {"commit_id": new_sha[0]} if not (args.release or args.input_file): # Dump json and exit. @@ -795,12 +748,13 @@ if not (args.release or args.input_file): try: import github3 - # Shut up the github module - github3.session.__logs__.setLevel(logging.WARNING) except ImportError: log.error("Publishing releases requires the github3 module. Please pip install github3.py") sys.exit(1) +# Shut up the github module +github3.session.__logs__.setLevel(logging.WARNING) + # Github authentication. token = os.getenv("FIREDRAKE_GITHUB_TOKEN") if token: @@ -820,85 +774,126 @@ variable FIREDRAKE_GITHUB_TOKEN to a Github personal access token with public_repo scope.""") sys.exit(1) -fd = gh.repository("firedrakeproject", "firedrake") -tag = time.strftime("Firedrake_%Y%m%d", time.localtime()) -index = -1 +def get_component_repository(component: str) -> github3.Repository: + return gh.repository("firedrakeproject", component) -for r in fd.tags(): - if r.name.startswith(tag): - newindex = int(r.name.split(".")[1]) - index = max(index, newindex) -tag += "." + str(index + 1) -# Verify commits. This ensures that an invalid sha will cause us to fail before we release any component. -# This step also replaces short shas with long ones. This seems to be necessary for release creation. +def generate_unique_release_tag() -> str: + tag = time.strftime("Zenodo_%Y%m%d", time.localtime()) + index = -1 + firedrake_repo = get_component_repository("firedrake") + for release in firedrake_repo.tags(): + if release.name.startswith(tag): + newindex = int(release.name.split(".")[1]) + index = max(index, newindex) + tag += "." + str(index + 1) + return tag -for component in components: - repo = gh.repository(projects[component], component) - try: - commit = repo.commit(shas[component]) - if not commit: - log.error("Failed to find specified commit for %s" % component) +def check_ref_exists(component): + if "version" in shas[component]: + # component is versioned, a GitHub release should already exist + if not release_already_exists(component, shas[component]["version"]): + log.error(f"A release of {component} is referenced but no corresponding release on GitHub can be found, aborting") + sys.exit(1) + else: + repo = get_component_repository(component) + repo.commit(shas[component]["commit_id"]) - shas[component] = commit.sha - except KeyError: - log.warning("No commit specified for %s. No release will be created for this component." % component) - # Also check that the tag name does not already exist. - if any(t.name == tag for t in repo.tags()): - log.warning(f"Repository {component} already has tag {tag}. This should not have happened.") - sys.exit(1) +def make_github_release_or_tag_existing(component: str) -> None: + repo = get_component_repository(component) + + if "version" in shas[component]: + # component is versioned, a GitHub release should already exist + release = get_matching_release(component, shas[component]["version"]) + tag_existing_release = True + commit_id = release.target_commitish + else: + # referencing a specific commit, may need to make a new release -# Now create releases. -message = """This release is specifically created to document the version of -{component} used in a particular set of experiments using -Firedrake. Please do not cite this as a general source for Firedrake -or any of its dependencies. Instead, refer to -https://www.firedrakeproject.org/citing.html""" - - -for component in (set(shas) & set(components)): - log.info("Releasing %s" % component) - repo = gh.repository(projects[component], component) - releases = repo.releases() - just_tag = False - date = datetime.datetime.utcnow().replace(microsecond=0, tzinfo=datetime.timezone.utc).isoformat() - tagger = {"name": "firedrake-zenodo", - "email": "firedrake@imperial.ac.uk", - "date": date} - - for release in releases: - if release.target_commitish == shas[component]: - just_tag = True - break - if just_tag: - repo.create_tag(tag, - message=descriptions[component], - sha=shas[component], + # make sure that 'commit_id' is not truncated + commit_id = repo.commit(shas[component]["commit_id"]) + + if commit_already_exists_in_release(component, commit_id): + tag_existing_release = True + else: + tag_existing_release = False + + if tag_existing_release: + log.info(f"Commit '{commit_id}' found in a pre-existing release for " + f"'{component}', adding tag '{release_tag}' to it") + date = datetime.datetime.utcnow().replace(microsecond=0, tzinfo=datetime.timezone.utc).isoformat() + tagger = {"name": "firedrake-zenodo", + "email": "firedrake@imperial.ac.uk", + "date": date} + repo.create_tag(release_tag, + message=DESCRIPTIONS[component], + sha=commit_id, obj_type="tree", tagger=tagger) else: + log.info(f"Pre-existing release for component '{component}' for commit '{commit_id}' not found, creating a new pre-release '{release_tag}' for it") + body = ( + "This release is specifically created to document the version of " + f"{component} used in a particular set of experiments using " + "Firedrake. Please do not cite this as a general source for Firedrake " + "or any of its dependencies. Instead, refer to " + "https://www.firedrakeproject.org/citing.html" + ) repo.create_release( - tag_name=tag, - target_commitish=shas[component], - name=descriptions[component], - body=message.format(component=component), + tag_name=release_tag, + target_commitish=commit_id, + name=DESCRIPTIONS[component], + body=body, draft=False, - prerelease=False) - -meta_file = "firedrake-meta-release.json" -with open(meta_file, "w") as f: - data = {"tag": tag, - "title": shas["title"], - "components": sorted(set(shas) & set(components)), - "info_file": shas.get("metarelease_info_file", None), - "new_version_of": shas.get("new_version_of", None), - "additional_dois": shas.get("additional_dois", None)} - f.write(json.dumps(data)) - -log.info("Releases complete. The release tag is %s" % tag) -log.info("Now, you need to create the meta-release") -log.info("Run 'firedrake-zenodo --create-meta-release %s' to do this" % os.path.abspath(meta_file)) -log.info("It is best to wait a short while to ensure that Zenodo is up to date") + prerelease=True) + + +def make_releases() -> None: + for component in components: + check_ref_exists(component) + + for component in components: + make_github_release_or_tag_existing(component) + + meta_file = "firedrake-meta-release.json" + with open(meta_file, "w") as f: + data = {"tag": release_tag, + "title": shas["title"], + "components": components, + "info_file": shas.get("metarelease_info_file", None), + "new_version_of": shas.get("new_version_of", None), + "additional_dois": shas.get("additional_dois", None)} + f.write(json.dumps(data)) + + log.info("Releases complete.") + log.info(f"Now you should create the meta-release with 'firedrake-zenodo --create-meta-release {os.path.abspath(meta_file)}'.") + log.info("It is best to wait a short while to ensure that the new releases are detected by Zenodo.") + + +def release_already_exists(component: str, release_name: str) -> bool: + try: + get_matching_release(component, release_name) + return True + except KeyError: + return False + + +def get_matching_release(component: str, release_name: str) -> github3.Release: + for release in get_component_repository(component).releases(): + if release.tag_name == release_name: + return release + raise KeyError + + +def commit_already_exists_in_release(component: str, commit_id: str) -> bool: + for release in get_component_repository(component).releases(): + if release.target_commitish == commit_id: + return True + return False + + +release_tag = generate_unique_release_tag() +make_releases() diff --git a/pyproject.toml b/pyproject.toml index 93aab13cf9..ffe34c9aa1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,7 @@ [project] name = "firedrake" -version = "0.14_dev" +# .. +version = "2025.4.0" description = "An automated system for the portable solution of partial differential equations using the finite element method" readme = "README.rst" license = {file = "LICENSE"} @@ -23,7 +24,8 @@ dependencies = [ "loopy>2024.1", # NOTE: If changing the PETSc/SLEPc version then firedrake-configure also needs # changing (as well as other references to petsc4py and slepc4py here) - "petsc4py==3.23.0", + # TODO RELEASE + # "petsc4py==3.23.0", "numpy", "packaging", "pkgconfig", @@ -35,6 +37,7 @@ dependencies = [ "rtree>=1.2", "scipy", "sympy", + # TODO RELEASE: use releases "fenics-ufl @ git+https://github.com/firedrakeproject/ufl.git", "firedrake-fiat @ git+https://github.com/firedrakeproject/fiat.git", "islpy>=2025.1.5; sys_platform == 'darwin'", @@ -70,13 +73,6 @@ check = [ "mpi-pytest", "pytest", ] -dev = [ # build dependencies that are needed to run 'make' - "Cython", - "mpi-pytest", - "pybind11", - "pytest", - "setuptools", -] docs = [ "bibtexparser", "matplotlib", # needed to resolve API @@ -95,10 +91,12 @@ jax = [ "jax", ] netgen = [ - "ngsPETSc", + # TODO RELEASE + # "ngsPETSc", ] slepc = [ - "slepc4py==3.23.0", + # TODO RELEASE + # "slepc4py==3.23.0", ] torch = [ # requires passing '--extra-index-url' to work "torch", @@ -114,13 +112,15 @@ ci = [ "matplotlib", "mpi-pytest", "nbval", - "ngsPETSc", + # TODO RELEASE + # "ngsPETSc", "pylit", "pytest", "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - "slepc4py==3.23.0", + # TODO RELEASE + # "slepc4py==3.23.0", "torch", # requires passing '--extra-index-url' to work "vtk", ] @@ -134,7 +134,8 @@ docker = [ # Used in firedrake-vanilla container "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - "slepc4py==3.23.0", + # TODO RELEASE + # "slepc4py==3.23.0", ] [build-system] @@ -147,7 +148,8 @@ requires = [ "pkgconfig", "pybind11", "setuptools>61.2", - "petsc4py==3.23.0", + # TODO RELEASE + # "petsc4py==3.23.0", "rtree>=1.2", ] build-backend = "setuptools.build_meta" @@ -162,6 +164,19 @@ script-files = [ [tool.setuptools.package-data] # Unless specified these files will not be installed along with the # rest of the package -firedrake = ["evaluate.h", "locate.c", "icons/*.png"] -firedrake_check = ["Makefile", "tests/firedrake/conftest.py", "tests/*/*/*.py"] -pyop2 = ["*.h", "*.pxd", "*.pyx", "codegen/c/*.c"] +firedrake = [ + "evaluate.h", + "locate.c", + "icons/*.png", +] +firedrake_check = [ + "Makefile", + "tests/firedrake/conftest.py", + "tests/*/*/*.py", +] +pyop2 = [ + "*.h", + "*.pxd", + "*.pyx", + "codegen/c/*.c", +] diff --git a/requirements-build.txt b/requirements-build.txt new file mode 100644 index 0000000000..cff27061d1 --- /dev/null +++ b/requirements-build.txt @@ -0,0 +1,13 @@ +# Core build dependencies (adapted from pyproject.toml) +Cython>=3.0 +libsupermesh +mpi4py>3; python_version >= '3.13' +mpi4py; python_version < '3.13' +numpy +pkgconfig +pybind11 +setuptools>61.2 +rtree>=1.2 + +# Transitive build dependencies +hatchling From aa682ef05107bf58d4af8644050e61baa7ffa0c0 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 29 Apr 2025 11:55:38 +0100 Subject: [PATCH 082/222] 2025.4.0 release candidate (#4263) --- docker/Dockerfile.vanilla | 4 ++-- pyproject.toml | 26 +++++++++----------------- 2 files changed, 11 insertions(+), 19 deletions(-) diff --git a/docker/Dockerfile.vanilla b/docker/Dockerfile.vanilla index 8fc0eaa93e..58c2cb92e1 100644 --- a/docker/Dockerfile.vanilla +++ b/docker/Dockerfile.vanilla @@ -26,7 +26,7 @@ ENV PIP_BREAK_SYSTEM_PACKAGES=1 ENV OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 # Download firedrake-configure -RUN curl -O --output-dir /opt https://raw.githubusercontent.com/firedrakeproject/firedrake/master/scripts/firedrake-configure +RUN curl -O --output-dir /opt https://raw.githubusercontent.com/firedrakeproject/firedrake/release/scripts/firedrake-configure # Install system dependencies RUN apt-get update \ @@ -62,4 +62,4 @@ ENV MPICC=$CC # Install Firedrake RUN pip install --verbose --no-binary h5py --src /opt \ - --editable git+https://github.com/firedrakeproject/firedrake.git#egg=firedrake[docker] + --editable git+https://github.com/firedrakeproject/firedrake.git@release#egg=firedrake[docker] diff --git a/pyproject.toml b/pyproject.toml index ffe34c9aa1..ae924ae289 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,13 +19,14 @@ dependencies = [ "decorator<=4.4.2", "mpi4py>3; python_version >= '3.13'", "mpi4py; python_version < '3.13'", + "fenics-ufl>=2025.1.0", + "firedrake-fiat>=2025.4.0", "h5py>3.12.1", "libsupermesh", "loopy>2024.1", # NOTE: If changing the PETSc/SLEPc version then firedrake-configure also needs # changing (as well as other references to petsc4py and slepc4py here) - # TODO RELEASE - # "petsc4py==3.23.0", + "petsc4py==3.23.0", "numpy", "packaging", "pkgconfig", @@ -37,9 +38,6 @@ dependencies = [ "rtree>=1.2", "scipy", "sympy", - # TODO RELEASE: use releases - "fenics-ufl @ git+https://github.com/firedrakeproject/ufl.git", - "firedrake-fiat @ git+https://github.com/firedrakeproject/fiat.git", "islpy>=2025.1.5; sys_platform == 'darwin'", ] classifiers = [ @@ -91,12 +89,10 @@ jax = [ "jax", ] netgen = [ - # TODO RELEASE - # "ngsPETSc", + "ngsPETSc", ] slepc = [ - # TODO RELEASE - # "slepc4py==3.23.0", + "slepc4py==3.23.0", ] torch = [ # requires passing '--extra-index-url' to work "torch", @@ -112,15 +108,13 @@ ci = [ "matplotlib", "mpi-pytest", "nbval", - # TODO RELEASE - # "ngsPETSc", + "ngsPETSc", "pylit", "pytest", "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - # TODO RELEASE - # "slepc4py==3.23.0", + "slepc4py==3.23.0", "torch", # requires passing '--extra-index-url' to work "vtk", ] @@ -134,8 +128,7 @@ docker = [ # Used in firedrake-vanilla container "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - # TODO RELEASE - # "slepc4py==3.23.0", + "slepc4py==3.23.0", ] [build-system] @@ -148,8 +141,7 @@ requires = [ "pkgconfig", "pybind11", "setuptools>61.2", - # TODO RELEASE - # "petsc4py==3.23.0", + "petsc4py==3.23.0", "rtree>=1.2", ] build-backend = "setuptools.build_meta" From fe0cfe62395178c6307558075e153a65cb9c71a0 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 29 Apr 2025 14:33:11 +0100 Subject: [PATCH 083/222] Post-release updates for master (#4265) * Post-release updates * Set source ref in workflows --- .github/workflows/core.yml | 12 ++++++++++++ .github/workflows/pr.yml | 1 + .github/workflows/push.yml | 1 + .github/workflows/release.yml | 5 +++-- docker/Dockerfile.vanilla | 4 ++-- pyproject.toml | 8 ++++---- 6 files changed, 23 insertions(+), 8 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 44a88e11d0..99040947e4 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -3,6 +3,10 @@ on: workflow_call: inputs: + source_ref: + description: The input Git ref to checkout + type: string + required: true target_branch: description: The target branch (usually 'master' or 'release') type: string @@ -30,6 +34,10 @@ on: workflow_dispatch: inputs: + source_ref: + description: The input Git ref to checkout + type: string + required: true target_branch: description: The target branch (usually 'master' or 'release') type: string @@ -98,6 +106,7 @@ jobs: - uses: actions/checkout@v4 with: path: firedrake-repo + ref: ${{ inputs.source_ref }} - name: Install system dependencies run: | @@ -403,6 +412,7 @@ jobs: - uses: actions/checkout@v4 with: path: firedrake-repo + ref: ${{ inputs.source_ref }} - name: Install system dependencies run: | @@ -475,6 +485,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + ref: ${{ inputs.source_ref }} - uses: actions/setup-python@v5 with: python-version: '3.10' diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index eea463bacc..8aca41386d 100644 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -7,6 +7,7 @@ jobs: test: uses: ./.github/workflows/core.yml with: + source_ref: ${{ github.head_ref }} target_branch: ${{ github.base_ref }} # Only run macOS tests if the PR is labelled 'macOS' test_macos: ${{ contains(github.event.pull_request.labels.*.name, 'macOS') }} diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml index 06b0e316fc..f6a06f9834 100644 --- a/.github/workflows/push.yml +++ b/.github/workflows/push.yml @@ -10,6 +10,7 @@ jobs: test: uses: ./.github/workflows/core.yml with: + source_ref: ${{ github.ref_name }} target_branch: ${{ github.ref_name }} test_macos: true deploy_website: true diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 4fa26d2c40..31df9ec16c 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -6,16 +6,17 @@ on: jobs: deploy: - if: ! startsWith( github.ref_name, 'Zenodo' ) + if: '!startsWith( github.ref_name, ''Zenodo'' )' uses: ./.github/workflows/core.yml with: + source_ref: release target_branch: release run_tests: false upload_pypi: true secrets: inherit docker: - if: ! startsWith( github.ref_name, 'Zenodo' ) + if: '!startsWith( github.ref_name, ''Zenodo'' )' name: Build Docker containers uses: ./.github/workflows/docker.yml with: diff --git a/docker/Dockerfile.vanilla b/docker/Dockerfile.vanilla index 8fc0eaa93e..58c2cb92e1 100644 --- a/docker/Dockerfile.vanilla +++ b/docker/Dockerfile.vanilla @@ -26,7 +26,7 @@ ENV PIP_BREAK_SYSTEM_PACKAGES=1 ENV OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 # Download firedrake-configure -RUN curl -O --output-dir /opt https://raw.githubusercontent.com/firedrakeproject/firedrake/master/scripts/firedrake-configure +RUN curl -O --output-dir /opt https://raw.githubusercontent.com/firedrakeproject/firedrake/release/scripts/firedrake-configure # Install system dependencies RUN apt-get update \ @@ -62,4 +62,4 @@ ENV MPICC=$CC # Install Firedrake RUN pip install --verbose --no-binary h5py --src /opt \ - --editable git+https://github.com/firedrakeproject/firedrake.git#egg=firedrake[docker] + --editable git+https://github.com/firedrakeproject/firedrake.git@release#egg=firedrake[docker] diff --git a/pyproject.toml b/pyproject.toml index ffe34c9aa1..71b3285cd0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "firedrake" # .. -version = "2025.4.0" +version = "2025.5.0.dev0" description = "An automated system for the portable solution of partial differential equations using the finite element method" readme = "README.rst" license = {file = "LICENSE"} @@ -19,6 +19,9 @@ dependencies = [ "decorator<=4.4.2", "mpi4py>3; python_version >= '3.13'", "mpi4py; python_version < '3.13'", + # TODO RELEASE: use releases + "fenics-ufl @ git+https://github.com/firedrakeproject/ufl.git", + "firedrake-fiat @ git+https://github.com/firedrakeproject/fiat.git", "h5py>3.12.1", "libsupermesh", "loopy>2024.1", @@ -37,9 +40,6 @@ dependencies = [ "rtree>=1.2", "scipy", "sympy", - # TODO RELEASE: use releases - "fenics-ufl @ git+https://github.com/firedrakeproject/ufl.git", - "firedrake-fiat @ git+https://github.com/firedrakeproject/fiat.git", "islpy>=2025.1.5; sys_platform == 'darwin'", ] classifiers = [ From 1d154ca12c51e84e78359249af332182e61e98eb Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 29 Apr 2025 15:36:32 +0100 Subject: [PATCH 084/222] Make linkcheck not prevent later steps in workflows (#4267) * Make linkcheck not prevent later steps * Set AIMS as a flaky link --- .github/workflows/core.yml | 40 +++++++++++++++++++++++++++++++------- docs/source/conf.py | 1 + 2 files changed, 34 insertions(+), 7 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 99040947e4..3435fdc56c 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -82,6 +82,9 @@ jobs: runs-on: [self-hosted, Linux] container: image: ubuntu:latest + outputs: + sdist_conclusion: ${{ steps.report_sdist.outputs.conclusion }} + docs_conclusion: ${{ steps.report_docs.outputs.conclusion }} env: OMPI_ALLOW_RUN_AS_ROOT: 1 OMPI_ALLOW_RUN_AS_ROOT_CONFIRM: 1 @@ -347,6 +350,7 @@ jobs: python -m build ./firedrake-repo --sdist - name: Upload sdist (release only) + id: upload_sdist if: | inputs.target_branch == 'release' && matrix.arch == 'default' && @@ -356,14 +360,19 @@ jobs: name: dist path: firedrake-repo/dist/* + - name: Report sdist build status + id: report_sdist + if: success() || steps.upload_sdist.conclusion == 'success' + run: echo "conclusion=success" >> "$GITHUB_OUTPUT" + - name: Check bibtex - if: always() && matrix.arch == 'default' + if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' run: | . venv/bin/activate make -C firedrake-repo/docs validate-bibtex - name: Check documentation links - if: always() && matrix.arch == 'default' + if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' run: | . venv/bin/activate make -C firedrake-repo/docs linkcheck @@ -382,12 +391,18 @@ jobs: - name: Upload documentation uses: actions/upload-pages-artifact@v3 + id: upload_docs if: (success() || steps.build_docs.conclusion == 'success') && matrix.arch == 'default' with: name: github-pages path: firedrake-repo/docs/build/html retention-days: 1 + - name: Report docs build status + id: report_docs + if: success() || steps.upload_docs.conclusion == 'success' + run: echo "conclusion=success" >> "$GITHUB_OUTPUT" + - name: Post-run cleanup if: always() run: find . -delete @@ -497,10 +512,13 @@ jobs: - name: Lint codebase run: make lint GITHUB_ACTIONS_FORMATTING=1 - deploy: - name: Deploy GitHub pages + deploy_website: + name: Deploy GitHub pages (optional) needs: test_linux - if: inputs.deploy_website + if: | + always() && + inputs.deploy_website && + needs.test_linux.outputs.docs_conclusion == 'success' permissions: pages: write id-token: write @@ -514,8 +532,12 @@ jobs: upload_pypi: name: Upload to PyPI (optional) - if: inputs.upload_pypi && inputs.target_branch == 'release' needs: test_linux + if: | + always() && + inputs.upload_pypi && + inputs.target_branch == 'release' && + needs.test_linux.outputs.sdist_conclusion == 'success' runs-on: ubuntu-latest environment: name: pypi @@ -531,8 +553,12 @@ jobs: upload_testpypi: name: Upload to TestPyPI (optional) - if: inputs.upload_testpypi && inputs.target_branch == 'release' needs: test_linux + if: | + always() && + inputs.upload_testpypi && + inputs.target_branch == 'release' && + needs.test_linux.outputs.sdist_conclusion == 'success' runs-on: ubuntu-latest environment: name: testpypi diff --git a/docs/source/conf.py b/docs/source/conf.py index 695b014a64..3fad3807e1 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -162,6 +162,7 @@ r'https://www.tuomaskarna.com', r'https://www.crosscountrytrains.co.uk/', r'https://www.siam.org/', + r'https://aims.ac.rw/', ] linkcheck_timeout = 30 From 4ba9c8cfc355bc48e3801dadd2c2a96d194d27ca Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 29 Apr 2025 16:19:08 +0100 Subject: [PATCH 085/222] Point to PyPI version of Firedrake on website (#4266) --- docs/source/install.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/install.rst b/docs/source/install.rst index cde921b8e2..77ded65c33 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -54,7 +54,7 @@ firedrake-configure To simplify the installation process, Firedrake provides a utility script called ``firedrake-configure``. This script can be downloaded by executing:: - $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/master/scripts/firedrake-configure + $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/release/scripts/firedrake-configure Unlike the now deprecated ``firedrake-install`` script, ``firedrake-configure`` **does not install Firedrake for you**. It is simply a helper script that emits @@ -180,7 +180,7 @@ install Firedrake. To do this perform the following steps: #. Install Firedrake:: $ pip cache remove petsc4py - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check]" + $ pip install --no-binary h5py 'firedrake[check]' .. note:: Though not strictly necessary to install Firedrake's optional @@ -219,7 +219,7 @@ Updating Firedrake involves following the same steps as above when :ref:`installing Firedrake`. First, use ``firedrake-configure`` to set the right environment variables and then run:: - $ pip install --upgrade git+https://github.com/firedrakeproject/firedrake.git@release + $ pip install --upgrade firedrake Updating PETSc ~~~~~~~~~~~~~~ @@ -326,7 +326,7 @@ To install Firedrake with SLEPc support you should: and install Firedrake with the ``slepc`` optional dependency. For example:: $ pip cache remove slepc4py - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,slepc]" + $ pip install --no-binary h5py 'firedrake[check,slepc]' VTK ~~~ @@ -334,7 +334,7 @@ VTK To install Firedrake with VTK, it should be installed using the ``vtk`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,vtk]" + $ pip install --no-binary h5py 'firedrake[check,vtk]' At present VTK wheels are not available for ARM Linux machines. Depending on your Python version you may be able to work around this by downloading and pip installing @@ -348,7 +348,7 @@ PyTorch To install Firedrake with `PyTorch `_, it should be installed using the ``torch`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,torch]" --extra-index-url https://download.pytorch.org/whl/cpu + $ pip install --no-binary h5py 'firedrake[check,torch]' --extra-index-url https://download.pytorch.org/whl/cpu Observe that, in addition to specifying ``torch``, an additional argument (``--extra-index-url``) is needed. More information on installing @@ -361,7 +361,7 @@ JAX To install Firedrake with JAX, it should be installed using the ``jax`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,jax]" + $ pip install --no-binary h5py 'firedrake[check,jax]' Netgen @@ -370,7 +370,7 @@ Netgen To install Firedrake with `Netgen `_ support, it should be installed with the ``netgen`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,netgen]" + $ pip install --no-binary h5py 'firedrake[check,netgen]' Customising PETSc From e78c8e75cacfe7b5a054969bd388d968ee359cd0 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Wed, 30 Apr 2025 09:25:12 +0100 Subject: [PATCH 086/222] Fix linkcheck (#4269) --- docs/source/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 695b014a64..b02cd34933 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -162,6 +162,7 @@ r'https://www.tuomaskarna.com', r'https://www.crosscountrytrains.co.uk/', r'https://www.siam.org/', + r'https://aims.ac.rw', ] linkcheck_timeout = 30 From 3d70102dc696aa97e42bc1ba81679611a5c6e240 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Wed, 30 Apr 2025 16:17:07 +0100 Subject: [PATCH 087/222] Release fixups (#4268) * Workflow fixups * Make linkcheck not prevent later steps in workflows (#4267) * Make linkcheck not prevent later steps * Set AIMS as a flaky link --- .github/workflows/core.yml | 52 ++++++++++++++++++++++++++++++----- .github/workflows/pr.yml | 1 + .github/workflows/push.yml | 1 + .github/workflows/release.yml | 5 ++-- 4 files changed, 50 insertions(+), 9 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 44a88e11d0..3435fdc56c 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -3,6 +3,10 @@ on: workflow_call: inputs: + source_ref: + description: The input Git ref to checkout + type: string + required: true target_branch: description: The target branch (usually 'master' or 'release') type: string @@ -30,6 +34,10 @@ on: workflow_dispatch: inputs: + source_ref: + description: The input Git ref to checkout + type: string + required: true target_branch: description: The target branch (usually 'master' or 'release') type: string @@ -74,6 +82,9 @@ jobs: runs-on: [self-hosted, Linux] container: image: ubuntu:latest + outputs: + sdist_conclusion: ${{ steps.report_sdist.outputs.conclusion }} + docs_conclusion: ${{ steps.report_docs.outputs.conclusion }} env: OMPI_ALLOW_RUN_AS_ROOT: 1 OMPI_ALLOW_RUN_AS_ROOT_CONFIRM: 1 @@ -98,6 +109,7 @@ jobs: - uses: actions/checkout@v4 with: path: firedrake-repo + ref: ${{ inputs.source_ref }} - name: Install system dependencies run: | @@ -338,6 +350,7 @@ jobs: python -m build ./firedrake-repo --sdist - name: Upload sdist (release only) + id: upload_sdist if: | inputs.target_branch == 'release' && matrix.arch == 'default' && @@ -347,14 +360,19 @@ jobs: name: dist path: firedrake-repo/dist/* + - name: Report sdist build status + id: report_sdist + if: success() || steps.upload_sdist.conclusion == 'success' + run: echo "conclusion=success" >> "$GITHUB_OUTPUT" + - name: Check bibtex - if: always() && matrix.arch == 'default' + if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' run: | . venv/bin/activate make -C firedrake-repo/docs validate-bibtex - name: Check documentation links - if: always() && matrix.arch == 'default' + if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' run: | . venv/bin/activate make -C firedrake-repo/docs linkcheck @@ -373,12 +391,18 @@ jobs: - name: Upload documentation uses: actions/upload-pages-artifact@v3 + id: upload_docs if: (success() || steps.build_docs.conclusion == 'success') && matrix.arch == 'default' with: name: github-pages path: firedrake-repo/docs/build/html retention-days: 1 + - name: Report docs build status + id: report_docs + if: success() || steps.upload_docs.conclusion == 'success' + run: echo "conclusion=success" >> "$GITHUB_OUTPUT" + - name: Post-run cleanup if: always() run: find . -delete @@ -403,6 +427,7 @@ jobs: - uses: actions/checkout@v4 with: path: firedrake-repo + ref: ${{ inputs.source_ref }} - name: Install system dependencies run: | @@ -475,6 +500,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + ref: ${{ inputs.source_ref }} - uses: actions/setup-python@v5 with: python-version: '3.10' @@ -485,10 +512,13 @@ jobs: - name: Lint codebase run: make lint GITHUB_ACTIONS_FORMATTING=1 - deploy: - name: Deploy GitHub pages + deploy_website: + name: Deploy GitHub pages (optional) needs: test_linux - if: inputs.deploy_website + if: | + always() && + inputs.deploy_website && + needs.test_linux.outputs.docs_conclusion == 'success' permissions: pages: write id-token: write @@ -502,8 +532,12 @@ jobs: upload_pypi: name: Upload to PyPI (optional) - if: inputs.upload_pypi && inputs.target_branch == 'release' needs: test_linux + if: | + always() && + inputs.upload_pypi && + inputs.target_branch == 'release' && + needs.test_linux.outputs.sdist_conclusion == 'success' runs-on: ubuntu-latest environment: name: pypi @@ -519,8 +553,12 @@ jobs: upload_testpypi: name: Upload to TestPyPI (optional) - if: inputs.upload_testpypi && inputs.target_branch == 'release' needs: test_linux + if: | + always() && + inputs.upload_testpypi && + inputs.target_branch == 'release' && + needs.test_linux.outputs.sdist_conclusion == 'success' runs-on: ubuntu-latest environment: name: testpypi diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index eea463bacc..8aca41386d 100644 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -7,6 +7,7 @@ jobs: test: uses: ./.github/workflows/core.yml with: + source_ref: ${{ github.head_ref }} target_branch: ${{ github.base_ref }} # Only run macOS tests if the PR is labelled 'macOS' test_macos: ${{ contains(github.event.pull_request.labels.*.name, 'macOS') }} diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml index 06b0e316fc..f6a06f9834 100644 --- a/.github/workflows/push.yml +++ b/.github/workflows/push.yml @@ -10,6 +10,7 @@ jobs: test: uses: ./.github/workflows/core.yml with: + source_ref: ${{ github.ref_name }} target_branch: ${{ github.ref_name }} test_macos: true deploy_website: true diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 4fa26d2c40..31df9ec16c 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -6,16 +6,17 @@ on: jobs: deploy: - if: ! startsWith( github.ref_name, 'Zenodo' ) + if: '!startsWith( github.ref_name, ''Zenodo'' )' uses: ./.github/workflows/core.yml with: + source_ref: release target_branch: release run_tests: false upload_pypi: true secrets: inherit docker: - if: ! startsWith( github.ref_name, 'Zenodo' ) + if: '!startsWith( github.ref_name, ''Zenodo'' )' name: Build Docker containers uses: ./.github/workflows/docker.yml with: From 5066a62f19917c9b63249c7f6ecdeb96e3bab422 Mon Sep 17 00:00:00 2001 From: Pablo Brubeck Date: Wed, 30 Apr 2025 17:05:13 +0100 Subject: [PATCH 088/222] BDDC: Fix block sizes (#4253) --- firedrake/preconditioners/fdm.py | 5 +++-- tests/firedrake/regression/test_bddc.py | 4 ++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/firedrake/preconditioners/fdm.py b/firedrake/preconditioners/fdm.py index 4e28f73a19..84ceae7360 100644 --- a/firedrake/preconditioners/fdm.py +++ b/firedrake/preconditioners/fdm.py @@ -777,10 +777,11 @@ def setup_block(self, Vrow, Vcol): P.setISAllowRepeated(self.allow_repeated) P.setLGMap(rmap, cmap) if on_diag and ptype == "is" and self.allow_repeated: - bsize = Vrow.finat_element.space_dimension() * Vrow.value_size + bsize = Vrow.finat_element.space_dimension() * Vrow.block_size local_mat = P.getISLocalMat() nblocks = local_mat.getSize()[0] // bsize - local_mat.setVariableBlockSizes([bsize] * nblocks) + sizes = numpy.full((nblocks,), bsize, dtype=PETSc.IntType) + local_mat.setVariableBlockSizes(sizes) P.setPreallocationNNZ((dnz, onz)) if not (ptype.endswith("sbaij") or ptype == "is"): diff --git a/tests/firedrake/regression/test_bddc.py b/tests/firedrake/regression/test_bddc.py index f1f1630e2d..ff82094ba9 100644 --- a/tests/firedrake/regression/test_bddc.py +++ b/tests/firedrake/regression/test_bddc.py @@ -111,8 +111,8 @@ def solve_riesz_map(mesh, family, degree, bcs, condense): @pytest.fixture(params=(2, 3), ids=("square", "cube")) def mesh(request): - nx = 4 dim = request.param + nx = 4 msh = UnitSquareMesh(nx, nx, quadrilateral=True) if dim == 3: msh = ExtrudedMesh(msh, nx) @@ -126,5 +126,5 @@ def mesh(request): def test_bddc_fdm(mesh, family, degree, condense): bcs = True tdim = mesh.topological_dimension() - expected = 6 if tdim == 2 else 11 + expected = 7 if tdim == 2 else 11 assert solve_riesz_map(mesh, family, degree, bcs, condense) <= expected From 8c19380b2fb839a0439423423c581687c5500f12 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 1 May 2025 08:49:57 +0100 Subject: [PATCH 089/222] Temporarily revert install command while sdist is fixed (#4275) --- docs/source/install.rst | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/install.rst b/docs/source/install.rst index 77ded65c33..d0cf5d6e7d 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -180,7 +180,7 @@ install Firedrake. To do this perform the following steps: #. Install Firedrake:: $ pip cache remove petsc4py - $ pip install --no-binary h5py 'firedrake[check]' + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check]" .. note:: Though not strictly necessary to install Firedrake's optional @@ -219,7 +219,7 @@ Updating Firedrake involves following the same steps as above when :ref:`installing Firedrake`. First, use ``firedrake-configure`` to set the right environment variables and then run:: - $ pip install --upgrade firedrake + $ pip install --upgrade git+https://github.com/firedrakeproject/firedrake.git@release Updating PETSc ~~~~~~~~~~~~~~ @@ -326,7 +326,7 @@ To install Firedrake with SLEPc support you should: and install Firedrake with the ``slepc`` optional dependency. For example:: $ pip cache remove slepc4py - $ pip install --no-binary h5py 'firedrake[check,slepc]' + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,slepc]" VTK ~~~ @@ -334,7 +334,7 @@ VTK To install Firedrake with VTK, it should be installed using the ``vtk`` optional dependency. For example:: - $ pip install --no-binary h5py 'firedrake[check,vtk]' + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,vtk]" At present VTK wheels are not available for ARM Linux machines. Depending on your Python version you may be able to work around this by downloading and pip installing @@ -348,7 +348,7 @@ PyTorch To install Firedrake with `PyTorch `_, it should be installed using the ``torch`` optional dependency. For example:: - $ pip install --no-binary h5py 'firedrake[check,torch]' --extra-index-url https://download.pytorch.org/whl/cpu + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,torch]" --extra-index-url https://download.pytorch.org/whl/cpu Observe that, in addition to specifying ``torch``, an additional argument (``--extra-index-url``) is needed. More information on installing @@ -361,7 +361,7 @@ JAX To install Firedrake with JAX, it should be installed using the ``jax`` optional dependency. For example:: - $ pip install --no-binary h5py 'firedrake[check,jax]' + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,jax]" Netgen @@ -370,7 +370,7 @@ Netgen To install Firedrake with `Netgen `_ support, it should be installed with the ``netgen`` optional dependency. For example:: - $ pip install --no-binary h5py 'firedrake[check,netgen]' + $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,netgen]" Customising PETSc From 139f85599a1f3489c7878603c7b7b670917cd0bb Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 1 May 2025 16:39:03 +0100 Subject: [PATCH 090/222] Fix sdist (#4276) * Fix sdist * Added extra check to CI to make sure that this works * Improve firedrake-check --- .github/workflows/core.yml | 43 +++++++++++----------------- .gitignore | 3 +- MANIFEST.in | 5 ++++ Makefile | 7 +++-- firedrake/_check/__init__.py | 9 ++++++ pyproject.toml | 8 ++---- setup.py | 54 +++++++++++------------------------- 7 files changed, 54 insertions(+), 75 deletions(-) create mode 100644 MANIFEST.in create mode 100644 firedrake/_check/__init__.py diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 3435fdc56c..d8f509da73 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -185,14 +185,29 @@ jobs: EXTRA_PIP_FLAGS='--no-build-isolation' fi + : # Install from an sdist so we can make sure that it is not ill-formed + pip install build + python -m build ./firedrake-repo --sdist + pip install --verbose $EXTRA_PIP_FLAGS \ --no-binary h5py \ --extra-index-url https://download.pytorch.org/whl/cpu \ - './firedrake-repo[ci,docs]' + "$(echo ./firedrake-repo/dist/firedrake-*.tar.gz)[ci,docs]" firedrake-clean pip list + - name: Upload sdist (default ARCH only) + if: matrix.arch == 'default' + uses: actions/upload-artifact@v4 + with: + name: dist + path: firedrake-repo/dist/* + + - name: Report sdist build status + id: report_sdist + run: echo "conclusion=success" >> "$GITHUB_OUTPUT" + - name: Run firedrake-check run: | . venv/bin/activate @@ -339,32 +354,6 @@ jobs: name: firedrake-logs-${{ matrix.arch }} path: pytest_*.log - - name: Build sdist (release only) - if: | - inputs.target_branch == 'release' && - matrix.arch == 'default' && - (success() || steps.install.conclusion == 'success') - run: | - . venv/bin/activate - pip install build - python -m build ./firedrake-repo --sdist - - - name: Upload sdist (release only) - id: upload_sdist - if: | - inputs.target_branch == 'release' && - matrix.arch == 'default' && - (success() || steps.install.conclusion == 'success') - uses: actions/upload-artifact@v4 - with: - name: dist - path: firedrake-repo/dist/* - - - name: Report sdist build status - id: report_sdist - if: success() || steps.upload_sdist.conclusion == 'success' - run: echo "conclusion=success" >> "$GITHUB_OUTPUT" - - name: Check bibtex if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default' run: | diff --git a/.gitignore b/.gitignore index f1f95add73..2ba4237dcd 100644 --- a/.gitignore +++ b/.gitignore @@ -27,7 +27,8 @@ __pycache__/ /docs/source/demos /firedrake/cython/*.c /firedrake.egg-info -/firedrake_check/ +/firedrake/_check/Makefile +/firedrake/_check/tests /docs/source/element_list.csv diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000000..545cd45965 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,5 @@ +# This file contains all the non-standard files that need to be included +# in the source distribution. + +recursive-include firedrake/cython *.pyx *.pxi +recursive-exclude firedrake/cython *.c diff --git a/Makefile b/Makefile index ef491f3efe..d993248f88 100644 --- a/Makefile +++ b/Makefile @@ -88,12 +88,13 @@ clean: @echo " RM tinyasm/*.so" -@rm -f tinyasm/*.so > /dev/null 2>&1 -# Do verbose checking if running on CI +# Do verbose checking if running on CI and always set no:cacheprovider because +# we don't want to generate any cache files in $VIRTUAL_ENV/lib/.../firedrake/_check check_flags = ifeq ($(FIREDRAKE_CI), 1) - check_flags = --verbose + check_flags = --verbose -p no:cacheprovider else - check_flags = --quiet + check_flags = --quiet -p no:cacheprovider endif CHECK_PYTEST_ARGS = diff --git a/firedrake/_check/__init__.py b/firedrake/_check/__init__.py new file mode 100644 index 0000000000..9d566ecb0c --- /dev/null +++ b/firedrake/_check/__init__.py @@ -0,0 +1,9 @@ +"""Run the Firedrake smoke tests.""" + +import pathlib +import subprocess + + +def main() -> None: + dir = pathlib.Path(__file__).parent + subprocess.run(f"make -C {dir} check".split(), errors=True) diff --git a/pyproject.toml b/pyproject.toml index ae924ae289..08ee4c42e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,7 +58,7 @@ Repository = "https://github.com/firedrakeproject/firedrake" Issues = "https://github.com/firedrakeproject/firedrake/issues/new/choose" [project.scripts] -firedrake-check = "firedrake_check:main" +firedrake-check = "firedrake._check:main" firedrake-clean = "firedrake.scripts.firedrake_clean:main" firedrake-preprocess-bibtex = "firedrake.scripts.firedrake_preprocess_bibtex:main" firedrake-status = "firedrake.scripts.firedrake_status:main" @@ -160,11 +160,7 @@ firedrake = [ "evaluate.h", "locate.c", "icons/*.png", -] -firedrake_check = [ - "Makefile", - "tests/firedrake/conftest.py", - "tests/*/*/*.py", + "_check/**", ] pyop2 = [ "*.h", diff --git a/setup.py b/setup.py index a5950b0110..03624bf88a 100644 --- a/setup.py +++ b/setup.py @@ -12,6 +12,7 @@ import pkgconfig from dataclasses import dataclass, field from setuptools import setup, find_packages, Extension +from setuptools.command.sdist import sdist as _sdist from glob import glob from pathlib import Path from Cython.Build import cythonize @@ -236,7 +237,9 @@ def extensions(): # TODO: It would be good to have a single source of truth for these files -FIREDRAKE_CHECK_TEST_FILES = ( +FIREDRAKE_CHECK_FILES = ( + "Makefile", + "tests/firedrake/conftest.py", "tests/firedrake/regression/test_stokes_mini.py", "tests/firedrake/regression/test_locate_cell.py", "tests/firedrake/supermesh/test_assemble_mixed_mass_matrix.py", @@ -247,46 +250,21 @@ def extensions(): ) -# This diabolical function is needed to allow 'firedrake-check' to work. Since -# installed packages are not allowed to contain files from outside that Python -# package we cannot access the Makefile or test files once installation is -# complete. Therefore, before we install, we create a dummy package, called -# 'firedrake-check' containing said Makefile and tests. -def make_firedrake_check_package(): - package_dir = "firedrake_check" - logging.info(f"Creating '{package_dir}' package") - if os.path.exists(package_dir): - logging.info(f"'{package_dir}' already found, removing") - shutil.rmtree(package_dir) +class sdist(_sdist): + def run(self): + self._copy_check_files() + super().run() - os.mkdir(package_dir) - with open(f"{package_dir}/__init__.py", "w") as f: - # set 'errors=True' to make sure that we propagate failures to the - # outer process - f.write("""import pathlib -import subprocess - -def main(): - dir = pathlib.Path(__file__).parent - subprocess.run(f'make -C {dir} check'.split(), errors=True) -""") - - # copy Makefile and tests into dummy package - shutil.copy("Makefile", package_dir) - for test_file in FIREDRAKE_CHECK_TEST_FILES: - package_test_dir = os.path.join(package_dir, Path(test_file).parent) - os.makedirs(package_test_dir, exist_ok=True) - shutil.copy(test_file, package_test_dir) - - # Also copy conftest.py so any markers are recognised - conftest_dir = os.path.join(package_dir, "tests", "firedrake") - shutil.copy("tests/firedrake/conftest.py", conftest_dir) - - -make_firedrake_check_package() + def _copy_check_files(self): + """Copy Makefile and tests into firedrake/_check.""" + dest_dir = Path("firedrake/_check") + for check_file in map(Path, FIREDRAKE_CHECK_FILES ): + os.makedirs(dest_dir / check_file.parent, exist_ok=True) + shutil.copy(check_file, dest_dir / check_file.parent) setup( + cmdclass={"sdist": sdist}, packages=find_packages(), - ext_modules=extensions() + ext_modules=extensions(), ) From 17afbd3b38273af13d311bddbbf12d9090625495 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 1 May 2025 16:44:12 +0100 Subject: [PATCH 091/222] Increment version number (#4280) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 08ee4c42e9..7f572acaa2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "firedrake" # .. -version = "2025.4.0" +version = "2025.4.0.post0" description = "An automated system for the portable solution of partial differential equations using the finite element method" readme = "README.rst" license = {file = "LICENSE"} From 1677d08e234c253d51063ea07ffaa9cdd030f634 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 1 May 2025 20:49:02 +0100 Subject: [PATCH 092/222] Point install page at the now working PyPI package --- docs/source/install.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/install.rst b/docs/source/install.rst index d0cf5d6e7d..d955a6c757 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -54,7 +54,7 @@ firedrake-configure To simplify the installation process, Firedrake provides a utility script called ``firedrake-configure``. This script can be downloaded by executing:: - $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/release/scripts/firedrake-configure + $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/refs/tags/2025.4.0.post0/scripts/firedrake-configure Unlike the now deprecated ``firedrake-install`` script, ``firedrake-configure`` **does not install Firedrake for you**. It is simply a helper script that emits @@ -180,7 +180,7 @@ install Firedrake. To do this perform the following steps: #. Install Firedrake:: $ pip cache remove petsc4py - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check]" + $ pip install --no-binary h5py 'firedrake[check]' .. note:: Though not strictly necessary to install Firedrake's optional @@ -219,7 +219,7 @@ Updating Firedrake involves following the same steps as above when :ref:`installing Firedrake`. First, use ``firedrake-configure`` to set the right environment variables and then run:: - $ pip install --upgrade git+https://github.com/firedrakeproject/firedrake.git@release + $ pip install --upgrade firedrake Updating PETSc ~~~~~~~~~~~~~~ @@ -326,7 +326,7 @@ To install Firedrake with SLEPc support you should: and install Firedrake with the ``slepc`` optional dependency. For example:: $ pip cache remove slepc4py - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,slepc]" + $ pip install --no-binary h5py 'firedrake[check,slepc]' VTK ~~~ @@ -334,7 +334,7 @@ VTK To install Firedrake with VTK, it should be installed using the ``vtk`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,vtk]" + $ pip install --no-binary h5py 'firedrake[check,vtk]' At present VTK wheels are not available for ARM Linux machines. Depending on your Python version you may be able to work around this by downloading and pip installing @@ -348,7 +348,7 @@ PyTorch To install Firedrake with `PyTorch `_, it should be installed using the ``torch`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,torch]" --extra-index-url https://download.pytorch.org/whl/cpu + $ pip install --no-binary h5py 'firedrake[check,torch]' --extra-index-url https://download.pytorch.org/whl/cpu Observe that, in addition to specifying ``torch``, an additional argument (``--extra-index-url``) is needed. More information on installing @@ -361,7 +361,7 @@ JAX To install Firedrake with JAX, it should be installed using the ``jax`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,jax]" + $ pip install --no-binary h5py 'firedrake[check,jax]' Netgen @@ -370,7 +370,7 @@ Netgen To install Firedrake with `Netgen `_ support, it should be installed with the ``netgen`` optional dependency. For example:: - $ pip install --no-binary h5py "firedrake @ git+https://github.com/firedrakeproject/firedrake.git@release#[check,netgen]" + $ pip install --no-binary h5py 'firedrake[check,netgen]' Customising PETSc From 0d5ba26a2699c08604b6455fd25b929be1076900 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Fri, 2 May 2025 15:37:33 +0100 Subject: [PATCH 093/222] unundo some bits needed in master --- docs/source/install.rst | 2 +- pyproject.toml | 21 ++++++++++++++------- 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/docs/source/install.rst b/docs/source/install.rst index d955a6c757..82d80ebf00 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -54,7 +54,7 @@ firedrake-configure To simplify the installation process, Firedrake provides a utility script called ``firedrake-configure``. This script can be downloaded by executing:: - $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/refs/tags/2025.4.0.post0/scripts/firedrake-configure + $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/master/scripts/firedrake-configure Unlike the now deprecated ``firedrake-install`` script, ``firedrake-configure`` **does not install Firedrake for you**. It is simply a helper script that emits diff --git a/pyproject.toml b/pyproject.toml index 5366d1409f..5cba98969b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,8 @@ dependencies = [ "loopy>2024.1", # NOTE: If changing the PETSc/SLEPc version then firedrake-configure also needs # changing (as well as other references to petsc4py and slepc4py here) - "petsc4py==3.23.0", + # TODO RELEASE + # "petsc4py==3.23.0", "numpy", "packaging", "pkgconfig", @@ -90,10 +91,12 @@ jax = [ "jax", ] netgen = [ - "ngsPETSc", + # TODO RELEASE + # "ngsPETSc", ] slepc = [ - "slepc4py==3.23.0", + # TODO RELEASE + # "slepc4py==3.23.0", ] torch = [ # requires passing '--extra-index-url' to work "torch", @@ -109,13 +112,15 @@ ci = [ "matplotlib", "mpi-pytest", "nbval", - "ngsPETSc", + # TODO RELEASE + # "ngsPETSc", "pylit", "pytest", "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - "slepc4py==3.23.0", + # TODO RELEASE + # "slepc4py==3.23.0", "torch", # requires passing '--extra-index-url' to work "vtk", ] @@ -129,7 +134,8 @@ docker = [ # Used in firedrake-vanilla container "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - "slepc4py==3.23.0", + # TODO RELEASE + # "slepc4py==3.23.0", ] [build-system] @@ -142,7 +148,8 @@ requires = [ "pkgconfig", "pybind11", "setuptools>61.2", - "petsc4py==3.23.0", + # TODO RELEASE + # "petsc4py==3.23.0", "rtree>=1.2", ] build-backend = "setuptools.build_meta" From 96ce83a1928427127a05d9045171f9155cfa5a96 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Fri, 2 May 2025 16:08:42 +0100 Subject: [PATCH 094/222] Fix sdist usage for master --- .github/workflows/core.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index d8f509da73..c0acee0dfe 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -169,6 +169,7 @@ jobs: pip cache remove slepc4py if [ ${{ inputs.target_branch }} = 'release' ]; then + EXTRA_BUILD_ARGS='' EXTRA_PIP_FLAGS='' else : # Install build dependencies @@ -182,12 +183,13 @@ jobs: pip install --no-deps ngsPETSc netgen-mesher netgen-occt : # We have to pass '--no-build-isolation' to use a custom petsc4py + EXTRA_BUILD_ARGS='--no-isolation' EXTRA_PIP_FLAGS='--no-build-isolation' fi : # Install from an sdist so we can make sure that it is not ill-formed pip install build - python -m build ./firedrake-repo --sdist + python -m build ./firedrake-repo --sdist "$EXTRA_BUILD_ARGS" pip install --verbose $EXTRA_PIP_FLAGS \ --no-binary h5py \ From 424e09f8c06abdea8cb6f184d46d4147e7298c60 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Tue, 6 May 2025 08:34:55 +0100 Subject: [PATCH 095/222] fdvrf: diffusion correlation operators --- firedrake/adjoint/__init__.py | 2 +- .../adjoint/fourdvar_reduced_functional.py | 328 ++++++++++++++++-- 2 files changed, 296 insertions(+), 34 deletions(-) diff --git a/firedrake/adjoint/__init__.py b/firedrake/adjoint/__init__.py index 0724821860..680bee08ee 100644 --- a/firedrake/adjoint/__init__.py +++ b/firedrake/adjoint/__init__.py @@ -37,7 +37,7 @@ from firedrake.adjoint.ufl_constraints import UFLInequalityConstraint, \ UFLEqualityConstraint # noqa F401 -from firedrake.adjoint.fourdvar_reduced_functional import FourDVarReducedFunctional # noqa F401 +from firedrake.adjoint.fourdvar_reduced_functional import * # noqa F401 from firedrake.adjoint.ensemble_reduced_functional import * # noqa F401 from firedrake.adjoint.ensemble_adjvec import EnsembleAdjVec # noqa F401 import numpy_adjoint # noqa F401 diff --git a/firedrake/adjoint/fourdvar_reduced_functional.py b/firedrake/adjoint/fourdvar_reduced_functional.py index 2434b010d7..7b9741d6e5 100644 --- a/firedrake/adjoint/fourdvar_reduced_functional.py +++ b/firedrake/adjoint/fourdvar_reduced_functional.py @@ -2,9 +2,10 @@ stop_annotating, get_working_tape, set_working_tape from pyadjoint.reduced_functional import AbstractReducedFunctional from pyadjoint.enlisting import Enlist +from firedrake.assemble import get_assembler from firedrake.function import Function from firedrake.ensemble import EnsembleFunction, EnsembleFunctionSpace -from firedrake import assemble, inner, dx, Constant +from firedrake import assemble, inner, dx, Constant, grad, TestFunction, TrialFunction, derivative, Cofunction, LinearVariationalProblem, LinearVariationalSolver, RieszMap from .composite_reduced_functional import CompositeReducedFunctional from .composite_reduced_functional import _rename, isolated_rf from .ensemble_reduced_functional import ( @@ -16,8 +17,10 @@ from types import SimpleNamespace from contextlib import contextmanager from firedrake.petsc import PETSc +from math import pi, sqrt +from abc import ABC, abstractmethod -__all__ = ['FourDVarReducedFunctional'] +__all__ = ['FourDVarReducedFunctional', 'CovarianceOperator'] def sc_passthrough(func): @@ -156,7 +159,7 @@ def __init__(self, control: Control, # RF to recalculate inner product |x_0 - x_b|_B self.background_norm = CovarianceNormReducedFunctional( self.background._ad_init_zero(), - background_covariance, + background_covariance, form="diffusion", control_name="bkg_err_vec_copy") if self.initial_observations: @@ -171,7 +174,7 @@ def __init__(self, control: Control, # RF to recalculate inner product |H(x_0) - y_0|_R self.initial_observation_norm = CovarianceNormReducedFunctional( self.initial_observation_error.functional, - observation_covariance, + observation_covariance, form="mass", control_name="obs_err_vec_0_copy") self.observation_rfs.append(self.initial_observation_error) @@ -212,19 +215,25 @@ def __init__(self, control: Control, # Strong constraint functional to be converted to ReducedFunctional later # penalty for straying from prior - self.background_norm = CovarianceNormReducedFunctional( - control.control._ad_init_zero(), background_covariance) + # self.background_norm = CovarianceNormReducedFunctional( + # control.control._ad_init_zero(), + # background_covariance, + # form="diffusion") + self.background_norm = background_covariance.reduced_functional bkg_err = Function(self.control_space) bkg_err.assign(control.control - self.background) - self._accumulate_functional( - covariance_norm(bkg_err, background_covariance)) + self._accumulate_functional(background_covariance.norm(bkg_err)) + # self._accumulate_functional( + # covariance_norm(bkg_err, background_covariance, form="diffusion")) # penalty for not hitting observations at initial time if self.initial_observations: self._accumulate_functional( - covariance_norm(observation_error(control.control), - observation_covariance)) + observation_covariance.norm( + observation_error(control.control))) + # covariance_norm(observation_error(control.control), + # observation_covariance, form="mass")) @property def controls(self): @@ -644,9 +653,12 @@ def set_observation(self, state: OverloadedType, if hasattr(self.aaorf, "_strong_reduced_functional"): raise ValueError("Cannot add observations once strong" " constraint ReducedFunctional instantiated") + # self.aaorf._accumulate_functional( + # covariance_norm(observation_error(state), + # observation_covariance, form="mass")) self.aaorf._accumulate_functional( - covariance_norm(observation_error(state), - observation_covariance)) + observation_covariance.norm( + observation_error(state))) # save the user's state to hand back for beginning of next stage self.state = state @@ -763,7 +775,7 @@ def set_observation(self, state: OverloadedType, self.model_norm = CovarianceNormReducedFunctional( state._ad_init_zero(), forward_model_covariance, - **names) + **names, form="diffusion") # Observations after tape cut because this is now a control, not a state @@ -785,7 +797,7 @@ def set_observation(self, state: OverloadedType, self.observation_norm = CovarianceNormReducedFunctional( self.observation_error.functional, observation_covariance, - **names) + **names, form="mass") # remove the stage initial condition "control" now we've finished recording delattr(self, "control") @@ -794,35 +806,285 @@ def set_observation(self, state: OverloadedType, set_working_tape() -def covariance_norm(x, covariance): - if isinstance(covariance, Collection): - covariance, power = covariance - else: - power = None - if isinstance(covariance, (float, int)): - weight = Constant(1/covariance) - else: - weight = 1/covariance - from firedrake import TestFunction +def covariance_norm(x, covariance, form='diffusion'): + # if isinstance(covariance, Collection): + # covariance, power = covariance + # else: + # power = None + power = None + + valid_forms = ("diffusion", "mass") + if form not in valid_forms: + raise ValueError( + f"Unknown covariance form type {form}, should be " + " or ".join(valid_forms)) + v = TestFunction(x.function_space()) - xB = inner(x*weight, v)*dx - val = assemble(xB(x)) + if form == 'diffusion': + sigma, L, bcs = covariance + # diffusion coefficient for lengthscale L + nu = 0.5*L*L + # normalisation for diffusion operator + lambda_g = L*sqrt(2*pi) + scale = lambda_g/sigma + + xB = scale*(inner(x, v) - nu*inner(grad(x), grad(v)))*dx + val = assemble(xB(x)) + elif form == 'mass': + if isinstance(covariance, (float, int)): + w = Constant(1/covariance) + else: + w = 1/covariance + xB = inner(x*w, v)*dx + val = assemble(xB(x)) + else: + raise ValueError("Forgot to handle a covariance form type") + from pyadjoint import AdjFloat result = val if power is None else val**power assert type(result) is AdjFloat, f"{type(result).__name__ = } is not AdjFloat." return result +class CovarianceOperatorBase(ABC) + @abstractmethod + def norm(self, x): + """Calculate (x^T)(B^-1)(x) : V x V -> V x V* -> R + """ + pass + + @abstractmethod + def action(self, x): + """Calculate B*x : V -> V* + """ + pass + + @property + @abstractmethod + def reduced_functional(self): + pass + + +class FormCovarianceOperator(CovarianceOperatorBase): + def __init__(self, V, sigma, bcs=bcs, implicit=True, + options_prefix=None, solver_parameters=None): + self.V = V + self.sigma = sigma + self.bcs = bcs + self.implicit = implicit + + self.x = Function(V) + + self._sol = Function(V) + if implicit: + self._b = Function(V) + rhs = inner(self._b, v)*dx + else: + self._b = Cofunction(V.dual()) + rhs = self._b + + self.two_form = derivative(self.one_form, self.x) + + self.solver = LinearVariationalSolver( + LinearVariationalProblem( + self.two_form, rhs, self._sol, + bcs=self.bcs, constant_jacobian=True), + options_prefix=options_prefix, + solver_parameters=solver_parameters) + + self._assemble_one_form = get_assembler( + self.one_form, bcs=self.bcs).assemble + + self._assemble_norm = get_assembler( + self.one_form(self.x)).assemble + + if implicit: + self._assemble_xBx = get_assembler( + inner(self._b, self._sol)*dx).assemble + + @property + @abstractmethod + def one_form(self): + raise NotImplementedError + + def norm(self, x): + if x.function_space() != self.V: + raise ValueError("Covariance norm acts on a Function") + + if self.implicit: # xT*L^{-1}*x + self._b.assign(x) + self.solver.solve() + return self._assemble_xBx() + + else: # xT*L*x + self.x.assign(x) + return self._assemble_norm() + + def action(self, x, tensor=None): + # B : V* -> V + if x.function_space() != self.V.dual(): + raise ValueError("Covariance operator acts on a Cofunction") + if tensor is None: + tensor = Function(self.V) + + if self.implicit: # L*x + self.x.assign(x.riesz_representation()) + Bx = self._assemble_one_form() + return tensor.assign(Bx.riesz_representation()) + + else: # L^{-1}*x + self._b.assign(x) + self.solver.solve() + return tensor.assign(self._sol) + + @cached_property + def reduced_functional(self): + pass + + +class MassCovarianceOperator(CovarianceOperatorBase): + @property + def one_form(self): + x = self.x + v = TestFunction(V) + w = sigma if self.implicit else 1/sigma + return inner(x*w, v)*dx + + +class DiffusionCovarianceOperator(CovarianceOperatorBase): + def __init__(self, V, sigma, L, bcs=bcs, implicit=True, + options_prefix=None, solver_parameters=None): + self.L = L + super().__init__(V, simgma, bcs=bcs, implicit=implicit, + options_prefix=options_prefix, + solver_parameters=solver_parameters) + + @property + def one_form(self): + L = self.L + nu = Constant(L*L/2) + lambda_g = Constant(L*sqrt(2*pi)) + self.nu = nu + self.lambda_g = lambda_g + w = sigma/lambda_g if self.implicit else lambda_g/sigma + x = self.x + v = TestFunction(self.V) + return w*(inner(x, v)*dx + inner(nu*grad(x), grad(v))*dx) + + +class CovarianceOperator: + def __init__(self, V, form_type, **kwargs): + valid_form_types = ("diffusion", "mass") + if form_type not in valid_form_types: + raise ValueError( + f"Unknown form_type {form} for {type(self).__name__}," + "should be "+" or ".join(valid_form_types)) + self.form_type = form_type + self.V = V + + x = Function(V) + u = TrialFunction(V) + v = TestFunction(V) + self.x = x + self.u = u + self.v = v + + sigma = kwargs["sigma"] + self.sigma = sigma + + if form_type == "mass": + w = 1/sigma + one_form = inner(x*w, v)*dx + + elif form_type == "diffusion": + L = kwargs["L"] + nu = Constant(0.5*L*L) + lambda_g = Constant(L*sqrt(2.*pi)) + self.L = L + self.nu = nu + self.lambda_g = lambda_g + + scale = sigma/lambda_g + one_form = scale*(inner(x, v) + nu*inner(grad(x), grad(v)))*dx + + self.one_form = one_form + self.two_form = derivative(self.one_form, x) + + self.bcs = kwargs.get("bcs", None) or [] + options_prefix = kwargs.get("options_prefix", None) + solver_parameters = kwargs.get("solver_parameters", None) + + self._assemble_action = get_assembler( + self.one_form, bcs=self.bcs).assemble + + self._b = Function(V) + rhs = inner(self._b, v)*dx + self._sol = Function(V) + self.solver = LinearVariationalSolver( + LinearVariationalProblem( + self.two_form, rhs, self._sol, + bcs=self.bcs, constant_jacobian=True), + options_prefix=options_prefix, + solver_parameters=solver_parameters) + + def norm(self, x): + if x.function_space() != self.V: + raise ValueError("Covariance norm taken over a Function") + + if self.form_type == "mass": + self.x.assign(x) + return assemble(self.one_form(self.x)) + + elif self.form_type == "diffusion": + self._b.assign(x) + self.solver.solve() + Binv_x = self._sol + return assemble(inner(x, Binv_x)*dx) + + def action(self, x, tensor=None): + if x.function_space() != self.V.dual(): + raise ValueError("Covariance operator acts on a Cofunction") + + if tensor is None: + tensor = Function(self.V) + + if self.form_type == "mass": + self._b.assign(x.riesz_representation()) + self._sol.zero() + self.solver.solve() + return tensor.assign(self._sol) + + elif self.form_type == "diffusion": + self.x.assign(x.riesz_representation()) + Bx = assemble(self.one_form, bcs=self.bcs) + return tensor.assign(Bx.riesz_representation()) + + @cached_property + def reduced_functional(self): + return CovarianceReducedFunctional(self) + + +class CovarianceReducedFunctional(ReducedFunctional): + def __init__(self, covariance): + self.covariance = covariance + rf = isolated_rf( + lambda x: covariance.norm(x), + covariance.x._ad_copy()) + super().__init__( + rf.functional, rf.controls[0], tape=rf.tape) + + class CovarianceNormReducedFunctional(ReducedFunctional): - def __init__(self, x, covariance, + def __init__(self, x, covariance, form='diffusion', functional_name=None, control_name=None): - if isinstance(covariance, Collection): - self.covariance, self.power = covariance - else: - self.covariance = covariance - self.power = None - cov_norm = partial(covariance_norm, covariance=covariance) + # if isinstance(covariance, Collection): + # self.covariance, self.power = covariance + # else: + # self.covariance = covariance + # self.power = None + self.covariance = covariance + cov_norm = partial(covariance_norm, covariance=covariance, form=form) rf = isolated_rf(cov_norm, x, functional_name=functional_name, control_name=control_name) From b51876f5f186f023d6d76484ff79d60c89155457 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 6 May 2025 14:24:11 +0100 Subject: [PATCH 096/222] Add option to build Docker containers manually (#4272) --- .github/workflows/docker.yml | 32 ++++++++------------------------ .github/workflows/release.yml | 6 ++---- 2 files changed, 10 insertions(+), 28 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index b26812f672..934b3e1193 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -7,14 +7,18 @@ on: description: Docker image tag type: string secrets: - # Docker login information DOCKERHUB_USER: required: true DOCKERHUB_TOKEN: required: true + workflow_dispatch: + inputs: + tag: + description: Docker image tag + type: string jobs: - # Firedrake container (just Firedrake) + # Firedrake only docker_build_vanilla: strategy: fail-fast: false @@ -48,7 +52,7 @@ jobs: tag: ${{ inputs.tag }} secrets: inherit - # # Firedrake container (Firedrake and friends) + # Firedrake and friends docker_build_firedrake: needs: docker_merge_vanilla uses: ./.github/workflows/docker_build.yml @@ -72,27 +76,7 @@ jobs: tag: ${{ inputs.tag }} secrets: inherit - # # Firedrake container with TeX - docker_build_docdeps: - needs: docker_merge_vanilla - uses: ./.github/workflows/docker_build.yml - with: - os: Linux - platform: linux/amd64 - target: firedrake-docdeps - tag: ${{ inputs.tag }} - dockerfile: docker/Dockerfile.docdeps - secrets: inherit - - docker_merge_docdeps: - uses: ./.github/workflows/docker_merge.yml - needs: docker_build_docdeps - with: - target: firedrake-docdeps - tag: ${{ inputs.tag }} - secrets: inherit - - # # Firedrake container with Jupyter notebooks + # Firedrake with Jupyter notebooks docker_build_jupyter: needs: docker_merge_firedrake uses: ./.github/workflows/docker_build.yml diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 31df9ec16c..37a02ef9c3 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -2,11 +2,10 @@ name: Publish release on: release: - types: [published] + types: [released] jobs: deploy: - if: '!startsWith( github.ref_name, ''Zenodo'' )' uses: ./.github/workflows/core.yml with: source_ref: release @@ -16,9 +15,8 @@ jobs: secrets: inherit docker: - if: '!startsWith( github.ref_name, ''Zenodo'' )' name: Build Docker containers uses: ./.github/workflows/docker.yml with: - tag: ${{ github.ref }} + tag: ${{ github.ref_name }} secrets: inherit From caf0f53d9b8ba129ef3a6359579fc93dd668f9c7 Mon Sep 17 00:00:00 2001 From: JHopeCollins Date: Wed, 7 May 2025 12:24:52 +0100 Subject: [PATCH 097/222] REVERT BEFORE MERGE: checkout pyadjoint branch --- .github/workflows/core.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index c0acee0dfe..d458b52ec2 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -195,6 +195,8 @@ jobs: --no-binary h5py \ --extra-index-url https://download.pytorch.org/whl/cpu \ "$(echo ./firedrake-repo/dist/firedrake-*.tar.gz)[ci,docs]" + : # TODO: Remove before merge + pip install --verbose --editable git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional#egg=pyadjoint-ad firedrake-clean pip list From 073fbd232908b8e9d2fbacea1f53c3eb85c385e9 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Wed, 7 May 2025 16:34:15 +0100 Subject: [PATCH 098/222] Only deploy the website for the master branch (#4278) GitHub pages only supports a single website per repository so we will host the master branch code at firedrakeproject.org/firedrake and the release code at firedrakeproject.org (using the other repository). --- .github/workflows/core.yml | 5 ++++- docs/source/install.rst | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index d8f509da73..efd4cac78a 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -504,16 +504,19 @@ jobs: deploy_website: name: Deploy GitHub pages (optional) needs: test_linux + # Only deploy the website for master here, the release branch is managed + # by https://github.com/firedrakeproject/firedrakeproject.github.io if: | always() && inputs.deploy_website && + inputs.target_branch == 'master' && needs.test_linux.outputs.docs_conclusion == 'success' permissions: pages: write id-token: write environment: name: github-pages - url: http://firedrakeproject.github.io/firedrake/${{ inputs.target_branch }} + url: http://firedrakeproject.github.io/firedrake runs-on: ubuntu-latest steps: - name: Deploy to GitHub Pages diff --git a/docs/source/install.rst b/docs/source/install.rst index d955a6c757..5fe950c531 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -477,7 +477,7 @@ Developer install .. warning:: You are currently looking at the documentation for the current stable release of Firedrake. For the most recent developer documentation you - should follow the instructions `here `__. + should follow the instructions `here `__. In order to install a development version of Firedrake the following steps should be followed: From 144746d50c10a23cb45e9ec4e327b0cd2f54749f Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Wed, 7 May 2025 16:36:13 +0100 Subject: [PATCH 099/222] Single source of truth (#4249) * Do not rely on an external configuration file * Add check for Firedrake version --- .github/workflows/core.yml | 8 ++- pyproject.toml | 2 - scripts/check-config | 110 ++++++++++++++++++++++++++++++++++++ scripts/firedrake-configure | 2 - 4 files changed, 117 insertions(+), 5 deletions(-) create mode 100755 scripts/check-config diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index efd4cac78a..2ea409fbc6 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -111,10 +111,16 @@ jobs: path: firedrake-repo ref: ${{ inputs.source_ref }} - - name: Install system dependencies + - name: Install Python run: | apt-get update apt-get -y install python3 python3-venv + + - name: Validate single source of truth + run: ./firedrake-repo/scripts/check-config + + - name: Install system dependencies + run: | apt-get -y install \ $(python3 ./firedrake-repo/scripts/firedrake-configure --arch ${{ matrix.arch }} --show-system-packages) : # Dependencies needed to run the test suite diff --git a/pyproject.toml b/pyproject.toml index 7f572acaa2..919cbe7496 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,8 +24,6 @@ dependencies = [ "h5py>3.12.1", "libsupermesh", "loopy>2024.1", - # NOTE: If changing the PETSc/SLEPc version then firedrake-configure also needs - # changing (as well as other references to petsc4py and slepc4py here) "petsc4py==3.23.0", "numpy", "packaging", diff --git a/scripts/check-config b/scripts/check-config new file mode 100755 index 0000000000..e23335b6cc --- /dev/null +++ b/scripts/check-config @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 + +"""Script that makes sure that hardcoded version numbers are consistent. + +It looks at files in the Firedrake repository and makes sure that everywhere a +version number is hardcoded that it matches. This allows for single-source-of-truth +of hardcoded values (via CI). + +""" + +import pathlib +import re + + +REPO_ROOT = pathlib.Path(__file__).parent.parent + + +class InvalidConfigurationException(Exception): + pass + + +def main(): + check_firedrake_version() + check_min_python_version() + check_petsc_version() + check_petsc_version_spec() + + +def check_firedrake_version(): + firedrake_version = check_file_contains_pattern( + REPO_ROOT / "pyproject.toml", + "version = \"(.*)\"" + ) + check_file_contains_pattern( + REPO_ROOT/"docs/source/install.rst", + f"https://raw.githubusercontent.com/firedrakeproject/firedrake/refs/tags/{firedrake_version}/scripts/firedrake-configure" + ) + + +def check_min_python_version() -> None: + min_python_version = check_file_contains_pattern( + REPO_ROOT / "pyproject.toml", + "requires-python = \">=(.*)\"" + ) + check_file_contains_pattern( + REPO_ROOT / "docs/source/install.rst", + f"Python \\({min_python_version} or greater\\)" + ) + + +def check_petsc_version() -> None: + petsc_version = check_file_contains_pattern( + REPO_ROOT / "scripts/firedrake-configure", + "SUPPORTED_PETSC_VERSION = \"v(.*)\"", + ) + check_file_contains_pattern( + REPO_ROOT / "pyproject.toml", + f"petsc4py=={petsc_version}", + 2, + ) + check_file_contains_pattern( + REPO_ROOT / "pyproject.toml", + f"slepc4py=={petsc_version}", + 3, + ) + + +def check_petsc_version_spec() -> None: + # TODO when https://github.com/firedrakeproject/firedrake/pull/4194 is merged + pass + + +def check_file_contains_pattern( + filename: pathlib.Path | str, + pattern: str, + num_expected_matches: int = 1, +) -> str: + """Check that the regex pattern exists in the file. + + Parameters + ---------- + filename : + The filename. + pattern : + The regular expression pattern to look for. + num_expected_matches : + The number of expected matches. + + Returns + ------- + str : + The matched value. This follows the semantics of `re.findall`. + + """ + with open(filename) as f: + text = f.read() + matches = re.findall(pattern, text) + if len(matches) != num_expected_matches: + raise InvalidConfigurationException( + f"Expected to find {num_expected_matches} matches for '{pattern}' in " + f"{filename} but found {len(matches)}" + ) + + # all the matches should be the same so just return one of them + match, = set(matches) + return match + + +if __name__ == "__main__": + main() diff --git a/scripts/firedrake-configure b/scripts/firedrake-configure index f8a93fdf5e..320786e4d4 100755 --- a/scripts/firedrake-configure +++ b/scripts/firedrake-configure @@ -39,8 +39,6 @@ ARCH_DEFAULT = FiredrakeArch.DEFAULT ARCH_COMPLEX = FiredrakeArch.COMPLEX -# NOTE: When updating this variable corresponding changes must be made inside -# pyproject.toml SUPPORTED_PETSC_VERSION = "v3.23.0" From 9b4f147377875e5445dd0cc703b31e0b15493189 Mon Sep 17 00:00:00 2001 From: Pablo Brubeck Date: Thu, 8 May 2025 09:44:06 +0100 Subject: [PATCH 100/222] Dual interpolation from/into MixedFunctionSpace (#4197) * Interpolation onto subfunctions * More generic approach using fieldsplit * Interpolate: Fix parloop args ordering * Fix dual * Interpolate from MixedFunctionSpace * Test Interpolate from vector/mixed to mixed * Interpolate from (scalar/mixed) to (scalar/mixed) Co-authored-by: Pablo Brubeck --------- Co-authored-by: David A. Ham --- firedrake/adjoint_utils/blocks/assembly.py | 6 +- firedrake/assemble.py | 95 +++++++++++++------ firedrake/interpolation.py | 67 +++++++------ firedrake/ufl_expr.py | 10 +- firedrake/variational_solver.py | 7 +- .../adjoint/test_reduced_functional.py | 49 ++++++++++ .../firedrake/regression/test_interp_dual.py | 67 +++++++++++++ .../test_restricted_function_space.py | 5 +- 8 files changed, 229 insertions(+), 77 deletions(-) diff --git a/firedrake/adjoint_utils/blocks/assembly.py b/firedrake/adjoint_utils/blocks/assembly.py index 8ba790ec74..969be25e9d 100644 --- a/firedrake/adjoint_utils/blocks/assembly.py +++ b/firedrake/adjoint_utils/blocks/assembly.py @@ -101,8 +101,7 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx, c = block_variable.output c_rep = block_variable.saved_output - from ufl.algorithms.analysis import extract_arguments - arity_form = len(extract_arguments(form)) + arity_form = len(form.arguments()) if isconstant(c): mesh = as_domain(self.form) @@ -157,8 +156,7 @@ def evaluate_hessian_component(self, inputs, hessian_inputs, adj_inputs, hessian_input = hessian_inputs[0] adj_input = adj_inputs[0] - from ufl.algorithms.analysis import extract_arguments - arity_form = len(extract_arguments(form)) + arity_form = len(form.arguments()) c1 = block_variable.output c1_rep = block_variable.saved_output diff --git a/firedrake/assemble.py b/firedrake/assemble.py index 97104b05cc..c40c38095b 100644 --- a/firedrake/assemble.py +++ b/firedrake/assemble.py @@ -18,6 +18,7 @@ import finat.ufl from firedrake import (extrusion_utils as eutils, matrix, parameters, solving, tsfc_interface, utils) +from firedrake.formmanipulation import split_form from firedrake.adjoint_utils import annotate_assemble from firedrake.ufl_expr import extract_unique_domain from firedrake.bcs import DirichletBC, EquationBC, EquationBCSplit @@ -230,34 +231,36 @@ def assemble(self, tensor=None, current_state=None): # Only Expr resulting in a Matrix if assembled are BaseFormOperator if not all(isinstance(op, matrix.AssembledMatrix) for op in (a, b)): raise TypeError('Mismatching Sum shapes') - return get_assembler(ufl.FormSum((a, 1), (b, 1))).assemble() + return assemble(ufl.FormSum((a, 1), (b, 1)), tensor=tensor) elif isinstance(expr, ufl.algebra.Product): a, b = expr.ufl_operands scalar = [e for e in expr.ufl_operands if is_scalar_constant_expression(e)] if scalar: base_form = a if a is scalar else b assembled_mat = assemble(base_form) - return get_assembler(ufl.FormSum((assembled_mat, scalar[0]))).assemble() + return assemble(ufl.FormSum((assembled_mat, scalar[0])), tensor=tensor) a, b = [assemble(e) for e in (a, b)] - return get_assembler(ufl.action(a, b)).assemble() + return assemble(ufl.action(a, b), tensor=tensor) # -- Linear combination of Functions and 1-form BaseFormOperators -- # # Example: a * u1 + b * u2 + c * N(u1; v*) + d * N(u2; v*) # with u1, u2 Functions, N a BaseFormOperator, and a, b, c, d scalars or 0-form BaseFormOperators. else: base_form_operators = extract_base_form_operators(expr) - assembled_bfops = [firedrake.assemble(e) for e in base_form_operators] # Substitute base form operators with their output before examining the expression # which avoids conflict when determining function space, for example: # extract_coefficients(Interpolate(u, V2)) with u \in V1 will result in an output function space V1 # instead of V2. if base_form_operators: - expr = ufl.replace(expr, dict(zip(base_form_operators, assembled_bfops))) - try: - coefficients = ufl.algorithms.extract_coefficients(expr) - V, = set(c.function_space() for c in coefficients) - {None} - except ValueError: - raise ValueError("Cannot deduce correct target space from pointwise expression") - return firedrake.Function(V).assign(expr) + assembled_bfops = {e: firedrake.assemble(e) for e in base_form_operators} + expr = ufl.replace(expr, assembled_bfops) + if tensor is None: + try: + coefficients = ufl.algorithms.extract_coefficients(expr) + V, = set(c.function_space() for c in coefficients) - {None} + except ValueError: + raise ValueError("Cannot deduce correct target space from pointwise expression") + tensor = firedrake.Function(V) + return tensor.assign(expr) class AbstractFormAssembler(abc.ABC): @@ -493,9 +496,10 @@ def base_form_assembly_visitor(self, expr, tensor, *args): if all(isinstance(op, numbers.Complex) for op in args): result = sum(weight * arg for weight, arg in zip(expr.weights(), args)) return tensor.assign(result) if tensor else result - elif all(isinstance(op, firedrake.Cofunction) for op in args): + elif (all(isinstance(op, firedrake.Cofunction) for op in args) + or all(isinstance(op, firedrake.Function) for op in args)): V, = set(a.function_space() for a in args) - result = tensor if tensor else firedrake.Cofunction(V) + result = tensor if tensor else firedrake.Function(V) result.dat.maxpy(expr.weights(), [a.dat for a in args]) return result elif all(isinstance(op, ufl.Matrix) for op in args): @@ -540,7 +544,10 @@ def base_form_assembly_visitor(self, expr, tensor, *args): if assembled_expression: # Occur in situations such as Interpolate composition expression = assembled_expression[0] - expr = expr._ufl_expr_reconstruct_(expression, v) + + reconstruct_interp = expr._ufl_expr_reconstruct_ + if (v, expression) != expr.argument_slots(): + expr = reconstruct_interp(expression, v=v) # Different assembly procedures: # 1) Interpolate(Argument(V1, 1), Argument(V2.dual(), 0)) -> Jacobian (Interpolate matrix) @@ -552,27 +559,59 @@ def base_form_assembly_visitor(self, expr, tensor, *args): # If argument numbers have been swapped => Adjoint. arg_expression = ufl.algorithms.extract_arguments(expression) is_adjoint = (arg_expression and arg_expression[0].number() == 0) + + # Dual interpolation from mixed source + if is_adjoint and len(expr.function_space()) > 1: + cur = 0 + sub_expressions = [] + components = numpy.reshape(expression, (-1,)) + for Vi in expr.function_space(): + sub_expressions.append(ufl.as_tensor(components[cur:cur+Vi.value_size].reshape(Vi.value_shape))) + cur += Vi.value_size + + # Component-split of the primal expression interpolated into the dual argument-split + split_interp = sum(reconstruct_interp(sub_expressions[i], v=vi) for (i,), vi in split_form(v)) + return assemble(split_interp, tensor=tensor) + + # Dual interpolation into mixed target + if is_adjoint and len(arg_expression[0].function_space()) > 1 and rank == 1: + V = arg_expression[0].function_space() + tensor = tensor or firedrake.Cofunction(V.dual()) + + # Argument-split of the Interpolate gets assembled into the corresponding sub-tensor + for (i,), sub_interp in split_form(expr): + assemble(sub_interp, tensor=tensor.subfunctions[i]) + return tensor + + # Get the primal space + V = expr.function_space() + if is_adjoint or rank == 0: + V = V.dual() + # Workaround: Renumber argument when needed since Interpolator assumes it takes a zero-numbered argument. - if not is_adjoint and rank != 1: + if not is_adjoint and rank == 2: _, v1 = expr.arguments() - expression = ufl.replace(expression, {v1: firedrake.Argument(v1.function_space(), number=0, part=v1.part())}) + expression = ufl.replace(expression, {v1: v1.reconstruct(number=0)}) # Get the interpolator interp_data = expr.interp_data default_missing_val = interp_data.pop('default_missing_val', None) - interpolator = firedrake.Interpolator(expression, expr.function_space(), **interp_data) + interpolator = firedrake.Interpolator(expression, V, **interp_data) # Assembly - if rank == 1: + if rank == 0: + Iu = interpolator._interpolate(default_missing_val=default_missing_val) + return assemble(ufl.Action(v, Iu), tensor=tensor) + elif rank == 1: # Assembling the action of the Jacobian adjoint. if is_adjoint: - output = tensor or firedrake.Cofunction(arg_expression[0].function_space().dual()) - return interpolator._interpolate(v, output=output, adjoint=True, default_missing_val=default_missing_val) + return interpolator._interpolate(v, output=tensor, adjoint=True, default_missing_val=default_missing_val) # Assembling the Jacobian action. - if interpolator.nargs: + elif interpolator.nargs: return interpolator._interpolate(expression, output=tensor, default_missing_val=default_missing_val) # Assembling the operator - if tensor is None: + elif tensor is None: return interpolator._interpolate(default_missing_val=default_missing_val) - return firedrake.Interpolator(expression, tensor, **interp_data)._interpolate(default_missing_val=default_missing_val) + else: + return firedrake.Interpolator(expression, tensor, **interp_data)._interpolate(default_missing_val=default_missing_val) elif rank == 2: res = tensor.petscmat if tensor else PETSc.Mat() # Get the interpolation matrix @@ -799,7 +838,7 @@ def restructure_base_form(expr, visited=None): replace_map = {arg: left} # Decrease number for all the other arguments since the lowest numbered argument will be replaced. other_args = [a for a in right.arguments() if a is not arg] - new_args = [firedrake.Argument(a.function_space(), number=a.number()-1, part=a.part()) for a in other_args] + new_args = [a.reconstruct(number=a.number()-1) for a in other_args] replace_map.update(dict(zip(other_args, new_args))) # Replace arguments return ufl.replace(right, replace_map) @@ -810,13 +849,13 @@ def restructure_base_form(expr, visited=None): u, v = B.arguments() # Let V1 and V2 be primal spaces, B: V1 -> V2 and B*: V2* -> V1*: # Adjoint(B(Argument(V1, 1), Argument(V2.dual(), 0))) = B(Argument(V1, 0), Argument(V2.dual(), 1)) - reordered_arguments = (firedrake.Argument(u.function_space(), number=v.number(), part=v.part()), - firedrake.Argument(v.function_space(), number=u.number(), part=u.part())) + reordered_arguments = {u: u.reconstruct(number=v.number()), + v: v.reconstruct(number=u.number())} # Replace arguments in argument slots - return ufl.replace(B, dict(zip((u, v), reordered_arguments))) + return ufl.replace(B, reordered_arguments) # -- Case (5) -- # - if isinstance(expr, ufl.core.base_form_operator.BaseFormOperator) and not expr.arguments(): + if isinstance(expr, ufl.core.base_form_operator.BaseFormOperator) and len(expr.arguments()) == 0: # We are assembling a BaseFormOperator of rank 0 (no arguments). # B(f, u*) be a BaseFormOperator with u* a Cofunction and f a Coefficient, then: # B(f, u*) <=> Action(B(f, v*), f) where v* is a Coargument diff --git a/firedrake/interpolation.py b/firedrake/interpolation.py index 05d3438318..3affb8b43c 100644 --- a/firedrake/interpolation.py +++ b/firedrake/interpolation.py @@ -85,14 +85,11 @@ def __init__(self, expr, v, the :meth:`interpolate` method or (b) set to zero. Ignored if interpolating within the same mesh or onto a :func:`.VertexOnlyMesh`. """ - # Check function space if isinstance(v, functionspaceimpl.WithGeometry): - v = Argument(v.dual(), 0) - - # Get the primal space (V** = V) - vv = v if not isinstance(v, ufl.Form) else v.arguments()[0] - self._function_space = vv.function_space().dual() + expr_args = extract_arguments(ufl.as_ufl(expr)) + is_adjoint = len(expr_args) and expr_args[0].number() == 0 + v = Argument(v.dual(), 1 if is_adjoint else 0) super().__init__(expr, v) # -- Interpolate data (e.g. `subset` or `access`) -- # @@ -101,8 +98,7 @@ def __init__(self, expr, v, "allow_missing_dofs": allow_missing_dofs, "default_missing_val": default_missing_val} - def function_space(self): - return self._function_space + function_space = ufl.Interpolate.ufl_function_space def _ufl_expr_reconstruct_(self, expr, v=None, **interp_data): interp_data = interp_data or self.interp_data.copy() @@ -293,9 +289,7 @@ def __init__( expr_args = extract_arguments(expr) if expr_args and expr_args[0].number() == 0: v, = expr_args - expr = replace(expr, {v: Argument(v.function_space(), - number=1, - part=v.part())}) + expr = replace(expr, {v: v.reconstruct(number=1)}) self.expr_renumbered = expr def _interpolate_future(self, *function, transpose=None, adjoint=False, default_missing_val=None): @@ -870,7 +864,7 @@ def _interpolate(self, *function, output=None, transpose=None, adjoint=False, ** raise ValueError("The expression had arguments: we therefore need to be given a Function (not an expression) to interpolate!") if adjoint: mul = assembled_interpolator.handle.multHermitian - V = self.arguments[0].function_space() + V = self.arguments[0].function_space().dual() else: mul = assembled_interpolator.handle.mult V = self.V @@ -1123,34 +1117,14 @@ def _interpolator(V, tensor, expr, subset, arguments, access, bcs=None): name = kernel.name kernel = op2.Kernel(ast, name, requires_zeroed_output_arguments=True, flop_count=kernel.flop_count, events=(kernel.event,)) + parloop_args = [kernel, cell_set] coefficients = tsfc_interface.extract_numbered_coefficients(expr, coefficient_numbers) if needs_external_coords: coefficients = [source_mesh.coordinates] + coefficients - if isinstance(target_mesh.topology, firedrake.mesh.VertexOnlyMeshTopology): - if target_mesh is not source_mesh: - # NOTE: TSFC will sometimes drop run-time arguments in generated - # kernels if they are deemed not-necessary. - # FIXME: Checking for argument name in the inner kernel to decide - # whether to add an extra coefficient is a stopgap until - # compile_expression_dual_evaluation - # (a) outputs a coefficient map to indicate argument ordering in - # parloops as `compile_form` does and - # (b) allows the dual evaluation related coefficients to be supplied to - # them rather than having to be added post-hoc (likely by - # replacing `to_element` with a CoFunction/CoArgument as the - # target `dual` which would contain `dual` related - # coefficient(s)) - if rt_var_name in [arg.name for arg in kernel.code[name].args]: - # Add the coordinates of the target mesh quadrature points in the - # source mesh's reference cell as an extra argument for the inner - # loop. (With a vertex only mesh this is a single point for each - # vertex cell.) - coefficients.append(target_mesh.reference_coordinates) - - if tensor in set((c.dat for c in coefficients)): + if any(c.dat == tensor for c in coefficients): output = tensor tensor = op2.Dat(tensor.dataset) if access is not op2.WRITE: @@ -1196,6 +1170,7 @@ def _interpolator(V, tensor, expr, subset, arguments, access, bcs=None): if needs_cell_sizes: cs = source_mesh.cell_sizes parloop_args.append(cs.dat(op2.READ, cs.cell_node_map())) + for coefficient in coefficients: if isinstance(target_mesh.topology, firedrake.mesh.VertexOnlyMeshTopology): coeff_mesh = extract_unique_domain(coefficient) @@ -1227,6 +1202,30 @@ def _interpolator(V, tensor, expr, subset, arguments, access, bcs=None): for const in extract_firedrake_constants(expr): parloop_args.append(const.dat(op2.READ)) + # Finally, add the target mesh reference coordinates if they appear in the kernel + if isinstance(target_mesh.topology, firedrake.mesh.VertexOnlyMeshTopology): + if target_mesh is not source_mesh: + # NOTE: TSFC will sometimes drop run-time arguments in generated + # kernels if they are deemed not-necessary. + # FIXME: Checking for argument name in the inner kernel to decide + # whether to add an extra coefficient is a stopgap until + # compile_expression_dual_evaluation + # (a) outputs a coefficient map to indicate argument ordering in + # parloops as `compile_form` does and + # (b) allows the dual evaluation related coefficients to be supplied to + # them rather than having to be added post-hoc (likely by + # replacing `to_element` with a CoFunction/CoArgument as the + # target `dual` which would contain `dual` related + # coefficient(s)) + if any(arg.name == rt_var_name for arg in kernel.code[name].args): + # Add the coordinates of the target mesh quadrature points in the + # source mesh's reference cell as an extra argument for the inner + # loop. (With a vertex only mesh this is a single point for each + # vertex cell.) + target_ref_coords = target_mesh.reference_coordinates + m_ = target_ref_coords.cell_node_map() + parloop_args.append(target_ref_coords.dat(op2.READ, m_)) + parloop = op2.ParLoop(*parloop_args) parloop_compute_callable = parloop.compute if isinstance(tensor, op2.Mat): diff --git a/firedrake/ufl_expr.py b/firedrake/ufl_expr.py index 1f9a66df75..912a05eae0 100644 --- a/firedrake/ufl_expr.py +++ b/firedrake/ufl_expr.py @@ -336,13 +336,9 @@ def adjoint(form, reordered_arguments=None, derivatives_expanded=None): # given. To avoid that, always pass reordered_arguments with # firedrake.Argument objects. if reordered_arguments is None: - v, u = extract_arguments(form) - reordered_arguments = (Argument(u.function_space(), - number=v.number(), - part=v.part()), - Argument(v.function_space(), - number=u.number(), - part=u.part())) + v, u = form.arguments() + reordered_arguments = (u.reconstruct(number=v.number()), + v.reconstruct(number=u.number())) return ufl.adjoint(form, reordered_arguments, derivatives_expanded=derivatives_expanded) diff --git a/firedrake/variational_solver.py b/firedrake/variational_solver.py index 8a26220765..34698f6b35 100644 --- a/firedrake/variational_solver.py +++ b/firedrake/variational_solver.py @@ -9,11 +9,11 @@ DEFAULT_SNES_PARAMETERS ) from firedrake.function import Function +from firedrake.interpolation import Interpolate from firedrake.matrix import MatrixBase -from firedrake.ufl_expr import TrialFunction, TestFunction, action +from firedrake.ufl_expr import TrialFunction, TestFunction from firedrake.bcs import DirichletBC, EquationBC, extract_subdomain_ids, restricted_function_space from firedrake.adjoint_utils import NonlinearVariationalProblemMixin, NonlinearVariationalSolverMixin -from firedrake.__future__ import interpolate from ufl import replace, Form __all__ = ["LinearVariationalProblem", @@ -100,7 +100,8 @@ def __init__(self, F, u, bcs=None, J=None, F_arg, = F.arguments() self.F = replace(F, {F_arg: v_res, self.u: self.u_restrict}) else: - self.F = action(replace(F, {self.u: self.u_restrict}), interpolate(v_res, V)) + self.F = Interpolate(v_res, replace(F, {self.u: self.u_restrict})) + v_arg, u_arg = self.J.arguments() self.J = replace(self.J, {v_arg: v_res, u_arg: u_res, self.u: self.u_restrict}) if self.Jp: diff --git a/tests/firedrake/adjoint/test_reduced_functional.py b/tests/firedrake/adjoint/test_reduced_functional.py index fd802d3cb4..edd483c040 100644 --- a/tests/firedrake/adjoint/test_reduced_functional.py +++ b/tests/firedrake/adjoint/test_reduced_functional.py @@ -202,3 +202,52 @@ def test_assemble_recompute(): h = Function(V) h.vector()[:] = 1 assert taylor_test(Jhat, f, h) > 1.9 + + +@pytest.mark.skipcomplex +def test_interpolate(): + mesh = UnitSquareMesh(2, 2) + V = FunctionSpace(mesh, "CG", 1) + Q = FunctionSpace(mesh, "DG", 0) + c = Cofunction(Q.dual()) + c.dat.data[:] = 1 + + f = Function(V) + f.dat.data[:] = 2 + J = assemble(Interpolate(f**2, c)) + Jhat = ReducedFunctional(J, Control(f)) + + h = Function(V) + h.dat.data[:] = 3 + assert taylor_test(Jhat, f, h) > 1.9 + + +@pytest.mark.skipcomplex +def test_interpolate_mixed(): + mesh = UnitSquareMesh(2, 2) + V1 = FunctionSpace(mesh, "RT", 1) + V2 = FunctionSpace(mesh, "CG", 1) + V = V1 * V2 + + Q1 = FunctionSpace(mesh, "DG", 0) + Q2 = FunctionSpace(mesh, "N1curl", 1) + Q = Q1 * Q2 + + c = Cofunction(Q.dual()) + c.subfunctions[0].dat.data[:] = 1 + c.subfunctions[1].dat.data[:] = 2 + + f = Function(V) + f.subfunctions[0].dat.data[:] = 3 + f.subfunctions[1].dat.data[:] = 4 + + f1, f2 = split(f) + exprs = [f2 * div(f1)**2, grad(f2) * div(f1)] + expr = as_vector([e[i] for e in exprs for i in np.ndindex(e.ufl_shape)]) + J = assemble(Interpolate(expr, c)) + Jhat = ReducedFunctional(J, Control(f)) + + h = Function(V) + h.subfunctions[0].dat.data[:] = 5 + h.subfunctions[1].dat.data[:] = 6 + assert taylor_test(Jhat, f, h) > 1.9 diff --git a/tests/firedrake/regression/test_interp_dual.py b/tests/firedrake/regression/test_interp_dual.py index 5e36610a33..04d9254963 100644 --- a/tests/firedrake/regression/test_interp_dual.py +++ b/tests/firedrake/regression/test_interp_dual.py @@ -277,3 +277,70 @@ def test_solve_interp_u(mesh): "ksp_type": "cg", "pc_type": "none"}) assert np.allclose(u.dat.data, u2.dat.data) + + +@pytest.fixture(params=[("DG", 1, "CG", 2), + ("DG", 0, "RT", 1), + ("CG", 1), + ("RT", 1)], + ids=lambda x: "-".join(map(str, x))) +def target_space(mesh, request): + spaces = [] + for i in range(0, len(request.param), 2): + family, degree = request.param[i:i+2] + spaces.append(FunctionSpace(mesh, family, degree)) + + W = spaces[0] if len(spaces) == 1 else MixedFunctionSpace(spaces) + return W + + +@pytest.fixture(params=["scalar", "vector", "mixed"]) +def source_space(mesh, request): + if request.param == "scalar": + return FunctionSpace(mesh, "DG", 0) + elif request.param == "vector": + return VectorFunctionSpace(mesh, "DG", 0, dim=3) + elif request.param == "mixed": + P0 = FunctionSpace(mesh, "DG", 0) + return P0 * P0 * P0 + else: + raise ValueError("Unrecognized parameter") + + +def test_interp_dual_mixed(source_space, target_space): + W = target_space + w = TestFunction(W) + + V = source_space + v = TestFunction(V) + + A_shape = V.value_shape + W.value_shape + if A_shape == (): + A = 1 + else: + mn = V.value_size * W.value_size + A = as_tensor(np.arange(1, 1+mn).reshape(A_shape)) + + if V.value_shape == (): + b = 1 + else: + m = V.value_size + b = as_tensor(np.arange(1, 1+m).reshape(V.value_shape)) + + expr = dot(A, w) if V.value_shape == () else A * w + + F_target = inner(b, expr)*dx(degree=0) + expected = assemble(F_target) + + F_source = inner(b, v)*dx + I_source = Interpolate(expr, F_source) + + c = Cofunction(W.dual()) + c.assign(99) + for tensor in (None, c): + result = assemble(I_source, tensor=tensor) + assert result.function_space() == W.dual() + if tensor: + assert result is tensor + for x, y, in zip(result.subfunctions, expected.subfunctions): + assert np.allclose(x.dat.data_ro, y.dat.data_ro) diff --git a/tests/firedrake/regression/test_restricted_function_space.py b/tests/firedrake/regression/test_restricted_function_space.py index 8d8a19a56d..67bb2e6861 100644 --- a/tests/firedrake/regression/test_restricted_function_space.py +++ b/tests/firedrake/regression/test_restricted_function_space.py @@ -176,7 +176,8 @@ def test_restricted_function_space_coord_change(j): compare_function_space_assembly(new_V, new_V_restricted, [bc]) -def test_poisson_restricted_mixed_space(): +@pytest.mark.parametrize("assembled_rhs", [False, True], ids=("Form", "Cofunction")) +def test_poisson_restricted_mixed_space(assembled_rhs): mesh = UnitSquareMesh(1, 1) V = FunctionSpace(mesh, "RT", 1) Q = FunctionSpace(mesh, "DG", 0) @@ -186,6 +187,8 @@ def test_poisson_restricted_mixed_space(): v, q = TestFunctions(Z) a = inner(u, v)*dx + inner(p, div(v))*dx + inner(div(u), q)*dx L = inner(1, q)*dx + if assembled_rhs: + L = assemble(L) bcs = [DirichletBC(Z.sub(0), 0, [1])] From 550311f8b9c5ad93844f7cc2f2334fb4a5c23062 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 8 May 2025 15:08:19 +0100 Subject: [PATCH 101/222] Use correct hook to prepare firedrake-check for editable installs (#4289) --- setup.py | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/setup.py b/setup.py index 03624bf88a..48def53670 100644 --- a/setup.py +++ b/setup.py @@ -12,6 +12,7 @@ import pkgconfig from dataclasses import dataclass, field from setuptools import setup, find_packages, Extension +from setuptools.command.editable_wheel import editable_wheel as _editable_wheel from setuptools.command.sdist import sdist as _sdist from glob import glob from pathlib import Path @@ -250,21 +251,28 @@ def extensions(): ) -class sdist(_sdist): +def copy_check_files(): + """Copy Makefile and tests into firedrake/_check.""" + dest_dir = Path("firedrake/_check") + for check_file in map(Path, FIREDRAKE_CHECK_FILES): + os.makedirs(dest_dir / check_file.parent, exist_ok=True) + shutil.copy(check_file, dest_dir / check_file.parent) + + +class editable_wheel(_editable_wheel): def run(self): - self._copy_check_files() + copy_check_files() super().run() - def _copy_check_files(self): - """Copy Makefile and tests into firedrake/_check.""" - dest_dir = Path("firedrake/_check") - for check_file in map(Path, FIREDRAKE_CHECK_FILES ): - os.makedirs(dest_dir / check_file.parent, exist_ok=True) - shutil.copy(check_file, dest_dir / check_file.parent) + +class sdist(_sdist): + def run(self): + copy_check_files() + super().run() setup( - cmdclass={"sdist": sdist}, + cmdclass={"editable_wheel": editable_wheel, "sdist": sdist}, packages=find_packages(), ext_modules=extensions(), ) From 502c2c911d9026ec1b01f5f70098bcf30ab35f21 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 8 May 2025 15:24:38 +0100 Subject: [PATCH 102/222] Add instruction to run pip cache remove firedrake (#4290) --- docs/source/install.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/install.rst b/docs/source/install.rst index 5fe950c531..0e43bb448e 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -180,6 +180,7 @@ install Firedrake. To do this perform the following steps: #. Install Firedrake:: $ pip cache remove petsc4py + $ pip cache remove firedrake $ pip install --no-binary h5py 'firedrake[check]' .. note:: From b8045e7275b22c9c33510e9f0153a6ecffff43a3 Mon Sep 17 00:00:00 2001 From: Josh Hope-Collins Date: Thu, 8 May 2025 15:27:16 +0100 Subject: [PATCH 103/222] tighten the solve tolerance for burgers eq hessian taylor test (#4291) --- tests/firedrake/adjoint/test_hessian.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/tests/firedrake/adjoint/test_hessian.py b/tests/firedrake/adjoint/test_hessian.py index ba0f0cad1e..403cb8a86e 100644 --- a/tests/firedrake/adjoint/test_hessian.py +++ b/tests/firedrake/adjoint/test_hessian.py @@ -264,12 +264,18 @@ def Dt(u, u_, timestep): timestep = Constant(1.0/n) + params = { + 'snes_rtol': 1e-10, + 'ksp_type': 'preonly', + 'pc_type': 'lu', + } + F = (Dt(u, ic, timestep)*v + u*u.dx(0)*v + nu*u.dx(0)*v.dx(0))*dx bc = DirichletBC(V, 0.0, "on_boundary") t = 0.0 - solve(F == 0, u, bc) + solve(F == 0, u, bc, solver_parameters=params) u_.assign(u) t += float(timestep) @@ -278,7 +284,7 @@ def Dt(u, u_, timestep): end = 0.2 while (t <= end): - solve(F == 0, u, bc) + solve(F == 0, u, bc, solver_parameters=params) u_.assign(u) t += float(timestep) From e56ec352283fc2ec656aa60fd114754f4d42d40f Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 8 May 2025 15:32:23 +0100 Subject: [PATCH 104/222] Fix branch used to set banner in docs (#4292) --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index b02cd34933..6c3fdc22e1 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -247,10 +247,10 @@ # Optional header warning about docs version rst_prolog = """ -.. only:: main +.. only:: master .. warning:: - You are reading a version of the website built against the unstable ``main`` branch. This content is liable to change without notice and may be inappropriate for your use case. + You are reading a version of the website built against the unstable ``master`` branch. This content is liable to change without notice and may be inappropriate for your use case. """ # -- Options for LaTeX output -------------------------------------------- From 97724b927ab679c64682dec23552e55d93593991 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 8 May 2025 16:30:03 +0100 Subject: [PATCH 105/222] Inc PETSc to 3.23.1 so macOs no longer needs to download CMake (#4277) --- pyproject.toml | 10 +++++----- scripts/firedrake-configure | 4 +--- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 919cbe7496..ba725a079c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,7 @@ dependencies = [ "h5py>3.12.1", "libsupermesh", "loopy>2024.1", - "petsc4py==3.23.0", + "petsc4py==3.23.1", "numpy", "packaging", "pkgconfig", @@ -90,7 +90,7 @@ netgen = [ "ngsPETSc", ] slepc = [ - "slepc4py==3.23.0", + "slepc4py==3.23.1", ] torch = [ # requires passing '--extra-index-url' to work "torch", @@ -112,7 +112,7 @@ ci = [ "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - "slepc4py==3.23.0", + "slepc4py==3.23.1", "torch", # requires passing '--extra-index-url' to work "vtk", ] @@ -126,7 +126,7 @@ docker = [ # Used in firedrake-vanilla container "pytest-split", # needed for firedrake-run-split-tests "pytest-timeout", "pytest-xdist", - "slepc4py==3.23.0", + "slepc4py==3.23.1", ] [build-system] @@ -139,7 +139,7 @@ requires = [ "pkgconfig", "pybind11", "setuptools>61.2", - "petsc4py==3.23.0", + "petsc4py==3.23.1", "rtree>=1.2", ] build-backend = "setuptools.build_meta" diff --git a/scripts/firedrake-configure b/scripts/firedrake-configure index 320786e4d4..84b6536ce0 100755 --- a/scripts/firedrake-configure +++ b/scripts/firedrake-configure @@ -39,7 +39,7 @@ ARCH_DEFAULT = FiredrakeArch.DEFAULT ARCH_COMPLEX = FiredrakeArch.COMPLEX -SUPPORTED_PETSC_VERSION = "v3.23.0" +SUPPORTED_PETSC_VERSION = "v3.23.1" def main(): @@ -427,8 +427,6 @@ def prepare_configure_options( # Avoid macos + openmpi + mumps segmentation violation issue; # see https://github.com/firedrakeproject/firedrake/issues/4102 and https://github.com/firedrakeproject/firedrake/issues/4113. configure_options.append("-download-mumps-avoid-mpi-in-place") - # PETSc does not support CMake 4.0 (hopefully fixed in v3.23.1) - configure_options.append("--download-cmake") else: configure_options.append(f"--FOPTFLAGS='-O3 -march=native -mtune=native{incflags}{libflags}'") From 7008cd7bab50fa899f33a747a3f2f152ff04d647 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 8 May 2025 16:32:59 +0100 Subject: [PATCH 106/222] Remove firedrake-install and friends (#4270) --- .github/ISSUE_TEMPLATE/bug_report.md | 2 +- docs/source/conf.py | 7 +- docs/source/install.rst | 7 +- firedrake/__init__.py | 11 +- firedrake/_version.py | 658 -------- firedrake/scripts/firedrake_status.py | 144 -- firedrake/version.py | 12 - pyop2/__init__.py | 8 - pyop2/_version.py | 658 -------- pyop2/local_kernel.py | 3 +- pyop2/version.py | 3 - pyproject.toml | 1 - requirements-ext.txt | 22 - requirements-git.txt | 6 - requirements.txt | 2 - scripts/firedrake-install | 2132 ------------------------ versioneer.py | 2205 ------------------------- 17 files changed, 10 insertions(+), 5871 deletions(-) delete mode 100644 firedrake/_version.py delete mode 100755 firedrake/scripts/firedrake_status.py delete mode 100644 firedrake/version.py delete mode 100644 pyop2/_version.py delete mode 100644 pyop2/version.py delete mode 100644 requirements-ext.txt delete mode 100644 requirements-git.txt delete mode 100644 requirements.txt delete mode 100755 scripts/firedrake-install delete mode 100644 versioneer.py diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index ab8e2db935..7c194d7617 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -27,7 +27,7 @@ Please do not add screenshots unless the bug is purely graphical. **Environment:** - OS: [eg: Linux, MacOS, WSL (Windows Subsystem for Linux)] add this as a label too! - Python version: [eg: 3.9.7] - - Output of `firedrake-status` + - Output of `pip list` - Any relevant environment variables or modifications [eg: PYOP2_DEBUG=1] **Additional Info** diff --git a/docs/source/conf.py b/docs/source/conf.py index b02cd34933..2b58d0faab 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -85,10 +85,11 @@ # built documents. # # The short X.Y version. -import firedrake -version = "%d.%d" % firedrake.__version_info__[0:2] +import importlib.metadata # The full version, including alpha/beta/rc tags. -release = firedrake.__version__ +release = importlib.metadata.version("firedrake") +# Short version dropping the extra tags +version = ".".join(release.split(".")[:3]) # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/docs/source/install.rst b/docs/source/install.rst index 82d80ebf00..48058a0d86 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -56,10 +56,9 @@ To simplify the installation process, Firedrake provides a utility script called $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/master/scripts/firedrake-configure -Unlike the now deprecated ``firedrake-install`` script, ``firedrake-configure`` -**does not install Firedrake for you**. It is simply a helper script that emits -the configuration options that Firedrake needs for the various steps needed -during installation. +Note that ``firedrake-configure`` **does not install Firedrake for you**. It +is simply a helper script that emits the configuration options that Firedrake +needs for the various steps needed during installation. To improve robustness, ``firedrake-configure`` is intentionally kept extremely minimal and simple. This means that if you want to install Firedrake in a diff --git a/firedrake/__init__.py b/firedrake/__init__.py index 35de00fc95..9230d91233 100644 --- a/firedrake/__init__.py +++ b/firedrake/__init__.py @@ -69,7 +69,6 @@ from firedrake.variational_solver import * from firedrake.eigensolver import * from firedrake.vector import * -from firedrake.version import __version__ as ver, __version_info__, check # noqa: F401 from firedrake.ensemble import * from firedrake.randomfunctiongen import * from firedrake.external_operators import * @@ -89,12 +88,7 @@ sys.modules["firedrake.plot"] = plot from firedrake.plot import * -check() -del check, sys - -from firedrake._version import get_versions -__version__ = get_versions()['version'] -del get_versions +del sys def set_blas_num_threads(): @@ -157,6 +151,3 @@ def warn_omp_num_threads(): _init_event.end() del _init_event del _is_logging - -from . import _version -__version__ = _version.get_versions()['version'] diff --git a/firedrake/_version.py b/firedrake/_version.py deleted file mode 100644 index 62b383e963..0000000000 --- a/firedrake/_version.py +++ /dev/null @@ -1,658 +0,0 @@ - -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. -# Generated by versioneer-0.28 -# https://github.com/python-versioneer/python-versioneer - -"""Git implementation of _version.py.""" - -import errno -import os -import re -import subprocess -import sys -from typing import Callable, Dict -import functools - - -def get_keywords(): - """Get the keywords needed to look up the version information.""" - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "$Format:%d$" - git_full = "$Format:%H$" - git_date = "$Format:%ci$" - keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} - return keywords - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - -def get_config(): - """Create, populate and return the VersioneerConfig() object.""" - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "pep440" - cfg.tag_prefix = "v" - cfg.parentdir_prefix = "firedrake-" - cfg.versionfile_source = "firedrake/_version.py" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs, method): # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f): - """Store f in HANDLERS[vcs][method].""" - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - - popen_kwargs = {} - if sys.platform == "win32": - # This hides the console window if pythonw.exe is used - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - popen_kwargs["startupinfo"] = startupinfo - - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None), **popen_kwargs) - break - except OSError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - print("stdout was %s" % stdout) - return None, process.returncode - return stdout, process.returncode - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%s', no digits" % ",".join(refs - tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - - # GIT_DIR can interfere with correct operation of Versioneer. - # It may be intended to be passed to the Versioneer-versioned project, - # but that should not change where we get our version from. - env = os.environ.copy() - env.pop("GIT_DIR", None) - runner = functools.partial(runner, env=env) - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=not verbose) - if rc != 0: - if verbose: - print("Directory %s not under git control" % root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, [ - "describe", "--tags", "--dirty", "--always", "--long", - "--match", f"{tag_prefix}[[:digit:]]*" - ], cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) - pieces["distance"] = len(out.split()) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def plus_or_dot(pieces): - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces): - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver): - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces): - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) - else: - rendered += ".post0.dev%d" % (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces): - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -def get_versions(): - """Get version information or return default if unable to do so.""" - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for _ in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree", - "date": None} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version", "date": None} diff --git a/firedrake/scripts/firedrake_status.py b/firedrake/scripts/firedrake_status.py deleted file mode 100755 index a256d70b69..0000000000 --- a/firedrake/scripts/firedrake_status.py +++ /dev/null @@ -1,144 +0,0 @@ -#! /usr/bin/env python3 -from six import iteritems - -from argparse import ArgumentParser, RawDescriptionHelpFormatter -from pprint import pformat -import logging -import json -import sys -import os -import subprocess -from collections import OrderedDict, defaultdict - - -def check_output(args, env=None): - return subprocess.check_output(args, stderr=subprocess.STDOUT, env=env) - - -def quit(log, message): - log.error(message) - sys.exit(1) - - -def main(): - parser = ArgumentParser(description="""Provide information on the currently downloaded version of Firedrake and its configuration. - This is particularly useful information to include when reporting bugs.""", - formatter_class=RawDescriptionHelpFormatter) - parser.add_argument("--log", action='store_true', - help="Log the output of the script to firedrake-status.log as well as to the console.") - - args = parser.parse_args() - - # Set up logging - if args.log: - logging.basicConfig(level=logging.DEBUG, - format='%(asctime)s %(levelname)-6s %(message)s', - filename='firedrake-status.log', - filemode='w') - console = logging.StreamHandler() - console.setLevel(logging.WARNING) - formatter = logging.Formatter('%(message)s') - console.setFormatter(formatter) - logging.getLogger().addHandler(console) - else: - logging.basicConfig(level=logging.WARNING, - format='%(message)s') - log = logging.getLogger() - - try: - firedrake_env = os.environ["VIRTUAL_ENV"] - except KeyError: - quit(log, "Unable to retrieve virtualenv name from the environment.\n Please ensure the virtualenv is active before running firedrake-update.") - - try: - with open(os.path.join(os.environ["VIRTUAL_ENV"], - ".configuration.json"), "r") as f: - config = json.load(f) - except FileNotFoundError: - config = defaultdict(dict) - - try: - config["system"] = check_output(["uname", "-a"]) - except subprocess.CalledProcessError: - log.error("Failed to retrieve system information.") - - print("Firedrake Configuration:") - if not config: - print("No configuration information found.") - else: - for key, val in iteritems(config["options"]): - print(" {}: {}".format(key, val)) - - print("Additions:") - if config["additions"]: - for a in config["additions"]: - print(" " + a) - else: - print(" None") - - for var in ["PYTHONPATH", "PETSC_ARCH", "PETSC_DIR"]: - config["environment"][var] = os.environ.get(var, None) - - print("Environment:") - for key, val in iteritems(config["environment"]): - print(" {}: {}".format(key, val)) - - status = OrderedDict() - for dir in sorted(os.listdir(firedrake_env + "/src")): - try: - os.chdir(firedrake_env + "/src/" + dir) - except OSError as e: - if e.errno == 20: - # Not a directory - continue - else: - raise - try: - revision = check_output(["git", "rev-parse", "--short", "HEAD"]).decode('ascii').strip() - branch = check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).decode('ascii').strip() - except subprocess.CalledProcessError: - log.error("Unable to retrieve git information from %s." % dir) - else: - try: - _ = check_output(["git", "diff-index", "--quiet", "HEAD"]) - modified = False - except subprocess.CalledProcessError: - modified = True - - status[dir] = {"branch": branch, - "revision": revision, - "modified": modified} - - status_string = "" - status_string += "Status of components:\n" - componentformat = "|{:20}|{:30}|{:10}|{!s:10}|\n" - header = componentformat.format("Package", "Branch", "Revision", "Modified") - line = "-" * (len(header) - 1) + "\n" - status_string += line + header + line - for dir, d in iteritems(status): - status_string += componentformat.format(dir, d["branch"], d["revision"], d["modified"]) - status_string += line - - print(status_string) - log.info("\n" + status_string) - - log.info("Firedrake configuration: ") - log.info(pformat(config)) - log.debug("\nPip packages installed:") - try: - log.debug(check_output(["pip", "freeze"])) - except subprocess.CalledProcessError: - log.error("""Failed to retrieve list of pip installed packages. Try running: - - pip freeze. - - """) - log.debug("\n Full environment:") - try: - log.debug(check_output(["env"])) - except subprocess.CalledProcessError: - log.error("""Shell command env failed.""") - - -if __name__ == "__main__": - main() diff --git a/firedrake/version.py b/firedrake/version.py deleted file mode 100644 index 2fe4e1b583..0000000000 --- a/firedrake/version.py +++ /dev/null @@ -1,12 +0,0 @@ - -__version_info__ = (0, 12, 0) -__version__ = '.'.join(map(str, __version_info__)) - - -def check(): - from pyop2.version import __version_info__ as pyop2_version_info - from pyop2.version import __version__ as pyop2_version - if pyop2_version_info[:2] != __version_info__[:2]: - raise RuntimeError( - """Firedrake version %s and PyOP2 version %s are incompatible.""" % - (__version__, pyop2_version)) diff --git a/pyop2/__init__.py b/pyop2/__init__.py index e9aeadf54a..7123e5ba35 100644 --- a/pyop2/__init__.py +++ b/pyop2/__init__.py @@ -2,11 +2,3 @@ PyOP2 is a library for parallel computations on unstructured meshes. """ from pyop2.op2 import * # noqa -from pyop2.version import __version_info__ # noqa: just expose - -from pyop2._version import get_versions -__version__ = get_versions()['version'] -del get_versions - -from . import _version -__version__ = _version.get_versions()['version'] diff --git a/pyop2/_version.py b/pyop2/_version.py deleted file mode 100644 index d9db778c32..0000000000 --- a/pyop2/_version.py +++ /dev/null @@ -1,658 +0,0 @@ - -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. -# Generated by versioneer-0.28 -# https://github.com/python-versioneer/python-versioneer - -"""Git implementation of _version.py.""" - -import errno -import os -import re -import subprocess -import sys -from typing import Callable, Dict -import functools - - -def get_keywords(): - """Get the keywords needed to look up the version information.""" - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "$Format:%d$" - git_full = "$Format:%H$" - git_date = "$Format:%ci$" - keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} - return keywords - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - -def get_config(): - """Create, populate and return the VersioneerConfig() object.""" - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "pep440" - cfg.tag_prefix = "v" - cfg.parentdir_prefix = "pyop2-" - cfg.versionfile_source = "pyop2/_version.py" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs, method): # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f): - """Store f in HANDLERS[vcs][method].""" - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - - popen_kwargs = {} - if sys.platform == "win32": - # This hides the console window if pythonw.exe is used - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - popen_kwargs["startupinfo"] = startupinfo - - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None), **popen_kwargs) - break - except OSError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - print("stdout was %s" % stdout) - return None, process.returncode - return stdout, process.returncode - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%s', no digits" % ",".join(refs - tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - - # GIT_DIR can interfere with correct operation of Versioneer. - # It may be intended to be passed to the Versioneer-versioned project, - # but that should not change where we get our version from. - env = os.environ.copy() - env.pop("GIT_DIR", None) - runner = functools.partial(runner, env=env) - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=not verbose) - if rc != 0: - if verbose: - print("Directory %s not under git control" % root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, [ - "describe", "--tags", "--dirty", "--always", "--long", - "--match", f"{tag_prefix}[[:digit:]]*" - ], cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) - pieces["distance"] = len(out.split()) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def plus_or_dot(pieces): - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces): - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver): - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces): - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) - else: - rendered += ".post0.dev%d" % (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces): - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -def get_versions(): - """Get version information or return default if unable to do so.""" - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for _ in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree", - "date": None} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version", "date": None} diff --git a/pyop2/local_kernel.py b/pyop2/local_kernel.py index 21e06ab043..79639c8ba7 100644 --- a/pyop2/local_kernel.py +++ b/pyop2/local_kernel.py @@ -9,7 +9,6 @@ from loopy.tools import LoopyKeyBuilder import numpy as np -from pyop2 import version from pyop2.configuration import configuration from pyop2.datatypes import ScalarType from pyop2.exceptions import NameTypeError @@ -126,7 +125,7 @@ def _immutable_cache_key(self): key = (code_key, self.name, self.cpp, self.flop_count, self.headers, self.include_dirs, self.ldargs, sorted(self.opts.items()), - self.requires_zeroed_output_arguments, self.user_code, version.__version__) + self.requires_zeroed_output_arguments, self.user_code) return hashlib.md5(str(key).encode()).hexdigest() @property diff --git a/pyop2/version.py b/pyop2/version.py deleted file mode 100644 index dcb98845b7..0000000000 --- a/pyop2/version.py +++ /dev/null @@ -1,3 +0,0 @@ - -__version_info__ = (0, 12, 0) -__version__ = '.'.join(map(str, __version_info__)) diff --git a/pyproject.toml b/pyproject.toml index 5cba98969b..cf54f8324e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -63,7 +63,6 @@ Issues = "https://github.com/firedrakeproject/firedrake/issues/new/choose" firedrake-check = "firedrake._check:main" firedrake-clean = "firedrake.scripts.firedrake_clean:main" firedrake-preprocess-bibtex = "firedrake.scripts.firedrake_preprocess_bibtex:main" -firedrake-status = "firedrake.scripts.firedrake_status:main" pyop2-clean = "pyop2.compilation:clear_compiler_disk_cache" spydump = "pyop2.scripts.spydump:main" diff --git a/requirements-ext.txt b/requirements-ext.txt deleted file mode 100644 index 67153f569d..0000000000 --- a/requirements-ext.txt +++ /dev/null @@ -1,22 +0,0 @@ -cachetools -Cython>=3.0 -decorator<=4.4.2 -flake8 -mpi4py -mpi-pytest -nbval -numpy -packaging -pkgconfig -progress -pybind11 -pycparser -pylint -pylit -pytest -pytest-xdist -pytools -requests -rtree>=1.2 -scipy -sympy diff --git a/requirements-git.txt b/requirements-git.txt deleted file mode 100644 index 4cf888c821..0000000000 --- a/requirements-git.txt +++ /dev/null @@ -1,6 +0,0 @@ -git+https://github.com/firedrakeproject/ufl.git#egg=fenics-ufl -git+https://github.com/firedrakeproject/fiat.git#egg=fenics-fiat -git+https://github.com/dolfin-adjoint/pyadjoint.git#egg=pyadjoint-ad -git+https://github.com/firedrakeproject/loopy.git@main#egg=loopy -git+https://github.com/firedrakeproject/petsc.git@firedrake#egg=petsc -git+https://github.com/firedrakeproject/libsupermesh.git#egg=libsupermesh diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index f430304ad4..0000000000 --- a/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ --r requirements-ext.txt --r requirements-git.txt diff --git a/scripts/firedrake-install b/scripts/firedrake-install deleted file mode 100755 index d56fb2978b..0000000000 --- a/scripts/firedrake-install +++ /dev/null @@ -1,2132 +0,0 @@ -#! /usr/bin/env python3 -import logging -import platform -import subprocess -import sys -import os -import shutil -from argparse import ArgumentParser, RawDescriptionHelpFormatter -import argparse -from collections import OrderedDict -import atexit -import json -import pprint -import shlex -from glob import iglob -from itertools import chain -import re -import importlib -import time -import warnings - - -warnings.warn( - "firedrake-install/firedrake-update is no longer the recommended way to " - "install/update Firedrake. Please use pip instead. For instructions on how " - "to do this please visit https://www.firedrakeproject.org/install.html.", - FutureWarning -) -# Give users time to see the warning -time.sleep(5) - - -class InstallError(Exception): - # Exception for generic install problems. - pass - - -try: - from pkg_resources.extern.packaging.version import Version, InvalidVersion -except ModuleNotFoundError: - try: - from packaging.version import Version, InvalidVersion - except ModuleNotFoundError: - raise InstallError("Neither setuptools or packaging found. Please " - "install one of these packages before trying again.") - -osname = platform.uname().system -arch = platform.uname().machine - -# Packages which we wish to ensure are always recompiled -wheel_blacklist = ["mpi4py", "randomgen", "numpy"] -# Packages for which we set CC=mpicc, CXX=mpicxx, F90=mpif90. -parallel_packages = ["h5py", "petsc4py", "slepc", "slepc4py", "libsupermesh", "firedrake"] - -# Firedrake application installation shortcuts. -firedrake_apps = { - "gusto": ("""Atmospheric dynamical core library. http://firedrakeproject.org/gusto""", - "git+ssh://github.com/firedrakeproject/gusto.git@main#egg=gusto"), - "thetis": ("""Coastal ocean model. http://thetisproject.org""", - "git+ssh://github.com/thetisproject/thetis#egg=thetis"), - "icepack": ("""Glacier and ice sheet model. https://icepack.github.io""", - "git+ssh://github.com/icepack/icepack.git#egg=icepack"), - "irksome": ("""Implicit Runge-Kutta methods. https://github.com/firedrakeproject/Irksome/""", - "git+ssh://github.com/firedrakeproject/Irksome.git#egg=Irksome"), - "femlium": ("""Interactive visualization of finite element simulations on geographic maps with folium. https://femlium.github.io/""", - "git+ssh://github.com/FEMlium/FEMlium.git@main#egg=FEMlium"), - "fascd": ("""Full approximation scheme constraint decomposition solver for variational inequalities. https://bitbucket.org/pefarrell/fascd""", - "git+ssh://bitbucket.org/pefarrell/fascd.git@master#egg=fascd"), - "defcon": ("""Deflated continuation algorithm for bifurcation analysis. https://bitbucket.org/pefarrell/defcon""", - "git+ssh://bitbucket.org/pefarrell/defcon.git@master#egg=defcon"), - "gadopt": ("""Geodynamic Adjoint Optimization Platform. http://g-adopt.github.io""", - "git+ssh://github.com/g-adopt/g-adopt.git@master#egg=gadopt"), - "asQ": ("""ParaDiag parallel-in-time methods. http://firedrakeproject.org/asQ""", - "git+ssh://github.com/firedrakeproject/asQ.git@master#egg=asQ"), -} - - -class FiredrakeConfiguration(dict): - """A dictionary extended to facilitate the storage of Firedrake - configuration information.""" - def __init__(self, args=None): - super(FiredrakeConfiguration, self).__init__() - - '''A record of the persistent options in force.''' - self["options"] = {} - '''Relevant environment variables.''' - self["environment"] = {} - '''Additional packages installed via the plugin interface.''' - self["additions"] = [] - - if args: - for o in self._persistent_options: - if o in args.__dict__.keys(): - self["options"][o] = args.__dict__[o] - - _persistent_options = ["package_manager", - "minimal_petsc", "mpicc", "mpicxx", "mpif90", "mpiexec", "mpihome", "disable_ssh", - "honour_petsc_dir", "with_parmetis", - "slepc", "packages", "honour_pythonpath", - "opencascade", "torch", "jax", - "petsc_int_type", "cache_dir", "complex", "remove_build_files", "with_blas", "netgen"] - - -def deep_update(this, that): - from collections import abc - for k, v in that.items(): - if isinstance(v, abc.Mapping) and k in this.keys(): - this[k] = deep_update(this.get(k, {}), v) - else: - this[k] = v - return this - - -if os.path.basename(__file__) == "firedrake-install": - mode = "install" - logfile_directory = os.path.abspath(os.getcwd()) - logfile_mode = "w" - -elif os.path.basename(__file__) == "firedrake-update": - mode = "update" - os.chdir(os.path.dirname(os.path.realpath(__file__)) + "/../..") - try: - logfile_directory = os.environ["VIRTUAL_ENV"] - except KeyError: - quit("Unable to retrieve venv name from the environment.\n Please ensure the venv is active before running firedrake-update.") - logfile_mode = "a" if "--no-update-script" in sys.argv else "w" -else: - sys.exit("Script must be invoked either as firedrake-install or firedrake-update") - - -# Set up logging -# Log to file at DEBUG level -if ("-h" in sys.argv) or ("--help" in sys.argv): - # Don't log if help displayed to avoid overwriting an existing log - logfile = os.devnull -else: - logfile = os.path.join(logfile_directory, 'firedrake-%s.log' % mode) -logging.basicConfig(level=logging.DEBUG, - format='%(asctime)s %(levelname)-6s %(message)s', - filename=logfile, - filemode=logfile_mode) -# Log to console at INFO level -console = logging.StreamHandler() -console.setLevel(logging.INFO) -formatter = logging.Formatter('%(message)s') -console.setFormatter(formatter) -logging.getLogger().addHandler(console) - -log = logging.getLogger() - -log.info("Running %s" % " ".join(sys.argv)) - -if sys.version_info >= (3, 14): - print("""\nCan not install Firedrake with Python 3.14 at the moment: -Some wheels are not yet available for Python 3.14 for some required package(s). -Please install with Python 3.13 (or an earlier version >= 3.10).""") - sys.exit(1) -elif sys.version_info < (3, 10): - if mode == "install": - print("""\nInstalling Firedrake requires Python 3, at least version 3.10. -You should run firedrake-install with python3.""") - if mode == "update": - if hasattr(sys, "real_prefix"): - # sys.real_prefix exists iff we are in an active virtualenv. - # - # Existing install trying to update past the py2/py3 barrier - print("""\nFiredrake is now Python 3 only. You cannot upgrade your existing installation. -Please follow the instructions at http://www.firedrakeproject.org/download.html to reinstall.""") - sys.exit(1) - else: - # Accidentally (?) running firedrake-update with python2. - print("""\nfiredrake-update must be run with Python 3, did you accidentally use Python 2?""") - sys.exit(1) - -branches = {} - - -def resolve_doi_branches(doi): - import requests - import hashlib - log.info("Installing Firedrake components specified by {}".format(doi)) - # Zenodo updated their API to use elastic search so simply querying with - # ?q=doi:10.5281/zenodo. now returns multiple results (not ideal) - # Instead use - # / - # as documented here https://developers.zenodo.org/#retrieve - id_ = doi.split('.')[-1] - response = requests.get("https://zenodo.org/api/records/{}".format(id_)) - if not response.ok: - log.error("Unable to obtain Zenodo record for doi {}".format(doi)) - log.error("Response was {}".format(response.json())) - sys.exit(1) - record = response.json() - files = record["files"] - try: - componentjson, = (f for f in files if f["key"] == "components.json") - except ValueError: - log.error("Expecting to find exactly one 'components.json' in record") - sys.exit(1) - download = requests.get(componentjson["links"]["self"]) - if download.status_code >= 400: - log.error("Unable to download 'components.json'") - log.error("Response was {}".format(download.json())) - sys.exit(1) - # component response has checksum as "md5:HEXDIGEST", strip the md5. - if hashlib.md5(download.content).hexdigest() != componentjson["checksum"][4:]: - log.error("Download failed checksum, expecting {expect}, got {got}".format( - expect=componentjson["checksum"][4:], - got=hashlib.md5(download.content).hexdigest())) - sys.exit(1) - componentjson = download.json() - branches = {} - for record in componentjson["components"]: - commit = record["commit"] - component = record["component"] - package = component[component.find("/")+1:].lower() - branches[package] = commit - log.info("Using commit {commit} for component {comp}".format( - commit=commit, comp=component)) - return branches - - -def honour_petsc_dir_get_petsc_dir(): - try: - petsc_dir = os.environ["PETSC_DIR"] - except KeyError: - raise InstallError("Unable to find installed PETSc (did you forget to set PETSC_DIR?)") - petsc_arch = os.environ.get("PETSC_ARCH", "") - return petsc_dir, petsc_arch - - -def honour_petsc_dir_fetch_petscconf(name): - # Return the line in "petscconf.h" that starts with name. - petsc_dir, petsc_arch = honour_petsc_dir_get_petsc_dir() - petscconf_h = os.path.join(petsc_dir, petsc_arch, "include", "petscconf.h") - with open(petscconf_h) as fh: - for line in fh.readlines(): - if line.startswith(name): - return line - - -def honour_petsc_dir_get_petsc_int_type(): - line = honour_petsc_dir_fetch_petscconf("#define PETSC_USE_64BIT_INDICES") - if line and line.split()[2] == '1': - return "int64" - else: - return "int32" - - -def honour_petsc_dir_get_petsc_packages(): - line = honour_petsc_dir_fetch_petscconf("#define PETSC_HAVE_PACKAGES") - return set(line.split()[2].strip().strip(':"').split(':')) - - -if mode == "install": - # Handle command line arguments. - parser = ArgumentParser(description="""Install firedrake and its dependencies.""", - epilog="""The install process has three steps. - -1. Any required system packages are installed using brew (MacOS) or apt (Ubuntu - and similar Linux systems). On a Linux system without apt, the installation - will fail if a dependency is not found. - -2. A set of standard and/or third party Python packages is installed to the - specified install location. - -3. The core set of Python packages is downloaded to ./firedrake/src/ and - installed to the specified location. - -The install creates a venv in ./firedrake (or in ./name where name is the -parameter given to --venv-name) and installs inside that venv. - -The installer will ensure that the required configuration options are -passed to PETSc. In addition, any configure options which you provide -in the PETSC_CONFIGURE_OPTIONS environment variable will be -honoured.""", - formatter_class=RawDescriptionHelpFormatter) - - parser.add_argument("--slepc", action="store_true", - help="Install SLEPc along with PETSc.") - parser.add_argument("--opencascade", action="store_true", - help="Install OpenCASCADE for CAD integration.") - parser.add_argument("--torch", const="cpu", default=False, nargs='?', choices=["cpu", "cuda"], - help="Install PyTorch for a CPU or CUDA backend (default: CPU).") - parser.add_argument("--jax", const="cpu", default=False, nargs='?', choices=["cpu", "cuda"], - help="Install JAX for a CPU or CUDA backend (default: CPU).") - parser.add_argument("--disable-ssh", action="store_true", - help="Do not attempt to use ssh to clone git repositories: fall immediately back to https.") - parser.add_argument("--no-package-manager", action='store_false', dest="package_manager", - help="Do not attempt to use apt or homebrew to install operating system packages on which we depend.") - group = parser.add_mutually_exclusive_group() - group.add_argument("--minimal-petsc", action="store_true", - help="Minimise the set of petsc dependencies installed. This creates faster build times (useful for build testing).") - group.add_argument("--with-parmetis", action="store_true", - help="Install PETSc with ParMETIS? (Non-free license, see http://glaros.dtc.umn.edu/gkhome/metis/parmetis/download)") - parser.add_argument("--honour-petsc-dir", action="store_true", - help="Usually it is best to let Firedrake build its own PETSc. If you wish to use another PETSc, set PETSC_DIR and pass this option.") - parser.add_argument("--petsc-int-type", choices=["int32", "int64"], - default="int32", type=str, - help="The integer type used by PETSc. Use int64 if you need to solve problems with more than 2 billion degrees of freedom. Only takes effect if firedrake-install builds PETSc.") - - parser.add_argument("--honour-pythonpath", action="store_true", - help="Pointing to external Python packages is usually a user error. Set this option if you know that you want PYTHONPATH set.") - parser.add_argument("--rebuild-script", action="store_true", - help="Only rebuild the firedrake-install script. Use this option if your firedrake-install script is broken and a fix has been released in upstream Firedrake. You will need to specify any other options which you wish to be honoured by your new update script.") - parser.add_argument("--doi", type=str, nargs=1, - help="Install a set of components matching a particular Zenodo DOI. The record should have been created with firedrake-zenodo.") - # Used for testing if Zenodo broke the API - # Tries to resolve to a known DOI and immediately exits. - parser.add_argument("--test-doi-resolution", action="store_true", - help=argparse.SUPPRESS) - parser.add_argument("--package-branch", type=str, nargs=2, action="append", metavar=("PACKAGE", "BRANCH"), - help="Specify which branch of a package to use. This takes two arguments, the package name and the branch.") - parser.add_argument("--verbose", "-v", action="store_true", help="Produce more verbose debugging output.") - parser.add_argument("--mpicc", type=str, - action="store", default=None, - help="C compiler to use when building with MPI. If not set, MPICH will be downloaded and used.") - parser.add_argument("--mpicxx", type=str, - action="store", default=None, - help="C++ compiler to use when building with MPI. If not set, MPICH will be downloaded and used.") - parser.add_argument("--mpif90", type=str, - action="store", default=None, - help="Fortran compiler to use when building with MPI. If not set, MPICH will be downloaded and used.") - parser.add_argument("--mpiexec", type=str, - action="store", default=None, - help="MPI launcher. If not set, MPICH will be downloaded and used.") - parser.add_argument("--mpihome", type=str, - action="store", default=None, - help="Location of MPI files. If not set, MPICH will be downloaded and used.") - parser.add_argument("--mpi4py-version", help="Specify an exact version of mpi4py to install") - parser.add_argument("--show-petsc-configure-options", action="store_true", - help="Print out the configure options passed to PETSc and exit") - parser.add_argument("--show-dependencies", action="store_true", - help="Print out the package manager used and packages installed/required.") - parser.add_argument("--venv-name", default="firedrake", - type=str, action="store", - help="Name of the venv to create and the name of the install directory relative to this install script (default is 'firedrake' with directory ./firedrake)") - parser.add_argument("--install", action="append", dest="packages", - help="Additional packages to be installed. The address should be in format vcs+protocol://repo_url/#egg=pkg&subdirectory=pkg_dir . Some additional packages have shortcut install options for more information see --install-help.") - parser.add_argument("--pip-install", action="append", dest="pip_packages", - help="Pip install additional packages into the venv") - parser.add_argument("--install-help", action="store_true", - help="Provide information on packages which can be installed using shortcut names.") - parser.add_argument("--cache-dir", type=str, - action="store", - help="Directory to use for disk caches of compiled code (default is the .cache subdirectory of the Firedrake installation).") - parser.add_argument("--complex", action="store_true", - help="Installs the complex version of Firedrake; rebuilds PETSc in complex mode.") - parser.add_argument("--documentation-dependencies", action="store_true", - help="Install the dependencies required to build the documentation") - parser.add_argument("--remove-build-files", action="store_true", - help="Cleans up build artefacts from Firedrake venv. Unless you really cannot spare the disk space, this option is not recommended") - parser.add_argument("--with-blas", default=("download" if osname == "Darwin" else None), - help="Specify path to system BLAS directory. Use '--with-blas=download' to download openblas") - parser.add_argument("--netgen", action="store_true", - help="Install NGSolve/Netgen and ngsPETSc.") - parser.add_argument("--no-vtk", action="store_true", - help="Do not install VTK into the Firedrake virtualenv") - - args = parser.parse_args() - - # If the user has set any MPI info, they must set them all - if args.mpicc or args.mpicxx or args.mpif90 or args.mpiexec or args.mpihome: - if not (args.mpicc and args.mpicxx and args.mpif90 and args.mpiexec and args.mpihome): - log.error("If you set any MPI information, you must set all of {mpicc, mpicxx, mpif90, mpiexec, mpihome}.") - sys.exit(1) - - if args.package_branch: - branches = {package.lower(): branch for package, branch in args.package_branch} - if args.test_doi_resolution: - actual = resolve_doi_branches("10.5281/zenodo.1322546") - expect = {'coffee': '87e50785d3a05b111f5423a66d461cd44cc4bdc9', - 'finat': 'aa74fd499304c8363a4520555fd62ef21e8e5e1f', - 'fiat': '184601a46c24fb5cbf8fd7961d22b16dd26890e7', - 'firedrake': '6a30b64da01eb587dcc0e04e8e6b84fe4839bdb7', - 'petsc': '413f72f04f5cb0a010c85e03ed029573ff6d4c63', - 'petsc4py': 'ac2690070a80211dfdbab04634bdb3496c14ca0a', - 'tsfc': 'fe9973eaacaa205fd491cd1cc9b3743b93a3d076', - 'ufl': 'c5eb7fbe89c1091132479c081e6fa9c182191dcc', - 'pyop2': '741a21ba9a62cb67c0aa300a2e199436ea8cb61c'} - if actual != expect: - log.error("Unable to resolve DOI correctly.") - log.error("You'll need to figure out how Zenodo have changed their API.") - sys.exit(1) - else: - log.info("DOI resolution test passed.") - sys.exit(0) - if args.doi: - branches = resolve_doi_branches(args.doi[0]) - if args.honour_petsc_dir: - petsc_int_type = honour_petsc_dir_get_petsc_int_type() - if petsc_int_type != args.petsc_int_type: - petsc_dir, petsc_arch = honour_petsc_dir_get_petsc_dir() - log.warning("Provided PETSc (PETSC_DIR=%s, PETSC_ARCH=%s) was compiled for %s, but given --petsc-int-type = %s: setting --petsc-int-type %s." % (petsc_dir, petsc_arch, petsc_int_type, args.petsc_int_type, petsc_int_type)) - args.petsc_int_type = petsc_int_type - args.prefix = False # Disabled as untested - args.packages = args.packages or [] - - config = FiredrakeConfiguration(args) -else: - try: - with open(os.path.join(os.environ["VIRTUAL_ENV"], - ".configuration.json"), "r") as f: - config = json.load(f) - except FileNotFoundError: - raise InstallError("Failed to find existing Firedrake configuration") - - parser = ArgumentParser(description="""Update this firedrake install to the latest versions of all packages.""", - formatter_class=RawDescriptionHelpFormatter) - parser.add_argument("--no-update-script", action="store_false", dest="update_script", - help="Do not update script before updating Firedrake.") - parser.add_argument("--rebuild", action="store_true", - help="Rebuild all packages even if no new version is available. Usually petsc and petsc4py are only rebuilt if they change. All other packages are always rebuilt.") - parser.add_argument("--rebuild-script", action="store_true", - help="Only rebuild the firedrake-install script. Use this option if your firedrake-install script is broken and a fix has been released in upstream Firedrake. You will need to specify any other options which you wish to be honoured by your new update script.") - parser.add_argument("--slepc", action="store_true", dest="slepc", default=config["options"]["slepc"], - help="Install SLEPc along with PETSc") - parser.add_argument("--opencascade", action="store_true", dest="opencascade", default=config["options"].get("opencascade", False), - help="Install OpenCASCADE for CAD integration.") - parser.add_argument("--torch", const="cpu", nargs='?', choices=["cpu", "cuda"], default=config["options"].get("torch", False), - help="Install PyTorch for a CPU or CUDA backend (default: CPU).") - parser.add_argument("--jax", const="cpu", nargs='?', choices=["cpu", "cuda"], default=config["options"].get("jax", False), - help="Install JAX for a CPU or CUDA backend (default: CPU).") - parser.add_argument("--honour-petsc-dir", action="store_true", - default=config["options"]["honour_petsc_dir"], - help="Usually it is best to let Firedrake build its own PETSc. If you wish to use another PETSc, set PETSC_DIR and pass this option.") - parser.add_argument("--petsc-int-type", choices=["int32", "int64"], - default=config["options"]["petsc_int_type"], type=str, - help="The integer type used by PETSc. Use int64 if you need to solve problems with more than 2 billion degrees of freedom. Only takes effect if firedrake-install builds PETSc.") - - parser.add_argument("--honour-pythonpath", action="store_true", default=config["options"].get("honour_pythonpath", False), - help="Pointing to external Python packages is usually a user error. Set this option if you know that you want PYTHONPATH set.") - parser.add_argument("--clean", action='store_true', - help="Delete any remnants of obsolete Firedrake components.") - parser.add_argument("--verbose", "-v", action="store_true", help="Produce more verbose debugging output.") - parser.add_argument("--install", action="append", dest="packages", default=config["options"].get("packages", []), - help="Additional packages to be installed. The address should be in format vcs+protocol://repo_url/#egg=pkg&subdirectory=pkg_dir. Some additional packages have shortcut install options for more information see --install-help.") - parser.add_argument("--pip-install", action="append", dest="pip_packages", - help="Pip install additional packages into the venv") - parser.add_argument("--install-help", action="store_true", - help="Provide information on packages which can be installed using shortcut names.") - parser.add_argument("--cache-dir", type=str, - action="store", default=config["options"].get("cache_dir", ""), - help="Directory to use for disk caches of compiled code (default is the .cache subdirectory of the Firedrake installation).") - complex_group = parser.add_mutually_exclusive_group() - complex_group.add_argument("--complex", action="store_true", - help="Updates to the complex version of Firedrake; rebuilds PETSc in complex mode.") - complex_group.add_argument("--real", action="store_true", - help="Updates to the real version of Firedrake; rebuilds PETSc in real mode.") - parser.add_argument("--documentation-dependencies", action="store_true", - help="Install the dependencies required to build the documentation") - parser.add_argument("--show-dependencies", action="store_true", - help="Print out the package manager used and packages installed/required.") - parser.add_argument("--remove-build-files", action="store_true", - help="Cleans up build artefacts from Firedrake venv. Unless you really cannot spare the disk space, this option is not recommended") - parser.add_argument("--with-blas", default=("download" if osname == "Darwin" else None), - help="Specify path to system BLAS directory. Use '--with-blas=download' to download openblas") - parser.add_argument("--netgen", action="store_true", dest="netgen", default=config["options"].get("netgen", False), - help="Install Netgen.") - parser.add_argument("--mpi4py-version", help="Specify an exact version of mpi4py to install") - - args = parser.parse_args() - - args.packages = list(set(args.packages)) # remove duplicates - - if args.honour_petsc_dir != config["options"]["honour_petsc_dir"]: - log.error("You installed Firedrake with --honour-petsc-dir=%s, but are trying to update it with --honour-petsc-dir=%s." % (config["options"]["honour_petsc_dir"], args.honour_petsc_dir)) - exit(1) - if args.honour_petsc_dir: - # Set int_type to the one found in petscconf.h - petsc_int_type = honour_petsc_dir_get_petsc_int_type() - if petsc_int_type != args.petsc_int_type: - petsc_dir, petsc_arch = honour_petsc_dir_get_petsc_dir() - log.warning("Provided PETSc (PETSC_DIR=%s, PETSC_ARCH=%s) was compiled for %s, but given --petsc-int-type = %s: setting --petsc-int-type %s." % (petsc_dir, petsc_arch, petsc_int_type, args.petsc_int_type, petsc_int_type)) - args.petsc_int_type = petsc_int_type - petsc_int_type_changed = False - if config["options"]["petsc_int_type"] != args.petsc_int_type: - petsc_int_type_changed = True - args.rebuild = True - if config["options"].get("with_blas") is not None: - if config["options"].get("with_blas") != args.with_blas: - # Rebuild if BLAS library explicitly changed at command line - args.rebuild = True - else: - # Prevent deep_update overwriting BLAS options if they exist in the config, but are unspecified at the command line - args.with_blas = config["options"].get("with_blas") - - petsc_complex_type_changed = False - previously_complex = config["options"].get("complex", False) - now_complex = not args.real and (args.complex or previously_complex) - if previously_complex != now_complex: - petsc_complex_type_changed = True - args.rebuild = True - args.complex = now_complex - - config = deep_update(config, FiredrakeConfiguration(args)) - -if args.install_help: - help_string = """ -You can install the following packages by passing --install shortname -where shortname is one of the names given below: - -""" - componentformat = "|{:10}|{:70}|\n" - header = componentformat.format("Name", "Description") - line = "-" * (len(header) - 1) + "\n" - help_string += line + header + line - for package, d in firedrake_apps.items(): - help_string += componentformat.format(package, d[0]) - help_string += line - print(help_string) - sys.exit(0) - - -def sniff_compiler(exe): - """Obtain the correct compiler class by calling the compiler executable. - Attempt to determine the compiler version number. - - :arg cpp: If set to True will use the C++ compiler rather than - the C compiler to determine the version number. - :arg exe: String with name or path to compiler executable - :returns: A compiler class - """ - # NAME - try: - output = subprocess.run( - [exe, "--version"], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=True, - encoding="utf-8" - ).stdout - except (subprocess.CalledProcessError, UnicodeDecodeError): - output = "" - # Find the name of the compiler family - if output.startswith("gcc") or output.startswith("g++") or output.startswith("GNU"): - name = "GNU" - elif output.startswith("clang"): - name = "clang" - elif output.startswith("Apple LLVM") or output.startswith("Apple clang"): - name = "clang" - elif output.startswith("icc"): - name = "Intel" - elif "Cray" in output.split("\n")[0]: - # Cray is more awkward eg: - # Cray clang version 11.0.4 () - # gcc (GCC) 9.3.0 20200312 (Cray Inc.) - name = "Cray" - else: - name = "unknown" - # VERSION - version = None - for dumpstring in ["-dumpfullversion", "-dumpversion"]: - try: - output = subprocess.run( - [exe, dumpstring], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=True, - encoding="utf-8" - ).stdout - version = Version(output) - break - except (subprocess.CalledProcessError, UnicodeDecodeError, InvalidVersion): - continue - return name, version - - -# Temporary catch for tigerlake processors with gcc/g++/gfortran 9.3 -if platform.system() == "Linux" and mode == "install": - # Get the cpu information - try: - cpuinfostring = subprocess.run( - ["lscpu"], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=True, - encoding="utf-8" - ).stdout - except (subprocess.CalledProcessError, UnicodeDecodeError): - output = "" - # Check if it is a tigerlake processor ie: "i3-11", "i5-11", "i7-11" - if any(bad_cpu in cpuinfostring for bad_cpu in ["i3-11", "i5-11", "i7-11"]): - # Get the compiler names and versions - try: - cc = sniff_compiler(args.mpicc or "gcc") - cxx = sniff_compiler(args.mpicxx or "g++") - fortran = sniff_compiler(args.mpif90 or "gfortran") - # Check if the combination is a problem - if any(map(lambda compiler: compiler[0] == "GNU" and compiler[1] < Version("9.4"), [cc, cxx, fortran])): - log.info( - "\nUsing a Tiger Lake (or newer) CPU with gcc, g++ and gfortran version<=9.3 is not possible." - " Please install at least gcc, g++ and gfortran version 9.4.0 or later" - ) - sys.exit(1) - except (subprocess.CalledProcessError, FileNotFoundError): - log.info( - "\nFailed to sniff compiler version, perhaps there is no compiler installed.\n" - "You can ignore this if you are not using a Tiger Lake (or newer) CPU with gcc, g++ or gfortran version<=9.3\n" - ) - - -@atexit.register -def print_log_location(): - log.info("\n\n%s log saved in %s" % (mode.capitalize(), logfile)) - - -class directory(object): - """Context manager that executes body in a given directory""" - def __init__(self, dir): - self.dir = os.path.abspath(dir) - - def __enter__(self): - self.olddir = os.path.abspath(os.getcwd()) - log.debug("Old path '%s'" % self.olddir) - log.debug("Pushing path '%s'" % self.dir) - os.chdir(self.dir) - - def __exit__(self, *args): - log.debug("Popping path '%s'" % self.dir) - os.chdir(self.olddir) - log.debug("New path '%s'" % self.olddir) - - -class environment(object): - def __init__(self, **env): - self.old = os.environ.copy() - self.new = env - - def __enter__(self): - os.environ.update(self.new) - - def __exit__(self, *args): - os.environ = self.old - - -options = config["options"] - -# Apply short cut package names. pyadjoint shoud no longer turn up the packages list because it is a hard dependency. -options["packages"] = [firedrake_apps.get(p, (None, p))[1] for p in options["packages"] if not p.endswith("pyadjoint")] - - -# Record of obsolete packages which --clean should remove from old installs. -old_git_packages = ["dolfin-adjoint", "libadjoint"] - -if mode == "install": - firedrake_env = os.path.abspath(args.venv_name) -else: - firedrake_env = os.environ["VIRTUAL_ENV"] - -if "cache_dir" not in config["options"] or not config["options"]["cache_dir"]: - config["options"]["cache_dir"] = os.path.join(firedrake_env, ".cache") - -# venv install -python = "%s/bin/python" % firedrake_env -# Use the pip from the venv -pip = [python, "-m", "pip"] -pipinstall = pip + ["install", - "--no-build-isolation", - "--no-binary", ",".join(wheel_blacklist)] - -if mode == "install": - # "petsc4py" is in `branches` when we are explicitly asking for a petsc4py - # branch that lives in firedrake-project/petsc4py. This happens when we do: - # python3 firedrake-install --doi some_old_zenodo_doi ... - # or: - # python3 firedrake-install --package-branch petsc4py some_old_branch ... - use_petsc4py_in_petsc = "petsc4py" not in branches - use_slepc4py_in_slepc = "slepc4py" not in branches -else: - use_petsc4py_in_petsc = True - use_slepc4py_in_slepc = True - - -# This context manager should be used whenever arguments should be temporarily added to pipinstall. -class pipargs(object): - def __init__(self, *args): - self.args = args - - def __enter__(self): - self.save = pipinstall.copy() - pipinstall.extend(self.args) - - def __exit__(self, *args, **kwargs): - global pipinstall - pipinstall = self.save - - -def check_call(arguments): - try: - log.debug("Running command '%s'", " ".join(arguments)) - log.debug(subprocess.check_output(arguments, stderr=subprocess.STDOUT, env=os.environ).decode()) - except subprocess.CalledProcessError as e: - log.debug(e.output.decode()) - raise - - -def check_output(args): - try: - log.debug("Running command '%s'", " ".join(args)) - return subprocess.check_output(args, stderr=subprocess.STDOUT, env=os.environ).decode() - except subprocess.CalledProcessError as e: - log.debug(e.output.decode()) - raise - - -if "PYTHONPATH" in os.environ and not args.honour_pythonpath: - quit("""The PYTHONPATH environment variable is set. This is probably an error. -If you really want to use your own Python packages, please run again with the ---honour-pythonpath option. -""") - - -def brew_gcc_libdir(): - brew_gcc_prefix = check_output(["brew", "--prefix", "gcc"])[:-1] - brew_gcc_info = json.loads(check_output(["brew", "info", "--json", "gcc"])[:-1]) - gcc_major_version = brew_gcc_info[0]["installed"][0]["version"].split(".")[0] - return brew_gcc_prefix + "/lib/gcc/" + gcc_major_version - - -# See .github/workflows/docker.yml to see the PETSc options used on CI. -# Edit these as appropriate if PETSc external packages are added or removed. -def get_minimal_petsc_packages(): - pkgs = set() - # File format - pkgs.add("hdf5") - # Parallel mesh partitioner - pkgs.add("ptscotch") - # Sparse direct solver - pkgs.add("scalapack") # Needed for mumps - pkgs.add("mumps") - return pkgs - - -def get_petsc_options(minimal=False): - # The logic in this function is getting out of hand... - petsc_options = {"--with-fortran-bindings=0", - "--with-debugging=0", - "--with-shared-libraries=1", - "--with-c2html=0", - "--with-strict-petscerrorcode", - # Parser generator - "--download-bison"} - for pkg in get_minimal_petsc_packages(): - petsc_options.add("--download-" + pkg) - if osname == "Darwin": - petsc_options.add("--with-x=0") - # These three lines are used to inspect the MacOS Command Line Tools (CLT) version - cmd = ["pkgutil", "--pkg-info=com.apple.pkg.CLTools_Executables"] - output = subprocess.run(cmd, stdout=subprocess.PIPE, encoding="UTF-8") - clt = {k.strip(): v.strip() for k, v in [line.split(':') for line in output.stdout.split('\n') if line]} - elif osname == "Linux": - # PETSc requires cmake version 3.18.1 or higher. - petsc_options.add("--download-cmake") - - if (not options["minimal_petsc"]) and (not minimal): - # Another sparse direct solver - petsc_options.add("--download-superlu_dist") - petsc_options.add("--with-zlib") - # File formats - petsc_options.add("--download-netcdf") - petsc_options.add("--download-pnetcdf") - # Sparse direct solvers - petsc_options.add("--download-suitesparse") - petsc_options.add("--download-pastix") - # Required by pastix - petsc_options.update({"--download-hwloc", "--download-netlib-lapack", "--with-netlib-lapack-c-bindings"}) - if osname == "Darwin": - petsc_options.add("--download-hwloc-configure-arguments=--disable-opencl") - # Serial mesh partitioner - petsc_options.add("--download-metis") - if options.get("with_parmetis", None): - # Non-free license. - petsc_options.add("--download-parmetis") - - if options["petsc_int_type"] != "int32": - petsc_options.add("--with-64-bit-indices") - - if options["complex"]: - petsc_options.add('--with-scalar-type=complex') - else: - # AMG - petsc_options.add("--download-hypre") - - if options.get("mpiexec") is not None: - petsc_options.add("--with-mpiexec={}".format(options["mpiexec"])) - petsc_options.add("--with-cc={}".format(options["mpicc"])) - petsc_options.add("--with-cxx={}".format(options["mpicxx"])) - petsc_options.add("--with-fc={}".format(options["mpif90"])) - else: - # Download mpich if the user does not tell us about an MPI. - petsc_options.add("--download-mpich") - if osname == "Darwin": - petsc_options.add("--download-mpich-configure-arguments=--disable-opencl") - - if options.get("with_blas") == "download": - # Download openblas if the user specifies. - petsc_options.add("--download-openblas") - petsc_options.add("--download-openblas-make-options='USE_THREAD=0 USE_LOCKING=1 USE_OPENMP=0'") - if osname == "Darwin": - petsc_options.add("--CFLAGS=-Wno-implicit-function-declaration") - if Version(clt["version"]) >= Version("15"): - # CLT >= 15 requires legacy linking behaviour (-ld_classic flag) - # The -dead_strip_dylibs flag is a workaround for MUMPS issues - # on macOS with parallel solvers. Its necessity is unclear, so a - # review in the future may be required. - petsc_options.add("--LDFLAGS=-Wl,-ld_classic,-dead_strip_dylibs") - if Version(clt["version"]) >= Version("16"): - # Should revisit this flag when the the issue https://gitlab.com/petsc/petsc/-/issues/1692 - # is resolved. - petsc_options.add("--with-cxx-dialect=17") - elif options.get("with_blas") is not None: - petsc_options.add("--with-blaslapack-dir={}".format(options["with_blas"])) - if osname == "Darwin": - petsc_options.add("--CFLAGS=-I{}/include -Wno-implicit-function-declaration".format(options["with_blas"])) - if Version(clt["version"]) >= Version("15"): - # CLT >= 15 requires legacy linking behaviour (-ld_classic flag) - # The -dead_strip_dylibs flag is a workaround for MUMPS issues - # on macOS with parallel solvers. Its necessity is unclear, so a - # review in the future may be required. - petsc_options.add("--LDFLAGS=-Wl,-ld_classic,-dead_strip_dylibs,-rpath,{0}/lib -L{0}/lib".format(options["with_blas"])) - else: - petsc_options.add("--CFLAGS=-I{}/include".format(options["with_blas"])) - petsc_options.add("--LDFLAGS=-Wl,-rpath,{0}/lib -L{0}/lib".format(options["with_blas"])) - elif osname == "Darwin": - brew_blas_prefix = check_output(["brew", "--prefix", "openblas"])[:-1] - petsc_options.add("--with-blaslapack-dir={}".format(brew_blas_prefix)) - petsc_options.add("--CFLAGS=-I{}/include -Wno-implicit-function-declaration".format(brew_blas_prefix)) - if Version(clt["version"]) >= Version("15"): - # CLT >= 15 requires legacy linking behaviour (-ld_classic flag) - # The -dead_strip_dylibs flag is a workaround for MUMPS issues - # on macOS with parallel solvers. Its necessity is unclear, so a - # review in the future may be required. - petsc_options.add("--LDFLAGS=-Wl,-ld_classic,-dead_strip_dylibs,-rpath,{0}/lib -L{0}/lib -L{1}".format(brew_blas_prefix, brew_gcc_libdir())) - else: - petsc_options.add("--LDFLAGS=-Wl,-rpath,{0}/lib -L{0}/lib -L{1}".format(brew_blas_prefix, brew_gcc_libdir())) - - if not minimal: - for option in shlex.split(os.environ.get("PETSC_CONFIGURE_OPTIONS", "")): - if option.startswith("--with-hdf5-dir"): - petsc_options.discard("--download-hdf5") - os.environ["HDF5_DIR"] = option.replace("--with-hdf5-dir=", "") - petsc_options.add(option) - if "HDF5_DIR" in os.environ and "--download-hdf5" in petsc_options: - del os.environ["HDF5_DIR"] - log.info("\nWarning: HDF5_DIR environment variable set, but ignored; " - "use PETSC_CONFIGURE_OPTIONS=\"--with-hdf5-dir=$HDF5_DIR\" " - "to have PETSc built against $HDF5_DIR.") - return list(petsc_options) - - -def get_petsc_config(): - petsc_dir, petsc_arch = get_petsc_dir() - with open(os.path.join(petsc_dir, petsc_arch, "include", "petscconfiginfo.h")) as fh: - for line in fh.readlines(): - if line.startswith("static const char *petscconfigureoptions"): - info = line.split("=", maxsplit=1)[1].strip(' ";\n').split('--')[1:] - break - return ["--" + arg for arg in info] - - -if mode == "update" and petsc_int_type_changed: - log.warning("""Force rebuilding all packages because PETSc int type changed""") - -if mode == "update" and petsc_complex_type_changed: - log.warning("""Force rebuilding all packages because PETSc scalar type changed between real and complex""") - - -def brew_install(name, options=None): - arguments = [name] - if options: - arguments = options + arguments - if args.verbose: - arguments = ["--verbose"] + arguments - check_call(["brew", "install"] + arguments) - - -def apt_check(name): - log.info("Checking for presence of package %s..." % name) - # Note that subprocess return codes have the opposite logical - # meanings to those of Python variables. - try: - check_call(["dpkg-query", "-s", name]) - log.info(" installed.") - return True - except subprocess.CalledProcessError: - log.info(" missing.") - return False - - -def apt_install(names): - log.info("Installing missing packages: %s." % ", ".join(names)) - if sys.stdin.isatty(): - subprocess.check_call(["sudo", "apt-get", "install"] + names) - else: - log.info("Non-interactive stdin detected; installing without prompts") - subprocess.check_call(["sudo", "apt-get", "-y", "install"] + names) - - -def split_requirements_url(url): - name = url.split(".git")[0].split("#")[0].split("/")[-1] - spliturl = url.split("://")[1].split("#")[0].split("@") - try: - plain_url, branch = spliturl - except ValueError: - plain_url = spliturl[0] - branch = "master" - return name, plain_url, branch - - -def git_url(plain_url, protocol): - if protocol == "ssh": - return "git@%s:%s" % tuple(plain_url.split("/", 1)) - elif protocol == "https": - return "https://%s" % plain_url - else: - raise ValueError("Unknown git protocol: %s" % protocol) - - -def git_clone(url): - name, plain_url, branch = split_requirements_url(url) - if name == "petsc" and args.honour_petsc_dir: - log.info("Using existing PETSc installation\n") - return name - elif name == "petsc4py" and use_petsc4py_in_petsc: - log.info("Not cloning petsc4py from firedrake-project repo: using petsc4py in PETSc source tree.\n") - return name - log.info("Cloning %s\n" % name) - branch = branches.get(name.lower(), branch) - try: - if options["disable_ssh"]: - raise InstallError("Skipping ssh clone because --disable-ssh") - # note: so far only loopy requires submodule - check_call(["git", "clone", "-q", "--recursive", git_url(plain_url, "ssh")]) - log.info("Successfully cloned repository %s" % name) - except (subprocess.CalledProcessError, InstallError): - if not options["disable_ssh"]: - log.warning("Failed to clone %s using ssh, falling back to https." % name) - try: - check_call(["git", "clone", "-q", "--recursive", git_url(plain_url, "https")]) - log.info("Successfully cloned repository %s." % name) - except subprocess.CalledProcessError: - log.error("Failed to clone %s branch %s." % (name, branch)) - raise - with directory(name): - try: - log.info("Checking out branch %s" % branch) - check_call(["git", "checkout", "-q", branch]) - log.info("Successfully checked out branch %s" % branch) - except subprocess.CalledProcessError: - log.error("Failed to check out branch %s" % branch) - raise - try: - log.info("Updating submodules.") - check_call(["git", "submodule", "update", "--recursive"]) - log.info("Successfully updated submodules.") - except subprocess.CalledProcessError: - log.error("Failed to update submodules.") - raise - return name - - -def list_cloned_dependencies(name): - log.info("Finding dependencies of %s\n" % name) - deps = OrderedDict() - try: - for dep in open(name + "/requirements-git.txt", "r"): - name = split_requirements_url(dep.strip())[0] - deps[name] = dep.strip() - except IOError: - pass - return deps - - -def clone_dependencies(name): - log.info("Cloning the dependencies of %s" % name) - deps = [] - try: - for dep in open(name + "/requirements-git.txt", "r"): - deps.append(git_clone(dep.strip())) - except IOError: - pass - return deps - - -def git_update(name, url=None): - # Update the named git repo and return true if the current branch actually changed. - log.info("Updating the git repository for %s" % name) - with directory(name): - git_sha = check_output(["git", "rev-parse", "HEAD"]) - # Ensure remotes get updated if and when we move repositories. - if url: - _, plain_url, branch = split_requirements_url(url) - current_url = check_output(["git", "remote", "-v"]).split()[1] - current_branch = check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]).strip() - protocol = "https" if current_url.startswith("https") else "ssh" - new_url = git_url(plain_url, protocol) - # Ensure we only change from bitbucket to github and not the reverse. - if (new_url != current_url - and ("bitbucket.org" in current_url) - and ("github.com/firedrakeproject" in plain_url - or "github.com/dolfin-adjoint" in plain_url)): - log.info("Updating git remote for %s" % name) - check_call(["git", "remote", "set-url", "origin", new_url]) - # Ensure we only switch loopy branch if loopy is on firedrake and not on a feature branch. - elif name == "loopy" and current_branch != branch and current_branch == "firedrake": - log.info("Updating loopy branch to main") - check_call(["git", "checkout", "-q", branch]) - - check_call(["git", "pull", "--recurse-submodules"]) - git_sha_new = check_output(["git", "rev-parse", "HEAD"]) - return git_sha != git_sha_new - - -def run_pip(args): - check_call(pip + args) - - -def run_pip_install(pipargs): - # Make pip verbose when logging, so we see what the - # subprocesses wrote out. - # Particularly important for debugging petsc fails. - with environment(**blas): - pipargs = ["-v"] + pipargs - check_call(pipinstall + pipargs) - - -def get_requirements(reqfname): - with open(reqfname, "r") as f: - reqs = f.readlines() - return [req.strip() for req in reqs] - - -def run_pip_install_wrap(reqs, parallel_compiler_env): - for req in reqs: - package_name = re.split('<|>|=', req)[0] - if package_name in parallel_packages: - with environment(**parallel_compiler_env): - run_pip_install([req]) - else: - run_pip_install([req]) - - -def pip_requirements(package, parallel_compiler_env): - log.info("Installing pip dependencies for %s" % package) - if os.path.isfile("%s/requirements-ext.txt" % package): - reqs = get_requirements("%s/requirements-ext.txt" % package) - run_pip_install_wrap(reqs, parallel_compiler_env) - elif os.path.isfile("%s/requirements.txt" % package): - if package == "COFFEE": - # FIXME: Horrible hack to work around - # https://github.com/coin-or/pulp/issues/123 - run_pip_install(["--no-deps", "-r", "%s/requirements.txt" % package]) - elif package == "pyadjoint": - reqs = get_requirements("%s/requirements.txt" % package) - for req in reqs: - try: - run_pip_install([req]) - except subprocess.CalledProcessError as e: - if req == "tensorflow": - log.debug("Skipping failing install of optional pyadjoint dependency tensorflow") - else: - raise e - elif package in {"loopy", "icepack"}: - # dependencies are installed in setup.py - return - else: - reqs = get_requirements("%s/requirements.txt" % package) - run_pip_install_wrap(reqs, parallel_compiler_env) - else: - log.info("No dependencies found. Skipping.") - - -def install(package): - log.info("Installing %s" % package) - if package == "petsc/": - build_and_install_petsc() - elif package == "slepc/": - build_and_install_slepc() - elif package == "petsc4py/": - build_and_install_x4py("petsc4py") - elif package == "slepc4py/": - build_and_install_x4py("slepc4py") - elif package == "h5py/": - # h5py tries to build against ancient version of numpy additional flag avoids this - run_pip_install(["--no-build-isolation", package]) - elif package == "loopy/" and osname == "Darwin": - # On MacOS loopy won't build unless additional flags are passed to the compiler - with environment(CFLAGS="-Wno-implicit-function-declaration"): - run_pip_install(["-e", package]) - else: - run_pip_install(["-e", package]) - - -def clean(package): - log.info("Cleaning %s" % package) - with directory(package): - with environment(**blas): - # loopy needs knowledge of the system BLAS just to run setup.py - if os.path.isfile("setup.py"): - check_call([python, "setup.py", "clean"]) - else: # PEP 518 build system - if os.path.exists("build") and os.path.isdir("build"): - shutil.rmtree("build") - - -def pip_uninstall(package): - log.info("Removing existing %s installations\n" % package) - # Uninstalling something with pip is an absolute disaster. We - # have to use pip freeze to list all available packages "locally" - # and keep on removing the one we want until it is gone from this - # list! Yes, pip will happily have two different versions of the - # same package co-existing. Moreover, depending on the phase of - # the moon, the order in which they are uninstalled is not the - # same as the order in which they appear on sys.path! - again = True - uninstalled = False - i = 0 - while again: - # List installed packages, "locally". In a venv, - # this just tells me packages in the venv, otherwise it - # gives me everything. - lines = check_output(pip + ["freeze", "-l"]) - again = False - for line in lines.split("\n"): - # Do we have a locally installed package? - if line.startswith(package+'=='): - # Uninstall it. - run_pip(["uninstall", "-y", line.strip()]) - uninstalled = True - # Go round again, because THERE MIGHT BE ANOTHER ONE! - again = True - i += 1 - if i > 10: - raise InstallError("pip claims it uninstalled %s more than 10 times. Something is probably broken.", package) - return uninstalled - - -def get_petsc_dir(): - if args.honour_petsc_dir: - return honour_petsc_dir_get_petsc_dir() - else: - petsc_dir = os.path.join(os.environ["VIRTUAL_ENV"], "src", "petsc") - petsc_arch = "default" - return petsc_dir, petsc_arch - - -def get_petsc4py_dir(): - if use_petsc4py_in_petsc: - petsc_dir, _ = get_petsc_dir() - # petsc4py is currently here: - return os.path.join(petsc_dir, "src/binding/petsc4py/") - else: - return "petsc4py/" - - -def get_slepc_dir(): - petsc_dir, petsc_arch = get_petsc_dir() - if args.honour_petsc_dir: - try: - slepc_dir = os.environ["SLEPC_DIR"] - except KeyError: - raise InstallError("Need to set SLEPC_DIR for --slepc with --honour-petsc-dir") - else: - slepc_dir = os.path.join(os.environ["VIRTUAL_ENV"], "src", "slepc") - return slepc_dir, petsc_arch - - -def get_slepc4py_dir(): - if use_slepc4py_in_slepc: - slepc_dir, _ = get_slepc_dir() - return os.path.join(slepc_dir, "src/binding/slepc4py") - else: - return "slepc4py/" - - -def build_and_install_petsc(): - log.info("Depending on your platform, PETSc may take an hour or more to build!") - assert not args.honour_petsc_dir # Belt and braces security before we start clobbering things. - with directory("petsc"): - petsc_dir, petsc_arch = get_petsc_dir() - check_call(["rm", "-rf", os.path.join(petsc_dir, petsc_arch)]) - petsc_options = get_petsc_options() - # Set MACOSX_DEPLOYMENT_TARGET to the current macos version - # to workaround issues configuring PETSc on macos. - # Choosing 12.0 as the oldest MacOS version supported as of 2024-02-08 - macosx_deployment_target = os.environ.get("MACOSX_DEPLOYMENT_TARGET", "12.0") - with environment(MACOSX_DEPLOYMENT_TARGET=macosx_deployment_target): - check_call([python, "./configure", "PETSC_DIR={}".format(petsc_dir), "PETSC_ARCH={}".format(petsc_arch)] + petsc_options) - check_call(["make", "PETSC_DIR={}".format(petsc_dir), "PETSC_ARCH={}".format(petsc_arch), "all"]) - if args.remove_build_files and not args.honour_petsc_dir: - log.info("Cleaning up PETSc build artifacts") - check_call(["rm", "-rf", os.path.join(petsc_dir, petsc_arch, "externalpackages")]) - check_call(["rm", "-rf", os.path.join(petsc_dir, "src", "docs")]) - tutorial_output = os.path.join(petsc_dir, "src", "**", "tutorials", "output", "*") - test_output = os.path.join(petsc_dir, "src", "**", "tests", "output", "*") - for deletefile in chain(iglob(tutorial_output, recursive=True), iglob(test_output, recursive=True)): - check_call(["rm", "-f", deletefile]) - - -def build_and_install_x4py(package="petsc4py"): - if package == "petsc4py": - x4py_dir = get_petsc4py_dir() - elif package == "slepc4py": - x4py_dir = get_slepc4py_dir() - else: - raise ValueError("build_and_install_x4py() should be called with 'petsc4py' or 'slepc4py' only") - with environment(**blas): - if args.honour_petsc_dir: - log.info("Copying {} to virtualenv to install".format(package)) - x4py_copy = os.path.join(os.environ['VIRTUAL_ENV'], 'src', package) - if not os.path.exists(x4py_copy): - shutil.copytree(x4py_dir, x4py_copy, dirs_exist_ok=True) - pipargs = ["-vvv", "--ignore-installed", x4py_copy] - try: - check_output(pipinstall + pipargs) - except subprocess.CalledProcessError: - with open(os.path.join(x4py_copy, "README.md")) as fh: - fh.write("# Unable to install {}\n".format(package)) - fh.write("This is just a copy of the {} source for debugging\n".format(package)) - fh.write("See `firedrake-install.log` for the error") - raise - else: - shutil.rmtree(x4py_copy) - else: - pipargs = ["-vvv", "--ignore-installed", x4py_dir] - check_output(pipinstall + pipargs) - - -def build_and_install_h5py(): - log.info("Installing h5py") - # Clean up old downloads - if os.path.exists("h5py-2.5.0"): - log.info("Removing old h5py-2.5.0 source") - shutil.rmtree("h5py-2.5.0") - if os.path.exists("h5py.tar.gz"): - log.info("Removing old h5py.tar.gz") - os.remove("h5py.tar.gz") - - url = "git+https://github.com/firedrakeproject/h5py.git@firedrake#egg=h5py" - if os.path.exists("h5py"): - changed = False - with directory("h5py"): - # Rewrite old h5py/h5py remote to firedrakeproject/h5py remote. - plain_url = split_requirements_url(url)[1] - current_remote = check_output(["git", "remote", "-v"]).split()[1] - proto = "https" if current_remote.startswith("https") else "ssh" - new_remote = git_url(plain_url, proto) - if new_remote != current_remote and "h5py/h5py.git" in current_remote: - log.info("Updating git remote for h5py from %s to %s", current_remote, new_remote) - check_call(["git", "remote", "set-url", "origin", new_remote]) - check_call(["git", "fetch", "-p", "origin"]) - check_call(["git", "checkout", "firedrake"]) - changed = True - changed |= git_update("h5py") - # If h5py installation fails for some reason, we end up having a - # situation where h5py is not installed, but h5py directory is - # up-to-date, so we need to handle this here. - changed |= not is_installed("h5py") - else: - git_clone(url) - changed = True - - petsc_dir, petsc_arch = get_petsc_dir() - hdf5_dir = os.environ.get("HDF5_DIR", "%s/%s" % (petsc_dir, petsc_arch)) - if changed or args.rebuild: - log.info("Linking h5py against HDF5_DIR=%s\n" % hdf5_dir) - with environment(HDF5_DIR=hdf5_dir, - HDF5_MPI="ON", - **blas): - # Only uninstall if things changed. - pip_uninstall("h5py") - # Pip installing from dirty directory is potentially unsafe. - with directory("h5py/"): - check_call(["git", "clean", "-fdx"]) - install("h5py/") - else: - log.info("No need to rebuild h5py") - - -def build_and_install_pythonocc(): - log.info("Installing pythonocc-core") - url = "git+https://github.com/tpaviot/pythonocc-core.git@595b0a4e8e60e8d6011bea0cdb54ac878efcfcd2" - if not os.path.exists("pythonocc-core"): - git_clone(url) - with directory("pythonocc-core"): - check_call(["git", "reset", "--hard"]) - check_call(["git", "clean", "-f", "-x", "-d"]) - check_call(["mkdir", "-p", "build"]) - with directory("build"): - check_call(["cmake", "..", - "-DCMAKE_INSTALL_PREFIX=" + firedrake_env, - "-DPYTHON_EXECUTABLE=" + python, - "-DMPI_C_COMPILER=" + cc, - "-DMPI_CXX_COMPILER=" + cxx, - "-DMPI_Fortran_COMPILER=" + f90, - "-DCMAKE_Fortran_COMPILER=" + f90]) - check_call(["make", "install"]) - else: - log.info("No need to rebuild pythonocc-core") - - -def build_and_install_torch(): - """Install PyTorch for a CPU or CUDA backend.""" - log.info("Installing PyTorch (backend: %s)" % args.torch) - if osname == "Darwin" and args.torch == "cuda": - raise InstallError("CUDA installation is not available on MacOS.") - if sys.version_info >= (3, 10): - run_pip_install(["typing_extensions"]) - extra_index_url = ["--extra-index-url", "https://download.pytorch.org/whl/cpu"] if args.torch == "cpu" else [] - run_pip_install(["torch"] + extra_index_url) - - -def build_and_install_jax(): - """Install JAX for a CPU or CUDA backend.""" - log.info("Installing JAX (backend: %s)" % args.jax) - version_name = "jax" if args.jax == "cpu" else "jax[cuda12]" - run_pip_install([version_name] + ["jaxlib"] + ["ml_dtypes"] + ["opt_einsum"]) - - -def build_and_install_slepc(): - petsc_dir, petsc_arch = get_petsc_dir() - slepc_dir, slepc_arch = get_slepc_dir() - assert petsc_arch == slepc_arch - - slepc_changed = False - if args.honour_petsc_dir: - log.info("Using installed SLEPc from %s/%s", slepc_dir, petsc_arch) - else: - log.info("Installing SLEPc.") - url = "git+https://github.com/firedrakeproject/slepc.git@firedrake" - if os.path.exists("slepc"): - slepc_changed |= git_update("slepc") - else: - git_clone(url) - slepc_changed = True - if slepc_changed: - with directory("slepc"): - check_call(["rm", "-rf", os.path.join(slepc_dir, petsc_arch)]) - with environment(PETSC_DIR=petsc_dir, - SLEPC_DIR=slepc_dir, - PETSC_ARCH=petsc_arch): - check_call([python, "./configure"]) - check_call(["make", "all"]) - check_call(["make", "check"]) - else: - log.info("No need to rebuild SLEPc") - - url = "git+https://github.com/firedrakeproject/slepc4py.git@firedrake" - if not use_slepc4py_in_slepc: - if os.path.exists("slepc4py"): - with directory("slepc4py"): - plain_url = split_requirements_url(url)[1] - current_remote = check_output(["git", "remote", "-v"]).split()[1] - proto = "https" if current_remote.startswith("https") else "ssh" - new_remote = git_url(plain_url, proto) - if new_remote != current_remote: - log.info("Updating git remote for slepc4py from %s to %s", current_remote, new_remote) - check_call(["git", "remote", "set-url", "origin", new_remote]) - check_call(["git", "fetch", "-p", "origin"]) - check_call(["git", "checkout", "firedrake"]) - else: - log.info("Cloning slepc4py from %s" % url) - git_clone(url) - - -def install_documentation_dependencies(): - """Just install the required dependencies. There are no provenance - issues here so no need to record this in the configuration dict.""" - log.info("Installing documentation dependencies") - run_pip(["install", "-U", "sphinx"]) - run_pip(["install", "-U", "sphinx-autobuild"]) - run_pip(["install", "-U", "sphinxcontrib-bibtex"]) - run_pip(["install", "-U", "sphinxcontrib-svg2pdfconverter"]) - run_pip(["install", "-U", "sphinxcontrib-jquery"]) - run_pip(["install", "-U", "bibtexparser"]) - run_pip(["install", "-U", "sphinxcontrib-youtube"]) - run_pip(["install", "-U", "numpydoc"]) - - -def clean_obsolete_packages(): - dirs = os.listdir(".") - for package in old_git_packages: - pip_uninstall(package) - if package in dirs: - shutil.rmtree(package) - - -def quit(message): - log.error(message) - sys.exit(1) - - -def build_update_script(): - log.info("Creating firedrake-update script.") - with open("firedrake/scripts/firedrake-install", "r") as f: - update_script = f.read() - - try: - os.mkdir("../bin") - except OSError: - pass - with open("../bin/firedrake-update", "w") as f: - f.write(update_script) - check_call(["chmod", "a+x", "../bin/firedrake-update"]) - - -if args.rebuild_script: - os.chdir(os.path.dirname(os.path.realpath(__file__)) + ("/../..")) - - build_update_script() - - log.info("Successfully rebuilt firedrake-update.\n") - - log.info("To upgrade firedrake, run firedrake-update") - sys.exit(0) - - -def create_compiler_env(cc, cxx, f90, mpihome): - env = dict() - if cc: - env["MPICC"] = cc - env["MPI_C_COMPILER"] = cc - env["CC"] = cc - # Work around homebrew adding /usr/local/lib to the library search path. - if osname == "Darwin": - env["CFLAGS"] = " ".join(( - os.environ.get("CFLAGS", ""), - "-L" + os.path.join(*get_petsc_dir(), "lib"), - "-I" + os.path.join(*get_petsc_dir(), "include") - )) - if cxx: - env["MPICXX"] = cxx - env["MPI_CXX_COMPILER"] = cxx - env["CXX"] = cxx - if f90: - env["MPIF90"] = f90 - env["MPI_Fortran_COMPILER"] = f90 - env["F90"] = f90 - if mpihome: - env["MPI_HOME"] = mpihome - - return env - - -if mode == "install" and args.show_petsc_configure_options: - log.info("*********************************************") - log.info("Would build PETSc with the following options:") - log.info("*********************************************\n") - petsc_options = get_petsc_options() - log.info("\n".join(petsc_options)) - sys.exit(0) - -if "PETSC_DIR" in os.environ and not args.honour_petsc_dir: - quit("""The PETSC_DIR environment variable is set. This is probably an error. -If you really want to use your own PETSc build, please run again with the ---honour-petsc-dir option. -""") - -if "PETSC_DIR" not in os.environ and args.honour_petsc_dir: - quit("""The --honour-petsc-dir is set, but PETSC_DIR environment variable is -not defined. If you have compiled PETSc manually, set PETSC_DIR -(and optionally PETSC_ARCH) variables to point to the build directory. -""") - -if "SLEPC_DIR" not in os.environ and args.honour_petsc_dir and options["slepc"]: - quit("""If you use --honour-petsc-dir, you must also build SLEPc manually -and set the SLEPC_DIR environment variable appropriately""") - -if "SLEPC_DIR" in os.environ and not args.honour_petsc_dir and options["slepc"]: - quit("""The SLEPC_DIR environment variable is set. If you want to use -your own SLEPc version, you must also build your own PETSc and run -with --honour-petsc-dir.""") - -if platform.uname()[0] == "Darwin" and args.opencascade: - quit("""Sorry, automatically installing opencascade on OSX hasn't been -implemented yet. (It's not a supported package in brew.) -Please contact us to get this working.""") - -log.debug("Installer running with: %s" % sys.executable) -log.debug("Python version: %s" % sys.version) - -log.debug("*** Current environment (output of 'env') ***") -log.debug(check_output(["env"])) -log.debug("\n\n") - -if mode == "install" and args.honour_petsc_dir: - minimal_packages = get_minimal_petsc_packages() - minimal_packages.update({"blaslapack", "mathlib", "mpi"}) - current_packages = honour_petsc_dir_get_petsc_packages() - log.debug("*****************************************************") - log.debug("Specified PETSc was built with the following options:") - log.debug("*****************************************************\n") - log.debug("PETSC_DIR={} PETSC_ARCH={}".format(*get_petsc_dir())) - log.debug("\n".join(sorted(get_petsc_config())) + '\n') - log.debug("The following external packages were detected and are used by PETSc:") - log.debug(", ".join(sorted(current_packages))) - log.debug("\n") - if not minimal_packages < current_packages: - log.error("*********************************************************************") - log.error("Specified PETSc is missing the following packages:") - log.error(", ".join(sorted(minimal_packages - current_packages)) + '\n') - log.error("You must ensure that PETSc has been built with these minimal options:") - log.error("*********************************************************************\n") - log.error("\n".join(sorted(get_petsc_options(minimal=True)))) - log.error("") - log.error('Use --show-petsc-configure-options as an argument to the firedrake-install script to display options if additional functionality is required.\n ') - log.error("FATAL: Unable to install with specified PETSc") - exit(1) - -if mode == "install" or not args.update_script: - # Check operating system. - required_packages = None - package_manager = None - if osname == "Darwin": - package_manager = "brew" - required_packages = ["gcc", - "pkg-config", - "autoconf", - "make", - "automake", - "cmake", - "ninja", - "libtool", - "boost"] - if args.with_blas is None and mode == "install": - required_packages.append("openblas") - elif osname == "Linux": - package_manager = "apt-get" - required_packages = ["build-essential", - "autoconf", - "automake", - "bison", # for ptscotch - "flex", # for ptscotch - "cmake", - "gfortran", - "pkg-config", - "git", - "libcurl4-openssl-dev", - "pkgconf", # for p4est - "libtool", - "libxml2-dev", - "ninja-build", # for meson/numpy - "python3-dev", - "python3-pip", - "python3-tk", - "python3-venv", - "zlib1g-dev", - "libboost-dev"] # For ROL in pyadjoint - if args.opencascade: - required_packages.append("liboce-ocaf-dev") - required_packages.append("swig") - - if args.with_blas is None and mode == "install": - required_packages.append("libopenblas-dev") - - else: - log.warning("You do not appear to be running Linux or MacOS. Please do not be surprised if this install fails.") - - if args.documentation_dependencies: - required_packages.append("graphviz") - - if args.show_dependencies: - log.info("\n************************************************") - log.info("The following dependencies need to be installed:") - log.info("************************************************\n") - log.info("* A C and C++ compiler (for example gcc/g++ or clang), GNU make") - log.info("* A Fortran compiler (for PETSc)") - log.info("* Blas and Lapack") - log.info("* Boost") - log.info("* Git") - log.info("* Python version >=3.6") - log.info("* The Python headers") - log.info("* autoconf, automake, libtool") - log.info("* CMake") - if osname == "Linux": - log.info("* zlib, bison, flex") - if package_manager: - log.info("\n********************************************") - log.info("The following %s packages are recommended:" % (package_manager)) - log.info("********************************************\n") - log.info("\n".join(required_packages)) - log.info("\nThey will be installed by %s unless --no-package-manager is used." % (package_manager)) - if osname == "Darwin": - log.info("\nIn addition, the command line tools will be installed using xcode") - sys.exit(0) - - blas_location = args.with_blas - - if osname == "Darwin": - try: - check_call(["brew", "--version"]) - except subprocess.CalledProcessError: - quit("Installing Firedrake on MacOS requires Homebrew. Please install " - "it using the instructions at http://brew.sh and then try again.") - - if options["package_manager"]: - log.info("Installing command line tools...") - try: - check_call(["xcode-select", "--install"]) - except subprocess.CalledProcessError: - # expected failure if already installed - pass - - log.info("Checking brew doctor...") - try: - check_call(["brew", "doctor"]) - except subprocess.CalledProcessError: - pass - log.info("Installing required packages via homebrew...") - # Ensure a fortran compiler is available - installed_packages = dict((p["name"], p) - for p in json.loads(check_output(["brew", "info", "--installed", "--json"]))) - # Handle homebrew migration of default python == python3 - if installed_packages.get("python", {}).get("oldname") == "python3": - installed_packages["python3"] = installed_packages["python"] - for package in required_packages: - if package in installed_packages: - log.info("Required package '{0}' is already installed via Homebrew.".format(package)) - log.debug(pprint.pformat(installed_packages[package])) - else: - log.info("Installing required package '{0}' via Homebrew.".format(package)) - brew_install(package) - else: - log.info("Xcode and homebrew installation disabled. Proceeding on the rash assumption that packaged dependencies are in place.") - log.info("Please use the --show-dependencies for more information.") - - # Unconditionally require openblas ("brew" is required above) - if args.with_blas is None and mode == "install": - config["options"]["with_blas"] = check_output(["brew", "--prefix", "openblas"])[:-1] - blas_location = config["options"]["with_blas"] - if not os.path.exists(blas_location): - raise InstallError("Automatic determination of BLAS looked for openblas in %s but didn't find it\n" - "Please provide location of a BLAS library with --with-blas (or use --with-blas=download)" % blas_location) - elif osname == "Linux": - # Check for apt. - try: - if not options["package_manager"]: - raise InstallError - check_call(["apt-get", "--version"]) - - missing_packages = [p for p in required_packages if not apt_check(p)] - if missing_packages: - apt_install(missing_packages) - - except (subprocess.CalledProcessError, InstallError): - log.info("apt-get not found or disabled. Proceeding on the rash assumption that your compiled dependencies are in place.") - log.info("Please use the --show-dependencies for more information.") -else: - blas_location = config["options"].get("with_blas") - -if mode == "install": - if os.path.exists(firedrake_env): - log.warning("Specified venv '%s' already exists", firedrake_env) - quit("Can't install into existing venv '%s'" % firedrake_env) - - log.info("Creating firedrake venv in '%s'." % firedrake_env) - # Debian's Python3 is screwed, they don't ship ensurepip as part - # of the base python package, so the default python -m venv - # doesn't work. Moreover, they have spiked the file such that it - # calls sys.exit, which will kill any attempts to create a venv - # with pip. - try: - import ensurepip # noqa: F401 - with_pip = True - except ImportError: - with_pip = False - import venv - venv.EnvBuilder(with_pip=with_pip).create(firedrake_env) - if not with_pip: - import urllib.request - log.debug("ensurepip unavailable, bootstrapping pip using get-pip.py") - urllib.request.urlretrieve("https://bootstrap.pypa.io/get-pip.py", filename="get-pip.py") - check_call([python, "get-pip.py"]) - log.debug("bootstrapping pip succeeded") - log.debug("Removing get-pip.py") - os.remove("get-pip.py") - - # We haven't activated the venv so we need to manually set the environment. - os.environ["VIRTUAL_ENV"] = firedrake_env - -# Ensure pip, setuptools, hatchling and wheel are at the latest version. -run_pip(["install", "-U", "setuptools"]) -run_pip(["install", "cffi"]) -run_pip(["install", "-U", "hatch"]) -run_pip(["install", "-U", "editables"]) -run_pip(["install", "-U", "pip"]) -run_pip(["install", "-U", "wheel"]) -run_pip(["install", "-U", "scikit-build-core"]) -run_pip(["install", "-U", "rtree>=1.2"]) - -# Extra numpy dependendencies, see -# https://github.com/numpy/numpy/blob/main/pyproject.toml -run_pip(["install", "-U", "meson-python>=0.15.0"]) -run_pip(["install", "-U", "Cython>=3.0.6"]) - -run_pip(["install", "-U", "numpy"]) - -# Loopy has additional build dependencies -run_pip(["install", "-U", "scikit-build"]) -run_pip(["install", "-U", "nanobind"]) -run_pip(["install", "-U", "make"]) -run_pip(["install", "-U", "pcpp"]) - -petsc_dir, petsc_arch = get_petsc_dir() -if options["slepc"]: - slepc_dir, _ = get_slepc_dir() - -if mode == "install": - should_link = not args.honour_petsc_dir or options.get("mpicc") is not None - - # Set up the MPI wrappers - for opt in ["mpicc", "mpicxx", "mpif90", "mpiexec"]: - if options.get(opt): - name = options[opt] - src = shutil.which(name) - else: - src = os.path.join(petsc_dir, petsc_arch, "bin", opt) - dest = os.path.join(firedrake_env, "bin", opt) - # Remove old symlinks - if os.path.lexists(dest): - os.remove(dest) - elif os.path.exists(dest): - os.remove(dest) - if should_link: - log.debug("Creating a wrapper for %s from %s to %s" % (opt, src, dest)) - # use low-level os.open to ensure permissions (incl. exec.) are set according to umask - fd = os.open(dest, os.O_CREAT | os.O_WRONLY) - with os.fdopen(fd, "w") as f: - f.write('#!/bin/bash' + os.linesep + src + ' "$@"') - -cc = options["mpicc"] -cxx = options["mpicxx"] -f90 = options["mpif90"] -mpiexec = options["mpiexec"] -try: - mpihome = options["mpihome"] -except KeyError: - if cc: - try: - mpihome = os.environ["MPI_HOME"] - except KeyError: - raise InstallError("MPI compilers specified but mpihome is missing from " - "configuration. Please set MPI_HOME to continue.") - else: - mpihome = None -compiler_env = create_compiler_env(cc, cxx, f90, mpihome) -os.chdir(firedrake_env) - -# Dict to store BLAS and OPENBLAS environment variables -blas = dict() -if blas_location == "download": - blas_location = os.path.join(petsc_dir, petsc_arch) -if blas_location is not None: - blas["BLAS"] = blas_location - blas["OPENBLAS"] = blas_location -if osname == "Darwin": - blas["LDFLAGS"] = "-L" + brew_gcc_libdir() - -log.info("BLAS config is %s" % blas) - -# Configuration is now complete, and we have a venv so we dump the -# config file in order to maximise the chance that firedrake-update -# can be run on a partial install. -config_location = os.path.join(firedrake_env, ".configuration.json") -json_output = json.dumps(config) -with open(config_location, "w") as f: - f.write(json_output) -log.info("Configuration saved to " + config_location) - - -def is_installed(name): - # Return if package `name` is installed. - try: - importlib.import_module(name) - except ModuleNotFoundError: - return False - else: - return True - - -if mode == "install": - os.mkdir("src") - os.chdir("src") - - if os.environ.get("FIREDRAKE_CI_TESTS", ""): - # When building Firedrake in CI, don't clone Firedrake again. - check_call(["ln", "-s", "../../firedrake", "firedrake"]) - else: - git_clone("git+https://github.com/firedrakeproject/firedrake.git") - - # Build the update script as soon as possible so that installation - # recovery can be attempted if required. - build_update_script() - - # Pre-install requested packages - if args.pip_packages is not None: - for package in args.pip_packages: - log.info("Pip installing %s to venv" % package) - run_pip_install_wrap(package.split(), {}) - - # Install VTK - if not args.no_vtk: - log.info("Pip installing VTK to venv") - run_pip_install_wrap(["vtk>=9.0.1"], {}) - - if sys.version_info[:2] >= (3, 9): - # Also lazy-object-proxy - run_pip(["install", "lazy-object-proxy==1.4.*"]) - - packages = clone_dependencies("firedrake") - packages = ["petsc4py"] + packages - packages += ["firedrake"] - - for p in options["packages"]: - name = git_clone(p) - packages.extend(clone_dependencies(name)) - packages += [name] - - if args.honour_petsc_dir: - try: - packages.remove("petsc") - except ValueError: - log.warning("PETSc wasn't in dependencies list") - else: - # Need to install petsc first in order to resolve hdf5 dependency. - with environment(**compiler_env): - with pipargs("--no-deps"): - try: - packages.remove("petsc") - except ValueError: - log.warning("PETSc wasn't in dependencies list") - install("petsc/") - os.environ["PETSC_DIR"] = petsc_dir - os.environ["PETSC_ARCH"] = petsc_arch - - # If petsc built mpich then we now need to set the compiler environment variables. - if not compiler_env: - compilerbin = os.path.join(petsc_dir, petsc_arch, "bin") - cc = os.path.join(compilerbin, "mpicc") - cxx = os.path.join(compilerbin, "mpicxx") - f90 = os.path.join(compilerbin, "mpif90") - mpiexec = os.path.join(compilerbin, "mpiexec") - mpihome = os.path.join(compilerbin, "..") - compiler_env = create_compiler_env(cc, cxx, f90, mpihome) - - # Make sure that we link against the right MPI and PETSc shared libraries - link_env = { - "LDFLAGS": ( - "-Wl,-rpath,{petsc_dir}/{petsc_arch}/lib -L{petsc_dir}/{petsc_arch}/lib" - .format(petsc_dir=petsc_dir, petsc_arch=petsc_arch) - ), - } - - # Install mpi4py - with environment(**compiler_env, **link_env): - if args.mpi4py_version: - run_pip_install(["--no-cache-dir", "mpi4py==" + args.mpi4py_version]) - else: - run_pip_install(["--no-cache-dir", "mpi4py"]) - - for p in packages: - pip_requirements(p, compiler_env) - - # FIAT requires recursivenodes, but does not include a requirements.txt - run_pip_install(["recursivenodes"]) - # Pyadjoint requires checkpoint-schedules, but does not include a requirements.txt - run_pip_install(["checkpoint_schedules"]) - - with environment(**compiler_env): - build_and_install_h5py() - - if "loopy" in packages: - # We do want to install loopy's dependencies. - install("loopy/") - packages.remove("loopy") - - with pipargs("--no-deps"): - try: - packages.remove("firedrake") - except ValueError: - pass - - for p in packages: - if p in parallel_packages: - with environment(**compiler_env, **link_env): - install(p+"/") - else: - install(p+"/") - sys.path.append(os.getcwd() + "/" + p) - - with pipargs("--no-deps"), environment(**compiler_env, **link_env): - install("firedrake/") - - packages.remove("petsc4py") - -else: - # Update mode - os.chdir("src") - - if args.update_script: - # Pull firedrake, rebuild update script, launch new script - git_update("firedrake") - build_update_script() - os.execv(sys.executable, [sys.executable, "../bin/firedrake-update", "--no-update-script"] + sys.argv[1:]) - - # Pre-install requested packages - if args.pip_packages is not None: - for package in args.pip_packages: - log.info("Pip installing %s to venv" % package) - run_pip_install(package.split()) - - deps = OrderedDict() - deps.update(list_cloned_dependencies("firedrake")) - for p in options["packages"]: - name = split_requirements_url(p)[0] - deps.update(list_cloned_dependencies(name)) - deps[name] = p - packages = ["petsc4py"] + list(deps.keys()) - packages += ["firedrake"] - - # update packages. - if not args.honour_petsc_dir: - update_petsc = git_update("petsc", deps["petsc"]) - else: - update_petsc = False - if os.path.exists(os.path.join(firedrake_env, "src/petsc4py")): - clean("petsc4py/") - shutil.move("petsc4py", "petsc4py_old") - update_petsc4py = True - else: - # Even if petsc has been installed, petsc4py might not, in - # which case we need to make sure that we install it. - # This situation occurs, e.g., when `firedrake-install` - # fails somewhere after successfully installing petsc, - # and the user continues installation with `firedrake-update`. - update_petsc4py = not is_installed("petsc4py") - if os.path.exists(os.path.join(firedrake_env, "src/slepc4py")): - clean("slepc4py") - shutil.move("slepc4py", "slepc4py_old") - - try: - packages.remove("petsc") - except ValueError: - log.warning("PETSc wasn't in dependencies list") - packages.remove("petsc4py") - - if args.clean: - clean_obsolete_packages() - for package in packages: - pip_uninstall(package) - if args.rebuild: - pip_uninstall("petsc4py") - - # Handle archived repositories - archived_repos = [("PyOP2", "firedrake", "PyOP2"), - ("tsfc", "firedrake+fiat", "tsfc"), - ("FInAT", "fiat", "FInAT")] - for src_repo, dest_repo, pip_pkg_name in archived_repos: - archived_path = os.path.join(firedrake_env, "src", src_repo) - if os.path.exists(archived_path): - log.warning("%s has been moved into %s, renaming to %s_old and pip uninstalling.\n" - % (src_repo, dest_repo, src_repo)) - pip_uninstall(pip_pkg_name) - new_path = os.path.join(firedrake_env, "src", "%s_old" % src_repo) - os.rename(archived_path, new_path) - - # If there is a petsc package to remove, then we're an old installation which will need to rebuild PETSc. - update_petsc = pip_uninstall("petsc") or update_petsc - - for p in packages: - try: - git_update(p, deps.get(p, None)) - except OSError as e: - if e.errno == 2: - log.warning("%s missing, cloning anew.\n" % p) - git_clone(deps[p]) - else: - raise - - with pipargs("--no-deps"): - with environment(**compiler_env): - # Only rebuild petsc if it has changed. - if not args.honour_petsc_dir and (args.rebuild or update_petsc): - clean("petsc/") - install("petsc/") - if not args.honour_petsc_dir: - os.environ["PETSC_DIR"] = petsc_dir - os.environ["PETSC_ARCH"] = petsc_arch - - # If petsc built mpich then we now need to set the compiler environment variables. - if not compiler_env: - compilerbin = os.path.join(petsc_dir, petsc_arch, "bin") - cc = os.path.join(compilerbin, "mpicc") - cxx = os.path.join(compilerbin, "mpicxx") - f90 = os.path.join(compilerbin, "mpif90") - mpiexec = os.path.join(compilerbin, "mpiexec") - mpihome = os.path.join(compilerbin, "..") - compiler_env = create_compiler_env(cc, cxx, f90, mpihome) - - # Make sure that we link against the right MPI and PETSc shared libraries - link_env = { - "LDFLAGS": ( - "-Wl,-rpath,{petsc_dir}/{petsc_arch}/lib -L{petsc_dir}/{petsc_arch}/lib" - .format(petsc_dir=petsc_dir, petsc_arch=petsc_arch) - ), - } - - # Install mpi4py - with environment(**compiler_env, **link_env): - if args.mpi4py_version: - run_pip_install(["--no-cache-dir", "mpi4py==" + args.mpi4py_version]) - else: - run_pip_install(["--no-cache-dir", "mpi4py"]) - - # update dependencies. - for p in packages: - pip_requirements(p, compiler_env) - - # FIAT requires recursivenodes, but does not include a requirements.txt - run_pip_install(["recursivenodes"]) - # Pyadjoint requires checkpoint-schedules, but does not include a requirements.txt - run_pip_install(["checkpoint_schedules"]) - - with pipargs("--no-deps"): - if args.rebuild or update_petsc or update_petsc4py: - # If not honour_petsc_dir, we have a version of petsc that contains - # petsc4py in its source tree (old petsc must have been updated - # in the above). - # If honour_petsc_dir, we can get here (e.g., with --rebuild flag) - # without obtaining the latest petsc in the above: - # thus, we must check if the user installed petsc contains petsc4py. - if args.honour_petsc_dir: - if not os.path.exists(get_petsc4py_dir()): - raise InstallError("""\npetsc4py not found in your installation of PETSc, -which indicates that your PETSc is relatively old. -Please consider updating your PETSc manually. -""") - clean(get_petsc4py_dir()) - with environment(**compiler_env): - install("petsc4py/") - # Always rebuild h5py. - with environment(**compiler_env): - build_and_install_h5py() - - if "loopy" in packages: - # We do want to install loopy's dependencies. - clean("loopy") - install("loopy/") - packages.remove("loopy") - - try: - log.info("Attempting to install symengine") - run_pip_install(["symengine"]) - except subprocess.CalledProcessError: - log.info("Symengine installation failed, this is NOT FATAL.") - - with pipargs("--no-deps"): - try: - packages.remove("firedrake") - except ValueError: - pass - - for p in packages: - clean(p) - if p in parallel_packages: - with environment(**compiler_env, **link_env): - install(p+"/") - else: - install(p+"/") - - with pipargs("--no-deps"), environment(**compiler_env, **link_env): - install("firedrake/") - - # Ensure pytest is at the latest version - run_pip(["install", "-U", "pytest"]) - -with environment(**compiler_env): - with pipargs("--no-deps"): - if options["slepc"]: - build_and_install_slepc() - with environment(PETSC_DIR=petsc_dir, - SLEPC_DIR=slepc_dir, - PETSC_ARCH=petsc_arch): - install("slepc4py/") - - if options["netgen"]: - packages += ["ngsPETSc"] - run_pip(["install", "-U", "ngsPETSc"]) - -with pipargs("--no-deps"): - if options["opencascade"]: - build_and_install_pythonocc() - -with pipargs("--no-deps"): - if options["torch"]: - build_and_install_torch() - -with pipargs("--no-deps"): - if options["jax"]: - build_and_install_jax() - -if args.documentation_dependencies: - install_documentation_dependencies() - -if mode == "update": - try: - from firedrake.configuration import setup_cache_dirs - setup_cache_dirs() - log.info("Clearing just in time compilation caches.") - from firedrake.tsfc_interface import clear_cache, TSFCKernel - from pyop2.compilation import clear_cache as pyop2_clear_cache - print('Removing cached TSFC kernels from %s' % TSFCKernel._cachedir) - clear_cache() - pyop2_clear_cache() - except: # noqa: E722 - # Unconditional except in order to avoid upgrade script failures. - log.error("Failed to clear caches. Try running firedrake-clean.") - - -os.chdir("../..") - -if mode == "install": - log.info("\n\nSuccessfully installed Firedrake.\n") - - log.info("\nFiredrake has been installed in a python venv. You activate it with:\n") - log.info(" . %s/bin/activate\n" % firedrake_env) - log.info("The venv can be deactivated by running:\n") - log.info(" deactivate\n\n") - log.info("To upgrade Firedrake activate the venv and run firedrake-update\n") -else: - log.info("\n\nSuccessfully updated Firedrake.\n") diff --git a/versioneer.py b/versioneer.py deleted file mode 100644 index 18e34c2f53..0000000000 --- a/versioneer.py +++ /dev/null @@ -1,2205 +0,0 @@ - -# Version: 0.28 - -"""The Versioneer - like a rocketeer, but for versions. - -The Versioneer -============== - -* like a rocketeer, but for versions! -* https://github.com/python-versioneer/python-versioneer -* Brian Warner -* License: Public Domain (Unlicense) -* Compatible with: Python 3.7, 3.8, 3.9, 3.10 and pypy3 -* [![Latest Version][pypi-image]][pypi-url] -* [![Build Status][travis-image]][travis-url] - -This is a tool for managing a recorded version number in setuptools-based -python projects. The goal is to remove the tedious and error-prone "update -the embedded version string" step from your release process. Making a new -release should be as easy as recording a new tag in your version-control -system, and maybe making new tarballs. - - -## Quick Install - -Versioneer provides two installation modes. The "classic" vendored mode installs -a copy of versioneer into your repository. The experimental build-time dependency mode -is intended to allow you to skip this step and simplify the process of upgrading. - -### Vendored mode - -* `pip install versioneer` to somewhere in your $PATH - * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is - available, so you can also use `conda install -c conda-forge versioneer` -* add a `[tool.versioneer]` section to your `pyproject.toml` or a - `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) - * Note that you will need to add `tomli; python_version < "3.11"` to your - build-time dependencies if you use `pyproject.toml` -* run `versioneer install --vendor` in your source tree, commit the results -* verify version information with `python setup.py version` - -### Build-time dependency mode - -* `pip install versioneer` to somewhere in your $PATH - * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is - available, so you can also use `conda install -c conda-forge versioneer` -* add a `[tool.versioneer]` section to your `pyproject.toml` or a - `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) -* add `versioneer` (with `[toml]` extra, if configuring in `pyproject.toml`) - to the `requires` key of the `build-system` table in `pyproject.toml`: - ```toml - [build-system] - requires = ["setuptools", "versioneer[toml]"] - build-backend = "setuptools.build_meta" - ``` -* run `versioneer install --no-vendor` in your source tree, commit the results -* verify version information with `python setup.py version` - -## Version Identifiers - -Source trees come from a variety of places: - -* a version-control system checkout (mostly used by developers) -* a nightly tarball, produced by build automation -* a snapshot tarball, produced by a web-based VCS browser, like github's - "tarball from tag" feature -* a release tarball, produced by "setup.py sdist", distributed through PyPI - -Within each source tree, the version identifier (either a string or a number, -this tool is format-agnostic) can come from a variety of places: - -* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows - about recent "tags" and an absolute revision-id -* the name of the directory into which the tarball was unpacked -* an expanded VCS keyword ($Id$, etc) -* a `_version.py` created by some earlier build step - -For released software, the version identifier is closely related to a VCS -tag. Some projects use tag names that include more than just the version -string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool -needs to strip the tag prefix to extract the version identifier. For -unreleased software (between tags), the version identifier should provide -enough information to help developers recreate the same tree, while also -giving them an idea of roughly how old the tree is (after version 1.2, before -version 1.3). Many VCS systems can report a description that captures this, -for example `git describe --tags --dirty --always` reports things like -"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the -0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has -uncommitted changes). - -The version identifier is used for multiple purposes: - -* to allow the module to self-identify its version: `myproject.__version__` -* to choose a name and prefix for a 'setup.py sdist' tarball - -## Theory of Operation - -Versioneer works by adding a special `_version.py` file into your source -tree, where your `__init__.py` can import it. This `_version.py` knows how to -dynamically ask the VCS tool for version information at import time. - -`_version.py` also contains `$Revision$` markers, and the installation -process marks `_version.py` to have this marker rewritten with a tag name -during the `git archive` command. As a result, generated tarballs will -contain enough information to get the proper version. - -To allow `setup.py` to compute a version too, a `versioneer.py` is added to -the top level of your source tree, next to `setup.py` and the `setup.cfg` -that configures it. This overrides several distutils/setuptools commands to -compute the version when invoked, and changes `setup.py build` and `setup.py -sdist` to replace `_version.py` with a small static file that contains just -the generated version data. - -## Installation - -See [INSTALL.md](./INSTALL.md) for detailed installation instructions. - -## Version-String Flavors - -Code which uses Versioneer can learn about its version string at runtime by -importing `_version` from your main `__init__.py` file and running the -`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can -import the top-level `versioneer.py` and run `get_versions()`. - -Both functions return a dictionary with different flavors of version -information: - -* `['version']`: A condensed version string, rendered using the selected - style. This is the most commonly used value for the project's version - string. The default "pep440" style yields strings like `0.11`, - `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section - below for alternative styles. - -* `['full-revisionid']`: detailed revision identifier. For Git, this is the - full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". - -* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the - commit date in ISO 8601 format. This will be None if the date is not - available. - -* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that - this is only accurate if run in a VCS checkout, otherwise it is likely to - be False or None - -* `['error']`: if the version string could not be computed, this will be set - to a string describing the problem, otherwise it will be None. It may be - useful to throw an exception in setup.py if this is set, to avoid e.g. - creating tarballs with a version string of "unknown". - -Some variants are more useful than others. Including `full-revisionid` in a -bug report should allow developers to reconstruct the exact code being tested -(or indicate the presence of local changes that should be shared with the -developers). `version` is suitable for display in an "about" box or a CLI -`--version` output: it can be easily compared against release notes and lists -of bugs fixed in various releases. - -The installer adds the following text to your `__init__.py` to place a basic -version in `YOURPROJECT.__version__`: - - from ._version import get_versions - __version__ = get_versions()['version'] - del get_versions - -## Styles - -The setup.cfg `style=` configuration controls how the VCS information is -rendered into a version string. - -The default style, "pep440", produces a PEP440-compliant string, equal to the -un-prefixed tag name for actual releases, and containing an additional "local -version" section with more detail for in-between builds. For Git, this is -TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags ---dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the -tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and -that this commit is two revisions ("+2") beyond the "0.11" tag. For released -software (exactly equal to a known tag), the identifier will only contain the -stripped tag, e.g. "0.11". - -Other styles are available. See [details.md](details.md) in the Versioneer -source tree for descriptions. - -## Debugging - -Versioneer tries to avoid fatal errors: if something goes wrong, it will tend -to return a version of "0+unknown". To investigate the problem, run `setup.py -version`, which will run the version-lookup code in a verbose mode, and will -display the full contents of `get_versions()` (including the `error` string, -which may help identify what went wrong). - -## Known Limitations - -Some situations are known to cause problems for Versioneer. This details the -most significant ones. More can be found on Github -[issues page](https://github.com/python-versioneer/python-versioneer/issues). - -### Subprojects - -Versioneer has limited support for source trees in which `setup.py` is not in -the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are -two common reasons why `setup.py` might not be in the root: - -* Source trees which contain multiple subprojects, such as - [Buildbot](https://github.com/buildbot/buildbot), which contains both - "master" and "slave" subprojects, each with their own `setup.py`, - `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI - distributions (and upload multiple independently-installable tarballs). -* Source trees whose main purpose is to contain a C library, but which also - provide bindings to Python (and perhaps other languages) in subdirectories. - -Versioneer will look for `.git` in parent directories, and most operations -should get the right version string. However `pip` and `setuptools` have bugs -and implementation details which frequently cause `pip install .` from a -subproject directory to fail to find a correct version string (so it usually -defaults to `0+unknown`). - -`pip install --editable .` should work correctly. `setup.py install` might -work too. - -Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in -some later version. - -[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking -this issue. The discussion in -[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the -issue from the Versioneer side in more detail. -[pip PR#3176](https://github.com/pypa/pip/pull/3176) and -[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve -pip to let Versioneer work correctly. - -Versioneer-0.16 and earlier only looked for a `.git` directory next to the -`setup.cfg`, so subprojects were completely unsupported with those releases. - -### Editable installs with setuptools <= 18.5 - -`setup.py develop` and `pip install --editable .` allow you to install a -project into a virtualenv once, then continue editing the source code (and -test) without re-installing after every change. - -"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a -convenient way to specify executable scripts that should be installed along -with the python package. - -These both work as expected when using modern setuptools. When using -setuptools-18.5 or earlier, however, certain operations will cause -`pkg_resources.DistributionNotFound` errors when running the entrypoint -script, which must be resolved by re-installing the package. This happens -when the install happens with one version, then the egg_info data is -regenerated while a different version is checked out. Many setup.py commands -cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into -a different virtualenv), so this can be surprising. - -[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes -this one, but upgrading to a newer version of setuptools should probably -resolve it. - - -## Updating Versioneer - -To upgrade your project to a new release of Versioneer, do the following: - -* install the new Versioneer (`pip install -U versioneer` or equivalent) -* edit `setup.cfg` and `pyproject.toml`, if necessary, - to include any new configuration settings indicated by the release notes. - See [UPGRADING](./UPGRADING.md) for details. -* re-run `versioneer install --[no-]vendor` in your source tree, to replace - `SRC/_version.py` -* commit any changed files - -## Future Directions - -This tool is designed to make it easily extended to other version-control -systems: all VCS-specific components are in separate directories like -src/git/ . The top-level `versioneer.py` script is assembled from these -components by running make-versioneer.py . In the future, make-versioneer.py -will take a VCS name as an argument, and will construct a version of -`versioneer.py` that is specific to the given VCS. It might also take the -configuration arguments that are currently provided manually during -installation by editing setup.py . Alternatively, it might go the other -direction and include code from all supported VCS systems, reducing the -number of intermediate scripts. - -## Similar projects - -* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time - dependency -* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of - versioneer -* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools - plugin - -## License - -To make Versioneer easier to embed, all its code is dedicated to the public -domain. The `_version.py` that it creates is also in the public domain. -Specifically, both are released under the "Unlicense", as described in -https://unlicense.org/. - -[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg -[pypi-url]: https://pypi.python.org/pypi/versioneer/ -[travis-image]: -https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg -[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer - -""" -# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring -# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements -# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error -# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with -# pylint:disable=attribute-defined-outside-init,too-many-arguments - -import configparser -import errno -import json -import os -import re -import subprocess -import sys -from pathlib import Path -from typing import Callable, Dict -import functools - -have_tomllib = True -if sys.version_info >= (3, 11): - import tomllib -else: - try: - import tomli as tomllib - except ImportError: - have_tomllib = False - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - -def get_root(): - """Get the project root directory. - - We require that all commands are run from the project root, i.e. the - directory that contains setup.py, setup.cfg, and versioneer.py . - """ - root = os.path.realpath(os.path.abspath(os.getcwd())) - setup_py = os.path.join(root, "setup.py") - versioneer_py = os.path.join(root, "versioneer.py") - if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): - # allow 'python path/to/setup.py COMMAND' - root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) - setup_py = os.path.join(root, "setup.py") - versioneer_py = os.path.join(root, "versioneer.py") - if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): - err = ("Versioneer was unable to run the project root directory. " - "Versioneer requires setup.py to be executed from " - "its immediate directory (like 'python setup.py COMMAND'), " - "or in a way that lets it use sys.argv[0] to find the root " - "(like 'python path/to/setup.py COMMAND').") - raise VersioneerBadRootError(err) - try: - # Certain runtime workflows (setup.py install/develop in a setuptools - # tree) execute all dependencies in a single python process, so - # "versioneer" may be imported multiple times, and python's shared - # module-import table will cache the first one. So we can't use - # os.path.dirname(__file__), as that will find whichever - # versioneer.py was first imported, even in later projects. - my_path = os.path.realpath(os.path.abspath(__file__)) - me_dir = os.path.normcase(os.path.splitext(my_path)[0]) - vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) - if me_dir != vsr_dir and "VERSIONEER_PEP518" not in globals(): - print("Warning: build in %s is using versioneer.py from %s" - % (os.path.dirname(my_path), versioneer_py)) - except NameError: - pass - return root - - -def get_config_from_root(root): - """Read the project setup.cfg file to determine Versioneer config.""" - # This might raise OSError (if setup.cfg is missing), or - # configparser.NoSectionError (if it lacks a [versioneer] section), or - # configparser.NoOptionError (if it lacks "VCS="). See the docstring at - # the top of versioneer.py for instructions on writing your setup.cfg . - root = Path(root) - pyproject_toml = root / "pyproject.toml" - setup_cfg = root / "setup.cfg" - section = None - if pyproject_toml.exists() and have_tomllib: - try: - with open(pyproject_toml, 'rb') as fobj: - pp = tomllib.load(fobj) - section = pp['tool']['versioneer'] - except (tomllib.TOMLDecodeError, KeyError): - pass - if not section: - parser = configparser.ConfigParser() - with open(setup_cfg) as cfg_file: - parser.read_file(cfg_file) - parser.get("versioneer", "VCS") # raise error if missing - - section = parser["versioneer"] - - cfg = VersioneerConfig() - cfg.VCS = section['VCS'] - cfg.style = section.get("style", "") - cfg.versionfile_source = section.get("versionfile_source") - cfg.versionfile_build = section.get("versionfile_build") - cfg.tag_prefix = section.get("tag_prefix") - if cfg.tag_prefix in ("''", '""', None): - cfg.tag_prefix = "" - cfg.parentdir_prefix = section.get("parentdir_prefix") - cfg.verbose = section.get("verbose") - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -# these dictionaries contain VCS-specific tools -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs, method): # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f): - """Store f in HANDLERS[vcs][method].""" - HANDLERS.setdefault(vcs, {})[method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - - popen_kwargs = {} - if sys.platform == "win32": - # This hides the console window if pythonw.exe is used - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - popen_kwargs["startupinfo"] = startupinfo - - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None), **popen_kwargs) - break - except OSError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - print("stdout was %s" % stdout) - return None, process.returncode - return stdout, process.returncode - - -LONG_VERSION_PY['git'] = r''' -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. -# Generated by versioneer-0.28 -# https://github.com/python-versioneer/python-versioneer - -"""Git implementation of _version.py.""" - -import errno -import os -import re -import subprocess -import sys -from typing import Callable, Dict -import functools - - -def get_keywords(): - """Get the keywords needed to look up the version information.""" - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" - git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" - git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s" - keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} - return keywords - - -class VersioneerConfig: - """Container for Versioneer configuration parameters.""" - - -def get_config(): - """Create, populate and return the VersioneerConfig() object.""" - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "%(STYLE)s" - cfg.tag_prefix = "%(TAG_PREFIX)s" - cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" - cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - """Exception raised if a method is not valid for the current scenario.""" - - -LONG_VERSION_PY: Dict[str, str] = {} -HANDLERS: Dict[str, Dict[str, Callable]] = {} - - -def register_vcs_handler(vcs, method): # decorator - """Create decorator to mark a method as the handler of a VCS.""" - def decorate(f): - """Store f in HANDLERS[vcs][method].""" - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): - """Call the given command(s).""" - assert isinstance(commands, list) - process = None - - popen_kwargs = {} - if sys.platform == "win32": - # This hides the console window if pythonw.exe is used - startupinfo = subprocess.STARTUPINFO() - startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW - popen_kwargs["startupinfo"] = startupinfo - - for command in commands: - try: - dispcmd = str([command] + args) - # remember shell=False, so use git.cmd on windows, not just git - process = subprocess.Popen([command] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None), **popen_kwargs) - break - except OSError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %%s" %% dispcmd) - print(e) - return None, None - else: - if verbose: - print("unable to find command, tried %%s" %% (commands,)) - return None, None - stdout = process.communicate()[0].strip().decode() - if process.returncode != 0: - if verbose: - print("unable to run %%s (error)" %% dispcmd) - print("stdout was %%s" %% stdout) - return None, process.returncode - return stdout, process.returncode - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %%s but none started with prefix %%s" %% - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %%d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%%s', no digits" %% ",".join(refs - tags)) - if verbose: - print("likely tags: %%s" %% ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %%s" %% r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - - # GIT_DIR can interfere with correct operation of Versioneer. - # It may be intended to be passed to the Versioneer-versioned project, - # but that should not change where we get our version from. - env = os.environ.copy() - env.pop("GIT_DIR", None) - runner = functools.partial(runner, env=env) - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=not verbose) - if rc != 0: - if verbose: - print("Directory %%s not under git control" %% root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, [ - "describe", "--tags", "--dirty", "--always", "--long", - "--match", f"{tag_prefix}[[:digit:]]*" - ], cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%%s'" - %% describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%%s' doesn't start with prefix '%%s'" - print(fmt %% (full_tag, tag_prefix)) - pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" - %% (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) - pieces["distance"] = len(out.split()) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def plus_or_dot(pieces): - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces): - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%%d.g%%s" %% (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver): - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces): - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%%d.dev%%d" %% (post_version + 1, pieces["distance"]) - else: - rendered += ".post0.dev%%d" %% (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%%d" %% pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%%s" %% pieces["short"] - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%%s" %% pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%%s" %% pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%%s" %% pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces): - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%%s'" %% style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -def get_versions(): - """Get version information or return default if unable to do so.""" - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for _ in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree", - "date": None} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version", "date": None} -''' - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - """Extract version information from the given file.""" - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - with open(versionfile_abs, "r") as fobj: - for line in fobj: - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - if line.strip().startswith("git_date ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["date"] = mo.group(1) - except OSError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - """Get version information from git keywords.""" - if "refnames" not in keywords: - raise NotThisMethod("Short version file found") - date = keywords.get("date") - if date is not None: - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - - # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant - # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 - # -like" string, which we must then edit to make compliant), because - # it's been around since git-1.5.3, and it's too difficult to - # discover which version we're using, or to work around using an - # older one. - date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = {r.strip() for r in refnames.strip("()").split(",")} - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = {r for r in refs if re.search(r'\d', r)} - if verbose: - print("discarding '%s', no digits" % ",".join(refs - tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - # Filter out refs that exactly match prefix or that don't start - # with a number once the prefix is stripped (mostly a concern - # when prefix is '') - if not re.match(r'\d', r): - continue - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): - """Get version from 'git describe' in the root of the source tree. - - This only gets called if the git-archive 'subst' keywords were *not* - expanded, and _version.py hasn't already been rewritten with a short - version string, meaning we're inside a checked out source tree. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - - # GIT_DIR can interfere with correct operation of Versioneer. - # It may be intended to be passed to the Versioneer-versioned project, - # but that should not change where we get our version from. - env = os.environ.copy() - env.pop("GIT_DIR", None) - runner = functools.partial(runner, env=env) - - _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=not verbose) - if rc != 0: - if verbose: - print("Directory %s not under git control" % root) - raise NotThisMethod("'git rev-parse --git-dir' returned error") - - # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] - # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = runner(GITS, [ - "describe", "--tags", "--dirty", "--always", "--long", - "--match", f"{tag_prefix}[[:digit:]]*" - ], cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], - cwd=root) - # --abbrev-ref was added in git-1.6.3 - if rc != 0 or branch_name is None: - raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") - branch_name = branch_name.strip() - - if branch_name == "HEAD": - # If we aren't exactly on a branch, pick a branch which represents - # the current commit. If all else fails, we are on a branchless - # commit. - branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) - # --contains was added in git-1.5.4 - if rc != 0 or branches is None: - raise NotThisMethod("'git branch --contains' returned error") - branches = branches.split("\n") - - # Remove the first line if we're running detached - if "(" in branches[0]: - branches.pop(0) - - # Strip off the leading "* " from the list of branches. - branches = [branch[2:] for branch in branches] - if "master" in branches: - branch_name = "master" - elif not branches: - branch_name = None - else: - # Pick the first branch that is returned. Good or bad. - branch_name = branches[0] - - pieces["branch"] = branch_name - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparsable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) - pieces["distance"] = len(out.split()) # total number of commits - - # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() - # Use only the last line. Previous lines may contain GPG signature - # information. - date = date.splitlines()[-1] - pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) - - return pieces - - -def do_vcs_install(versionfile_source, ipy): - """Git-specific installation logic for Versioneer. - - For Git, this means creating/changing .gitattributes to mark _version.py - for export-subst keyword substitution. - """ - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - files = [versionfile_source] - if ipy: - files.append(ipy) - if "VERSIONEER_PEP518" not in globals(): - try: - my_path = __file__ - if my_path.endswith((".pyc", ".pyo")): - my_path = os.path.splitext(my_path)[0] + ".py" - versioneer_file = os.path.relpath(my_path) - except NameError: - versioneer_file = "versioneer.py" - files.append(versioneer_file) - present = False - try: - with open(".gitattributes", "r") as fobj: - for line in fobj: - if line.strip().startswith(versionfile_source): - if "export-subst" in line.strip().split()[1:]: - present = True - break - except OSError: - pass - if not present: - with open(".gitattributes", "a+") as fobj: - fobj.write(f"{versionfile_source} export-subst\n") - files.append(".gitattributes") - run_command(GITS, ["add", "--"] + files) - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - """Try to determine the version from the parent directory name. - - Source tarballs conventionally unpack into a directory that includes both - the project name and a version string. We will also support searching up - two directory levels for an appropriately named parent directory - """ - rootdirs = [] - - for _ in range(3): - dirname = os.path.basename(root) - if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} - rootdirs.append(root) - root = os.path.dirname(root) # up a level - - if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - - -SHORT_VERSION_PY = """ -# This file was generated by 'versioneer.py' (0.28) from -# revision-control system data, or from the parent directory name of an -# unpacked source archive. Distribution tarballs contain a pre-generated copy -# of this file. - -import json - -version_json = ''' -%s -''' # END VERSION_JSON - - -def get_versions(): - return json.loads(version_json) -""" - - -def versions_from_file(filename): - """Try to determine the version from _version.py if present.""" - try: - with open(filename) as f: - contents = f.read() - except OSError: - raise NotThisMethod("unable to read _version.py") - mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) - if not mo: - mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) - if not mo: - raise NotThisMethod("no version_json in _version.py") - return json.loads(mo.group(1)) - - -def write_to_version_file(filename, versions): - """Write the given version number to the given _version.py file.""" - os.unlink(filename) - contents = json.dumps(versions, sort_keys=True, - indent=1, separators=(",", ": ")) - with open(filename, "w") as f: - f.write(SHORT_VERSION_PY % contents) - - print("set %s to '%s'" % (filename, versions["version"])) - - -def plus_or_dot(pieces): - """Return a + if we don't already have one, else return a .""" - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - """Build up version string, with post-release "local version identifier". - - Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - Exceptions: - 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_branch(pieces): - """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . - - The ".dev0" means not master branch. Note that .dev0 sorts backwards - (a feature branch will appear "older" than the master branch). - - Exceptions: - 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0" - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def pep440_split_post(ver): - """Split pep440 version string at the post-release segment. - - Returns the release segments before the post-release and the - post-release version number (or -1 if no post-release segment is present). - """ - vc = str.split(ver, ".post") - return vc[0], int(vc[1] or 0) if len(vc) == 2 else None - - -def render_pep440_pre(pieces): - """TAG[.postN.devDISTANCE] -- No -dirty. - - Exceptions: - 1: no tags. 0.post0.devDISTANCE - """ - if pieces["closest-tag"]: - if pieces["distance"]: - # update the post release segment - tag_version, post_version = pep440_split_post(pieces["closest-tag"]) - rendered = tag_version - if post_version is not None: - rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) - else: - rendered += ".post0.dev%d" % (pieces["distance"]) - else: - # no commits, use the tag as the version - rendered = pieces["closest-tag"] - else: - # exception #1 - rendered = "0.post0.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX] . - - The ".dev0" means dirty. Note that .dev0 sorts backwards - (a dirty tree will appear "older" than the corresponding clean one), - but you shouldn't be releasing software with -dirty anyways. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_post_branch(pieces): - """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . - - The ".dev0" means not master branch. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["branch"] != "master": - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_old(pieces): - """TAG[.postDISTANCE[.dev0]] . - - The ".dev0" means dirty. - - Exceptions: - 1: no tags. 0.postDISTANCE[.dev0] - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - """TAG[-DISTANCE-gHEX][-dirty]. - - Like 'git describe --tags --dirty --always'. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - """TAG-DISTANCE-gHEX[-dirty]. - - Like 'git describe --tags --dirty --always -long'. - The distance/hash is unconditional. - - Exceptions: - 1: no tags. HEX[-dirty] (note: no 'g' prefix) - """ - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - """Render the given version pieces into the requested style.""" - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-branch": - rendered = render_pep440_branch(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-post-branch": - rendered = render_pep440_post_branch(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} - - -class VersioneerBadRootError(Exception): - """The project root directory is unknown or missing key files.""" - - -def get_versions(verbose=False): - """Get the project version from whatever source is available. - - Returns dict with two keys: 'version' and 'full'. - """ - if "versioneer" in sys.modules: - # see the discussion in cmdclass.py:get_cmdclass() - del sys.modules["versioneer"] - - root = get_root() - cfg = get_config_from_root(root) - - assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" - handlers = HANDLERS.get(cfg.VCS) - assert handlers, "unrecognized VCS '%s'" % cfg.VCS - verbose = verbose or cfg.verbose - assert cfg.versionfile_source is not None, \ - "please set versioneer.versionfile_source" - assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" - - versionfile_abs = os.path.join(root, cfg.versionfile_source) - - # extract version from first of: _version.py, VCS command (e.g. 'git - # describe'), parentdir. This is meant to work for developers using a - # source checkout, for users of a tarball created by 'setup.py sdist', - # and for users of a tarball/zipball created by 'git archive' or github's - # download-from-tag feature or the equivalent in other VCSes. - - get_keywords_f = handlers.get("get_keywords") - from_keywords_f = handlers.get("keywords") - if get_keywords_f and from_keywords_f: - try: - keywords = get_keywords_f(versionfile_abs) - ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) - if verbose: - print("got version from expanded keyword %s" % ver) - return ver - except NotThisMethod: - pass - - try: - ver = versions_from_file(versionfile_abs) - if verbose: - print("got version from file %s %s" % (versionfile_abs, ver)) - return ver - except NotThisMethod: - pass - - from_vcs_f = handlers.get("pieces_from_vcs") - if from_vcs_f: - try: - pieces = from_vcs_f(cfg.tag_prefix, root, verbose) - ver = render(pieces, cfg.style) - if verbose: - print("got version from VCS %s" % ver) - return ver - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - if verbose: - print("got version from parentdir %s" % ver) - return ver - except NotThisMethod: - pass - - if verbose: - print("unable to compute version") - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, "error": "unable to compute version", - "date": None} - - -def get_version(): - """Get the short version string for this project.""" - return get_versions()["version"] - - -def get_cmdclass(cmdclass=None): - """Get the custom setuptools subclasses used by Versioneer. - - If the package uses a different cmdclass (e.g. one from numpy), it - should be provide as an argument. - """ - if "versioneer" in sys.modules: - del sys.modules["versioneer"] - # this fixes the "python setup.py develop" case (also 'install' and - # 'easy_install .'), in which subdependencies of the main project are - # built (using setup.py bdist_egg) in the same python process. Assume - # a main project A and a dependency B, which use different versions - # of Versioneer. A's setup.py imports A's Versioneer, leaving it in - # sys.modules by the time B's setup.py is executed, causing B to run - # with the wrong versioneer. Setuptools wraps the sub-dep builds in a - # sandbox that restores sys.modules to it's pre-build state, so the - # parent is protected against the child's "import versioneer". By - # removing ourselves from sys.modules here, before the child build - # happens, we protect the child from the parent's versioneer too. - # Also see https://github.com/python-versioneer/python-versioneer/issues/52 - - cmds = {} if cmdclass is None else cmdclass.copy() - - # we add "version" to setuptools - from setuptools import Command - - class cmd_version(Command): - description = "report generated version string" - user_options = [] - boolean_options = [] - - def initialize_options(self): - pass - - def finalize_options(self): - pass - - def run(self): - vers = get_versions(verbose=True) - print("Version: %s" % vers["version"]) - print(" full-revisionid: %s" % vers.get("full-revisionid")) - print(" dirty: %s" % vers.get("dirty")) - print(" date: %s" % vers.get("date")) - if vers["error"]: - print(" error: %s" % vers["error"]) - cmds["version"] = cmd_version - - # we override "build_py" in setuptools - # - # most invocation pathways end up running build_py: - # distutils/build -> build_py - # distutils/install -> distutils/build ->.. - # setuptools/bdist_wheel -> distutils/install ->.. - # setuptools/bdist_egg -> distutils/install_lib -> build_py - # setuptools/install -> bdist_egg ->.. - # setuptools/develop -> ? - # pip install: - # copies source tree to a tempdir before running egg_info/etc - # if .git isn't copied too, 'git describe' will fail - # then does setup.py bdist_wheel, or sometimes setup.py install - # setup.py egg_info -> ? - - # pip install -e . and setuptool/editable_wheel will invoke build_py - # but the build_py command is not expected to copy any files. - - # we override different "build_py" commands for both environments - if 'build_py' in cmds: - _build_py = cmds['build_py'] - else: - from setuptools.command.build_py import build_py as _build_py - - class cmd_build_py(_build_py): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - _build_py.run(self) - if getattr(self, "editable_mode", False): - # During editable installs `.py` and data files are - # not copied to build_lib - return - # now locate _version.py in the new build/ directory and replace - # it with an updated value - if cfg.versionfile_build: - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - cmds["build_py"] = cmd_build_py - - if 'build_ext' in cmds: - _build_ext = cmds['build_ext'] - else: - from setuptools.command.build_ext import build_ext as _build_ext - - class cmd_build_ext(_build_ext): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - _build_ext.run(self) - if self.inplace: - # build_ext --inplace will only build extensions in - # build/lib<..> dir with no _version.py to write to. - # As in place builds will already have a _version.py - # in the module dir, we do not need to write one. - return - # now locate _version.py in the new build/ directory and replace - # it with an updated value - if not cfg.versionfile_build: - return - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) - if not os.path.exists(target_versionfile): - print(f"Warning: {target_versionfile} does not exist, skipping " - "version update. This can happen if you are running build_ext " - "without first running build_py.") - return - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - cmds["build_ext"] = cmd_build_ext - - if "cx_Freeze" in sys.modules: # cx_freeze enabled? - from cx_Freeze.dist import build_exe as _build_exe - # nczeczulin reports that py2exe won't like the pep440-style string - # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. - # setup(console=[{ - # "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION - # "product_version": versioneer.get_version(), - # ... - - class cmd_build_exe(_build_exe): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - target_versionfile = cfg.versionfile_source - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - - _build_exe.run(self) - os.unlink(target_versionfile) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - cmds["build_exe"] = cmd_build_exe - del cmds["build_py"] - - if 'py2exe' in sys.modules: # py2exe enabled? - try: - from py2exe.setuptools_buildexe import py2exe as _py2exe - except ImportError: - from py2exe.distutils_buildexe import py2exe as _py2exe - - class cmd_py2exe(_py2exe): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - target_versionfile = cfg.versionfile_source - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - - _py2exe.run(self) - os.unlink(target_versionfile) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - cmds["py2exe"] = cmd_py2exe - - # sdist farms its file list building out to egg_info - if 'egg_info' in cmds: - _egg_info = cmds['egg_info'] - else: - from setuptools.command.egg_info import egg_info as _egg_info - - class cmd_egg_info(_egg_info): - def find_sources(self): - # egg_info.find_sources builds the manifest list and writes it - # in one shot - super().find_sources() - - # Modify the filelist and normalize it - root = get_root() - cfg = get_config_from_root(root) - self.filelist.append('versioneer.py') - if cfg.versionfile_source: - # There are rare cases where versionfile_source might not be - # included by default, so we must be explicit - self.filelist.append(cfg.versionfile_source) - self.filelist.sort() - self.filelist.remove_duplicates() - - # The write method is hidden in the manifest_maker instance that - # generated the filelist and was thrown away - # We will instead replicate their final normalization (to unicode, - # and POSIX-style paths) - from setuptools import unicode_utils - normalized = [unicode_utils.filesys_decode(f).replace(os.sep, '/') - for f in self.filelist.files] - - manifest_filename = os.path.join(self.egg_info, 'SOURCES.txt') - with open(manifest_filename, 'w') as fobj: - fobj.write('\n'.join(normalized)) - - cmds['egg_info'] = cmd_egg_info - - # we override different "sdist" commands for both environments - if 'sdist' in cmds: - _sdist = cmds['sdist'] - else: - from setuptools.command.sdist import sdist as _sdist - - class cmd_sdist(_sdist): - def run(self): - versions = get_versions() - self._versioneer_generated_versions = versions - # unless we update this, the command will keep using the old - # version - self.distribution.metadata.version = versions["version"] - return _sdist.run(self) - - def make_release_tree(self, base_dir, files): - root = get_root() - cfg = get_config_from_root(root) - _sdist.make_release_tree(self, base_dir, files) - # now locate _version.py in the new base_dir directory - # (remembering that it may be a hardlink) and replace it with an - # updated value - target_versionfile = os.path.join(base_dir, cfg.versionfile_source) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, - self._versioneer_generated_versions) - cmds["sdist"] = cmd_sdist - - return cmds - - -CONFIG_ERROR = """ -setup.cfg is missing the necessary Versioneer configuration. You need -a section like: - - [versioneer] - VCS = git - style = pep440 - versionfile_source = src/myproject/_version.py - versionfile_build = myproject/_version.py - tag_prefix = - parentdir_prefix = myproject- - -You will also need to edit your setup.py to use the results: - - import versioneer - setup(version=versioneer.get_version(), - cmdclass=versioneer.get_cmdclass(), ...) - -Please read the docstring in ./versioneer.py for configuration instructions, -edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. -""" - -SAMPLE_CONFIG = """ -# See the docstring in versioneer.py for instructions. Note that you must -# re-run 'versioneer.py setup' after changing this section, and commit the -# resulting files. - -[versioneer] -#VCS = git -#style = pep440 -#versionfile_source = -#versionfile_build = -#tag_prefix = -#parentdir_prefix = - -""" - -OLD_SNIPPET = """ -from ._version import get_versions -__version__ = get_versions()['version'] -del get_versions -""" - -INIT_PY_SNIPPET = """ -from . import {0} -__version__ = {0}.get_versions()['version'] -""" - - -def do_setup(): - """Do main VCS-independent setup function for installing Versioneer.""" - root = get_root() - try: - cfg = get_config_from_root(root) - except (OSError, configparser.NoSectionError, - configparser.NoOptionError) as e: - if isinstance(e, (OSError, configparser.NoSectionError)): - print("Adding sample versioneer config to setup.cfg", - file=sys.stderr) - with open(os.path.join(root, "setup.cfg"), "a") as f: - f.write(SAMPLE_CONFIG) - print(CONFIG_ERROR, file=sys.stderr) - return 1 - - print(" creating %s" % cfg.versionfile_source) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - - ipy = os.path.join(os.path.dirname(cfg.versionfile_source), - "__init__.py") - if os.path.exists(ipy): - try: - with open(ipy, "r") as f: - old = f.read() - except OSError: - old = "" - module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0] - snippet = INIT_PY_SNIPPET.format(module) - if OLD_SNIPPET in old: - print(" replacing boilerplate in %s" % ipy) - with open(ipy, "w") as f: - f.write(old.replace(OLD_SNIPPET, snippet)) - elif snippet not in old: - print(" appending to %s" % ipy) - with open(ipy, "a") as f: - f.write(snippet) - else: - print(" %s unmodified" % ipy) - else: - print(" %s doesn't exist, ok" % ipy) - ipy = None - - # Make VCS-specific changes. For git, this means creating/changing - # .gitattributes to mark _version.py for export-subst keyword - # substitution. - do_vcs_install(cfg.versionfile_source, ipy) - return 0 - - -def scan_setup_py(): - """Validate the contents of setup.py against Versioneer's expectations.""" - found = set() - setters = False - errors = 0 - with open("setup.py", "r") as f: - for line in f.readlines(): - if "import versioneer" in line: - found.add("import") - if "versioneer.get_cmdclass()" in line: - found.add("cmdclass") - if "versioneer.get_version()" in line: - found.add("get_version") - if "versioneer.VCS" in line: - setters = True - if "versioneer.versionfile_source" in line: - setters = True - if len(found) != 3: - print("") - print("Your setup.py appears to be missing some important items") - print("(but I might be wrong). Please make sure it has something") - print("roughly like the following:") - print("") - print(" import versioneer") - print(" setup( version=versioneer.get_version(),") - print(" cmdclass=versioneer.get_cmdclass(), ...)") - print("") - errors += 1 - if setters: - print("You should remove lines like 'versioneer.VCS = ' and") - print("'versioneer.versionfile_source = ' . This configuration") - print("now lives in setup.cfg, and should be removed from setup.py") - print("") - errors += 1 - return errors - - -def setup_command(): - """Set up Versioneer and exit with appropriate error code.""" - errors = do_setup() - errors += scan_setup_py() - sys.exit(1 if errors else 0) - - -if __name__ == "__main__": - cmd = sys.argv[1] - if cmd == "setup": - setup_command() From 302bf2de922f0308a2595d20e2c1290728aa3d3a Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Thu, 8 May 2025 17:06:04 +0100 Subject: [PATCH 107/222] Add check for uncommitted changes to autogenerated files (#4281) * Remove the unconventional image caching behaviour from team.ini because the URL is only accessed once. * Add author photo * Update AUTHORS.rst and do not autogenerate the citations file --- .github/workflows/core.yml | 53 ++++++++++++++++-------- AUTHORS.rst | 2 - Makefile | 2 +- docs/Makefile | 2 +- docs/source/citation.rst_t | 37 ----------------- docs/source/images/umbertozerbinati.jpg | Bin 0 -> 22244 bytes docs/source/team.ini | 23 +++++----- docs/source/team2.py | 32 -------------- 8 files changed, 49 insertions(+), 102 deletions(-) delete mode 100644 docs/source/citation.rst_t create mode 100644 docs/source/images/umbertozerbinati.jpg diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 2ea409fbc6..3ac51d46ad 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -106,41 +106,41 @@ jobs: # Make sure the current directory is empty run: find . -delete + # Git is needed for actions/checkout and Python for firedrake-configure + - name: Install system dependencies (1) + run: | + apt-get update + apt-get -y install git python3 + - uses: actions/checkout@v4 with: path: firedrake-repo ref: ${{ inputs.source_ref }} - - name: Install Python - run: | - apt-get update - apt-get -y install python3 python3-venv - - name: Validate single source of truth run: ./firedrake-repo/scripts/check-config - - name: Install system dependencies - run: | - apt-get -y install \ - $(python3 ./firedrake-repo/scripts/firedrake-configure --arch ${{ matrix.arch }} --show-system-packages) - : # Dependencies needed to run the test suite - apt-get -y install parallel - : # Dependencies needed to build the documentation - apt-get -y install inkscape texlive-full - # Raise an error if any 'TODO RELEASE' comments remain - name: Check no 'TODO RELEASE' comments (release only) if: inputs.target_branch == 'release' + working-directory: firedrake-repo run: | - cd firedrake-repo - if [ -z "$( git grep 'TODO RELEASE' )" ]; then + if [ -z "$( grep -r --exclude-dir='.*' 'TODO RELEASE' )" ]; then exit 0 else exit 1 fi - # TODO: is there a 'blessed commit' or do we just point to 'main'? - # I prefer the latter because then failures are everyone's problem, not just mine! + - name: Install system dependencies (2) + run: | + apt-get -y install \ + $(python3 ./firedrake-repo/scripts/firedrake-configure --arch ${{ matrix.arch }} --show-system-packages) + apt-get -y install python3-venv + : # Dependencies needed to run the test suite + apt-get -y install parallel + : # Dependencies needed to build the documentation + apt-get -y install inkscape texlive-full + - name: Install PETSc run: | if [ ${{ inputs.target_branch }} = 'release' ]; then @@ -398,6 +398,23 @@ jobs: if: success() || steps.upload_docs.conclusion == 'success' run: echo "conclusion=success" >> "$GITHUB_OUTPUT" + # Some files are automatically generated when we install or build the + # docs (e.g. AUTHORS.rst). These files are committed to the repository + # so uncommitted changes at this point reflect changes in the automatically + # generated files that ought to be committed. + - name: Check no untracked files + if: always() + working-directory: firedrake-repo + run: | + if [ -z "$( git status --porcelain )" ]; then + exit 0 + else + echo Error: there are untracked files in the repository + echo The output from 'git status' is: + git status + exit 1 + fi + - name: Post-run cleanup if: always() run: find . -delete diff --git a/AUTHORS.rst b/AUTHORS.rst index cb83a641ab..a7b4a25fb2 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -80,8 +80,6 @@ Thomas H. Gibson Tim Greaves -Thomas J. Gregory............. - Alastair Gregory David A. Ham.................. diff --git a/Makefile b/Makefile index d993248f88..66c4f8e8ef 100644 --- a/Makefile +++ b/Makefile @@ -39,7 +39,7 @@ actionlint: @docker pull rhysd/actionlint:latest @# Exclude SC2046 so it doesn't complain about unquoted $ characters (the @# quoting can prevent proper parsing) - @docker run -e SHELLCHECK_OPTS='--exclude=SC2046,SC2078' --rm -v $$(pwd):/repo --workdir /repo rhysd/actionlint -color + @docker run -e SHELLCHECK_OPTS='--exclude=SC2046,SC2078,SC2143' --rm -v $$(pwd):/repo --workdir /repo rhysd/actionlint -color .PHONY: dockerlint dockerlint: diff --git a/docs/Makefile b/docs/Makefile index 2e922b109a..f9fc8fd981 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -82,7 +82,7 @@ source/element_list.csv: source/element_list.py cd source; python element_list.py source/team.rst: source/team2.py - cd source; python team2.py; mv AUTHORS.rst CITATION.rst ../.. + cd source; python team2.py; mv AUTHORS.rst ../.. source/firedrake_citations.rst: sphinx-apidoc -f -T -o source/ ../firedrake_citations diff --git a/docs/source/citation.rst_t b/docs/source/citation.rst_t deleted file mode 100644 index 894758ccd3..0000000000 --- a/docs/source/citation.rst_t +++ /dev/null @@ -1,37 +0,0 @@ -.. - This file is generated by team2.py using the citations.rst_t template and team.ini. - DO NOT EDIT DIRECTLY - To add your name and/or institution edit the relevant sections of - docs/source/team.ini - -================ -Citing Firedrake -================ - -If you publish results using Firedrake, we would be grateful if you would cite the Firedrake user manual:: - - @manual{FiredrakeUserManual, - title = {Firedrake User Manual}, - author = {{ '{' }}{{ author }}{{ '}' }}, - organization = {{ '{' }}{{ institution }}{{ '}' }}, - edition = {First edition}, - year = {2023}, - month = {5}, - doi = {10.25561/104839}, - } - -The simplest way to determine any additional relevant papers to cite is by asking Firedrake itself. You can ask that a list of citations relevant to your computation be printed when exiting by calling `Citations.print_at_exit` after importing Firedrake:: - - from firedrake import * - - Citations.print_at_exit() - -Alternatively, you can pass a command-line option `-citations` to obtain the same result. - -Please visit https://www.firedrakeproject.org/citing.html for further details. - -Archiving your code with Zenodo -------------------------------- - -In order to make your simulation results traceable and reproducible, we can provide you with a citeable archive of the exact version of Firedrake and its key dependencies that you used in your simulations. For information on how to do this see https://firedrakeproject.org/zenodo.html. - diff --git a/docs/source/images/umbertozerbinati.jpg b/docs/source/images/umbertozerbinati.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ff78d019602108082a61644fe02831fdd91d58da GIT binary patch literal 22244 zcmbTdXH-*P^fnj>5ZVu<3KEJ0rHT~61QM#!rAd<}2#82cLhr?b7($bxLI9;#>Ai^% zdXO$nAc08lRlt}3%&d3Tnol!x?n=Jo z2jJf&KvCWJp{j|2v4ViJ&*NV^fQ1%t z1~>y!2m&ZsD1a;!|9$}Y0RRf>E2#g2_Wv3blt8MhRnpSYGh9umxdx!500Jqm)_(=y zYIexg?*J+mYSx?5sx)jy_8>tdyG%qvHZ4T0qJzWu=Z=uP#+~SF)gQL@P=NB$7(cV73e*OW0QLkRVc^myMCNb$l za!P92$4_6r=H%w(7Zes%R#n&3*3~yOe((Iz)s64z?Hd^#8=sh*`Zc|{L?kV*kpHZ% z?e6U#93CB?oSyv;E(!qf|Ah6wk^Mh#v0UMzyfOGJ zWjr7YKpC)7{b#YE`aUN)7$pg|vl9g)C;zs@(Nr|+W=r^XZw+&o_DmamPU40$p7{{} z0mh$v`tSUcmijf!*M)2OVzi8)0p|L(Af}ckUBy4n1~5~{7^D5SPn!r3ZoD2T%*gP9 zg#OiS=@u}1X&<~Czf%;jKKYsqJCu{)WRq)DC{b~nWdH*-srEYv!h3ly#b|Qjw$C>C z(GpBvbQiLhK`yI)30#nO9Pb&6mI13nLByG;Vf5Hfok{o4Lr23eS6_2Jl+j+U)q2r8 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S`SN_D?;hUt+8r%ZKmXZG9Uevi literal 0 HcmV?d00001 diff --git a/docs/source/team.ini b/docs/source/team.ini index 974b627615..308be57a0e 100644 --- a/docs/source/team.ini +++ b/docs/source/team.ini @@ -32,27 +32,28 @@ Applied Physics Laboratory: https://www.apl.washington.edu/ [active-team] # Use format -# name : personal-webpage, image-link +# name : personal-webpage +# Note that if you add your name here you should also add a picture of yourself +# in /docs/source/images. David A. Ham: https://www.imperial.ac.uk/people/david.ham Paul H. J. Kelly: https://www.imperial.ac.uk/people/p.kelly -Lawrence Mitchell: https://www.wence.uk/ Colin J. Cotter: https://www.imperial.ac.uk/people/colin.cotter Robert C. Kirby: https://www.baylor.edu/math/index.php?id=90540 Koki Sagiyama: https://www.imperial.ac.uk/people/k.sagiyama -Nacime Bouziani: https://www.imperial.ac.uk/people/n.bouziani18 -Thomas J. Gregory: https://www.imperial.ac.uk/people/t.gregory18 -Jack Betteridge: https://www.imperial.ac.uk/people/j.betteridge, https://avatars.githubusercontent.com/u/43041811 +Jack Betteridge: https://www.imperial.ac.uk/people/j.betteridge Daniel R. Shapero: https://psc.apl.uw.edu/people/investigators/daniel-shapero/ -Reuben W. Nixon-Hill: https://www.imperial.ac.uk/people/reuben.nixon-hill10 Connor J. Ward: https://www.imperial.ac.uk/people/c.ward20 -Patrick E. Farrell: https://pefarrell.org, https://pefarrell.org/images/profile.jpg -Pablo D. Brubeck: https://www.maths.ox.ac.uk/people/pablo.brubeckmartinez, https://avatars.githubusercontent.com/u/6486130 -India Marsden: https://www.maths.ox.ac.uk/people/india.marsden, https://pefarrell.org/images/marsden.jpg -Daiane I. Dolci: https://www.imperial.ac.uk/people/d.dolci, https://avatars.githubusercontent.com/u/63597005?v=4 +Patrick E. Farrell: https://pefarrell.org +Pablo D. Brubeck: https://www.maths.ox.ac.uk/people/pablo.brubeckmartinez +India Marsden: https://www.maths.ox.ac.uk/people/india.marsden +Daiane I. Dolci: https://www.imperial.ac.uk/people/d.dolci Joshua Hope-Collins: https://www.imperial.ac.uk/people/joshua.hope-collins13 -Umberto Zerbinati: https://www.uzerbinati.eu, https://www.uzerbinati.eu/assets/logo.jpeg +Umberto Zerbinati: https://www.uzerbinati.eu [inactive-team] +Lawrence Mitchell: https://www.wence.uk/ +Reuben W. Nixon-Hill: https://www.imperial.ac.uk/people/reuben.nixon-hill10 +Nacime Bouziani: https://www.imperial.ac.uk/people/n.bouziani18 Sophia Vorderwuelbecke: https://www.imperial.ac.uk/people/s.vorderwuelbecke18 Thomas H. Gibson: Miklós Homolya: diff --git a/docs/source/team2.py b/docs/source/team2.py index 3299b3a3bc..70aa227f73 100644 --- a/docs/source/team2.py +++ b/docs/source/team2.py @@ -39,30 +39,11 @@ def rows(self): return range(self.nrows) -def cache_web_image(name, url): - img_name = "".join(name.split()).lower().encode("punycode").decode() - img_name = img_name[:-1] if img_name[-1] == "-" else img_name - with urlopen(url) as response: - filetype = response.getheader("Content-Type") - ext = filetype.split("/")[1] - if ext == "jpeg": - ext = "jpg" - with open("images/" + img_name + "." + ext, "wb") as fh: - fh.write(response.read()) - - # Read the current team information from configuration file team = ConfigParser(interpolation=ExtendedInterpolation()) team.optionxform = lambda x: x team.read("team.ini") -# Grab images from provided URLs and cahce them (if necessary) -for name, links in team["active-team"].items(): - parts = links.split(",") - if parts[1:]: - website = parts[1] - cache_web_image(name, website) - # Environment for applying templates env = Environment( loader=FileSystemLoader("."), @@ -105,16 +86,3 @@ def cache_web_image(name, url): institution_set=institution_set, individual_set=individual_set )) - -# Create citations file for the Github repository -citation_rst = env.get_template("citation.rst_t") -institution_list = [inst[0] for inst in team["dev-institution"].items()] -institution = ' and '.join(institution_list) -author_list = list(team['active-team'].keys()) -author_list += list(team['inactive-team'].keys()) -author = ' and '.join(author_list) -with open("CITATION.rst", "w") as fh: - fh.write(citation_rst.render( - author=author, - institution=institution - )) From 9dd8f75768d4f25b9d0d511cb9052fe559546a3b Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Mon, 12 May 2025 13:43:42 +0100 Subject: [PATCH 108/222] petsc4py docs hotfix (#4294) --- .github/workflows/core.yml | 4 ++++ docs/source/install.rst | 15 +++++++++++++++ 2 files changed, 19 insertions(+) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 3ac51d46ad..5222cc582c 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -169,6 +169,10 @@ jobs: python3 -m venv venv . venv/bin/activate + : # Hotfix for petsc4py build, see https://gitlab.com/petsc/petsc/-/issues/1759 + echo 'Cython<3.1' > constraints.txt + export PIP_CONSTRAINT=constraints.txt + : # Force a rebuild of petsc4py as the cached one will not link to the fresh : # install of PETSc. pip cache remove petsc4py diff --git a/docs/source/install.rst b/docs/source/install.rst index 0e43bb448e..424191d5e5 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -183,6 +183,21 @@ install Firedrake. To do this perform the following steps: $ pip cache remove firedrake $ pip install --no-binary h5py 'firedrake[check]' + .. warning:: + The ``pip install`` instruction above does not currently work due to + `this issue `__. To fix it you should + first: + + 1. Run:: + + $ echo 'Cython<3.1' > constraints.txt + + 2. Run:: + + $ export PIP_CONSTRAINT=constraints.txt + + 3. Continue the installation as usual + .. note:: Though not strictly necessary to install Firedrake's optional dependencies with ``[check]`` it is recommended because it allows you From 54a9ddc7b454694e33da60653d54b9abb68a7f12 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 13 May 2025 12:04:18 +0100 Subject: [PATCH 109/222] Add TODO RELEASE to check-config --- scripts/check-config | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scripts/check-config b/scripts/check-config index e23335b6cc..4c66b97075 100755 --- a/scripts/check-config +++ b/scripts/check-config @@ -20,7 +20,8 @@ class InvalidConfigurationException(Exception): def main(): - check_firedrake_version() + # TODO RELEASE + # check_firedrake_version() check_min_python_version() check_petsc_version() check_petsc_version_spec() From 4abfa46a8464fdce91849d9a639fae6de8651126 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Tue, 13 May 2025 13:44:51 +0100 Subject: [PATCH 110/222] Install documentation improvements and fix macos CI (#4310) --- .github/workflows/core.yml | 4 ++++ docs/source/install.rst | 18 +++++++++++++++++- 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml index 5222cc582c..b75c626735 100644 --- a/.github/workflows/core.yml +++ b/.github/workflows/core.yml @@ -482,6 +482,10 @@ jobs: : # like h5py or mpi4py if changing HDF5/MPI libraries. pip cache remove petsc4py + : # Hotfix for petsc4py build, see https://gitlab.com/petsc/petsc/-/issues/1759 + echo 'Cython<3.1' > constraints.txt + export PIP_CONSTRAINT=constraints.txt + if [ ${{ inputs.target_branch }} = 'release' ]; then EXTRA_PIP_FLAGS='' else diff --git a/docs/source/install.rst b/docs/source/install.rst index 424191d5e5..5fde32a24f 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -107,7 +107,7 @@ do the following steps: #. Clone the PETSc repository, checking out the right version:: - $ git clone --depth 1 --branch $(python3 firedrake-configure --show-petsc-version) https://gitlab.com/petsc/petsc.git + $ git clone --branch $(python3 firedrake-configure --show-petsc-version) https://gitlab.com/petsc/petsc.git $ cd petsc #. Run PETSc ``configure``, passing in the flags generated by ``firedrake-configure``:: @@ -534,6 +534,22 @@ should be followed: $ pip install --no-build-isolation --no-binary h5py --editable './firedrake[check]' + .. warning:: + The ``pip install`` instruction above does not currently work due to + `this issue `__. To fix it you should + first: + + 1. Run:: + + $ echo 'Cython<3.1' > constraints.txt + + 2. Run:: + + $ export PIP_CONSTRAINT=constraints.txt + + 3. Continue the installation as usual + + Editing subpackages ------------------- From 54528aeb8fba10891bed7dbcada121f1e7b7bdb2 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Tue, 13 May 2025 14:47:02 +0100 Subject: [PATCH 111/222] cause actions to trigger when PR target is changed (#4312) --- .github/workflows/pr.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/pr.yml b/.github/workflows/pr.yml index 8aca41386d..3a5572fd24 100644 --- a/.github/workflows/pr.yml +++ b/.github/workflows/pr.yml @@ -2,6 +2,7 @@ name: Test pull request on: pull_request: + types: [ opened, synchronize, reopened, edited ] jobs: test: From dcb0cac7acd96d0023cc7e97ae3710ded7d485a8 Mon Sep 17 00:00:00 2001 From: India Marsden <37078108+indiamai@users.noreply.github.com> Date: Tue, 13 May 2025 16:16:06 +0100 Subject: [PATCH 112/222] Put dual spaces in the manual (#4304) * Add dual manual page --------- Co-authored-by: David Ham --- docs/source/conf.py | 2 +- docs/source/duals.rst | 160 ++++++++++++++++++++++++++++++++++ docs/source/interpolation.rst | 2 + docs/source/manual.rst | 1 + 4 files changed, 164 insertions(+), 1 deletion(-) create mode 100644 docs/source/duals.rst diff --git a/docs/source/conf.py b/docs/source/conf.py index 6c3fdc22e1..302938e308 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -402,7 +402,7 @@ intersphinx_mapping = { 'pyop2': ('https://op2.github.io/PyOP2', None), - 'ufl': ('https://fenics.readthedocs.io/projects/ufl/en/latest/', None), + 'ufl': ('https://docs.fenicsproject.org/ufl/main/', None), 'FIAT': ('https://fenics.readthedocs.io/projects/fiat/en/latest/', None), 'FInAT': ('https://finat.github.io/FInAT/', None), 'mpi4py': ('https://mpi4py.readthedocs.io/en/stable/', None), diff --git a/docs/source/duals.rst b/docs/source/duals.rst new file mode 100644 index 0000000000..706718c632 --- /dev/null +++ b/docs/source/duals.rst @@ -0,0 +1,160 @@ +Dual spaces +===================================== + +Mathematical background +-------------------------- + +If :math:`V` is a vector space, then define the (anti-)dual space :math:`V^*` to be the space of bounded conjugate linear functionals :math:`V \to K`. Therefore, the dual space is the space containing functions from :math:`V` to :math:`K`, where :math:`K` can either be :math:`\mathbb{R}` or :math:`\mathbb{C}`. + +If :math:`\{\phi_i\}` is a basis for a finite vector space :math:`V` then there exists :math:`\{\phi_i^*\}` a basis for :math:`V^*` such that: + +.. math:: + + \phi_i^*(\phi_j) = \delta_{ij} + +The basis :math:`\{\phi_i^*\}` is termed the *dual basis*. Where it is +necessary to make the distinction, we will refer to the space to which a dual +space is dual as the *primal space* and its basis as the *primal basis*. + +Since UFL function spaces are finite-dimensional Hilbert spaces which result +from the discretisation of infinite-dimensional Hilbert spaces, all of the +function spaces with which we are concerned are reflexive, ie :math:`(V^*)^*` +is isomorphic to V under the canonical map. That is, we can identify +:math:`(V^*)^*` and :math:`V`: + +.. math:: + + (V^*)^* \equiv V + +A form defined over an unknown :math:`a` in the primal space :math:`V` is a +known object in the dual space. For example: + +.. math:: + h(a) &= \int_\Omega \phi_i\, \mathrm{ d}x\ a_i \\ + &= \int_\Omega \phi_i\, \mathrm{ d}x\ I_{ij}\ a_j \\ + &= \int_\Omega \phi_i\, \mathrm{ d}x\ \phi_i^*(\phi_j)\ a_j \\ + &= \int_\Omega \phi_i\, \mathrm{ d}x\ \phi_i^*(\phi_j\ a_j ) \\ + &= \int_\Omega \phi_i\, \mathrm{ d}x\ \phi_i^*(a)\\ + &= h_i \phi_i^*(a) + +with basis coefficients :math:`h_i = \displaystyle\int_\Omega \phi_i \text{ +d}x`. + + + + +Dual objects in UFL +-------------------------- + +For an arbitrary :py:class:`~.ufl.FunctionSpace`, ``V``, the corresponding dual space :math:`V^*` can be obtained by calling the :py:meth:`~.ufl.FunctionSpace.dual` method: + +.. code-block:: python3 + + from firedrake import * + mesh = UnitSquareMesh(10, 10) + V = FunctionSpace(mesh, "CG", 1) + V_star = V.dual() + +A :py:class:`~.ufl.Coefficient` defines a *known* function ``c`` in ``V``. A :py:class:`~.Function` is a subclass of :py:class:`~.ufl.Coefficient`. +Consequently, + +.. code-block:: python3 + + c = Function(V) + f_0 = c * dx + +is a symbolic expression for the integral of ``c`` over the domain and represents a scalar value. ``f_0`` is a Python object of type :py:class:`~.ufl.Form`, once assembled, it is a scalar object. + + +Conversely, :py:class:`~.Argument` defines a placeholder symbol ``a`` for an *unknown* function in ``V``. :py:class:`~firedrake.ufl_expr.TestFunction` and :py:class:`~firedrake.ufl_expr.TestFunction` are syntactic sugar for ``Argument(V, 0)`` and ``Argument(V, 1)`` respectively. + +.. code-block:: python3 + + a = TrialFunction(V) + f_1 = a * dx + +represents the integration of the unknown function ``a`` over the domain. It's therefore a linear 1-form, or a function in the dual space :math:`V^* = V \rightarrow K`. ``f_1`` is also a Python object of type :py:class:`~.ufl.Form`. When assembled, it is an object of type :py:class:`~.ufl.Cofunction`: + +.. code-block:: python3 + + cf = assemble(f_1) # type Cofunction + +``cf`` is a known object in the dual space, and the dual equivalent of :py:class:`~.ufl.Coefficient`. The more consistent name ``Cocoefficient`` was rejected as confusing and risible. :py:class:`~.ufl.Cofunction` objects can be combined with symbolic :py:class:`~.ufl.Form` objects: + +.. code-block:: + + v = TestFunction(V) + a = v * dx + b = assemble(a) + res = a + b + c = assemble(res) + + +Furthermore, we will want to express unknown objects in the dual space. For example, in order to represent interpolation from a space :math:`U` to a space :math:`V`, it is convienent to reframe this as a problem involving the dual space: + +.. math:: + + V \to U = V \times U^* \to R + +Using the reflexivity of the function space :math:`U`. This form therefore has two arguments, one in the primal space :math:`V` and one in the dual space :math:`U^*`. Therefore, we need to represent *arguments* in the dual space - we will call these *coarguments*. The details of interpolation will be discussed in :ref:`its own section `. + +A :py:class:`~.Coargument` can be constructed by either calling :py:class:`~.ufl.Argument` on a dual space object or calling :py:class:`~.Coargument` on a dual space. + +.. code-block:: + + v = Argument (V , 1) # type Argument + u = Argument (V.dual() , 2) # type Coargument + w = Coargument (V.dual() , 3) # type Coargument + + +There is a further dual-related type avalilable in UFL. In :py:class:`~.ufl.Cofunction`, we have represented an assembled 1-form. However, commonly we also assemble 2-forms. :py:class:`~.Matrix` allows an analogous use, and assembled 2-forms can be naturally combined with 2-forms that have not yet been assembled: + +.. code-block:: + + mesh = UnitSquareMesh(10,10) + V = FunctionSpace(mesh, "Lagrange", 1) + u = TrialFunction(V) + v = TestFunction(V) + + a = (u*v - inner(grad(u),grad(v)) ) * dx + M = assemble(a) # type Matrix + res = assemble(M + a) + +Operations supported symbolically, such as the adjoint and action, are also supported on the dual space equivalent. + +.. code-block:: + + mesh = UnitSquareMesh(10,10) + V = FunctionSpace(mesh, "Lagrange", 1) + U = FunctionSpace(mesh, "Lagrange", 1) + u = TrialFunction(U) + v = TestFunction(V) + + a = u * v * dx + a = assemble(a) # type Matrix + + adj = adjoint(a) + + b = Matrix(V, U.dual()) + u = Coefficient(U) + u_a = Argument(U, 0) + u_form = u_a * dx + + primal_action = action(a, u) + dual_action = action(b, u_form) + + +In summary, this table describes the dual types corresponding to primal finite element spaces, and to known and unknown functions in those spaces: + +.. list-table:: + :widths: 25 25 + :header-rows: 1 + + * - Primal quantity + - Dual quantity + * - :py:class:`~.ufl.FunctionSpace` + - :py:class:`~.ufl.classes.DualSpace` + * - :py:class:`~.ufl.Coefficient` + - :py:class:`~.ufl.Cofunction` + * - :py:class:`~.Argument` + - :py:class:`~.Coargument` \ No newline at end of file diff --git a/docs/source/interpolation.rst b/docs/source/interpolation.rst index e604a1fbf9..b079a9e352 100644 --- a/docs/source/interpolation.rst +++ b/docs/source/interpolation.rst @@ -2,6 +2,8 @@ .. contents:: +.. _firedrake_interpolation: + Interpolation ============= diff --git a/docs/source/manual.rst b/docs/source/manual.rst index 7e17383edf..a0e3da659e 100644 --- a/docs/source/manual.rst +++ b/docs/source/manual.rst @@ -16,6 +16,7 @@ Manual r-space extruded-meshes mesh-coordinates + duals interpolation point-evaluation external_operators From bcf484de780f50633dae3e80ebdbb3b9a670f884 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Tue, 13 May 2025 17:29:25 +0100 Subject: [PATCH 113/222] status badge for the right branch (#4311) --- docs/source/documentation.rst | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst index 1a90527867..48de7143d5 100644 --- a/docs/source/documentation.rst +++ b/docs/source/documentation.rst @@ -8,9 +8,22 @@ Firedrake is continually tested using `GitHub actions `__. - Latest Firedrake status: |firedrakebuild|_ - .. |firedrakebuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml/badge.svg - .. _firedrakebuild: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml + .. only:: release + + Latest Firedrake **release** branch status: + + |firedrakereleasebuild|_ + + .. only:: not release + + Latest Firedrake **master** branch status: + + |firedrakemasterbuild|_ + + .. |firedrakereleasebuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml/badge.svg?branch=release + .. _firedrakereleasebuild: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml?branch=release + .. |firedrakemasterbuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml/badge.svg?query=branch%3Amaster + .. _firedrakemasterbuild: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml?query=branch%3Amaster Firedrake and its components are developed on `GitHub `__. From 9014b041a824d79d8969a7822341c68fbd3e12a9 Mon Sep 17 00:00:00 2001 From: "David A. Ham" Date: Tue, 13 May 2025 17:42:11 +0100 Subject: [PATCH 114/222] intersphinx the current fiat documentation (#4305) * intersphinx the current fiat documentation --- docs/source/conf.py | 1 + firedrake/functionspace.py | 14 ++++---------- firedrake/functionspaceimpl.py | 9 ++++----- 3 files changed, 9 insertions(+), 15 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 302938e308..45046e43ce 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -402,6 +402,7 @@ intersphinx_mapping = { 'pyop2': ('https://op2.github.io/PyOP2', None), + 'ufl': ('https://fenics.readthedocs.io/projects/ufl/en/latest/', None), 'ufl': ('https://docs.fenicsproject.org/ufl/main/', None), 'FIAT': ('https://fenics.readthedocs.io/projects/fiat/en/latest/', None), 'FInAT': ('https://finat.github.io/FInAT/', None), diff --git a/firedrake/functionspace.py b/firedrake/functionspace.py index 509f7a2863..6f10a4720b 100644 --- a/firedrake/functionspace.py +++ b/firedrake/functionspace.py @@ -171,12 +171,8 @@ def VectorFunctionSpace(mesh, family, degree=None, dim=None, ----- The ``family`` argument may be an existing :class:`finat.ufl.finiteelementbase.FiniteElementBase`, in which case all other arguments - are ignored and the appropriate :class:`.FunctionSpace` is returned. In - this case, the provided element must have an empty - :attr:`finat.ufl.finiteelementbase.FiniteElementBase.value_shape`. - - The element that you provide need be a scalar element (with empty - ``value_shape``), however, it should not be an existing + are ignored and the appropriate :class:`.FunctionSpace` is returned. + The element that you provide need be a scalar element, however, it should not be an existing :class:`finat.ufl.mixedelement.VectorElement`. If you already have an existing :class:`finat.ufl.mixedelement.VectorElement`, you should pass it to :class:`.FunctionSpace` directly instead. @@ -227,11 +223,9 @@ def TensorFunctionSpace(mesh, family, degree=None, shape=None, The ``family`` argument may be an existing :class:`finat.ufl.finiteelementbase.FiniteElementBase`, in which case all other arguments are ignored and the appropriate `FunctionSpace` is - returned. In this case, the provided element must have an empty - :attr:`finat.ufl.finiteelementbase.FiniteElementBase.value_shape`. + returned. - The element that you provide must be a scalar element (with empty - ``value_shape``). If you already have an existing + The element that you provide must be a scalar element. If you already have an existing :class:`finat.ufl.mixedelement.TensorElement`, you should pass it to `FunctionSpace` directly instead. diff --git a/firedrake/functionspaceimpl.py b/firedrake/functionspaceimpl.py index cec9dd860c..d4dd168fdb 100644 --- a/firedrake/functionspaceimpl.py +++ b/firedrake/functionspaceimpl.py @@ -504,11 +504,10 @@ def __init__(self, mesh, element, name=None): self.rank = len(self.shape) r"""The rank of this :class:`FunctionSpace`. Spaces where the - element is scalar-valued (or intrinsically vector-valued) have - rank zero. Spaces built on :class:`finat.ufl.mixedelement.VectorElement` or - :class:`finat.ufl.mixedelement.TensorElement` instances have rank equivalent to - the number of components of their - :attr:`finat.ufl.finiteelementbase.FiniteElementBase.value_shape`.""" + element is scalar-valued (or intrinsically vector-valued) have rank + zero. Spaces built on :class:`finat.ufl.mixedelement.VectorElement` or + :class:`finat.ufl.mixedelement.TensorElement` have rank 1 and 2 + respectively.""" self.block_size = int(numpy.prod(self.shape, dtype=int)) r"""The total number of degrees of freedom at each function From b358e33ab12b3c4bc3819c9c6e9ed0930082b750 Mon Sep 17 00:00:00 2001 From: qbisicwate Date: Wed, 14 May 2025 00:52:09 +0800 Subject: [PATCH 115/222] pyproject: move spydump to script-files (#4301) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index ba725a079c..f5bcd2dd95 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,7 +61,6 @@ firedrake-clean = "firedrake.scripts.firedrake_clean:main" firedrake-preprocess-bibtex = "firedrake.scripts.firedrake_preprocess_bibtex:main" firedrake-status = "firedrake.scripts.firedrake_status:main" pyop2-clean = "pyop2.compilation:clear_compiler_disk_cache" -spydump = "pyop2.scripts.spydump:main" [project.optional-dependencies] @@ -149,6 +148,7 @@ build-backend = "setuptools.build_meta" script-files = [ "firedrake/scripts/firedrake-zenodo", "scripts/firedrake-run-split-tests", + "pyop2/scripts/spydump", ] [tool.setuptools.package-data] From fd30db6664b382f158de079d4ed28be0fe49b985 Mon Sep 17 00:00:00 2001 From: Connor Ward Date: Wed, 14 May 2025 15:14:49 +0100 Subject: [PATCH 116/222] Use SPDX string for pyproject license (#4324) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index f5bcd2dd95..55dbcd6427 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ name = "firedrake" version = "2025.4.0.post0" description = "An automated system for the portable solution of partial differential equations using the finite element method" readme = "README.rst" -license = {file = "LICENSE"} +license = "LGPL-3.0-or-later" maintainers = [ {name = "Pablo D. Brubeck"}, {name = "Daiane I. Dolci"}, From bab5c969a2ce7158b2ad7bf1c8dea81b6fc398e9 Mon Sep 17 00:00:00 2001 From: India Marsden <37078108+indiamai@users.noreply.github.com> Date: Wed, 14 May 2025 15:15:10 +0100 Subject: [PATCH 117/222] fix typos, make my code examples flake8 compliant (#4321) --- docs/source/duals.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/duals.rst b/docs/source/duals.rst index 706718c632..156b92e49a 100644 --- a/docs/source/duals.rst +++ b/docs/source/duals.rst @@ -66,7 +66,7 @@ Consequently, is a symbolic expression for the integral of ``c`` over the domain and represents a scalar value. ``f_0`` is a Python object of type :py:class:`~.ufl.Form`, once assembled, it is a scalar object. -Conversely, :py:class:`~.Argument` defines a placeholder symbol ``a`` for an *unknown* function in ``V``. :py:class:`~firedrake.ufl_expr.TestFunction` and :py:class:`~firedrake.ufl_expr.TestFunction` are syntactic sugar for ``Argument(V, 0)`` and ``Argument(V, 1)`` respectively. +Conversely, :py:class:`~.Argument` defines a placeholder symbol ``a`` for an *unknown* function in ``V``. :py:class:`~firedrake.ufl_expr.TestFunction` and :py:class:`~firedrake.ufl_expr.TrialFunction` are syntactic sugar for ``Argument(V, 0)`` and ``Argument(V, 1)`` respectively. .. code-block:: python3 @@ -102,9 +102,9 @@ A :py:class:`~.Coargument` can be constructed by either calling :py:class:`~.ufl .. code-block:: - v = Argument (V , 1) # type Argument - u = Argument (V.dual() , 2) # type Coargument - w = Coargument (V.dual() , 3) # type Coargument + v = Argument(V, 1) # type Argument + u = Argument(V.dual(), 2) # type Coargument + w = Coargument(V.dual(), 3) # type Coargument There is a further dual-related type avalilable in UFL. In :py:class:`~.ufl.Cofunction`, we have represented an assembled 1-form. However, commonly we also assemble 2-forms. :py:class:`~.Matrix` allows an analogous use, and assembled 2-forms can be naturally combined with 2-forms that have not yet been assembled: From e3b7c29a8739439bb5f8ca8b43ea13eb5493ae37 Mon Sep 17 00:00:00 2001 From: Leo Collins Date: Thu, 15 May 2025 10:58:25 +0100 Subject: [PATCH 118/222] add meson-python requirement to requirements-build.txt (#4327) --- requirements-build.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements-build.txt b/requirements-build.txt index cff27061d1..09b1f06535 100644 --- a/requirements-build.txt +++ b/requirements-build.txt @@ -11,3 +11,4 @@ rtree>=1.2 # Transitive build dependencies hatchling +meson-python From 155d48b8eba5bb7d962c3feaa6cd365101d44d3f Mon Sep 17 00:00:00 2001 From: KarsKnook <57411502+KarsKnook@users.noreply.github.com> Date: Thu, 15 May 2025 12:05:42 +0100 Subject: [PATCH 119/222] demo for steady Boussinesq problem with integral constraints (#4318) * demo for steady Boussinesq 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zu04C^nDf?w7?>pJ;HR_9%D;Ml&(C(P3}T(DkYc%FV`GVOypos)+x75i z3I8x@X#U^m_kaJY{}?wA9P&#*fI8qSRLPJ9#Q(*@{SQR`&qYD|H^i|2=l8}oq>!V# zHUD$Qe~|kB9NIT80vk7||Nr|7{<{!Z5{OC|-YWKg{s=I62r^z|r_lfJIsb#zY0zTM lgj?|c`~3eG!x@p@zm^Ks!^x@d^8lcitfZ1et(bAp{{`__ and `Kars Knook + `__. + +We consider a nondimensionalised steady Boussinesq problem. +The domain is :math:`\Omega \subset \mathbb{R}^2` +with boundary :math:`\Gamma`. The boundary value problem is: + +.. math:: + + -\nabla \cdot (\mu(T) \epsilon (u)) + \nabla p - f T &= 0 \quad \textrm{in}\ \Omega, + + \nabla \cdot u &= 0 \quad \textrm{in}\ \Omega, + + u \cdot \nabla T - \nabla^2 T &= 0 \quad \textrm{in}\ \Omega, + + u &= 0 \quad \textrm{on}\ \Gamma, + + \nabla T \cdot n &= g \quad \textrm{on}\ \Gamma. + +The unknowns are the :math:`\mathbb{R}^2`-valued velocity :math:`u`, +scalar pressure :math:`p` and temperature :math:`T`. +The viscosity :math:`\mu(T)` is assumed to be a known function of :math:`T`. +Moreover :math:`\epsilon (u)` denotes the symmetric gradient of :math:`u` +and :math:`f = (0, -1)^T` the acceleration due to gravity. +Inhomogeneous Neumann boundary conditions are imposed on :math:`\nabla T \cdot n` through +given data :math:`g` which must satisfy a compatibility condition + +.. math:: + + \int_{\Gamma} g \ {\rm d} s = 0. + +Evidently the pressure :math:`p` is only determined up to a constant, since it only appears in +the problem through its gradient. This choice of constant is arbitrary and does not affect the model. +The situation regarding the temperature :math:`T` is, however, more subtle. +For the sake of discussion let us first assume that :math:`\mu(T) = \mu_0` is a constant that does +not depend on :math:`T`. It is then clear that, just like the pressure, the temperature :math:`T` +is undetermined up to a constant. We shall pin this down by enforcing that the mean of :math:`T` +is a user-supplied constant :math:`T_0`, + +.. math:: + + \int_{\Omega} (T - T_0) \ {\rm d} x = 0. + +The Boussinesq approximation assumes that density varies linearly with temperature. +Hence, this constraint can be viewed as an indirect imposition on the total mass of fluid in :math:`\Omega`. + +Now suppose that :math:`\mu(T)` does depend on :math:`T`. +For simplicity we use a basic power law :math:`\mu(T) = 10^{-4} \cdot T^{1/2}` +but emphasise that the precise functional form of :math:`\mu(T)` is unimportant to this demo. +We must still impose the integral constraint on :math:`T` to obtain a unique solution, +but the value of :math:`T_0` now affects the model in a non-trivial way since :math:`\mu(T)` and +:math:`T` are coupled (c.f. the figures at the bottom of the demo). +**In particular, this is not a "trivial" situation like the incompressible +Stokes problem where the discretized Jacobians have a nullspace corresponding to the constant pressures. +Instead, we have an integral constraint on** :math:`T` **even though +the discretized Jacobians do not have a nullspace corresponding to the constant temperatures.** + +We build the mesh using :doc:`netgen `, choosing a trapezoidal geometry +to prevent hydrostatic equilibrium and allow for a non-trivial velocity solution.:: + + from firedrake import * + import netgen.occ as ngocc + + wp = ngocc.WorkPlane() + wp.MoveTo(0, 0) + wp.LineTo(2, 0) + wp.LineTo(2, 1) + wp.LineTo(0, 2) + wp.LineTo(0, 0) + + shape = wp.Face() + shape.edges.Min(ngocc.X).name = "left" + shape.edges.Max(ngocc.X).name = "right" + + ngmesh = ngocc.OCCGeometry(shape, dim=2).GenerateMesh(maxh=0.1) + + left_id = [i+1 for i, name in enumerate(ngmesh.GetRegionNames(dim=1)) if name == "left"] + right_id = [i+1 for i, name in enumerate(ngmesh.GetRegionNames(dim=1)) if name == "right"] + + mesh = Mesh(ngmesh) + x, y = SpatialCoordinate(mesh) + +Next we set up the discrete function spaces. +We use lowest-order Taylor--Hood elements for the velocity and pressure, +and continuous piecewise-linear elements for the temperature. +We introduce a Lagrange multiplier to enforce the integral constraint on :math:`T`:: + + U = VectorFunctionSpace(mesh, "CG", degree=2) + V = FunctionSpace(mesh, "CG", degree=1) + W = FunctionSpace(mesh, "CG", degree=1) + R = FunctionSpace(mesh, "R", 0) + + Z = U * V * W * R + +The trial and test functions are::: + + z = Function(Z) + (u, p, T_aux, l) = split(z) + (v, q, w, s) = split(TestFunction(Z)) + + T = T_aux + l + +The test Lagrange multiplier :code:`s` will allow us to impose the integral constraint on the temperature. +We use the trial Lagrange multiplier :code:`l` by decomposing the discretized temperature field :code:`T` +as :code:`T = T_aux + l` where :code:`T_aux` is the trial function from :code:`W`. +The value of :code:`l` will then be determined by the integral constraint on :code:`T`. + +The remaining problem data to be specified is the Neumann data, +viscosity, acceleration due to gravity and :math:`T_0`. +For the Neumann data we choose a parabolic profile on the left and right edges, +and zero data on the top and bottom.:: + + g_left = y*(y-2) # Neumann data on the left + g_right = -8*y*(y-1) # Neumann data on the right + mu = (1e-4) * T ** (1/2) # Viscosity + f = as_vector([0, -1]) # Acceleration due to gravity + T0 = Constant(1) # Mean of the temperature + +The nonlinear form for the problem is::: + + F = (mu * inner(sym(grad(u)), sym(grad(v))) * dx # Viscous terms + - inner(p, div(v)) * dx # Pressure gradient + - inner(f, T*v) * dx # Buoyancy term + - inner(div(u), q) * dx # Incompressibility constraint + + inner(dot(u, grad(T)), w) * dx # Temperature advection term + + inner(grad(T), grad(w)) * dx # Temperature diffusion term + - inner(g_left, w) * ds(tuple(left_id)) # Weakly imposed Neumann BC terms + - inner(g_right, w) * ds(tuple(right_id)) # Weakly imposed Neumann BC terms + + inner(T - T0, s) * dx # Integral constraint on T + ) + +and the (strongly enforced) Dirichlet boundary conditions on :math:`u` are enforced by:: + + bc_u = DirichletBC(Z.sub(0), 0, "on_boundary") + +At this point we could form and solve a :class:`~.NonlinearVariationalProblem` +using :code:`F` and :code:`bc_u`. However, the resultant problem has a nullspace of +dimension 2, corresponding to (i) shifting :math:`p` by a constant :math:`C_1` +and (ii) shifting :math:`l` by a constant :math:`C_2` while simultaneuosly shifting +:math:`T_{\textrm{aux}}` by :math:`-C_2`. + +One way of dealing with nullspaces in Firedrake is to pass a :code:`nullspace` and +:code:`transpose_nullspace` to :class:`~.NonlinearVariationalSolver`. However, sometimes +this approach may not be practical. First, for nonlinear problems with Jacobians that +are not symmetric, it may not obvious what the :code:`transpose_nullspace` is. A second +reason is that, when using customised PETSc linear solvers, it may be desirable +to directly eliminate the nullspace from the assembled Jacobian matrix, since one +cannot always be sure that the linear solver at hand is correctly utilising the provided +:code:`nullspace` and :code:`transpose_nullspace`. + +To directly eliminate the nullspace we introduce a class :code:`FixAtPointBC` which +implements a boundary condition that fixes a field at a single point.:: + + import firedrake.utils as firedrake_utils + + class FixAtPointBC(firedrake.DirichletBC): + r'''A special BC object for pinning a function at a point. + + :arg V: the :class:`.FunctionSpace` on which the boundary condition should be applied. + :arg g: the boundary condition value. + :arg bc_point: the point at which to pin the function. + The location of the finite element DOF nearest to bc_point is actually used. + ''' + def __init__(self, V, g, bc_point): + super(FixAtPointBC, self).__init__(V, g, bc_point) + if isinstance(bc_point, tuple): + bc_point = as_vector(bc_point) + self.bc_point = bc_point + + @firedrake_utils.cached_property + def nodes(self): + V = self.function_space() + x = firedrake.SpatialCoordinate(V.mesh()) + xdist = x - self.bc_point + + test = firedrake.TestFunction(V) + trial = firedrake.TrialFunction(V) + xphi = firedrake.assemble(ufl.inner(xdist * test, xdist * trial) * ufl.dx, diagonal=True) + phi = firedrake.assemble(ufl.inner(test, trial) * ufl.dx, diagonal=True) + with xphi.dat.vec as xu, phi.dat.vec as u: + xu.pointwiseDivide(xu, u) + min_index, min_value = xu.min() # Find the index of the DOF closest to bc_point + + nodes = V.dof_dset.lgmap.applyInverse([min_index]) + nodes = nodes[nodes >= 0] + return nodes + +We use this to fix the pressure and auxiliary temperature at the origin::: + + aux_bcs = [FixAtPointBC(Z.sub(1), 0, as_vector([0, 0])), + FixAtPointBC(Z.sub(2), 0, as_vector([0, 0]))] + +:code:`FixAtPointBC` takes three arguments: the function space to fix, the value with which it +will be fixed, and the location at which to fix. Generally :code:`FixAtPointBC` will not fix +the function at exactly the supplied point; it will fix it at the finite element DOF closest +to that point. By default CG elements have DOFs on all mesh vertices, so if the supplied +point if a mesh vertex then CG fields will be fixed at exactly the supplied point. + +.. warning:: + + A :code:`FixAtPointBC` does more than just fix the corresponding trial function + at the chosen DOF. It also ensures that the corresponding test function + (which is equal to one at that DOF and zero at all others) + will no longer be used in the discretized variational problem. + One must be sure that it is mathematically acceptable to do this. + + In the present setting this is acceptable owing to the homogeneous Dirichlet + boundary conditions on :math:`u` and compatibility condition :math:`\int_{\Gamma} g \ {\rm d} s = 0` + on the Neumann data. The former ensures that the rows in the discretized Jacobian + corresponding to the incompressibility constraint are linearly dependent + (if there are :math:`M` rows, only :math:`M-1` of them are linearly independent, since + the boundary conditions on :math:`u` ensure that + :math:`\int_{\Omega} \nabla \cdot u {\rm d} x = 0` automatically). + Similarily the rows in the Jacobian corresponding to the temperature advection-diffusion + equation are linearly independent (again, if there are :math:`M` rows, + only :math:`M-1` of them are linearly independent). + The effect of :code:`FixAtPointBC` will be to remove one of the rows corresponding + to the incompressibility constraint and one corresponding to the temperature advection-diffusion + equation. Which row ends up getting removed is determined by the location of :code:`bc_point`, + but in the present setting removing a given row is mathematically equivalent to removing any one of the others. + + One could envisage a more complicated scenario than the one in this demo, wherein the Neumann data + depends nonlinearly on some other problem unknowns, and it only satisfies the compatibility condition + approximately (e.g. up to some discretization error). + In this case one would have to be very careful when using :code:`FixAtPointBC` -- + although similar cautionary behaviour would also have to be taken if using + :code:`nullspace` and :code:`transpose_nullspace` instead. + +Finally, we form and solve the nonlinear variational problem for :math:`T_0 \in \{1, 10, 100, 1000, 10000 \}`:: + + NLVP = NonlinearVariationalProblem(F, z, bcs=[bc_u]+aux_bcs) + NLVS = NonlinearVariationalSolver(NLVP) + + (u, p, T_aux, l) = z.subfunctions + File = VTKFile(f"output/boussinesq.pvd") + + for i in range(0, 5): + T0.assign(10**(i)) + l.assign(Constant(T0)) + NLVS.solve() + + u_out = assemble(project(u, Z.sub(0))) + p_out = assemble(project(p, Z.sub(1))) + T_out = assemble(project(T, Z.sub(2))) + + u_out.rename("u") + p_out.rename("p") + T_out.rename("T") + + File.write(u_out, p_out, T_out, time=i) + +The temperature and stream lines for :math:`T_0=1` and :math:`T_0=10000` are displayed below on the left and right respectively. + ++-------------------------+-------------------------+ +| .. image:: T0_1.png | .. image:: T0_10000.png | +| :width: 100% | :width: 100% | ++-------------------------+-------------------------+ + +A Python script version of this demo can be found :demo:`here +`. diff --git a/docs/source/advanced_tut.rst b/docs/source/advanced_tut.rst index d9fbf252ba..6794207abc 100644 --- a/docs/source/advanced_tut.rst +++ b/docs/source/advanced_tut.rst @@ -24,3 +24,4 @@ element systems. Rayleigh-Benard convection. Netgen support. Full-waveform inversion: spatial and wave sources parallelism. + Steady Boussinesq problem with integral constraints. diff --git a/docs/source/conf.py b/docs/source/conf.py index 45046e43ce..dfb14aba20 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -422,9 +422,19 @@ bibtex_bibfiles = ['demos/demo_references.bib', '_static/bibliography.bib', '_static/firedrake-apps.bib', '_static/references.bib'] # -- Options for sphinx.ext.extlinks ------------------------------------ -extlinks = { - 'demo': ('https://firedrakeproject.org/demos/%s', None) -} +extlinks = {} +if tags.has('master'): + extlinks['demo'] = ( + 'https://firedrakeproject.org/firedrake/demos/%s', None + ) +elif tags.has('release'): + extlinks['demo'] = ( + 'https://firedrakeproject.org/demos/%s', None + ) +else: + extlinks['demo'] = ( + '%s', None + ) # -- Options for sphinx_reredirects ------------------------------------ redirects = { diff --git a/docs/source/optimising.rst b/docs/source/optimising.rst index 12497405b2..2b87ac07c2 100644 --- a/docs/source/optimising.rst +++ b/docs/source/optimising.rst @@ -38,8 +38,8 @@ informative flame graphs giving a lot of insight into the internals of Firedrake and PETSc. As an example, here is a flame graph showing the performance of the -:demo:`scalar wave equation with higher-order mass lumping demo -`. +:doc:`scalar wave equation with higher-order mass lumping demo +`. It is interactive and you can zoom in on functions by clicking. .. raw:: html diff --git a/tests/firedrake/demos/test_demos_run.py b/tests/firedrake/demos/test_demos_run.py index fe5665a0fe..cc0938272b 100644 --- a/tests/firedrake/demos/test_demos_run.py +++ b/tests/firedrake/demos/test_demos_run.py @@ -19,6 +19,7 @@ SERIAL_DEMOS = [ Demo(("benney_luke", "benney_luke"), ["vtk"]), + Demo(("boussinesq", "boussinesq"), []), Demo(("burgers", "burgers"), ["vtk"]), Demo(("camassa-holm", "camassaholm"), ["vtk"]), Demo(("DG_advection", "DG_advection"), ["matplotlib"]), From ab3409bc7b39985a71f3d38e95cbb484ce0af7d6 Mon Sep 17 00:00:00 2001 From: Pablo Brubeck Date: Fri, 16 May 2025 15:40:38 +0100 Subject: [PATCH 120/222] Cleanup Interpolate assembly (#4288) --- firedrake/assemble.py | 50 +++++++++++++++++++++---------------------- 1 file changed, 24 insertions(+), 26 deletions(-) diff --git a/firedrake/assemble.py b/firedrake/assemble.py index c40c38095b..cac73acc64 100644 --- a/firedrake/assemble.py +++ b/firedrake/assemble.py @@ -539,15 +539,15 @@ def base_form_assembly_visitor(self, expr, tensor, *args): return tensor.assign(result) if tensor else result elif isinstance(expr, ufl.Interpolate): # Replace assembled children - _, expression = expr.argument_slots() - v, *assembled_expression = args - if assembled_expression: + _, operand = expr.argument_slots() + v, *assembled_operand = args + if assembled_operand: # Occur in situations such as Interpolate composition - expression = assembled_expression[0] + operand = assembled_operand[0] reconstruct_interp = expr._ufl_expr_reconstruct_ - if (v, expression) != expr.argument_slots(): - expr = reconstruct_interp(expression, v=v) + if (v, operand) != expr.argument_slots(): + expr = reconstruct_interp(operand, v=v) # Different assembly procedures: # 1) Interpolate(Argument(V1, 1), Argument(V2.dual(), 0)) -> Jacobian (Interpolate matrix) @@ -557,25 +557,28 @@ def base_form_assembly_visitor(self, expr, tensor, *args): # This can be generalized to the case where the first slot is an arbitray expression. rank = len(expr.arguments()) # If argument numbers have been swapped => Adjoint. - arg_expression = ufl.algorithms.extract_arguments(expression) - is_adjoint = (arg_expression and arg_expression[0].number() == 0) + arg_operand = ufl.algorithms.extract_arguments(operand) + is_adjoint = (arg_operand and arg_operand[0].number() == 0) + + # Get the target space + V = v.function_space().dual() # Dual interpolation from mixed source - if is_adjoint and len(expr.function_space()) > 1: + if is_adjoint and len(V) > 1: cur = 0 - sub_expressions = [] - components = numpy.reshape(expression, (-1,)) - for Vi in expr.function_space(): - sub_expressions.append(ufl.as_tensor(components[cur:cur+Vi.value_size].reshape(Vi.value_shape))) + sub_operands = [] + components = numpy.reshape(operand, (-1,)) + for Vi in V: + sub_operands.append(ufl.as_tensor(components[cur:cur+Vi.value_size].reshape(Vi.value_shape))) cur += Vi.value_size - # Component-split of the primal expression interpolated into the dual argument-split - split_interp = sum(reconstruct_interp(sub_expressions[i], v=vi) for (i,), vi in split_form(v)) + # Component-split of the primal operands interpolated into the dual argument-split + split_interp = sum(reconstruct_interp(sub_operands[i], v=vi) for (i,), vi in split_form(v)) return assemble(split_interp, tensor=tensor) # Dual interpolation into mixed target - if is_adjoint and len(arg_expression[0].function_space()) > 1 and rank == 1: - V = arg_expression[0].function_space() + if is_adjoint and len(arg_operand[0].function_space()) > 1 and rank == 1: + V = arg_operand[0].function_space() tensor = tensor or firedrake.Cofunction(V.dual()) # Argument-split of the Interpolate gets assembled into the corresponding sub-tensor @@ -583,19 +586,14 @@ def base_form_assembly_visitor(self, expr, tensor, *args): assemble(sub_interp, tensor=tensor.subfunctions[i]) return tensor - # Get the primal space - V = expr.function_space() - if is_adjoint or rank == 0: - V = V.dual() - # Workaround: Renumber argument when needed since Interpolator assumes it takes a zero-numbered argument. if not is_adjoint and rank == 2: _, v1 = expr.arguments() - expression = ufl.replace(expression, {v1: v1.reconstruct(number=0)}) + operand = ufl.replace(operand, {v1: v1.reconstruct(number=0)}) # Get the interpolator interp_data = expr.interp_data default_missing_val = interp_data.pop('default_missing_val', None) - interpolator = firedrake.Interpolator(expression, V, **interp_data) + interpolator = firedrake.Interpolator(operand, V, **interp_data) # Assembly if rank == 0: Iu = interpolator._interpolate(default_missing_val=default_missing_val) @@ -606,12 +604,12 @@ def base_form_assembly_visitor(self, expr, tensor, *args): return interpolator._interpolate(v, output=tensor, adjoint=True, default_missing_val=default_missing_val) # Assembling the Jacobian action. elif interpolator.nargs: - return interpolator._interpolate(expression, output=tensor, default_missing_val=default_missing_val) + return interpolator._interpolate(operand, output=tensor, default_missing_val=default_missing_val) # Assembling the operator elif tensor is None: return interpolator._interpolate(default_missing_val=default_missing_val) else: - return firedrake.Interpolator(expression, tensor, **interp_data)._interpolate(default_missing_val=default_missing_val) + return firedrake.Interpolator(operand, tensor, **interp_data)._interpolate(default_missing_val=default_missing_val) elif rank == 2: res = tensor.petscmat if tensor else PETSc.Mat() # Get the interpolation matrix From c579a94bdc2c45f6f03e8d76e3083002674a7a33 Mon Sep 17 00:00:00 2001 From: Daiane Iglesia Dolci <63597005+Ig-dolci@users.noreply.github.com> Date: Wed, 21 May 2025 10:44:28 +0100 Subject: [PATCH 121/222] checkpointing bcs (#4284) Ensure that restoring a BC from checkpoint returns a new BC and doesn't incorrectly modify the program variable BC. --------- Co-authored-by: David A. Ham Co-authored-by: Pablo Brubeck --- firedrake/adjoint_utils/dirichletbc.py | 8 +++-- .../adjoint/test_disk_checkpointing.py | 32 +++++++++++++++++++ .../regression/test_adjoint_operators.py | 22 +++++++++++-- 3 files changed, 56 insertions(+), 6 deletions(-) diff --git a/firedrake/adjoint_utils/dirichletbc.py b/firedrake/adjoint_utils/dirichletbc.py index bf3c7e46ef..5496701754 100644 --- a/firedrake/adjoint_utils/dirichletbc.py +++ b/firedrake/adjoint_utils/dirichletbc.py @@ -42,7 +42,9 @@ def _ad_create_checkpoint(self): return deps[0] - def _ad_restore_at_checkpoint(self, checkpoint): - if checkpoint is not None: - self.set_value(checkpoint.saved_output) + def _ad_restore_at_checkpoint(self, bv): + if bv is not None: + bc = self.reconstruct(g=bv.saved_output) + bc.block = self.block + return bc return self diff --git a/tests/firedrake/adjoint/test_disk_checkpointing.py b/tests/firedrake/adjoint/test_disk_checkpointing.py index 3707204256..6c4e5deb45 100644 --- a/tests/firedrake/adjoint/test_disk_checkpointing.py +++ b/tests/firedrake/adjoint/test_disk_checkpointing.py @@ -185,3 +185,35 @@ def delta_expr(x0, x, y, sigma_x=2000.0): J_hat = ReducedFunctional(J, Control(c)) assert taylor_test(J_hat, c, Function(V).interpolate(0.1)) > 1.9 + + +@pytest.mark.skipcomplex +def test_bcs(): + + enable_disk_checkpointing() + + tape = get_working_tape() + tape.enable_checkpointing(SingleDiskStorageSchedule()) + + mesh = checkpointable_mesh(UnitSquareMesh(5, 5)) + V = FunctionSpace(mesh, "CG", 2) + T = Function(V) + u = TrialFunction(V) + v = TestFunction(V) + a = inner(grad(u), grad(v)) * dx + x = SpatialCoordinate(mesh) + F = Function(V) + control = Control(F) + F.interpolate(sin(x[0] * pi) * sin(2 * x[1] * pi)) + L = F * v * dx + uu = Function(V) + bcs = [DirichletBC(V, T, (1,))] + problem = LinearVariationalProblem(a, L, uu, bcs=bcs) + solver = LinearVariationalSolver(problem) + + for i in tape.timestepper(iter(range(3))): + T.assign(T + 1.0) + solver.solve() + obj = assemble(uu * uu * dx) + rf = ReducedFunctional(obj, control) + assert np.allclose(rf(F), obj) diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py index 8d4c0ab796..b7ecdd6ed0 100644 --- a/tests/firedrake/regression/test_adjoint_operators.py +++ b/tests/firedrake/regression/test_adjoint_operators.py @@ -1005,6 +1005,7 @@ def test_cofunction_assign_functional(): @pytest.mark.skipcomplex # Taping for complex-valued 0-forms not yet done def test_bdy_control(): + from firedrake.adjoint_utils.dirichletbc import DirichletBCBlock # Test for the case the boundary condition is a control for a # domain with length different from 1. mesh = IntervalMesh(10, 0, 2) @@ -1024,13 +1025,28 @@ def test_bdy_control(): problem = LinearVariationalProblem(lhs(F), rhs(F), sol, bcs=bc) solver = LinearVariationalSolver(problem) solver.solve() + # Analytical solution of the analytical Laplace equation is: # u(x) = a + (b - a)/2 * x - u_analytical = a + (b - a)/2 * X[0] - der_analytical0 = assemble(derivative((u_analytical**2) * dx, a)) - der_analytical1 = assemble(derivative((u_analytical**2) * dx, b)) + def u_analytical(x, a, b): + return a + (b - a)/2 * x + der_analytical0 = assemble(derivative( + (u_analytical(X[0], a, b)**2) * dx, a)) + der_analytical1 = assemble(derivative( + (u_analytical(X[0], a, b)**2) * dx, b)) J = assemble(sol * sol * dx) J_hat = ReducedFunctional(J, [Control(a), Control(b)]) adj_derivatives = J_hat.derivative(options={"riesz_representation": "l2"}) assert np.allclose(adj_derivatives[0].dat.data_ro, der_analytical0.dat.data_ro) assert np.allclose(adj_derivatives[1].dat.data_ro, der_analytical1.dat.data_ro) + a = Function(R, val=1.5) + b = Function(R, val=2.5) + J_hat([a, b]) + tape = get_working_tape() + # Check the checkpointed boundary conditions are not updating the + # user-defined boundary conditions ``bc_left`` and ``bc_right``. + assert isinstance(tape._blocks[0], DirichletBCBlock) and \ + tape._blocks[0]._outputs[0].checkpoint.checkpoint is not bc_left._original_arg + # tape._blocks[1] is the DirichletBC block for the right boundary + assert isinstance(tape._blocks[1], DirichletBCBlock) and \ + tape._blocks[1]._outputs[0].checkpoint.checkpoint is not bc_right._original_arg From ca3931816fe8cd934c884a28da5d628415875c2c Mon Sep 17 00:00:00 2001 From: Aaron Baier-Reinio <130484015+ABaierReinio@users.noreply.github.com> Date: Wed, 21 May 2025 16:35:18 +0100 Subject: [PATCH 122/222] Fix minor typos in Boussinesq demo (#4336) --- demos/boussinesq/boussinesq.py.rst | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/demos/boussinesq/boussinesq.py.rst b/demos/boussinesq/boussinesq.py.rst index ff0a29d4b4..ac093bd556 100644 --- a/demos/boussinesq/boussinesq.py.rst +++ b/demos/boussinesq/boussinesq.py.rst @@ -60,12 +60,12 @@ We must still impose the integral constraint on :math:`T` to obtain a unique sol but the value of :math:`T_0` now affects the model in a non-trivial way since :math:`\mu(T)` and :math:`T` are coupled (c.f. the figures at the bottom of the demo). **In particular, this is not a "trivial" situation like the incompressible -Stokes problem where the discretized Jacobians have a nullspace corresponding to the constant pressures. +Stokes problem where the discretised Jacobians have a nullspace corresponding to the constant pressures. Instead, we have an integral constraint on** :math:`T` **even though -the discretized Jacobians do not have a nullspace corresponding to the constant temperatures.** +the discretised Jacobians do not have a nullspace corresponding to the constant temperatures.** We build the mesh using :doc:`netgen `, choosing a trapezoidal geometry -to prevent hydrostatic equilibrium and allow for a non-trivial velocity solution.:: +to prevent hydrostatic equilibrium and allow for a non-trivial velocity solution. :: from firedrake import * import netgen.occ as ngocc @@ -101,7 +101,7 @@ We introduce a Lagrange multiplier to enforce the integral constraint on :math:` Z = U * V * W * R -The trial and test functions are::: +The trial and test functions are:: z = Function(Z) (u, p, T_aux, l) = split(z) @@ -110,14 +110,14 @@ The trial and test functions are::: T = T_aux + l The test Lagrange multiplier :code:`s` will allow us to impose the integral constraint on the temperature. -We use the trial Lagrange multiplier :code:`l` by decomposing the discretized temperature field :code:`T` +We use the trial Lagrange multiplier :code:`l` by decomposing the discretised temperature field :code:`T` as :code:`T = T_aux + l` where :code:`T_aux` is the trial function from :code:`W`. The value of :code:`l` will then be determined by the integral constraint on :code:`T`. The remaining problem data to be specified is the Neumann data, viscosity, acceleration due to gravity and :math:`T_0`. For the Neumann data we choose a parabolic profile on the left and right edges, -and zero data on the top and bottom.:: +and zero data on the top and bottom. :: g_left = y*(y-2) # Neumann data on the left g_right = -8*y*(y-1) # Neumann data on the right @@ -125,7 +125,7 @@ and zero data on the top and bottom.:: f = as_vector([0, -1]) # Acceleration due to gravity T0 = Constant(1) # Mean of the temperature -The nonlinear form for the problem is::: +The nonlinear form for the problem is:: F = (mu * inner(sym(grad(u)), sym(grad(v))) * dx # Viscous terms - inner(p, div(v)) * dx # Pressure gradient @@ -158,7 +158,7 @@ cannot always be sure that the linear solver at hand is correctly utilising the :code:`nullspace` and :code:`transpose_nullspace`. To directly eliminate the nullspace we introduce a class :code:`FixAtPointBC` which -implements a boundary condition that fixes a field at a single point.:: +implements a boundary condition that fixes a field at a single point. :: import firedrake.utils as firedrake_utils @@ -194,7 +194,7 @@ implements a boundary condition that fixes a field at a single point.:: nodes = nodes[nodes >= 0] return nodes -We use this to fix the pressure and auxiliary temperature at the origin::: +We use this to fix the pressure and auxiliary temperature at the origin:: aux_bcs = [FixAtPointBC(Z.sub(1), 0, as_vector([0, 0])), FixAtPointBC(Z.sub(2), 0, as_vector([0, 0]))] @@ -210,16 +210,16 @@ point if a mesh vertex then CG fields will be fixed at exactly the supplied poin A :code:`FixAtPointBC` does more than just fix the corresponding trial function at the chosen DOF. It also ensures that the corresponding test function (which is equal to one at that DOF and zero at all others) - will no longer be used in the discretized variational problem. + will no longer be used in the discretised variational problem. One must be sure that it is mathematically acceptable to do this. In the present setting this is acceptable owing to the homogeneous Dirichlet boundary conditions on :math:`u` and compatibility condition :math:`\int_{\Gamma} g \ {\rm d} s = 0` - on the Neumann data. The former ensures that the rows in the discretized Jacobian + on the Neumann data. The former ensures that the rows in the discretised Jacobian corresponding to the incompressibility constraint are linearly dependent (if there are :math:`M` rows, only :math:`M-1` of them are linearly independent, since the boundary conditions on :math:`u` ensure that - :math:`\int_{\Omega} \nabla \cdot u {\rm d} x = 0` automatically). + :math:`\int_{\Omega} \nabla \cdot u \ {\rm d} x = 0` automatically). Similarily the rows in the Jacobian corresponding to the temperature advection-diffusion equation are linearly independent (again, if there are :math:`M` rows, only :math:`M-1` of them are linearly independent). From c730277b0a1376ac44580b8d6fee7bf7dd3db15c Mon Sep 17 00:00:00 2001 From: Robert Kirby Date: Wed, 21 May 2025 16:46:39 +0100 Subject: [PATCH 123/222] Rckirby/patchdemo (#4317) Co-authored-by: Pablo Brubeck --------- Co-authored-by: Pablo Brubeck --- demos/patch/hcurl_riesz_star.py.rst | 123 +++++++++++++++ demos/patch/hdiv_riesz_star.py.rst | 101 +++++++++++++ demos/patch/poisson_mg_patches.py.rst | 190 ++++++++++++++++++++++++ demos/patch/star.svg | 153 +++++++++++++++++++ demos/patch/stokes_vanka_patches.py.rst | 159 ++++++++++++++++++++ demos/patch/vanka.svg | 83 +++++++++++ docs/Makefile | 2 +- docs/source/advanced_tut.rst | 4 + tests/firedrake/demos/test_demos_run.py | 4 + 9 files changed, 818 insertions(+), 1 deletion(-) create mode 100644 demos/patch/hcurl_riesz_star.py.rst create mode 100644 demos/patch/hdiv_riesz_star.py.rst create mode 100644 demos/patch/poisson_mg_patches.py.rst create mode 100644 demos/patch/star.svg create mode 100644 demos/patch/stokes_vanka_patches.py.rst create mode 100644 demos/patch/vanka.svg diff --git a/demos/patch/hcurl_riesz_star.py.rst b/demos/patch/hcurl_riesz_star.py.rst new file mode 100644 index 0000000000..e15b824c08 --- /dev/null +++ b/demos/patch/hcurl_riesz_star.py.rst @@ -0,0 +1,123 @@ +Using patch relaxation for H(curl) +================================== + +Contributed by `Robert Kirby `_ +and `Pablo Brubeck `_. + +Multigrid in H(div) and H(curl) also requires relaxation based on topological patches. +Here, we demonstrate how to do this in the latter case.:: + + from firedrake import * + + base = UnitCubeMesh(2, 2, 2) + mh = MeshHierarchy(base, 3) + mesh = mh[-1] + +We consider the Riesz map on H(curl), discretized with lowest order +Nedelec elements. We force the system with a random right-hand side and +impose homogeneous Dirichlet boundary conditions:: + + + def run_solve(mesh, params): + V = FunctionSpace(mesh, "N1curl", 1) + u = TrialFunction(V) + v = TestFunction(V) + uh = Function(V) + a = inner(curl(u), curl(v)) * dx + inner(u, v) * dx + rg = RandomGenerator(PCG64(seed=123456789)) + L = rg.uniform(V.dual(), -1, 1) + bcs = DirichletBC(V, 0, "on_boundary") + + problem = LinearVariationalProblem(a, L, uh, bcs) + solver = LinearVariationalSolver(problem, solver_parameters=params) + + solver.solve() + + return solver.snes.getLinearSolveIterations() + +Having done both :class:`~.ASMStarPC` and :class:`~.PatchPC` in other demos, +here we simply opt for the former. Arnold, Falk, and Winther show that vertex +patches yield a robust method.:: + + + def mg_params(relax): + return { + "ksp_type": "cg", + "pc_type": "mg", + "mg_levels": { + "ksp_type": "chebyshev", + "ksp_max_it": 1, + **relax + }, + "mg_coarse": { + "ksp_type": "preonly", + "pc_type": "cholesky" + } + } + + + def asm_params(construct_dim): + return { + "pc_type": "python", + "pc_python_type": "firedrake.ASMStarPC", + "pc_star_construct_dim": construct_dim, + "pc_star_backend_type": "tinyasm" + } + +Hiptmair proposed a finer space decomposition for Nedelec elements using edge +patches on the original Nedelec space and vertex patches on the gradient of a Lagrange space. The python type +preconditioner :class:`~.HiptmairPC` automatically sets up an additive two-level method +using the auxiliary Lagrange space in a multigrid hierarchy. Therefore, the overall multigrid relaxation composes the edge patches with the auxiliary space relaxation. For the latter, the residual on each level is restricted from the dual of H(curl) into the dual of H1 via the adjoint of the gradient, where a vertex patch relaxation is applied to obtain a correction that is prolonged from H1 into H(curl) via the gradient. :: + + + def hiptmair_params(): + return { + "pc_type": "python", + "pc_python_type": "firedrake.HiptmairPC", + "hiptmair_mg_coarse": asm_params(0), + "hiptmair_mg_levels": asm_params(1), + "hiptmair_mg_levels_ksp_type": "richardson", + "hiptmair_mg_levels_ksp_max_it": 1, + "hiptmair_mg_coarse_ksp_type": "preonly", + } + + +Now, for each parameter choice, we report the iteration count for the Riesz map +over a range of meshes. We see that vertex patches approach give lower +iteration counts than the Hiptmair approach, but they are more expensive.:: + + names = { + "Vertex Star": mg_params(asm_params(0)), + "Hiptmair": mg_params(hiptmair_params()), + } + + for name, parameters in names.items(): + print(f"{name}") + print("Level | Iterations") + for lvl, msh in enumerate(mh[1:], start=1): + its = run_solve(msh, parameters) + print(f"{lvl} | {its}") + +For vertex patches, we expect output like, + +======== ============ + Level Iterations +======== ============ + 1 10 + 2 14 + 3 16 +======== ============ + +and with Hiptmair (edge patches + vertex patches on gradients of Lagrange) + +======== ============ + Level Iterations +======== ============ + 1 18 + 2 20 + 3 21 +======== ============ + +and additional mesh refinement will lead to these numbers leveling off. + +A runnable python version of this demo can be found :demo:`here`. diff --git a/demos/patch/hdiv_riesz_star.py.rst b/demos/patch/hdiv_riesz_star.py.rst new file mode 100644 index 0000000000..7e1e132919 --- /dev/null +++ b/demos/patch/hdiv_riesz_star.py.rst @@ -0,0 +1,101 @@ +Using patch relaxation for H(div) +================================= + +Contributed by `Robert Kirby `_ +and `Pablo Brubeck `_. + +Multigrid in H(div) and H(curl) also requires relaxation based on topological patches. +Here, we demonstrate how to do this in the former case.:: + + from firedrake import * + + base = UnitCubeMesh(2, 2, 2) + mh = MeshHierarchy(base, 3) + mesh = mh[-1] + +We consider the Riesz map on H(div), discretized with lowest order +Raviart--Thomas elements. We force the system with a random right-hand side and +impose homogeneous Dirichlet boundary conditions:: + + + def run_solve(mesh, params): + V = FunctionSpace(mesh, "RT", 1) + u = TrialFunction(V) + v = TestFunction(V) + uh = Function(V) + a = inner(div(u), div(v)) * dx + inner(u, v) * dx + rg = RandomGenerator(PCG64(seed=123456789)) + L = rg.uniform(V.dual(), -1, 1) + bcs = DirichletBC(V, 0, "on_boundary") + + problem = LinearVariationalProblem(a, L, uh, bcs) + solver = LinearVariationalSolver(problem, solver_parameters=params) + + solver.solve() + + return solver.snes.getLinearSolveIterations() + +Having done both :class:`~.ASMStarPC` and :class:`~.PatchPC` in other demos, here we simply opt for the former. +Arnold, Falk, and Winther show that either vertex (`construct_dim=0`) or edge patches (`construct_dim=1`) will be acceptable in three dimensions.:: + + + def mg_params(relax): + return { + "ksp_type": "cg", + "pc_type": "mg", + "mg_levels": { + "ksp_type": "chebyshev", + "ksp_max_it": 1, + **relax + }, + "mg_coarse": { + "ksp_type": "preonly", + "pc_type": "cholesky" + } + } + + + def asm_params(construct_dim): + return { + "pc_type": "python", + "pc_python_type": "firedrake.ASMStarPC", + "pc_star_construct_dim": construct_dim, + "pc_star_backend_type": "tinyasm" + } + +Now, for each parameter choice, we report the iteration count for the Riesz map +over a range of meshes. We see that vertex patches give lower iteration counts than +edge patches, but they are more expensive.:: + + + for cdim in (0, 1): + params = mg_params(asm_params(cdim)) + print(f"Relaxation with patches of dimension {cdim}") + print("Level | Iterations") + for lvl, msh in enumerate(mh[1:], start=1): + its = run_solve(msh, params) + print(f"{lvl} | {its}") + +For vertex patches, we expect output like, + +======== ============ + Level Iterations +======== ============ + 1 9 + 2 10 + 3 13 +======== ============ + +and with edge patches + +======== ============ + Level Iterations +======== ============ + 1 25 + 2 29 + 3 32 +======== ============ + +and additional mesh refinement will lead to these numbers leveling off. + +A runnable python version of this demo can be found :demo:`here`. diff --git a/demos/patch/poisson_mg_patches.py.rst b/demos/patch/poisson_mg_patches.py.rst new file mode 100644 index 0000000000..03813c17fb --- /dev/null +++ b/demos/patch/poisson_mg_patches.py.rst @@ -0,0 +1,190 @@ +Using patch relaxation for multigrid +==================================== + +Contributed by `Robert Kirby `_ +and `Pablo Brubeck `_. + +Simple relaxation like point Jacobi are not optimal or even suitable +smoothers for all applications. Firedrake supports additive Schwarz methods +based on local patch-based decompositions through two different paths. + +This demonstration illustrates basic usage of these methods for the Poisson +problem. Here, multigrid with point Jacobi relaxation works, but the iteration +count degrades with polynomial degree, while vertex star patches give +degree-independent iteration counts. Here, all the degrees of freedom in the cells +(but not on the boundary) around each vertex are included in a patch + +.. figure:: star.svg + :align: center + +For many problems, point Jacobi is even worse, and patches are required even to +get a convergent method. We refer the reader to other demos. + +We start by importing firedrake and setting up a mesh hierarchy and the +exact solution and forcing data.:: + + from firedrake import * + + base = UnitSquareMesh(4, 4) + mh = MeshHierarchy(base, 1) + mesh = mh[-1] + +Next, this function solves the Poisson equation discretized with +a user-provided degree of Lagrange elements and set of solver +parameters and returns the iteration count required for convergence. +To stress-test the solver, the forcing function is taken as a randomly +generated cofunction.:: + + + def run_solve(deg, params): + V = FunctionSpace(mesh, "CG", deg) + u = TrialFunction(V) + v = TestFunction(V) + uh = Function(V) + a = inner(grad(u), grad(v)) * dx + rg = RandomGenerator(PCG64(seed=123456789)) + L = rg.uniform(V.dual(), -1, 1) + bcs = DirichletBC(V, 0, "on_boundary") + + problem = LinearVariationalProblem(a, L, uh, bcs) + solver = LinearVariationalSolver(problem, solver_parameters=params) + + solver.solve() + + return solver.snes.getLinearSolveIterations() + + +These two dictionaries specify parameters for sparse direct method, to be used +on the coarsest level of the multigrid hierarchy.:: + + lu = { + "ksp_type": "preonly", + "pc_type": "lu" + } + +When we use a matrix-free method, there will not be an assembled matrix to factor +on the coarse level. This forces the matrix to be assembled.:: + + assembled_lu = { + "ksp_type": "preonly", + "pc_type": "python", + "pc_python_type": "firedrake.AssembledPC", + "assembled": lu + } + +This function creates multigrid parameters using a given set of +relaxation options and matrix assembled type.:: + + + def mg_params(relax, mat_type="aij"): + if mat_type == "aij": + coarse = lu + else: + coarse = assembled_lu + + return { + "mat_type": mat_type, + "ksp_type": "cg", + "pc_type": "mg", + "mg_levels": { + "ksp_type": "chebyshev", + "ksp_max_it": 1, + **relax + }, + "mg_coarse": coarse + } + +The simplest parameter case will use point Jacobi smoothing on each level. +Here, a matrix-free implementation is appropriate, and Firedrake will +automatically assemble the diagonal for us. +Point Jacobi, however, will require more multigrid iterations as the polynomial +degree increases.:: + + + jacobi_relax = mg_params({"pc_type": "jacobi"}, mat_type="matfree") + +These options specify an additive Schwarz relaxation through :class:`~.PatchPC`. +:class:`~.PatchPC` builds the patch operators by assembling the bilinear form over +each subdomain. Hence, it does not require the global stiffness +matrix to be assembled. +These options tell the patch mechanism to use vertex star patches, storing +the element matrices in a dense format. The patch problems are solved by +LU factorizations without a Krylov iteration. As an optimization, +patch is told to precompute all the element matrices and store the inverses +in dense format.:: + + patch_relax = mg_params({ + "pc_type": "python", + "pc_python_type": "firedrake.PatchPC", + "patch": { + "pc_patch": { + "construct_type": "star", + "construct_dim": 0, + "sub_mat_type": "seqdense", + "dense_inverse": True, + "save_operators": True, + "precompute_element_tensors": True}, + "sub_ksp_type": "preonly", + "sub_pc_type": "lu"}}, + mat_type="matfree") + +:class:`~.ASMStarPC`, on the other hand, does no re-discretization, but extracts the +submatrices for each patch from the already-assembled global stiffness matrix. + + +The tinyasm backend uses LAPACK to invert all the patch operators. If this option +is not specified, PETSc's ASM framework will set up a small KSP for each patch. +This can be useful when the patches become larger and one wants to use a sparse +direct solver or a Krylov iteration on each one.:: + + asm_relax = mg_params({ + "pc_type": "python", + "pc_python_type": "firedrake.ASMStarPC", + "pc_star_backend_type": "tinyasm"}) + +Now, for each parameter choice, we report the iteration count for the Poisson problem +over a range of polynomial degrees. We see that the Jacobi relaxation leads to growth +in iteration count, while both :class:`~.PatchPC` and :class:`~.ASMStarPC` do not. Mathematically, the two +latter options do the same operations, just via different code paths.:: + + names = {"Jacobi": jacobi_relax, + "Patch": patch_relax, + "ASM Star": asm_relax} + + for name, method in names.items(): + print(name) + print("Degree | Iterations") + print("-------------------") + for deg in range(1, 8): + its = run_solve(deg, method) + print(f" {deg} | {its}") + +For Jacobi, we expect output such as + +======== ================ + Degree Iterations +======== ================ + 1 8 + 2 8 + 3 10 + 4 11 + 5 14 + 6 16 + 7 19 +======== ================ + +While for either :class:`~.PatchPC` or :class:`~.ASMStarPC`, we expect + +======== ================ + Degree Iterations +======== ================ + 1 8 + 2 8 + 3 8 + 4 8 + 5 8 + 6 8 + 7 8 +======== ================ + +A runnable python version of this demo can be found :demo:`here`. diff --git a/demos/patch/star.svg b/demos/patch/star.svg new file mode 100644 index 0000000000..29280a713e --- /dev/null +++ b/demos/patch/star.svg @@ -0,0 +1,153 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/demos/patch/stokes_vanka_patches.py.rst b/demos/patch/stokes_vanka_patches.py.rst new file mode 100644 index 0000000000..9a4bb6ca96 --- /dev/null +++ b/demos/patch/stokes_vanka_patches.py.rst @@ -0,0 +1,159 @@ +Using Vanka relaxation for Stokes flow +====================================== + +Contributed by `Robert Kirby `_ +and `Pablo Brubeck `_. + +Vanka relaxation enables monolithic multigrid algorithms for Stokes flow and +other coupled problems. Here, specially chosen patches are used to define +additive Schwarz methods. For a Stokes discretization with continuous pressure +spaces, we orient those patches around vertices, taking velocity values on the boundary +of the patch but not pressures. + +.. figure:: vanka.svg + :align: center + +In practice, we arrive at mesh-independent multigrid convergence using these relaxation. +We can construct Vanka patches either through :class:`~.PatchPC`, in which the bilinear form +is assembled on each vertex patch, or through :class:`~.ASMVankaPC`, in which the patch +operators are extracted from the globally assembled stiffness matrix.:: + + from firedrake import * + + base = UnitSquareMesh(4, 4) + mh = MeshHierarchy(base, 3) + mesh = mh[-1] + +Next, this function solves the Stokes equation discretized with Taylor-Hood +elements and user-provided solver parameters and returns the iteration count +required for convergence. Here, we use a driven cavity problem:: + + + def run_solve(mesh, params): + V = VectorFunctionSpace(mesh, "CG", 2) + Q = FunctionSpace(mesh, "CG", 1) + Z = V * Q + u, p = TrialFunctions(Z) + v, q = TestFunctions(Z) + up = Function(Z) + a = inner(grad(u), grad(v)) * dx - inner(p, div(v)) * dx - inner(div(u), q) * dx + L = 0 + bcs = [DirichletBC(Z.sub(0), Constant((1, 0)), (4, )), + DirichletBC(Z.sub(0), 0, (1, 2, 3))] + nsp = MixedVectorSpaceBasis( + Z, [Z.sub(0), VectorSpaceBasis(constant=True, comm=Z.comm)] + ) + + problem = LinearVariationalProblem(a, L, up, bcs) + solver = LinearVariationalSolver(problem, solver_parameters=params, nullspace=nsp) + + solver.solve() + + return solver.snes.getLinearSolveIterations() + + +These two dictionaries specify parameters for sparse direct method, to be used +on the coarsest level of the multigrid hierarchy.:: + + ldlt = { + "ksp_type": "preonly", + "pc_type": "cholesky", + "pc_factor_shift_type": "nonzero" + } + +When we use a matrix-free method, there will not be an assembled matrix to factor +on the coarse level. This forces the matrix to be assembled.:: + + assembled_ldlt = { + "ksp_type": "preonly", + "pc_type": "python", + "pc_python_type": "firedrake.AssembledPC", + "assembled": ldlt + } + +This function creates multigrid parameters using a given set of +relaxation options and matrix assembled type.:: + + + def mg_params(relax, mat_type="aij"): + if mat_type == "aij": + coarse = ldlt + else: + coarse = assembled_ldlt + + return { + "mat_type": mat_type, + "ksp_type": "gmres", + "pc_type": "mg", + "mg_levels": { + "ksp_type": "chebyshev", + "ksp_max_it": 2, + **relax + }, + "mg_coarse": coarse + } + + +These options specify an additive Schwarz relaxation through :class:`~.PatchPC`. +:class:`~.PatchPC` builds the patch operators by assembling the bilineary form over +each subdomain. Hence, it does not require the global stiffness +matrix to be assembled. These are quite similar to the options used in +:: + + patch_relax = mg_params( + {"pc_type": "python", + "pc_python_type": "firedrake.PatchPC", + "patch": { + "pc_patch_construct_type": "vanka", + "pc_patch_construct_dim": 0, + "pc_patch_exclude_subspaces": 1, + "pc_patch_sub_mat_type": "seqdense", + "sub_ksp_type": "preonly", + "sub_pc_type": "lu", + "pc_patch_dense_inverse": True, + "pc_patch_save_operators": True, + "pc_patch_precompute_element_tensors": None}}, + mat_type="matfree") + +:class:`~.ASMStarPC`, on the other hand, does no re-discretization, but extracts the +patch operators for each patch from the already-assembled global stiffness matrix.:: + + asm_relax = mg_params( + {"pc_type": "python", + "pc_python_type": "firedrake.ASMVankaPC", + "pc_vanka_construct_dim": 0, + "pc_vanka_exclude_subspaces": 1, + "pc_vanka_backend_type": "tinyasm" + }) + +The `tinyasm` backend uses LAPACK to invert all the patch operators. If this option +is not specified, PETSc's ASM framework will set up a small KSP for each patch. +This can be useful when the patches become larger and one wants to use a sparse +direct or Krylov method on each one. + +Now, for each parameter choice, we report the iteration count for the Poisson problem +over a range of polynomial degrees. We see that the Jacobi relaxation leads to growth +in iteration count, while both :class:`~.PatchPC` and :class:`~.ASMStarPC` do not. Mathematically, the two +latter options do the same operations, just via different code paths.:: + + names = {"ASM Vanka": asm_relax, + "Patch Vanka": patch_relax} + + for name, params in names.items(): + print(f"{name}") + print("Level | Iterations") + for lvl, msh in enumerate(mh[1:], start=1): + its = run_solve(msh, params) + print(f"{lvl} | {its}") + +For either set of options, we expect 10 iterations to convergence for each mesh level. + +======== ============ + Level Iterations +======== ============ + 1 10 + 2 10 + 3 10 +======== ============ + +A runnable python version of this demo can be found :demo:`here`. diff --git a/demos/patch/vanka.svg b/demos/patch/vanka.svg new file mode 100644 index 0000000000..dbbaceb780 --- /dev/null +++ b/demos/patch/vanka.svg @@ -0,0 +1,83 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/Makefile b/docs/Makefile index f9fc8fd981..67d6cc4c0b 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -65,7 +65,7 @@ source/demos: copy_demos copy_demos: install -d source/demos - cp ../demos/*/*.rst ../demos/demo_references.bib ../demos/*/*.geo ../demos/*/*.msh ../demos/*/*.png source/demos + cp ../demos/*/*.rst ../demos/demo_references.bib ../demos/*/*.geo ../demos/*/*.msh ../demos/*/*.png ../demos/*/*.svg source/demos for file in source/demos/*.py.rst; do pylit $$file; done reset_notebooks: diff --git a/docs/source/advanced_tut.rst b/docs/source/advanced_tut.rst index 6794207abc..8c08858801 100644 --- a/docs/source/advanced_tut.rst +++ b/docs/source/advanced_tut.rst @@ -24,4 +24,8 @@ element systems. Rayleigh-Benard convection. Netgen support. Full-waveform inversion: spatial and wave sources parallelism. + Degree-independent multigrid convergence using patch relaxation. + Monolithic multigrid with Vanka relaxation for Stokes. + Vertex/edge star multigrid relaxation for H(div). + Auxiliary space patch relaxation multigrid for H(curl). Steady Boussinesq problem with integral constraints. diff --git a/tests/firedrake/demos/test_demos_run.py b/tests/firedrake/demos/test_demos_run.py index cc0938272b..31cb8d4721 100644 --- a/tests/firedrake/demos/test_demos_run.py +++ b/tests/firedrake/demos/test_demos_run.py @@ -40,6 +40,10 @@ Demo(("nonlinear_QG_winddrivengyre", "qg_winddrivengyre"), ["vtk"]), Demo(("parallel-printing", "parprint"), []), Demo(("poisson", "poisson_mixed"), ["vtk"]), + Demo(("patch", "poisson_mg_patches"), []), + Demo(("patch", "stokes_vanka_patches"), []), + Demo(("patch", "hcurl_riesz_star"), []), + Demo(("patch", "hdiv_riesz_star"), []), Demo(("quasigeostrophy_1layer", "qg_1layer_wave"), ["hypre", "vtk"]), Demo(("saddle_point_pc", "saddle_point_systems"), ["hypre", "mumps"]), ] From 80b6edd1659455068399bb4e6a000ec8bce6c531 Mon Sep 17 00:00:00 2001 From: Pablo Brubeck Date: Thu, 22 May 2025 12:46:03 +0100 Subject: [PATCH 124/222] Fix Real space assembly (#4331) * Fix Real space assembly --- tests/firedrake/regression/test_real_space.py | 38 ++++++++++++++++++- tsfc/kernel_interface/common.py | 12 ++++-- 2 files changed, 45 insertions(+), 5 deletions(-) diff --git a/tests/firedrake/regression/test_real_space.py b/tests/firedrake/regression/test_real_space.py index a8ee58760e..b989ae2c86 100644 --- a/tests/firedrake/regression/test_real_space.py +++ b/tests/firedrake/regression/test_real_space.py @@ -58,7 +58,8 @@ def test_real_nonsquare_two_form_assembly(): @pytest.mark.skipcomplex -def test_real_mixed_one_form_assembly(): +@pytest.mark.parametrize("coefficient", (False, True)) +def test_real_mixed_one_form_assembly(coefficient): mesh = UnitIntervalMesh(3) rfs = FunctionSpace(mesh, "Real", 0) cgfs = FunctionSpace(mesh, "CG", 1) @@ -66,7 +67,13 @@ def test_real_mixed_one_form_assembly(): mfs = cgfs*rfs v, q = TestFunctions(mfs) - A = assemble(conj(v) * dx + q * dx) + if coefficient: + z = Function(mfs) + z.subfunctions[1].assign(1) + u, p = split(z) + A = assemble(inner(u, v) * dx + inner(p, q) * dx) + else: + A = assemble(conj(v) * dx + q * dx) qq = TestFunction(rfs) @@ -131,6 +138,33 @@ def test_real_mixed_empty_component_assembly(): assemble(derivative(inner(grad(v), grad(v)) * dx, w)) +@pytest.mark.skipcomplex +@pytest.mark.parametrize("coefficient", (False, True)) +def test_real_extruded_mixed_one_form_assembly(coefficient): + m = UnitIntervalMesh(3) + mesh = ExtrudedMesh(m, 10) + rfs = FunctionSpace(mesh, "Real", 0) + cgfs = FunctionSpace(mesh, "CG", 1) + + mfs = cgfs*rfs + v, q = TestFunctions(mfs) + + if coefficient: + z = Function(mfs) + z.subfunctions[1].assign(1) + u, p = split(z) + A = assemble(inner(u, v) * dx + inner(p, q) * dx) + else: + A = assemble(conj(v) * dx + q * dx) + + qq = TestFunction(rfs) + + AA = assemble(qq * dx) + + np.testing.assert_almost_equal(A.dat.data[1], + AA.dat.data) + + @pytest.mark.skipcomplex def test_real_extruded_mixed_two_form_assembly(): m = UnitIntervalMesh(3) diff --git a/tsfc/kernel_interface/common.py b/tsfc/kernel_interface/common.py index 667dab75a8..ae04a48878 100644 --- a/tsfc/kernel_interface/common.py +++ b/tsfc/kernel_interface/common.py @@ -16,6 +16,7 @@ from numpy import asarray from tsfc import fem, ufl_utils from finat.element_factory import as_fiat_cell, create_element +from finat.ufl import MixedElement from tsfc.kernel_interface import KernelInterface from tsfc.logging import logger from ufl.utils.sequences import max_degree @@ -298,10 +299,16 @@ def set_quad_rule(params, cell, integral_type, functions): # Check if the integral has a quad degree or quad element attached, # otherwise use the estimated polynomial degree attached by compute_form_data quad_rule = params.get("quadrature_rule", "default") + elements = [] + for f in functions: + e = f.ufl_element() + if type(e) is MixedElement: + elements.extend(e.sub_elements) + else: + elements.append(e) try: quadrature_degree = params["quadrature_degree"] except KeyError: - elements = [f.ufl_function_space().ufl_element() for f in functions] quad_data = set((e.degree(), e.quadrature_scheme() or "default") for e in elements if e.family() in {"Quadrature", "Boundary Quadrature"}) if len(quad_data) == 0: @@ -320,8 +327,7 @@ def set_quad_rule(params, cell, integral_type, functions): if isinstance(quad_rule, str): scheme = quad_rule fiat_cell = as_fiat_cell(cell) - finat_elements = set(create_element(f.ufl_element()) for f in functions - if f.ufl_element().family() != "Real") + finat_elements = set(create_element(e) for e in elements if e.family() != "Real") fiat_cells = [fiat_cell] + [finat_el.complex for finat_el in finat_elements] fiat_cell = max_complex(fiat_cells) From 5144bf85ee557d62d87d6844bd82d39ccdfc4389 Mon Sep 17 00:00:00 2001 From: ksagiyam <46749170+ksagiyam@users.noreply.github.com> Date: Thu, 22 May 2025 20:50:37 +0100 Subject: [PATCH 125/222] submesh: allow for extracting codim-1 submeshes (#4329) --- firedrake/cython/dmcommon.pyx | 229 ++++++++++++++++-- firedrake/interpolation.py | 6 +- firedrake/mesh.py | 81 ++++--- tests/firedrake/submesh/test_submesh_facet.py | 124 ++++++++++ .../submesh/test_submesh_interpolate.py | 108 ++++++++- 5 files changed, 491 insertions(+), 57 deletions(-) create mode 100644 tests/firedrake/submesh/test_submesh_facet.py diff --git a/firedrake/cython/dmcommon.pyx b/firedrake/cython/dmcommon.pyx index b67c0c21ec..e1666425a4 100644 --- a/firedrake/cython/dmcommon.pyx +++ b/firedrake/cython/dmcommon.pyx @@ -1,6 +1,7 @@ # cython: language_level=3 # Utility functions common to all DMs used in Firedrake +import enum import functools import math import cython @@ -25,6 +26,28 @@ FACE_SETS_LABEL = "Face Sets" CELL_SETS_LABEL = "Cell Sets" +class DistributedMeshOverlapType(enum.Enum): + """How should the mesh overlap be grown for distributed meshes? + + Possible options are: + + - :attr:`NONE`: Don't overlap distributed meshes, only useful for problems with + no interior facet integrals. + - :attr:`FACET`: Add ghost entities in the closure of the star of + facets. + - :attr:`RIDGE`: Add ghost entities in the closure of the star of + ridges. + - :attr:`VERTEX`: Add ghost entities in the closure of the star + of vertices. + + Defaults to :attr:`FACET`. + """ + NONE = 4 + FACET = 1 + RIDGE = 2 + VERTEX = 3 + + def get_topological_dimension(PETSc.DM dm): """ Get the topological dimension of a DMPlex or DMSwarm. Assumes that @@ -3463,13 +3486,111 @@ cdef int DMPlexGetAdjacency_Facet_Support(PETSc.PetscDM dm, return 0 -def set_adjacency_callback(PETSc.DM dm not None): +cdef int DMPlexGetAdjacency_Closure_Star_Ridge( + PETSc.PetscDM dm, + PetscInt p, + PetscInt *adjSize, + PetscInt adj[], + void *ctx +) noexcept nogil: + """Custom adjacency callback for closure(star(ridge)) halo. + + Parameters + ---------- + dm + The DMPlex object. + p + The mesh point to compute the adjacency of. + adjSize + Output parameter, the size of the computed adjacency. + adj + Output parameter, the adjacent mesh points. + ctx + User context. + + The halo we need for owner-computes is everything in the stencil + of the owned mesh points. For cells, we already have everything, + for edges, if we own the edge, we need the mesh points in + closure(star(edge)). This function returns non-zero adjacency + only for edges, which then means that everything else falls + through right. + + """ + cdef: + const PetscInt *star = NULL; + PetscInt numAdj = 0 + PetscInt maxAdjSize = adjSize[0] + PetscInt starSize + PetscInt s + PetscInt pStart, pEnd + PetscInt point, closureSize, ci, q + PetscInt *closure = NULL + DMLabel label = ctx; + PetscBool flg = PETSC_TRUE + + # This function is general, and should also be able to replace + # `DMPlexGetAdjacency_Facet_Support()` just by replacing + # 2 with 1 in the line below. + CHKERR(DMPlexGetHeightStratum(dm, 2, &pStart, &pEnd)) + if not (pStart <= p < pEnd): + # Not a point of interest, no adjacent points + adjSize[0] = 0 + return 0 + if label != NULL: + # If a label is provided to filter out points, use it. + # Requires that the label has already had an index created. + # The label should mark those points that are not owned. + # If the point is owned, then we would like to grow the halo. + # So we need the remote process to donate those points. + # Hence, if we own the point, we return an empty adjacency (we + # don't want to donate those points to the remote process), + # and vice versa. + CHKERR(DMLabelHasPoint(label, p, &flg)) + if not flg: + # This point is owned, no adjacency. + adjSize[0] = 0 + return 0 + # A remote point, gather the adjacency + CHKERR(DMPlexGetTransitiveClosure(dm, p, PETSC_FALSE, &starSize, &star)) + for s in range(starSize): + point = star[2*s] + CHKERR(DMPlexGetTransitiveClosure(dm, point, PETSC_TRUE, &closureSize, &closure)) + for ci in range(closureSize): + # This is just ensuring that the adjacency is unique. + for q in range(numAdj): + if closure[2*ci] == adj[q]: + break + else: + adj[numAdj] = closure[2*ci] + numAdj += 1 + # Too many adjacent points for the provided output array. + if numAdj > maxAdjSize: + SETERR(77) + CHKERR(DMPlexRestoreTransitiveClosure(dm, point, PETSC_TRUE, &closureSize, &closure)) + CHKERR(DMPlexRestoreTransitiveClosure(dm, p, PETSC_FALSE, &starSize, &star)) + adjSize[0] = numAdj + return 0 + + +def set_adjacency_callback( + PETSc.DM dm not None, + overlap_type, +): """Set the callback for DMPlexGetAdjacency. - :arg dm: The DMPlex object. + Parameters + ---------- + dm : PETSc.DM + The DMPlex object. + overlap_type : DistributedMeshOverlapType + `DistributedMeshOverlapType.FACET` or `DistributedMeshOverlapType.RIDGE`. + Notes + ----- This is used during DMPlexDistributeOverlap to determine where to - grow the halos.""" + grow the halos. + + """ cdef: PetscInt pStart, pEnd, p DMLabel label = NULL @@ -3491,7 +3612,12 @@ def set_adjacency_callback(PETSc.DM dm not None): for p in range(nleaves): CHKERR(DMLabelSetValue(label, ilocal[p] if ilocal else p, 1)) CHKERR(DMLabelCreateIndex(label, pStart, pEnd)) - CHKERR(DMPlexSetAdjacencyUser(dm.dm, DMPlexGetAdjacency_Facet_Support, NULL)) + if overlap_type == DistributedMeshOverlapType.FACET: + CHKERR(DMPlexSetAdjacencyUser(dm.dm, DMPlexGetAdjacency_Facet_Support, NULL)) + elif overlap_type == DistributedMeshOverlapType.RIDGE: + CHKERR(DMPlexSetAdjacencyUser(dm.dm, DMPlexGetAdjacency_Closure_Star_Ridge, NULL)) + else: + raise NotImplementedError(f"Unknown overlap type: {overlap_type}") def clear_adjacency_callback(PETSc.DM dm not None): @@ -3690,6 +3816,7 @@ def create_halo_exchange_sf(PETSc.DM dm): @cython.boundscheck(False) @cython.wraparound(False) def submesh_create(PETSc.DM dm, + PetscInt subdim, label_name, PetscInt label_value): """Create submesh. @@ -3698,6 +3825,8 @@ def submesh_create(PETSc.DM dm, ---------- dm : PETSc.DM DMPlex representing the mesh topology + subdim : int + Topological dimension of the submesh label_name : str Name of the label label_value : int @@ -3706,11 +3835,35 @@ def submesh_create(PETSc.DM dm, """ cdef: PETSc.DM subdm = PETSc.DMPlex() - PETSc.DMLabel label + PETSc.DMLabel label, temp_label PETSc.SF ownership_transfer_sf = PETSc.SF() + char *temp_label_name = "firedrake_submesh_temp_label" + PetscInt pStart, pEnd, p, i, stratum_size + PETSc.PetscIS stratum_is = NULL + const PetscInt *stratum_indices = NULL label = dm.getLabel(label_name) - CHKERR(DMPlexFilter(dm.dm, label.dmlabel, label_value, PETSC_FALSE, PETSC_TRUE, &ownership_transfer_sf.sf, &subdm.dm)) + # Create temp_label that contains no lower-dimensional points. + dm.createLabel(temp_label_name) + temp_label = dm.getLabel(temp_label_name) + CHKERR(DMLabelGetStratumSize(label.dmlabel, label_value, &stratum_size)) + if stratum_size > 0: + CHKERR(DMLabelGetStratumIS(label.dmlabel, label_value, &stratum_is)) + CHKERR(ISGetIndices(stratum_is, &stratum_indices)) + CHKERR(DMPlexGetDepthStratum(dm.dm, subdim, &pStart, &pEnd)) + for i in range(stratum_size): + p = stratum_indices[i] + # Only include points on the submesh topological dimension, + # culling all lower-dimensional points. + if pStart <= p < pEnd: + CHKERR(DMLabelSetValue(temp_label.dmlabel, p, label_value)) + CHKERR(ISRestoreIndices(stratum_is, &stratum_indices)) + CHKERR(ISDestroy(&stratum_is)) + # Make submesh using temp_label. + CHKERR(DMPlexFilter(dm.dm, temp_label.dmlabel, label_value, PETSC_FALSE, PETSC_TRUE, &ownership_transfer_sf.sf, &subdm.dm)) + # Destroy temp_label. + dm.removeLabel(temp_label_name) + subdm.removeLabel(temp_label_name) submesh_update_facet_labels(dm, subdm) submesh_correct_entity_classes(dm, subdm, ownership_transfer_sf) return subdm @@ -3823,13 +3976,17 @@ def submesh_update_facet_labels(PETSc.DM dm, PETSc.DM subdm): PETSc.DMLabel sub_int_facet_label, sub_ext_facet_label PetscBool has_point + dim = dm.getDimension() + subdim = subdm.getDimension() + if subdim != dim: + # What labels the submesh should have by default is not trivial. + # Think harder and do something useful here if necessary. + return # Mark interior and exterior facets label_facets(subdm) sub_int_facet_label = subdm.getLabel("interior_facets") sub_ext_facet_label = subdm.getLabel("exterior_facets") # Mark new exterior facets with current max label value + 1 in "Face Sets" - dim = dm.getDimension() - subdim = subdm.getDimension() subpoint_is = subdm.getSubpointIS() CHKERR(ISGetIndices(subpoint_is.iset, &subpoint_indices)) if subdim == dim: @@ -3866,11 +4023,14 @@ def submesh_update_facet_labels(PETSc.DM dm, PETSc.DM subdm): @cython.boundscheck(False) @cython.wraparound(False) -def submesh_create_cell_closure_cell_submesh(PETSc.DM subdm, - PETSc.DM dm, - PETSc.Section subcell_numbering, - PETSc.Section cell_numbering, - np.ndarray cell_closure): +def submesh_create_cell_closure( + PETSc.DM subdm, + PETSc.DM dm, + PETSc.Section subcell_numbering, + PETSc.Section cell_numbering, + np.ndarray cell_closure, + np.ndarray entity_per_subcell, +): """Inherit cell_closure from parent. Parameters @@ -3885,17 +4045,26 @@ def submesh_create_cell_closure_cell_submesh(PETSc.DM subdm, The cell_numbering of the parent mesh. cell_closure : numpy.ndarray The cell_closure of the parent mesh. + entity_per_subcell : numpy.ndarray + List of the number of entity points in each dimension on subcell. """ cdef: PETSc.IS subpoint_is const PetscInt *subpoint_indices = NULL PetscInt *subpoint_indices_inv = NULL + PetscInt dim, subdim PetscInt subpStart, subpEnd, subp, subcStart, subcEnd, subc, subcell PetscInt pStart, pEnd, p, cStart, cEnd, c, cell - PetscInt nclosure, cl + PetscInt nclosure, cl, nsubclosure, subcl, nsupport + PetscInt *subclosure = NULL + const PetscInt *support np.ndarray subcell_closure + dim = get_topological_dimension(dm) + subdim = get_topological_dimension(subdm) + if subdim != dim and subdim != dim - 1: + raise NotImplementedError(f"subdim = {subdim} and dim = {dim}") get_chart(subdm.dm, &subpStart, &subpEnd) get_height_stratum(subdm.dm, 0, &subcStart, &subcEnd) get_chart(dm.dm, &pStart, &pEnd) @@ -3905,21 +4074,41 @@ def submesh_create_cell_closure_cell_submesh(PETSc.DM subdm, CHKERR(PetscMalloc1(pEnd - pStart, &subpoint_indices_inv)) for p in range(pStart, pEnd): subpoint_indices_inv[p - pStart] = -1 - for subp in range(subpStart, subpEnd): - subpoint_indices_inv[subpoint_indices[subp] - pStart] = subp + subcell_closure = np.empty((subcEnd - subcStart, sum(entity_per_subcell)), dtype=IntType) nclosure = cell_closure.shape[1] - subcell_closure = np.empty((subcEnd - subcStart, nclosure), dtype=IntType) + nsubclosure = subcell_closure.shape[1] for subc in range(subcStart, subcEnd): c = subpoint_indices[subc] + if subdim == dim: + pass + elif subdim == dim - 1: + CHKERR(DMPlexGetSupportSize(dm.dm, c, &nsupport)) + CHKERR(DMPlexGetSupport(dm.dm, c, &support)) + if nsupport <= 0: + raise RuntimeError(f"Num. support = {nsupport} (<= 0) for parent facet {c}") + # Assume single cell type mesh and pick arbitrary side. + c = support[0] CHKERR(PetscSectionGetOffset(subcell_numbering.sec, subc, &subcell)) CHKERR(PetscSectionGetOffset(cell_numbering.sec, c, &cell)) + get_transitive_closure(subdm.dm, subc, PETSC_TRUE, &nsubclosure, &subclosure) + for subcl in range(nsubclosure): + subp = subclosure[2*subcl] + p = subpoint_indices[subp] + subpoint_indices_inv[p - pStart] = subp # set to non-negative subp. + subcl = 0 for cl in range(nclosure): p = cell_closure[cell, cl] subp = subpoint_indices_inv[p] if subp >= 0: - subcell_closure[subcell, cl] = subp - else: - raise RuntimeError(f"subcell = {subcell}, cell = {cell}, p = {p}, subp = {subp}") + subcell_closure[subcell, subcl] = subp + subcl += 1 + if subcl != nsubclosure: + raise RuntimeError(f"subcl {(subcl)} != nsubclosure {(nsubclosure)}") + for subcl in range(nsubclosure): + subp = subclosure[2*subcl] + p = subpoint_indices[subp] + subpoint_indices_inv[p - pStart] = -1 # set back to -1. + restore_transitive_closure(subdm.dm, subc, PETSC_TRUE, &nsubclosure, &subclosure) CHKERR(PetscFree(subpoint_indices_inv)) CHKERR(ISRestoreIndices(subpoint_is.iset, &subpoint_indices)) return subcell_closure diff --git a/firedrake/interpolation.py b/firedrake/interpolation.py index 3affb8b43c..06d124934e 100644 --- a/firedrake/interpolation.py +++ b/firedrake/interpolation.py @@ -257,7 +257,11 @@ class Interpolator(abc.ABC): def __new__(cls, expr, V, **kwargs): target_mesh = as_domain(V) source_mesh = extract_unique_domain(expr) or target_mesh - if target_mesh is source_mesh or all(isinstance(m.topology, firedrake.mesh.MeshTopology) for m in [target_mesh, source_mesh]) and target_mesh.submesh_ancesters[-1] is source_mesh.submesh_ancesters[-1]: + submesh_interp_implemented = \ + all(isinstance(m.topology, firedrake.mesh.MeshTopology) for m in [target_mesh, source_mesh]) and \ + target_mesh.submesh_ancesters[-1] is source_mesh.submesh_ancesters[-1] and \ + target_mesh.topological_dimension() == source_mesh.topological_dimension() + if target_mesh is source_mesh or submesh_interp_implemented: return object.__new__(SameMeshInterpolator) else: if isinstance(target_mesh.topology, firedrake.mesh.VertexOnlyMeshTopology): diff --git a/firedrake/mesh.py b/firedrake/mesh.py index 0be58d6d95..7b8b74122f 100644 --- a/firedrake/mesh.py +++ b/firedrake/mesh.py @@ -24,6 +24,7 @@ from pyop2.utils import as_tuple import firedrake.cython.dmcommon as dmcommon +from firedrake.cython.dmcommon import DistributedMeshOverlapType import firedrake.cython.extrusion_numbering as extnum import firedrake.extrusion_utils as eutils import firedrake.cython.spatialindex as spatialindex @@ -146,25 +147,6 @@ def _generate_default_mesh_topology_permutation_name(reorder): return "_".join(["firedrake", "default", str(reorder)]) -class DistributedMeshOverlapType(enum.Enum): - """How should the mesh overlap be grown for distributed meshes? - - Possible options are: - - - :attr:`NONE`: Don't overlap distributed meshes, only useful for problems with - no interior facet integrals. - - :attr:`FACET`: Add ghost entities in the closure of the star of - facets. - - :attr:`VERTEX`: Add ghost entities in the closure of the star - of vertices. - - Defaults to :attr:`FACET`. - """ - NONE = 1 - FACET = 2 - VERTEX = 3 - - class _Facets(object): """Wrapper class for facet interation information on a :func:`Mesh` @@ -1182,8 +1164,8 @@ def _add_overlap(self): if overlap_type == DistributedMeshOverlapType.NONE: if overlap > 0: raise ValueError("Can't have NONE overlap with overlap > 0") - elif overlap_type == DistributedMeshOverlapType.FACET: - dmcommon.set_adjacency_callback(self.topology_dm) + elif overlap_type in [DistributedMeshOverlapType.FACET, DistributedMeshOverlapType.RIDGE]: + dmcommon.set_adjacency_callback(self.topology_dm, overlap_type) original_name = self.topology_dm.getName() sfBC = self.topology_dm.distributeOverlap(overlap) self.topology_dm.setName(original_name) @@ -1262,12 +1244,26 @@ def cell_closure(self): cell = self.ufl_cell() assert tdim == cell.topological_dimension() - if self.submesh_parent is not None: - return dmcommon.submesh_create_cell_closure_cell_submesh(plex, - self.submesh_parent.topology_dm, - cell_numbering, - self.submesh_parent._cell_numbering, - self.submesh_parent.cell_closure) + if self.submesh_parent is not None and \ + not (self.submesh_parent.ufl_cell().cellname() == "hexahedron" and cell.cellname() == "quadrilateral"): + # Codim-1 submesh of a hex mesh (i.e. a quad submesh) can not + # inherit cell_closure from the hex mesh as the cell_closure + # must follow the special orientation restriction. This means + # that, when the quad submesh works with the parent hex mesh, + # quadrature points must be permuted (i.e. use the canonical + # quadrature point ordering based on the cone ordering). + topology = FIAT.ufc_cell(cell).get_topology() + entity_per_cell = np.zeros(len(topology), dtype=IntType) + for d, ents in topology.items(): + entity_per_cell[d] = len(ents) + return dmcommon.submesh_create_cell_closure( + plex, + self.submesh_parent.topology_dm, + cell_numbering, + self.submesh_parent._cell_numbering, + self.submesh_parent.cell_closure, + entity_per_cell, + ) elif cell.is_simplex(): topology = FIAT.ufc_cell(cell).get_topology() entity_per_cell = np.zeros(len(topology), dtype=IntType) @@ -4619,8 +4615,24 @@ def Submesh(mesh, subdim, subdomain_id, label_name=None, name=None): Notes ----- - Currently, one can only make submeshes that have the same - topological dimension as the parent mesh. + Currently, one can only make submeshes of co-dimension 0 or 1. + + To make a submesh of co-dimension 1, the parent mesh must have + been overlapped with :class:`DistributedMeshOverlapType` of + {``None``, `VERTEX``, ``RIDGE``}; see ``distribution_parameters`` + kwarg of :func:`~.Mesh`. + + To use interior facet integration on a submesh of co-dimension 1, + the parent mesh must have been overlapped with + ``DistributedMeshOverlapType`` of {`VERTEX``, ``RIDGE``}, and the + facets of the parent mesh must have been labeled such that the + ridges (entities of co-dim 2) to be contained in the submesh are + shared by at most two facets. + + Currently, to make a quadrilateral submesh from a hexahedral mesh, + the facets of the hex mesh must have been labeled such that the + ridges to be contained in the quad mesh are shared by at most two + facets to make the quad mesh orientation algorithm work. Examples -------- @@ -4657,12 +4669,15 @@ def Submesh(mesh, subdim, subdomain_id, label_name=None, name=None): mesh.topology.init() plex = mesh.topology_dm dim = plex.getDimension() - if subdim != dim: - raise NotImplementedError(f"Found submesh dim ({subdim}) != parent dim ({dim})") + if subdim not in [dim, dim - 1]: + raise NotImplementedError(f"Found submesh dim ({subdim}) and parent dim ({dim})") if label_name is None: - label_name = dmcommon.CELL_SETS_LABEL + if subdim == dim: + label_name = dmcommon.CELL_SETS_LABEL + elif subdim == dim - 1: + label_name = dmcommon.FACE_SETS_LABEL name = name or _generate_default_submesh_name(mesh.name) - subplex = dmcommon.submesh_create(plex, label_name, subdomain_id) + subplex = dmcommon.submesh_create(plex, subdim, label_name, subdomain_id) subplex.setName(_generate_default_mesh_topology_name(name)) if subplex.getDimension() != subdim: raise RuntimeError(f"Found subplex dim ({subplex.getDimension()}) != expected ({subdim})") diff --git a/tests/firedrake/submesh/test_submesh_facet.py b/tests/firedrake/submesh/test_submesh_facet.py new file mode 100644 index 0000000000..1e9554c0c6 --- /dev/null +++ b/tests/firedrake/submesh/test_submesh_facet.py @@ -0,0 +1,124 @@ +import pytest +from firedrake import * +from firedrake.mesh import plex_from_cell_list +from pyop2.mpi import COMM_WORLD + + +@pytest.mark.parallel(nprocs=2) +def test_submesh_facet_corner_case_1(): + # mesh and ownership: + # + # +------+ + # / 0 /| + # +------+------+ + # / 0 / 1 /| + # +------+------+ | + # | | | + + # | | |/ + # +------+------+ + # + # If (DistributedMeshOverlapType.FACET, 1) was used for the parent mesh: + # + # subm would look like: + # + # + + # /| + # +------+ +------+ + # | + | | | + # |/ | | | + # +------+ +------+ + # + # rank 0 rank 1 + # + # This is sane, but current quad orientation implementation + # can not handle such asymmetric overlaps. + # + # If (DistributedMeshOverlapType.RIDGE, 1) is used for the parent mesh: + # + # subm looks like: + # + # + + + # /| /| + # +------+ +------+ + # | + | | + | + # |/ | |/ | + # +------+ +------+ + # + # rank 0 rank 1 + # + # This works, and this is better. + vertices = [ + [0., 0., 0.], + [0., 0., 1.], + [0., 1., 0.], + [0., 1., 1.], + [0., 2., 0.], + [0., 2., 1.], + [1., 0., 0.], + [1., 0., 1.], + [1., 1., 0.], + [1., 1., 1.], + [1., 2., 0.], + [1., 2., 1.], + [2., 0., 0.], + [2., 0., 1.], + [2., 1., 0.], + [2., 1., 1.], + ] + cell_vert_map = [ + [0, 2, 8, 6, 1, 7, 9, 3], + [2, 4, 10, 8, 3, 9, 11, 5], + [6, 8, 14, 12, 7, 13, 15, 9], + ] + plex = plex_from_cell_list(3, cell_vert_map, vertices, COMM_WORLD) + mesh = Mesh( + plex, + distribution_parameters={ + "partition": True, + "partitioner_type": "simple", + "overlap_type": (DistributedMeshOverlapType.RIDGE, 1), + }, + ) + x, y, z = SpatialCoordinate(mesh) + facet0 = And(And(x > 0.9, x < 1.1), y > 0.9) + facet1 = And(And(y > 0.9, y < 1.1), x > 0.9) + V = FunctionSpace(mesh, "Q", 2) + f = Function(V).interpolate(conditional(Or(facet0, facet1), 1, 0)) + mesh = RelabeledMesh(mesh, [f], [999]) + subm = Submesh(mesh, mesh.topological_dimension() - 1, 999) + v = assemble(Constant(1.) * dx(domain=subm)) + assert abs(v - 2.) < 2.e-15 + + +@pytest.mark.parallel(nprocs=2) +def test_submesh_facet_corner_case_2(): + # Naively, one would have a submesh like: + # + # +--------+ +--------+ + # | /| /| + # | / | / | + # | / | / | + # +--------+ +--------+ + # rank 0 rank 1 + # + # , which might be fine in general, but + # Firedrake does not expect this kind of mesh. + # Thus, in dmcommon.create_submesh(), we cull + # redundant lower-dimensional points in the + # label to obtain a submesh like: + # + # +--------+ + + # | /| /| + # | / | / | + # | / | / | + # +--------+ +--------+ + # rank 0 rank 1 + mesh = UnitCubeMesh(1, 1, 1) + V = FunctionSpace(mesh, "HDiv Trace", 0) + x, y, z = SpatialCoordinate(mesh) + facet_function = Function(V).interpolate( + conditional(x > .999, 1., 0.,) + ) + facet_value = 999 + mesh = RelabeledMesh(mesh, [facet_function], [facet_value]) + _ = Submesh(mesh, mesh.topological_dimension() - 1, facet_value) diff --git a/tests/firedrake/submesh/test_submesh_interpolate.py b/tests/firedrake/submesh/test_submesh_interpolate.py index cc6dac2231..774d163a2b 100644 --- a/tests/firedrake/submesh/test_submesh_interpolate.py +++ b/tests/firedrake/submesh/test_submesh_interpolate.py @@ -34,11 +34,11 @@ def _test_submesh_interpolate_cell_cell(mesh, subdomain_cond, fe_fesub): indicator_function = Function(DG0).interpolate(subdomain_cond) label_value = 999 mesh.mark_entities(indicator_function, label_value) - msub = Submesh(mesh, dim, label_value) + subm = Submesh(mesh, dim, label_value) V = FunctionSpace(mesh, family, degree) V_ = FunctionSpace(mesh, family_sub, degree_sub) - Vsub = FunctionSpace(msub, family_sub, degree_sub) - Vsub_ = FunctionSpace(msub, family, degree) + Vsub = FunctionSpace(subm, family_sub, degree_sub) + Vsub_ = FunctionSpace(subm, family, degree) f = Function(V).interpolate(_get_expr(V)) gsub_ = Function(Vsub_).interpolate(_get_expr(Vsub_)) gsub = Function(Vsub).interpolate(gsub_) @@ -124,3 +124,105 @@ def test_submesh_interpolate_cell_cell_quad_3_processes(fe_fesub, condx, condy, cond = conditional(condx(x, 0.5), 1, conditional(condy(y, 0.5), 1, 0)) # noqa: E128 _test_submesh_interpolate_cell_cell(mesh, cond, fe_fesub) + + +@pytest.mark.parallel(nprocs=5) +@pytest.mark.parametrize('hexahedral', [False, True]) +@pytest.mark.parametrize('direction', ['x', 'y', 'z']) +@pytest.mark.parametrize('facet_type', ['interior', 'exterior']) +def test_submesh_interpolate_3Dcell_2Dfacet(hexahedral, direction, facet_type): + def expr(m): + x, y, z = SpatialCoordinate(m) + return x + y**2 + z**3 + degree = 3 + distribution_parameters = { + "partition": True, + "overlap_type": (DistributedMeshOverlapType.RIDGE, 1), + } + if hexahedral: + mesh = Mesh( + join(cwd, "..", "meshes", "cube_hex.msh"), + distribution_parameters=distribution_parameters, + ) + V = FunctionSpace(mesh, "CG", 2) + else: + mesh = UnitCubeMesh(8, 8, 8) + V = FunctionSpace(mesh, "HDiv Trace", 0) + x, y, z = SpatialCoordinate(mesh) + facet_function = Function(V).interpolate( + conditional( + { + ('x', 'interior'): And(x > .499, x < .501), + ('y', 'interior'): And(y > .499, y < .501), + ('z', 'interior'): And(z > .499, z < .501), + ('x', 'exterior'): x > .999, + ('y', 'exterior'): y > .999, + ('z', 'exterior'): z > .999, + }[(direction, facet_type)], + 1., 0., + ) + ) + facet_value = 999 + mesh = RelabeledMesh(mesh, [facet_function], [facet_value]) + subm = Submesh(mesh, mesh.topological_dimension() - 1, facet_value) + DG3d = FunctionSpace(mesh, "DG", degree) + dg3d = Function(DG3d).interpolate(expr(mesh)) + DG2d = FunctionSpace(subm, "DG", degree) + dg2d = Function(DG2d).interpolate(expr(subm)) + value3d_int = assemble(inner(dg3d('+'), dg3d('-')) * dS(facet_value)) + value3d_ext = assemble(inner(dg3d, dg3d) * ds(facet_value)) + value2d = assemble(inner(dg2d, dg2d) * dx) + assert abs(value2d - (value3d_int + value3d_ext)) < 1.e-14 + if facet_type == 'exterior': + x, y, z = SpatialCoordinate(subm) + RT2d = FunctionSpace(subm, "RTCE" if hexahedral else "RTE", 4) + tangent_expr = { + 'x': as_vector([0, y**2, z**3]), + 'y': as_vector([x, 0, z**3]), + 'z': as_vector([x, y**2, 0]), + }[direction] + rt2d = Function(RT2d).project( + tangent_expr, + solver_parameters={ + "ksp_rtol": 1.e-14, + }, + ) + error_expr = rt2d - tangent_expr + error = assemble(inner(error_expr, error_expr) * dx)**0.5 + assert abs(error) < 1.e-14 + + +@pytest.mark.parallel(nprocs=4) +def test_submesh_interpolate_3Dcell_2Dfacet_simplex_sckelton(): + # The usage of sckelton meshes is limited as + # number of support cells of a facet can be > 2. + # We can not make sckelton mesh of hex meshes as, + # already in the quad orientation implementation, + # we assume that number of support cells <= 2. + def expr(m): + x, y, z = SpatialCoordinate(m) + return x + y**2 + z**3 + degree = 3 + distribution_parameters = { + "partition": True, + "overlap_type": (DistributedMeshOverlapType.RIDGE, 1), + } + mesh = UnitCubeMesh(8, 8, 8, distribution_parameters=distribution_parameters) + V = FunctionSpace(mesh, "HDiv Trace", 0) + facet_function = Function(V).interpolate(Constant(1.)) + facet_value = 999 + mesh = RelabeledMesh(mesh, [facet_function], [facet_value]) + subm = Submesh(mesh, mesh.topological_dimension() - 1, facet_value) + HDivT3d = FunctionSpace(mesh, "HDiv Trace", degree) + hdivt3d = Function(HDivT3d).interpolate(expr(mesh)) + DG2d = FunctionSpace(subm, "DG", degree) + dg2d = Function(DG2d).interpolate(expr(subm)) + value3d_int = assemble(inner(hdivt3d('+'), hdivt3d('-')) * dS(facet_value)) + value3d_ext = assemble(inner(hdivt3d, hdivt3d) * ds(facet_value)) + value2d = assemble(inner(dg2d, dg2d) * dx) + assert abs(value2d - (value3d_int + value3d_ext)) < 5.e-13 + DG3d = FunctionSpace(mesh, "DG", degree) + dg3d = Function(DG3d).interpolate(expr(mesh)) + dg2d_ = Function(DG2d).interpolate(dg3d) + error = assemble(inner(dg2d_ - expr(subm), dg2d_ - expr(subm)) * dx)**0.5 + assert abs(error) < 1.e-14 From 16e086edde9df2cb47ed6189a884cbede65b6721 Mon Sep 17 00:00:00 2001 From: Colin J Cotter Date: Wed, 28 May 2025 17:00:09 +0100 Subject: [PATCH 126/222] Vlasov demo (#4320) * Vlasov demo --------- Co-authored-by: David Ham Co-authored-by: Josh Hope-Collins Co-authored-by: Connor Ward --- demos/vlasov_poisson_1d/vlasov_0s_HR.png | Bin 0 -> 63345 bytes demos/vlasov_poisson_1d/vlasov_0s_LR.png | Bin 0 -> 62268 bytes demos/vlasov_poisson_1d/vlasov_15_HR.png | Bin 0 -> 234092 bytes demos/vlasov_poisson_1d/vlasov_15_LR.png | Bin 0 -> 224728 bytes demos/vlasov_poisson_1d/vp1d.py.rst | 390 +++++++++++++++++++++ docs/source/advanced_tut.rst | 1 + tests/firedrake/demos/test_demos_run.py | 1 + tests/firedrake/output/test_io_function.py | 2 +- 8 files changed, 393 insertions(+), 1 deletion(-) create mode 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diff --git a/demos/vlasov_poisson_1d/vp1d.py.rst b/demos/vlasov_poisson_1d/vp1d.py.rst
new file mode 100644
index 0000000000..be950f2ac3
--- /dev/null
+++ b/demos/vlasov_poisson_1d/vp1d.py.rst
@@ -0,0 +1,390 @@
+1D Vlasov-Poisson Equation
+===========================
+
+This tutorial was contributed by `Colin Cotter
+`__ and Werner Bauer.
+
+A plasma is a continuum of moving particles with nonunique velocity
+at each point in space. In :math:`d` dimensions, the plasma is
+described by a density :math:`f(x,v,t)` where :math:`x\in \mathbb{R}^d`
+are the physical coordinates and :math:`v \in \mathbb{R}^d` are velocity
+coordinates. Hence, in :math:`d` dimensions, a :math:`2d`
+dimensional mesh is required. To deal with this curse of
+dimensionality, particle-in-cell methods are usually used. However,
+in 1 dimension, it is tractable to simulate the plasma on a 2
+dimensional mesh.
+
+The Vlasov equation models the (collisionless) conservation of plasma
+particles, according to 
+
+.. math::
+   f_t + \nabla_{\vec{x}} \cdot (\vec{v}f) + \nabla_{\vec{v}} \cdot (\vec{a}f) = 0,
+
+where
+
+.. math::
+   \nabla_{\vec{x}} = (\partial_{x_1},\ldots, \partial_{x_d}), \quad
+   \nabla_{\vec{v}} = (\partial_{v_1},\ldots, \partial_{v_d}).
+
+To close the system, we need a formula for the acceleration :math:`\vec{a}`.
+In the (single species) Vlasov-Poisson model, the acceleration is
+determined by the electrostatic force,
+
+.. math::
+   \vec{a} = -\frac{1}{m}\nabla\phi,
+
+where :math:`m`
+is the mass per plasma particle, and :math:`\phi` is the electrostatic
+potential determined by the Poisson equation,
+
+.. math::
+   -\nabla^2\phi = q_0\int_{\mathbb{R}^d} f(\vec{x},\vec{v},t)\,\mathrm{d} v,
+
+where :math:`q_0` is the electric charge per plasma particle.
+
+In this demo we specialise to :math:`d=1`, and the equations become
+
+.. math::
+   f_t + (fv)_x + (-f\phi_x/m)_v = 0, \quad
+   -\phi_{xx} = q_0\int f(x,v,t)\,\mathrm{d} v,
+
+with coordinates :math:`(x,v)\in \mathbb{R}^2`. From now on we will
+relabel these coordinates :math:`(x,v)\mapsto (x_1,x_2)`, obtaining
+the equivalent form,
+
+.. math::
+   f_t + \nabla\cdot(\vec{u}f) = 0, \quad \vec{u} = (v,-\phi_x/m), \quad
+   -\phi_{x_1x_1} = q_0\int f(x_1,x_2,t)\,\mathrm{d} x_2,
+
+where :math:`\nabla=(\partial_{x_1},\partial_{x_2})`. From now on we will
+choose units such that :math:`q_0,m` are absorbed into the definition of
+:math:`f`.
+
+To proceed, we need to develop variational formulations of these
+equations.
+
+For the density we will use a discontinuous Galerkin formulation,
+and the continuity equation becomes 
+
+.. math::
+
+   \int_\Omega \! q \frac{\partial f}{\partial t} \, \mathrm{d} x
+   &= \int_\Omega \! f \nabla \cdot (q \vec{u}) \, \mathrm{d} x\\
+   &\quad- \int_{\Gamma_\mathrm{int}} \! \widetilde{f}(q_+ \vec{u} \cdot \vec{n}_+
+     + q_- \vec{u} \cdot \vec{n}_-) \, \mathrm{d} S\\
+   &\quad- \int_{\Gamma_I} q f_\mathrm{in} \vec{u} \cdot
+   \vec{n} \, \mathrm{d} s\\
+   &\quad- \int_{\Gamma_O} q f \vec{u} \cdot
+   \vec{n} \, \mathrm{d} s
+   \qquad \forall q \in V,
+
+where :math:`\Omega` is the computational domain in :math:`(x,v)`
+space, :math:`V` is the discontinuous finite element space,
+:math:`\Gamma_\mathrm{int}` is the set of interior cell edges,
+:math:`\Gamma_I` is the inlet part of
+exterior boundary where :math:`\vec{u}\cdot\vec{n}<0`,
+:math:`\Gamma_O` is the outlet part of
+exterior boundary where :math:`\vec{u}\cdot\vec{n}>0`, :math:`n` is
+the normal to each edge, :math:`\tilde{f}` is the upwind value of
+:math:`f`, and :math:`f_{\mathrm{in}}` is the inflow boundary value
+for :math:`f`. See the Discontinuous Galerkin advection
+:doc:`demo` for more details. The unapproximated
+problem should have :math:`-\infty < x_2 < \infty`, i.e. unbounded velocities, but we approximate
+the problem by solving in the domain :math:`\Omega=I_1\times [-H/2, H/2]`,
+where :math:`I` is some chosen interval in the spatial dimension.
+
+For the Poisson equation, we will use a regular Galerkin formulation.
+The difficulty in the formulation is the integral over :math:`x_2`. We
+deal with this by considering a space :math:`\bar{W}` which is restricted
+to functions that are constant in the vertical. Multiplying by a
+test function :math:`\psi\in \bar{W}` and integrating by parts gives
+
+.. math::
+
+   \int \psi_{x_1}\phi_{x_1}\, \mathrm{d} x_1
+   = \int \int f(x_1,x_2,t) \psi\, \mathrm{d} x_1\,\mathrm{d} x_2, \quad
+   \forall \psi \in \bar{W}.
+
+Since the left hand side integrand is independent of :math:`v=x_2`, we
+can integrate over :math:`x_2` and divide by :math:`H`, to obtain
+
+.. math::
+
+   \int_\Omega \psi_{x_1}\phi_{x_1}/H\, \mathrm{d} x
+   = \int f \psi\, \mathrm{d} x, \quad
+   \forall \psi \in \bar{W},
+
+which is now in a form which we can implement easily in Firedrake. One
+final issue is that this problem only has a solution up to an additive
+constant, so we further restrict :math:`\phi \in \mathring{\bar{W}}`,
+where
+
+.. math::
+   \mathring{\bar{W}} = \{ w\in \bar{W}: \bar{w}=0\},
+
+where here the bar indicates a spatial average,
+
+.. math::
+
+   \bar{w} = \frac{\int_{\Omega} w\, \mathrm{d} x}{\int_{\Omega} 1 \mathrm{d} x}.
+   
+Then we seek the solution of 
+
+.. math::
+
+   \int_\Omega \psi_{x_1}\phi_{x_1}\,\mathrm{d} x
+   = \int H(f-\bar{f}) \psi\, \mathrm{d} x, \quad
+   \forall \psi \in \mathring{\bar{W}}.
+
+To discretise in time, we will use an SSPRK3 time discretisation, similar to the DG advection :doc:`demo`.  At
+each Runge-Kutta stage, we must solve for the electrostatic potential,
+and then use it to compute :math:`\vec{u}`, in order to compute
+:math:`\partial f/\partial t`.
+   
+As usual, to implement this problem, we start by importing the
+Firedrake namespace. ::
+
+  from firedrake import *
+
+We build the mesh by constructing a 1D mesh, which will be extruded in
+the vertical. Here we will use periodic boundary conditions in the
+:math:`x_1` direction, ::
+  
+  ncells = 50
+  L = 8*pi
+  base_mesh = PeriodicIntervalMesh(ncells, L)
+
+The mesh is then extruded upwards in the "velocity" direction. ::
+  
+  H = 10.0
+  nlayers = 50
+  mesh = ExtrudedMesh(base_mesh, layers=nlayers, layer_height=H/nlayers)
+
+We want to have :math:`v=0` in the middle of the domain, so that we
+can have negative and positive velocities. This requires to edit the
+coordinate field. ::
+		      
+  x, v = SpatialCoordinate(mesh)
+  mesh.coordinates.interpolate(as_vector([x, v-H/2]))
+
+Now we build a discontinuous finite element space for the density, ::
+  
+  V = FunctionSpace(mesh, 'DQ', 1)
+
+and a continuous finite element space for the electostatic potential.
+The space is continuous in the horizontal and constant in the vertical,
+specified through the ``vfamily``. ::
+  
+  Wbar = FunctionSpace(mesh, 'CG', 1, vfamily='R', vdegree=0)
+
+We create a :class:`~.Function` to store the solution at the current
+time, and then set its initial condition,
+
+.. math::
+
+   f(x,v,0) = \frac{1}{\sqrt{2\pi}}v^2\exp(-v^2/2)(1+ A\cos(kx)),
+   \quad A=0.05, \quad k=0.5.
+
+::
+  
+  fn = Function(V, name="density")
+  A = Constant(0.05)
+  k = Constant(0.5)
+  fn.interpolate(v**2*exp(-v**2/2)*(1 + A*cos(k*x))/(2*pi)**0.5)
+
+We will need the (conserved) average :math:`\bar{f}` for the Poisson
+equation. ::
+
+  One = Function(V).assign(1.0)
+  fbar = assemble(fn*dx)/assemble(One*dx)
+
+We create a :class:`~.Function` to store the electrostatic potential. ::
+
+  phi = Function(Wbar, name="potential")
+
+The next task is to create the solver for the electrostatic potential, which
+will be called every timestep. 
+  
+We create a :class:`~.Function` to store the intermediate densities at each
+Runge-Kutta stage. The right hand side of the Poisson equation will be
+evaluated using this :class:`~.Function` to obtain the potential at each
+stage. Defining this beforehand will enable us to reuse the solver. ::
+
+  fstar = Function(V)
+
+Now we express the Poisson equation in UFL. ::
+  
+  psi = TestFunction(Wbar)
+  dphi = TrialFunction(Wbar)
+  phi_eqn = dphi.dx(0)*psi.dx(0)*dx - H*(fstar-fbar)*psi*dx
+
+To deal mathematically with the null space of the potential, we expressed the
+problem in :math:`\mathring{\bar{W}}`. Programmatically we will express the
+problem in :math:`\bar{W}` and deal with the null space by defining a basis
+for the space of globally constant functions, which we will later pass to PETSc
+so the solver can remove it from the solution. ::
+
+  nullspace = VectorSpaceBasis(constant=True, comm=COMM_WORLD)
+
+However, the null space also means that the assembled matrix of the
+Poisson problem will be singular, which will prevent us from using a
+direct solver. To deal with this, we will precondition the Poisson problem
+with a version shifted by :math:`\int_{\Omega}\phi\psi\mathrm{d}x`. The
+shifted problem is well-posed on :math:`\bar{W}`, so the assembled matrix
+will be non-singular and so solvable with direct methods. ::
+  
+  shift_eqn = dphi.dx(0)*psi.dx(0)*dx + dphi*psi*dx
+
+We use these to define a :class:`~.LinearVariationalProblem`. ::
+  
+  phi_problem = LinearVariationalProblem(lhs(phi_eqn), rhs(phi_eqn),
+                                         phi, aP=shift_eqn)
+
+Now we build the :class:`~.LinearVariationalSolver`. The problem
+is preconditioned by the shifted operator which is solved using a direct
+solver, and we pass the nullspace of globally constant functions to
+the solver. ::
+					 
+  params = {
+     'ksp_type': 'gmres',
+     'pc_type': 'lu',
+     'ksp_rtol': 1.0e-8,
+     }
+  phi_solver = LinearVariationalSolver(phi_problem,
+                                       nullspace=nullspace,
+				       solver_parameters=params)
+
+Now we move onto the solver to compute :math:`\partial f/\partial t`. We
+define a symbolic :math:`\Delta t` which we will update later. ::
+  
+  dtc = Constant(0)
+
+At each stage, the solver will take in the intermediate solution ``fstar`` and
+return the stage increment :math:`\Delta t\partial f/\partial t` in ``df_out``. ::
+
+  df_out = Function(V)
+
+Now we express the equation in UFL, starting with the left hand side
+bilinear form ::
+  
+  q = TestFunction(V)
+  u = as_vector([v, -phi.dx(0)])
+  n = FacetNormal(mesh)
+  un = 0.5*(dot(u, n) + abs(dot(u, n)))
+  df = TrialFunction(V)
+  df_a = q*df*dx
+
+The problem is defined on an extruded mesh, so the interior facets are
+separated into horizontal and vertical ones. ::
+
+  dS = dS_h + dS_v
+
+Now we build the right hand side linear form. A conditional operator
+is used to deal with the inflow and outflow parts of the exterior
+boundary. Due to the periodic boundary conditions in :math:`x_1`, the only exterior boundary is at the top and bottom of the domain, with measure `ds_tb`. ::
+  
+  df_L = dtc*(div(u*q)*fstar*dx
+     - (q('+') - q('-'))*(un('+')*fstar('+') - un('-')*fstar('-'))*dS
+     - conditional(dot(u, n) > 0, q*dot(u, n)*fstar, 0.)*ds_tb
+      )
+
+We then use this to build a solver. ::
+
+  df_problem = LinearVariationalProblem(df_a, df_L, df_out)
+  df_solver = LinearVariationalSolver(df_problem)
+
+We are getting close to the time loop. We set up some timestepping
+parameters. ::
+  
+  T = 50.0 # maximum timestep
+  t = 0. # model time
+  ndump = 100 # frequency of file dumps
+  dumpn = 0 # dump counter
+  nsteps = 5000
+  dt = T/nsteps
+  dtc.assign(dt)
+
+We set up some :class:`~.Function`\s to store Runge-Kutta stage variables. ::
+  
+  f1 = Function(V)
+  f2 = Function(V)
+
+We set up a ``VTKFile`` object to write output every ``ndump``
+timesteps. ::
+
+  outfile = VTKFile("vlasov.pvd")
+
+We want to output the initial condition, so need to solve for the electrostatic
+potential that corresponds to the initial density. ::
+
+  fstar.assign(fn)
+  phi_solver.solve()
+  outfile.write(fn, phi)
+  phi.assign(.0)
+
+Now we start the timeloop using a lovely progress bar. Note that
+we have 5000 timesteps so this may take a few minutes to run::
+
+  for step in ProgressBar("Timestep").iter(range(nsteps)):
+
+Each Runge-Kutta stage involves solving for :math:`\phi` before solving
+for :math:`\partial f/\partial t`. Here is the first stage. ::
+
+  #
+      fstar.assign(fn)
+      phi_solver.solve()
+      df_solver.solve()
+      f1.assign(fn + df_out)
+
+The second stage. ::
+
+  #
+      fstar.assign(f1)
+      phi_solver.solve()
+      df_solver.solve()
+      f2.assign(3*fn/4 + (f1 + df_out)/4)
+
+The third stage. ::
+
+  #
+      fstar.assign(f2)
+      phi_solver.solve()
+      df_solver.solve()
+      fn.assign(fn/3 + 2*(f2 + df_out)/3)
+      t += dt
+
+Finally we output to the VTK file if it is time to do that. ::
+
+  #
+      dumpn += 1
+      if dumpn % ndump == 0:
+          dumpn = 0
+          outfile.write(fn, phi)
+
+Images of the solution at shown below.
+
+.. figure:: vlasov_0s_LR.png
+   :align: center
+
+   Solution at :math:`t = 0.`
+
+.. figure:: vlasov_15_LR.png
+   :align: center
+
+   Solution at :math:`t = 15.`
+
+We also present solutions at double the resolution, by doubling the number
+of horizontal cells and the number of layers, halving the timestep (by doubling the number of steps), and doubling ``nsteps``.
+
+.. figure:: vlasov_0s_HR.png
+   :align: center
+
+   Solution at :math:`t = 0.`
+
+.. figure:: vlasov_15_HR.png
+   :align: center
+
+   Solution at :math:`t = 15.`
+
+   
+A Python script version of this demo can be found :demo:`here `.
diff --git a/docs/source/advanced_tut.rst b/docs/source/advanced_tut.rst
index 8c08858801..9b83643c5f 100644
--- a/docs/source/advanced_tut.rst
+++ b/docs/source/advanced_tut.rst
@@ -24,6 +24,7 @@ element systems.
    Rayleigh-Benard convection.
    Netgen support.
    Full-waveform inversion: spatial and wave sources parallelism.
+   1D Vlasov-Poisson equation using vertical independent function spaces.
    Degree-independent multigrid convergence using patch relaxation.
    Monolithic multigrid with Vanka relaxation for Stokes.
    Vertex/edge star multigrid relaxation for H(div).
diff --git a/tests/firedrake/demos/test_demos_run.py b/tests/firedrake/demos/test_demos_run.py
index 31cb8d4721..4d8eb623d8 100644
--- a/tests/firedrake/demos/test_demos_run.py
+++ b/tests/firedrake/demos/test_demos_run.py
@@ -46,6 +46,7 @@
     Demo(("patch", "hdiv_riesz_star"), []),
     Demo(("quasigeostrophy_1layer", "qg_1layer_wave"), ["hypre", "vtk"]),
     Demo(("saddle_point_pc", "saddle_point_systems"), ["hypre", "mumps"]),
+    Demo(('vlasov_poisson_1d', 'vp1d'), []),
 ]
 PARALLEL_DEMOS = [
     Demo(("full_waveform_inversion", "full_waveform_inversion"), ["adjoint"]),
diff --git a/tests/firedrake/output/test_io_function.py b/tests/firedrake/output/test_io_function.py
index 03bad95b71..e84d778733 100644
--- a/tests/firedrake/output/test_io_function.py
+++ b/tests/firedrake/output/test_io_function.py
@@ -142,7 +142,7 @@ def _load_check_save_functions(filename, func_name, comm, method, mesh_name, var
     VB = fB.function_space()
     fBe = Function(VB)
     _initialise_function(fBe, _get_expr(VB), method)
-    assert assemble(inner(fB - fBe, fB - fBe) * dx) < 5.e-12
+    assert assemble(inner(fB - fBe, fB - fBe) * dx) < 6.e-12
     # Save
     with CheckpointFile(filename, 'w', comm=comm) as afile:
         afile.save_function(fB)

From 17dc7308f4c83fe9db8f8d454f2290f25c7ea2c9 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 29 May 2025 14:53:12 +0100
Subject: [PATCH 127/222] Add macOS compiler failure to install FAQ (#4345)

---
 docs/source/install.rst | 53 ++++++++++++++++++++++++++++-------------
 1 file changed, 37 insertions(+), 16 deletions(-)

diff --git a/docs/source/install.rst b/docs/source/install.rst
index 5fde32a24f..cd971160f6 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -262,23 +262,23 @@ No such file or directory: '/tmp/.../petsc/conf/petscvariables'
 
 If you encounter the error:
 
-   .. code-block:: text
-
-      FileNotFoundError: [Errno 2] No such file or directory: '/tmp/.../petsc/conf/petscvariables'
+.. code-block:: text
 
-when running the ``pip install`` instruction this is usually a sign that the
-environment variables ``PETSC_DIR`` or ``PETSC_ARCH`` are not set correctly.
-You can check this by making sure that you can run the following command
-without error::
-
-   $ ls $PETSC_DIR/$PETSC_ARCH/lib/petsc/conf/petscvariables 
+   FileNotFoundError: [Errno 2] No such file or directory: '/tmp/.../petsc/conf/petscvariables'
 
-If this raises an error then you should re-``export`` the variables necessary to
-build Firedrake as described :ref:`above`::
+when running the ``pip install`` instruction this is usually a sign that either:
+you are using a cached version of petsc4py that is linked incorrectly, or that
+the environment variables ``PETSC_DIR`` or ``PETSC_ARCH`` are not set correctly.
+To fix this we suggest purging the pip cache before re-exporting the environment
+variables::
 
+   $ pip cache purge
    $ export $(python3 firedrake-configure --show-env)
 
-and try the install again.
+You can check that ``PETSC_DIR`` and ``PETSC_ARCH`` are set correctly by making
+sure that you can run the following command without error::
+
+   $ ls $PETSC_DIR/$PETSC_ARCH/lib/petsc/conf/petscvariables 
 
 Missing symbols post install
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -291,13 +291,34 @@ the wrong compiled library. This is usually caused by issues with caching.
 To resolve the problem you should first remove any existing cached packages::
 
    $ pip uninstall -y h5py mpi4py petsc4py slepc4py
-   $ pip cache remove h5py
-   $ pip cache remove mpi4py
-   $ pip cache remove petsc4py
-   $ pip cache remove slepc4py
+   $ pip cache purge
 
 before re-running the instruction to install Firedrake.
 
+Unable to configure PETSc on macOS
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you are running on macOS and encounter error messages during PETSc
+``configure`` like the following:
+
+.. code-block:: text
+
+   *********************************************************************************************
+              UNABLE to CONFIGURE with GIVEN OPTIONS (see configure.log for details):
+   ---------------------------------------------------------------------------------------------
+            Cannot use scalapack without Fortran, make sure you do NOT have --with-fc=0
+   *********************************************************************************************
+
+then this is usually a sign that your Homebrew and/or Xcode are too old. We
+recommend making sure that they are both up-to-date before trying again.
+
+For Homebrew it is sometimes useful to run the command::
+
+   $ brew doctor
+
+as this can flag issues with your system that should be resolved before
+installing Firedrake.
+
 .. _customising:
 
 Customising Firedrake

From 6e9738102c7365e90f4b7643ecdec0eb28ec7e06 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Tue, 3 Jun 2025 13:08:53 +0100
Subject: [PATCH 128/222] Use a different mirror to download apt packages
 (#4347)

* Use a different mirror to download apt packages
---
 .github/workflows/core.yml | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index b75c626735..51fe7a987a 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -106,11 +106,18 @@ jobs:
         # Make sure the current directory is empty
         run: find . -delete
 
-      # Git is needed for actions/checkout and Python for firedrake-configure
-      - name: Install system dependencies (1)
+      # Use a different mirror to fetch apt packages from to get around
+      # temporary outage.
+      # (https://askubuntu.com/questions/1549622/problem-with-archive-ubuntu-com-most-of-the-servers-are-not-responding)
+      # The mirror was chosen from https://launchpad.net/ubuntu/+archivemirrors.
+      - name: Configure apt
         run: |
+          sed -i 's|http://archive.ubuntu.com/ubuntu|http://www.mirrorservice.org/sites/archive.ubuntu.com/ubuntu/|g' /etc/apt/sources.list.d/ubuntu.sources
           apt-get update
-          apt-get -y install git python3
+
+      # Git is needed for actions/checkout and Python for firedrake-configure
+      - name: Install system dependencies (1)
+        run: apt-get -y install git python3
 
       - uses: actions/checkout@v4
         with:
@@ -138,8 +145,6 @@ jobs:
           apt-get -y install python3-venv
           : # Dependencies needed to run the test suite
           apt-get -y install parallel
-          : # Dependencies needed to build the documentation
-          apt-get -y install inkscape texlive-full
 
       - name: Install PETSc
         run: |
@@ -364,6 +369,9 @@ jobs:
           name: firedrake-logs-${{ matrix.arch }}
           path: pytest_*.log
 
+      - name: Install system dependencies (3)
+        run: apt-get -y install inkscape texlive-full
+
       - name: Check bibtex
         if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default'
         run: |

From 8448807424972f918a8b1c2310b4771797d5c42a Mon Sep 17 00:00:00 2001
From: Angus Gibson 
Date: Wed, 4 Jun 2025 19:58:22 +1000
Subject: [PATCH 129/222] checkpointing bcs (#4284) (#4351)

Ensure that restoring a BC from checkpoint returns a new BC and doesn't incorrectly modify the program variable BC.

---------

Co-authored-by: Daiane Iglesia Dolci <63597005+Ig-dolci@users.noreply.github.com>
Co-authored-by: David A. Ham 
Co-authored-by: Pablo Brubeck 
---
 firedrake/adjoint_utils/dirichletbc.py        |  8 +++--
 .../adjoint/test_disk_checkpointing.py        | 32 +++++++++++++++++++
 .../regression/test_adjoint_operators.py      | 22 +++++++++++--
 3 files changed, 56 insertions(+), 6 deletions(-)

diff --git a/firedrake/adjoint_utils/dirichletbc.py b/firedrake/adjoint_utils/dirichletbc.py
index bf3c7e46ef..5496701754 100644
--- a/firedrake/adjoint_utils/dirichletbc.py
+++ b/firedrake/adjoint_utils/dirichletbc.py
@@ -42,7 +42,9 @@ def _ad_create_checkpoint(self):
 
         return deps[0]
 
-    def _ad_restore_at_checkpoint(self, checkpoint):
-        if checkpoint is not None:
-            self.set_value(checkpoint.saved_output)
+    def _ad_restore_at_checkpoint(self, bv):
+        if bv is not None:
+            bc = self.reconstruct(g=bv.saved_output)
+            bc.block = self.block
+            return bc
         return self
diff --git a/tests/firedrake/adjoint/test_disk_checkpointing.py b/tests/firedrake/adjoint/test_disk_checkpointing.py
index 3707204256..6c4e5deb45 100644
--- a/tests/firedrake/adjoint/test_disk_checkpointing.py
+++ b/tests/firedrake/adjoint/test_disk_checkpointing.py
@@ -185,3 +185,35 @@ def delta_expr(x0, x, y, sigma_x=2000.0):
 
     J_hat = ReducedFunctional(J, Control(c))
     assert taylor_test(J_hat, c, Function(V).interpolate(0.1)) > 1.9
+
+
+@pytest.mark.skipcomplex
+def test_bcs():
+
+    enable_disk_checkpointing()
+
+    tape = get_working_tape()
+    tape.enable_checkpointing(SingleDiskStorageSchedule())
+
+    mesh = checkpointable_mesh(UnitSquareMesh(5, 5))
+    V = FunctionSpace(mesh, "CG", 2)
+    T = Function(V)
+    u = TrialFunction(V)
+    v = TestFunction(V)
+    a = inner(grad(u), grad(v)) * dx
+    x = SpatialCoordinate(mesh)
+    F = Function(V)
+    control = Control(F)
+    F.interpolate(sin(x[0] * pi) * sin(2 * x[1] * pi))
+    L = F * v * dx
+    uu = Function(V)
+    bcs = [DirichletBC(V, T, (1,))]
+    problem = LinearVariationalProblem(a, L, uu, bcs=bcs)
+    solver = LinearVariationalSolver(problem)
+
+    for i in tape.timestepper(iter(range(3))):
+        T.assign(T + 1.0)
+        solver.solve()
+    obj = assemble(uu * uu * dx)
+    rf = ReducedFunctional(obj, control)
+    assert np.allclose(rf(F), obj)
diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py
index 8d4c0ab796..b7ecdd6ed0 100644
--- a/tests/firedrake/regression/test_adjoint_operators.py
+++ b/tests/firedrake/regression/test_adjoint_operators.py
@@ -1005,6 +1005,7 @@ def test_cofunction_assign_functional():
 
 @pytest.mark.skipcomplex  # Taping for complex-valued 0-forms not yet done
 def test_bdy_control():
+    from firedrake.adjoint_utils.dirichletbc import DirichletBCBlock
     # Test for the case the boundary condition is a control for a
     # domain with length different from 1.
     mesh = IntervalMesh(10, 0, 2)
@@ -1024,13 +1025,28 @@ def test_bdy_control():
     problem = LinearVariationalProblem(lhs(F), rhs(F), sol, bcs=bc)
     solver = LinearVariationalSolver(problem)
     solver.solve()
+
     # Analytical solution of the analytical Laplace equation is:
     # u(x) = a + (b - a)/2 * x
-    u_analytical = a + (b - a)/2 * X[0]
-    der_analytical0 = assemble(derivative((u_analytical**2) * dx, a))
-    der_analytical1 = assemble(derivative((u_analytical**2) * dx, b))
+    def u_analytical(x, a, b):
+        return a + (b - a)/2 * x
+    der_analytical0 = assemble(derivative(
+        (u_analytical(X[0], a, b)**2) * dx, a))
+    der_analytical1 = assemble(derivative(
+        (u_analytical(X[0], a, b)**2) * dx, b))
     J = assemble(sol * sol * dx)
     J_hat = ReducedFunctional(J, [Control(a), Control(b)])
     adj_derivatives = J_hat.derivative(options={"riesz_representation": "l2"})
     assert np.allclose(adj_derivatives[0].dat.data_ro, der_analytical0.dat.data_ro)
     assert np.allclose(adj_derivatives[1].dat.data_ro, der_analytical1.dat.data_ro)
+    a = Function(R, val=1.5)
+    b = Function(R, val=2.5)
+    J_hat([a, b])
+    tape = get_working_tape()
+    # Check the checkpointed boundary conditions are not updating the
+    # user-defined boundary conditions ``bc_left`` and ``bc_right``.
+    assert isinstance(tape._blocks[0], DirichletBCBlock) and \
+        tape._blocks[0]._outputs[0].checkpoint.checkpoint is not bc_left._original_arg
+    # tape._blocks[1] is the DirichletBC block for the right boundary
+    assert isinstance(tape._blocks[1], DirichletBCBlock) and \
+        tape._blocks[1]._outputs[0].checkpoint.checkpoint is not bc_right._original_arg

From fa94acac8ff05794cd0715b38e229105a3053ca0 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 5 Jun 2025 15:47:01 +0100
Subject: [PATCH 130/222] Always install system dependencies (#4353)

---
 .github/workflows/core.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index 51fe7a987a..b5248a61be 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -370,6 +370,7 @@ jobs:
           path: pytest_*.log
 
       - name: Install system dependencies (3)
+        if: always()
         run: apt-get -y install inkscape texlive-full
 
       - name: Check bibtex

From 6ea914558ec4f76d3fb7d179915677469c9c78a9 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Fri, 6 Jun 2025 08:15:12 +0100
Subject: [PATCH 131/222] SLATE: support dual space (#4342)

* Allow primal/dual error in Mult to make tests pass

* SCPC: fixes for dualspace
---
 firedrake/assemble.py                          |  4 ++--
 firedrake/slate/slate.py                       | 15 ++++++++-------
 .../slate/static_condensation/hybridization.py |  8 ++++----
 firedrake/slate/static_condensation/scpc.py    |  4 ++--
 firedrake/ufl_expr.py                          |  6 +++---
 tests/firedrake/slate/test_assemble_tensors.py | 18 ++++++++++++++++++
 .../slate/test_slate_infrastructure.py         |  6 ++++--
 7 files changed, 41 insertions(+), 20 deletions(-)

diff --git a/firedrake/assemble.py b/firedrake/assemble.py
index 97104b05cc..04e4bd56b1 100644
--- a/firedrake/assemble.py
+++ b/firedrake/assemble.py
@@ -1199,10 +1199,10 @@ def allocate(self):
         rank = len(self._form.arguments())
         if rank == 1:
             test, = self._form.arguments()
-            return firedrake.Cofunction(test.function_space().dual())
+            return firedrake.Function(test.function_space().dual())
         elif rank == 2 and self._diagonal:
             test, _ = self._form.arguments()
-            return firedrake.Cofunction(test.function_space().dual())
+            return firedrake.Function(test.function_space().dual())
         else:
             raise RuntimeError(f"Not expected: found rank = {rank} and diagonal = {self._diagonal}")
 
diff --git a/firedrake/slate/slate.py b/firedrake/slate/slate.py
index 07854a9843..942faf2bd8 100644
--- a/firedrake/slate/slate.py
+++ b/firedrake/slate/slate.py
@@ -60,7 +60,7 @@ class RemoveNegativeRestrictions(MultiFunction):
     """UFL MultiFunction which removes any negative restrictions
     in a form.
     """
-    expr = MultiFunction.reuse_if_untouched
+    ufl_type = MultiFunction.reuse_if_untouched
 
     def negative_restricted(self, o):
         return Zero(o.ufl_shape, o.ufl_free_indices, o.ufl_index_dimensions)
@@ -464,7 +464,7 @@ def arg_function_spaces(self):
         if isinstance(tensor, BaseForm):
             return tuple(a.function_space() for a in tensor.arguments())
         else:
-            return (tensor.ufl_function_space(),)
+            return (tensor.ufl_function_space().dual(),)
 
     @cached_property
     def _argument(self):
@@ -1084,14 +1084,14 @@ def arg_function_spaces(self):
         is defined on.
         """
         tensor, = self.operands
-        return tensor.arg_function_spaces[::-1]
+        return tuple(V.dual() for V in reversed(tensor.arg_function_spaces))
 
     def arguments(self):
         """Returns the expected arguments of the resulting tensor of
         performing a specific unary operation on a tensor.
         """
         tensor, = self.operands
-        return tensor.arguments()[::-1]
+        return tuple(a.reconstruct(a.function_space().dual()) for a in reversed(tensor.arguments()))
 
     def _output_string(self, prec=None):
         """Creates a string representation of the inverse of a tensor."""
@@ -1267,9 +1267,9 @@ def __init__(self, A, B):
         fsA = A.arg_function_spaces[-1]
         fsB = B.arg_function_spaces[0]
 
-        assert space_equivalence(fsA, fsB), (
+        assert space_equivalence(fsA, fsB.dual()), (
             "Cannot perform argument contraction over middle indices. "
-            "They must be in the same function space."
+            "They should be in dual function spaces."
         )
 
         super(Mul, self).__init__(A, B)
@@ -1341,7 +1341,8 @@ def __init__(self, A, B, decomposition=None):
 
         super(Solve, self).__init__(A_factored, B)
 
-        self._args = A_factored.arguments()[::-1][:-1] + B.arguments()[1:]
+        Ainv_args = tuple(a.reconstruct(a.function_space().dual()) for a in reversed(A.arguments()))
+        self._args = Ainv_args[:-1] + B.arguments()[1:]
         self._arg_fs = [arg.function_space() for arg in self._args]
 
     @cached_property
diff --git a/firedrake/slate/static_condensation/hybridization.py b/firedrake/slate/static_condensation/hybridization.py
index 63edd7cbd7..bd0fc761f0 100644
--- a/firedrake/slate/static_condensation/hybridization.py
+++ b/firedrake/slate/static_condensation/hybridization.py
@@ -91,11 +91,11 @@ def initialize(self, pc):
 
         # Set up the functions for the original, hybridized
         # and schur complement systems
-        self.broken_solution = Cofunction(V_d.dual())
-        self.broken_residual = Function(V_d)
+        self.broken_solution = Function(V_d)
+        self.broken_residual = Cofunction(V_d.dual())
         self.trace_solution = Function(TraceSpace)
-        self.unbroken_solution = Cofunction(V.dual())
-        self.unbroken_residual = Function(V)
+        self.unbroken_solution = Function(V)
+        self.unbroken_residual = Cofunction(V.dual())
 
         shapes = (V[self.vidx].finat_element.space_dimension(),
                   V[self.vidx].block_size)
diff --git a/firedrake/slate/static_condensation/scpc.py b/firedrake/slate/static_condensation/scpc.py
index 9ba54b778d..a7536d7bda 100644
--- a/firedrake/slate/static_condensation/scpc.py
+++ b/firedrake/slate/static_condensation/scpc.py
@@ -79,8 +79,8 @@ def initialize(self, pc):
 
         self.c_field = c_field
         self.condensed_rhs = Cofunction(Vc.dual())
-        self.residual = Function(W)
-        self.solution = Cofunction(W.dual())
+        self.residual = Cofunction(W.dual())
+        self.solution = Function(W)
 
         shapes = (Vc.finat_element.space_dimension(),
                   np.prod(Vc.shape))
diff --git a/firedrake/ufl_expr.py b/firedrake/ufl_expr.py
index 1f9a66df75..3883dcdf1a 100644
--- a/firedrake/ufl_expr.py
+++ b/firedrake/ufl_expr.py
@@ -142,7 +142,7 @@ def reconstruct(self, function_space=None,
             raise TypeError(f"Expecting an int, not {number}")
         if function_space.value_shape != self.function_space().value_shape:
             raise ValueError("Cannot reconstruct an Coargument with a different value shape.")
-        return Coargument(function_space, number, part=part)
+        return Argument(function_space, number, part=part)
 
     def equals(self, other):
         if type(other) is not Coargument:
@@ -379,7 +379,7 @@ def extract_domains(func):
     list of firedrake.mesh.MeshGeometry
         Extracted domains.
     """
-    if isinstance(func, (function.Function, cofunction.Cofunction)):
+    if isinstance(func, (function.Function, cofunction.Cofunction, Argument, Coargument)):
         return [func.function_space().mesh()]
     else:
         return ufl.domain.extract_domains(func)
@@ -398,7 +398,7 @@ def extract_unique_domain(func):
     list of firedrake.mesh.MeshGeometry
         Extracted domains.
     """
-    if isinstance(func, (function.Function, cofunction.Cofunction)):
+    if isinstance(func, (function.Function, cofunction.Cofunction, Argument, Coargument)):
         return func.function_space().mesh()
     else:
         return ufl.domain.extract_unique_domain(func)
diff --git a/tests/firedrake/slate/test_assemble_tensors.py b/tests/firedrake/slate/test_assemble_tensors.py
index c35d43e27e..d9222f6fa2 100644
--- a/tests/firedrake/slate/test_assemble_tensors.py
+++ b/tests/firedrake/slate/test_assemble_tensors.py
@@ -130,6 +130,24 @@ def test_assemble_matrix(rank_two_tensor):
     assert np.allclose(M.M.values, assemble(rank_two_tensor.form).M.values, rtol=1e-14)
 
 
+def test_assemble_solve(mesh):
+    V = FunctionSpace(mesh, "DG", 0)
+    u = TrialFunction(V)
+    v = TestFunction(V)
+
+    M = inner(u, v)*dx
+    f = Cofunction(V.dual())
+    f.dat.data[...] = 1
+
+    u1 = Function(V)
+    u2 = Function(V)
+    # Assemble a SLATE tensor into u1
+    assemble(Inverse(Tensor(M)) * AssembledVector(f), tensor=u1)
+    # Solve in the usual way
+    solve(M == f, u2)
+    assert np.allclose(u1.dat.data, u2.dat.data, rtol=1e-14)
+
+
 def test_assemble_vector_into_tensor(mesh):
     V = FunctionSpace(mesh, "DG", 1)
     v = TestFunction(V)
diff --git a/tests/firedrake/slate/test_slate_infrastructure.py b/tests/firedrake/slate/test_slate_infrastructure.py
index f29afae582..6067e481fc 100644
--- a/tests/firedrake/slate/test_slate_infrastructure.py
+++ b/tests/firedrake/slate/test_slate_infrastructure.py
@@ -112,6 +112,8 @@ def test_arguments(mass, stiffness, load, boundary_load, zero_rank_tensor):
     f, = F.form.coefficients()
     g, = G.form.coefficients()
     v, u = M.form.arguments()
+    vdual = v.reconstruct(v.function_space().dual())
+    udual = u.reconstruct(u.function_space().dual())
 
     assert len(N.arguments()) == N.rank
     assert len(M.arguments()) == M.rank
@@ -123,8 +125,8 @@ def test_arguments(mass, stiffness, load, boundary_load, zero_rank_tensor):
     assert len(S.arguments()) == S.rank
     assert S.arguments() == ()
     assert (M.T).arguments() == (u, v)
-    assert (N.inv).arguments() == (u, v)
-    assert (N.T + M.inv).arguments() == (u, v)
+    assert (N.inv).arguments() == (udual, vdual)
+    assert (N.T + M.T).arguments() == (u, v)
     assert (F.T).arguments() == (v,)
     assert (F.T + G.T).arguments() == (v,)
     assert (M*F).arguments() == (v,)

From 057a716a562433171350a03e3714dac423d08c2e Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Fri, 6 Jun 2025 08:16:33 +0100
Subject: [PATCH 132/222] PETSc update (#4348)

* Bump PETSc version to 3.23.3

This includes bugfixes for Cython 3.1.
---
 .github/workflows/core.yml  |  4 ----
 docs/source/install.rst     | 39 +++++++------------------------------
 pyproject.toml              |  4 ++--
 scripts/check-config        |  6 +++++-
 scripts/firedrake-configure |  2 +-
 5 files changed, 15 insertions(+), 40 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index b5248a61be..b11acbe1e6 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -174,10 +174,6 @@ jobs:
           python3 -m venv venv
           . venv/bin/activate
 
-          : # Hotfix for petsc4py build, see https://gitlab.com/petsc/petsc/-/issues/1759
-          echo 'Cython<3.1' > constraints.txt
-          export PIP_CONSTRAINT=constraints.txt
-
           : # Force a rebuild of petsc4py as the cached one will not link to the fresh
           : # install of PETSc.
           pip cache remove petsc4py
diff --git a/docs/source/install.rst b/docs/source/install.rst
index cd971160f6..f734e76e30 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -183,21 +183,6 @@ install Firedrake. To do this perform the following steps:
       $ pip cache remove firedrake
       $ pip install --no-binary h5py 'firedrake[check]'
 
-   .. warning::
-      The ``pip install`` instruction above does not currently work due to
-      `this issue `__. To fix it you should
-      first:
-
-      1. Run::
-
-         $ echo 'Cython<3.1' > constraints.txt
-
-      2. Run::
-
-         $ export PIP_CONSTRAINT=constraints.txt
-
-      3. Continue the installation as usual
-
    .. note::
       Though not strictly necessary to install Firedrake's optional
       dependencies with ``[check]`` it is recommended because it allows you
@@ -240,10 +225,15 @@ to set the right environment variables and then run::
 Updating PETSc
 ~~~~~~~~~~~~~~
 
-To update PETSc you simply need to run::
+To update PETSc you should:
+
+#. Re-download ``firedrake-configure``.
+
+#. Run::
 
    $ cd /path/to/petsc
-   $ git pull
+   $ git fetch
+   $ git checkout -b $(python3 /path/to/firedrake-configure --show-petsc-version)
    $ make
 
 Note that this will only recompile PETSc's source code, not that of the external
@@ -555,21 +545,6 @@ should be followed:
 
    $ pip install --no-build-isolation --no-binary h5py --editable './firedrake[check]'
 
-   .. warning::
-      The ``pip install`` instruction above does not currently work due to
-      `this issue `__. To fix it you should
-      first:
-
-      1. Run::
-
-         $ echo 'Cython<3.1' > constraints.txt
-
-      2. Run::
-
-         $ export PIP_CONSTRAINT=constraints.txt
-
-      3. Continue the installation as usual
-
 
 Editing subpackages
 -------------------
diff --git a/pyproject.toml b/pyproject.toml
index 55dbcd6427..298b4fe483 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,7 @@ dependencies = [
   "h5py>3.12.1",
   "libsupermesh",
   "loopy>2024.1",
-  "petsc4py==3.23.1",
+  "petsc4py==3.23.3",
   "numpy",
   "packaging",
   "pkgconfig",
@@ -138,7 +138,7 @@ requires = [
   "pkgconfig",
   "pybind11",
   "setuptools>61.2",
-  "petsc4py==3.23.1",
+  "petsc4py==3.23.3",
   "rtree>=1.2",
 ]
 build-backend = "setuptools.build_meta"
diff --git a/scripts/check-config b/scripts/check-config
index e23335b6cc..8a62b5c765 100755
--- a/scripts/check-config
+++ b/scripts/check-config
@@ -23,6 +23,7 @@ def main():
     check_firedrake_version()
     check_min_python_version()
     check_petsc_version()
+    check_slepc_version()
     check_petsc_version_spec()
 
 
@@ -58,9 +59,12 @@ def check_petsc_version() -> None:
         f"petsc4py=={petsc_version}",
         2,
     )
+
+
+def check_slepc_version() -> None:
     check_file_contains_pattern(
         REPO_ROOT / "pyproject.toml",
-        f"slepc4py=={petsc_version}",
+        "slepc4py==(.*)",
         3,
     )
 
diff --git a/scripts/firedrake-configure b/scripts/firedrake-configure
index 84b6536ce0..bab1fc839b 100755
--- a/scripts/firedrake-configure
+++ b/scripts/firedrake-configure
@@ -39,7 +39,7 @@ ARCH_DEFAULT = FiredrakeArch.DEFAULT
 ARCH_COMPLEX = FiredrakeArch.COMPLEX
 
 
-SUPPORTED_PETSC_VERSION = "v3.23.1"
+SUPPORTED_PETSC_VERSION = "v3.23.3"
 
 
 def main():

From e53bd189202ef7321afd0da1a875b3cfaf408008 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Fri, 6 Jun 2025 09:40:46 +0100
Subject: [PATCH 133/222] Set version number to 2025.4.1 (#4355)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 298b4fe483..056af084db 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "firedrake"
 # ..
-version = "2025.4.0.post0"
+version = "2025.4.1"
 description = "An automated system for the portable solution of partial differential equations using the finite element method"
 readme = "README.rst"
 license = "LGPL-3.0-or-later"

From 0b42ad90b9902ff7df779182eec25cecd5f9d751 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Fri, 6 Jun 2025 10:02:19 +0100
Subject: [PATCH 134/222] Fix version number in install.rst

---
 docs/source/install.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/install.rst b/docs/source/install.rst
index f734e76e30..d6ac3e9ab8 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -54,7 +54,7 @@ firedrake-configure
 To simplify the installation process, Firedrake provides a utility script called
 ``firedrake-configure``. This script can be downloaded by executing::
 
-  $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/refs/tags/2025.4.0.post0/scripts/firedrake-configure
+  $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/refs/tags/2025.4.1/scripts/firedrake-configure
 
 Unlike the now deprecated ``firedrake-install`` script, ``firedrake-configure``
 **does not install Firedrake for you**. It is simply a helper script that emits

From 43211b9eb4bb4ee983d0b3da046e3af2754a6efd Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Mon, 9 Jun 2025 14:53:59 +0100
Subject: [PATCH 135/222] Set setuptools min version (#4357)

* Bump setuptools minimum version

This is to make sure that the 'license' field is interpreted correctly
in the pyproject.toml. See
https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license-and-license-files.

* Change setuptools min version in requirements-build.txt
---
 pyproject.toml         | 2 +-
 requirements-build.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 056af084db..5844da9bca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -137,7 +137,7 @@ requires = [
   "numpy",
   "pkgconfig",
   "pybind11",
-  "setuptools>61.2",
+  "setuptools>=77.0.3",
   "petsc4py==3.23.3",
   "rtree>=1.2",
 ]
diff --git a/requirements-build.txt b/requirements-build.txt
index cff27061d1..fdcd04ad9a 100644
--- a/requirements-build.txt
+++ b/requirements-build.txt
@@ -6,7 +6,7 @@ mpi4py; python_version < '3.13'
 numpy
 pkgconfig
 pybind11
-setuptools>61.2
+setuptools>=77.0.3
 rtree>=1.2
 
 # Transitive build dependencies

From 45bbc145b3e048797d0f8d4bc9ecc3a43026c4dc Mon Sep 17 00:00:00 2001
From: ksagiyam <46749170+ksagiyam@users.noreply.github.com>
Date: Tue, 10 Jun 2025 22:45:37 +0100
Subject: [PATCH 136/222] TensorProductElement: rename sub_elements
 factor_elements (#4354)

---
 firedrake/embedding.py                |  2 +-
 firedrake/extrusion_utils.py          |  2 +-
 firedrake/functionspaceimpl.py        |  2 +-
 firedrake/interpolation.py            | 10 ++--------
 firedrake/mg/embedded.py              |  7 +++++--
 firedrake/preconditioners/hiptmair.py |  2 +-
 firedrake/preconditioners/pmg.py      |  2 +-
 7 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/firedrake/embedding.py b/firedrake/embedding.py
index e16d799902..517be9387e 100644
--- a/firedrake/embedding.py
+++ b/firedrake/embedding.py
@@ -59,7 +59,7 @@ def get_embedding_method_for_checkpointing(element):
                               'S', 'DPC', 'Real']:
         return "interpolate"
     elif isinstance(element, finat.ufl.TensorProductElement):
-        methods = [get_embedding_method_for_checkpointing(elem) for elem in element.sub_elements]
+        methods = [get_embedding_method_for_checkpointing(elem) for elem in element.factor_elements]
         if any(method == "project" for method in methods):
             return "project"
         else:
diff --git a/firedrake/extrusion_utils.py b/firedrake/extrusion_utils.py
index 4298a21d7b..0b65f6d11d 100644
--- a/firedrake/extrusion_utils.py
+++ b/firedrake/extrusion_utils.py
@@ -50,7 +50,7 @@ def make_extruded_coords(extruded_topology, base_coords, ext_coords,
     coordinates on the extruded cell (to write to), the fixed layer
     height, and the current cell layer.
     """
-    _, vert_space = ext_coords.function_space().ufl_element().sub_elements[0].sub_elements
+    _, vert_space = ext_coords.function_space().ufl_element().sub_elements[0].factor_elements
     if kernel is None and not (vert_space.degree() == 1
                                and vert_space.family() in ['Lagrange',
                                                            'Discontinuous Lagrange']):
diff --git a/firedrake/functionspaceimpl.py b/firedrake/functionspaceimpl.py
index d4dd168fdb..00f517c088 100644
--- a/firedrake/functionspaceimpl.py
+++ b/firedrake/functionspaceimpl.py
@@ -60,7 +60,7 @@ def check_element(element, top=True):
     elif type(element) is finat.ufl.EnrichedElement:
         inner = element._elements
     elif type(element) is finat.ufl.TensorProductElement:
-        inner = element.sub_elements
+        inner = element.factor_elements
     elif isinstance(element, finat.ufl.MixedElement):
         if not top:
             raise ValueError(f"{type(element).__name__} modifier must be outermost")
diff --git a/firedrake/interpolation.py b/firedrake/interpolation.py
index 06d124934e..351b393394 100644
--- a/firedrake/interpolation.py
+++ b/firedrake/interpolation.py
@@ -543,9 +543,7 @@ def __init__(
         # input ordering VOM will only contain the points on rank 0!
         # QUESTION: Should any of the below have annotation turned off?
         ufl_scalar_element = V_dest.ufl_element()
-        if ufl_scalar_element.num_sub_elements and not isinstance(
-            ufl_scalar_element, finat.ufl.TensorProductElement
-        ):
+        if isinstance(ufl_scalar_element, finat.ufl.MixedElement):
             if all(
                 ufl_scalar_element.sub_elements[0] == e
                 for e in ufl_scalar_element.sub_elements
@@ -558,7 +556,7 @@ def __init__(
                     raise NotImplementedError(
                         "Can't yet cross-mesh interpolate onto function spaces made from VectorElements or TensorElements made from sub elements with value shape other than ()."
                     )
-            elif type(ufl_scalar_element) is finat.ufl.MixedElement:
+            else:
                 # Build and save an interpolator for each sub-element
                 # separately for MixedFunctionSpaces. NOTE: since we can't have
                 # expressions for MixedFunctionSpaces we know that the input
@@ -597,10 +595,6 @@ def __init__(
                     )
                     self.sub_interpolators.append(sub_interpolator)
                 return
-            else:
-                raise NotImplementedError(
-                    f"Unhandled cross-mesh interpolation ufl element type: {repr(ufl_scalar_element)}"
-                )
 
         from firedrake.assemble import assemble
         V_dest_vec = firedrake.VectorFunctionSpace(dest_mesh, ufl_scalar_element)
diff --git a/firedrake/mg/embedded.py b/firedrake/mg/embedded.py
index 551c09a7e2..d452bd1a4b 100644
--- a/firedrake/mg/embedded.py
+++ b/firedrake/mg/embedded.py
@@ -70,8 +70,11 @@ def __init__(self, *, native_transfers=None, use_averaging=True):
     def is_native(self, element, op):
         if element in self.native_transfers.keys():
             return self.native_transfers[element][op] is not None
-        if isinstance(element.cell, ufl.TensorProductCell) and len(element.sub_elements) > 0:
-            return all(self.is_native(e, op) for e in element.sub_elements)
+        if isinstance(element.cell, ufl.TensorProductCell):
+            if isinstance(element, finat.ufl.TensorProductElement):
+                return all(self.is_native(e, op) for e in element.factor_elements)
+            elif isinstance(element, finat.ufl.MixedElement):
+                return all(self.is_native(e, op) for e in element.sub_elements)
         return (element.family() in native_families) and not (element.variant() in non_native_variants)
 
     def _native_transfer(self, element, op):
diff --git a/firedrake/preconditioners/hiptmair.py b/firedrake/preconditioners/hiptmair.py
index f292579eb5..f4071d7a10 100644
--- a/firedrake/preconditioners/hiptmair.py
+++ b/firedrake/preconditioners/hiptmair.py
@@ -250,7 +250,7 @@ def div_to_curl(ele):
     elif isinstance(ele, finat.ufl.EnrichedElement):
         return type(ele)(*(div_to_curl(e) for e in reversed(ele._elements)))
     elif isinstance(ele, finat.ufl.TensorProductElement):
-        return type(ele)(*(div_to_curl(e) for e in ele.sub_elements), cell=ele.cell)
+        return type(ele)(*(div_to_curl(e) for e in ele.factor_elements), cell=ele.cell)
     elif isinstance(ele, finat.ufl.WithMapping):
         return type(ele)(div_to_curl(ele.wrapee), ele.mapping())
     elif isinstance(ele, finat.ufl.BrokenElement):
diff --git a/firedrake/preconditioners/pmg.py b/firedrake/preconditioners/pmg.py
index c5eb1ccaac..c90961bbff 100644
--- a/firedrake/preconditioners/pmg.py
+++ b/firedrake/preconditioners/pmg.py
@@ -423,7 +423,7 @@ def reconstruct_degree(ele, degree):
             return type(ele)(*(PMGBase.reconstruct_degree(e, PMGBase.max_degree(e) + shift) for e in ele._elements))
         elif isinstance(ele, finat.ufl.TensorProductElement):
             shift = degree - PMGBase.max_degree(ele)
-            return type(ele)(*(PMGBase.reconstruct_degree(e, PMGBase.max_degree(e) + shift) for e in ele.sub_elements), cell=ele.cell)
+            return type(ele)(*(PMGBase.reconstruct_degree(e, PMGBase.max_degree(e) + shift) for e in ele.factor_elements), cell=ele.cell)
         elif isinstance(ele, finat.ufl.MixedElement):
             shift = degree - PMGBase.max_degree(ele)
             return type(ele)(*(PMGBase.reconstruct_degree(e, PMGBase.max_degree(e) + shift) for e in ele.sub_elements))

From d7293a76750ae778ca3fed6facde479cf5e07aa7 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 11 Jun 2025 16:33:42 +0100
Subject: [PATCH 137/222] Fast diagonalization demo (#4309)

* Fast diagonalization demo
---
 demos/demo_references.bib                     |  22 ++
 .../fast_diagonalisation_poisson.py.rst       | 200 ++++++++++++++++++
 docs/source/_static/firedrake-apps.bib        |  11 -
 docs/source/advanced_tut.rst                  |   1 +
 docs/source/preconditioning.rst               |   2 +-
 firedrake/preconditioners/facet_split.py      |   2 +-
 firedrake/preconditioners/fdm.py              |  24 ---
 firedrake_citations/__init__.py               |  26 +++
 tests/firedrake/demos/test_demos_run.py       |   1 +
 9 files changed, 252 insertions(+), 37 deletions(-)
 create mode 100644 demos/fast_diagonalisation/fast_diagonalisation_poisson.py.rst

diff --git a/demos/demo_references.bib b/demos/demo_references.bib
index cb4633b5ef..652eb1176d 100644
--- a/demos/demo_references.bib
+++ b/demos/demo_references.bib
@@ -308,3 +308,25 @@ @article{Ricker:1953
   publisher={Society of Exploration Geophysicists},
   doi={https://doi.org/10.1190/1.1437843}
 }
+
+@article{Brubeck2022,
+  author        = {Brubeck, Pablo D. and Farrell, Patrick E.},
+  doi           = {10.1137/21M1444187},
+  journal       = {SIAM J. Sci. Comput.},
+  number        = {5},
+  pages         = {A2991-A3017},
+  title         = {A scalable and robust vertex-star relaxation for high-order {FEM}},
+  volume        = {44},
+  year          = {2022}
+}
+
+@article{Brubeck2024,
+  author        = {Brubeck, Pablo D. and Farrell, Patrick E.},
+  doi           = {10.1137/22M1537370},
+  journal       = {SIAM J. Sci. Comput.},
+  number        = {3},
+  pages         = {A1549-A1573},
+  title         = {{Multigrid solvers for the de Rham complex with optimal complexity in polynomial degree}},
+  volume        = {46},
+  year          = {2024}
+}
diff --git a/demos/fast_diagonalisation/fast_diagonalisation_poisson.py.rst b/demos/fast_diagonalisation/fast_diagonalisation_poisson.py.rst
new file mode 100644
index 0000000000..bc558ebc25
--- /dev/null
+++ b/demos/fast_diagonalisation/fast_diagonalisation_poisson.py.rst
@@ -0,0 +1,200 @@
+Using fast diagonalisation solvers in Firedrake
+===============================================
+
+Contributed by `Pablo Brubeck `_.
+
+In this demo we show how to efficiently solve the Poisson equation using
+high-order tensor-product elements. This is accomplished through a special
+basis, obtained from the fast diagonalisation method (FDM). We first construct
+an auxiliary operator that is sparse in this basis, with as many zeros as a
+low-order method.  We then combine this with an additive Schwarz method.
+Finally, we show how to do static condensation using fieldsplit. A detailed
+description of these solvers is found in :cite:`Brubeck2022` and
+:cite:`Brubeck2024`.
+
+
+Creating an extruded mesh
+-------------------------
+
+The fast diagonalisation method produces a basis of discrete eigenfunctions.
+These are polynomials, and can be efficiently computed on tensor
+product-elements by solving an eigenproblem on the interval. Therefore, we will
+require quadrilateral or hexahedral meshes.  Currently, the solver only supports
+extruded hexahedral meshes, so we must create an :func:`~.ExtrudedMesh`. ::
+
+  from firedrake import *
+
+  base = UnitSquareMesh(8, 8, quadrilateral=True)
+  mesh = ExtrudedMesh(base, 8)
+
+
+Defining the problem: the Poisson equation
+------------------------------------------
+
+Having defined the mesh we now need to set up our problem.  The crucial step
+for fast diagonalisation is a special choice of basis functions. We obtain them
+by passing ``variant="fdm"`` to the :func:`~.FunctionSpace` constructor.  The
+solvers in this demo work also with other element variants, but each iteration
+would involve an additional a basis transformation.  To stress-test the solver,
+we prescribe a random :class:`~.Cofunction` as right-hand side.
+
+We'll demonstrate a few different sets of solver parameters, so let's define a
+function that takes in set of parameters and uses them on a
+:class:`~.LinearVariationalSolver`. ::
+
+
+  def run_solve(degree, parameters):
+      V = FunctionSpace(mesh, "Q", degree, variant="fdm")
+      u = TrialFunction(V)
+      v = TestFunction(V)
+      uh = Function(V)
+      a = inner(grad(u), grad(v)) * dx
+      bcs = [DirichletBC(V, 0, sub) for sub in ("on_boundary", "top", "bottom")]
+
+      rg = RandomGenerator(PCG64(seed=123456789))
+      L = rg.uniform(V.dual(), -1, 1)
+
+      problem = LinearVariationalProblem(a, L, uh, bcs=bcs)
+      solver = LinearVariationalSolver(problem, solver_parameters=parameters)
+      solver.solve()
+      return solver.snes.getLinearSolveIterations()
+
+Specifying the solver
+---------------------
+
+The solver avoids the assembly of a matrix with dense element submatrices, and
+instead applies a matrix-free conjugate gradient method with a preconditioner
+obtained by assembling a sparse matrix.  This is done through the python type
+preconditioner :class:`~.FDMPC`.  We define a function that enables us to
+compose :class:`~.FDMPC` with an inner relaxation. ::
+
+
+  def fdm_params(relax):
+      return {
+          "mat_type": "matfree",
+          "ksp_type": "cg",
+          "pc_type": "python",
+          "pc_python_type": "firedrake.FDMPC",
+          "fdm": relax,
+      }
+
+Let's start with our first test.  We'll confirm a working solve by
+using a sparse direct LU factorization. ::
+
+
+  lu_params = {
+      "pc_type": "lu",
+      "pc_factor_mat_solver_type": "mumps",
+  }
+
+  fdm_lu_params = fdm_params(lu_params)
+  its = run_solve(5, fdm_lu_params)
+  print(f"LU iterations {its}")
+
+
+.. note::
+    On this Cartesian mesh, the sparse operator constructed by :class:`~.FDMPC`
+    corresponds to the original operator. This is no longer the case with non-Cartesian
+    meshes or more general PDEs, as the FDM basis will fail to diagonalise the
+    problem.  For such cases, :class:`~.FDMPC` will produce a sparse
+    approximation of the original operator.
+
+Moving on to a more complicated solver, we'll employ a two-level solver with
+the lowest-order coarse space via :class:`~.P1PC`.  As the fine level
+relaxation we define an additive Schwarz method on vertex-star patches
+implemented via :class:`~.ASMExtrudedStarPC` as we have an extruded mesh::
+
+  asm_params = {
+      "pc_type": "python",
+      "pc_python_type": "firedrake.P1PC",
+      "pmg_mg_coarse_mat_type": "aij",
+      "pmg_mg_coarse": lu_params,
+      "pmg_mg_levels": {
+          "ksp_max_it": 1,
+          "ksp_type": "chebyshev",
+          "pc_type": "python",
+          "pc_python_type": "firedrake.ASMExtrudedStarPC",
+          "sub_sub_pc_type": "lu",
+      },
+  }
+
+  fdm_asm_params = fdm_params(asm_params)
+
+  print("FDM + ASM")
+  print("Degree\tIterations")
+  for degree in range(3, 6):
+      its = run_solve(degree, fdm_asm_params)
+      print(f"{degree}\t{its}")
+
+We observe degree-independent iteration counts:
+
+======== ============
+ Degree   Iterations
+======== ============
+  3        13
+  4        13
+  5        12
+======== ============
+
+Static condensation
+-------------------
+
+Finally, we construct :class:`~.FDMPC` solver parameters using static
+condensation.  The fast diagonalisation basis diagonalises the operator on cell
+interiors. So we define a solver that splits the interior and facet degrees of
+freedom via :class:`~.FacetSplitPC` and fieldsplit options.  We set the option
+``fdm_static_condensation`` to tell :class:`~.FDMPC` to assemble a 2-by-2 block
+preconditioner where the lower-right block is replaced by the Schur complement
+resulting from eliminating the interior degrees of freedom.  The Krylov
+solver is posed on the full set of degrees of freedom, and the preconditioner
+applies a symmetrized multiplicative sweep on the interior and the facet
+degrees of freedom. In general, we are not able to fully eliminate the
+interior, as the sparse operator constructed by :class:`~.FDMPC` is only an
+approximation on non-Cartesian meshes.  We apply point-Jacobi on the interior
+block, and the two-level additive Schwarz method on the facets. ::
+
+
+  def fdm_static_condensation_params(relax):
+      return {
+          "mat_type": "matfree",
+          "ksp_type": "cg",
+          "pc_type": "python",
+          "pc_python_type": "firedrake.FacetSplitPC",
+          "facet_pc_type": "python",
+          "facet_pc_python_type": "firedrake.FDMPC",
+          "facet_fdm_static_condensation": True,
+          "facet_fdm_pc_use_amat": False,
+          "facet_fdm_pc_type": "fieldsplit",
+          "facet_fdm_pc_fieldsplit_type": "symmetric_multiplicative",
+          "facet_fdm_fieldsplit_ksp_type": "preonly",
+          "facet_fdm_fieldsplit_0_pc_type": "jacobi",
+          "facet_fdm_fieldsplit_1": relax,
+      }
+
+
+  fdm_sc_asm_params = fdm_static_condensation_params(asm_params)
+
+  print('FDM + SC + ASM')
+  print("Degree\tIterations")
+  for degree in range(3, 6):
+      its = run_solve(degree, fdm_sc_asm_params)
+      print(f"{degree}\t{its}")
+
+We also observe degree-independent iteration counts:
+
+======== ============
+ Degree   Iterations
+======== ============
+  3        10
+  4        10
+  5        10
+======== ============
+
+A runnable python version of this demo can be found :demo:`here
+`.
+
+
+.. rubric:: References
+
+.. bibliography:: demo_references.bib
+   :filter: docname in docnames
diff --git a/docs/source/_static/firedrake-apps.bib b/docs/source/_static/firedrake-apps.bib
index a2e08f4e6b..72b718c98a 100644
--- a/docs/source/_static/firedrake-apps.bib
+++ b/docs/source/_static/firedrake-apps.bib
@@ -198,17 +198,6 @@ @article{Braun2017
   year          = {2017}
 }
 
-@article{Brubeck2022,
-  author        = {Brubeck, Pablo D. and Farrell, Patrick E.},
-  doi           = {10.1137/21M1444187},
-  journal       = {SIAM J. Sci. Comput.},
-  number        = {5},
-  pages         = {A2991-A3017},
-  title         = {A scalable and robust vertex-star relaxation for high-order {FEM}},
-  volume        = {44},
-  year          = {2022}
-}
-
 @misc{Brugnoli2020,
   author        = {Brugnoli, Andrea and Alazar, Daniel and Pommier-Budinger, Valérie and Matignon, Denis},
   eprint        = {2002.12816},
diff --git a/docs/source/advanced_tut.rst b/docs/source/advanced_tut.rst
index 9b83643c5f..9bf7da1eab 100644
--- a/docs/source/advanced_tut.rst
+++ b/docs/source/advanced_tut.rst
@@ -30,3 +30,4 @@ element systems.
    Vertex/edge star multigrid relaxation for H(div).
    Auxiliary space patch relaxation multigrid for H(curl).
    Steady Boussinesq problem with integral constraints.
+   Preconditioning using fast diagonalisation.
diff --git a/docs/source/preconditioning.rst b/docs/source/preconditioning.rst
index f2e6aeebe9..b0f56325b8 100644
--- a/docs/source/preconditioning.rst
+++ b/docs/source/preconditioning.rst
@@ -138,7 +138,7 @@ operator instead.
    implemented for quadrilateral and hexahedral cells. The assembled
    matrix becomes as sparse as a low-order refined preconditioner, to
    which one may apply other preconditioners such as :class:`.ASMStarPC` or
-   :class:`.ASMExtrudedStarPC`. See details in :cite:`Brubeck2022`.
+   :class:`.ASMExtrudedStarPC`. See details in :cite:`Brubeck2022` and :cite:`Brubeck2024`.
 :class:`.MassInvPC`
    Preconditioner for applying an inverse mass matrix.
 :class:`~.PCDPC`
diff --git a/firedrake/preconditioners/facet_split.py b/firedrake/preconditioners/facet_split.py
index 2d49f9e908..26e3f984ca 100644
--- a/firedrake/preconditioners/facet_split.py
+++ b/firedrake/preconditioners/facet_split.py
@@ -19,7 +19,7 @@ class FacetSplitPC(PCBase):
     This allows for statically-condensed preconditioners to be applied to
     linear systems involving the matrix applied to the full set of DOFs. Code
     generated for the matrix-free operator evaluation in the space with full
-    DOFs will run faster than the one with interior-facet decoposition, since
+    DOFs will run faster than the one with interior-facet decomposition, since
     the full element has a simpler structure.
     """
 
diff --git a/firedrake/preconditioners/fdm.py b/firedrake/preconditioners/fdm.py
index 4e28f73a19..042956ca8f 100644
--- a/firedrake/preconditioners/fdm.py
+++ b/firedrake/preconditioners/fdm.py
@@ -36,30 +36,6 @@
 import numpy
 import ctypes
 
-Citations().add("Brubeck2022", """
-@article{Brubeck2022,
-  title={A scalable and robust vertex-star relaxation for high-order {FEM}},
-  author={Brubeck, Pablo D. and Farrell, Patrick E.},
-  journal = {SIAM J. Sci. Comput.},
-  volume = {44},
-  number = {5},
-  pages = {A2991-A3017},
-  year = {2022},
-  doi = {10.1137/21M1444187}
-""")
-
-Citations().add("Brubeck2024", """
-@article{Brubeck2024,
-  title={{Multigrid solvers for the de Rham complex with optimal complexity in polynomial degree}},
-  author={Brubeck, Pablo D. and Farrell, Patrick E.},
-  journal = {SIAM J. Sci. Comput.},
-  volume = {46},
-  number = {3},
-  pages = {A1549-A1573},
-  year = {2024},
-  doi = {10.1137/22M1537370}
-""")
-
 
 __all__ = ("FDMPC", "PoissonFDMPC")
 
diff --git a/firedrake_citations/__init__.py b/firedrake_citations/__init__.py
index 46e6a0a77a..6ad0108c2f 100644
--- a/firedrake_citations/__init__.py
+++ b/firedrake_citations/__init__.py
@@ -375,3 +375,29 @@ def print_at_exit(cls):
   title = {ngsPETSc: A coupling between NETGEN/NGSolve and PETSc},
   journal = {Journal of Open Source Software} }
 """)
+
+Citations().add("Brubeck2022", """
+@article{Brubeck2022,
+  author        = {Brubeck, Pablo D. and Farrell, Patrick E.},
+  doi           = {10.1137/21M1444187},
+  journal       = {SIAM J. Sci. Comput.},
+  number        = {5},
+  pages         = {A2991-A3017},
+  title         = {A scalable and robust vertex-star relaxation for high-order {FEM}},
+  volume        = {44},
+  year          = {2022}
+}
+""")
+
+Citations().add("Brubeck2024", """
+@article{Brubeck2024,
+  author        = {Brubeck, Pablo D. and Farrell, Patrick E.},
+  doi           = {10.1137/22M1537370},
+  journal       = {SIAM J. Sci. Comput.},
+  number        = {3},
+  pages         = {A1549-A1573},
+  title         = {{Multigrid solvers for the de Rham complex with optimal complexity in polynomial degree}},
+  volume        = {46},
+  year          = {2024}
+}
+""")
diff --git a/tests/firedrake/demos/test_demos_run.py b/tests/firedrake/demos/test_demos_run.py
index 4d8eb623d8..be2f038d67 100644
--- a/tests/firedrake/demos/test_demos_run.py
+++ b/tests/firedrake/demos/test_demos_run.py
@@ -46,6 +46,7 @@
     Demo(("patch", "hdiv_riesz_star"), []),
     Demo(("quasigeostrophy_1layer", "qg_1layer_wave"), ["hypre", "vtk"]),
     Demo(("saddle_point_pc", "saddle_point_systems"), ["hypre", "mumps"]),
+    Demo(("fast_diagonalisation", "fast_diagonalisation_poisson"), ["mumps"]),
     Demo(('vlasov_poisson_1d', 'vp1d'), []),
 ]
 PARALLEL_DEMOS = [

From a05fe6b805e578216ace1d0d5d5f2aff4bb0bf10 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 11 Jun 2025 16:37:39 +0100
Subject: [PATCH 138/222] Fix homogeneous DirichletBC residual (#4370)

---
 firedrake/bcs.py                       |  6 +++++-
 tests/firedrake/regression/test_bcs.py | 15 +++++++++++++++
 2 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/firedrake/bcs.py b/firedrake/bcs.py
index 6cd6f3d5d7..932b75d5b6 100644
--- a/firedrake/bcs.py
+++ b/firedrake/bcs.py
@@ -451,7 +451,11 @@ def apply(self, r, u=None):
             if u:
                 u = u.sub(idx)
         if u:
-            r.assign(u - self.function_arg, subset=self.node_set)
+            if self.function_arg == 0:
+                bc_residual = u
+            else:
+                bc_residual = u - self.function_arg
+            r.assign(bc_residual, subset=self.node_set)
         else:
             r.assign(self.function_arg, subset=self.node_set)
 
diff --git a/tests/firedrake/regression/test_bcs.py b/tests/firedrake/regression/test_bcs.py
index 16cbc669c6..737e96bd5c 100644
--- a/tests/firedrake/regression/test_bcs.py
+++ b/tests/firedrake/regression/test_bcs.py
@@ -439,3 +439,18 @@ def test_bcs_mixed_real_vector():
     A = assemble(a, bcs=[bc, ])
     assert np.allclose(A.M[0][1].values, [[[0.25], [0.], [0.25], [0.], [0.25], [0.25], [0.25], [0.25]]])
     assert np.allclose(A.M[1][0].values, [[0.25, 0., 0.25, 0., 0.25, 0.25, 0.25, 0.25]])
+
+
+def test_homogeneous_bc_residual():
+    mesh = UnitSquareMesh(2, 2)
+    V = VectorFunctionSpace(mesh, "CG", 1)
+    bc = DirichletBC(V, 0, "on_boundary")
+
+    u = Function(V).assign(42)
+    r = Function(V).assign(333)
+    bc.apply(r, u=u)
+
+    assert np.allclose(r.dat.data_ro[bc.nodes], u.dat.data_ro[bc.nodes])
+
+    interior = np.setdiff1d(range(r.dat.data_ro.shape[0]), bc.nodes)
+    assert np.allclose(r.dat.data_ro[interior], 333)

From 0453c8dc9e53696f80618453f391b4521eb09e22 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 11 Jun 2025 18:03:00 +0100
Subject: [PATCH 139/222] ImplicitMatrixContext: Fix dual space for matfree
 SLATE Inverse (#4360)

* ImplicitMatrixContext: Fix dual space for matfree SLATE Inverse

* add a test
---
 firedrake/matrix_free/operators.py            | 11 +++---
 firedrake/ufl_expr.py                         |  2 +-
 .../firedrake/slate/test_assemble_tensors.py  | 18 ----------
 tests/firedrake/slate/test_linear_algebra.py  | 34 +++++++++++++++++++
 4 files changed, 40 insertions(+), 25 deletions(-)

diff --git a/firedrake/matrix_free/operators.py b/firedrake/matrix_free/operators.py
index 9a5a042f0e..18a767557d 100644
--- a/firedrake/matrix_free/operators.py
+++ b/firedrake/matrix_free/operators.py
@@ -11,7 +11,6 @@
 from firedrake.petsc import PETSc
 from firedrake.utils import cached_property
 from firedrake.function import Function
-from firedrake.cofunction import Cofunction
 from ufl.form import ZeroBaseForm
 
 
@@ -114,18 +113,18 @@ def __init__(self, a, row_bcs=[], col_bcs=[],
             arg.function_space() for arg in a.arguments()
         )
         # Need a cofunction since y receives the assembled result of Ax
-        self._ystar = Cofunction(test_space.dual())
+        self._ystar = Function(test_space.dual())
         self._y = Function(test_space)
         self._x = Function(trial_space)
-        self._xstar = Cofunction(trial_space.dual())
+        self._xstar = Function(trial_space.dual())
 
         # These are temporary storage for holding the BC
         # values during matvec application.  _xbc is for
         # the action and ._ybc is for transpose.
         if len(self.bcs) > 0:
-            self._xbc = Cofunction(trial_space.dual())
+            self._xbc = Function(trial_space.dual())
         if len(self.col_bcs) > 0:
-            self._ybc = Cofunction(test_space.dual())
+            self._ybc = Function(test_space.dual())
 
         # Get size information from template vecs on test and trial spaces
         trial_vec = trial_space.dof_dset.layout_vec
@@ -178,7 +177,7 @@ def __init__(self, a, row_bcs=[], col_bcs=[],
     @cached_property
     def _diagonal(self):
         assert self.on_diag
-        return Cofunction(self._x.function_space().dual())
+        return Function(self._x.function_space().dual())
 
     @cached_property
     def _assemble_diagonal(self):
diff --git a/firedrake/ufl_expr.py b/firedrake/ufl_expr.py
index 3883dcdf1a..4a5b1344ba 100644
--- a/firedrake/ufl_expr.py
+++ b/firedrake/ufl_expr.py
@@ -301,7 +301,7 @@ def action(form, coefficient, derivatives_expanded=None):
     if isinstance(form, firedrake.slate.TensorBase):
         if form.rank == 0:
             raise ValueError("Can't take action of rank-0 tensor")
-        return form * firedrake.AssembledVector(coefficient)
+        return form * coefficient
     else:
         return ufl.action(form, coefficient, derivatives_expanded=derivatives_expanded)
 
diff --git a/tests/firedrake/slate/test_assemble_tensors.py b/tests/firedrake/slate/test_assemble_tensors.py
index d9222f6fa2..c35d43e27e 100644
--- a/tests/firedrake/slate/test_assemble_tensors.py
+++ b/tests/firedrake/slate/test_assemble_tensors.py
@@ -130,24 +130,6 @@ def test_assemble_matrix(rank_two_tensor):
     assert np.allclose(M.M.values, assemble(rank_two_tensor.form).M.values, rtol=1e-14)
 
 
-def test_assemble_solve(mesh):
-    V = FunctionSpace(mesh, "DG", 0)
-    u = TrialFunction(V)
-    v = TestFunction(V)
-
-    M = inner(u, v)*dx
-    f = Cofunction(V.dual())
-    f.dat.data[...] = 1
-
-    u1 = Function(V)
-    u2 = Function(V)
-    # Assemble a SLATE tensor into u1
-    assemble(Inverse(Tensor(M)) * AssembledVector(f), tensor=u1)
-    # Solve in the usual way
-    solve(M == f, u2)
-    assert np.allclose(u1.dat.data, u2.dat.data, rtol=1e-14)
-
-
 def test_assemble_vector_into_tensor(mesh):
     V = FunctionSpace(mesh, "DG", 1)
     v = TestFunction(V)
diff --git a/tests/firedrake/slate/test_linear_algebra.py b/tests/firedrake/slate/test_linear_algebra.py
index 124520651c..12d6223a43 100644
--- a/tests/firedrake/slate/test_linear_algebra.py
+++ b/tests/firedrake/slate/test_linear_algebra.py
@@ -116,3 +116,37 @@ def test_local_solve(decomp):
     x = assemble(A.solve(b, decomposition=decomp))
 
     assert np.allclose(x.dat.data, f.dat.data, rtol=1.e-13)
+
+
+@pytest.mark.parametrize("mat_type, rhs_type", [
+    ("slate", "slate"), ("slate", "form"), ("slate", "cofunction"),
+    ("aij", "cofunction"), ("aij", "form"),
+    ("matfree", "cofunction"), ("matfree", "form")])
+def test_inverse_action(mat_type, rhs_type):
+    """Test combined UFL/SLATE expressions
+    """
+    mesh = UnitSquareMesh(3, 3)
+    V = FunctionSpace(mesh, "DG", 1)
+    u = TrialFunction(V)
+    v = TestFunction(V)
+
+    A = Tensor(inner(u, v)*dx)
+    if mat_type == "slate":
+        Ainv = A.inv
+    else:
+        Ainv = assemble(A.inv, mat_type=mat_type)
+
+    f = Function(V).assign(1.0)
+    L = inner(f, v)*dx
+    if rhs_type == "form":
+        b = L
+    elif rhs_type == "cofunction":
+        b = assemble(L)
+    elif rhs_type == "slate":
+        b = Tensor(L)
+    else:
+        raise ValueError("Invalid rhs type")
+
+    x = Function(V)
+    assemble(action(Ainv, b), tensor=x)
+    assert np.allclose(x.dat.data, f.dat.data, rtol=1.e-13)

From d7c230c767fa611e70d3129e3c44f4b2b79ca7b6 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 11 Jun 2025 18:24:16 +0100
Subject: [PATCH 140/222] Fix typo in test (#4359)

---
 tests/firedrake/multigrid/test_hiptmair.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/firedrake/multigrid/test_hiptmair.py b/tests/firedrake/multigrid/test_hiptmair.py
index f1451ec53f..99ab52fe33 100644
--- a/tests/firedrake/multigrid/test_hiptmair.py
+++ b/tests/firedrake/multigrid/test_hiptmair.py
@@ -61,7 +61,7 @@ def asm(k):
         "ksp_type": "preonly",
         "pc_type": "python",
         "pc_python_type": "firedrake.ASMExtrudedStarPC",
-        "pc_star_patch_construct_dim": k,
+        "pc_star_construct_dim": k,
     }
 
 
@@ -166,5 +166,5 @@ def test_pmg_hiptmair_hcurl():
     family = "NCE"
     mat_type = "aij"
     V = FunctionSpace(mesh, family, degree=3)
-    max_it = 10
+    max_it = 12
     assert run_riesz_map(V, mat_type, max_it, solver_type="pmg") < 1E-6

From 92e85cc8ba669eea16c8fbdd08c0c410afb1ea94 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 11 Jun 2025 18:28:18 +0100
Subject: [PATCH 141/222] ASM: support Extruded Periodic meshes (#4352)

* ASM: support Extruded Periodic meshes

* pyop2: fix PermutedMap on extruded periodic meshes
---
 firedrake/preconditioners/asm.py              | 55 ++++++++++++-------
 pyop2/codegen/builder.py                      | 18 +++---
 tests/firedrake/multigrid/test_p_multigrid.py | 46 +++++++++++++---
 tests/firedrake/regression/test_star_pc.py    | 53 ++++++++++++++++++
 4 files changed, 134 insertions(+), 38 deletions(-)

diff --git a/firedrake/preconditioners/asm.py b/firedrake/preconditioners/asm.py
index f91261586d..7bff0e35de 100644
--- a/firedrake/preconditioners/asm.py
+++ b/firedrake/preconditioners/asm.py
@@ -379,6 +379,13 @@ def get_basemesh_nodes(W):
         basemeshdof[p - pstart] = dof_per_layer
         basemeshlayeroffset[p - pstart] = layer_offset
 
+    if W.mesh().extruded_periodic:
+        # Account for missing dofs from the top layer
+        for dim in range(W.mesh().topological_dimension()):
+            qstart, qend = W.mesh().topology_dm.getDepthStratum(dim)
+            quotient = len(W.finat_element.entity_dofs()[(dim, 0)][0])
+            basemeshdof[qstart-pstart:qend-pstart] += quotient
+
     return basemeshoff, basemeshdof, basemeshlayeroffset
 
 
@@ -399,6 +406,7 @@ def get_patches(self, V):
         nlayers = mesh.layers
         if not mesh.cell_set._extruded:
             return super(ASMExtrudedStarPC, self).get_patches(V)
+        periodic = mesh.extruded_periodic
 
         # Obtain the topological entities to use to construct the stars
         opts = PETSc.Options(self.prefix)
@@ -433,13 +441,19 @@ def get_patches(self, V):
         # Face-stars: depth = 2 = 2 + 0 = 1 + 1.
         # 2 + 0 -> horizontal face-star = (2D interior) x (1D vertex-star)
         # 1 + 1 -> vertical face-star = (2D edge-star) x (1D interior)
+        pstart, _ = mesh_dm.getChart()
         for base_depth in range(depth+1):
             interval_depth = depth - base_depth
-            if interval_depth > 1:
+            if interval_depth == 0:
+                # extrude by 1D vertex-star
+                layer_entities = [(1, 1), (1, 0), (0, 0)]
+            elif interval_depth == 1:
+                # extrude by 1D interior
+                layer_entities = [(1, 0)]
+            else:
                 continue
 
             start, end = mesh_dm.getDepthStratum(base_depth)
-            pstart, _ = mesh_dm.getChart()
             for seed in range(start, end):
                 # Only build patches over owned DoFs
                 if mesh_dm.getLabelValue("pyop2_ghost", seed) != -1:
@@ -449,25 +463,24 @@ def get_patches(self, V):
                 points, _ = mesh_dm.getTransitiveClosure(seed, useCone=False)
                 points = order_points(mesh_dm, points, ordering, self.prefix)
                 points -= pstart  # offset by chart start
-                for k in range(nlayers-interval_depth):
-                    if interval_depth == 1:
-                        # extrude by 1D interior
-                        planes = [1]
-                    elif k == 0:
-                        # extrude by 1D vertex-star on the bottom
-                        planes = [1, 0]
-                    elif k == nlayers - 1:
-                        # extrude by 1D vertex-star on the top
-                        planes = [-1, 0]
-                    else:
-                        # extrude by 1D vertex-star
-                        planes = [-1, 1, 0]
 
+                num_seeds = nlayers
+                if periodic or interval_depth:
+                    num_seeds -= 1
+                for layer_seed in range(num_seeds):
                     indices = []
                     # Get DoF indices for patch
                     for i, W in enumerate(V):
                         iset = V_ises[i]
-                        for plane in planes:
+                        for layer_dim, layer_shift in layer_entities:
+                            layer = layer_seed - layer_shift
+                            if periodic:
+                                # Handle periodic case
+                                layer = layer % (nlayers-1)
+                            elif layer < 0 or (layer + layer_dim) >= nlayers:
+                                # We are out of bounds
+                                continue
+
                             for p in points:
                                 # How to walk up one layer
                                 blayer_offset = basemeshlayeroffsets[i][p]
@@ -483,12 +496,12 @@ def get_patches(self, V):
                                 # entity
                                 dof = basemeshdof[i][p]
                                 # Hard-code taking the star
-                                if plane == 0:
-                                    begin = off + k * blayer_offset
-                                    end = off + k * blayer_offset + dof
+                                if layer_dim == 0:
+                                    begin = off + layer * blayer_offset
+                                    end = off + layer * blayer_offset + dof
                                 else:
-                                    begin = off + min(k, k+plane) * blayer_offset + dof
-                                    end = off + max(k, k+plane) * blayer_offset
+                                    begin = off + layer * blayer_offset + dof
+                                    end = off + (layer + 1) * blayer_offset
                                 zlice = slice(W.block_size * begin, W.block_size * end)
                                 indices.extend(iset[zlice])
 
diff --git a/pyop2/codegen/builder.py b/pyop2/codegen/builder.py
index 505dc5d2bf..82cd5dfe21 100644
--- a/pyop2/codegen/builder.py
+++ b/pyop2/codegen/builder.py
@@ -157,18 +157,20 @@ def __init__(self, map_, permutation):
         self.values = map_.values
         self.offset = map_.offset
         offset = map_.offset
+        quotient = map_.offset_quotient
         # TODO: this is a hack, rep2loopy should be in charge of
         # generating all names!
         count = next(map_._pmap_count)
-        if offset is not None:
-            if offset.shape:
-                # Have a named literal
-                offset = offset.value[permutation]
-                offset = NamedLiteral(offset, parent=self.values, suffix=f"permutation{count}_offset")
-            else:
-                offset = map_.offset
+        if offset is not None and offset.shape:
+            # Have a named literal
+            offset = offset.value[permutation]
+            offset = NamedLiteral(offset, parent=self.values, suffix=f"permutation{count}_offset")
+        if quotient is not None and quotient.shape:
+            # Have a named literal
+            quotient = quotient.value[permutation]
+            quotient = NamedLiteral(quotient, parent=self.values, suffix=f"permutation{count}_offset_quotient")
         self.offset = offset
-        self.offset_quotient = map_.offset_quotient
+        self.offset_quotient = quotient
         self.permutation = NamedLiteral(permutation, parent=self.values, suffix=f"permutation{count}")
 
     def _permute(self, x):
diff --git a/tests/firedrake/multigrid/test_p_multigrid.py b/tests/firedrake/multigrid/test_p_multigrid.py
index 39437b0204..d028c54d30 100644
--- a/tests/firedrake/multigrid/test_p_multigrid.py
+++ b/tests/firedrake/multigrid/test_p_multigrid.py
@@ -2,14 +2,21 @@
 from firedrake import *
 
 
-@pytest.fixture(params=[2, 3],
-                ids=["Rectangle", "Box"])
+@pytest.fixture(params=[(2, False, False), (2, True, False), (1, True, True)],
+                ids=["Rectangle", "Rectangle-Extruded", "Interval-Extruded-Periodic"])
 def tp_mesh(request):
-    nx = 4
+    dim, extruded, periodic = request.param
+
+    nx = 2
     distribution = {"overlap_type": (DistributedMeshOverlapType.VERTEX, 1)}
-    m = UnitSquareMesh(nx, nx, quadrilateral=True, distribution_parameters=distribution)
-    if request.param == 3:
-        m = ExtrudedMesh(m, nx)
+    if dim == 1:
+        m = UnitIntervalMesh(nx, distribution_parameters=distribution)
+    elif dim == 2:
+        m = UnitSquareMesh(nx, nx, quadrilateral=True, distribution_parameters=distribution)
+    else:
+        m = UnitCubeMesh(nx, nx, nx, hexahedral=True, distribution_parameters=distribution)
+    if extruded:
+        m = ExtrudedMesh(m, nx, periodic=periodic)
 
     x = SpatialCoordinate(m)
     xnew = as_vector([acos(1-2*xj)/pi for xj in x])
@@ -66,6 +73,23 @@ def test_reconstruct_degree(tp_mesh, mixed_family):
         assert e == PMGPC.reconstruct_degree(elist[0], degree)
 
 
+@pytest.mark.parametrize("family", ["Q", "NCE", "NCF", "DQ"])
+def test_prolong_basic(tp_mesh, family):
+    """ Interpolate a constant function between low-order and high-order spaces
+    """
+    from firedrake.preconditioners.pmg import prolongation_matrix_matfree
+    if tp_mesh.topological_dimension() == 2:
+        family = family.replace("N", "RT")
+
+    fs = [FunctionSpace(tp_mesh, family, degree) for degree in (1, 2)]
+    u, v = [Function(V) for V in fs]
+
+    u.assign(1)
+    P = prolongation_matrix_matfree(u, v).getPythonContext()
+    P._prolong()
+    assert np.allclose(v.dat.data, 1)
+
+
 def test_prolong_de_rham(tp_mesh):
     """ Interpolate a linear vector function between [H1]^d, HCurl and HDiv spaces
         where it can be exactly represented
@@ -74,13 +98,17 @@ def test_prolong_de_rham(tp_mesh):
 
     tdim = tp_mesh.topological_dimension()
     b = Constant(list(range(tdim)))
-    mat = diag(Constant([tdim+1]*tdim)) + Constant([[-1]*tdim]*tdim)
-    expr = dot(mat, SpatialCoordinate(tp_mesh)) + b
+    if tp_mesh.extruded_periodic:
+        expr = b
+    else:
+        mat = diag(Constant([tdim+1]*tdim)) + Constant([[-1]*tdim]*tdim)
+        expr = b + dot(mat, SpatialCoordinate(tp_mesh))
 
     cell = tp_mesh.ufl_cell()
     elems = [VectorElement(FiniteElement("Q", cell=cell, degree=2)),
              FiniteElement("NCE" if tdim == 3 else "RTCE", cell=cell, degree=2),
              FiniteElement("NCF" if tdim == 3 else "RTCF", cell=cell, degree=2)]
+
     fs = [FunctionSpace(tp_mesh, e) for e in elems]
     us = [Function(V) for V in fs]
     us[0].interpolate(expr)
@@ -89,7 +117,7 @@ def test_prolong_de_rham(tp_mesh):
             if u != v:
                 P = prolongation_matrix_matfree(u, v).getPythonContext()
                 P._prolong()
-                assert norm(v-expr, "L2") < 1E-14
+                assert errornorm(expr, v) < 1E-14
 
 
 def test_prolong_low_order_to_restricted(tp_mesh, tp_family, variant):
diff --git a/tests/firedrake/regression/test_star_pc.py b/tests/firedrake/regression/test_star_pc.py
index eb02c665e8..cb82ad435c 100644
--- a/tests/firedrake/regression/test_star_pc.py
+++ b/tests/firedrake/regression/test_star_pc.py
@@ -361,3 +361,56 @@ def test_vanka_equivalence(problem_type):
     comp_its = comp_solver.snes.getLinearSolveIterations()
 
     assert star_its == comp_its
+
+
+@pytest.fixture(params=["interval", "square"])
+def base(request):
+    dp = {"overlap_type": (DistributedMeshOverlapType.VERTEX, 1)}
+    if request.param == "interval":
+        return UnitIntervalMesh(5, distribution_parameters=dp)
+    elif request.param == "square":
+        return UnitSquareMesh(5, 5, quadrilateral=True, distribution_parameters=dp)
+
+
+@pytest.mark.parametrize("family,degree", [("Q", 3), ("NCF", 3)])
+@pytest.mark.parametrize("periodic", (False, True), ids=("extruded", "extruded-periodic"))
+def test_asm_extruded_star(base, periodic, family, degree):
+    mesh = ExtrudedMesh(base, 5, periodic=periodic)
+    if mesh.topological_dimension() == 2:
+        family = family.replace("N", "RT")
+    V = FunctionSpace(mesh, family, degree)
+    space = V.ufl_element().sobolev_space
+    d = {H1: grad, HCurl: curl, HDiv: div}[space]
+
+    x = SpatialCoordinate(mesh)
+    uexact = x[0]*(1-x[0])
+    if V.value_shape != uexact.ufl_shape:
+        uexact = grad(uexact)
+
+    u = TrialFunction(V)
+    v = TestFunction(V)
+    a = inner(d(u), d(v)) * dx + inner(u, v)*dx
+    L = a(v, uexact)
+
+    patch_dim = 1 if family == "NCF" else 0
+    params = {
+        "ksp_type": "cg",
+        "pc_type": "python",
+        "pc_python_type": "firedrake.P1PC",
+        "pmg_mg_coarse_ksp_type": "preonly",
+        "pmg_mg_coarse_pc_type": "lu",
+        "pmg_mg_coarse_pc_factor_mat_solver_type": "mumps",
+        "pmg_mg_levels_ksp_type": "chebyshev",
+        "pmg_mg_levels_pc_type": "python",
+        "pmg_mg_levels_pc_python_type": "firedrake.ASMExtrudedStarPC",
+        "pmg_mg_levels_pc_star_construct_dim": patch_dim,
+    }
+
+    uh = Function(V)
+    problem = LinearVariationalProblem(a, L, uh)
+    solver = LinearVariationalSolver(problem, solver_parameters=params)
+    solver.solve()
+
+    expected = 7
+    assert solver.snes.getLinearSolveIterations() <= expected
+    assert errornorm(uexact, uh) < 1E-7

From 1156c4af29d4d0369beb1871e5a475557aba8cb4 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 12 Jun 2025 10:32:13 +0100
Subject: [PATCH 142/222] Proposal: use upstream UFL instead of our fork
 (#4344)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 2b76d2ed84..c932940e12 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,7 @@ dependencies = [
   "mpi4py>3; python_version >= '3.13'",
   "mpi4py; python_version < '3.13'",
   # TODO RELEASE: use releases
-  "fenics-ufl @ git+https://github.com/firedrakeproject/ufl.git",
+  "fenics-ufl @ git+https://github.com/FEniCS/ufl.git",
   "firedrake-fiat @ git+https://github.com/firedrakeproject/fiat.git",
   "h5py>3.12.1",
   "libsupermesh",

From 3e5e7401b6bd6e1f1375d27097bd6b0fdd153a61 Mon Sep 17 00:00:00 2001
From: KarsKnook <57411502+KarsKnook@users.noreply.github.com>
Date: Thu, 12 Jun 2025 12:54:35 +0100
Subject: [PATCH 143/222] Knook/EquationBC_GMG (#4338)

Co-authored-by: Patrick Farrell 
Co-authored-by: Aaron Baier-Reinio <130484015+ABaierReinio@users.noreply.github.com>
Co-authored-by: Pablo Brubeck 
---
 firedrake/bcs.py                              |   3 +-
 firedrake/mg/ufl_utils.py                     | 102 +++++++--------
 firedrake/solving_utils.py                    |  13 +-
 .../equation_bcs/test_equation_bcs.py         |  14 ++-
 tests/firedrake/multigrid/test_equation_bc.py | 119 ++++++++++++++++++
 5 files changed, 186 insertions(+), 65 deletions(-)
 create mode 100644 tests/firedrake/multigrid/test_equation_bc.py

diff --git a/firedrake/bcs.py b/firedrake/bcs.py
index 932b75d5b6..21235c145d 100644
--- a/firedrake/bcs.py
+++ b/firedrake/bcs.py
@@ -243,7 +243,7 @@ def increment_bc_depth(self):
         for bc in itertools.chain(*self.bcs):
             bc._bc_depth += 1
 
-    def extract_forms(self, form_type):
+    def extract_form(self, form_type):
         # Return boundary condition objects actually used in assembly.
         raise NotImplementedError("Method to extract form objects not implemented.")
 
@@ -524,6 +524,7 @@ def __init__(self, *args, bcs=None, J=None, Jp=None, V=None, is_linear=False, Jp
                 J = J or ufl_expr.derivative(F, u)
                 Jp = Jp or J
                 self.is_linear = False
+            self.eq = eq
             # Check form style consistency
             is_form_consistent(self.is_linear, bcs)
             # Argument checking
diff --git a/firedrake/mg/ufl_utils.py b/firedrake/mg/ufl_utils.py
index 9e92159d25..6a89bb2eed 100644
--- a/firedrake/mg/ufl_utils.py
+++ b/firedrake/mg/ufl_utils.py
@@ -5,15 +5,12 @@
 from ufl.duals import is_dual
 
 from functools import singledispatch, partial
-from itertools import chain
 import firedrake
-from firedrake.utils import unique
 from firedrake.petsc import PETSc
 from firedrake.dmhooks import (get_transfer_manager, get_appctx, push_appctx, pop_appctx,
                                get_parent, add_hook)
 
 from . import utils
-import weakref
 
 
 __all__ = ["coarsen"]
@@ -29,8 +26,10 @@ class CoarsenIntegrand(MultiFunction):
     """'Coarsen' a :class:`ufl.Expr` by replacing coefficients,
     arguments and domain data with coarse mesh equivalents."""
 
-    def __init__(self, coarsen, coefficient_mapping):
-        self.coefficient_mapping = coefficient_mapping or {}
+    def __init__(self, coarsen, coefficient_mapping=None):
+        if coefficient_mapping is None:
+            coefficient_mapping = {}
+        self.coefficient_mapping = coefficient_mapping
         self.coarsen = coarsen
         super(CoarsenIntegrand, self).__init__()
 
@@ -96,8 +95,7 @@ def coarsen_form(form, self, coefficient_mapping=None):
     for it in form.integrals():
         integrand = map_expr_dag(mapper, it.integrand())
         mesh = as_domain(it)
-        hierarchy, level = utils.get_level(mesh)
-        new_mesh = hierarchy[level-1]
+        new_mesh = self(mesh, self)
         if isinstance(integrand, ufl.classes.Zero):
             continue
         if it.subdomain_data() is not None:
@@ -106,7 +104,6 @@ def coarsen_form(form, self, coefficient_mapping=None):
                                  domain=new_mesh)
         integrals.append(new_itg)
     form = ufl.Form(integrals)
-    form._cache["coefficient_mapping"] = coefficient_mapping
     return form
 
 
@@ -126,6 +123,21 @@ def coarsen_bc(bc, self, coefficient_mapping=None):
     return type(bc)(V, val, subdomain)
 
 
+@coarsen.register(firedrake.EquationBC)
+def coarsen_equation_bc(ebc, self, coefficient_mapping=None):
+    J = self(ebc._J.f, self, coefficient_mapping=coefficient_mapping)
+    Jp = self(ebc._Jp.f, self, coefficient_mapping=coefficient_mapping)
+    u = self(ebc._F.u, self, coefficient_mapping=coefficient_mapping)
+    sub_domain = ebc._F.sub_domain
+    bcs = [self(bc, self, coefficient_mapping=coefficient_mapping)
+           for bc in ebc.dirichlet_bcs()]
+    V = self(ebc._F.function_space(), self, coefficient_mapping=coefficient_mapping)
+    lhs = self(ebc.eq.lhs, self, coefficient_mapping=coefficient_mapping)
+    rhs = self(ebc.eq.rhs, self, coefficient_mapping=coefficient_mapping)
+
+    return type(ebc)(lhs == rhs, u, sub_domain, V=V, bcs=bcs, J=J, Jp=Jp)
+
+
 @coarsen.register(firedrake.functionspaceimpl.WithGeometryBase)
 def coarsen_function_space(V, self, coefficient_mapping=None):
     if hasattr(V, "_coarse"):
@@ -150,12 +162,12 @@ def coarsen_function(expr, self, coefficient_mapping=None):
         Vf = expr.function_space()
         Vc = self(Vf, self)
         new = firedrake.Function(Vc, name=f"coarse_{expr.name()}")
-        expr._child = weakref.proxy(new)
         manager = get_transfer_manager(Vf.dm)
         if is_dual(expr):
             manager.restrict(expr, new)
         else:
             manager.inject(expr, new)
+        coefficient_mapping[expr] = new
     return new
 
 
@@ -165,48 +177,36 @@ def coarsen_nlvp(problem, self, coefficient_mapping=None):
         return problem._coarse
 
     def inject_on_restrict(fine, restriction, rscale, injection, coarse):
-        from firedrake.bcs import DirichletBC
         manager = get_transfer_manager(fine)
-        finectx = get_appctx(fine)
-        forms = (finectx.F, finectx.J, finectx.Jp)
-        coefficients = unique(chain.from_iterable(form.coefficients()
-                              for form in forms if form is not None))
-        for c in coefficients:
-            if hasattr(c, '_child'):
-                if is_dual(c):
-                    manager.restrict(c, c._child)
-                else:
-                    manager.inject(c, c._child)
-        # Apply bcs and also inject them
-        for bc in chain(*finectx._problem.bcs):
-            if isinstance(bc, DirichletBC):
-                if finectx.pre_apply_bcs:
-                    bc.apply(finectx._x)
-                g = bc.function_arg
-                if isinstance(g, firedrake.Function) and hasattr(g, "_child"):
-                    manager.inject(g, g._child)
-
-    V = problem.u.function_space()
-    if not hasattr(V, "_coarse"):
+        cctx = get_appctx(coarse)
+        cmapping = cctx._coefficient_mapping
+        if cmapping is None:
+            return
+        for c in cmapping:
+            if is_dual(c):
+                manager.restrict(c, cmapping[c])
+            else:
+                manager.inject(c, cmapping[c])
+        # Apply bcs
+        if cctx.pre_apply_bcs:
+            for bc in cctx._problem.dirichlet_bcs():
+                bc.apply(cctx._x)
+
+    dm = problem.u.function_space().dm
+    if not dm.getAttr("_coarsen_hook"):
         # The hook is persistent and cumulative, but also problem-independent.
         # Therefore, we are only adding it once.
-        V.dm.addCoarsenHook(None, inject_on_restrict)
+        dm.addCoarsenHook(None, inject_on_restrict)
+        dm.setAttr("_coarsen_hook", True)
 
-    # Build set of coefficients we need to coarsen
-    forms = (problem.F, problem.J, problem.Jp)
-    coefficients = unique(chain.from_iterable(form.coefficients() for form in forms if form is not None))
-    # Coarsen them, and remember where from.
     if coefficient_mapping is None:
         coefficient_mapping = {}
-    for c in coefficients:
-        coefficient_mapping[c] = self(c, self, coefficient_mapping=coefficient_mapping)
-
-    u = coefficient_mapping[problem.u]
 
-    bcs = [self(bc, self) for bc in problem.bcs]
+    bcs = [self(bc, self, coefficient_mapping=coefficient_mapping) for bc in problem.bcs]
+    F = self(problem.F, self, coefficient_mapping=coefficient_mapping)
     J = self(problem.J, self, coefficient_mapping=coefficient_mapping)
     Jp = self(problem.Jp, self, coefficient_mapping=coefficient_mapping)
-    F = self(problem.F, self, coefficient_mapping=coefficient_mapping)
+    u = coefficient_mapping[problem.u]
 
     fine = problem
     problem = firedrake.NonlinearVariationalProblem(F, u, bcs=bcs, J=J, Jp=Jp, is_linear=problem.is_linear,
@@ -217,7 +217,9 @@ def inject_on_restrict(fine, restriction, rscale, injection, coarse):
 
 @coarsen.register(firedrake.VectorSpaceBasis)
 def coarsen_vectorspacebasis(basis, self, coefficient_mapping=None):
-    coarse_vecs = [self(vec, self, coefficient_mapping=coefficient_mapping) for vec in basis._vecs]
+    # Do not add basis._vecs to the coefficient_mapping,
+    # as they need to be normalized, and are not meant to be reinjected
+    coarse_vecs = [self(vec, self) for vec in basis._vecs]
     vsb = firedrake.VectorSpaceBasis(coarse_vecs, constant=basis._constant, comm=basis.comm)
     vsb.orthonormalize()
     return vsb
@@ -267,13 +269,14 @@ def coarsen_snescontext(context, self, coefficient_mapping=None):
                            appctx=new_appctx,
                            transfer_manager=context.transfer_manager,
                            pre_apply_bcs=context.pre_apply_bcs)
+    coarse._coefficient_mapping = coefficient_mapping
     coarse._fine = context
     context._coarse = coarse
 
     # Now that we have the coarse snescontext, push it to the coarsened DMs
     # Otherwise they won't have the right transfer manager when they are
     # coarsened in turn
-    for val in chain(coefficient_mapping.values(), (bc.function_arg for bc in problem.bcs)):
+    for val in coefficient_mapping.values():
         if isinstance(val, (firedrake.Function, firedrake.Cofunction)):
             V = val.function_space()
             coarseneddm = V.dm
@@ -352,7 +355,7 @@ def __init__(self, cfn, ffn, manager, cbcs=None):
         self.cbcs = cbcs or []
         self.manager = manager
 
-    def multTranspose(self, mat, x, y):
+    def mult(self, mat, x, y):
         with self.ffn.dat.vec_wo as v:
             x.copy(v)
         self.manager.inject(self.ffn, self.cfn)
@@ -363,7 +366,6 @@ def multTranspose(self, mat, x, y):
 
 
 def create_interpolation(dmc, dmf):
-
     cctx = get_appctx(dmc)
     fctx = get_appctx(dmf)
 
@@ -377,8 +379,8 @@ def create_interpolation(dmc, dmf):
 
     cfn = firedrake.Function(V_c)
     ffn = firedrake.Function(V_f)
-    cbcs = cctx._problem.bcs
-    fbcs = fctx._problem.bcs
+    cbcs = tuple(cctx._problem.dirichlet_bcs())
+    fbcs = tuple(fctx._problem.dirichlet_bcs())
 
     ctx = Interpolation(cfn, ffn, manager, cbcs, fbcs)
     mat = PETSc.Mat().create(comm=dmc.comm)
@@ -398,8 +400,8 @@ def create_injection(dmc, dmf):
     V_c = cctx._problem.u.function_space()
     V_f = fctx._problem.u.function_space()
 
-    row_size = V_f.dof_dset.layout_vec.getSizes()
-    col_size = V_c.dof_dset.layout_vec.getSizes()
+    row_size = V_c.dof_dset.layout_vec.getSizes()
+    col_size = V_f.dof_dset.layout_vec.getSizes()
 
     cfn = firedrake.Function(V_c)
     ffn = firedrake.Function(V_f)
diff --git a/firedrake/solving_utils.py b/firedrake/solving_utils.py
index 55d228a0ce..393e5e2b8c 100644
--- a/firedrake/solving_utils.py
+++ b/firedrake/solving_utils.py
@@ -228,7 +228,7 @@ def __init__(self, problem, mat_type, pmat_type, appctx=None,
         self.bcs_Jp = tuple(bc.extract_form('Jp') for bc in problem.bcs)
 
         self._bc_residual = None
-        if not pre_apply_bcs and len(self.bcs_F) > 0:
+        if not pre_apply_bcs and next(problem.dirichlet_bcs(), None) is not None:
             # Delayed lifting of DirichletBCs
             self._bc_residual = Function(self._x.function_space())
             if problem.is_linear:
@@ -251,6 +251,7 @@ def __init__(self, problem, mat_type, pmat_type, appctx=None,
         self._nullspace = None
         self._nullspace_T = None
         self._near_nullspace = None
+        self._coefficient_mapping = None
         self._transfer_manager = transfer_manager
 
     @property
@@ -426,7 +427,7 @@ def form_function(snes, X, F):
 
         if not ctx.pre_apply_bcs:
             # Compute DirichletBC residual
-            for bc in ctx.bcs_F:
+            for bc in ctx._problem.dirichlet_bcs():
                 bc.apply(ctx._bc_residual, u=ctx._x)
 
         ctx._assemble_residual(tensor=ctx._F, current_state=ctx._x)
@@ -499,14 +500,6 @@ def compute_operators(ksp, J, P):
             return
         ctx._jacobian_assembled = True
 
-        fine = ctx._fine
-        if fine is not None:
-            manager = dmhooks.get_transfer_manager(fine._x.function_space().dm)
-            manager.inject(fine._x, ctx._x)
-            if ctx.pre_apply_bcs:
-                for bc in problem.dirichlet_bcs():
-                    bc.apply(ctx._x)
-
         ctx._assemble_jac(ctx._jac)
         if ctx.Jp is not None:
             assert P.handle == ctx._pjac.petscmat.handle
diff --git a/tests/firedrake/equation_bcs/test_equation_bcs.py b/tests/firedrake/equation_bcs/test_equation_bcs.py
index fdd05b7f2e..d487aa1e27 100644
--- a/tests/firedrake/equation_bcs/test_equation_bcs.py
+++ b/tests/firedrake/equation_bcs/test_equation_bcs.py
@@ -8,7 +8,7 @@
 import math
 
 
-def nonlinear_poisson(solver_parameters, mesh_num, porder):
+def nonlinear_poisson(solver_parameters, mesh_num, porder, pre_apply_bcs=True):
 
     mesh = UnitSquareMesh(mesh_num, mesh_num)
 
@@ -29,7 +29,7 @@ def nonlinear_poisson(solver_parameters, mesh_num, porder):
     e2 = as_vector([0., 1.])
     bc1 = EquationBC((-inner(dot(grad(u), e2), dot(grad(v), e2)) + 3 * pi * pi * inner(u, v) + 1 * pi * pi * inner(g, v)) * ds(1) == 0, u, 1)
 
-    solve(a - L == 0, u, bcs=[bc1], solver_parameters=solver_parameters)
+    solve(a - L == 0, u, bcs=[bc1], solver_parameters=solver_parameters, pre_apply_bcs=pre_apply_bcs)
 
     f = cos(x * pi * 2) * cos(y * pi * 2)
     return sqrt(assemble(inner(u - f, u - f) * dx))
@@ -199,7 +199,13 @@ def linear_poisson_mixed(solver_parameters, mesh_num, porder):
 
 @pytest.mark.parametrize("eq_type", ["linear", "nonlinear"])
 @pytest.mark.parametrize("with_bbc", [False, True])
-def test_EquationBC_poisson_matrix(eq_type, with_bbc):
+@pytest.mark.parametrize("pre_apply_bcs", [False, True])
+def test_EquationBC_poisson_matrix(eq_type, with_bbc, pre_apply_bcs):
+
+    # Only test pre_apply_bcs=False for nonlinear case
+    if not pre_apply_bcs and (eq_type == "linear"):
+        pytest.skip(reason="Only test pre_apply_bcs=False in the nonlinear case")
+
     mat_type = "aij"
     porder = 2
     # Test standard poisson with EquationBCs
@@ -225,7 +231,7 @@ def test_EquationBC_poisson_matrix(eq_type, with_bbc):
                 err.append(linear_poisson(solver_parameters, mesh_num, porder))
         elif eq_type == "nonlinear":
             for mesh_num in mesh_sizes:
-                err.append(nonlinear_poisson(solver_parameters, mesh_num, porder))
+                err.append(nonlinear_poisson(solver_parameters, mesh_num, porder, pre_apply_bcs=pre_apply_bcs))
 
     assert abs(math.log2(err[0]) - math.log2(err[1]) - (porder+1)) < 0.05
 
diff --git a/tests/firedrake/multigrid/test_equation_bc.py b/tests/firedrake/multigrid/test_equation_bc.py
new file mode 100644
index 0000000000..d0372c9fe1
--- /dev/null
+++ b/tests/firedrake/multigrid/test_equation_bc.py
@@ -0,0 +1,119 @@
+import pytest
+from firedrake import *
+
+
+def test_poisson_NLVP():
+    mesh = UnitIntervalMesh(10)
+    mesh_hierarchy = MeshHierarchy(mesh, 1)
+    mesh = mesh_hierarchy[-1]
+
+    V = FunctionSpace(mesh, "CG", 1)
+    u = Function(V)
+    v = TestFunction(V)
+
+    x, = SpatialCoordinate(mesh)
+    u_exact = Function(V).interpolate(x**2)
+
+    F = inner(grad(u - u_exact), grad(v))*dx
+    bcs = [EquationBC(inner(u - u_exact, v) * ds == 0, u, "on_boundary", V=V)]
+    NLVP = NonlinearVariationalProblem(F, u, bcs=bcs)
+
+    sp = {"ksp_rtol": 1E-10, "pc_type": "mg"}
+    NLVS = NonlinearVariationalSolver(NLVP, solver_parameters=sp)
+    NLVS.solve()
+
+    assert errornorm(u_exact, u) < 1e-9
+    assert NLVS.snes.getLinearSolveIterations() <= 9
+    assert all(isinstance(bc, EquationBC) for bc in NLVP._coarse.bcs)
+
+
+def test_poisson_LVP():
+    mesh = UnitIntervalMesh(10)
+    mesh_hierarchy = MeshHierarchy(mesh, 1)
+    mesh = mesh_hierarchy[-1]
+
+    V = FunctionSpace(mesh, "CG", 1)
+    u = TrialFunction(V)
+    u_ = Function(V)
+    v = TestFunction(V)
+
+    x, = SpatialCoordinate(mesh)
+    u_exact = Function(V).interpolate(x**2)
+
+    a = inner(grad(u), grad(v))*dx
+    L = action(a, u_exact)
+    bcs = [EquationBC(inner(u, v) * ds == inner(u_exact, v) * ds, u_, "on_boundary", V=V)]
+    LVP = LinearVariationalProblem(a, L, u_, bcs=bcs)
+
+    sp = {"ksp_rtol": 1E-10, "pc_type": "mg"}
+    LVS = LinearVariationalSolver(LVP, solver_parameters=sp)
+    LVS.solve()
+
+    assert errornorm(u_exact, u_) < 1e-9
+    assert LVS.snes.getLinearSolveIterations() <= 9
+    assert all(isinstance(bc, EquationBC) for bc in LVP._coarse.bcs)
+
+
+@pytest.mark.parametrize("dim", (2, 3))
+def test_nested_equation_bc(dim):
+    Nbase = 2
+    refine = 2
+    if dim == 2:
+        mesh = UnitSquareMesh(Nbase, Nbase)
+    elif dim == 3:
+        mesh = UnitCubeMesh(Nbase, Nbase, Nbase)
+
+    mesh_hierarchy = MeshHierarchy(mesh, refine)
+    mesh = mesh_hierarchy[-1]
+
+    V = FunctionSpace(mesh, "CG", 1)
+    u = Function(V)
+    v = TestFunction(V)
+
+    x = SpatialCoordinate(mesh)
+    u_exact = Function(V).interpolate(x[0]*x[1])
+
+    if dim == 2:
+        ridges = [(1, 3), (1, 4), (2, 3), (2, 4)]
+        num_ridge_dofs = 4
+    elif dim == 3:
+        ridges = [(1, 3), (1, 4), (1, 5), (1, 6),
+                  (2, 3), (2, 4), (2, 5), (2, 6),
+                  (3, 5), (3, 6), (4, 5), (4, 6)]
+        num_ridge_dofs = 8 + 12*(Nbase*2**refine-1)
+
+    F = inner(grad(u - u_exact), grad(v))*dx
+
+    u_bc = Function(u_exact)
+    n = FacetNormal(mesh)
+    Ps = Identity(dim) - outer(n, n)
+    Fs = inner(dot(grad(u - u_bc), Ps), grad(v)) * ds
+
+    bcs_ridges = [DirichletBC(V, u_bc, ridges)]
+    bcs_facets = [EquationBC(Fs == 0, u, "on_boundary", V=V, bcs=bcs_ridges)]
+    problem = NonlinearVariationalProblem(F, u, bcs=bcs_facets)
+
+    nodes = set(sum((tuple(e.nodes) for e in problem.dirichlet_bcs()), ()))
+    assert len(nodes) == num_ridge_dofs
+
+    sp = {
+        "ksp_type": "fgmres",
+        "ksp_rtol": 1e-10,
+        "pc_type": "mg",
+        "mg_levels": {
+            "ksp_max_it": "2",
+            "ksp_convergence_test": "skip",
+            "ksp_type": "gmres",
+            "pc_type": "jacobi",
+        },
+        "mg_coarse": {
+            "ksp_type": "preonly",
+            "pc_type": "lu",
+        }
+    }
+
+    solver = NonlinearVariationalSolver(problem, solver_parameters=sp)
+    solver.solve()
+    assert errornorm(u_exact, u) < 1e-9
+    assert solver.snes.getLinearSolveIterations() <= 10
+    assert all(isinstance(bc, EquationBC) for bc in problem._coarse.bcs)

From fd63370b915be79648de8514c9e18c132d4ae4d2 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Fri, 13 Jun 2025 16:23:53 +0100
Subject: [PATCH 144/222] GTMGPC: use the new Interpolate

---
 firedrake/preconditioners/gtmg.py | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/firedrake/preconditioners/gtmg.py b/firedrake/preconditioners/gtmg.py
index 1d76a13e60..d60a2f090f 100644
--- a/firedrake/preconditioners/gtmg.py
+++ b/firedrake/preconditioners/gtmg.py
@@ -2,6 +2,11 @@
 from firedrake.preconditioners.base import PCBase
 import firedrake.dmhooks as dmhooks
 
+from firedrake.parameters import parameters
+from firedrake.interpolation import Interpolate
+from firedrake.solving_utils import _SNESContext
+from firedrake.matrix_free.operators import ImplicitMatrixContext
+
 
 __all__ = ['GTMGPC']
 
@@ -12,11 +17,6 @@ class GTMGPC(PCBase):
     _prefix = "gt_"
 
     def initialize(self, pc):
-        from firedrake import TestFunction, parameters
-        from firedrake.assemble import get_assembler
-        from firedrake.interpolation import Interpolator
-        from firedrake.solving_utils import _SNESContext
-        from firedrake.matrix_free.operators import ImplicitMatrixContext
 
         _, P = pc.getOperators()
         appctx = self.get_appctx(pc)
@@ -37,6 +37,7 @@ def initialize(self, pc):
 
         # Handle the fine operator if type is python
         if P.getType() == "python":
+            from firedrake.assemble import get_assembler
             ictx = P.getPythonContext()
             if ictx is None:
                 raise ValueError("No context found on matrix")
@@ -102,11 +103,12 @@ def initialize(self, pc):
 
         interp_petscmat = appctx.get("interpolation_matrix", None)
         if interp_petscmat is None:
+            from firedrake.assemble import assemble
             # Create interpolation matrix from coarse space to fine space
             fine_space = ctx.J.arguments()[0].function_space()
-            interpolator = Interpolator(TestFunction(coarse_space), fine_space)
-            interpolation_matrix = interpolator.callable()
-            interp_petscmat = interpolation_matrix.handle
+            coarse_test, coarse_trial = coarse_operator.arguments()
+            interp = assemble(Interpolate(coarse_trial, fine_space))
+            interp_petscmat = interp.petscmat
 
         # We set up a PCMG object that uses the constructed interpolation
         # matrix to generate the restriction/prolongation operators.

From 382e63ff6d20ccb6ae2c087d7376185ec961c05a Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Fri, 13 Jun 2025 16:26:35 +0100
Subject: [PATCH 145/222] Revert "GTMGPC: use the new Interpolate"

This reverts commit fd63370b915be79648de8514c9e18c132d4ae4d2.
---
 firedrake/preconditioners/gtmg.py | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/firedrake/preconditioners/gtmg.py b/firedrake/preconditioners/gtmg.py
index d60a2f090f..1d76a13e60 100644
--- a/firedrake/preconditioners/gtmg.py
+++ b/firedrake/preconditioners/gtmg.py
@@ -2,11 +2,6 @@
 from firedrake.preconditioners.base import PCBase
 import firedrake.dmhooks as dmhooks
 
-from firedrake.parameters import parameters
-from firedrake.interpolation import Interpolate
-from firedrake.solving_utils import _SNESContext
-from firedrake.matrix_free.operators import ImplicitMatrixContext
-
 
 __all__ = ['GTMGPC']
 
@@ -17,6 +12,11 @@ class GTMGPC(PCBase):
     _prefix = "gt_"
 
     def initialize(self, pc):
+        from firedrake import TestFunction, parameters
+        from firedrake.assemble import get_assembler
+        from firedrake.interpolation import Interpolator
+        from firedrake.solving_utils import _SNESContext
+        from firedrake.matrix_free.operators import ImplicitMatrixContext
 
         _, P = pc.getOperators()
         appctx = self.get_appctx(pc)
@@ -37,7 +37,6 @@ def initialize(self, pc):
 
         # Handle the fine operator if type is python
         if P.getType() == "python":
-            from firedrake.assemble import get_assembler
             ictx = P.getPythonContext()
             if ictx is None:
                 raise ValueError("No context found on matrix")
@@ -103,12 +102,11 @@ def initialize(self, pc):
 
         interp_petscmat = appctx.get("interpolation_matrix", None)
         if interp_petscmat is None:
-            from firedrake.assemble import assemble
             # Create interpolation matrix from coarse space to fine space
             fine_space = ctx.J.arguments()[0].function_space()
-            coarse_test, coarse_trial = coarse_operator.arguments()
-            interp = assemble(Interpolate(coarse_trial, fine_space))
-            interp_petscmat = interp.petscmat
+            interpolator = Interpolator(TestFunction(coarse_space), fine_space)
+            interpolation_matrix = interpolator.callable()
+            interp_petscmat = interpolation_matrix.handle
 
         # We set up a PCMG object that uses the constructed interpolation
         # matrix to generate the restriction/prolongation operators.

From 32611a79ed56978c19ca004a1633addf31724aa4 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Mon, 16 Jun 2025 09:27:56 +0100
Subject: [PATCH 146/222] Use petsctools package (#4194)

* Add version_spec check to setup.py
* Move OptionsManager to petsctools
---
 docs/source/conf.py                           |   1 +
 docs/source/optimising.rst                    |   2 +-
 firedrake/__init__.py                         |  46 ++-
 firedrake/checkpointing.py                    |   3 +-
 firedrake/eigensolver.py                      |   2 +-
 firedrake/mesh.py                             |   5 +-
 firedrake/petsc.py                            | 281 ++----------------
 firedrake/slate/slac/compiler.py              |   4 +-
 firedrake/utils.py                            |  20 +-
 firedrake/variational_solver.py               |   6 +-
 pyproject.toml                                |  10 +-
 requirements-build.txt                        |   1 +
 scripts/check-config                          |  10 +-
 setup.py                                      |  68 ++---
 .../regression/test_solvers_options_prefix.py |   3 +-
 15 files changed, 119 insertions(+), 343 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 8c74556594..ca8ce26a4f 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -135,6 +135,7 @@
     (r'py:.*', r'ufl\..*'),
     (r'py:.*', r'PETSc\..*'),
     (r'py:.*', r'progress\..*'),
+    (r'py:.*', r'petsctools\..*'),
     # Ignore undocumented PyOP2
     ('py:class', 'pyop2.caching.Cached'),
     # Ignore mission docs from Firedrake internal "private" code
diff --git a/docs/source/optimising.rst b/docs/source/optimising.rst
index 2b87ac07c2..93a43e5cad 100644
--- a/docs/source/optimising.rst
+++ b/docs/source/optimising.rst
@@ -136,7 +136,7 @@ Caveats
 
   .. code-block:: python
 
-    from firedrake.petsc import OptionsManager
+    from petsctools import OptionsManager
 
     # If the -log_view flag is passed you don't need to call
     # PETSc.Log.begin because it is done automatically.
diff --git a/firedrake/__init__.py b/firedrake/__init__.py
index 9230d91233..5c975f8770 100644
--- a/firedrake/__init__.py
+++ b/firedrake/__init__.py
@@ -1,24 +1,38 @@
-import sys
-from firedrake.configuration import setup_cache_dirs
+# The range of PETSc versions supported by Firedrake. Note that unlike in
+# firedrake-configure and pyproject.toml where we want to be strict about
+# the specific version, here we are more permissive. This is to catch the
+# case where users don't update their PETSc for a really long time or
+# accidentally install a too-new release that isn't yet supported.
+PETSC_SUPPORTED_VERSIONS = ">=3.23.0"
+
+
+def init_petsc():
+    import os
+    import sys
+    import petsctools
+
+    # We conditionally pass '-options_left no' as in some circumstances (e.g.
+    # when running pytest) PETSc complains that command line options are not
+    # PETSc options.
+    if os.getenv("FIREDRAKE_DISABLE_OPTIONS_LEFT") == "1":
+        petsctools.init(sys.argv + ["-options_left", "no"], version_spec=PETSC_SUPPORTED_VERSIONS)
+    else:
+        petsctools.init(sys.argv, version_spec=PETSC_SUPPORTED_VERSIONS)
+
+
+# Ensure petsc is initialised right away
+init_petsc()
 
 # Set up the cache directories before importing PyOP2.
+from firedrake.configuration import setup_cache_dirs
+
 setup_cache_dirs()
 
-# Ensure petsc is initialised by us before anything else gets in there.
-# We conditionally pass '-options_left no' as in some circumstances (e.g.
-# when running pytest) PETSc complains that command line options are not
-# PETSc options.
-import os
-import petsc4py
-if os.getenv("FIREDRAKE_DISABLE_OPTIONS_LEFT") == "1":
-    petsc4py.init(sys.argv + ["-options_left", "no"])
-else:
-    petsc4py.init(sys.argv)
-del os, petsc4py
 
 # Initialise PETSc events for both import and entire duration of program
+import petsctools
 from firedrake import petsc
-_is_logging = "log_view" in petsc.OptionsManager.commandline_options
+_is_logging = "log_view" in petsctools.OptionsManager.commandline_options
 if _is_logging:
     _main_event = petsc.PETSc.Log.Event("firedrake")
     _main_event.begin()
@@ -29,6 +43,7 @@
     import atexit
     atexit.register(lambda: _main_event.end())
     del atexit
+del petsctools
 del petsc
 
 from ufl import *
@@ -85,6 +100,7 @@
 #   from firedrake._deprecation import output
 #   sys.modules["firedrake.output"] = output
 from firedrake.output import *
+import sys
 sys.modules["firedrake.plot"] = plot
 from firedrake.plot import *
 
@@ -104,7 +120,7 @@ def set_blas_num_threads():
 
     """
     from ctypes import cdll
-    from firedrake.petsc import get_blas_library
+    from petsctools import get_blas_library
 
     try:
         blas_lib_path = get_blas_library()
diff --git a/firedrake/checkpointing.py b/firedrake/checkpointing.py
index 5070a96315..a9fba2dd00 100644
--- a/firedrake/checkpointing.py
+++ b/firedrake/checkpointing.py
@@ -4,9 +4,10 @@
 import finat.ufl
 from pyop2 import op2
 from pyop2.mpi import COMM_WORLD, internal_comm, MPI
+from petsctools import OptionsManager
 from firedrake.cython import hdf5interface as h5i
 from firedrake.cython import dmcommon
-from firedrake.petsc import PETSc, OptionsManager
+from firedrake.petsc import PETSc
 from firedrake.mesh import MeshTopology, ExtrudedMeshTopology, DEFAULT_MESH_NAME, make_mesh_from_coordinates, DistributedMeshOverlapType
 from firedrake.functionspace import FunctionSpace
 from firedrake import functionspaceimpl as impl
diff --git a/firedrake/eigensolver.py b/firedrake/eigensolver.py
index 9294393a6e..bb8a11a5ab 100644
--- a/firedrake/eigensolver.py
+++ b/firedrake/eigensolver.py
@@ -1,10 +1,10 @@
 """Specify and solve finite element eigenproblems."""
+from petsctools import OptionsManager, flatten_parameters
 from firedrake.assemble import assemble
 from firedrake.bcs import extract_subdomain_ids, restricted_function_space
 from firedrake.function import Function
 from firedrake.ufl_expr import TrialFunction, TestFunction
 from firedrake import utils
-from firedrake.petsc import OptionsManager, flatten_parameters
 from firedrake.exceptions import ConvergenceError
 from ufl import replace, inner, dx
 try:
diff --git a/firedrake/mesh.py b/firedrake/mesh.py
index 7b8b74122f..e30a4e0ed7 100644
--- a/firedrake/mesh.py
+++ b/firedrake/mesh.py
@@ -22,6 +22,7 @@
     MPI, COMM_WORLD, internal_comm, is_pyop2_comm, temp_internal_comm
 )
 from pyop2.utils import as_tuple
+from petsctools import OptionsManager, get_external_packages
 
 import firedrake.cython.dmcommon as dmcommon
 from firedrake.cython.dmcommon import DistributedMeshOverlapType
@@ -32,9 +33,7 @@
 from firedrake.utils import as_cstr, IntType, RealType
 from firedrake.logging import info_red
 from firedrake.parameters import parameters
-from firedrake.petsc import (
-    PETSc, OptionsManager, get_external_packages, DEFAULT_PARTITIONER
-)
+from firedrake.petsc import PETSc, DEFAULT_PARTITIONER
 from firedrake.adjoint_utils import MeshGeometryMixin
 from pyadjoint import stop_annotating
 import gem
diff --git a/firedrake/petsc.py b/firedrake/petsc.py
index 7bb84dd1bf..65cc465871 100644
--- a/firedrake/petsc.py
+++ b/firedrake/petsc.py
@@ -1,23 +1,20 @@
-import functools
 import gc
-import itertools
-import os
-import subprocess
-from contextlib import contextmanager
 from copy import deepcopy
 from types import MappingProxyType
 from typing import Any
 from warnings import warn
 
-import petsc4py
+import petsctools
 from mpi4py import MPI
 from petsc4py import PETSc
 from pyop2 import mpi
 
+from firedrake import utils
+
 
 __all__ = (
     "PETSc",
-    "OptionsManager",
+    # TODO: These are all now deprecated
     "get_petsc_variables",
     "get_petscconf_h",
     "get_external_packages"
@@ -28,273 +25,29 @@ class FiredrakePETScError(Exception):
     pass
 
 
-def flatten_parameters(parameters, sep="_"):
-    """Flatten a nested parameters dict, joining keys with sep.
-
-    :arg parameters: a dict to flatten.
-    :arg sep: separator of keys.
-
-    Used to flatten parameter dictionaries with nested structure to a
-    flat dict suitable to pass to PETSc.  For example:
-
-    .. code-block:: python3
-
-       flatten_parameters({"a": {"b": {"c": 4}, "d": 2}, "e": 1}, sep="_")
-       => {"a_b_c": 4, "a_d": 2, "e": 1}
-
-    If a "prefix" key already ends with the provided separator, then
-    it is not used to concatenate the keys.  Hence:
-
-    .. code-block:: python3
-
-       flatten_parameters({"a_": {"b": {"c": 4}, "d": 2}, "e": 1}, sep="_")
-       => {"a_b_c": 4, "a_d": 2, "e": 1}
-       # rather than
-       => {"a__b_c": 4, "a__d": 2, "e": 1}
-    """
-    from firedrake.logging import warning
-    new = type(parameters)()
-
-    if not len(parameters):
-        return new
-
-    def flatten(parameters, *prefixes):
-        """Iterate over nested dicts, yielding (*keys, value) pairs."""
-        sentinel = object()
-        try:
-            option = sentinel
-            for option, value in parameters.items():
-                # Recurse into values to flatten any dicts.
-                for pair in flatten(value, option, *prefixes):
-                    yield pair
-            # Make sure zero-length dicts come back.
-            if option is sentinel:
-                yield (prefixes, parameters)
-        except AttributeError:
-            # Non dict values are just returned.
-            yield (prefixes, parameters)
-
-    def munge(keys):
-        """Ensure that each intermediate key in keys ends in sep.
-
-        Also, reverse the list."""
-        for key in reversed(keys[1:]):
-            if len(key) and not key.endswith(sep):
-                yield key + sep
-            else:
-                yield key
-        else:
-            yield keys[0]
+@utils.deprecated("petsctools.flatten_parameters")
+def flatten_parameters(*args, **kwargs):
+    return petsctools.flatten_parameters(*args, **kwargs)
 
-    for keys, value in flatten(parameters):
-        option = "".join(map(str, munge(keys)))
-        if option in new:
-            warning("Ignoring duplicate option: %s (existing value %s, new value %s)",
-                    option, new[option], value)
-        new[option] = value
-    return new
 
-
-@functools.lru_cache()
+@utils.deprecated("petsctools.get_petscvariables")
 def get_petsc_variables():
-    """Get dict of PETSc environment variables from the file:
-    $PETSC_DIR/$PETSC_ARCH/lib/petsc/conf/petscvariables
-
-    The result is memoized to avoid constantly reading the file.
-    """
-    config = petsc4py.get_config()
-    path = [config["PETSC_DIR"], config["PETSC_ARCH"], "lib/petsc/conf/petscvariables"]
-    variables_path = os.path.join(*path)
-    with open(variables_path) as fh:
-        # Split lines on first '=' (assignment)
-        splitlines = (line.split("=", maxsplit=1) for line in fh.readlines())
-    return {k.strip(): v.strip() for k, v in splitlines}
+    return petsctools.get_petscvariables()
 
 
-@functools.lru_cache()
+@utils.deprecated("petsctools.get_petscconf_h")
 def get_petscconf_h():
-    """Get dict of PETSc include variables from the file:
-    $PETSC_DIR/$PETSC_ARCH/include/petscconf.h
-
-    The ``#define`` and ``PETSC_`` prefix are dropped in the dictionary key.
-
-    The result is memoized to avoid constantly reading the file.
-    """
-    config = petsc4py.get_config()
-    path = [config["PETSC_DIR"], config["PETSC_ARCH"], "include/petscconf.h"]
-    petscconf_h = os.path.join(*path)
-    with open(petscconf_h) as fh:
-        # TODO: use `removeprefix("#define PETSC_")` in place of
-        # `lstrip("#define PETSC")[1:]` when support for Python 3.8 is dropped
-        splitlines = (
-            line.lstrip("#define PETSC")[1:].split(" ", maxsplit=1)
-            for line in filter(lambda x: x.startswith("#define PETSC_"), fh.readlines())
-        )
-    return {k: v.strip() for k, v in splitlines}
+    return petsctools.get_petscconf_h()
 
 
+@utils.deprecated("petsctools.get_external_packages")
 def get_external_packages():
-    """Return a list of PETSc external packages that are installed.
-
-    """
-    # The HAVE_PACKAGES variable uses delimiters at both ends
-    # so we drop the empty first and last items
-    return get_petscconf_h()["HAVE_PACKAGES"].split(":")[1:-1]
-
-
-def _get_dependencies(filename):
-    """Get all the dependencies of a shared object library"""
-    # Linux uses `ldd` to look at shared library linkage, MacOS uses `otool`
-    try:
-        program = ["ldd"]
-        cmd = subprocess.run([*program, filename], stdout=subprocess.PIPE)
-        # Filter out the VDSO and the ELF interpreter on Linux
-        results = [line for line in cmd.stdout.decode("utf-8").split("\n") if "=>" in line]
-        return [line.split()[2] for line in results]
-    except FileNotFoundError:
-        program = ["otool", "-L"]
-        cmd = subprocess.run([*program, filename], stdout=subprocess.PIPE)
-        # Meanwhile MacOS puts garbage at the beginning and end of `otool` output
-        return [line.split()[0] for line in cmd.stdout.decode("utf-8").split("\n")[1:-1]]
+    return petsctools.get_external_packages()
 
 
+@utils.deprecated("petsctools.get_blas_library")
 def get_blas_library():
-    """Get the path to the BLAS library that PETSc links to"""
-    petsc_py_dependencies = _get_dependencies(PETSc.__file__)
-    library_names = ["blas", "libmkl"]
-    for filename in petsc_py_dependencies:
-        if any(name in filename for name in library_names):
-            return filename
-
-    # On newer MacOS versions, the PETSc Python extension library doesn't link
-    # to BLAS or MKL directly, so we check the PETSc C library.
-    petsc_c_library = [f for f in petsc_py_dependencies if "libpetsc" in f][0]
-    petsc_c_dependencies = _get_dependencies(petsc_c_library)
-    for filename in petsc_c_dependencies:
-        if any(name in filename for name in library_names):
-            return filename
-
-    return None
-
-
-class OptionsManager(object):
-
-    # What appeared on the commandline, we should never clear these.
-    # They will override options passed in as a dict if an
-    # options_prefix was supplied.
-    commandline_options = frozenset(PETSc.Options().getAll())
-
-    options_object = PETSc.Options()
-
-    count = itertools.count()
-
-    """Mixin class that helps with managing setting petsc options.
-
-    :arg parameters: The dictionary of parameters to use.
-    :arg options_prefix: The prefix to look up items in the global
-        options database (may be ``None``, in which case only entries
-        from ``parameters`` will be considered.  If no trailing
-        underscore is provided, one is appended.  Hence ``foo_`` and
-        ``foo`` are treated equivalently.  As an exception, if the
-        prefix is the empty string, no underscore is appended.
-
-    To use this, you must call its constructor to with the parameters
-    you want in the options database.
-
-    You then call :meth:`set_from_options`, passing the PETSc object
-    you'd like to call ``setFromOptions`` on.  Note that this will
-    actually only call ``setFromOptions`` the first time (so really
-    this parameters object is a once-per-PETSc-object thing).
-
-    So that the runtime monitors which look in the options database
-    actually see options, you need to ensure that the options database
-    is populated at the time of a ``SNESSolve`` or ``KSPSolve`` call.
-    Do that using the :meth:`inserted_options` context manager.
-
-    .. code-block:: python3
-
-       with self.inserted_options():
-           self.snes.solve(...)
-
-    This ensures that the options database has the relevant entries
-    for the duration of the ``with`` block, before removing them
-    afterwards.  This is a much more robust way of dealing with the
-    fixed-size options database than trying to clear it out using
-    destructors.
-
-    This object can also be used only to manage insertion and deletion
-    into the PETSc options database, by using the context manager.
-    """
-    def __init__(self, parameters, options_prefix):
-        super().__init__()
-        if parameters is None:
-            parameters = {}
-        else:
-            # Convert nested dicts
-            parameters = flatten_parameters(parameters)
-        if options_prefix is None:
-            self.options_prefix = "firedrake_%d_" % next(self.count)
-            self.parameters = parameters
-            self.to_delete = set(parameters)
-        else:
-            if len(options_prefix) and not options_prefix.endswith("_"):
-                options_prefix += "_"
-            self.options_prefix = options_prefix
-            # Remove those options from the dict that were passed on
-            # the commandline.
-            self.parameters = {k: v for k, v in parameters.items()
-                               if options_prefix + k not in self.commandline_options}
-            self.to_delete = set(self.parameters)
-            # Now update parameters from options, so that they're
-            # available to solver setup (for, e.g., matrix-free).
-            # Can't ask for the prefixed guy in the options object,
-            # since that does not DTRT for flag options.
-            for k, v in self.options_object.getAll().items():
-                if k.startswith(self.options_prefix):
-                    self.parameters[k[len(self.options_prefix):]] = v
-        self._setfromoptions = False
-
-    def set_default_parameter(self, key, val):
-        """Set a default parameter value.
-
-        :arg key: The parameter name
-        :arg val: The parameter value.
-
-        Ensures that the right thing happens cleaning up the options
-        database.
-        """
-        k = self.options_prefix + key
-        if k not in self.options_object and key not in self.parameters:
-            self.parameters[key] = val
-            self.to_delete.add(key)
-
-    def set_from_options(self, petsc_obj):
-        """Set up petsc_obj from the options database.
-
-        :arg petsc_obj: The PETSc object to call setFromOptions on.
-
-        Matt says: "Only ever call setFromOptions once".  This
-        function ensures we do so.
-        """
-        if not self._setfromoptions:
-            with self.inserted_options():
-                petsc_obj.setOptionsPrefix(self.options_prefix)
-                # Call setfromoptions inserting appropriate options into
-                # the options database.
-                petsc_obj.setFromOptions()
-                self._setfromoptions = True
-
-    @contextmanager
-    def inserted_options(self):
-        """Context manager inside which the petsc options database
-    contains the parameters from this object."""
-        try:
-            for k, v in self.parameters.items():
-                self.options_object[self.options_prefix + k] = v
-            yield
-        finally:
-            for k in self.to_delete:
-                del self.options_object[self.options_prefix + k]
+    return petsctools.get_blas_library()
 
 
 def _extract_comm(obj: Any) -> MPI.Comm:
@@ -376,7 +129,7 @@ def garbage_view(obj: Any):
         raise FiredrakePETScError("No comm found, cannot view garbage")
 
 
-external_packages = get_external_packages()
+external_packages = petsctools.get_external_packages()
 
 # Setup default partitioner
 # Manually define the priority until
@@ -429,7 +182,7 @@ def garbage_view(obj: Any):
 # Parameters must be flattened for `set_defaults` in `solving_utils.py` to
 # mutate options dictionaries "correctly".
 # TODO: refactor `set_defaults` in `solving_utils.py`
-_DEFAULT_KSP_PARAMETERS = flatten_parameters({
+_DEFAULT_KSP_PARAMETERS = petsctools.flatten_parameters({
     "mat_type": "aij",
     "ksp_type": "preonly",
     "ksp_rtol": 1e-7,
diff --git a/firedrake/slate/slac/compiler.py b/firedrake/slate/slac/compiler.py
index 0d312d02c6..98d2b4a48c 100644
--- a/firedrake/slate/slac/compiler.py
+++ b/firedrake/slate/slac/compiler.py
@@ -19,7 +19,6 @@
 from firedrake import tsfc_interface
 from firedrake.logging import logger
 from firedrake.parameters import parameters
-from firedrake.petsc import get_petsc_variables
 from firedrake.utils import complex_mode
 from gem import impero_utils
 from itertools import chain
@@ -33,6 +32,7 @@
 import numpy as np
 import loopy
 import gem
+import petsctools
 from gem import indices as make_indices
 from tsfc.kernel_args import OutputKernelArg, CoefficientKernelArg
 from tsfc.loopy import generate as generate_loopy
@@ -54,7 +54,7 @@
 BLASLAPACK_LIB = None
 BLASLAPACK_INCLUDE = None
 if COMM_WORLD.rank == 0:
-    petsc_variables = get_petsc_variables()
+    petsc_variables = petsctools.get_petscvariables()
     BLASLAPACK_LIB = petsc_variables.get("BLASLAPACK_LIB", "")
     BLASLAPACK_LIB = COMM_WORLD.bcast(BLASLAPACK_LIB, root=0)
     BLASLAPACK_INCLUDE = petsc_variables.get("BLASLAPACK_INCLUDE", "")
diff --git a/firedrake/utils.py b/firedrake/utils.py
index e8bda53b95..901e2694ae 100644
--- a/firedrake/utils.py
+++ b/firedrake/utils.py
@@ -1,5 +1,6 @@
 # Some generic python utilities not really specific to our work.
 import collections.abc
+import warnings
 from decorator import decorator
 from pyop2.utils import cached_property  # noqa: F401
 from pyop2.datatypes import ScalarType, as_cstr
@@ -7,7 +8,7 @@
 from pyop2.datatypes import IntType      # noqa: F401
 from pyop2.datatypes import as_ctypes    # noqa: F401
 from pyop2.mpi import MPI
-from firedrake.petsc import get_petsc_variables
+import petsctools
 
 
 # MPI key value for storing a per communicator universal identifier
@@ -17,7 +18,7 @@
 ScalarType_c = as_cstr(ScalarType)
 IntType_c = as_cstr(IntType)
 
-complex_mode = (get_petsc_variables()["PETSC_SCALAR"].lower() == "complex")
+complex_mode = (petsctools.get_petscvariables()["PETSC_SCALAR"].lower() == "complex")
 
 # Remove this (and update test suite) when Slate supports complex mode.
 SLATE_SUPPORTS_COMPLEX = False
@@ -152,3 +153,18 @@ def assert_empty(iterator):
         raise AssertionError("Iterator is not empty")
     except StopIteration:
         pass
+
+
+# NOTE: Python 3.13 has warnings.deprecated which does exactly this
+def deprecated(prefer=None, internal=False):
+    """Decorator that emits a warning saying that the function is deprecated."""
+    def decorator(fn):
+        def wrapper(*args, **kwargs):
+            msg = f"{fn.__qualname__} is deprecated and will be removed"
+            if prefer:
+                msg += f", please use {prefer} instead"
+            warning_type = DeprecationWarning if internal else FutureWarning
+            warnings.warn(msg, warning_type)
+            return fn(*args, **kwargs)
+        return wrapper
+    return decorator
diff --git a/firedrake/variational_solver.py b/firedrake/variational_solver.py
index 34698f6b35..f6b13aacb3 100644
--- a/firedrake/variational_solver.py
+++ b/firedrake/variational_solver.py
@@ -2,12 +2,10 @@
 from itertools import chain
 from contextlib import ExitStack
 from types import MappingProxyType
+from petsctools import OptionsManager, flatten_parameters
 
 from firedrake import dmhooks, slate, solving, solving_utils, ufl_expr, utils
-from firedrake.petsc import (
-    PETSc, OptionsManager, flatten_parameters, DEFAULT_KSP_PARAMETERS,
-    DEFAULT_SNES_PARAMETERS
-)
+from firedrake.petsc import PETSc, DEFAULT_KSP_PARAMETERS, DEFAULT_SNES_PARAMETERS
 from firedrake.function import Function
 from firedrake.interpolation import Interpolate
 from firedrake.matrix import MatrixBase
diff --git a/pyproject.toml b/pyproject.toml
index c932940e12..9f4f2f22d8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,10 +25,11 @@ dependencies = [
   "h5py>3.12.1",
   "libsupermesh",
   "loopy>2024.1",
-  # TODO RELEASE
-  # "petsc4py==3.23.3",
   "numpy",
   "packaging",
+  # TODO RELEASE
+  # "petsc4py==3.23.3",
+  "petsctools @ git+https://github.com/firedrakeproject/petsctools.git",
   "pkgconfig",
   "progress",
   "pyadjoint-ad>=2025.04",
@@ -142,10 +143,11 @@ requires = [
   "mpi4py; python_version < '3.13'",
   "numpy",
   "pkgconfig",
-  "pybind11",
-  "setuptools>=77.0.3",
   # TODO RELEASE
   # "petsc4py==3.23.3",
+  "petsctools @ git+https://github.com/firedrakeproject/petsctools.git",
+  "pybind11",
+  "setuptools>=77.0.3",
   "rtree>=1.2",
 ]
 build-backend = "setuptools.build_meta"
diff --git a/requirements-build.txt b/requirements-build.txt
index 2115e2c3be..36790d8f26 100644
--- a/requirements-build.txt
+++ b/requirements-build.txt
@@ -5,6 +5,7 @@ mpi4py>3; python_version >= '3.13'
 mpi4py; python_version < '3.13'
 numpy
 pkgconfig
+petsctools @ git+https://github.com/firedrakeproject/petsctools.git
 pybind11
 setuptools>=77.0.3
 rtree>=1.2
diff --git a/scripts/check-config b/scripts/check-config
index 4fccd64a94..957d110e16 100755
--- a/scripts/check-config
+++ b/scripts/check-config
@@ -71,8 +71,14 @@ def check_slepc_version() -> None:
 
 
 def check_petsc_version_spec() -> None:
-    # TODO when https://github.com/firedrakeproject/firedrake/pull/4194 is merged
-    pass
+    petsc_version_spec = check_file_contains_pattern(
+        REPO_ROOT / "firedrake/__init__.py",
+        "PETSC_SUPPORTED_VERSIONS = \"(.*)\"",
+    )
+    check_file_contains_pattern(
+        REPO_ROOT / "setup.py",
+        f"petsctools.init\\(version_spec=\"{petsc_version_spec}\"\\)",
+    )
 
 
 def check_file_contains_pattern(
diff --git a/setup.py b/setup.py
index 48def53670..56ab7dba55 100644
--- a/setup.py
+++ b/setup.py
@@ -1,47 +1,26 @@
-import logging
 import os
+import pkgconfig
 import platform
-import sys
-import site
 import shutil
+import site
+from dataclasses import dataclass, field
+from glob import glob
+from pathlib import Path
+
+import libsupermesh
 import numpy as np
 import pybind11
-import petsc4py
+import petsctools
 import rtree
-import libsupermesh
-import pkgconfig
-from dataclasses import dataclass, field
+from Cython.Build import cythonize
 from setuptools import setup, find_packages, Extension
 from setuptools.command.editable_wheel import editable_wheel as _editable_wheel
 from setuptools.command.sdist import sdist as _sdist
-from glob import glob
-from pathlib import Path
-from Cython.Build import cythonize
-
 
-petsc_config = petsc4py.get_config()
 
-
-def get_petsc_dir():
-    """Attempts to find the PETSc directory on the system
-    """
-    petsc_dir = petsc_config["PETSC_DIR"]
-    petsc_arch = petsc_config["PETSC_ARCH"]
-    pathlist = [petsc_dir]
-    if petsc_arch:
-        pathlist.append(os.path.join(petsc_dir, petsc_arch))
-    return pathlist
-
-
-def get_petsc_variables():
-    """Attempts obtain a dictionary of PETSc configuration settings
-    """
-    path = [get_petsc_dir()[-1], "lib/petsc/conf/petscvariables"]
-    variables_path = os.path.join(*path)
-    with open(variables_path) as fh:
-        # Split lines on first '=' (assignment)
-        splitlines = (line.split("=", maxsplit=1) for line in fh.readlines())
-    return {k.strip(): v.strip() for k, v in splitlines}
+# Ensure that the PETSc getting linked against is compatible
+petsctools.init(version_spec=">=3.23.0")
+import petsc4py
 
 
 @dataclass
@@ -94,16 +73,18 @@ def __getitem__(self, key):
 # example:
 # gcc -I$PETSC_DIR/include -I$PETSC_DIR/$PETSC_ARCH/include -I/petsc4py/include
 # gcc -L$PETSC_DIR/$PETSC_ARCH/lib -lpetsc -Wl,-rpath,$PETSC_DIR/$PETSC_ARCH/lib
-petsc_dirs = get_petsc_dir()
+petsc_dir = petsctools.get_petsc_dir()
+petsc_arch = petsctools.get_petsc_arch()
+petsc_dirs = [petsc_dir, os.path.join(petsc_dir, petsc_arch)]
 petsc_ = ExternalDependency(
     libraries=["petsc"],
     include_dirs=[petsc4py.get_include()] + [os.path.join(d, "include") for d in petsc_dirs],
     library_dirs=[os.path.join(petsc_dirs[-1], "lib")],
     runtime_library_dirs=[os.path.join(petsc_dirs[-1], "lib")],
 )
-petsc_variables = get_petsc_variables()
-petsc_hdf5_compile_args = petsc_variables.get("HDF5_INCLUDE", "")
-petsc_hdf5_link_args = petsc_variables.get("HDF5_LIB", "")
+petscvariables = petsctools.get_petscvariables()
+petsc_hdf5_compile_args = petscvariables.get("HDF5_INCLUDE", "")
+petsc_hdf5_link_args = petscvariables.get("HDF5_LIB", "")
 
 # HDF5
 # example:
@@ -113,15 +94,15 @@ def __getitem__(self, key):
     # We almost always want to be in this first case!!!
     # PETSc variables only contains the compile/link args, not the paths
     hdf5_ = ExternalDependency(
-        extra_compile_args = petsc_hdf5_compile_args.split(),
-        extra_link_args = petsc_hdf5_link_args.split()
+        extra_compile_args=petsc_hdf5_compile_args.split(),
+        extra_link_args=petsc_hdf5_link_args.split()
     )
 elif os.environ.get("HDF5_DIR"):
     hdf5_dir = Path(os.environ.get("HDF5_DIR"))
     hdf5_ = ExternalDependency(
         libraries=["hdf5"],
-        include_dirs = [str(hdf5_dir.joinpath("include"))],
-        library_dirs = [str(hdf5_dir.joinpath("lib"))]
+        include_dirs=[str(hdf5_dir.joinpath("include"))],
+        library_dirs=[str(hdf5_dir.joinpath("lib"))]
     )
 elif pkgconfig.exists("hdf5"):
     hdf5_ = ExternalDependency(**pkgconfig.parse("hdf5"))
@@ -165,9 +146,10 @@ def __getitem__(self, key):
     libraries=["supermesh"],
 )
 
+
 # The following extensions need to be linked accordingly:
 def extensions():
-    ## CYTHON EXTENSIONS
+    # CYTHON EXTENSIONS
     cython_list = []
     # firedrake/cython/dmcommon.pyx: petsc, numpy
     cython_list.append(Extension(
@@ -225,7 +207,7 @@ def extensions():
         sources=[os.path.join("pyop2", "sparsity.pyx")],
         **(petsc_ + numpy_)
     ))
-    ## PYBIND11 EXTENSIONS
+    # PYBIND11 EXTENSIONS
     pybind11_list = []
     # tinyasm/tinyasm.cpp: petsc, pybind11
     pybind11_list.append(Extension(
diff --git a/tests/firedrake/regression/test_solvers_options_prefix.py b/tests/firedrake/regression/test_solvers_options_prefix.py
index 3d9fab01bf..396df42983 100644
--- a/tests/firedrake/regression/test_solvers_options_prefix.py
+++ b/tests/firedrake/regression/test_solvers_options_prefix.py
@@ -1,6 +1,7 @@
 from firedrake import *
 from firedrake.matrix import ImplicitMatrix
-from firedrake.petsc import PETSc, OptionsManager
+from firedrake.petsc import PETSc
+from petsctools import OptionsManager
 import pytest
 
 

From 6e1f8b937ef9c93c24cf28d59ee62296f0bc54dc Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Mon, 16 Jun 2025 14:01:45 +0100
Subject: [PATCH 147/222] Add Firedrake 25 information to the website (#4371)

---
 docs/source/events.rst                        |   8 ++
 docs/source/firedrake_25.rst                  | 118 ++++++++++++++++++
 docs/source/images/leeds_som.png              | Bin 0 -> 1590601 bytes
 docs/source/images/university_house_leeds.jpg | Bin 0 -> 378248 bytes
 4 files changed, 126 insertions(+)
 create mode 100644 docs/source/firedrake_25.rst
 create mode 100644 docs/source/images/leeds_som.png
 create mode 100644 docs/source/images/university_house_leeds.jpg

diff --git a/docs/source/events.rst b/docs/source/events.rst
index 3d29b37754..7463562487 100644
--- a/docs/source/events.rst
+++ b/docs/source/events.rst
@@ -8,6 +8,14 @@
 Firedrake events
 ----------------
 
+Firedrake '25
+~~~~~~~~~~~~~
+
+The tenth Firedrake user and developer workshop will be held
+between 15-17 September 2025 at the University of Leeds. For more
+information see :doc:`the workshop page `.
+
+
 CCP-DCM Hackathon
 ~~~~~~~~~~~~~~~~~
 
diff --git a/docs/source/firedrake_25.rst b/docs/source/firedrake_25.rst
new file mode 100644
index 0000000000..4b0d04746b
--- /dev/null
+++ b/docs/source/firedrake_25.rst
@@ -0,0 +1,118 @@
+:orphan: true
+
+.. title:: Firedrake '25
+
+.. image:: images/leeds_som.png
+   :alt: University of Leeds, School of Mathematics
+   :width: 100%
+   :align: center
+
+Firedrake '25
+-------------
+
+The tenth Firedrake user and developer workshop will be held at the
+`School of Mathematics `__ together
+with
+`Leeds Institute of Fluid Dynamics `__,
+University of Leeds, from 15-17 September 2025.
+
+The workshop will be an opportunity for Firedrake users and developers
+to discuss the latest developments in the project, anticipated future
+developments, and how Firedrake can be used in the numerical solution
+of partial differential equations. The event will provide Firedrake
+users with the opportunity to interact directly with developers and
+with other users.
+
+The conference will begin around lunchtime on 15 September and end on the
+evening of 17 September.
+
+Conference venue
+----------------
+
+The conference will take place in the School of Mathematics, the MALL and
+the Reading Room (Room 9.31). Its main entrance (building 84 on the
+`campus map `__) is located
+next to the Edward Boyle library (building 83 on the
+campus map).
+
+Accommodation
+-------------
+
+Accommodation is to be arranged by the participants themselves. We recommend
+the following hotels, in particular the ibis Leeds Centre Marlborough Street,
+which is a ten-minute walk to the School of Mathematics.
+
+* `ibis Leeds Centre Marlborough Street `__
+* `The Queens Hotel `__
+* `Radisson Blu Leeds `__
+* `The Marriott Leeds `__
+* `Park Plaza Leeds `__
+* `Hilton Leeds City `__
+* `Novotel Leeds Centre `__
+* `ibis Styles Leeds City Centre Arena `__
+* `Kspace apartments, Waterloo Court, Leeds `__
+
+Conference dinner
+-----------------
+
+Registration includes a drinks reception and three-course dinner at University House, University of Leeds. The dinner will take place on the evening of 16 September. 
+
+It is possible during registration to purchase additional tickets for the dinner to bring non-registered companions.
+
+.. image:: images/university_house_leeds.jpg
+   :width: 80%
+   :alt: University House, University of Leeds
+   :align: center
+
+
+Registration
+------------
+
+Registration will open soon and be closed on August 17th 2025.
+
+The registration fees will be as follows:
+
+.. list-table::
+   :widths: 25 25
+
+   * - Student
+     - £25
+   * - Non-student
+     - £115
+
+Abstract submission
+-------------------
+
+Please submit your abstracts to the LIFD email: fluids-institute@leeds.ac.uk.
+
+Support
+-------
+
+The conference has been kindly supported by EPSRC grant EP/W026163/1 and EPSRC
+grant EP/W026066/1 with support by the School of Mathematics and Leeds
+Institute of Fluid Dynamics.
+
+Travel to Leeds
+----------------
+
+Coming to Leeds from abroad, the best options are:
+
+* Fly to Heathrow or Gatwick
+
+  This is usually the best option for those flying long distance. Subsequently,
+  one can take the train to Leeds.
+
+* Fly to Leeds-Bradford (LBA)
+
+  This is a good option for European flights; Leeds-Bradford is a much smaller
+  airport than Heathrow and is more convenient. There is a bus service to the
+  centre of Leeds. Alternatively, one can take a taxi.
+
+If coming from the UK it is simplest to take the train to Leeds station.
+
+Organising committee
+--------------------
+
+* Onno Bokhove o.bokhove@leeds.ac.uk, School of Mathematics, University of Leeds
+* Claire Savy and Deborah Clarke fluids-institute@leeds.ac.uk, Leeds Institute of
+  Fluid Dynamics, University of Leeds
diff --git a/docs/source/images/leeds_som.png b/docs/source/images/leeds_som.png
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z(ds-
Date: Wed, 18 Jun 2025 15:59:03 +0100
Subject: [PATCH 148/222] use ufl CoefficientSplitter (#4349)

Co-authored-by: Connor Ward 
---
 firedrake/mg/kernels.py                  |   2 +-
 firedrake/preconditioners/patch.py       |  13 ++-
 firedrake/tsfc_interface.py              | 100 ++++++++++++++---------
 tsfc/driver.py                           |  96 ++++++++++++++--------
 tsfc/kernel_interface/common.py          |   5 +-
 tsfc/kernel_interface/firedrake_loopy.py |  54 ++++--------
 tsfc/ufl_utils.py                        |  70 ++--------------
 7 files changed, 154 insertions(+), 186 deletions(-)

diff --git a/firedrake/mg/kernels.py b/firedrake/mg/kernels.py
index 6ce9c4eb55..09436bdbbd 100644
--- a/firedrake/mg/kernels.py
+++ b/firedrake/mg/kernels.py
@@ -485,7 +485,6 @@ def __init__(self, scalar_type, num_entities):
 
     def set_coefficients(self, coefficients):
         self.coefficients = []
-        self.coefficient_split = {}
         self.kernel_args = []
         for i, coefficient in enumerate(coefficients):
             if type(coefficient.ufl_element()) == finat.ufl.MixedElement:
@@ -560,6 +559,7 @@ def dg_injection_kernel(Vf, Vc, ncell):
                                 domain_number=0,
                                 arguments=(ufl.TestFunction(Vc), ),
                                 coefficients=(),
+                                coefficient_split={},
                                 coefficient_numbers=())
 
     coarse_builder = firedrake_interface.KernelBuilder(info, parameters["scalar_type"])
diff --git a/firedrake/preconditioners/patch.py b/firedrake/preconditioners/patch.py
index 093827d69e..30ce3c3e74 100644
--- a/firedrake/preconditioners/patch.py
+++ b/firedrake/preconditioners/patch.py
@@ -14,7 +14,6 @@
 import numpy
 from finat.ufl import VectorElement, MixedElement
 from ufl.domain import extract_unique_domain
-from tsfc.kernel_interface.firedrake_loopy import make_builder
 from tsfc.ufl_utils import extract_firedrake_constants
 import weakref
 
@@ -144,11 +143,11 @@ def matrix_funptr(form, state):
         raise NotImplementedError("Only for matching test and trial spaces")
 
     if state is not None:
-        interface = make_builder(dont_split=(state, ))
+        dont_split = (state, )
     else:
-        interface = None
+        dont_split = ()
 
-    kernels = compile_form(form, "subspace_form", split=False, interface=interface)
+    kernels = compile_form(form, "subspace_form", split=False, dont_split=dont_split)
 
     cell_kernels = []
     int_facet_kernels = []
@@ -238,11 +237,11 @@ def residual_funptr(form, state):
         raise NotImplementedError("State and test space must be dual to one-another")
 
     if state is not None:
-        interface = make_builder(dont_split=(state, ))
+        dont_split = (state, )
     else:
-        interface = None
+        dont_split = ()
 
-    kernels = compile_form(form, "subspace_form", split=False, interface=interface)
+    kernels = compile_form(form, "subspace_form", split=False, dont_split=dont_split)
 
     cell_kernels = []
     int_facet_kernels = []
diff --git a/firedrake/tsfc_interface.py b/firedrake/tsfc_interface.py
index d6c663f5de..2999a9e9ef 100644
--- a/firedrake/tsfc_interface.py
+++ b/firedrake/tsfc_interface.py
@@ -7,9 +7,7 @@
 from os import path, environ, getuid, makedirs
 import tempfile
 import collections
-import functools
 
-from tsfc.kernel_interface.firedrake_loopy import KernelBuilder
 import ufl
 import finat.ufl
 from ufl import conj, Form, ZeroBaseForm
@@ -54,12 +52,12 @@
 )
 
 
-def tsfc_compile_form_hashkey(form, prefix, parameters, interface, diagonal):
+def tsfc_compile_form_hashkey(form, prefix, parameters, dont_split_numbers, diagonal):
     return default_parallel_hashkey(
         form.signature(),
         prefix,
         utils.tuplify(parameters),
-        _make_interface_key(interface, form),
+        dont_split_numbers,
         diagonal,
     )
 
@@ -84,21 +82,31 @@ def __init__(
         parameters,
         coefficient_numbers,
         constant_numbers,
-        interface,
+        dont_split_numbers,
         diagonal=False
     ):
         """A wrapper object for one or more TSFC kernels compiled from a given :class:`~ufl.classes.Form`.
 
-        :arg form: the :class:`~ufl.classes.Form` from which to compile the kernels.
-        :arg name: a prefix to be applied to the compiled kernel names. This is primarily useful for debugging.
-        :arg parameters: a dict of parameters to pass to the form compiler.
-        :arg coefficient_numbers: Map from coefficient numbers in the provided (split) form to coefficient numbers in the original form.
-        :arg constant_numbers: Map from local constant numbers in the provided (split) form to constant numbers in the original form.
-        :arg interface: the KernelBuilder interface for TSFC (may be None)
-        :arg diagonal: If assembling a matrix is it diagonal?
+        Parameters
+        ----------
+        form : ufl.classes.Form
+            The :class:`~ufl.classes.Form` from which to compile the kernels.
+        name : str
+            A prefix to be applied to the compiled kernel names. This is primarily useful for debugging.
+        parameters : dict
+            A dict of parameters to pass to the form compiler.
+        coefficient_numbers : dict
+            Map from coefficient numbers in the provided (split) form to coefficient numbers in the original form.
+        constant_numbers : dict
+            Map from local constant numbers in the provided (split) form to constant numbers in the original form.
+        dont_split_numbers : tuple
+            Block-local coefficient numbers of coefficients that are not to be split into components by form compiler.
+        diagonal : bool
+            If assembling a matrix is it diagonal?
+
         """
         tree = tsfc_compile_form(form, prefix=name, parameters=parameters,
-                                 interface=interface,
+                                 dont_split_numbers=dont_split_numbers,
                                  diagonal=diagonal)
         kernels = []
         for kernel in tree:
@@ -138,13 +146,13 @@ def __init__(
 SplitKernel = collections.namedtuple("SplitKernel", ["indices", "kinfo"])
 
 
-def _compile_form_hashkey(form, name, parameters=None, split=True, interface=None, diagonal=False):
+def _compile_form_hashkey(form, name, parameters=None, split=True, dont_split=(), diagonal=False):
     return (
         form.signature(),
         name,
         utils.tuplify(parameters),
         split,
-        _make_interface_key(interface, form),
+        _make_dont_split_numbers(dont_split, form),
         diagonal,
     )
 
@@ -156,20 +164,35 @@ def _compile_form_hashkey(form, name, parameters=None, split=True, interface=Non
     cachedir=_cachedir
 )
 @PETSc.Log.EventDecorator()
-def compile_form(form, name, parameters=None, split=True, interface=None, diagonal=False):
+def compile_form(form, name, parameters=None, split=True, dont_split=(), diagonal=False):
     """Compile a form using TSFC.
 
-    :arg form: the :class:`~ufl.classes.Form` to compile.
-    :arg name: a prefix for the generated kernel functions.
-    :arg parameters: optional dict of parameters to pass to the form
-         compiler. If not provided, parameters are read from the
-         ``form_compiler`` slot of the Firedrake
-         :data:`~.parameters` dictionary (which see).
-    :arg split: If ``False``, then don't split mixed forms.
-
-    Returns a tuple of tuples of
-    (index, integral type, subdomain id, coordinates, coefficients, needs_orientations, ``pyop2.op2.Kernel``).
-
+    Parameters
+    ----------
+    form : ufl.classes.Form
+        The :class:`~ufl.classes.Form` to compile.
+    name : str
+        A prefix for the generated kernel functions.
+    parameters : dict
+        Optional dict of parameters to pass to the form
+        compiler. If not provided, parameters are read from the
+        ``form_compiler`` slot of the Firedrake
+        :data:`~.parameters` dictionary (which see).
+    split : bool
+        If ``False``, then don't split mixed forms.
+    dont_split : tuple
+        Coefficients that are not to be split into components by form compiler.
+    diagonal : bool
+        If assembling a matrix is it diagonal?
+
+    Returns
+    -------
+    Tuple
+        A tuple of tuples of
+        (index, integral type, subdomain id, coordinates, coefficients, needs_orientations, ``pyop2.op2.Kernel``).
+
+    Notes
+    ----
     ``needs_orientations`` indicates whether the form requires cell
     orientation information (for correctly pulling back to reference
     elements on embedded manifolds).
@@ -216,6 +239,7 @@ def compile_form(form, name, parameters=None, split=True, interface=None, diagon
         constant_numbers = tuple(
             numbering[c] for c in extract_firedrake_constants(f)
         )
+        dont_split_numbers = _make_dont_split_numbers(dont_split, f)
         prefix = name + "".join(map(str, (i for i in idx if i is not None)))
         tsfc_kernel = TSFCKernel(
             f,
@@ -223,7 +247,8 @@ def compile_form(form, name, parameters=None, split=True, interface=None, diagon
             parameters,
             coefficient_numbers,
             constant_numbers,
-            interface, diagonal
+            dont_split_numbers,
+            diagonal,
         )
         for kinfo in tsfc_kernel.kernels:
             kernels.append(SplitKernel(idx, kinfo))
@@ -315,14 +340,11 @@ def extract_numbered_coefficients(expr, numbers):
     return coefficients
 
 
-def _make_interface_key(interface, form):
-    if interface:
-        # The 'interface' argument is a small hack done in patch.py. When
-        # specified, what really matters for caching is which coeffients
-        # are passed to the 'dont_split' kwarg.
-        assert isinstance(interface, functools.partial)
-        assert interface.func is KernelBuilder
-        coefficients = form.coefficients()
-        return tuple(coefficients.index(f) for f in interface.keywords["dont_split"])
-    else:
-        return None
+def _make_dont_split_numbers(dont_split, form):
+    return tuple(
+        sorted(
+            form.coefficients().index(c)
+            for c in dont_split
+            if c in form.coefficients()
+        )
+    )
diff --git a/tsfc/driver.py b/tsfc/driver.py
index 32a65f0542..1cbbec56c3 100644
--- a/tsfc/driver.py
+++ b/tsfc/driver.py
@@ -7,6 +7,7 @@
 import ufl
 from ufl.algorithms import extract_arguments, extract_coefficients
 from ufl.algorithms.analysis import has_type
+from ufl.algorithms.apply_coefficient_split import CoefficientSplitter
 from ufl.classes import Form, GeometricQuantity
 from ufl.domain import extract_unique_domain
 
@@ -28,7 +29,7 @@
 TSFCIntegralDataInfo = collections.namedtuple("TSFCIntegralDataInfo",
                                               ["domain", "integral_type", "subdomain_id", "domain_number",
                                                "arguments",
-                                               "coefficients", "coefficient_numbers"])
+                                               "coefficients", "coefficient_split", "coefficient_numbers"])
 TSFCIntegralDataInfo.__doc__ = """
     Minimal set of objects for kernel builders.
 
@@ -47,14 +48,27 @@
     """
 
 
-def compile_form(form, prefix="form", parameters=None, interface=None, diagonal=False):
+def compile_form(form, prefix="form", parameters=None, dont_split_numbers=(), diagonal=False):
     """Compiles a UFL form into a set of assembly kernels.
 
-    :arg form: UFL form
-    :arg prefix: kernel name will start with this string
-    :arg parameters: parameters object
-    :arg diagonal: Are we building a kernel for the diagonal of a rank-2 element tensor?
-    :returns: list of kernels
+    Parameters
+    ----------
+    form : ufl.classes.Form
+        UFL form
+    prefix : str
+        Kernel name will start with this string
+    parameters : dict
+        Parameters object
+    dont_split_numbers : tuple
+        Coefficient numbers of coefficients that are not to be split into components by form compiler.
+    diagonal : bool
+        Are we building a kernel for the diagonal of a rank-2 element tensor?
+
+    Returns
+    -------
+    list
+        list of kernels
+
     """
     cpu_time = time.time()
 
@@ -64,13 +78,21 @@ def compile_form(form, prefix="form", parameters=None, interface=None, diagonal=
 
     # Determine whether in complex mode:
     complex_mode = parameters and is_complex(parameters.get("scalar_type"))
-    fd = ufl_utils.compute_form_data(form, complex_mode=complex_mode)
+    form_data = ufl_utils.compute_form_data(
+        form,
+        coefficients_to_split=tuple(
+            c
+            for i, c in enumerate(form.coefficients())
+            if type(c.ufl_element()) == finat.ufl.MixedElement and i not in dont_split_numbers
+        ),
+        complex_mode=complex_mode,
+    )
     logger.info(GREEN % "compute_form_data finished in %g seconds.", time.time() - cpu_time)
-
+    # Create local kernels.
     kernels = []
-    for integral_data in fd.integral_data:
+    for integral_data in form_data.integral_data:
         start = time.time()
-        kernel = compile_integral(integral_data, fd, prefix, parameters, interface=interface, diagonal=diagonal)
+        kernel = compile_integral(integral_data, form_data, prefix, parameters, diagonal=diagonal)
         if kernel is not None:
             kernels.append(kernel)
         logger.info(GREEN % "compile_integral finished in %g seconds.", time.time() - start)
@@ -79,20 +101,17 @@ def compile_form(form, prefix="form", parameters=None, interface=None, diagonal=
     return kernels
 
 
-def compile_integral(integral_data, form_data, prefix, parameters, interface, *, diagonal=False):
+def compile_integral(integral_data, form_data, prefix, parameters, *, diagonal=False):
     """Compiles a UFL integral into an assembly kernel.
 
     :arg integral_data: UFL integral data
     :arg form_data: UFL form data
     :arg prefix: kernel name will start with this string
     :arg parameters: parameters object
-    :arg interface: backend module for the kernel interface
     :arg diagonal: Are we building a kernel for the diagonal of a rank-2 element tensor?
     :returns: a kernel constructed by the kernel interface
     """
     parameters = preprocess_parameters(parameters)
-    if interface is None:
-        interface = firedrake_interface_loopy.KernelBuilder
     scalar_type = parameters["scalar_type"]
     integral_type = integral_data.integral_type
     mesh = integral_data.domain
@@ -103,27 +122,38 @@ def compile_integral(integral_data, form_data, prefix, parameters, interface, *,
     # Dict mapping domains to index in original_form.ufl_domains()
     domain_numbering = form_data.original_form.domain_numbering()
     domain_number = domain_numbering[integral_data.domain]
-    coefficients = [form_data.function_replace_map[c] for c in integral_data.integral_coefficients]
     # This is which coefficient in the original form the
     # current coefficient is.
     # Consider f*v*dx + g*v*ds, the full form contains two
     # coefficients, but each integral only requires one.
-    coefficient_numbers = tuple(form_data.original_coefficient_positions[i]
-                                for i, (_, enabled) in enumerate(zip(form_data.reduced_coefficients, integral_data.enabled_coefficients))
-                                if enabled)
-    integral_data_info = TSFCIntegralDataInfo(domain=integral_data.domain,
-                                              integral_type=integral_data.integral_type,
-                                              subdomain_id=integral_data.subdomain_id,
-                                              domain_number=domain_number,
-                                              arguments=arguments,
-                                              coefficients=coefficients,
-                                              coefficient_numbers=coefficient_numbers)
-    builder = interface(integral_data_info,
-                        scalar_type,
-                        diagonal=diagonal)
+    coefficients = []
+    coefficient_split = {}
+    coefficient_numbers = []
+    for i, (coeff_orig, enabled) in enumerate(zip(form_data.reduced_coefficients, integral_data.enabled_coefficients)):
+        if enabled:
+            coeff = form_data.function_replace_map[coeff_orig]
+            coefficients.append(coeff)
+            if coeff in form_data.coefficient_split:
+                coefficient_split[coeff] = form_data.coefficient_split[coeff]
+            coefficient_numbers.append(form_data.original_coefficient_positions[i])
+    integral_data_info = TSFCIntegralDataInfo(
+        domain=integral_data.domain,
+        integral_type=integral_data.integral_type,
+        subdomain_id=integral_data.subdomain_id,
+        domain_number=domain_number,
+        arguments=arguments,
+        coefficients=coefficients,
+        coefficient_split=coefficient_split,
+        coefficient_numbers=coefficient_numbers,
+    )
+    builder = firedrake_interface_loopy.KernelBuilder(
+        integral_data_info,
+        scalar_type,
+        diagonal=diagonal,
+    )
     builder.set_coordinates(mesh)
     builder.set_cell_sizes(mesh)
-    builder.set_coefficients(integral_data, form_data)
+    builder.set_coefficients()
     # TODO: We do not want pass constants to kernels that do not need them
     # so we should attach the constants to integral data instead
     builder.set_constants(form_data.constants)
@@ -131,8 +161,7 @@ def compile_integral(integral_data, form_data, prefix, parameters, interface, *,
     for integral in integral_data.integrals:
         params = parameters.copy()
         params.update(integral.metadata())  # integral metadata overrides
-        integrand = ufl.replace(integral.integrand(), form_data.function_replace_map)
-        integrand_exprs = builder.compile_integrand(integrand, params, ctx)
+        integrand_exprs = builder.compile_integrand(integral.integrand(), params, ctx)
         integral_exprs = builder.construct_integrals(integrand_exprs, params)
         builder.stash_integrals(integral_exprs, params, ctx)
     return builder.construct_kernel(kernel_name, ctx, parameters["add_petsc_events"])
@@ -227,7 +256,8 @@ def compile_expression_dual_evaluation(expression, to_element, ufl_element, *,
     builder.set_constants(constants)
 
     # Split mixed coefficients
-    expression = ufl_utils.split_coefficients(expression, builder.coefficient_split)
+    coeff_splitter = CoefficientSplitter(builder.coefficient_split)
+    expression = coeff_splitter(expression)
 
     # Set up kernel config for translation of UFL expression to gem
     kernel_cfg = dict(interface=builder,
diff --git a/tsfc/kernel_interface/common.py b/tsfc/kernel_interface/common.py
index ae04a48878..f2369cec0d 100644
--- a/tsfc/kernel_interface/common.py
+++ b/tsfc/kernel_interface/common.py
@@ -14,7 +14,7 @@
 from gem.optimise import constant_fold_zero
 from gem.optimise import remove_componenttensors as prune
 from numpy import asarray
-from tsfc import fem, ufl_utils
+from tsfc import fem
 from finat.element_factory import as_fiat_cell, create_element
 from finat.ufl import MixedElement
 from tsfc.kernel_interface import KernelInterface
@@ -132,9 +132,6 @@ def compile_integrand(self, integrand, params, ctx):
 
         See :meth:`create_context` for typical calling sequence.
         """
-        # Split Coefficients
-        if self.coefficient_split:
-            integrand = ufl_utils.split_coefficients(integrand, self.coefficient_split)
         # Compile: ufl -> gem
         info = self.integral_data_info
         functions = [*info.arguments, self.coordinate(info.domain), *info.coefficients]
diff --git a/tsfc/kernel_interface/firedrake_loopy.py b/tsfc/kernel_interface/firedrake_loopy.py
index c767fc976e..f13a7d1e33 100644
--- a/tsfc/kernel_interface/firedrake_loopy.py
+++ b/tsfc/kernel_interface/firedrake_loopy.py
@@ -1,9 +1,7 @@
 import numpy
 from collections import namedtuple, OrderedDict
-from functools import partial
 
 from ufl import Coefficient, FunctionSpace
-from ufl.domain import extract_unique_domain
 from finat.ufl import MixedElement as ufl_MixedElement, FiniteElement
 
 import gem
@@ -25,10 +23,6 @@
                                                    'flop_count', 'event'])
 
 
-def make_builder(*args, **kwargs):
-    return partial(KernelBuilder, *args, **kwargs)
-
-
 class Kernel:
     __slots__ = ("ast", "arguments", "integral_type", "oriented", "subdomain_id",
                  "domain_number", "needs_cell_sizes", "tabulations",
@@ -253,7 +247,7 @@ class KernelBuilder(KernelBuilderBase, KernelBuilderMixin):
     """Helper class for building a :class:`Kernel` object."""
 
     def __init__(self, integral_data_info, scalar_type,
-                 dont_split=(), diagonal=False):
+                 diagonal=False):
         """Initialise a kernel builder."""
         integral_type = integral_data_info.integral_type
         super(KernelBuilder, self).__init__(scalar_type, integral_type.startswith("interior_facet"))
@@ -261,9 +255,7 @@ def __init__(self, integral_data_info, scalar_type,
 
         self.diagonal = diagonal
         self.local_tensor = None
-        self.coefficient_split = {}
         self.coefficient_number_index_map = OrderedDict()
-        self.dont_split = frozenset(dont_split)
 
         # Facet number
         if integral_type in ['exterior_facet', 'exterior_facet_vert']:
@@ -314,38 +306,20 @@ def set_arguments(self, arguments):
         self.return_variables = return_variables
         self.argument_multiindices = argument_multiindices
 
-    def set_coefficients(self, integral_data, form_data):
-        """Prepare the coefficients of the form.
-
-        :arg integral_data: UFL integral data
-        :arg form_data: UFL form data
-        """
-        # enabled_coefficients is a boolean array that indicates which
-        # of reduced_coefficients the integral requires.
-        n, k = 0, 0
-        for i in range(len(integral_data.enabled_coefficients)):
-            if integral_data.enabled_coefficients[i]:
-                original = form_data.reduced_coefficients[i]
-                coefficient = form_data.function_replace_map[original]
-                if type(coefficient.ufl_element()) == ufl_MixedElement:
-                    if original in self.dont_split:
-                        self.coefficient_split[coefficient] = [coefficient]
-                        self._coefficient(coefficient, f"w_{k}")
-                        self.coefficient_number_index_map[coefficient] = (n, 0)
-                        k += 1
-                    else:
-                        self.coefficient_split[coefficient] = []
-                        for j, element in enumerate(coefficient.ufl_element().sub_elements):
-                            c = Coefficient(FunctionSpace(extract_unique_domain(coefficient), element))
-                            self.coefficient_split[coefficient].append(c)
-                            self._coefficient(c, f"w_{k}")
-                            self.coefficient_number_index_map[c] = (n, j)
-                            k += 1
-                else:
-                    self._coefficient(coefficient, f"w_{k}")
-                    self.coefficient_number_index_map[coefficient] = (n, 0)
+    def set_coefficients(self):
+        """Prepare the coefficients of the form."""
+        info = self.integral_data_info
+        k = 0
+        for n, coeff in enumerate(info.coefficients):
+            if coeff in info.coefficient_split:
+                for i, c in enumerate(info.coefficient_split[coeff]):
+                    self.coefficient_number_index_map[c] = (n, i)
+                    self._coefficient(c, f"w_{k}")
                     k += 1
-                n += 1
+            else:
+                self.coefficient_number_index_map[coeff] = (n, 0)
+                self._coefficient(coeff, f"w_{k}")
+                k += 1
 
     def set_constants(self, constants):
         for i, const in enumerate(constants):
diff --git a/tsfc/ufl_utils.py b/tsfc/ufl_utils.py
index e6e5cc5ac3..18173a9660 100644
--- a/tsfc/ufl_utils.py
+++ b/tsfc/ufl_utils.py
@@ -17,23 +17,19 @@
 from ufl.algorithms.comparison_checker import do_comparison_check
 from ufl.algorithms.remove_complex_nodes import remove_complex_nodes
 from ufl.algorithms.signature import compute_expression_signature
-from ufl.corealg.map_dag import map_expr_dag
 from ufl.corealg.multifunction import MultiFunction
 from ufl.geometry import QuadratureWeight
 from ufl.geometry import Jacobian, JacobianDeterminant, JacobianInverse
-from ufl.classes import (Abs, Argument, CellOrientation, Coefficient,
-                         ComponentTensor, Expr, FloatValue, Division,
-                         MultiIndex, Product,
+from ufl.classes import (Abs, Argument, CellOrientation,
+                         Expr, FloatValue, Division,
+                         Product,
                          ScalarValue, Sqrt, Zero, CellVolume, FacetArea)
-from ufl.domain import extract_unique_domain
-from finat.ufl import MixedElement
 from ufl.utils.sorting import sorted_by_count
+from ufl.domain import extract_unique_domain
 
 from gem.node import MemoizerArg
 
-from tsfc.modified_terminals import (is_modified_terminal,
-                                     analyse_modified_terminal,
-                                     construct_modified_terminal)
+from tsfc.modified_terminals import is_modified_terminal, analyse_modified_terminal
 
 
 preserve_geometry_types = (CellVolume, FacetArea)
@@ -46,6 +42,7 @@ def compute_form_data(form,
                       preserve_geometry_types=preserve_geometry_types,
                       do_apply_restrictions=True,
                       do_estimate_degrees=True,
+                      coefficients_to_split=None,
                       complex_mode=False):
     """Preprocess UFL form in a format suitable for TSFC. Return
     form data.
@@ -62,6 +59,8 @@ def compute_form_data(form,
         preserve_geometry_types=preserve_geometry_types,
         do_apply_restrictions=do_apply_restrictions,
         do_estimate_degrees=do_estimate_degrees,
+        do_replace_functions=True,
+        coefficients_to_split=coefficients_to_split,
         complex_mode=complex_mode
     )
     constants = extract_firedrake_constants(form)
@@ -174,59 +173,6 @@ def _modified_terminal(self, o):
     terminal = _modified_terminal
 
 
-class CoefficientSplitter(MultiFunction, ModifiedTerminalMixin):
-    def __init__(self, split):
-        MultiFunction.__init__(self)
-        self._split = split
-
-    expr = MultiFunction.reuse_if_untouched
-
-    def modified_terminal(self, o):
-        mt = analyse_modified_terminal(o)
-        terminal = mt.terminal
-
-        if not isinstance(terminal, Coefficient):
-            # Only split coefficients
-            return o
-
-        if type(terminal.ufl_element()) != MixedElement:
-            # Only split mixed coefficients
-            return o
-
-        # Reference value expected
-        assert mt.reference_value
-
-        # Derivative indices
-        beta = indices(mt.local_derivatives)
-
-        components = []
-        for subcoeff in self._split[terminal]:
-            # Apply terminal modifiers onto the subcoefficient
-            component = construct_modified_terminal(mt, subcoeff)
-            # Collect components of the subcoefficient
-            for alpha in numpy.ndindex(subcoeff.ufl_element().reference_value_shape):
-                # New modified terminal: component[alpha + beta]
-                components.append(component[alpha + beta])
-        # Repack derivative indices to shape
-        c, = indices(1)
-        return ComponentTensor(as_tensor(components)[c], MultiIndex((c,) + beta))
-
-
-def split_coefficients(expression, split):
-    """Split mixed coefficients, so mixed elements need not be
-    implemented.
-
-    :arg split: A :py:class:`dict` mapping each mixed coefficient to a
-                sequence of subcoefficients.  If None, calling this
-                function is a no-op.
-    """
-    if split is None:
-        return expression
-
-    splitter = CoefficientSplitter(split)
-    return map_expr_dag(splitter, expression)
-
-
 class PickRestriction(MultiFunction, ModifiedTerminalMixin):
     """Pick out parts of an expression with specified restrictions on
     the arguments.

From 5cd91d788fa03298361ed44bd74eba93ca313bc6 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 18 Jun 2025 16:47:54 +0100
Subject: [PATCH 149/222] GTMGPC: use the new Interpolate (#4374)

---
 firedrake/preconditioners/gtmg.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/firedrake/preconditioners/gtmg.py b/firedrake/preconditioners/gtmg.py
index 1d76a13e60..160382e31f 100644
--- a/firedrake/preconditioners/gtmg.py
+++ b/firedrake/preconditioners/gtmg.py
@@ -1,5 +1,9 @@
 from firedrake.petsc import PETSc
 from firedrake.preconditioners.base import PCBase
+from firedrake.parameters import parameters
+from firedrake.interpolation import Interpolate
+from firedrake.solving_utils import _SNESContext
+from firedrake.matrix_free.operators import ImplicitMatrixContext
 import firedrake.dmhooks as dmhooks
 
 
@@ -12,11 +16,7 @@ class GTMGPC(PCBase):
     _prefix = "gt_"
 
     def initialize(self, pc):
-        from firedrake import TestFunction, parameters
-        from firedrake.assemble import get_assembler
-        from firedrake.interpolation import Interpolator
-        from firedrake.solving_utils import _SNESContext
-        from firedrake.matrix_free.operators import ImplicitMatrixContext
+        from firedrake.assemble import assemble, get_assembler
 
         _, P = pc.getOperators()
         appctx = self.get_appctx(pc)
@@ -104,9 +104,9 @@ def initialize(self, pc):
         if interp_petscmat is None:
             # Create interpolation matrix from coarse space to fine space
             fine_space = ctx.J.arguments()[0].function_space()
-            interpolator = Interpolator(TestFunction(coarse_space), fine_space)
-            interpolation_matrix = interpolator.callable()
-            interp_petscmat = interpolation_matrix.handle
+            coarse_test, coarse_trial = coarse_operator.arguments()
+            interp = assemble(Interpolate(coarse_trial, fine_space))
+            interp_petscmat = interp.petscmat
 
         # We set up a PCMG object that uses the constructed interpolation
         # matrix to generate the restriction/prolongation operators.

From dd78d1aed3231c9476d578469f6b7250ac8b9ff5 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Tue, 24 Jun 2025 07:28:15 +0100
Subject: [PATCH 150/222] Use petsc.org instead of mcs.anl.gov for PETSc links
 (#4379)

---
 demos/parallel-printing/parprint.py.rst    | 4 ++--
 docs/notebooks/03-elasticity.ipynb         | 2 +-
 docs/notebooks/08-composable-solvers.ipynb | 4 ++--
 docs/source/boundary_conditions.rst        | 2 +-
 docs/source/checkpointing.rst              | 4 ++--
 docs/source/events.rst                     | 2 +-
 docs/source/firedrake_18.rst               | 2 +-
 docs/source/firedrake_24.rst               | 4 ++--
 docs/source/index.rst                      | 2 +-
 docs/source/install.rst                    | 2 +-
 firedrake/mesh.py                          | 2 +-
 pyop2/types/mat.py                         | 2 +-
 12 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/demos/parallel-printing/parprint.py.rst b/demos/parallel-printing/parprint.py.rst
index cd4b7fd7c8..a39663293f 100644
--- a/demos/parallel-printing/parprint.py.rst
+++ b/demos/parallel-printing/parprint.py.rst
@@ -15,8 +15,8 @@ Run this example in parallel using :math:`P` processes by doing
 
 We start with the usual import but we also import `petsc4py `_
 so that classes ``PETSc.X`` are available.  Here ``X`` is one of the
-`PETSc object types `_,
-including types like `Vec `_::
+`PETSc object types `_,
+including types like `Vec `_::
 
     from firedrake import *
     from firedrake.petsc import PETSc
diff --git a/docs/notebooks/03-elasticity.ipynb b/docs/notebooks/03-elasticity.ipynb
index 85cd065285..15feea6d97 100644
--- a/docs/notebooks/03-elasticity.ipynb
+++ b/docs/notebooks/03-elasticity.ipynb
@@ -264,7 +264,7 @@
    "source": [
     "# Solving bigger problems\n",
     "\n",
-    "Up to now, we've only really solved quite small problems, and therefore haven't really had to worry about tuning the solver. As we increase the size of the problem we're solving, the direct solver approach will no longer be good enough.  Firedrake uses [PETSc](http://www.mcs.anl.gov/petsc) to provide solvers, and uses PETSc solver parameters to control them.\n",
+    "Up to now, we've only really solved quite small problems, and therefore haven't really had to worry about tuning the solver. As we increase the size of the problem we're solving, the direct solver approach will no longer be good enough.  Firedrake uses [PETSc](http://petsc.org) to provide solvers, and uses PETSc solver parameters to control them.\n",
     "\n",
     "Let's dive straight in.  We'll write a function that solves the same elasticity problem, but takes parameters for the number of cells in the mesh, as well as a dictionary of solver options."
    ]
diff --git a/docs/notebooks/08-composable-solvers.ipynb b/docs/notebooks/08-composable-solvers.ipynb
index ccc42c43d2..39e26a0fe7 100644
--- a/docs/notebooks/08-composable-solvers.ipynb
+++ b/docs/notebooks/08-composable-solvers.ipynb
@@ -346,7 +346,7 @@
    "source": [
     "### Block preconditioning\n",
     "\n",
-    "Firedrake hooks up all the necessary machinery to access PETSc's [`PCFIELDSPLIT`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/PC/PCFIELDSPLIT.html#PCFIELDSPLIT) preconditioner. This provides mechanisms for building preconditioners based on block factorisations. The Stokes problem \n",
+    "Firedrake hooks up all the necessary machinery to access PETSc's [`PCFIELDSPLIT`](https://petsc.org/release/manualpages/PC/PCFIELDSPLIT/) preconditioner. This provides mechanisms for building preconditioners based on block factorisations. The Stokes problem \n",
     "$$\n",
     "\\begin{align}\n",
     "  \\nu\\int_\\Omega \\color{#800020}{\\nabla u : \\nabla v}\\,\\mathrm{d}x - \\int_\\Omega\n",
@@ -873,7 +873,7 @@
     "\n",
     "So far, we've only used direct solvers for the blocks. We can also use iterative methods. Here we'll use geometric multigrid to solve\n",
     "\n",
-    "In the same way that Firedrake hooks up solvers such that [`PCFIELDSPLIT`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/PC/PCFIELDSPLIT.html#PCFIELDSPLIT) is enabled, if a problem was defined on a mesh from a `MeshHierarchy`, [`PCMG`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/PC/PCMG.html) and [`SNESFAS`](https://www.mcs.anl.gov/petsc/petsc-current/manualpages/SNES/SNESFASType.html) are also available."
+    "In the same way that Firedrake hooks up solvers such that [`PCFIELDSPLIT`](https://petsc.org/release/manualpages/PC/PCFIELDSPLIT/) is enabled, if a problem was defined on a mesh from a `MeshHierarchy`, [`PCMG`](https://petsc.org/release/manualpages/PC/PCMG/) and [`SNESFAS`](https://petsc.org/release/manualpages/SNESFAS/SNESFAS/) are also available."
    ]
   },
   {
diff --git a/docs/source/boundary_conditions.rst b/docs/source/boundary_conditions.rst
index 0c341544a3..da8d9eefdc 100644
--- a/docs/source/boundary_conditions.rst
+++ b/docs/source/boundary_conditions.rst
@@ -243,7 +243,7 @@ Variational problems
 ~~~~~~~~~~~~~~~~~~~~
 
 Both linear and nonlinear PDEs are solved in residual form in
-Firedrake using the `PETSc SNES interface `_. In the case of linear
+Firedrake using the `PETSc SNES interface `_. In the case of linear
 systems, a single step of Newton is employed. 
 
 In the following we will use ``F`` for the residual :class:`~ufl.form.Form`
diff --git a/docs/source/checkpointing.rst b/docs/source/checkpointing.rst
index 841674b035..770bb08969 100644
--- a/docs/source/checkpointing.rst
+++ b/docs/source/checkpointing.rst
@@ -634,9 +634,9 @@ Instructions for how to do this can be found here_.
 
 .. _HDF5: https://www.hdfgroup.org/HDF5/
 
-.. _PETSc: http://www.mcs.anl.gov/petsc/
+.. _PETSc: https://petsc.org/
 
-.. _Viewer: http://www.mcs.anl.gov/petsc/petsc-current/manualpages/Viewer/index.html
+.. _Viewer: https://petsc.org/release/manualpages/Viewer/
 .. _h5py: http://www.h5py.org
 
 .. _here: https://docs.h5py.org/en/latest/build.html#custom-installation
diff --git a/docs/source/events.rst b/docs/source/events.rst
index 7463562487..5885ec267f 100644
--- a/docs/source/events.rst
+++ b/docs/source/events.rst
@@ -152,7 +152,7 @@ PETSc '18
 ~~~~~~~~~
 
 The Firedrake team hosted PETSc '18 at Imperial College London from
-4-6 Junr 2018. For more information, see `the meeting page `_.
+4-6 Junr 2018. For more information, see `the meeting page `_.
 
 Firedrake tutorial Jan 12 2018
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/source/firedrake_18.rst b/docs/source/firedrake_18.rst
index 55f325ceba..692ef26cc9 100644
--- a/docs/source/firedrake_18.rst
+++ b/docs/source/firedrake_18.rst
@@ -95,7 +95,7 @@ Co-location with PETSc '18 users meeting
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This year, Imperial College is also hosting the `PETSc users
-`_ meeting, which will
+`_ meeting, which will
 be held in the first half of the week: 4-6 June 2018.  if you are
 interested in more general aspects of high-performance computational
 science, and the intricacies of composable solvers, we would encourage
diff --git a/docs/source/firedrake_24.rst b/docs/source/firedrake_24.rst
index 5a36b5f835..de3ddc6c78 100644
--- a/docs/source/firedrake_24.rst
+++ b/docs/source/firedrake_24.rst
@@ -101,9 +101,9 @@ Support
 -------
 
 The conference has been kindly supported by `EPSRC grant EP/W026163/1
-`__ and
+`__ and
 `EPSRC grant EP/W026066/1
-`__.
+`__.
 
 ..
     Talk videos
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 79cbaa7ed5..ddebc3ea9b 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -21,7 +21,7 @@ Features:
 * Expressive specification of any PDE using the Unified Form Language
   from `the FEniCS Project `_.
 * Sophisticated, programmable solvers through seamless coupling with `PETSc
-  `_.
+  `_.
 * Triangular, quadrilateral, and tetrahedral unstructured meshes.
 * Layered meshes of triangular wedges or hexahedra.
 * Vast range of finite element spaces.
diff --git a/docs/source/install.rst b/docs/source/install.rst
index d6ac3e9ab8..55de3456ec 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -566,7 +566,7 @@ package.
 .. _discussion: https://github.com/firedrakeproject/firedrake/discussions
 .. _issue: https://github.com/firedrakeproject/firedrake/issues
 .. _homebrew: https://brew.sh/
-.. _PETSc: https://www.mcs.anl.gov/petsc/
+.. _PETSc: https://petsc.org/
 .. _petsc4py: https://petsc.org/release/petsc4py/reference/petsc4py.html
 .. _venv: https://docs.python.org/3/tutorial/venv.html
 .. _WSL: https://github.com/firedrakeproject/firedrake/wiki/Installing-on-Windows-Subsystem-for-Linux
diff --git a/firedrake/mesh.py b/firedrake/mesh.py
index 0be58d6d95..4d0bbc68c5 100644
--- a/firedrake/mesh.py
+++ b/firedrake/mesh.py
@@ -3482,7 +3482,7 @@ def _pic_swarm_in_mesh(
         would be initialised with ``fields = [("DMSwarmPIC_coor", coordsdim,
         RealType)]``. All fields must have the same number of points. For more
         information see `the DMSWARM API reference
-        _.
+        _.
     :kwarg tolerance: The relative tolerance (i.e. as defined on the reference
         cell) for the distance a point can be from a cell and still be
         considered to be in the cell. Note that this tolerance uses an L1
diff --git a/pyop2/types/mat.py b/pyop2/types/mat.py
index 91eaa90555..d0fb9e2404 100644
--- a/pyop2/types/mat.py
+++ b/pyop2/types/mat.py
@@ -32,7 +32,7 @@ class Sparsity(caching.ObjectCached):
         Sparsity((row_dset, col_dset),
                  [(first_rowmap, first_colmap), (second_rowmap, second_colmap), None])
 
-    .. _MatMPIAIJSetPreallocation: http://www.mcs.anl.gov/petsc/petsc-current/docs/manualpages/Mat/MatMPIAIJSetPreallocation.html
+    .. _MatMPIAIJSetPreallocation: https://petsc.org/release/manualpages/Mat/MatMPIAIJSetPreallocation/
     """
 
     def __init__(self, dsets, maps_and_regions, name=None, nest=None, block_sparse=None, diagonal_block=True):

From 7432acb7a0a7e4a27cd32bffc28b940ce9b95d89 Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Tue, 24 Jun 2025 10:00:02 +0100
Subject: [PATCH 151/222] import get_external_packages from petsctools not
 firedrake.petsc (#4381)

---
 tests/firedrake/conftest.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/firedrake/conftest.py b/tests/firedrake/conftest.py
index 10059578c5..18b5c949ce 100644
--- a/tests/firedrake/conftest.py
+++ b/tests/firedrake/conftest.py
@@ -7,7 +7,8 @@
 os.environ["FIREDRAKE_DISABLE_OPTIONS_LEFT"] = "1"
 
 import pytest
-from firedrake.petsc import PETSc, get_external_packages
+from firedrake.petsc import PETSc
+from petsctools import get_external_packages
 
 
 def _skip_test_dependency(dependency):

From 2e90c7fcf467813f4a9669c221bf3efe8792a85a Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Wed, 25 Jun 2025 12:09:38 +0100
Subject: [PATCH 152/222] remove stale link

---
 docs/source/team.ini | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/team.ini b/docs/source/team.ini
index 308be57a0e..264312f258 100644
--- a/docs/source/team.ini
+++ b/docs/source/team.ini
@@ -101,7 +101,7 @@ Nicolas Loriant:
 Fabio Luporini:
 Scott P. MacLachlan: https://www.math.mun.ca/~smaclachlan/
 Geordie McBain:
-Oliver Meister: https://www5.in.tum.de/wiki/index.php/Dipl.-Inf._Oliver_Meister
+Oliver Meister:
 Eike H. Mueller:
 Alberto Paganini: https://le.ac.uk/people/alberto-paganini
 Francis J. Poulin:

From 1044796a282e43bcbe2d9e8e6499ec9ffeb7b7cd Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 25 Jun 2025 16:12:57 +0100
Subject: [PATCH 153/222] 'Fix' petsc raises (#4384)

* Specialise petsc_raises on Python version (<3.12.11)

Since https://gitlab.com/petsc/petsc/-/commit/4237731afe36b4db0a3147aff0aed51d4657afa2
and the introduction of `petsc_raises`
(https://github.com/firedrakeproject/firedrake/commit/c9d392201d03f1736258a9d8f915e909ca215339)
we have been seeing random failures on CI that I have been unable to
reproduce locally.

I was able to reproduce the stochastic failures with a Docker image
so my best guess is that these are caused by differences in the Python
version (since the PETSc error handling touches a *lot* of the Python
internals for handling exceptions).

The best approach to me seems to be to allow the more permissive
exception check until we reach a Python version that we think should
be fine. We can revisit then if the issue continues.

* update comment
---
 tests/firedrake/conftest.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/tests/firedrake/conftest.py b/tests/firedrake/conftest.py
index 10059578c5..5f3b3a921e 100644
--- a/tests/firedrake/conftest.py
+++ b/tests/firedrake/conftest.py
@@ -1,6 +1,7 @@
 """Global test configuration."""
 
 import os
+import sys
 
 # Disable warnings for missing options when running with pytest as PETSc does
 # not know what to do with the pytest arguments.
@@ -203,8 +204,15 @@ def __enter__(self):
         pass
 
     def __exit__(self, exc_type, exc_val, traceback):
-        if exc_type is PETSc.Error and isinstance(exc_val.__cause__, self.exc_type):
-            return True
+        # There is a bug where 'exc_val' is occasionally 'None'. In my tests
+        # this error only exists for Python < 3.12.11.
+        if exc_type is PETSc.Error:
+            if sys.version_info < (3, 12, 11):
+                if exc_val is None or isinstance(exc_val.__cause__, self.exc_type):
+                    return True
+            else:
+                if isinstance(exc_val.__cause__, self.exc_type):
+                    return True
 
 
 @pytest.fixture

From a116dd8ecca48c177b318b1e5b6ed190cab7e641 Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Fri, 27 Jun 2025 13:20:44 +0100
Subject: [PATCH 154/222] remove erroneous docs file

---
 AUTHORS.rst | 176 ----------------------------------------------------
 1 file changed, 176 deletions(-)
 delete mode 100644 AUTHORS.rst

diff --git a/AUTHORS.rst b/AUTHORS.rst
deleted file mode 100644
index a7b4a25fb2..0000000000
--- a/AUTHORS.rst
+++ /dev/null
@@ -1,176 +0,0 @@
-..
-   This file is generated by team2.py using the team.rst_t template and team.ini.
-      DO NOT EDIT DIRECTLY
-   To add your name and/or institution edit the relevant sections of
-      docs/source/team.ini
-
-=======
-AUTHORS
-=======
-
-Firedrake has been contributed to by the following:
-
-Institutions
-------------
-
-Baylor University.............
-
-Durham University.............
-
-Imperial College London.......
-
-University of Bath............
-
-University of Leeds...........
-
-University of Oxford..........
-
-University of Washington......
-
-
-
-Individuals
------------
-
-Julian Andrej
-
-Kaito Arai
-
-Nicolas Barral................
-
-Nicholas Barton
-
-Thomas M. Bendall
-
-Gheorghe-Teodor Bercea
-
-Jack Betteridge...............
-
-George Boutsioukis
-
-Nacime Bouziani...............
-
-Romain Brault
-
-Pablo D. Brubeck..............
-
-Ed Bueler
-
-Henrik Buesing
-
-Justin Chang
-
-Cyrus Cheng
-
-Teodoro Fields Collin
-
-Colin J. Cotter...............
-
-Joshua Coutinho
-
-Daiane I. Dolci...............
-
-Patrick E. Farrell............
-
-Simon W. Funke................
-
-Melina Giagiozis
-
-Thomas H. Gibson
-
-Tim Greaves
-
-Alastair Gregory
-
-David A. Ham..................
-
-Christopher Hawkes
-
-Miklós Homolya
-
-Joshua Hope-Collins...........
-
-Christian T. Jacobs
-
-Darko Janeković
-
-Nick Johnson
-
-Anna Kalogirou
-
-Paul H. J. Kelly..............
-
-Robert C. Kirby...............
-
-Stephan C. Kramer.............
-
-Tuomas Kärnä..................
-
-Michael Lange.................
-
-Nicolas Loriant
-
-Fabio Luporini................
-
-Scott P. MacLachlan...........
-
-Graham R. Markall.............
-
-India Marsden.................
-
-Geordie McBain
-
-Andrew T. T. McRae
-
-Oliver Meister................
-
-Lawrence Mitchell.............
-
-Eike H. Mueller
-
-Reuben W. Nixon-Hill..........
-
-Alberto Paganini..............
-
-Francis J. Poulin
-
-Florian Rathgeber.............
-
-Asbjørn Nilsen Riseth
-
-Hannah Rittich
-
-Thomas Roy
-
-Francis P. Russell
-
-Koki Sagiyama.................
-
-Tomasz J. Salwa
-
-Kaho Sato
-
-Matthew Scroggs...............
-
-Ben Sepanski..................
-
-Daniel R. Shapero.............
-
-Jemma Shipton
-
-Tianjiao Sun..................
-
-Sophia Vorderwuelbecke........
-
-Joseph G. Wallwork............
-
-Connor J. Ward................
-
-Florian Wechsung..............
-
-Yian Zeng
-
-Umberto Zerbinati.............
-
-Fangyi Zhou
-

From bc5c842e3839248f3ab7e5916b84b1158032d88b Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Fri, 27 Jun 2025 13:22:27 +0100
Subject: [PATCH 155/222] remove erroneous docs file

---
 AUTHORS.rst | 176 ----------------------------------------------------
 1 file changed, 176 deletions(-)
 delete mode 100644 AUTHORS.rst

diff --git a/AUTHORS.rst b/AUTHORS.rst
deleted file mode 100644
index a7b4a25fb2..0000000000
--- a/AUTHORS.rst
+++ /dev/null
@@ -1,176 +0,0 @@
-..
-   This file is generated by team2.py using the team.rst_t template and team.ini.
-      DO NOT EDIT DIRECTLY
-   To add your name and/or institution edit the relevant sections of
-      docs/source/team.ini
-
-=======
-AUTHORS
-=======
-
-Firedrake has been contributed to by the following:
-
-Institutions
-------------
-
-Baylor University.............
-
-Durham University.............
-
-Imperial College London.......
-
-University of Bath............
-
-University of Leeds...........
-
-University of Oxford..........
-
-University of Washington......
-
-
-
-Individuals
------------
-
-Julian Andrej
-
-Kaito Arai
-
-Nicolas Barral................
-
-Nicholas Barton
-
-Thomas M. Bendall
-
-Gheorghe-Teodor Bercea
-
-Jack Betteridge...............
-
-George Boutsioukis
-
-Nacime Bouziani...............
-
-Romain Brault
-
-Pablo D. Brubeck..............
-
-Ed Bueler
-
-Henrik Buesing
-
-Justin Chang
-
-Cyrus Cheng
-
-Teodoro Fields Collin
-
-Colin J. Cotter...............
-
-Joshua Coutinho
-
-Daiane I. Dolci...............
-
-Patrick E. Farrell............
-
-Simon W. Funke................
-
-Melina Giagiozis
-
-Thomas H. Gibson
-
-Tim Greaves
-
-Alastair Gregory
-
-David A. Ham..................
-
-Christopher Hawkes
-
-Miklós Homolya
-
-Joshua Hope-Collins...........
-
-Christian T. Jacobs
-
-Darko Janeković
-
-Nick Johnson
-
-Anna Kalogirou
-
-Paul H. J. Kelly..............
-
-Robert C. Kirby...............
-
-Stephan C. Kramer.............
-
-Tuomas Kärnä..................
-
-Michael Lange.................
-
-Nicolas Loriant
-
-Fabio Luporini................
-
-Scott P. MacLachlan...........
-
-Graham R. Markall.............
-
-India Marsden.................
-
-Geordie McBain
-
-Andrew T. T. McRae
-
-Oliver Meister................
-
-Lawrence Mitchell.............
-
-Eike H. Mueller
-
-Reuben W. Nixon-Hill..........
-
-Alberto Paganini..............
-
-Francis J. Poulin
-
-Florian Rathgeber.............
-
-Asbjørn Nilsen Riseth
-
-Hannah Rittich
-
-Thomas Roy
-
-Francis P. Russell
-
-Koki Sagiyama.................
-
-Tomasz J. Salwa
-
-Kaho Sato
-
-Matthew Scroggs...............
-
-Ben Sepanski..................
-
-Daniel R. Shapero.............
-
-Jemma Shipton
-
-Tianjiao Sun..................
-
-Sophia Vorderwuelbecke........
-
-Joseph G. Wallwork............
-
-Connor J. Ward................
-
-Florian Wechsung..............
-
-Yian Zeng
-
-Umberto Zerbinati.............
-
-Fangyi Zhou
-

From 7349d96d07c74f47e0e730f71e39b41223a57a31 Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Fri, 27 Jun 2025 16:02:12 +0100
Subject: [PATCH 156/222] mpecdt now behind authorisation check

---
 AUTHORS.rst         | 176 ++++++++++++++++++++++++++++++++++++++++++++
 docs/source/conf.py |   1 +
 2 files changed, 177 insertions(+)
 create mode 100644 AUTHORS.rst

diff --git a/AUTHORS.rst b/AUTHORS.rst
new file mode 100644
index 0000000000..a7b4a25fb2
--- /dev/null
+++ b/AUTHORS.rst
@@ -0,0 +1,176 @@
+..
+   This file is generated by team2.py using the team.rst_t template and team.ini.
+      DO NOT EDIT DIRECTLY
+   To add your name and/or institution edit the relevant sections of
+      docs/source/team.ini
+
+=======
+AUTHORS
+=======
+
+Firedrake has been contributed to by the following:
+
+Institutions
+------------
+
+Baylor University.............
+
+Durham University.............
+
+Imperial College London.......
+
+University of Bath............
+
+University of Leeds...........
+
+University of Oxford..........
+
+University of Washington......
+
+
+
+Individuals
+-----------
+
+Julian Andrej
+
+Kaito Arai
+
+Nicolas Barral................
+
+Nicholas Barton
+
+Thomas M. Bendall
+
+Gheorghe-Teodor Bercea
+
+Jack Betteridge...............
+
+George Boutsioukis
+
+Nacime Bouziani...............
+
+Romain Brault
+
+Pablo D. Brubeck..............
+
+Ed Bueler
+
+Henrik Buesing
+
+Justin Chang
+
+Cyrus Cheng
+
+Teodoro Fields Collin
+
+Colin J. Cotter...............
+
+Joshua Coutinho
+
+Daiane I. Dolci...............
+
+Patrick E. Farrell............
+
+Simon W. Funke................
+
+Melina Giagiozis
+
+Thomas H. Gibson
+
+Tim Greaves
+
+Alastair Gregory
+
+David A. Ham..................
+
+Christopher Hawkes
+
+Miklós Homolya
+
+Joshua Hope-Collins...........
+
+Christian T. Jacobs
+
+Darko Janeković
+
+Nick Johnson
+
+Anna Kalogirou
+
+Paul H. J. Kelly..............
+
+Robert C. Kirby...............
+
+Stephan C. Kramer.............
+
+Tuomas Kärnä..................
+
+Michael Lange.................
+
+Nicolas Loriant
+
+Fabio Luporini................
+
+Scott P. MacLachlan...........
+
+Graham R. Markall.............
+
+India Marsden.................
+
+Geordie McBain
+
+Andrew T. T. McRae
+
+Oliver Meister................
+
+Lawrence Mitchell.............
+
+Eike H. Mueller
+
+Reuben W. Nixon-Hill..........
+
+Alberto Paganini..............
+
+Francis J. Poulin
+
+Florian Rathgeber.............
+
+Asbjørn Nilsen Riseth
+
+Hannah Rittich
+
+Thomas Roy
+
+Francis P. Russell
+
+Koki Sagiyama.................
+
+Tomasz J. Salwa
+
+Kaho Sato
+
+Matthew Scroggs...............
+
+Ben Sepanski..................
+
+Daniel R. Shapero.............
+
+Jemma Shipton
+
+Tianjiao Sun..................
+
+Sophia Vorderwuelbecke........
+
+Joseph G. Wallwork............
+
+Connor J. Ward................
+
+Florian Wechsung..............
+
+Yian Zeng
+
+Umberto Zerbinati.............
+
+Fangyi Zhou
+
diff --git a/docs/source/conf.py b/docs/source/conf.py
index ca8ce26a4f..4c93ff3cd3 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -165,6 +165,7 @@
     r'https://www.crosscountrytrains.co.uk/',
     r'https://www.siam.org/',
     r'https://aims.ac.rw',
+    r'https://mpecdt.ac.uk',
 ]
 linkcheck_timeout = 30
 

From 9bc79e069cc0cf1fe87860010461f250ff952cef Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Fri, 27 Jun 2025 18:40:44 +0100
Subject: [PATCH 157/222] modified AUTHORS.rst

---
 AUTHORS.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index a7b4a25fb2..d8787ed90e 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -122,7 +122,7 @@ Geordie McBain
 
 Andrew T. T. McRae
 
-Oliver Meister................
+Oliver Meister
 
 Lawrence Mitchell.............
 

From 22e6730ac9ff5411c0d3f0334ce821faf253632e Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Mon, 30 Jun 2025 19:37:45 +0100
Subject: [PATCH 158/222] loosen unnecessarily tight tolerances on tao
 optimisation tests

---
 tests/firedrake/adjoint/test_optimisation.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/tests/firedrake/adjoint/test_optimisation.py b/tests/firedrake/adjoint/test_optimisation.py
index 80fc476f51..f7bbaf96ce 100644
--- a/tests/firedrake/adjoint/test_optimisation.py
+++ b/tests/firedrake/adjoint/test_optimisation.py
@@ -68,18 +68,20 @@ def test_petsc_roundtrip_multiple():
 def minimize_tao_lmvm(rf):
     problem = MinimizationProblem(rf)
     solver = TAOSolver(problem, {"tao_type": "lmvm",
-                                 "tao_gatol": 1.0e-7,
+                                 "tao_gatol": 1.0e-6,
                                  "tao_grtol": 0.0,
-                                 "tao_gttol": 0.0})
+                                 "tao_gttol": 1.0e-6,
+                                 "tao_monitor": None})
     return solver.solve()
 
 
 def minimize_tao_nls(rf):
     problem = MinimizationProblem(rf)
     solver = TAOSolver(problem, {"tao_type": "nls",
-                                 "tao_gatol": 1.0e-7,
+                                 "tao_gatol": 1.0e-6,
                                  "tao_grtol": 0.0,
-                                 "tao_gttol": 0.0})
+                                 "tao_gttol": 1.0e-6,
+                                 "tao_monitor": None})
     return solver.solve()
 
 

From f5ec7d1ae5707a31357efdb47e65a789e4243309 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Tue, 1 Jul 2025 12:16:02 +0100
Subject: [PATCH 159/222] Fix Cython build for more restrictive compilers
 (#4407)

---
 firedrake/cython/dmcommon.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/firedrake/cython/dmcommon.pyx b/firedrake/cython/dmcommon.pyx
index e1666425a4..c3ee78600b 100644
--- a/firedrake/cython/dmcommon.pyx
+++ b/firedrake/cython/dmcommon.pyx
@@ -3517,7 +3517,7 @@ cdef int DMPlexGetAdjacency_Closure_Star_Ridge(
 
     """
     cdef:
-        const PetscInt *star = NULL;
+        PetscInt *star = NULL;
         PetscInt numAdj = 0
         PetscInt maxAdjSize = adjSize[0]
         PetscInt starSize

From e286da3b2ca30284b60eb3efbc0749238a2534a1 Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Tue, 1 Jul 2025 12:16:56 +0100
Subject: [PATCH 160/222] CI: Only install docs system dependencies when the
 docs will be built. (#4416)

---
 .github/workflows/core.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index c1e7e25a59..c2b7aae6d5 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -368,7 +368,7 @@ jobs:
           path: pytest_*.log
 
       - name: Install system dependencies (3)
-        if: always()
+        if: (success() || steps.install.conclusion == 'success') && matrix.arch == 'default'
         run: apt-get -y install inkscape texlive-full
 
       - name: Check bibtex

From 8b974e17264c008d263dc723cdd3c9c474a6d824 Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Tue, 1 Jul 2025 12:17:49 +0100
Subject: [PATCH 161/222] remove stale link (#4417)

---
 docs/source/team.ini | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/team.ini b/docs/source/team.ini
index 308be57a0e..264312f258 100644
--- a/docs/source/team.ini
+++ b/docs/source/team.ini
@@ -101,7 +101,7 @@ Nicolas Loriant:
 Fabio Luporini:
 Scott P. MacLachlan: https://www.math.mun.ca/~smaclachlan/
 Geordie McBain:
-Oliver Meister: https://www5.in.tum.de/wiki/index.php/Dipl.-Inf._Oliver_Meister
+Oliver Meister:
 Eike H. Mueller:
 Alberto Paganini: https://le.ac.uk/people/alberto-paganini
 Francis J. Poulin:

From 310d28cef2a090110723f780bb22203eb327635e Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 3 Jul 2025 09:19:18 +0100
Subject: [PATCH 162/222] Fixes for changes to petsctools OptionsManager API
 (#4418)

---
 docs/source/optimising.rst                            |  4 ++--
 firedrake/__init__.py                                 |  2 +-
 .../regression/test_solvers_options_prefix.py         | 11 ++++++-----
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/docs/source/optimising.rst b/docs/source/optimising.rst
index 93a43e5cad..305a4889c6 100644
--- a/docs/source/optimising.rst
+++ b/docs/source/optimising.rst
@@ -136,11 +136,11 @@ Caveats
 
   .. code-block:: python
 
-    from petsctools import OptionsManager
+    import petsctools
 
     # If the -log_view flag is passed you don't need to call
     # PETSc.Log.begin because it is done automatically.
-    if "log_view" not in OptionsManager.commandline_options:
+    if "log_view" not in petsctools.get_commandline_options():
         PETSc.Log.begin()
 
 Common performance issues
diff --git a/firedrake/__init__.py b/firedrake/__init__.py
index 5c975f8770..4f97abd898 100644
--- a/firedrake/__init__.py
+++ b/firedrake/__init__.py
@@ -32,7 +32,7 @@ def init_petsc():
 # Initialise PETSc events for both import and entire duration of program
 import petsctools
 from firedrake import petsc
-_is_logging = "log_view" in petsctools.OptionsManager.commandline_options
+_is_logging = "log_view" in petsctools.get_commandline_options()
 if _is_logging:
     _main_event = petsc.PETSc.Log.Event("firedrake")
     _main_event.begin()
diff --git a/tests/firedrake/regression/test_solvers_options_prefix.py b/tests/firedrake/regression/test_solvers_options_prefix.py
index 396df42983..80361f554e 100644
--- a/tests/firedrake/regression/test_solvers_options_prefix.py
+++ b/tests/firedrake/regression/test_solvers_options_prefix.py
@@ -1,8 +1,9 @@
+import petsctools.options
+import pytest
+
 from firedrake import *
 from firedrake.matrix import ImplicitMatrix
 from firedrake.petsc import PETSc
-from petsctools import OptionsManager
-import pytest
 
 
 @pytest.fixture(params=[None, "", "foo_"])
@@ -32,13 +33,13 @@ def opts(request, prefix, global_parameters):
         opts[prefix + k] = v
 
     # Pretend these came from the commandline
-    OptionsManager.commandline_options = frozenset(opts.getAll())
+    petsctools.options._commandline_options = frozenset(opts.getAll())
 
     def finalize():
+        # And remove again
         for k in global_parameters.keys():
             del opts[prefix + k]
-        # And remove again
-        OptionsManager.commandline_options = frozenset(opts.getAll())
+        petsctools.options._commandline_options = frozenset(opts.getAll())
 
     request.addfinalizer(finalize)
 

From 1829d994ffbcbe476cbf012b6d5143cb15966196 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 3 Jul 2025 13:59:03 +0100
Subject: [PATCH 163/222] Bump PETSc and SLEPc versions (#4419)

---
 pyproject.toml              | 10 +++++-----
 scripts/firedrake-configure |  2 +-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5844da9bca..e025906aee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,7 @@ dependencies = [
   "h5py>3.12.1",
   "libsupermesh",
   "loopy>2024.1",
-  "petsc4py==3.23.3",
+  "petsc4py==3.23.4",
   "numpy",
   "packaging",
   "pkgconfig",
@@ -89,7 +89,7 @@ netgen = [
   "ngsPETSc",
 ]
 slepc = [
-  "slepc4py==3.23.1",
+  "slepc4py==3.23.2",
 ]
 torch = [  # requires passing '--extra-index-url' to work
   "torch",
@@ -111,7 +111,7 @@ ci = [
   "pytest-split",  # needed for firedrake-run-split-tests
   "pytest-timeout",
   "pytest-xdist",
-  "slepc4py==3.23.1",
+  "slepc4py==3.23.2",
   "torch",  # requires passing '--extra-index-url' to work
   "vtk",
 ]
@@ -125,7 +125,7 @@ docker = [  # Used in firedrake-vanilla container
   "pytest-split",  # needed for firedrake-run-split-tests
   "pytest-timeout",
   "pytest-xdist",
-  "slepc4py==3.23.1",
+  "slepc4py==3.23.2",
 ]
 
 [build-system]
@@ -138,7 +138,7 @@ requires = [
   "pkgconfig",
   "pybind11",
   "setuptools>=77.0.3",
-  "petsc4py==3.23.3",
+  "petsc4py==3.23.4",
   "rtree>=1.2",
 ]
 build-backend = "setuptools.build_meta"
diff --git a/scripts/firedrake-configure b/scripts/firedrake-configure
index bab1fc839b..8f74642951 100755
--- a/scripts/firedrake-configure
+++ b/scripts/firedrake-configure
@@ -39,7 +39,7 @@ ARCH_DEFAULT = FiredrakeArch.DEFAULT
 ARCH_COMPLEX = FiredrakeArch.COMPLEX
 
 
-SUPPORTED_PETSC_VERSION = "v3.23.3"
+SUPPORTED_PETSC_VERSION = "v3.23.4"
 
 
 def main():

From 8f0f02d6d1d688316639ea326b1d223d69891288 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 3 Jul 2025 14:17:44 +0100
Subject: [PATCH 164/222] Fix PyPI publishing workflow

---
 .github/workflows/core.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index b11acbe1e6..e3b912c20f 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -569,8 +569,6 @@ jobs:
     runs-on: ubuntu-latest
     environment:
       name: pypi
-    permissions:
-      id-token: write
     steps:
       - uses: actions/download-artifact@v4
         with:
@@ -578,6 +576,8 @@ jobs:
           path: dist
       - name: Push to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          password: ${{ secrets.PYPI_API_TOKEN }}
 
   upload_testpypi:
     name: Upload to TestPyPI (optional)

From 11b13a461562148cf494f8238a5bd52cfd0a63d8 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 3 Jul 2025 14:19:14 +0100
Subject: [PATCH 165/222] Bump version number to 2025.4.2

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index e025906aee..7562610df4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "firedrake"
 # ..
-version = "2025.4.1"
+version = "2025.4.2"
 description = "An automated system for the portable solution of partial differential equations using the finite element method"
 readme = "README.rst"
 license = "LGPL-3.0-or-later"

From 6f853a7370f6ef730a6e6e3bcf01089423abe50d Mon Sep 17 00:00:00 2001
From: Leo Collins 
Date: Thu, 3 Jul 2025 14:46:19 +0100
Subject: [PATCH 166/222] Move `__future__` interpolation into
 `interpolation.py` (#4346)

* replace `Interpolator.__new__`

* change `Interpolator._interpolate_future` to `Interpolator.interpolate`

* replace `interpolate` function

* update `interpolate` docstring

* remove `firedrake.__future__` imports

* delete contents of `__future__`

* remove instruction to import `__future__.interpolate` from interpolation docs

* update `Interpolator.interpolate` docstring

* update benny_luke demo

* update full_waveform_inversion demo

* add deprecation warning to `__future__.interpolate`

* wrap `Interpolate` class in `__future__` with deprecation warning
---
 demos/benney_luke/benney_luke.py.rst          |  14 +--
 .../full_waveform_inversion.py.rst            |   2 -
 .../higher_order_mass_lumping.py.rst          |   2 +-
 docs/notebooks/01-spd-helmholtz.ipynb         |   3 +-
 docs/notebooks/03-elasticity.ipynb            |   2 +-
 .../06-pde-constrained-optimisation.ipynb     |   2 +-
 docs/notebooks/07-geometric-multigrid.ipynb   |   4 +-
 .../11-extract-adjoint-solutions.ipynb        |   2 +-
 docs/source/interpolation.rst                 |  12 --
 firedrake/__future__.py                       |  54 ++------
 firedrake/interpolation.py                    | 117 +++---------------
 firedrake/preconditioners/hypre_ads.py        |   2 +-
 firedrake/preconditioners/hypre_ams.py        |  11 +-
 tests/firedrake/adjoint/test_assignment.py    |   1 -
 .../adjoint/test_disk_checkpointing.py        |   1 -
 tests/firedrake/adjoint/test_projection.py    |   1 -
 .../adjoint/test_shape_derivatives.py         |   1 -
 .../extrusion/test_variable_layers_bcs.py     |   1 -
 .../test_variable_layers_numbering.py         |   1 -
 .../extrusion/test_variable_layers_poisson.py |   1 -
 .../test_variable_layers_steady_advection.py  |   1 -
 tests/firedrake/macro/test_macro_multigrid.py |   1 -
 .../firedrake/multigrid/test_grid_transfer.py |   1 -
 tests/firedrake/output/test_plotting.py       |   1 -
 .../regression/test_adjoint_operators.py      |   1 -
 tests/firedrake/regression/test_cellorigin.py |   1 -
 .../firedrake/regression/test_interp_dual.py  |   1 -
 .../firedrake/regression/test_interpolate.py  |   1 -
 .../regression/test_interpolate_cross_mesh.py |   1 -
 .../test_interpolate_p3intmoments.py          |   1 -
 .../regression/test_interpolate_vs_project.py |   1 -
 .../regression/test_interpolation_manual.py   |   1 -
 .../regression/test_interpolation_nodes.py    |   1 -
 tests/firedrake/regression/test_netgen.py     |   1 -
 tests/firedrake/regression/test_norm.py       |   1 -
 tests/firedrake/regression/test_nullspace.py  |   1 -
 .../regression/test_octahedral_hemisphere.py  |   1 -
 tests/firedrake/regression/test_par_loops.py  |   1 -
 .../regression/test_project_interp_KMV.py     |   1 -
 tests/firedrake/regression/test_real_space.py |   1 -
 tests/firedrake/slate/test_facet_tensors.py   |   1 -
 .../slate/test_facet_tensors_extr.py          |   1 -
 .../test_interpolation_from_parent.py         |   1 -
 .../test_point_eval_immersed_manifold.py      |   1 -
 .../test_poisson_inverse_conductivity.py      |   1 -
 tests/firedrake/vertexonly/test_swarm.py      |   1 -
 .../vertexonly/test_vertex_only_fs.py         |   1 -
 .../vertexonly/test_vertex_only_manual.py     |   1 -
 48 files changed, 46 insertions(+), 216 deletions(-)

diff --git a/demos/benney_luke/benney_luke.py.rst b/demos/benney_luke/benney_luke.py.rst
index 2931926249..0d035af52e 100644
--- a/demos/benney_luke/benney_luke.py.rst
+++ b/demos/benney_luke/benney_luke.py.rst
@@ -177,14 +177,8 @@ and define the exact solutions, which need to be updated at every time-step::
   expr_eta = 1/3.0*c*pow(cosh(0.5*sqrt(c*epsilon/mu)*(x[0]-x0-t_-epsilon*c*t_/6.0)),-2)
   expr_phi = 2/3.0*sqrt(c*mu/epsilon)*(tanh(0.5*sqrt(c*epsilon/mu)*(x[0]-x0-t_-epsilon*c*t_/6.0))+1)
 
-Since we will interpolate these values again and again, we use an
-:class:`~.Interpolator` whose :meth:`~.Interpolator.interpolate`
-method we can call to perform the interpolation. ::
-
-  eta_interpolator = Interpolator(expr_eta, ex_eta)
-  phi_interpolator = Interpolator(expr_phi, ex_phi)
-  phi_interpolator.interpolate()
-  eta_interpolator.interpolate()
+  ex_eta.interpolate(expr_eta)
+  ex_phi.interpolate(expr_phi)
 
 For visualisation, we save the computed and exact solutions to
 an output file.  Note that the visualised data will be interpolated
@@ -201,8 +195,8 @@ We are now ready to enter the main time iteration loop::
 
         t_.assign(t)
 
-        eta_interpolator.interpolate()
-        phi_interpolator.interpolate()
+        ex_eta.interpolate(expr_eta)
+        ex_phi.interpolate(expr_phi)
 
         phi_solver_h.solve()
         q_solver_h.solve()
diff --git a/demos/full_waveform_inversion/full_waveform_inversion.py.rst b/demos/full_waveform_inversion/full_waveform_inversion.py.rst
index eb4c135756..fcc2ffaadc 100644
--- a/demos/full_waveform_inversion/full_waveform_inversion.py.rst
+++ b/demos/full_waveform_inversion/full_waveform_inversion.py.rst
@@ -217,8 +217,6 @@ The receiver mesh is required in order to interpolate the wave equation solution
 
 We are now able to proceed with the synthetic data computations and record them on the receivers::
 
-    from firedrake.__future__ import interpolate
-
     true_data_receivers = []
     total_steps = int(final_time / dt) + 1
     f = Cofunction(V.dual())  # Wave equation forcing term.
diff --git a/demos/higher_order_mass_lumping/higher_order_mass_lumping.py.rst b/demos/higher_order_mass_lumping/higher_order_mass_lumping.py.rst
index 3f4e3bde28..193047aa27 100644
--- a/demos/higher_order_mass_lumping/higher_order_mass_lumping.py.rst
+++ b/demos/higher_order_mass_lumping/higher_order_mass_lumping.py.rst
@@ -135,7 +135,7 @@ Using Firedrake, we specify the mass matrix using the special quadrature rule wi
     m = (u - 2.0 * u_n + u_nm1) / Constant(dt * dt) * v * dxlump
 
 .. note::
-    Mass lumping is a common technique in finite elements to produce a diagonal mass matrix that can be trivially inverted resulting in a in very efficient explicit time integration scheme. It's usually done with nodal basis functions and an inexact quadrature rule for the mass matrix. A diagonal matrix is obtained when the integration points coincide with the nodes of the basis function. However, when using elements of :math:`p \ge 2`, this technique does not result in a stable and accurate finite element scheme and new elements must be found such as those detailed in :cite:Chin:1999 .
+    Mass lumping is a common technique in finite elements to produce a diagonal mass matrix that can be trivially inverted resulting in a very efficient explicit time integration scheme. It's usually done with nodal basis functions and an inexact quadrature rule for the mass matrix. A diagonal matrix is obtained when the integration points coincide with the nodes of the basis function. However, when using elements of :math:`p \ge 2`, this technique does not result in a stable and accurate finite element scheme and new elements must be found such as those detailed in :cite:Chin:1999 .
 
 The stiffness matrix :math:`a(u,v)` is formed using a standard quadrature rule and is treated explicitly::
 
diff --git a/docs/notebooks/01-spd-helmholtz.ipynb b/docs/notebooks/01-spd-helmholtz.ipynb
index 021ce7fcfc..adb2849b60 100644
--- a/docs/notebooks/01-spd-helmholtz.ipynb
+++ b/docs/notebooks/01-spd-helmholtz.ipynb
@@ -129,8 +129,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from firedrake import *\n",
-    "from firedrake.__future__ import interpolate"
+    "from firedrake import *"
    ]
   },
   {
diff --git a/docs/notebooks/03-elasticity.ipynb b/docs/notebooks/03-elasticity.ipynb
index 15feea6d97..97d290d6f0 100644
--- a/docs/notebooks/03-elasticity.ipynb
+++ b/docs/notebooks/03-elasticity.ipynb
@@ -187,7 +187,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "displaced_coordinates = interpolate(SpatialCoordinate(mesh) + uh, V)\n",
+    "displaced_coordinates = assemble(interpolate(SpatialCoordinate(mesh) + uh, V))\n",
     "displaced_mesh = Mesh(displaced_coordinates)"
    ]
   },
diff --git a/docs/notebooks/06-pde-constrained-optimisation.ipynb b/docs/notebooks/06-pde-constrained-optimisation.ipynb
index 1b3cbda968..0a4e965448 100644
--- a/docs/notebooks/06-pde-constrained-optimisation.ipynb
+++ b/docs/notebooks/06-pde-constrained-optimisation.ipynb
@@ -226,7 +226,7 @@
     "u_inflow = as_vector([y*(10-y)/25.0, 0])\n",
     "\n",
     "noslip = DirichletBC(W.sub(0), (0, 0), (3, 5))\n",
-    "inflow = DirichletBC(W.sub(0), interpolate(u_inflow, V), 1)\n",
+    "inflow = DirichletBC(W.sub(0), assemble(interpolate(u_inflow, V)), 1)\n",
     "static_bcs = [inflow, noslip]"
    ]
   },
diff --git a/docs/notebooks/07-geometric-multigrid.ipynb b/docs/notebooks/07-geometric-multigrid.ipynb
index 3eefe36deb..ace7b7caa2 100644
--- a/docs/notebooks/07-geometric-multigrid.ipynb
+++ b/docs/notebooks/07-geometric-multigrid.ipynb
@@ -211,7 +211,7 @@
     "                       0)\n",
     "\n",
     "    value = as_vector([gbar*(1 - (12*t)**2), 0])\n",
-    "    bcs = [DirichletBC(W.sub(0), interpolate(value, V), (1, 2)),\n",
+    "    bcs = [DirichletBC(W.sub(0), assemble(interpolate(value, V)), (1, 2)),\n",
     "           DirichletBC(W.sub(0), zero(2), (3, 4))]\n",
     "    \n",
     "    a = (nu*inner(grad(u), grad(v)) - p*div(v) + q*div(u))*dx\n",
@@ -524,7 +524,7 @@
     "                       0)\n",
     "\n",
     "    value = as_vector([gbar*(1 - (12*t)**2), 0])\n",
-    "    bcs = [DirichletBC(W.sub(0), interpolate(value, V), (1, 2)),\n",
+    "    bcs = [DirichletBC(W.sub(0), assemble(interpolate(value, V)), (1, 2)),\n",
     "           DirichletBC(W.sub(0), zero(2), (3, 4))]\n",
     "    \n",
     "    a = (nu*inner(grad(u), grad(v)) - p*div(v) + q*div(u))*dx\n",
diff --git a/docs/notebooks/11-extract-adjoint-solutions.ipynb b/docs/notebooks/11-extract-adjoint-solutions.ipynb
index 79b779a96a..c3f9ec0fcb 100644
--- a/docs/notebooks/11-extract-adjoint-solutions.ipynb
+++ b/docs/notebooks/11-extract-adjoint-solutions.ipynb
@@ -133,7 +133,7 @@
     "\n",
     "# Assign initial condition\n",
     "x, y = SpatialCoordinate(mesh)\n",
-    "u_ = interpolate(as_vector([sin(pi*x), 0]), V)\n",
+    "u_ = assemble(interpolate(as_vector([sin(pi*x), 0]), V))\n",
     "u.assign(u_)\n",
     "\n",
     "# Set diffusivity constant\n",
diff --git a/docs/source/interpolation.rst b/docs/source/interpolation.rst
index b079a9e352..66082da969 100644
--- a/docs/source/interpolation.rst
+++ b/docs/source/interpolation.rst
@@ -38,18 +38,6 @@ where :math:`\bar{\phi}^*_i` is the :math:`i`-th dual basis function to
 The interpolate operator
 ------------------------
 
-.. note::
-   The semantics for interpolation in Firedrake are in the course of changing.
-   The documentation provided here is for the new behaviour, in which the
-   `interpolate` operator is symbolic. In order to access the behaviour
-   documented here (which is recommended), users need to use the following
-   import line:
-
-   .. code-block:: python3
-
-      from firedrake.__future__ import interpolate
-
-
 The basic syntax for interpolation is:
 
 .. literalinclude:: ../../tests/firedrake/regression/test_interpolation_manual.py
diff --git a/firedrake/__future__.py b/firedrake/__future__.py
index b0c67effdb..d3656f79ad 100644
--- a/firedrake/__future__.py
+++ b/firedrake/__future__.py
@@ -1,46 +1,16 @@
-from ufl.domain import as_domain, extract_unique_domain
-from ufl.algorithms import extract_arguments
-from firedrake.mesh import MeshTopology, VertexOnlyMeshTopology
-from firedrake.interpolation import (interpolate as interpolate_old,
-                                     Interpolator as InterpolatorOld,
-                                     SameMeshInterpolator as SameMeshInterpolatorOld,
-                                     CrossMeshInterpolator as CrossMeshInterpolatorOld)
-from firedrake.cofunction import Cofunction
-from functools import wraps
+from firedrake.interpolation import interpolate as interpolate_default, Interpolator as Interpolator_default
+import warnings
 
 
-__all__ = ("interpolate", "Interpolator")
-
-
-class Interpolator(InterpolatorOld):
-    def __new__(cls, expr, V, **kwargs):
-        target_mesh = as_domain(V)
-        source_mesh = extract_unique_domain(expr) or target_mesh
-        if target_mesh is source_mesh or all(isinstance(m.topology, MeshTopology) for m in [target_mesh, source_mesh]) and target_mesh.submesh_ancesters[-1] is source_mesh.submesh_ancesters[-1]:
-            return object.__new__(SameMeshInterpolator)
-        else:
-            if isinstance(target_mesh.topology, VertexOnlyMeshTopology):
-                return object.__new__(SameMeshInterpolator)
-            else:
-                return object.__new__(CrossMeshInterpolator)
-
-    interpolate = InterpolatorOld._interpolate_future
-
-
-class SameMeshInterpolator(Interpolator, SameMeshInterpolatorOld):
-    pass
-
-
-class CrossMeshInterpolator(Interpolator, CrossMeshInterpolatorOld):
-    pass
+def interpolate(expr, V, *args, **kwargs):
+    warnings.warn("""The symbolic `interpolate` has been moved into `firedrake`
+                  and is now the default method for interpolating in Firedrake. The need to
+                  `from firedrake.__future__ import interpolate` is now deprecated.""", FutureWarning)
+    return interpolate_default(expr, V, *args, **kwargs)
 
 
-@wraps(interpolate_old)
-def interpolate(expr, V, *args, **kwargs):
-    default_missing_val = kwargs.pop("default_missing_val", None)
-    if isinstance(V, Cofunction):
-        adjoint = bool(extract_arguments(expr))
-        return Interpolator(
-            expr, V.function_space().dual(), *args, **kwargs
-        ).interpolate(V, adjoint=adjoint, default_missing_val=default_missing_val)
-    return Interpolator(expr, V, *args, **kwargs).interpolate(default_missing_val=default_missing_val)
+class Interpolator(Interpolator_default):
+    def __new__(cls, *args, **kwargs):
+        warnings.warn("""The symbolic `Interpolator` has been moved into `firedrake`.
+                      The need to `from firedrake.__future__ import Interpolator` is now deprecated.""", FutureWarning)
+        return Interpolator_default(*args, **kwargs)
diff --git a/firedrake/interpolation.py b/firedrake/interpolation.py
index 351b393394..ccf0a81cfc 100644
--- a/firedrake/interpolation.py
+++ b/firedrake/interpolation.py
@@ -24,9 +24,10 @@
 import firedrake
 from firedrake import tsfc_interface, utils, functionspaceimpl
 from firedrake.ufl_expr import Argument, action, adjoint as expr_adjoint
-from firedrake.mesh import MissingPointsBehaviour, VertexOnlyMeshMissingPointsError
+from firedrake.mesh import MissingPointsBehaviour, VertexOnlyMeshMissingPointsError, VertexOnlyMeshTopology
 from firedrake.petsc import PETSc
 from firedrake.halo import _get_mtype as get_dat_mpi_type
+from firedrake.cofunction import Cofunction
 from mpi4py import MPI
 
 from pyadjoint import stop_annotating, no_annotations
@@ -142,20 +143,13 @@ def _ufl_expr_reconstruct_(self, expr, v=None, **interp_data):
 
 
 @PETSc.Log.EventDecorator()
-def interpolate(
-    expr,
-    V,
-    subset=None,
-    access=op2.WRITE,
-    allow_missing_dofs=False,
-    default_missing_val=None,
-    ad_block_tag=None
-):
-    """Interpolate an expression onto a new function in V.
+def interpolate(expr, V, *args, **kwargs):
+    """Returns a UFL expression for the interpolation operation of ``expr`` into ``V``.
 
     :arg expr: a UFL expression.
     :arg V: the :class:`.FunctionSpace` to interpolate into (or else
-        an existing :class:`.Function`).
+        an existing :class:`.Function` or :class:`.Cofunction`).
+        Adjoint interpolation requires ``V`` to be a :class:`.Cofunction`.
     :kwarg subset: An optional :class:`pyop2.types.set.Subset` to apply the
         interpolation over. Cannot, at present, be used when interpolating
         across meshes unless the target mesh is a :func:`.VertexOnlyMesh`.
@@ -182,8 +176,7 @@ def interpolate(
         to zero. Ignored if interpolating within the same mesh or onto a
         :func:`.VertexOnlyMesh`.
     :kwarg ad_block_tag: An optional string for tagging the resulting assemble block on the Pyadjoint tape.
-    :returns: a new :class:`.Function` in the space ``V`` (or ``V`` if
-        it was a Function).
+    :returns: A synbolic :class:`.Interpolate` object
 
     .. note::
 
@@ -204,9 +197,13 @@ def interpolate(
        performance by using an :class:`Interpolator` instead.
 
     """
-    return Interpolator(
-        expr, V, subset=subset, access=access, allow_missing_dofs=allow_missing_dofs
-    ).interpolate(default_missing_val=default_missing_val, ad_block_tag=ad_block_tag)
+    default_missing_val = kwargs.pop("default_missing_val", None)
+    if isinstance(V, Cofunction):
+        adjoint = bool(extract_arguments(expr))
+        return Interpolator(
+            expr, V.function_space().dual(), *args, **kwargs
+        ).interpolate(V, adjoint=adjoint, default_missing_val=default_missing_val)
+    return Interpolator(expr, V, *args, **kwargs).interpolate(default_missing_val=default_missing_val)
 
 
 class Interpolator(abc.ABC):
@@ -264,7 +261,7 @@ def __new__(cls, expr, V, **kwargs):
         if target_mesh is source_mesh or submesh_interp_implemented:
             return object.__new__(SameMeshInterpolator)
         else:
-            if isinstance(target_mesh.topology, firedrake.mesh.VertexOnlyMeshTopology):
+            if isinstance(target_mesh.topology, VertexOnlyMeshTopology):
                 return object.__new__(SameMeshInterpolator)
             else:
                 return object.__new__(CrossMeshInterpolator)
@@ -296,7 +293,7 @@ def __init__(
             expr = replace(expr, {v: v.reconstruct(number=1)})
         self.expr_renumbered = expr
 
-    def _interpolate_future(self, *function, transpose=None, adjoint=False, default_missing_val=None):
+    def interpolate(self, *function, transpose=None, adjoint=False, default_missing_val=None):
         """Define the :class:`Interpolate` object corresponding to the interpolation operation of interest.
 
         Parameters
@@ -322,11 +319,6 @@ def _interpolate_future(self, *function, transpose=None, adjoint=False, default_
         -------
         firedrake.interpolation.Interpolate or ufl.action.Action or ufl.adjoint.Adjoint
             The symbolic object representing the interpolation operation.
-
-        Notes
-        -----
-        This method is the default future behaviour of interpolation. In a future release, the
-        ``Interpolator.interpolate`` method will be replaced by this method.
         """
 
         V = self.V
@@ -351,79 +343,6 @@ def _interpolate_future(self, *function, transpose=None, adjoint=False, default_
         # Return the `ufl.Interpolate` object
         return interp
 
-    @PETSc.Log.EventDecorator()
-    def interpolate(self, *function, output=None, transpose=None, adjoint=False, default_missing_val=None,
-                    ad_block_tag=None):
-        """Compute the interpolation by assembling the appropriate :class:`Interpolate` object.
-
-        Parameters
-        ----------
-        *function: firedrake.function.Function or firedrake.cofunction.Cofunction
-                   If the expression being interpolated contains an argument,
-                   then the function value to interpolate.
-        output: firedrake.function.Function or firedrake.cofunction.Cofunction
-                A function to contain the output.
-        transpose : bool
-                   Deprecated, use adjoint instead.
-        adjoint: bool
-                   Set to true to apply the adjoint of the interpolation
-                   operator.
-        default_missing_val: bool
-                             For interpolation across meshes: the
-                             optional value to assign to DoFs in the target mesh that are
-                             outside the source mesh. If this is not set then the values are
-                             either (a) unchanged if some ``output`` is specified to the
-                             :meth:`interpolate` method or (b) set to zero. This does not affect
-                             adjoint interpolation. Ignored if interpolating within the same
-                             mesh or onto a :func:`.VertexOnlyMesh`.
-        ad_block_tag: str
-                      An optional string for tagging the resulting assemble block on the Pyadjoint tape.
-
-        Returns
-        -------
-        firedrake.function.Function or firedrake.cofunction.Cofunction
-            The resulting interpolated function.
-        """
-        from firedrake.assemble import assemble
-
-        warnings.warn("""The use of `interpolate` to perform the numerical interpolation is deprecated.
-This feature will be removed very shortly.
-
-Instead, import `interpolate` from the `firedrake.__future__` module to update
-the interpolation's behaviour to return the symbolic `ufl.Interpolate` object associated
-with this interpolation.
-
-You can then assemble the resulting object to get the interpolated quantity
-of interest. For example,
-
-```
-from firedrake.__future__ import interpolate
-...
-
-assemble(interpolate(expr, V))
-```
-
-Alternatively, you can also perform other symbolic operations on the interpolation operator, such as taking
-the derivative, and then assemble the resulting form.
-""", FutureWarning)
-        if transpose is not None:
-            warnings.warn("'transpose' argument is deprecated, use 'adjoint' instead", FutureWarning)
-            adjoint = transpose or adjoint
-
-        # Get the Interpolate object
-        interp = self._interpolate_future(*function, adjoint=adjoint,
-                                          default_missing_val=default_missing_val)
-
-        if isinstance(self.V, firedrake.Function) and not output:
-            # V can be the Function to interpolate into (e.g. see `Function.interpolate``).
-            output = self.V
-
-        # Assemble the `ufl.Interpolate` object, which will then call `Interpolator._interpolate`
-        # to perform the interpolation. Having this structure ensures consistency between
-        # `Interpolator` and `Interp`. This mechanism handles annotation since performing interpolation will drop an
-        # `AssembleBlock` on the tape.
-        return assemble(interp, tensor=output, ad_block_tag=ad_block_tag)
-
     @abc.abstractmethod
     def _interpolate(self, *args, **kwargs):
         """
@@ -715,10 +634,10 @@ def _interpolate(
                     # so the sub_interpolators are already prepared to interpolate
                     # without needing to be given a Function
                     assert not self.nargs
-                    interp = sub_interpolator._interpolate_future(adjoint=adjoint, **kwargs)
+                    interp = sub_interpolator.interpolate(adjoint=adjoint, **kwargs)
                     assemble(interp, tensor=output_sub_func)
                 else:
-                    interp = sub_interpolator._interpolate_future(adjoint=adjoint, **kwargs)
+                    interp = sub_interpolator.interpolate(adjoint=adjoint, **kwargs)
                     assemble(action(interp, f_src_sub_func), tensor=output_sub_func)
             return output
 
diff --git a/firedrake/preconditioners/hypre_ads.py b/firedrake/preconditioners/hypre_ads.py
index 13dc0d1a50..b639c21f05 100644
--- a/firedrake/preconditioners/hypre_ads.py
+++ b/firedrake/preconditioners/hypre_ads.py
@@ -5,7 +5,7 @@
 from firedrake.ufl_expr import TestFunction
 from firedrake.dmhooks import get_function_space
 from firedrake.preconditioners.hypre_ams import chop
-from firedrake.__future__ import interpolate
+from firedrake.interpolation import interpolate
 from ufl import grad, curl, SpatialCoordinate
 from pyop2.utils import as_tuple
 
diff --git a/firedrake/preconditioners/hypre_ams.py b/firedrake/preconditioners/hypre_ams.py
index 9616b142b9..5f995c3d90 100644
--- a/firedrake/preconditioners/hypre_ams.py
+++ b/firedrake/preconditioners/hypre_ams.py
@@ -1,4 +1,4 @@
-import firedrake.assemble as assemble
+import firedrake as fd
 from firedrake.preconditioners.base import PCBase
 from firedrake.petsc import PETSc
 from firedrake.functionspace import FunctionSpace, VectorFunctionSpace
@@ -6,9 +6,6 @@
 from firedrake.dmhooks import get_function_space
 from firedrake.utils import complex_mode
 from firedrake_citations import Citations
-from firedrake import SpatialCoordinate
-from firedrake.__future__ import interpolate
-from ufl import grad
 from pyop2.utils import as_tuple
 
 __all__ = ("HypreAMS",)
@@ -50,7 +47,7 @@ def initialize(self, obj):
         P1 = FunctionSpace(mesh, "Lagrange", 1)
         G_callback = appctx.get("get_gradient", None)
         if G_callback is None:
-            G = chop(assemble.assemble(interpolate(grad(TestFunction(P1)), V)).petscmat)
+            G = chop(fd.assemble(fd.interpolate(fd.grad(TestFunction(P1)), V)).petscmat)
         else:
             G = G_callback(P1, V)
 
@@ -68,8 +65,8 @@ def initialize(self, obj):
             pc.setHYPRESetBetaPoissonMatrix(None)
 
         VectorP1 = VectorFunctionSpace(mesh, "Lagrange", 1)
-        interp = interpolate(SpatialCoordinate(mesh), VectorP1)
-        pc.setCoordinates(assemble.assemble(interp).dat.data_ro.copy())
+        interp = fd.interpolate(fd.SpatialCoordinate(mesh), VectorP1)
+        pc.setCoordinates(fd.assemble(interp).dat.data_ro.copy())
         pc.setFromOptions()
         self.pc = pc
 
diff --git a/tests/firedrake/adjoint/test_assignment.py b/tests/firedrake/adjoint/test_assignment.py
index c4b9fcd467..93bee8f265 100644
--- a/tests/firedrake/adjoint/test_assignment.py
+++ b/tests/firedrake/adjoint/test_assignment.py
@@ -1,7 +1,6 @@
 import pytest
 
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.adjoint import *
 
 from numpy.random import rand
diff --git a/tests/firedrake/adjoint/test_disk_checkpointing.py b/tests/firedrake/adjoint/test_disk_checkpointing.py
index 6c4e5deb45..f264e5cb11 100644
--- a/tests/firedrake/adjoint/test_disk_checkpointing.py
+++ b/tests/firedrake/adjoint/test_disk_checkpointing.py
@@ -1,7 +1,6 @@
 import pytest
 
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.adjoint import *
 from firedrake.adjoint_utils.checkpointing import disk_checkpointing
 from checkpoint_schedules import SingleDiskStorageSchedule
diff --git a/tests/firedrake/adjoint/test_projection.py b/tests/firedrake/adjoint/test_projection.py
index 1b38b1170f..d77e6a9df0 100644
--- a/tests/firedrake/adjoint/test_projection.py
+++ b/tests/firedrake/adjoint/test_projection.py
@@ -1,7 +1,6 @@
 import pytest
 
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.adjoint import *
 
 from numpy.random import rand
diff --git a/tests/firedrake/adjoint/test_shape_derivatives.py b/tests/firedrake/adjoint/test_shape_derivatives.py
index 88cee34f78..cb23522ce5 100644
--- a/tests/firedrake/adjoint/test_shape_derivatives.py
+++ b/tests/firedrake/adjoint/test_shape_derivatives.py
@@ -2,7 +2,6 @@
 
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.adjoint import *
 from pyadjoint import taylor_to_dict
 
diff --git a/tests/firedrake/extrusion/test_variable_layers_bcs.py b/tests/firedrake/extrusion/test_variable_layers_bcs.py
index 6c7ceb4b55..1dc3498415 100644
--- a/tests/firedrake/extrusion/test_variable_layers_bcs.py
+++ b/tests/firedrake/extrusion/test_variable_layers_bcs.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.utils import IntType
 import pytest
 import numpy
diff --git a/tests/firedrake/extrusion/test_variable_layers_numbering.py b/tests/firedrake/extrusion/test_variable_layers_numbering.py
index 326a06a499..e13358f57d 100644
--- a/tests/firedrake/extrusion/test_variable_layers_numbering.py
+++ b/tests/firedrake/extrusion/test_variable_layers_numbering.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.mesh import plex_from_cell_list
 from firedrake.utils import IntType
 
diff --git a/tests/firedrake/extrusion/test_variable_layers_poisson.py b/tests/firedrake/extrusion/test_variable_layers_poisson.py
index 7c3244eb53..7ab9bdac21 100644
--- a/tests/firedrake/extrusion/test_variable_layers_poisson.py
+++ b/tests/firedrake/extrusion/test_variable_layers_poisson.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 import numpy
 from firedrake.utils import IntType
 
diff --git a/tests/firedrake/extrusion/test_variable_layers_steady_advection.py b/tests/firedrake/extrusion/test_variable_layers_steady_advection.py
index f26838bfb9..d8439b73bd 100644
--- a/tests/firedrake/extrusion/test_variable_layers_steady_advection.py
+++ b/tests/firedrake/extrusion/test_variable_layers_steady_advection.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 import numpy
 from firedrake.utils import IntType
 
diff --git a/tests/firedrake/macro/test_macro_multigrid.py b/tests/firedrake/macro/test_macro_multigrid.py
index 8f880bd48d..e3d8b11e9f 100644
--- a/tests/firedrake/macro/test_macro_multigrid.py
+++ b/tests/firedrake/macro/test_macro_multigrid.py
@@ -1,7 +1,6 @@
 import numpy
 import pytest
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.utils import complex_mode
 
 
diff --git a/tests/firedrake/multigrid/test_grid_transfer.py b/tests/firedrake/multigrid/test_grid_transfer.py
index 40b21bb3b2..09c279baec 100644
--- a/tests/firedrake/multigrid/test_grid_transfer.py
+++ b/tests/firedrake/multigrid/test_grid_transfer.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.utils import complex_mode
 
 
diff --git a/tests/firedrake/output/test_plotting.py b/tests/firedrake/output/test_plotting.py
index b3195d0038..2cf840e2e9 100644
--- a/tests/firedrake/output/test_plotting.py
+++ b/tests/firedrake/output/test_plotting.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 
 try:
     from firedrake.pyplot import *
diff --git a/tests/firedrake/regression/test_adjoint_operators.py b/tests/firedrake/regression/test_adjoint_operators.py
index b7ecdd6ed0..6410d74957 100644
--- a/tests/firedrake/regression/test_adjoint_operators.py
+++ b/tests/firedrake/regression/test_adjoint_operators.py
@@ -6,7 +6,6 @@
 
 from firedrake import *
 from firedrake.adjoint import *
-from firedrake.__future__ import *
 
 
 @pytest.fixture(autouse=True)
diff --git a/tests/firedrake/regression/test_cellorigin.py b/tests/firedrake/regression/test_cellorigin.py
index c763de56b0..754fbf23d3 100644
--- a/tests/firedrake/regression/test_cellorigin.py
+++ b/tests/firedrake/regression/test_cellorigin.py
@@ -2,7 +2,6 @@
 import numpy as np
 from ufl.geometry import CellOrigin
 from firedrake import *
-from firedrake.__future__ import *
 
 
 @pytest.fixture(params=["interval", "triangle", "quadrilateral", "tetrahedron"])
diff --git a/tests/firedrake/regression/test_interp_dual.py b/tests/firedrake/regression/test_interp_dual.py
index 04d9254963..ae3993534c 100644
--- a/tests/firedrake/regression/test_interp_dual.py
+++ b/tests/firedrake/regression/test_interp_dual.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.utils import complex_mode
 import ufl
 
diff --git a/tests/firedrake/regression/test_interpolate.py b/tests/firedrake/regression/test_interpolate.py
index e8860a32e7..45a2d9c89a 100644
--- a/tests/firedrake/regression/test_interpolate.py
+++ b/tests/firedrake/regression/test_interpolate.py
@@ -2,7 +2,6 @@
 import numpy as np
 import pytest
 from firedrake import *
-from firedrake.__future__ import *
 
 cwd = abspath(dirname(__file__))
 
diff --git a/tests/firedrake/regression/test_interpolate_cross_mesh.py b/tests/firedrake/regression/test_interpolate_cross_mesh.py
index c19b028a19..c3e3c39ece 100644
--- a/tests/firedrake/regression/test_interpolate_cross_mesh.py
+++ b/tests/firedrake/regression/test_interpolate_cross_mesh.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.petsc import DEFAULT_PARTITIONER
 from firedrake.ufl_expr import extract_unique_domain
 import numpy as np
diff --git a/tests/firedrake/regression/test_interpolate_p3intmoments.py b/tests/firedrake/regression/test_interpolate_p3intmoments.py
index 6ef0ef34d2..5a7e18c1d6 100644
--- a/tests/firedrake/regression/test_interpolate_p3intmoments.py
+++ b/tests/firedrake/regression/test_interpolate_p3intmoments.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 from FIAT.dual_set import DualSet
 from FIAT.finite_element import CiarletElement
 from FIAT.reference_element import UFCInterval
diff --git a/tests/firedrake/regression/test_interpolate_vs_project.py b/tests/firedrake/regression/test_interpolate_vs_project.py
index 84fabd6a45..3a0fcc571f 100644
--- a/tests/firedrake/regression/test_interpolate_vs_project.py
+++ b/tests/firedrake/regression/test_interpolate_vs_project.py
@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 from firedrake import *
-from firedrake.__future__ import *
 
 
 @pytest.fixture(params=["square", "cube"], scope="module")
diff --git a/tests/firedrake/regression/test_interpolation_manual.py b/tests/firedrake/regression/test_interpolation_manual.py
index ffe515ddd1..3b2dcfa041 100644
--- a/tests/firedrake/regression/test_interpolation_manual.py
+++ b/tests/firedrake/regression/test_interpolation_manual.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 import pytest
 import numpy as np
 
diff --git a/tests/firedrake/regression/test_interpolation_nodes.py b/tests/firedrake/regression/test_interpolation_nodes.py
index 6c6e338afb..13b52aa193 100644
--- a/tests/firedrake/regression/test_interpolation_nodes.py
+++ b/tests/firedrake/regression/test_interpolation_nodes.py
@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 from firedrake import *
-from firedrake.__future__ import *
 
 '''
 The spaces N1div, N1curl, N2div and N2curl have the special property that the interpolation in these
diff --git a/tests/firedrake/regression/test_netgen.py b/tests/firedrake/regression/test_netgen.py
index 6e728ef653..51371d36da 100644
--- a/tests/firedrake/regression/test_netgen.py
+++ b/tests/firedrake/regression/test_netgen.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import interpolate
 import numpy as np
 import pytest
 
diff --git a/tests/firedrake/regression/test_norm.py b/tests/firedrake/regression/test_norm.py
index d66c55ee85..35762c4c38 100644
--- a/tests/firedrake/regression/test_norm.py
+++ b/tests/firedrake/regression/test_norm.py
@@ -1,6 +1,5 @@
 import pytest
 from firedrake import *
-from firedrake.__future__ import *
 import numpy
 
 
diff --git a/tests/firedrake/regression/test_nullspace.py b/tests/firedrake/regression/test_nullspace.py
index e4f436ff8d..5b3538c41e 100644
--- a/tests/firedrake/regression/test_nullspace.py
+++ b/tests/firedrake/regression/test_nullspace.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.petsc import PETSc
 import pytest
 import numpy as np
diff --git a/tests/firedrake/regression/test_octahedral_hemisphere.py b/tests/firedrake/regression/test_octahedral_hemisphere.py
index be2c0696c9..da8ed1aada 100644
--- a/tests/firedrake/regression/test_octahedral_hemisphere.py
+++ b/tests/firedrake/regression/test_octahedral_hemisphere.py
@@ -1,7 +1,6 @@
 from __future__ import absolute_import, print_function, division
 import pytest
 from firedrake import *
-from firedrake.__future__ import *
 import numpy
 
 
diff --git a/tests/firedrake/regression/test_par_loops.py b/tests/firedrake/regression/test_par_loops.py
index 8222e0d5c7..f4df8f51fa 100644
--- a/tests/firedrake/regression/test_par_loops.py
+++ b/tests/firedrake/regression/test_par_loops.py
@@ -2,7 +2,6 @@
 import numpy as np
 from firedrake import *
 from firedrake.utils import ScalarType_c
-from firedrake.__future__ import *
 
 
 @pytest.fixture(scope="module")
diff --git a/tests/firedrake/regression/test_project_interp_KMV.py b/tests/firedrake/regression/test_project_interp_KMV.py
index 44ca5ec59c..7eef071841 100644
--- a/tests/firedrake/regression/test_project_interp_KMV.py
+++ b/tests/firedrake/regression/test_project_interp_KMV.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 import finat
 
 
diff --git a/tests/firedrake/regression/test_real_space.py b/tests/firedrake/regression/test_real_space.py
index b989ae2c86..74f4015c66 100644
--- a/tests/firedrake/regression/test_real_space.py
+++ b/tests/firedrake/regression/test_real_space.py
@@ -2,7 +2,6 @@
 import numpy as np
 
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.petsc import DEFAULT_DIRECT_SOLVER
 
 
diff --git a/tests/firedrake/slate/test_facet_tensors.py b/tests/firedrake/slate/test_facet_tensors.py
index 834db4e09b..eacf020266 100644
--- a/tests/firedrake/slate/test_facet_tensors.py
+++ b/tests/firedrake/slate/test_facet_tensors.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 
 
 @pytest.fixture(scope='module', params=[False, True])
diff --git a/tests/firedrake/slate/test_facet_tensors_extr.py b/tests/firedrake/slate/test_facet_tensors_extr.py
index fb4443ac76..1a0d058b67 100644
--- a/tests/firedrake/slate/test_facet_tensors_extr.py
+++ b/tests/firedrake/slate/test_facet_tensors_extr.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 
 
 @pytest.fixture(scope='module', params=[False, True])
diff --git a/tests/firedrake/vertexonly/test_interpolation_from_parent.py b/tests/firedrake/vertexonly/test_interpolation_from_parent.py
index c722d7b9b3..275c729cf2 100644
--- a/tests/firedrake/vertexonly/test_interpolation_from_parent.py
+++ b/tests/firedrake/vertexonly/test_interpolation_from_parent.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 import pytest
 import numpy as np
 from functools import reduce
diff --git a/tests/firedrake/vertexonly/test_point_eval_immersed_manifold.py b/tests/firedrake/vertexonly/test_point_eval_immersed_manifold.py
index 30d8057748..bfae5673e1 100644
--- a/tests/firedrake/vertexonly/test_point_eval_immersed_manifold.py
+++ b/tests/firedrake/vertexonly/test_point_eval_immersed_manifold.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 import numpy as np
 import pytest
 
diff --git a/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py b/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py
index df28b196f2..9975aa6dd1 100644
--- a/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py
+++ b/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py
@@ -1,7 +1,6 @@
 import pytest
 import numpy as np
 from firedrake import *
-from firedrake.__future__ import *
 from pyadjoint.tape import get_working_tape, pause_annotation
 
 
diff --git a/tests/firedrake/vertexonly/test_swarm.py b/tests/firedrake/vertexonly/test_swarm.py
index 4bd5cde939..555351cd01 100644
--- a/tests/firedrake/vertexonly/test_swarm.py
+++ b/tests/firedrake/vertexonly/test_swarm.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 from firedrake.utils import IntType, RealType
 import pytest
 import numpy as np
diff --git a/tests/firedrake/vertexonly/test_vertex_only_fs.py b/tests/firedrake/vertexonly/test_vertex_only_fs.py
index ce071267b6..6fa9fddd7c 100644
--- a/tests/firedrake/vertexonly/test_vertex_only_fs.py
+++ b/tests/firedrake/vertexonly/test_vertex_only_fs.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 import pytest
 import numpy as np
 from mpi4py import MPI
diff --git a/tests/firedrake/vertexonly/test_vertex_only_manual.py b/tests/firedrake/vertexonly/test_vertex_only_manual.py
index 7578b5c69b..f44b30758e 100644
--- a/tests/firedrake/vertexonly/test_vertex_only_manual.py
+++ b/tests/firedrake/vertexonly/test_vertex_only_manual.py
@@ -1,5 +1,4 @@
 from firedrake import *
-from firedrake.__future__ import *
 import pytest
 
 

From 1420433f10658aa8e068613b1cbbeb2b072f2da5 Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Thu, 3 Jul 2025 15:21:48 +0100
Subject: [PATCH 167/222] bump pyadjoint dependency version (#4424)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7562610df4..16efd0c079 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,7 +29,7 @@ dependencies = [
   "packaging",
   "pkgconfig",
   "progress",
-  "pyadjoint-ad>=2025.04",
+  "pyadjoint-ad>=2025.04.1",
   "pycparser",
   "pytools[siphash]",
   "requests",

From 247552e8ab423d452b6d2e706bfd82b948af96fc Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 3 Jul 2025 15:23:36 +0100
Subject: [PATCH 168/222] Bump version in install.rst link

---
 docs/source/install.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/install.rst b/docs/source/install.rst
index 55de3456ec..a16b5d6a08 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -54,7 +54,7 @@ firedrake-configure
 To simplify the installation process, Firedrake provides a utility script called
 ``firedrake-configure``. This script can be downloaded by executing::
 
-  $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/refs/tags/2025.4.1/scripts/firedrake-configure
+  $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/refs/tags/2025.4.2/scripts/firedrake-configure
 
 Unlike the now deprecated ``firedrake-install`` script, ``firedrake-configure``
 **does not install Firedrake for you**. It is simply a helper script that emits

From 0de241ea0f3ba893de2d5b86d4b14098567ac9db Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Fri, 4 Jul 2025 15:00:26 +0100
Subject: [PATCH 169/222] Inner product jacobian is constant.

Co-authored-by: Josh Hope-Collins 
---
 firedrake/cofunction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py
index 3a54ba910d..29d60e7b10 100644
--- a/firedrake/cofunction.py
+++ b/firedrake/cofunction.py
@@ -464,7 +464,7 @@ def _solver(self):
         soln = Function(self._function_space)
         lvp = LinearVariationalProblem(
             self._inner_product, rhs, soln, bcs=self._bcs,
-            restrict=self._restrict,
+            restrict=self._restrict, constant_jacobian=True,
             form_compiler_parameters=self._form_compiler_parameters)
         solver = LinearVariationalSolver(
             lvp, solver_parameters=self._solver_parameters

From d85339c1e309064dee0361a324e404cf7e3f8467 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Mon, 7 Jul 2025 11:06:14 +0100
Subject: [PATCH 170/222] Speed up caching (#4383)

---
 firedrake/interpolation.py       |   5 +-
 firedrake/slate/slac/compiler.py |   7 +-
 firedrake/tsfc_interface.py      |   4 +-
 pyop2/caching.py                 | 187 ++++++++++++++++++-------------
 pyop2/compilation.py             |  34 ++++--
 tests/pyop2/test_caching.py      | 175 +++++++++++++++++------------
 6 files changed, 249 insertions(+), 163 deletions(-)

diff --git a/firedrake/interpolation.py b/firedrake/interpolation.py
index ccf0a81cfc..a18239865b 100644
--- a/firedrake/interpolation.py
+++ b/firedrake/interpolation.py
@@ -1,9 +1,10 @@
 import numpy
-from functools import partial, singledispatch
 import os
 import tempfile
 import abc
 import warnings
+from functools import partial, singledispatch
+from typing import Hashable
 
 import FIAT
 import ufl
@@ -1158,7 +1159,7 @@ def _interpolator(V, tensor, expr, subset, arguments, access, bcs=None):
                                   f"firedrake-tsfc-expression-kernel-cache-uid{os.getuid()}")
 
 
-def _compile_expression_key(comm, expr, to_element, ufl_element, domain, parameters):
+def _compile_expression_key(comm, expr, to_element, ufl_element, domain, parameters) -> tuple[Hashable, ...]:
     """Generate a cache key suitable for :func:`tsfc.compile_expression_dual_evaluation`."""
     return (hash_expr(expr), hash(ufl_element), utils.tuplify(parameters))
 
diff --git a/firedrake/slate/slac/compiler.py b/firedrake/slate/slac/compiler.py
index 98d2b4a48c..7edad22f14 100644
--- a/firedrake/slate/slac/compiler.py
+++ b/firedrake/slate/slac/compiler.py
@@ -8,6 +8,7 @@
 expressions (finite element variational forms written in UFL).
 """
 import time
+from typing import Hashable
 
 from firedrake_citations import Citations
 from firedrake.tsfc_interface import SplitKernel, KernelInfo, TSFCKernel
@@ -71,7 +72,7 @@ def __init__(self, expr, compiler_parameters):
         self.split_kernel = generate_loopy_kernel(expr, compiler_parameters)
 
 
-def _compile_expression_hashkey(slate_expr, compiler_parameters=None):
+def _compile_expression_hashkey(slate_expr, compiler_parameters=None) -> tuple[Hashable, ...]:
     params = copy.deepcopy(parameters)
     if compiler_parameters and "slate_compiler" in compiler_parameters.keys():
         params["slate_compiler"].update(compiler_parameters.pop("slate_compiler"))
@@ -79,7 +80,7 @@ def _compile_expression_hashkey(slate_expr, compiler_parameters=None):
         params["form_compiler"].update(compiler_parameters)
     # The getattr here is to defer validation to the `compile_expression` call
     # as the test suite checks the correct exceptions are raised on invalid input.
-    return getattr(slate_expr, "expression_hash", "ERROR") + str(sorted(params.items()))
+    return (getattr(slate_expr, "expression_hash", "ERROR") + str(sorted(params.items())))
 
 
 def _compile_expression_comm(*args, **kwargs):
@@ -90,7 +91,7 @@ def _compile_expression_comm(*args, **kwargs):
 
 @memory_and_disk_cache(
     hashkey=_compile_expression_hashkey,
-    comm_getter=_compile_expression_comm,
+    get_comm=_compile_expression_comm,
     cachedir=tsfc_interface._cachedir
 )
 @PETSc.Log.EventDecorator()
diff --git a/firedrake/tsfc_interface.py b/firedrake/tsfc_interface.py
index 2999a9e9ef..e7d5904d1c 100644
--- a/firedrake/tsfc_interface.py
+++ b/firedrake/tsfc_interface.py
@@ -69,7 +69,7 @@ def _compile_form_comm(form, *args, **kwargs):
 # Decorate the original tsfc.compile_form with a cache
 tsfc_compile_form = memory_and_disk_cache(
     hashkey=tsfc_compile_form_hashkey,
-    comm_getter=_compile_form_comm,
+    get_comm=_compile_form_comm,
     cachedir=_cachedir
 )(original_tsfc_compile_form)
 
@@ -160,7 +160,7 @@ def _compile_form_hashkey(form, name, parameters=None, split=True, dont_split=()
 @PETSc.Log.EventDecorator()
 @memory_and_disk_cache(
     hashkey=_compile_form_hashkey,
-    comm_getter=_compile_form_comm,
+    get_comm=_compile_form_comm,
     cachedir=_cachedir
 )
 @PETSc.Log.EventDecorator()
diff --git a/pyop2/caching.py b/pyop2/caching.py
index c49e621a3d..03017783fc 100644
--- a/pyop2/caching.py
+++ b/pyop2/caching.py
@@ -34,17 +34,19 @@
 """Provides common base classes for cached objects."""
 import atexit
 import cachetools
+import functools
 import hashlib
 import os
 import pickle
 import weakref
-from collections.abc import MutableMapping
+from collections.abc import Mapping, MutableMapping
 from pathlib import Path
 from warnings import warn  # noqa F401
 from collections import defaultdict
 from itertools import count
 from functools import wraps
 from tempfile import mkstemp
+from typing import Any, Callable, Hashable
 
 from pyop2.configuration import configuration
 from pyop2.exceptions import CachingError, HashError  # noqa: F401
@@ -56,11 +58,9 @@
 from petsc4py import PETSc
 
 
-# Caches created here are registered as a tuple of
-#     (creation_index, comm, comm.name, function, cache)
-# in _KNOWN_CACHES
 _CACHE_CIDX = count()
 _KNOWN_CACHES = []
+
 # Flag for outputting information at the end of testing (do not abuse!)
 _running_on_ci = bool(os.environ.get('PYOP2_CI_TESTS'))
 
@@ -178,9 +178,56 @@ def cache_filter(comm=None, comm_name=None, alive=True, function=None, cache_typ
     return [*caches]
 
 
-class _CacheRecord:
-    """ Object for keeping a record of Pyop2 Cache statistics.
+def get_comm_caches(comm: MPI.Comm) -> dict[Hashable, Mapping]:
+    """Return the collection of caches that are stored on a comm.
+
+    If a cache stash has not already been created then a new `dict` is
+    created and stored.
+
+    Parameters
+    ----------
+    comm :
+        The communicator to get the caches from.
+
+    Returns
+    -------
+    dict :
+        The collection of caches.
+
     """
+    comm_caches = comm.Get_attr(comm_cache_keyval)
+    if comm_caches is None:
+        comm_caches = {}
+        comm.Set_attr(comm_cache_keyval, comm_caches)
+    return comm_caches
+
+
+def get_cache_entry(comm: MPI.Comm, cache: Mapping, key: Hashable) -> Any:
+    if (
+        configuration["spmd_strict"]
+        and not pytools.is_single_valued(comm.allgather(key))
+    ):
+        raise ValueError(
+            f"Cache keys differ between ranks. On rank {comm.rank} got:\n{key}"
+        )
+
+    value = cache.get(key, CACHE_MISS)
+
+    if configuration["debug"]:
+        message = [f"{COMM_WORLD.name} R{COMM_WORLD.rank}, {comm.name} R{comm.rank}: "]
+        message.append(f"key={key} in cache: '{cache}' ")
+        if value is CACHE_MISS:
+            message.append("miss")
+        else:
+            message.append("hit")
+        message = "".join(message)
+        debug(message)
+
+    return value
+
+
+class _CacheRecord:
+    """Object that records cache statistics."""
     def __init__(self, cidx, comm, func, cache):
         self.cidx = cidx
         self.comm = comm
@@ -276,7 +323,16 @@ class _CacheMiss:
 CACHE_MISS = _CacheMiss()
 
 
-def _as_hexdigest(*args):
+@functools.cache
+def as_hexdigest(*args) -> str:
+    """Return ``args`` as a hash string.
+
+    Notes
+    -----
+    This function is relatively expensive to compute so one should avoid
+    calling it wherever possible.
+
+    """
     hash_ = hashlib.md5()
     for a in args:
         if isinstance(a, MPI.Comm):
@@ -297,13 +353,11 @@ def __init__(self, cachedir, extension=".pickle"):
         self.cachedir = cachedir
         self.extension = extension
 
-    def __getitem__(self, key):
-        """Retrieve a value from the disk cache.
+    def __getitem__(self, key: Hashable) -> Any:
+        """Retrieve a value from the disk cache."""
+        key = as_hexdigest(key)
 
-        :arg key: The cache key, a 2-tuple of strings.
-        :returns: The cached object if found.
-        """
-        filepath = Path(self.cachedir, key[0][:2], key[0][2:] + key[1])
+        filepath = Path(self.cachedir, key[:2], key[2:])
         try:
             with self.open(filepath.with_suffix(self.extension), mode="rb") as fh:
                 value = self.read(fh)
@@ -311,13 +365,11 @@ def __getitem__(self, key):
             raise KeyError("File not on disk, cache miss")
         return value
 
-    def __setitem__(self, key, value):
-        """Store a new value in the disk cache.
+    def __setitem__(self, key: Hashable, value: Any) -> None:
+        """Store a new value in the disk cache."""
+        key = as_hexdigest(key)
 
-        :arg key: The cache key, a 2-tuple of strings.
-        :arg value: The new item to store in the cache.
-        """
-        k1, k2 = key[0][:2], key[0][2:] + key[1]
+        k1, k2 = key[:2], key[2:]
         basedir = Path(self.cachedir, k1)
         basedir.mkdir(parents=True, exist_ok=True)
 
@@ -366,7 +418,7 @@ def write(self, filehandle, value):
         pickle.dump(value, filehandle)
 
 
-def default_comm_getter(*args, **kwargs):
+def default_get_comm(*args, **kwargs):
     """ A sensible default comm fetcher for use with `parallel_cache`.
     """
     comms = filter(
@@ -380,7 +432,7 @@ def default_comm_getter(*args, **kwargs):
     return comm
 
 
-def default_parallel_hashkey(*args, **kwargs):
+def default_parallel_hashkey(*args, **kwargs) -> Hashable:
     """ A sensible default hash key for use with `parallel_cache`.
     """
     # We now want to actively remove any comms from args and kwargs to get
@@ -441,10 +493,11 @@ class DEFAULT_CACHE(dict):
     DictLikeDiskAccess = instrument(DictLikeDiskAccess)
 
 
+# TODO: One day should use the compilation comm to do the bcast
 def parallel_cache(
     hashkey=default_parallel_hashkey,
-    comm_getter=default_comm_getter,
-    cache_factory=lambda: DEFAULT_CACHE(),
+    get_comm: Callable = default_get_comm,
+    make_cache: Callable[[], Mapping] = lambda: DEFAULT_CACHE(),
     bcast=False,
 ):
     """Parallel cache decorator.
@@ -453,10 +506,10 @@ def parallel_cache(
     ----------
     hashkey :
         Callable taking ``*args`` and ``**kwargs`` and returning a hash.
-    comm_getter :
+    get_comm :
         Callable taking ``*args`` and ``**kwargs`` and returning the
         appropriate communicator.
-    cache_factory :
+    make_cache :
         Callable that will build a new cache (if one does not exist).
         This will be called every time the decorated function is called, and must return an instance
         of the same type every time it is called.
@@ -467,53 +520,32 @@ def parallel_cache(
         serial; else it will deadlock.
 
     """
+    # Store a unique integer for each 'parallel_cache' decorator so we can
+    # identify the different caches when we wrap a function in multiple of
+    # them (this happens for memory and disk caches for example). This
+    # identifier is different between ranks but that is fine as it is only
+    # used locally.
+    cache_id = next(_CACHE_CIDX)
+
     def decorator(func):
-        @PETSc.Log.EventDecorator("pyop2: cache wrapper")
+        @PETSc.Log.EventDecorator(f"pyop2.caching.parallel_cache.wrapper({func.__qualname__})")
         @wraps(func)
         def wrapper(*args, **kwargs):
-            # Extract the key and then try the memory cache before falling back
-            # to calling the function and populating the cache.
-            k = hashkey(*args, **kwargs)
-            key = _as_hexdigest(*k), func.__qualname__
-
-            # Create a PyOP2 comm associated with the key, so it is decrefed when the wrapper exits
-            with temp_internal_comm(comm_getter(*args, **kwargs)) as comm:
-                if configuration["spmd_strict"] and not pytools.is_single_valued(comm.allgather(key)):
-                    raise ValueError("Cache keys differ between ranks")
-
-                # Fetch the per-comm cache_collection or set it up if not present
-                # A collection is required since different types of cache can be set up on the same comm
-                cache_collection = comm.Get_attr(comm_cache_keyval)
-                if cache_collection is None:
-                    cache_collection = {}
-                    comm.Set_attr(comm_cache_keyval, cache_collection)
-                # If this kind of cache is already present on the
-                # cache_collection, get it, otherwise create it
-                local_cache = cache_collection.setdefault(
-                    (cf := cache_factory()).__class__.__name__,
-                    cf
-                )
-                local_cache = cache_collection[cf.__class__.__name__]
-
-                # If this is a new cache or function add it to the list of known caches
-                if (comm, comm.name, func, local_cache) not in [(c.comm, c.comm_name, c.func, c.cache()) for c in _KNOWN_CACHES]:
-                    # When a comm is freed we do not hold a reference to the cache.
-                    # We attach a finalizer that extracts the stats before the cache
-                    # is deleted.
-                    _KNOWN_CACHES.append(_CacheRecord(next(_CACHE_CIDX), comm, func, local_cache))
-
-                # Grab value from all ranks cache and broadcast cache hit/miss
-                value = local_cache.get(key, CACHE_MISS)
-                debug_string = f"{COMM_WORLD.name} R{COMM_WORLD.rank}, {comm.name} R{comm.rank}: "
-                debug_string += f"key={k} in cache: {local_cache.__class__.__name__} cache "
-                if value is CACHE_MISS:
-                    debug(debug_string + "miss")
-                    cache_hit = False
-                else:
-                    debug(debug_string + "hit")
-                    cache_hit = True
+            # Create a PyOP2 comm associated with the key, so it is decrefed
+            # when the wrapper exits
+            with temp_internal_comm(get_comm(*args, **kwargs)) as comm:
+                # Get the right cache from the comm
+                comm_caches = get_comm_caches(comm)
+                try:
+                    cache = comm_caches[cache_id]
+                except KeyError:
+                    cache = comm_caches.setdefault(cache_id, make_cache())
+                    _KNOWN_CACHES.append(_CacheRecord(cache_id, comm, func, cache))
+
+                key = hashkey(*args, **kwargs)
+                value = get_cache_entry(comm, cache, key)
 
-                if isinstance(local_cache, DictLikeDiskAccess):
+                if isinstance(cache, DictLikeDiskAccess):
                     if bcast:
                         # Since disk caches share state between ranks there are extra
                         # opportunities for mismatching hit/miss results and hence
@@ -535,29 +567,30 @@ def wrapper(*args, **kwargs):
                         # will do a broadcast and others will not, ruining MPI synchronisation.
                         # To fix this we check to see if any ranks have hit cache and, if so,
                         # nominate that rank as the root of the subsequent broadcast.
-                        root = comm.rank if cache_hit else -1
+                        root = comm.rank if value is not CACHE_MISS else -1
                         root = comm.allreduce(root, op=MPI.MAX)
                         if root >= 0:
                             # Found a rank with a cache hit, broadcast 'value' from it
                             value = comm.bcast(value, root=root)
-                            cache_hit = True
-                        else:
-                            # Cache miss on all ranks, recompute below
-                            cache_hit = False
                 else:
                     # In-memory caches are stashed on the comm and so must always agree
                     # on their contents.
-                    if configuration["spmd_strict"] and not pytools.is_single_valued(comm.allgather(cache_hit)):
+                    if (
+                        configuration["spmd_strict"]
+                        and not pytools.is_single_valued(
+                            comm.allgather(value is not CACHE_MISS)
+                        )
+                    ):
                         raise ValueError("Cache hit on some ranks but missed on others")
 
-            if not cache_hit:
+            if value is CACHE_MISS:
                 if bcast:
                     value = func(*args, **kwargs) if comm.rank == 0 else None
                     value = comm.bcast(value, root=0)
                 else:
                     value = func(*args, **kwargs)
 
-            return local_cache.setdefault(key, value)
+            return cache.setdefault(key, value)
         return wrapper
     return decorator
 
@@ -579,7 +612,7 @@ def serial_cache(hashkey, cache_factory=lambda: DEFAULT_CACHE()):
 
 
 def disk_only_cache(*args, cachedir=configuration["cache_dir"], **kwargs):
-    return parallel_cache(*args, **kwargs, cache_factory=lambda: DictLikeDiskAccess(cachedir))
+    return parallel_cache(*args, **kwargs, make_cache=lambda: DictLikeDiskAccess(cachedir))
 
 
 def memory_and_disk_cache(*args, cachedir=configuration["cache_dir"], **kwargs):
diff --git a/pyop2/compilation.py b/pyop2/compilation.py
index 77835b3592..0c8014c477 100644
--- a/pyop2/compilation.py
+++ b/pyop2/compilation.py
@@ -47,11 +47,12 @@
 from pathlib import Path
 from contextlib import contextmanager
 from tempfile import gettempdir, mkstemp
+from typing import Hashable
 from random import randint
 
 
 from pyop2 import mpi
-from pyop2.caching import parallel_cache, memory_cache, default_parallel_hashkey, _as_hexdigest, DictLikeDiskAccess
+from pyop2.caching import parallel_cache, memory_cache, default_parallel_hashkey, DictLikeDiskAccess, as_hexdigest
 from pyop2.configuration import configuration
 from pyop2.logger import warning, debug, progress, INFO
 from pyop2.exceptions import CompilationError
@@ -248,6 +249,24 @@ def __repr__(self):
     def __str__(self):
         return f"<{self._name} compiler, version {self._version or 'unknown'}>"
 
+    def __hash__(self) -> int:
+        return hash((
+            type(self),
+            self._extra_compiler_flags,
+            self._extra_linker_flags,
+            self._version,
+            self._debug,
+        ))
+
+    def __eq__(self, other) -> bool:
+        return (
+            type(other) is type(self)
+            and other._extra_compiler_flags == self._extra_compiler_flags
+            and other._extra_linker_flags == self._extra_linker_flags
+            and other._version == self._version
+            and other._debug == self._debug
+        )
+
     @property
     def cc(self):
         return self._cc or petsc_variables["CC"]
@@ -409,9 +428,10 @@ class AnonymousCompiler(Compiler):
     _name = "Unknown"
 
 
-def load_hashkey(*args, **kwargs):
-    code_hash = md5(args[0].encode()).hexdigest()
-    return default_parallel_hashkey(code_hash, *args[1:], **kwargs)
+def load_hashkey(code, extension, cppargs=(), ldargs=(), comm=None):
+    cppargs = tuple(cppargs)
+    ldargs = tuple(ldargs)
+    return default_parallel_hashkey(code, extension, cppargs, ldargs)
 
 
 @mpi.collective
@@ -481,7 +501,7 @@ def setdefault(self, key, default=None):
         return self[key]
 
 
-def _make_so_hashkey(compiler, code, extension, comm):
+def _make_so_hashkey(compiler, code, extension, comm) -> tuple[Hashable, ...]:
     if extension == "cpp":
         exe = compiler.cxx
         compiler_flags = compiler.cxxflags
@@ -501,7 +521,7 @@ def check_source_hashes(compiler, code, extension, comm):
     :arg comm: Communicator over which to perform compilation.
     """
     # Reconstruct hash from filename
-    hashval = _as_hexdigest(_make_so_hashkey(compiler, code, extension, comm))
+    hashval = as_hexdigest(_make_so_hashkey(compiler, code, extension, comm))
     with mpi.temp_internal_comm(comm) as icomm:
         matching = icomm.allreduce(hashval, op=_check_op)
         if matching != hashval:
@@ -520,7 +540,7 @@ def check_source_hashes(compiler, code, extension, comm):
 @mpi.collective
 @parallel_cache(
     hashkey=_make_so_hashkey,
-    cache_factory=lambda: CompilerDiskAccess(configuration['cache_dir'], extension=".so")
+    make_cache=lambda: CompilerDiskAccess(configuration['cache_dir'], extension=".so")
 )
 @PETSc.Log.EventDecorator()
 def make_so(compiler, code, extension, comm, filename=None):
diff --git a/tests/pyop2/test_caching.py b/tests/pyop2/test_caching.py
index eebd6743d0..c924cf81d4 100644
--- a/tests/pyop2/test_caching.py
+++ b/tests/pyop2/test_caching.py
@@ -43,9 +43,11 @@
 from pyop2.caching import (
     DEFAULT_CACHE,
     disk_only_cache,
+    get_comm_caches,
     memory_cache,
     memory_and_disk_cache,
-    clear_memory_cache
+    clear_memory_cache,
+    _KNOWN_CACHES,
 )
 from pyop2.compilation import load
 from pyop2.configuration import configuration
@@ -53,7 +55,6 @@
     MPI,
     COMM_WORLD,
     COMM_SELF,
-    comm_cache_keyval,
     internal_comm,
     temp_internal_comm
 )
@@ -297,27 +298,38 @@ def test_sparsity_cache_miss(self, base_set, base_set2,
         assert not sp == sp2
 
 
-class TestGeneratedCodeCache:
+def get_cache(comm, func_name):
+    cache_id = None
+    for cache_info in _KNOWN_CACHES:
+        if cache_info.func_name == func_name:
+            assert cache_id is None
+            cache_id = cache_info.cidx
 
-    """
-    Generated Code Cache Tests.
-    """
+    if cache_id is None:
+        # haven't hit the function yet so the cache doesn't exist
+        return None
+
+    caches = get_comm_caches(comm)
+    return caches[cache_id]
+
+
+def get_caches(comm, func_name):
+    cache_ids = []
+    for cache_info in _KNOWN_CACHES:
+        if cache_info.func_name == func_name and cache_info.comm is comm:
+            cache_ids.append(cache_info.cidx)
+
+    caches = get_comm_caches(comm)
+    return tuple(caches[cache_id] for cache_id in cache_ids)
+
+
+class TestGeneratedCodeCache:
+    """Generated Code Cache Tests."""
 
     @property
     def cache(self):
-        int_comm = internal_comm(COMM_WORLD, self)
-        _cache_collection = int_comm.Get_attr(comm_cache_keyval)
-        if _cache_collection is None:
-            _cache_collection = {default_cache_name: DEFAULT_CACHE()}
-            int_comm.Set_attr(comm_cache_keyval, _cache_collection)
-        return _cache_collection[default_cache_name]
-
-    def code_cache_len_equals(self, expected):
-        # We need to do this check because different things also get
-        # put into self.cache
-        return sum(
-            1 for key in self.cache if key[1] == "compile_global_kernel"
-        ) == expected
+        icomm = internal_comm(COMM_WORLD, self)
+        return get_cache(icomm, "compile_global_kernel")
 
     @pytest.fixture
     def a(cls, diterset):
@@ -328,8 +340,9 @@ def b(cls, diterset):
         return op2.Dat(diterset, list(range(nelems)), numpy.uint32, "b")
 
     def test_same_args(self, iterset, iter2ind1, x, a):
-        self.cache.clear()
-        assert len(self.cache) == 0
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
 
         kernel_cpy = "static void cpy(unsigned int* dst, unsigned int* src) { *dst = *src; }"
 
@@ -338,18 +351,19 @@ def test_same_args(self, iterset, iter2ind1, x, a):
                      a(op2.WRITE),
                      x(op2.READ, iter2ind1))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         op2.par_loop(op2.Kernel(kernel_cpy, "cpy"),
                      iterset,
                      a(op2.WRITE),
                      x(op2.READ, iter2ind1))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
     def test_diff_kernel(self, iterset, iter2ind1, x, a):
-        self.cache.clear()
-        assert len(self.cache) == 0
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
 
         kernel_cpy = "static void cpy(unsigned int* dst, unsigned int* src) { *dst = *src; }"
 
@@ -358,7 +372,7 @@ def test_diff_kernel(self, iterset, iter2ind1, x, a):
                      a(op2.WRITE),
                      x(op2.READ, iter2ind1))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         kernel_cpy = "static void cpy(unsigned int* DST, unsigned int* SRC) { *DST = *SRC; }"
 
@@ -367,11 +381,12 @@ def test_diff_kernel(self, iterset, iter2ind1, x, a):
                      a(op2.WRITE),
                      x(op2.READ, iter2ind1))
 
-        assert self.code_cache_len_equals(2)
+        assert len(self.cache) == 2
 
     def test_invert_arg_similar_shape(self, iterset, iter2ind1, x, y):
-        self.cache.clear()
-        assert len(self.cache) == 0
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
 
         kernel_swap = """
 static void swap(unsigned int* x, unsigned int* y)
@@ -387,18 +402,19 @@ def test_invert_arg_similar_shape(self, iterset, iter2ind1, x, y):
                      x(op2.RW, iter2ind1),
                      y(op2.RW, iter2ind1))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         op2.par_loop(op2.Kernel(kernel_swap, "swap"),
                      iterset,
                      y(op2.RW, iter2ind1),
                      x(op2.RW, iter2ind1))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
     def test_dloop_ignore_scalar(self, iterset, a, b):
-        self.cache.clear()
-        assert len(self.cache) == 0
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
 
         kernel_swap = """
 static void swap(unsigned int* x, unsigned int* y)
@@ -414,18 +430,19 @@ def test_dloop_ignore_scalar(self, iterset, a, b):
                      a(op2.RW),
                      b(op2.RW))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         op2.par_loop(op2.Kernel(kernel_swap, "swap"),
                      iterset,
                      b(op2.RW),
                      a(op2.RW))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
     def test_vector_map(self, iterset, x2, iter2ind2):
-        self.cache.clear()
-        assert len(self.cache) == 0
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
 
         kernel_swap = """
 static void swap(unsigned int* x)
@@ -441,60 +458,65 @@ def test_vector_map(self, iterset, x2, iter2ind2):
                      iterset,
                      x2(op2.RW, iter2ind2))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         op2.par_loop(op2.Kernel(kernel_swap, "swap"),
                      iterset,
                      x2(op2.RW, iter2ind2))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
     def test_same_iteration_space_works(self, iterset, x2, iter2ind2):
-        self.cache.clear()
-        assert len(self.cache) == 0
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
+
         k = op2.Kernel("""static void k(void *x) {}""", 'k')
 
         op2.par_loop(k, iterset,
                      x2(op2.INC, iter2ind2))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         op2.par_loop(k, iterset,
                      x2(op2.INC, iter2ind2))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
     def test_change_dat_dtype_matters(self, iterset, diterset):
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
+
         d = op2.Dat(diterset, list(range(nelems)), numpy.uint32)
-        self.cache.clear()
-        assert len(self.cache) == 0
 
         k = op2.Kernel("""static void k(void *x) {}""", 'k')
 
         op2.par_loop(k, iterset, d(op2.WRITE))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         d = op2.Dat(diterset, list(range(nelems)), numpy.int32)
         op2.par_loop(k, iterset, d(op2.WRITE))
 
-        assert self.code_cache_len_equals(2)
+        assert len(self.cache) == 2
 
     def test_change_global_dtype_matters(self, iterset, diterset):
-        g = op2.Global(1, 0, dtype=numpy.uint32, comm=COMM_WORLD)
-        self.cache.clear()
-        assert len(self.cache) == 0
+        if self.cache is not None:
+            self.cache.clear()
+            assert len(self.cache) == 0
 
+        g = op2.Global(1, 0, dtype=numpy.uint32, comm=COMM_WORLD)
         k = op2.Kernel("""static void k(void *x) {}""", 'k')
 
         op2.par_loop(k, iterset, g(op2.INC))
 
-        assert self.code_cache_len_equals(1)
+        assert len(self.cache) == 1
 
         g = op2.Global(1, 0, dtype=numpy.float64, comm=COMM_WORLD)
         op2.par_loop(k, iterset, g(op2.INC))
 
-        assert self.code_cache_len_equals(2)
+        assert len(self.cache) == 2
 
 
 class TestSparsityCache:
@@ -558,11 +580,6 @@ def test_sparsities_different_ordered_map_tuple_cached(self, m1, m2, ds2):
 
 class TestDiskCachedDecorator:
 
-    @staticmethod
-    def myfunc(arg, comm):
-        """Example function to cache the outputs of."""
-        return {arg}
-
     @pytest.fixture
     def comm(self):
         """This fixture provides a temporary comm so that each test gets it's own
@@ -578,12 +595,16 @@ def cachedir(cls):
         return tempfile.TemporaryDirectory()
 
     def test_decorator_in_memory_cache_reuses_results(self, cachedir, comm):
+        def myfunc(arg, comm):
+            """Example function to cache the outputs of."""
+            return {arg}
+
         decorated_func = memory_and_disk_cache(
             cachedir=cachedir.name
-        )(self.myfunc)
+        )(myfunc)
 
         obj1 = decorated_func("input1", comm=comm)
-        mem_cache = comm.Get_attr(comm_cache_keyval)[default_cache_name]
+        mem_cache, disk_cache = get_caches(comm, myfunc.__qualname__)
         assert len(mem_cache) == 1
         assert len(os.listdir(cachedir.name)) == 1
 
@@ -593,50 +614,60 @@ def test_decorator_in_memory_cache_reuses_results(self, cachedir, comm):
         assert len(os.listdir(cachedir.name)) == 1
 
     def test_decorator_uses_different_in_memory_caches_on_different_comms(self, cachedir, comm):
+        def myfunc(arg, comm):
+            """Example function to cache the outputs of."""
+            return {arg}
+
         comm_world_func = memory_and_disk_cache(
             cachedir=cachedir.name
-        )(self.myfunc)
+        )(myfunc)
 
         temporary_comm = COMM_SELF.Dup()
         temporary_comm.name = "pytest temp COMM_SELF"
         with temp_internal_comm(temporary_comm) as comm_self:
             comm_self_func = memory_and_disk_cache(
                 cachedir=cachedir.name
-            )(self.myfunc)
+            )(myfunc)
 
             # obj1 should be cached on the COMM_WORLD cache
             obj1 = comm_world_func("input1", comm=comm)
-            comm_world_cache = comm.Get_attr(comm_cache_keyval)[default_cache_name]
-            assert len(comm_world_cache) == 1
+            comm_world_mem_cache, comm_world_disk_cache = get_caches(comm, myfunc.__qualname__)
+            assert len(comm_world_mem_cache) == 1
             assert len(os.listdir(cachedir.name)) == 1
 
             # obj2 should be cached on the COMM_SELF cache
             obj2 = comm_self_func("input1", comm=comm_self)
-            comm_self_cache = comm_self.Get_attr(comm_cache_keyval)[default_cache_name]
+            comm_self_mem_cache, comm_self_disk_cache = get_caches(comm_self, myfunc.__qualname__)
             assert obj1 == obj2 and obj1 is not obj2
-            assert len(comm_world_cache) == 1
-            assert len(comm_self_cache) == 1
+            assert len(comm_world_mem_cache) == 1
+            assert len(comm_self_mem_cache) == 1
             assert len(os.listdir(cachedir.name)) == 1
 
         temporary_comm.Free()
 
     def test_decorator_disk_cache_reuses_results(self, cachedir, comm):
-        decorated_func = memory_and_disk_cache(cachedir=cachedir.name)(self.myfunc)
+        def myfunc(arg, comm):
+            return {arg}
+
+        decorated_func = memory_and_disk_cache(cachedir=cachedir.name)(myfunc)
 
         obj1 = decorated_func("input1", comm=comm)
         clear_memory_cache(comm)
         obj2 = decorated_func("input1", comm=comm)
-        mem_cache = comm.Get_attr(comm_cache_keyval)[default_cache_name]
+        old_mem_cache, old_disk_cache, mem_cache, disk_cache = get_caches(comm, myfunc.__qualname__)
         assert obj1 == obj2 and obj1 is not obj2
         assert len(mem_cache) == 1
         assert len(os.listdir(cachedir.name)) == 1
 
     def test_decorator_cache_misses(self, cachedir, comm):
-        decorated_func = memory_and_disk_cache(cachedir=cachedir.name)(self.myfunc)
+        def myfunc(arg, comm):
+            return {arg}
+
+        decorated_func = memory_and_disk_cache(cachedir=cachedir.name)(myfunc)
 
         obj1 = decorated_func("input1", comm=comm)
         obj2 = decorated_func("input2", comm=comm)
-        mem_cache = comm.Get_attr(comm_cache_keyval)[default_cache_name]
+        mem_cache, disk_cache = get_caches(comm, myfunc.__qualname__)
         assert obj1 != obj2
         assert len(mem_cache) == 2
         assert len(os.listdir(cachedir.name)) == 2
@@ -850,7 +881,7 @@ def func():
             hashkey=lambda: "cachekey",
             cachedir=tmpdir,
             bcast=bcast,
-            comm_getter=lambda: comm,
+            get_comm=lambda: comm,
         )(func)
 
         # Ensure ranks are synchronised at the start (so we can force them to not be)
@@ -898,7 +929,7 @@ def func():
             hashkey=lambda: "cachekey",
             cachedir=tmpdir,
             bcast=bcast,
-            comm_getter=lambda: subcomm,
+            get_comm=lambda: subcomm,
         )(func)
 
         # Ensure ranks are synchronised at the start (so we can force them to not be)

From bd7aa77640bef811a6e63e4c12b748ebe31688f1 Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Wed, 9 Jul 2025 14:28:13 +0100
Subject: [PATCH 171/222] Apply suggestions from code review

Co-authored-by: Pablo Brubeck 
---
 firedrake/cofunction.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py
index 29d60e7b10..6acd3665d5 100644
--- a/firedrake/cofunction.py
+++ b/firedrake/cofunction.py
@@ -409,7 +409,8 @@ class RieszMap:
 
     def __init__(self, function_space_or_inner_product=None,
                  sobolev_space=ufl.L2, *, bcs=None, solver_parameters=None,
-                 form_compiler_parameters=None, restrict=True):
+                 form_compiler_parameters=None, restrict=True, 
+                 constant_jacobian=False):
         if isinstance(function_space_or_inner_product, ufl.Form):
             args = ufl.algorithms.extract_arguments(
                 function_space_or_inner_product
@@ -442,6 +443,7 @@ def __init__(self, function_space_or_inner_product=None,
         self._solver_parameters = solver_parameters or {}
         self._form_compiler_parameters = form_compiler_parameters or {}
         self._restrict = restrict
+        self._constant_jacobian = constant_jacobian
 
     @staticmethod
     def _inner_product_form(sobolev_space, u, v):
@@ -464,7 +466,8 @@ def _solver(self):
         soln = Function(self._function_space)
         lvp = LinearVariationalProblem(
             self._inner_product, rhs, soln, bcs=self._bcs,
-            restrict=self._restrict, constant_jacobian=True,
+            restrict=self._restrict,
+            constant_jacobian=self._constant_jacobian,
             form_compiler_parameters=self._form_compiler_parameters)
         solver = LinearVariationalSolver(
             lvp, solver_parameters=self._solver_parameters

From 6a3b0c4c4b2dfcbf8733a740687fe885a7499614 Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Wed, 9 Jul 2025 16:20:22 +0100
Subject: [PATCH 172/222] Firedrake '25 abstract submission and registration.

* Fix documentation issues

* disable linkcheck on links that are timing out

* registration and abstracts
---
 AUTHORS.rst                  | 2 +-
 docs/source/conf.py          | 3 +++
 docs/source/firedrake_25.rst | 8 ++++++--
 3 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index a7b4a25fb2..d8787ed90e 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -122,7 +122,7 @@ Geordie McBain
 
 Andrew T. T. McRae
 
-Oliver Meister................
+Oliver Meister
 
 Lawrence Mitchell.............
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index dfb14aba20..c902bb74f6 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -163,6 +163,9 @@
     r'https://www.crosscountrytrains.co.uk/',
     r'https://www.siam.org/',
     r'https://aims.ac.rw',
+    r'https://www.hilton.com/en/hotels/leehnhn-hilton-leeds-city/',
+    r'https://www.radissonhotels.com/en-us/hotels/park-plaza-leeds',
+    r'https://www.radissonhotels.com/en-us/hotels/radisson-blu-leeds'
 ]
 linkcheck_timeout = 30
 
diff --git a/docs/source/firedrake_25.rst b/docs/source/firedrake_25.rst
index 4b0d04746b..2bf9086665 100644
--- a/docs/source/firedrake_25.rst
+++ b/docs/source/firedrake_25.rst
@@ -68,7 +68,9 @@ It is possible during registration to purchase additional tickets for the dinner
 Registration
 ------------
 
-Registration will open soon and be closed on August 17th 2025.
+`Registration
+`__
+is now open.
 
 The registration fees will be as follows:
 
@@ -83,7 +85,9 @@ The registration fees will be as follows:
 Abstract submission
 -------------------
 
-Please submit your abstracts to the LIFD email: fluids-institute@leeds.ac.uk.
+We welcome presentations about any aspect of Firedrake development or
+application. Please submit your abstract `here
+`__.
 
 Support
 -------

From 758a6bf5d0135e43f458b845990bd4f532cd8c66 Mon Sep 17 00:00:00 2001
From: Leo Collins 
Date: Wed, 9 Jul 2025 16:42:45 +0100
Subject: [PATCH 173/222] Allow construction of interpolation matrix between a
 `VertexOnlyMesh` and its input-ordering (#4341)

* added create_petsc_mat method

* convert back to AIJ. It looked like `MatMatMult` wasn't supported between BAIJ and AIJ matrices

---------

Co-authored-by: Connor Ward 
---
 firedrake/interpolation.py                    | 121 +++++++++++++++---
 .../regression/test_interpolate_cross_mesh.py |  53 ++++++++
 .../vertexonly/test_vertex_only_fs.py         |  38 +++---
 3 files changed, 177 insertions(+), 35 deletions(-)

diff --git a/firedrake/interpolation.py b/firedrake/interpolation.py
index a18239865b..a5704231de 100644
--- a/firedrake/interpolation.py
+++ b/firedrake/interpolation.py
@@ -49,7 +49,8 @@ def __init__(self, expr, v,
                  subset=None,
                  access=op2.WRITE,
                  allow_missing_dofs=False,
-                 default_missing_val=None):
+                 default_missing_val=None,
+                 matfree=True):
         """Symbolic representation of the interpolation operator.
 
         Parameters
@@ -86,6 +87,10 @@ def __init__(self, expr, v,
                               then the values are either (a) unchanged if some ``output`` is given to
                               the :meth:`interpolate` method or (b) set to zero.
                               Ignored if interpolating within the same mesh or onto a :func:`.VertexOnlyMesh`.
+        matfree : bool
+                If ``False``, then construct the permutation matrix for interpolating
+                between a VOM and its input ordering. Defaults to ``True`` which uses SF broadcast
+                and reduce operations.
         """
         # Check function space
         if isinstance(v, functionspaceimpl.WithGeometry):
@@ -98,7 +103,8 @@ def __init__(self, expr, v,
         self.interp_data = {"subset": subset,
                             "access": access,
                             "allow_missing_dofs": allow_missing_dofs,
-                            "default_missing_val": default_missing_val}
+                            "default_missing_val": default_missing_val,
+                            "matfree": matfree}
 
     function_space = ufl.Interpolate.ufl_function_space
 
@@ -177,7 +183,7 @@ def interpolate(expr, V, *args, **kwargs):
         to zero. Ignored if interpolating within the same mesh or onto a
         :func:`.VertexOnlyMesh`.
     :kwarg ad_block_tag: An optional string for tagging the resulting assemble block on the Pyadjoint tape.
-    :returns: A synbolic :class:`.Interpolate` object
+    :returns: A symbolic :class:`.Interpolate` object
 
     .. note::
 
@@ -240,6 +246,9 @@ class Interpolator(abc.ABC):
         Ignored if interpolating within the same mesh or onto a
         :func:`.VertexOnlyMesh` (the behaviour of a :func:`.VertexOnlyMesh` in
         this scenario is, at present, set when it is created).
+    :kwarg matfree: If ``False``, then construct the permutation matrix for interpolating
+        between a VOM and its input ordering. Defaults to ``True`` which uses SF broadcast
+        and reduce operations.
 
     This object can be used to carry out the same interpolation
     multiple times (for example in a timestepping loop).
@@ -276,6 +285,7 @@ def __init__(
         access=op2.WRITE,
         bcs=None,
         allow_missing_dofs=False,
+        matfree=True
     ):
         self.expr = expr
         self.V = V
@@ -284,6 +294,7 @@ def __init__(
         self.access = access
         self.bcs = bcs
         self._allow_missing_dofs = allow_missing_dofs
+        self.matfree = matfree
         self.callable = None
         # Cope with the different convention of `Interpolate` and `Interpolator`:
         #  -> Interpolate(Argument(V1, 1), Argument(V2.dual(), 0))
@@ -330,7 +341,8 @@ def interpolate(self, *function, transpose=None, adjoint=False, default_missing_
                              subset=self.subset,
                              access=self.access,
                              allow_missing_dofs=self._allow_missing_dofs,
-                             default_missing_val=default_missing_val)
+                             default_missing_val=default_missing_val,
+                             matfree=self.matfree)
         if transpose is not None:
             warnings.warn("'transpose' argument is deprecated, use 'adjoint' instead", FutureWarning)
             adjoint = transpose or adjoint
@@ -403,6 +415,7 @@ def __init__(
         access=op2.WRITE,
         bcs=None,
         allow_missing_dofs=False,
+        matfree=True
     ):
         if subset:
             raise NotImplementedError("subset not implemented")
@@ -423,7 +436,7 @@ def __init__(
                 "Can only interpolate into spaces with point evaluation nodes."
             )
 
-        super().__init__(expr, V, subset, freeze_expr, access, bcs, allow_missing_dofs)
+        super().__init__(expr, V, subset, freeze_expr, access, bcs, allow_missing_dofs, matfree)
 
         self.arguments = extract_arguments(expr)
         self.nargs = len(self.arguments)
@@ -738,10 +751,12 @@ class SameMeshInterpolator(Interpolator):
     """
 
     @no_annotations
-    def __init__(self, expr, V, subset=None, freeze_expr=False, access=op2.WRITE, bcs=None, **kwargs):
-        super().__init__(expr, V, subset, freeze_expr, access, bcs)
+    def __init__(self, expr, V, subset=None, freeze_expr=False, access=op2.WRITE,
+                 bcs=None, matfree=True, **kwargs):
+        super().__init__(expr, V, subset=subset, freeze_expr=freeze_expr,
+                         access=access, bcs=bcs, matfree=matfree)
         try:
-            self.callable, arguments = make_interpolator(expr, V, subset, access, bcs=bcs)
+            self.callable, arguments = make_interpolator(expr, V, subset, access, bcs=bcs, matfree=matfree)
         except FIAT.hdiv_trace.TraceError:
             raise NotImplementedError("Can't interpolate onto traces sorry")
         self.arguments = arguments
@@ -812,7 +827,7 @@ def _interpolate(self, *function, output=None, transpose=None, adjoint=False, **
 
 
 @PETSc.Log.EventDecorator()
-def make_interpolator(expr, V, subset, access, bcs=None):
+def make_interpolator(expr, V, subset, access, bcs=None, matfree=True):
     assert isinstance(expr, ufl.classes.Expr)
     arguments = extract_arguments(expr)
     target_mesh = as_domain(V)
@@ -887,8 +902,7 @@ def make_interpolator(expr, V, subset, access, bcs=None):
         raise ValueError("Cannot interpolate an expression with %d arguments" % len(arguments))
 
     if vom_onto_other_vom:
-        # To interpolate between vertex-only meshes we use a PETSc SF
-        wrapper = VomOntoVomWrapper(V, source_mesh, target_mesh, expr, arguments)
+        wrapper = VomOntoVomWrapper(V, source_mesh, target_mesh, expr, arguments, matfree)
         # NOTE: get_dat_mpi_type ensures we get the correct MPI type for the
         # data, including the correct data size and dimensional information
         # (so for vector function spaces in 2 dimensions we might need a
@@ -916,7 +930,10 @@ def callable():
             wrapper.mpi_type, _ = get_dat_mpi_type(temp_source_func.dat)
 
             # Leave wrapper inside a callable so we can access the handle
-            # property (which is pretending to be a petsc mat)
+            # property. If matfree is True, then the handle is a PETSc SF
+            # pretending to be a PETSc Mat. If matfree is False, then this
+            # will be a PETSc Mat representing the equivalent permutation
+            # matrix
             def callable():
                 return wrapper
 
@@ -1392,9 +1409,14 @@ class VomOntoVomWrapper(object):
     arguments : list of `ufl.Argument`
         The arguments in the expression. These are not extracted from expr here
         since, where we use this, we already have them.
+    matfree : bool
+        If ``False``, the matrix representating the permutation of the points is
+        constructed and used to perform the interpolation. If ``True``, then the
+        interpolation is performed using the broadcast and reduce operations on the
+        PETSc Star Forest.
     """
 
-    def __init__(self, V, source_vom, target_vom, expr, arguments):
+    def __init__(self, V, source_vom, target_vom, expr, arguments, matfree):
         reduce = False
         if source_vom.input_ordering is target_vom:
             reduce = True
@@ -1411,9 +1433,15 @@ def __init__(self, V, source_vom, target_vom, expr, arguments):
         self.arguments = arguments
         self.reduce = reduce
         # note that interpolation doesn't include halo cells
-        self.handle = VomOntoVomDummyMat(
+        self.dummy_mat = VomOntoVomDummyMat(
             original_vom.input_ordering_without_halos_sf, reduce, V, source_vom, expr, arguments
         )
+        if matfree:
+            # If matfree, we use the SF to perform the interpolation
+            self.handle = self.dummy_mat._wrap_dummy_mat()
+        else:
+            # Otherwise we create the permutation matrix
+            self.handle = self.dummy_mat._create_permutation_mat()
 
     @property
     def mpi_type(self):
@@ -1426,10 +1454,10 @@ def mpi_type(self):
 
     @mpi_type.setter
     def mpi_type(self, val):
-        self.handle.mpi_type = val
+        self.dummy_mat.mpi_type = val
 
     def forward_operation(self, target_dat):
-        coeff = self.handle.expr_as_coeff()
+        coeff = self.dummy_mat.expr_as_coeff()
         with coeff.dat.vec_ro as coeff_vec, target_dat.vec_wo as target_vec:
             self.handle.mult(coeff_vec, target_vec)
 
@@ -1467,6 +1495,12 @@ def __init__(self, sf, forward_reduce, V, source_vom, expr, arguments):
         self.source_vom = source_vom
         self.expr = expr
         self.arguments = arguments
+        # Calculate correct local and global sizes for the matrix
+        nroots, leaves, _ = sf.getGraph()
+        nleaves = len(leaves)
+        self._local_sizes = V.comm.allgather(nroots)
+        self.source_size = (nroots, sum(self._local_sizes))
+        self.target_size = (nleaves, self.V.comm.allreduce(nleaves, op=MPI.SUM))
 
     @property
     def mpi_type(self):
@@ -1515,7 +1549,7 @@ def expr_as_coeff(self, source_vec=None):
         return coeff
 
     def reduce(self, source_vec, target_vec):
-        source_arr = source_vec.getArray()
+        source_arr = source_vec.getArray(readonly=True)
         target_arr = target_vec.getArray()
         self.sf.reduceBegin(
             self.mpi_type,
@@ -1531,7 +1565,7 @@ def reduce(self, source_vec, target_vec):
         )
 
     def broadcast(self, source_vec, target_vec):
-        source_arr = source_vec.getArray()
+        source_arr = source_vec.getArray(readonly=True)
         target_arr = target_vec.getArray()
         self.sf.bcastBegin(
             self.mpi_type,
@@ -1546,7 +1580,7 @@ def broadcast(self, source_vec, target_vec):
             MPI.REPLACE,
         )
 
-    def mult(self, source_vec, target_vec):
+    def mult(self, mat, source_vec, target_vec):
         # need to evaluate expression before doing mult
         coeff = self.expr_as_coeff(source_vec)
         with coeff.dat.vec_ro as coeff_vec:
@@ -1555,7 +1589,10 @@ def mult(self, source_vec, target_vec):
             else:
                 self.broadcast(coeff_vec, target_vec)
 
-    def multHermitian(self, source_vec, target_vec):
+    def multHermitian(self, mat, source_vec, target_vec):
+        self.multTranspose(mat, source_vec, target_vec)
+
+    def multTranspose(self, mat, source_vec, target_vec):
         # can only do adjoint if our expression exclusively contains a
         # single argument, making the application of the adjoint operator
         # straightforward (haven't worked out how to do this otherwise!)
@@ -1581,3 +1618,47 @@ def multHermitian(self, source_vec, target_vec):
             # matrix will then have rows of zeros for those points.
             target_vec.zeroEntries()
             self.reduce(source_vec, target_vec)
+
+    def _get_sizes(self):
+        nroots, leaves, _ = self.sf.getGraph()
+        nleaves = len(leaves)
+        local_sizes = self.V.comm.allgather(nroots)
+        source_size = (nroots, sum(local_sizes))
+        target_size = (nleaves, self.V.comm.allreduce(nleaves, op=MPI.SUM))
+        return source_size, target_size
+
+    def _create_permutation_mat(self):
+        """Creates the PETSc matrix that represents the interpolation operator from a vertex-only mesh to
+        its input ordering vertex-only mesh"""
+        mat = PETSc.Mat().createAIJ((self.target_size, self.source_size), nnz=1, comm=self.V.comm)
+        mat.setUp()
+        start = sum(self._local_sizes[:self.V.comm.rank])
+        end = start + self.source_size[0]
+        contiguous_indices = numpy.arange(start, end, dtype=utils.IntType)
+        perm = numpy.zeros(self.target_size[0], dtype=utils.IntType)
+        self.sf.bcastBegin(MPI.INT, contiguous_indices, perm, MPI.REPLACE)
+        self.sf.bcastEnd(MPI.INT, contiguous_indices, perm, MPI.REPLACE)
+        rows = numpy.arange(self.target_size[0] + 1, dtype=utils.IntType)
+        mat.setValuesCSR(rows, perm, numpy.ones_like(perm, dtype=utils.IntType))
+        mat.assemble()
+        if self.forward_reduce:
+            mat.transpose()
+        return mat
+
+    def _wrap_dummy_mat(self):
+        mat = PETSc.Mat().create(comm=self.V.comm)
+        dim = self.V.value_size
+        source_size = tuple(dim * i for i in self.source_size)
+        target_size = tuple(dim * i for i in self.target_size)
+        if self.forward_reduce:
+            mat_size = (source_size, target_size)
+        else:
+            mat_size = (target_size, source_size)
+        mat.setSizes(mat_size)
+        mat.setType(mat.Type.PYTHON)
+        mat.setPythonContext(self)
+        mat.setUp()
+        return mat
+
+    def duplicate(self, mat=None, op=None):
+        return self._wrap_dummy_mat()
diff --git a/tests/firedrake/regression/test_interpolate_cross_mesh.py b/tests/firedrake/regression/test_interpolate_cross_mesh.py
index c3e3c39ece..a3944bc526 100644
--- a/tests/firedrake/regression/test_interpolate_cross_mesh.py
+++ b/tests/firedrake/regression/test_interpolate_cross_mesh.py
@@ -736,6 +736,54 @@ def test_line_integral():
     assert np.isclose(assemble(f_line_square * dx), 1.0)
 
 
+def test_interpolate_matrix_cross_mesh():
+    source_mesh = UnitSquareMesh(4, 4)
+    target_mesh = UnitSquareMesh(5, 5)
+    U = FunctionSpace(source_mesh, "CG", 2)
+    V = FunctionSpace(target_mesh, "CG", 3)
+
+    # For comparison later
+    x, y = SpatialCoordinate(source_mesh)
+    f = Function(U).interpolate(x**2 + y**2)
+    x2, y2 = SpatialCoordinate(target_mesh)
+    g = Function(V).interpolate(x2**2 + y2**2)
+
+    # We get the VOM at the point evaluation node coords of the target FS
+    w = VectorFunctionSpace(target_mesh, V.ufl_element())
+    X = assemble(interpolate(target_mesh.coordinates, w))
+    vom = VertexOnlyMesh(source_mesh, X.dat.data_ro, redundant=False)
+    P0DG = FunctionSpace(vom, "DG", 0)
+    # We get the interpolation matrix U -> P0DG which performs point evaluation
+    A = assemble(interpolate(TestFunction(U), P0DG))
+    f_at_points = assemble(A @ f)
+    f_at_points2 = assemble(interpolate(f, P0DG))
+    assert np.allclose(f_at_points.dat.data_ro, f_at_points2.dat.data_ro)
+    # To get the points in the correct order in V we interpolate into vom.input_ordering
+    # We pass matfree=False which constructs the permutation matrix instead of using SFs
+    P0DG_io = FunctionSpace(vom.input_ordering, "DG", 0)
+    B = assemble(interpolate(TestFunction(P0DG), P0DG_io, matfree=False))
+    f_at_points_correct_order = assemble(B @ f_at_points)
+    f_at_points_correct_order2 = assemble(interpolate(f_at_points, P0DG_io))
+    assert np.allclose(f_at_points_correct_order.dat.data_ro, f_at_points_correct_order2.dat.data_ro)
+
+    # f_at_points_correct_order has the correct coefficients of the function in V
+    # It is a function in P0DG_io, so we just directly assign it to a function in V
+    f_interp = Function(V)
+    f_interp.dat.data_wo[:] = f_at_points_correct_order.dat.data_ro[:]
+    assert np.allclose(f_interp.dat.data_ro, g.dat.data_ro)
+
+    # Hence interpolation from U to V is the product of the following three matrices:
+    # C*B*A
+    # A is the interpolation matrix from U to P0DG
+    # B is the interpolation matrix from P0DG to vom.input_ordering
+    # C is direct assignment to the function in V
+    interp_mat = assemble(Action(B, A))
+    f_at_points_correct_order3 = assemble(interp_mat @ f)
+    f_interp2 = Function(V)
+    f_interp2.dat.data_wo[:] = f_at_points_correct_order3.dat.data_ro[:]
+    assert np.allclose(f_interp2.dat.data_ro, g.dat.data_ro)
+
+
 @pytest.mark.parallel
 def test_interpolate_cross_mesh_parallel(parameters):
     test_interpolate_cross_mesh(parameters)
@@ -756,6 +804,11 @@ def test_exact_refinement_parallel():
     test_exact_refinement()
 
 
+@pytest.mark.parallel
+def test_interpolate_matrix_cross_mesh_parallel():
+    test_interpolate_matrix_cross_mesh()
+
+
 def voting_algorithm_edgecases(nprocs):
     # this triggers lots of cases where the VOM voting algorithm has to deal
     # with points being claimed by multiple ranks: there are cases where each
diff --git a/tests/firedrake/vertexonly/test_vertex_only_fs.py b/tests/firedrake/vertexonly/test_vertex_only_fs.py
index 6fa9fddd7c..e152fee26b 100644
--- a/tests/firedrake/vertexonly/test_vertex_only_fs.py
+++ b/tests/firedrake/vertexonly/test_vertex_only_fs.py
@@ -120,6 +120,7 @@ def functionspace_tests(vm):
     idxs_to_include = input_ordering_parent_cell_nums != -1
     assert np.allclose(h.dat.data_ro_with_halos[idxs_to_include], np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
     assert np.all(h.dat.data_ro_with_halos[~idxs_to_include] == -1)
+
     # check other interpolation APIs work identically
     h2 = assemble(interpolate(g, W))
     assert np.allclose(h2.dat.data_ro_with_halos[idxs_to_include], h.dat.data_ro_with_halos[idxs_to_include])
@@ -154,8 +155,11 @@ def functionspace_tests(vm):
     assert np.allclose(g.dat.data_ro_with_halos, np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
     with pytest.raises(NotImplementedError):
         # Can't use adjoint on interpolators with expressions yet
-        g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
-        assert np.allclose(g2.dat.data_ro_with_halos, 2*np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
+        try:
+            g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
+            assert np.allclose(g2.dat.data_ro_with_halos, 2*np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
+        except PETSc.Error as e:
+            raise e.__cause__ from None
 
     I_io_adjoint = Interpolator(TestFunction(W), V)
     I2_io_adjoint = Interpolator(2*TestFunction(W), V)
@@ -163,11 +167,13 @@ def functionspace_tests(vm):
     h = h_star.riesz_representation(riesz_map="l2")
     assert np.allclose(h.dat.data_ro_with_halos[idxs_to_include], np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
     assert np.all(h.dat.data_ro_with_halos[~idxs_to_include] == 0)
-
     with pytest.raises(NotImplementedError):
         # Can't use adjoint on interpolators with expressions yet
-        h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
-        assert np.allclose(h2.dat.data_ro_with_halos[idxs_to_include], 2*np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
+        try:
+            h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
+            assert np.allclose(h2.dat.data_ro_with_halos[idxs_to_include], 2*np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
+        except PETSc.Error as e:
+            raise e.__cause__ from None
     g = assemble(I_io_adjoint.interpolate(h))
     assert np.allclose(g.dat.data_ro_with_halos, np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
     g2 = assemble(I2_io_adjoint.interpolate(h))
@@ -259,8 +265,11 @@ def vectorfunctionspace_tests(vm):
     assert np.allclose(g.dat.data_ro_with_halos, 2*vm.coordinates.dat.data_ro_with_halos)
     with pytest.raises(NotImplementedError):
         # Can't use adjoint on interpolators with expressions yet
-        g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
-        assert np.allclose(g2.dat.data_ro_with_halos, 4*vm.coordinates.dat.data_ro_with_halos)
+        try:
+            g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
+            assert np.allclose(g2.dat.data_ro_with_halos, 4*vm.coordinates.dat.data_ro_with_halos)
+        except PETSc.Error as e:
+            raise e.__cause__ from None
 
     I_io_adjoint = Interpolator(TestFunction(W), V)
     I2_io_adjoint = Interpolator(2*TestFunction(W), V)
@@ -268,9 +277,12 @@ def vectorfunctionspace_tests(vm):
     assert np.allclose(h_star.dat.data_ro[idxs_to_include], 2*vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include])
     assert np.all(h_star.dat.data_ro_with_halos[~idxs_to_include] == 0)
     with pytest.raises(NotImplementedError):
-        # Can't use adjoint on interpolators with expressions yet
-        h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
-        assert np.allclose(h2.dat.data_ro[idxs_to_include], 4*vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include])
+        try:
+            # Can't use adjoint on interpolators with expressions yet
+            h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
+            assert np.allclose(h2.dat.data_ro[idxs_to_include], 4*vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include])
+        except PETSc.Error as e:
+            raise e.__cause__ from None
 
     h = h_star.riesz_representation(riesz_map="l2")
     g = assemble(I_io_adjoint.interpolate(h))
@@ -279,6 +291,7 @@ def vectorfunctionspace_tests(vm):
     assert np.allclose(g2.dat.data_ro_with_halos, 4*vm.coordinates.dat.data_ro_with_halos)
 
 
+@pytest.mark.parallel([1, 3])
 def test_functionspaces(parentmesh, vertexcoords):
     vm = VertexOnlyMesh(parentmesh, vertexcoords, missing_points_behaviour=None)
     functionspace_tests(vm)
@@ -287,11 +300,6 @@ def test_functionspaces(parentmesh, vertexcoords):
     vectorfunctionspace_tests(vm.input_ordering)
 
 
-@pytest.mark.parallel
-def test_functionspaces_parallel(parentmesh, vertexcoords):
-    test_functionspaces(parentmesh, vertexcoords)
-
-
 @pytest.mark.parallel(nprocs=2)
 def test_simple_line():
     m = UnitIntervalMesh(4)

From 70cae78a453a13b4bd7beb5a5dcaaf1d66a3e2e7 Mon Sep 17 00:00:00 2001
From: Eike Hermann Mueller 
Date: Wed, 9 Jul 2025 16:44:34 +0100
Subject: [PATCH 174/222] Eikehmueller/gtmgpc restriction (#4373)

* Add functionality to pass an optional restriction matrix to GTMGPC

---------

Co-authored-by: Pablo Brubeck 
Co-authored-by: Eike Mueller 
---
 firedrake/preconditioners/gtmg.py             | 78 ++++++++++++++++++-
 firedrake_citations/__init__.py               | 16 ++++
 .../firedrake/multigrid/test_poisson_gtmg.py  | 28 +++++--
 3 files changed, 115 insertions(+), 7 deletions(-)

diff --git a/firedrake/preconditioners/gtmg.py b/firedrake/preconditioners/gtmg.py
index 160382e31f..4d25ce7259 100644
--- a/firedrake/preconditioners/gtmg.py
+++ b/firedrake/preconditioners/gtmg.py
@@ -1,3 +1,6 @@
+"""Non-nested multigrid preconditioner"""
+
+from firedrake_citations import Citations
 from firedrake.petsc import PETSc
 from firedrake.preconditioners.base import PCBase
 from firedrake.parameters import parameters
@@ -11,13 +14,60 @@
 
 
 class GTMGPC(PCBase):
+    """Non-nested multigrid preconditioner
+
+    Implements the method described in [1]. Note that while the authors of
+    this paper consider a non-nested function space hierarchy, the algorithm
+    can also be applied if the spaces are nested.
+
+    Uses PCMG to implement a two-level multigrid method involving two spaces:
+
+        * `V`: the fine space on which the problem is formulated
+        * `V_coarse`: a user-defined coarse space
+
+    The following options must be passed through the `appctx` dictionary:
+
+        * `get_coarse_space`: method which returns the user-defined coarse space
+        * `get_coarse_operator`: method which returns the operator on the coarse space
+
+    The following options (also passed through the `appctx`) are optional:
+
+        * `form_compiler_parameters`: parameters for assembling operators on
+          both levels of the hierarchy.
+        * `coarse_space_bcs`: boundary conditions to be used on coarse space.
+        * `get_coarse_op_nullspace`: method which returns the nullspace of the
+          coarse operator.
+        * `get_coarse_op_transpose_nullspace`: method which returns the
+          nullspace of the transpose of the coarse operator.
+        * `interpolation_matrix`: PETSc Mat which describes the interpolation
+          from the coarse to the fine space. If omitted, this will be
+          constructed automatically with an :class:`.Interpolate` object.
+        * `restriction_matrix`: PETSc Mat which describes the restriction
+          from the fine space dual to the coarse space dual. It defaults
+          to the transpose of the interpolation matrix.
+
+    PETSc options for the underlying PCMG object can be set with the
+    prefix ``gt_``.
+
+    Reference:
+
+        [1] Gopalakrishnan, J. and Tan, S., 2009: "A convergent multigrid
+        cycle for the hybridized mixed method". Numerical Linear Algebra
+        with Applications, 16(9), pp.689-714. https://doi.org/10.1002/nla.636
+
+    """
 
     needs_python_pmat = False
     _prefix = "gt_"
 
     def initialize(self, pc):
+        """Initialize new instance
+
+        :arg pc: PETSc preconditioner instance
+        """
         from firedrake.assemble import assemble, get_assembler
 
+        Citations().register("Gopalakrishnan2009")
         _, P = pc.getOperators()
         appctx = self.get_appctx(pc)
         fcp = appctx.get("form_compiler_parameters")
@@ -58,7 +108,7 @@ def initialize(self, pc):
         else:
             fine_petscmat = P
 
-        # Transfer fine operator null space
+        # Transfer fine operator nullspace
         fine_petscmat.setNullSpace(P.getNullSpace())
         fine_transpose_nullspace = P.getTransposeNullSpace()
         if fine_transpose_nullspace.handle != 0:
@@ -107,6 +157,7 @@ def initialize(self, pc):
             coarse_test, coarse_trial = coarse_operator.arguments()
             interp = assemble(Interpolate(coarse_trial, fine_space))
             interp_petscmat = interp.petscmat
+        restr_petscmat = appctx.get("restriction_matrix", None)
 
         # We set up a PCMG object that uses the constructed interpolation
         # matrix to generate the restriction/prolongation operators.
@@ -119,6 +170,8 @@ def initialize(self, pc):
         pcmg.setMGLevels(2)
         pcmg.setMGCycleType(pc.MGCycleType.V)
         pcmg.setMGInterpolation(1, interp_petscmat)
+        if restr_petscmat is not None:
+            pcmg.setMGRestriction(1, restr_petscmat)
         pcmg.setOperators(A=fine_petscmat, P=fine_petscmat)
 
         coarse_solver = pcmg.getMGCoarseSolve()
@@ -147,6 +200,12 @@ def initialize(self, pc):
             coarse_solver.setFromOptions()
 
     def update(self, pc):
+        """Update preconditioner
+
+        Re-assemble operators on both levels of the hierarchy
+
+        :arg pc: PETSc preconditioner instance
+        """
         if hasattr(self, "fine_op"):
             self._assemble_fine_op(tensor=self.fine_op)
 
@@ -154,16 +213,33 @@ def update(self, pc):
         self.pc.setUp()
 
     def apply(self, pc, X, Y):
+        """Apply preconditioner
+
+        :arg pc: PETSc preconditioner instance
+        :arg X: right hand side PETSc vector
+        :arg Y: PETSc vector with resulting solution
+        """
         dm = self._dm
         with dmhooks.add_hooks(dm, self, appctx=self._ctx_ref):
             self.pc.apply(X, Y)
 
     def applyTranspose(self, pc, X, Y):
+        """Apply transpose preconditioner
+
+        :arg pc: PETSc preconditioner instance
+        :arg X: right hand side PETSc vector
+        :arg Y: PETSc vector with resulting solution
+        """
         dm = self._dm
         with dmhooks.add_hooks(dm, self, appctx=self._ctx_ref):
             self.pc.applyTranspose(X, Y)
 
     def view(self, pc, viewer=None):
+        """View preconditioner options
+
+        :arg pc: preconditioner instance
+        :arg viewer: PETSc viewer instance
+        """
         super(GTMGPC, self).view(pc, viewer)
         if hasattr(self, "pc"):
             viewer.printfASCII("PC using Gopalakrishnan and Tan algorithm\n")
diff --git a/firedrake_citations/__init__.py b/firedrake_citations/__init__.py
index 6ad0108c2f..1b69e752b1 100644
--- a/firedrake_citations/__init__.py
+++ b/firedrake_citations/__init__.py
@@ -292,6 +292,22 @@ def print_at_exit(cls):
 }
 """)
 
+Citations().add("Gopalakrishnan2009", """
+@article{Gopalakrishnan2009,
+  author    = {Jayadeep Gopalakrishnan and Shuguang Tan},
+  doi       = {10.1002/nla.636},
+  journal   = {Numerical Linear Algebra with Applications},
+  month     = {sep},
+  number    = {9},
+  pages     = {689--714},
+  publisher = {Wiley},
+  title     = {A convergent multigrid cycle for the hybridized mixed method},
+  url       = {https://doi.org/10.1002/nla.636},
+  volume    = {16},
+  year      = {2009}
+}
+""")
+
 Citations().add("nixonhill2023consistent", """
 @article{nixonhill2023consistent,
   author        = {Nixon-Hill, R. W. and Shapero, D. and Cotter, C. J. and Ham, D. A.},
diff --git a/tests/firedrake/multigrid/test_poisson_gtmg.py b/tests/firedrake/multigrid/test_poisson_gtmg.py
index 5c2be75b95..f70e5c6825 100644
--- a/tests/firedrake/multigrid/test_poisson_gtmg.py
+++ b/tests/firedrake/multigrid/test_poisson_gtmg.py
@@ -2,7 +2,7 @@
 import pytest
 
 
-def run_gtmg_mixed_poisson():
+def run_gtmg_mixed_poisson(custom_transfer=False):
 
     m = UnitSquareMesh(10, 10)
     nlevels = 2
@@ -14,7 +14,7 @@ def get_p1_space():
         return FunctionSpace(mesh, "CG", 1)
 
     def get_p1_prb_bcs():
-        return DirichletBC(get_p1_space(), Constant(0.0), "on_boundary")
+        return DirichletBC(get_p1_space(), 0, "on_boundary")
 
     def p1_callback():
         P1 = get_p1_space()
@@ -57,13 +57,28 @@ def p1_callback():
     appctx = {'get_coarse_operator': p1_callback,
               'get_coarse_space': get_p1_space,
               'coarse_space_bcs': get_p1_prb_bcs()}
-
-    solve(a == L, w, solver_parameters=params, appctx=appctx)
+    if custom_transfer:
+        P1 = get_p1_space()
+        V = FunctionSpace(mesh, "DGT", degree - 1)
+        I = assemble(Interpolate(TrialFunction(P1), V)).petscmat
+        R = PETSc.Mat().createTranspose(I)
+        appctx['interpolation_matrix'] = I
+        appctx['restriction_matrix'] = R
+
+    problem = LinearVariationalProblem(a, L, w)
+    solver = LinearVariationalSolver(problem, solver_parameters=params, appctx=appctx)
+    solver.solve()
     _, uh = w.subfunctions
 
     # Analytical solution
     f.interpolate(x[0]*(1-x[0])*x[1]*(1-x[1]))
 
+    if custom_transfer:
+        hyb = solver.snes.ksp.pc.getPythonContext()
+        gtmg = hyb.trace_ksp.pc.getPythonContext()
+        assert I.handle != R.handle
+        assert gtmg.pc.getMGInterpolation(1).handle == I.handle
+        assert gtmg.pc.getMGRestriction(1).handle == R.handle
     return errornorm(f, uh, norm_type="L2")
 
 
@@ -144,8 +159,9 @@ def p1_callback():
 
 
 @pytest.mark.skipcomplexnoslate
-def test_mixed_poisson_gtmg():
-    assert run_gtmg_mixed_poisson() < 1e-5
+@pytest.mark.parametrize("custom_transfer", [False, True])
+def test_mixed_poisson_gtmg(custom_transfer):
+    assert run_gtmg_mixed_poisson(custom_transfer) < 1e-5
 
 
 @pytest.mark.skipcomplexnoslate

From aa8566d2d88469a7f9c95b4a8ba507b093b575ef Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 9 Jul 2025 16:45:55 +0100
Subject: [PATCH 175/222] Clarify install docs (#4366)

* Clarify install docs

* Add minimal set of packages to docs so it is clear that PETSc will
  download most of the external packages.
* Add link to HPC installation wiki page.
* Remove cmake, openmpi and openblas from 'essential' package list

---------

Co-authored-by: David A. Ham 
Co-authored-by: Josh Hope-Collins 
---
 .gitignore                  |  1 +
 docs/Makefile               |  9 +++++-
 docs/source/install.rst     | 22 +++++++++++--
 scripts/firedrake-configure | 61 ++++++++++++++++++++++++++-----------
 4 files changed, 72 insertions(+), 21 deletions(-)

diff --git a/.gitignore b/.gitignore
index 2ba4237dcd..dede58dcd7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,6 +19,7 @@ __pycache__/
 /docs/source/firedrake.rst
 /docs/source/firedrake.*.rst
 /docs/source/homebrew_deps.txt
+/docs/source/minimal_apt_deps.txt
 /docs/source/obtaining_pyop2.rst
 /docs/source/petsc_configure_options.txt
 /docs/source/team.rst
diff --git a/docs/Makefile b/docs/Makefile
index 67d6cc4c0b..eaf4cd14f0 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -43,7 +43,7 @@ help:
 
 TARGETS = html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
 
-GENERATED_FILES = source/team.rst source/demos source/element_list.csv source/firedrake_citations.rst source/apt_deps.txt source/homebrew_deps.txt source/petsc_configure_options.txt
+GENERATED_FILES = source/team.rst source/demos source/element_list.csv source/firedrake_citations.rst source/apt_deps.txt source/homebrew_deps.txt source/minimal_apt_deps.txt source/petsc_configure_options.txt
 
 publishpdf:
 	env FIREDRAKE_MANUAL_RELEASE=`date +%Y-%m` $(MAKE) latexpdf
@@ -101,6 +101,13 @@ source/homebrew_deps.txt:
 		--show-system-packages > source/homebrew_deps.tmp
 	mv source/homebrew_deps.tmp source/homebrew_deps.txt
 
+source/minimal_apt_deps.txt:
+	python ../scripts/firedrake-configure \
+		--package-manager apt-x86_64 \
+		--show-minimal-system-packages > source/minimal_apt_deps.tmp
+	mv source/minimal_apt_deps.tmp source/minimal_apt_deps.txt
+
+
 source/petsc_configure_options.txt:
 	python ../scripts/firedrake-configure \
 		--no-package-manager \
diff --git a/docs/source/install.rst b/docs/source/install.rst
index a16b5d6a08..85505a5c4b 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -20,6 +20,13 @@ it should be installable on any Linux distribution. Windows users are encouraged
 to use WSL_ or one of Firedrake's
 :ref:`alternative installation mechanisms`.
 
+If installing on an HPC system most of the following steps should remain
+applicable, though care will have to be taken to make sure that the right system
+packages are used. A community-maintained collection of instructions for how to install
+Firedrake onto a number of different HPC systems may be found
+`here `__.
+If install on an HPC system not included in the wiki, please consider contributing a
+page describing the installation on that system.
 
 .. _pip_install_firedrake:
 
@@ -92,9 +99,20 @@ which will install the following packages:
 .. literalinclude:: homebrew_deps.txt
    :language: text
 
-If you do not have one of these systems then these dependencies will need to
-be installed manually.
+The packages installed here are a combination of system dependencies,
+like a C compiler, BLAS, and MPI,  and 'external packages' that are used by PETSc, like
+MUMPS and HDF5.
 
+If you are not installing onto Ubuntu or macOS then it is your responsibility to
+ensure that these system dependencies are in place. Some of the dependencies
+(e.g. a C compiler) must come from your system whereas others, if desired, may be
+downloaded by PETSc ``configure`` by passing additional flags like
+``--download-mpich`` or ``--download-openblas`` (run ``./configure --help | less`` to
+see what is available). To give you a guide as to what system dependencies are
+needed, on Ubuntu they are:
+
+.. literalinclude:: minimal_apt_deps.txt
+   :language: text
 
 .. _install_petsc:
 
diff --git a/scripts/firedrake-configure b/scripts/firedrake-configure
index 8f74642951..359faacef9 100755
--- a/scripts/firedrake-configure
+++ b/scripts/firedrake-configure
@@ -82,7 +82,18 @@ Please see https://firedrakeproject.org/install for more information."""
         "--show-system-packages",
         "--sysdeps",  # alias
         action="store_true",
-        help="Print out the system packages Firedrake needs. The user must install these.",
+        help=(
+            "Print out the system packages Firedrake needs including those "
+            "expected by PETSc."
+        ),
+    )
+    cmd_group.add_argument(
+        "--show-minimal-system-packages",
+        action="store_true",
+        help=(
+            "Print out the minimal system packages Firedrake needs to build. "
+            "The user must install these."
+        ),
     )
     cmd_group.add_argument(
         "--show-petsc-configure-options",
@@ -120,6 +131,13 @@ Please see https://firedrakeproject.org/install for more information."""
                 "please install them manually"
             )
         print(" ".join(SYSTEM_PACKAGES[package_manager, arch]), end="")
+    elif args.show_minimal_system_packages:
+        if package_manager is None:
+            raise RuntimeError(
+                "Cannot install Firedrake dependencies without a package manager, "
+                "please install them manually"
+            )
+        print(" ".join(MINIMAL_SYSTEM_PACKAGES[package_manager]), end="")
     elif args.show_petsc_configure_options:
         print(" ".join(PETSC_CONFIGURE_OPTIONS[package_manager, arch]), end="")
     elif args.show_petsc_version:
@@ -166,21 +184,21 @@ def has_homebrew() -> bool:
         raise RuntimeError("Unexpected error occurred searching for homebrew")
 
 
-BASE_LINUX_APT_PACKAGES = (
-    "bison",
+MINIMAL_LINUX_APT_PACKAGES = (
     "build-essential",
-    "cmake",
     "flex",
     "gfortran",
     "git",
-    "libopenblas-dev",
-    "libopenmpi-dev",
     "ninja-build",
     "pkg-config",
     "python3-dev",
     "python3-pip",
 )
 
+BASE_LINUX_APT_PACKAGES = (
+    MINIMAL_LINUX_APT_PACKAGES + ("bison", "cmake", "libopenblas-dev", "libopenmpi-dev")
+)
+
 PETSC_EXTRAS_LINUX_APT_PACKAGES = (
     "libfftw3-dev",
     "libfftw3-mpi-dev",
@@ -197,23 +215,24 @@ PETSC_EXTRAS_LINUX_APT_PACKAGES = (
     "libsuperlu-dist-dev",
 )
 
-COMMON_LINUX_APT_PACKAGES = BASE_LINUX_APT_PACKAGES + PETSC_EXTRAS_LINUX_APT_PACKAGES
+LINUX_APT_PACKAGES = BASE_LINUX_APT_PACKAGES + PETSC_EXTRAS_LINUX_APT_PACKAGES
 
-BASE_MACOS_HOMEBREW_PACKAGES = (
+MINIMAL_MACOS_HOMEBREW_PACKAGES = (
     "autoconf",
     "automake",
     "boost",
-    "cmake",
     "gcc",
     "libtool",
     "make",
     "ninja",
-    "openblas",
-    "openmpi",
     "pkg-config",
     "python",
 )
 
+BASE_MACOS_HOMEBREW_PACKAGES = (
+    MINIMAL_MACOS_HOMEBREW_PACKAGES + ("cmake", "openblas", "openmpi")
+)
+
 PETSC_EXTRAS_MACOS_HOMEBREW_PACKAGES = (
     "fftw",
     "hwloc",
@@ -225,17 +244,23 @@ PETSC_EXTRAS_MACOS_HOMEBREW_PACKAGES = (
     "zlib",
 )
 
-COMMON_MACOS_HOMEBREW_PACKAGES = (
+MACOS_HOMEBREW_PACKAGES = (
     BASE_MACOS_HOMEBREW_PACKAGES + PETSC_EXTRAS_MACOS_HOMEBREW_PACKAGES
 )
 
+MINIMAL_SYSTEM_PACKAGES = {
+    LINUX_APT_X86_64: MINIMAL_LINUX_APT_PACKAGES,
+    LINUX_APT_AARCH64: MINIMAL_LINUX_APT_PACKAGES,
+    MACOS_HOMEBREW_ARM64: MINIMAL_MACOS_HOMEBREW_PACKAGES,
+}
+
 SYSTEM_PACKAGES = {
-    (LINUX_APT_X86_64, ARCH_DEFAULT): COMMON_LINUX_APT_PACKAGES,
-    (LINUX_APT_X86_64, ARCH_COMPLEX): COMMON_LINUX_APT_PACKAGES,
-    (LINUX_APT_AARCH64, ARCH_DEFAULT): COMMON_LINUX_APT_PACKAGES,
-    (LINUX_APT_AARCH64, ARCH_COMPLEX): COMMON_LINUX_APT_PACKAGES,
-    (MACOS_HOMEBREW_ARM64, ARCH_DEFAULT): COMMON_MACOS_HOMEBREW_PACKAGES,
-    (MACOS_HOMEBREW_ARM64, ARCH_COMPLEX): COMMON_MACOS_HOMEBREW_PACKAGES,
+    (LINUX_APT_X86_64, ARCH_DEFAULT): LINUX_APT_PACKAGES,
+    (LINUX_APT_X86_64, ARCH_COMPLEX): LINUX_APT_PACKAGES,
+    (LINUX_APT_AARCH64, ARCH_DEFAULT): LINUX_APT_PACKAGES,
+    (LINUX_APT_AARCH64, ARCH_COMPLEX): LINUX_APT_PACKAGES,
+    (MACOS_HOMEBREW_ARM64, ARCH_DEFAULT): MACOS_HOMEBREW_PACKAGES,
+    (MACOS_HOMEBREW_ARM64, ARCH_COMPLEX): MACOS_HOMEBREW_PACKAGES,
 }
 
 COMMON_PETSC_CONFIGURE_OPTIONS = (

From bfcfb96d7a05de791af875e31af70cbcd1661d6f Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 9 Jul 2025 16:46:45 +0100
Subject: [PATCH 176/222] Better check for VCS info in firedrake-zenodo (#4433)

---
 firedrake/scripts/firedrake-zenodo | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/firedrake/scripts/firedrake-zenodo b/firedrake/scripts/firedrake-zenodo
index 44fdc0f42f..3a751af02b 100755
--- a/firedrake/scripts/firedrake-zenodo
+++ b/firedrake/scripts/firedrake-zenodo
@@ -176,13 +176,12 @@ def get_component_version(component: str) -> ComponentVersion:
 
     if direct_url_json:
         direct_url = json.loads(direct_url_json)
-        is_editable = direct_url.get("dir_info", {}).get("editable", False)
-        if is_editable:
+        try:
+            commit_id = direct_url["vcs_info"]["commit_id"]
+        except KeyError:
             # sniff the commit info from the repository
             repo = direct_url["url"].removeprefix("file://")
             commit_id = get_git_commit_info(repo)
-        else:
-            commit_id = direct_url["vcs_info"]["commit_id"]
         return VCSComponentVersion(commit_id)
     else:
         # 'direct_url_json' is 'None' if the package is installed via PyPI

From cc8f9d40de5fa4a3afd371c3e8378ce36f87afff Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Wed, 9 Jul 2025 16:48:20 +0100
Subject: [PATCH 177/222] Use python3 in firedrake-run-split-tests so it works
 in the docker containers (#4428)

---
 scripts/firedrake-run-split-tests | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/firedrake-run-split-tests b/scripts/firedrake-run-split-tests
index 30a413c280..d13ff42ca9 100755
--- a/scripts/firedrake-run-split-tests
+++ b/scripts/firedrake-run-split-tests
@@ -36,10 +36,10 @@ cache_cmd="PYOP2_CACHE_DIR=\$VIRTUAL_ENV/.cache/pyop2/job{#} \
            FIREDRAKE_TSFC_KERNEL_CACHE_DIR=\$VIRTUAL_ENV/.cache/tsfc/job{#}"
 
 if [ $num_procs = 1 ]; then
-    pytest_exec="python -m pytest"
+    pytest_exec="python3 -m pytest"
     marker_spec="\"parallel[1] or not parallel\""
 else
-    pytest_exec="mpiexec -n ${num_procs} python -m pytest"
+    pytest_exec="mpiexec -n ${num_procs} python3 -m pytest"
     marker_spec="parallel[${num_procs}]"
 fi
 

From 9af5cad69a4e3520286bc67094bb959d5466d1e4 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 9 Jul 2025 16:49:54 +0100
Subject: [PATCH 178/222] Add initial Firedrake 26 information to the website
 (#4426)

---
 docs/source/events.rst       | 13 ++++++++++---
 docs/source/firedrake_26.rst | 32 ++++++++++++++++++++++++++++++++
 2 files changed, 42 insertions(+), 3 deletions(-)
 create mode 100644 docs/source/firedrake_26.rst

diff --git a/docs/source/events.rst b/docs/source/events.rst
index 5885ec267f..88424219cf 100644
--- a/docs/source/events.rst
+++ b/docs/source/events.rst
@@ -8,6 +8,13 @@
 Firedrake events
 ----------------
 
+PETSc annual user meeting and Firedrake '26
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The eleventh Firedrake user and developer workshop will be held
+jointly with the PETSc annual user meeting near London between
+1-5 June 2026. For more information see :doc:`the workshop page `.
+
 Firedrake '25
 ~~~~~~~~~~~~~
 
@@ -19,7 +26,7 @@ information see :doc:`the workshop page `.
 CCP-DCM Hackathon
 ~~~~~~~~~~~~~~~~~
 
-In conjunction with our friends from FEniCS we're hosting a 3 day retreat in
+In conjunction with our friends from FEniCS we hosted a 3 day retreat in
 Totnes to write up demos based on users' use-cases for Firedrake and its
 downstream applications. For more details see `the website
 `__.
@@ -27,14 +34,14 @@ downstream applications. For more details see `the website
 Finite Element Course at AIMS Rwanda
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The Firedrake team will be presenting a short course on the Finite Element
+The Firedrake team presented a short course on the Finite Element
 Method at the African Institute for Mathematical Sciences in Kigali between 8
 and 12 April 2025. For more information see :doc:`the course page `.
 
 Firedrake USA'25
 ~~~~~~~~~~~~~~~~
 
-The ninth Firedrake user and developer workshop will be held 28 February
+The ninth Firedrake user and developer workshop was held 28 February
 and 1 March 2025 at Baylor University in Texas, USA.  For more information
 see :doc:`the workshop page `.
 
diff --git a/docs/source/firedrake_26.rst b/docs/source/firedrake_26.rst
new file mode 100644
index 0000000000..f2e8b9ebfd
--- /dev/null
+++ b/docs/source/firedrake_26.rst
@@ -0,0 +1,32 @@
+:orphan: true
+
+.. title:: PETSc annual user meeting and Firedrake '26
+
+PETSc annual user meeting and Firedrake '26
+-------------------------------------------
+
+.. warning::
+   This page is for the Firedrake workshop to be held in 2026. For the
+   2025 workshop please see :doc:`here `.
+
+The eleventh Firedrake user and developer workshop will be held jointly
+with the `PETSc annual user meeting `__
+near London between 1-5 June 2026.
+
+The workshop will be an opportunity for Firedrake users and developers
+to discuss the latest developments in the project, anticipated future
+developments, and how Firedrake can be used in the numerical solution
+of partial differential equations. The event will provide Firedrake
+users with the opportunity to interact directly with developers and
+with other users.
+
+Organising committee
+--------------------
+
+* `David Ham `__, Imperial College London
+* `Connor Ward `__, Imperial College London
+
+Further details
+---------------
+
+For any queries, please contact `Connor Ward `_.

From af2f5ba30c916ea2c3b6e417c81335e73c67786d Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 9 Jul 2025 16:53:38 +0100
Subject: [PATCH 179/222] Fix AUTHORS.rst (#4420)

* Fix AUTHORS.rst

* Also run git diff
---
 .github/workflows/core.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index e3b912c20f..1e671b3474 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -421,6 +421,8 @@ jobs:
             echo Error: there are untracked files in the repository
             echo The output from 'git status' is:
             git status
+            echo The output from 'git diff' is:
+            git diff
             exit 1
           fi
 

From a8ddb726397763aeb94e6ead7b5278c50766e09b Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 9 Jul 2025 16:54:05 +0100
Subject: [PATCH 180/222] Fix demo links (#4422)

---
 demos/patch/poisson_mg_patches.py.rst   | 2 +-
 demos/patch/stokes_vanka_patches.py.rst | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/demos/patch/poisson_mg_patches.py.rst b/demos/patch/poisson_mg_patches.py.rst
index 03813c17fb..4ab35b0b43 100644
--- a/demos/patch/poisson_mg_patches.py.rst
+++ b/demos/patch/poisson_mg_patches.py.rst
@@ -187,4 +187,4 @@ While for either :class:`~.PatchPC` or :class:`~.ASMStarPC`, we expect
    7         8
 ======== ================
 
-A runnable python version of this demo can be found :demo:`here`.
+A runnable python version of this demo can be found :demo:`here`.
diff --git a/demos/patch/stokes_vanka_patches.py.rst b/demos/patch/stokes_vanka_patches.py.rst
index 9a4bb6ca96..d47b4af693 100644
--- a/demos/patch/stokes_vanka_patches.py.rst
+++ b/demos/patch/stokes_vanka_patches.py.rst
@@ -156,4 +156,4 @@ For either set of options, we expect 10 iterations to convergence for each mesh
   3        10
 ======== ============
 
-A runnable python version of this demo can be found :demo:`here`.
+A runnable python version of this demo can be found :demo:`here`.

From bf5d7326fd0b781ab2e0670acd9b7c6726dbe90f Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 9 Jul 2025 16:55:14 +0100
Subject: [PATCH 181/222] Use petsctools release (#4423)

---
 pyproject.toml         | 4 ++--
 requirements-build.txt | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 9f4f2f22d8..f86a67837c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,7 +29,7 @@ dependencies = [
   "packaging",
   # TODO RELEASE
   # "petsc4py==3.23.3",
-  "petsctools @ git+https://github.com/firedrakeproject/petsctools.git",
+  "petsctools",
   "pkgconfig",
   "progress",
   "pyadjoint-ad>=2025.04",
@@ -145,7 +145,7 @@ requires = [
   "pkgconfig",
   # TODO RELEASE
   # "petsc4py==3.23.3",
-  "petsctools @ git+https://github.com/firedrakeproject/petsctools.git",
+  "petsctools",
   "pybind11",
   "setuptools>=77.0.3",
   "rtree>=1.2",
diff --git a/requirements-build.txt b/requirements-build.txt
index 36790d8f26..3e63fbea9f 100644
--- a/requirements-build.txt
+++ b/requirements-build.txt
@@ -5,7 +5,7 @@ mpi4py>3; python_version >= '3.13'
 mpi4py; python_version < '3.13'
 numpy
 pkgconfig
-petsctools @ git+https://github.com/firedrakeproject/petsctools.git
+petsctools
 pybind11
 setuptools>=77.0.3
 rtree>=1.2

From 8ac1f2471bff7543b9892ab646850fd37786bd35 Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Wed, 9 Jul 2025 16:56:25 +0100
Subject: [PATCH 182/222] Recommend running firedrake-clean after updating
 (#4404)

---
 docs/source/install.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/docs/source/install.rst b/docs/source/install.rst
index 6453f255b5..6d45e288c5 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -221,6 +221,12 @@ to set the right environment variables and then run::
 
      $ pip install --upgrade firedrake
 
+Previously generated code may not be compatible with a newer
+Firedrake installation, and may crash with cryptic messages.
+We recommend removing any cached code after updating by running::
+
+     $ firedrake-clean
+
 Updating PETSc
 ~~~~~~~~~~~~~~
 

From 44b1bf1062b2f108ac227aa22d2ee54a5466a534 Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Thu, 10 Jul 2025 13:01:58 +0100
Subject: [PATCH 183/222] payment links (#4435)

---
 docs/source/firedrake_25.rst | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/docs/source/firedrake_25.rst b/docs/source/firedrake_25.rst
index 2bf9086665..b27bdcfa3e 100644
--- a/docs/source/firedrake_25.rst
+++ b/docs/source/firedrake_25.rst
@@ -72,14 +72,17 @@ Registration
 `__
 is now open.
 
+In addition to registering to attend, participants need to pay for their
+registration using the links below.
+
 The registration fees will be as follows:
 
 .. list-table::
    :widths: 25 25
 
-   * - Student
+   * - `Student `__
      - £25
-   * - Non-student
+   * - `Non-student `__
      - £115
 
 Abstract submission

From 4a2fb06a36659d34c078d13982f9cd58d411025f Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Fri, 11 Jul 2025 13:37:10 +0100
Subject: [PATCH 184/222] Dham/firedrake 25 payment (#4438)

* payment links

* plus deadlines
---
 docs/source/firedrake_25.rst | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/docs/source/firedrake_25.rst b/docs/source/firedrake_25.rst
index b27bdcfa3e..b6c8c45f7b 100644
--- a/docs/source/firedrake_25.rst
+++ b/docs/source/firedrake_25.rst
@@ -26,6 +26,13 @@ with other users.
 The conference will begin around lunchtime on 15 September and end on the
 evening of 17 September.
 
+Key Dates
+---------
+
+- Abstract deadline: 17 August 2025
+- Registration deadline: 25 August 2025
+- Workshop: 15-17 September 2025
+
 Conference venue
 ----------------
 

From 11d377dfefdbbd117cb91e9c52f9dce3468fae7b Mon Sep 17 00:00:00 2001
From: Leo Collins 
Date: Tue, 15 Jul 2025 13:53:50 +0100
Subject: [PATCH 185/222] Fix typos in point evaluation docs (#4436)

---
 docs/source/point-evaluation.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/docs/source/point-evaluation.rst b/docs/source/point-evaluation.rst
index 0da44f8396..731a0081f5 100644
--- a/docs/source/point-evaluation.rst
+++ b/docs/source/point-evaluation.rst
@@ -31,10 +31,10 @@ a few points.
    # evaluate f at one 2-dimensional point
    f.at([0.2, 0.4])
 
-   # evaluate f at two 2-dimensional point
+   # evaluate f at two 2-dimensional points
    f.at([0.2, 0.4], [1.2, 0.5])
 
-   # evaluate f at two 2-dimensional point (same as above)
+   # evaluate f at two 2-dimensional points (same as above)
    f.at([[0.2, 0.4], [1.2, 0.5]])
 
 While in these examples we have only shown lists, other *iterables*
@@ -210,7 +210,7 @@ by rank B then they will be moved to rank B.
 
 If the same coordinates are supplied more than once, they are always assumed to
 be a new vertex: this is true for both ``redundant = True`` and
-``redunant = False``. So if we have the same set of points on all MPI processes
+``redundant = False``. So if we have the same set of points on all MPI processes
 and switch from ``redundant = True`` to ``redundant = False`` we will get point
 duplication.
 
@@ -218,7 +218,7 @@ duplication.
 Points outside the domain
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Be default points outside the domain by more than the :ref:`specified
+By default points outside the domain by more than the :ref:`specified
 tolerance ` will generate
 a :class:`~.VertexOnlyMeshMissingPointsError`. This can be switched to a
 warning or switched off entirely:
@@ -262,7 +262,7 @@ These equivalent expressions for point evaluation
 
 where :math:`N` is the number of points, :math:`x_i` is the :math:`i`\th point,
 :math:`\Omega` is a 'parent' mesh, :math:`f` is a function on that mesh,
-:math:`\delta` is a dirac delta distribition can therefore be written in
+:math:`\delta` is a Dirac delta distribution can therefore be written in
 Firedrake using :func:`~.VertexOnlyMesh` and :func:`~.interpolate` as
 
 .. literalinclude:: ../../tests/firedrake/vertexonly/test_vertex_only_manual.py
@@ -320,7 +320,7 @@ distributed** we can use :py:attr:`~.VertexOnlyMeshTopology.input_ordering` as f
 
 .. note::
 
-   When a a vertex-only mesh is created with ``redundant = True`` (which is the
+   When a vertex-only mesh is created with ``redundant = True`` (which is the
    default when creating a :func:`~.VertexOnlyMesh`) the
    :py:attr:`~.VertexOnlyMeshTopology.input_ordering` method will return a vertex-only
    mesh with all points on rank 0.
@@ -331,7 +331,7 @@ If we ran the example in parallel, the above code would print
 print ``[0.02, 0.08, 0.18]`` on all ranks and we would have point duplication.
 
 If any of the specified points were not found in the mesh, the value on the
-input ordering vertex-only mesh will not be effected by the interpolation from
+input ordering vertex-only mesh will not be affected by the interpolation from
 the original vertex-only mesh. In the above example, the values would be zero
 at those points. To make it more obvious that those points were not found, it's
 a good idea to set the values to ``nan`` before the interpolation:
@@ -430,7 +430,7 @@ works on any :py:class:`~.ufl.core.expr.Expr`, for example:
 
 .. code-block:: python3
 
-   (f*sin(f)([0.2, 0.4])
+   (f*sin(f))([0.2, 0.4])
 
 will evaluate :math:`f \cdot \sin(f)` at :math:`(0.2, 0.4)`.
 

From 8f421c42e551c9a722f030bd79746f3da869f74d Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Tue, 15 Jul 2025 14:36:25 +0100
Subject: [PATCH 186/222] Do not use Makefile for firedrake-check (#4391)

* Fix firedrake-check for some systems where running the parallel tests would fail.

* Move firedrake-check outside firedrake package

This should avoid CI failures due to forking mpiexec after importing
Firedrake.
---
 Makefile                          | 27 ----------
 firedrake/_check/__init__.py      |  9 ----
 pyproject.toml                    |  8 +--
 scripts/firedrake-check           | 88 +++++++++++++++++++++++++++++++
 scripts/firedrake-run-split-tests |  9 +---
 setup.py                          |  2 -
 6 files changed, 94 insertions(+), 49 deletions(-)
 create mode 100644 scripts/firedrake-check

diff --git a/Makefile b/Makefile
index 66c4f8e8ef..06cca02e41 100644
--- a/Makefile
+++ b/Makefile
@@ -88,33 +88,6 @@ clean:
 	@echo "    RM tinyasm/*.so"
 	-@rm -f tinyasm/*.so > /dev/null 2>&1
 
-# Do verbose checking if running on CI and always set no:cacheprovider because
-# we don't want to generate any cache files in $VIRTUAL_ENV/lib/.../firedrake/_check
-check_flags =
-ifeq ($(FIREDRAKE_CI), 1)
-	check_flags = --verbose -p no:cacheprovider
-else
-	check_flags = --quiet -p no:cacheprovider
-endif
-
-CHECK_PYTEST_ARGS =
-
-.PHONY: check
-check:
-	@echo "    Running serial smoke tests"
-	@python3 -m pytest $(check_flags) $(CHECK_PYTEST_ARGS) \
-		tests/firedrake/regression/test_stokes_mini.py::test_stokes_mini \
-		tests/firedrake/regression/test_locate_cell.py  `# spatialindex` \
-		tests/firedrake/supermesh/test_assemble_mixed_mass_matrix.py::test_assemble_mixed_mass_matrix[2-CG-CG-0-0]  `# supermesh` \
-		tests/firedrake/regression/test_matrix_free.py::test_fieldsplitting[parameters3-cofunc_rhs-variational]  `# fieldsplit` \
-		tests/firedrake/regression/test_nullspace.py::test_near_nullspace  `# near nullspace`
-	@echo "    Serial tests passed"
-	@echo "    Running parallel smoke tests"
-	@mpiexec -n 3 python3 -m pytest $(check_flags) $(CHECK_PYTEST_ARGS) -m parallel[3] \
-		tests/firedrake/regression/test_dg_advection.py::test_dg_advection_icosahedral_sphere \
-		tests/firedrake/regression/test_interpolate_cross_mesh.py::test_interpolate_cross_mesh_parallel[extrudedcube]  `# vertex-only mesh`
-	@echo "    Parallel tests passed"
-
 .PHONY: durations
 durations:
 	@echo "    Generate timings to optimise pytest-split"
diff --git a/firedrake/_check/__init__.py b/firedrake/_check/__init__.py
index 9d566ecb0c..e69de29bb2 100644
--- a/firedrake/_check/__init__.py
+++ b/firedrake/_check/__init__.py
@@ -1,9 +0,0 @@
-"""Run the Firedrake smoke tests."""
-
-import pathlib
-import subprocess
-
-
-def main() -> None:
-    dir = pathlib.Path(__file__).parent
-    subprocess.run(f"make -C {dir} check".split(), errors=True)
diff --git a/pyproject.toml b/pyproject.toml
index 16efd0c079..8558b4a073 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,7 +56,6 @@ Repository = "https://github.com/firedrakeproject/firedrake"
 Issues = "https://github.com/firedrakeproject/firedrake/issues/new/choose"
 
 [project.scripts]
-firedrake-check = "firedrake._check:main"
 firedrake-clean = "firedrake.scripts.firedrake_clean:main"
 firedrake-preprocess-bibtex = "firedrake.scripts.firedrake_preprocess_bibtex:main"
 firedrake-status = "firedrake.scripts.firedrake_status:main"
@@ -65,7 +64,7 @@ pyop2-clean = "pyop2.compilation:clear_compiler_disk_cache"
 
 [project.optional-dependencies]
 check = [
-  "mpi-pytest",
+  "mpi-pytest>=2025.7",
   "pytest",
 ]
 docs = [
@@ -103,7 +102,7 @@ ci = [
   "ipympl",  # needed for notebook testing
   "jax",
   "matplotlib",
-  "mpi-pytest",
+  "mpi-pytest>=2025.7",
   "nbval",
   "ngsPETSc",
   "pylit",
@@ -118,7 +117,7 @@ ci = [
 docker = [  # Used in firedrake-vanilla container
   "ipympl",  # needed for notebook testing
   "matplotlib",
-  "mpi-pytest",
+  "mpi-pytest>=2025.7",
   "nbval",
   "pylit",
   "pytest",
@@ -147,6 +146,7 @@ build-backend = "setuptools.build_meta"
 [tool.setuptools]
 script-files = [
   "firedrake/scripts/firedrake-zenodo",
+  "scripts/firedrake-check",
   "scripts/firedrake-run-split-tests",
   "pyop2/scripts/spydump",
 ]
diff --git a/scripts/firedrake-check b/scripts/firedrake-check
new file mode 100644
index 0000000000..b04d943a7c
--- /dev/null
+++ b/scripts/firedrake-check
@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+
+"""Run the Firedrake smoke tests."""
+
+import argparse
+import logging
+import importlib
+import os
+import pathlib
+import subprocess
+import sys
+
+
+# smoke tests grouped by number of processors
+TESTS = {
+    1: (
+        "tests/firedrake/regression/test_stokes_mini.py::test_stokes_mini",
+        # spatialindex
+        "tests/firedrake/regression/test_locate_cell.py",
+        # supermesh
+        "tests/firedrake/supermesh/test_assemble_mixed_mass_matrix.py::test_assemble_mixed_mass_matrix[2-CG-CG-0-0]",
+        # fieldsplit
+        "tests/firedrake/regression/test_matrix_free.py::test_fieldsplitting[parameters3-cofunc_rhs-variational]",
+        # near nullspace
+        "tests/firedrake/regression/test_nullspace.py::test_near_nullspace",
+    ),
+    3: (
+        "tests/firedrake/regression/test_dg_advection.py::test_dg_advection_icosahedral_sphere[nprocs=3]",
+        # vertex-only mesh
+        "tests/firedrake/regression/test_interpolate_cross_mesh.py::test_interpolate_cross_mesh_parallel[extrudedcube]",
+    ),
+}
+
+
+# log to terminal at INFO level
+logging.basicConfig(format="%(message)s", level=logging.INFO)
+
+
+def main() -> None:
+    args = parse_args()
+
+    for nprocs, tests in TESTS.items():
+        if nprocs == 1:
+            logging.info("    Running serial smoke tests")
+        else:
+            logging.info(f"    Running parallel smoke tests (nprocs={nprocs})")
+        run_tests(tests, nprocs, args.mpiexec)
+        logging.info("    Tests passed")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--mpiexec",
+        type=str,
+        default="mpiexec -n",
+        help=(
+            "Command used to launch MPI. The command must end with the flag "
+            "taking the number of processors, e.g. '-n' for mpiexec."
+        ),
+    )
+    return parser.parse_args()
+
+
+def run_tests(tests: tuple[str], nprocs: int, mpiexec: str) -> None:
+    # Find the path to firedrake._check. Don't actually import Firedrake here
+    # because that will initialise MPI and can prevent us calling 'mpiexec'
+    # below. This only causes issues on some systems.
+    firedrake_dir = pathlib.Path(
+        importlib.util.find_spec("firedrake").origin
+    ).parent
+    test_dir = firedrake_dir / "_check"
+
+    # Do verbose checking if running on CI and always set no:cacheprovider because
+    # we don't want to generate any cache files in $VIRTUAL_ENV/lib/.../firedrake/_check
+    if "FIREDRAKE_CI" in os.environ:
+        check_flags = "--verbose -p no:cacheprovider"
+    else:
+        check_flags = "--quiet -p no:cacheprovider"
+
+    cmd = f"{mpiexec} {nprocs} {sys.executable} -m pytest {check_flags} -m parallel[{nprocs}]".split()
+    cmd.extend(tests)
+
+    subprocess.run(cmd, cwd=test_dir, check=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/firedrake-run-split-tests b/scripts/firedrake-run-split-tests
index d13ff42ca9..3a2b450640 100755
--- a/scripts/firedrake-run-split-tests
+++ b/scripts/firedrake-run-split-tests
@@ -35,13 +35,8 @@ extra_args=${@:3}
 cache_cmd="PYOP2_CACHE_DIR=\$VIRTUAL_ENV/.cache/pyop2/job{#} \
            FIREDRAKE_TSFC_KERNEL_CACHE_DIR=\$VIRTUAL_ENV/.cache/tsfc/job{#}"
 
-if [ $num_procs = 1 ]; then
-    pytest_exec="python3 -m pytest"
-    marker_spec="\"parallel[1] or not parallel\""
-else
-    pytest_exec="mpiexec -n ${num_procs} python3 -m pytest"
-    marker_spec="parallel[${num_procs}]"
-fi
+pytest_exec="mpiexec -n ${num_procs} python -m pytest"
+marker_spec="parallel[${num_procs}]"
 
 pytest_cmd="${pytest_exec} -v \
             --splits ${num_jobs} --group {#} \
diff --git a/setup.py b/setup.py
index 48def53670..a43e399fc5 100644
--- a/setup.py
+++ b/setup.py
@@ -237,9 +237,7 @@ def extensions():
     return cythonize(cython_list) + pybind11_list
 
 
-# TODO: It would be good to have a single source of truth for these files
 FIREDRAKE_CHECK_FILES = (
-    "Makefile",
     "tests/firedrake/conftest.py",
     "tests/firedrake/regression/test_stokes_mini.py",
     "tests/firedrake/regression/test_locate_cell.py",

From b071ea18f38d01325f8e59b47607df14bacd22c9 Mon Sep 17 00:00:00 2001
From: David Ham 
Date: Wed, 16 Jul 2025 10:23:38 +0100
Subject: [PATCH 187/222] Github issue for the UFL problem

---
 firedrake/adjoint_utils/blocks/solving.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/firedrake/adjoint_utils/blocks/solving.py b/firedrake/adjoint_utils/blocks/solving.py
index 940ae8ce43..2c1fb4f876 100644
--- a/firedrake/adjoint_utils/blocks/solving.py
+++ b/firedrake/adjoint_utils/blocks/solving.py
@@ -788,6 +788,7 @@ def evaluate_adj_component(self, inputs, adj_inputs, block_variable, idx,
 
         if isinstance(dFdm, firedrake.Argument):
             #  Corner case. Should be fixed more permanently upstream in UFL.
+            #  See: https://github.com/FEniCS/ufl/issues/395
             dFdm = ufl.Action(dFdm, adj_sol)
         else:
             dFdm = dFdm * adj_sol

From f3f5d11b8f5bbc24236ecb24ca4852e9a3534f5d Mon Sep 17 00:00:00 2001
From: David Ham 
Date: Wed, 16 Jul 2025 10:29:14 +0100
Subject: [PATCH 188/222] lint

---
 firedrake/cofunction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/firedrake/cofunction.py b/firedrake/cofunction.py
index 6acd3665d5..a2dbb10321 100644
--- a/firedrake/cofunction.py
+++ b/firedrake/cofunction.py
@@ -409,7 +409,7 @@ class RieszMap:
 
     def __init__(self, function_space_or_inner_product=None,
                  sobolev_space=ufl.L2, *, bcs=None, solver_parameters=None,
-                 form_compiler_parameters=None, restrict=True, 
+                 form_compiler_parameters=None, restrict=True,
                  constant_jacobian=False):
         if isinstance(function_space_or_inner_product, ufl.Form):
             args = ufl.algorithms.extract_arguments(

From a90df34891ecacaf88ef1661f4ac74600a4cbb68 Mon Sep 17 00:00:00 2001
From: Leo Collins 
Date: Wed, 16 Jul 2025 16:22:13 +0100
Subject: [PATCH 189/222] Fix imports in `hypre_ams.py` (#4440)

* move `assemble` import inside function
---
 firedrake/preconditioners/hypre_ams.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/firedrake/preconditioners/hypre_ams.py b/firedrake/preconditioners/hypre_ams.py
index 5f995c3d90..1277d5505c 100644
--- a/firedrake/preconditioners/hypre_ams.py
+++ b/firedrake/preconditioners/hypre_ams.py
@@ -1,10 +1,11 @@
-import firedrake as fd
 from firedrake.preconditioners.base import PCBase
 from firedrake.petsc import PETSc
 from firedrake.functionspace import FunctionSpace, VectorFunctionSpace
 from firedrake.ufl_expr import TestFunction
 from firedrake.dmhooks import get_function_space
 from firedrake.utils import complex_mode
+from firedrake.interpolation import interpolate
+from firedrake import grad, SpatialCoordinate
 from firedrake_citations import Citations
 from pyop2.utils import as_tuple
 
@@ -28,6 +29,8 @@ def chop(A, tol=1E-10):
 
 class HypreAMS(PCBase):
     def initialize(self, obj):
+        from firedrake.assemble import assemble
+
         if complex_mode:
             raise NotImplementedError("HypreAMS preconditioner not yet implemented in complex mode")
 
@@ -47,7 +50,7 @@ def initialize(self, obj):
         P1 = FunctionSpace(mesh, "Lagrange", 1)
         G_callback = appctx.get("get_gradient", None)
         if G_callback is None:
-            G = chop(fd.assemble(fd.interpolate(fd.grad(TestFunction(P1)), V)).petscmat)
+            G = chop(assemble(interpolate(grad(TestFunction(P1)), V)).petscmat)
         else:
             G = G_callback(P1, V)
 
@@ -65,8 +68,8 @@ def initialize(self, obj):
             pc.setHYPRESetBetaPoissonMatrix(None)
 
         VectorP1 = VectorFunctionSpace(mesh, "Lagrange", 1)
-        interp = fd.interpolate(fd.SpatialCoordinate(mesh), VectorP1)
-        pc.setCoordinates(fd.assemble(interp).dat.data_ro.copy())
+        interp = interpolate(SpatialCoordinate(mesh), VectorP1)
+        pc.setCoordinates(assemble(interp).dat.data_ro.copy())
         pc.setFromOptions()
         self.pc = pc
 

From 5360642e089ed9bd79439e0179bffc0e24ae17f7 Mon Sep 17 00:00:00 2001
From: KarsKnook <57411502+KarsKnook@users.noreply.github.com>
Date: Wed, 16 Jul 2025 16:44:49 +0100
Subject: [PATCH 190/222] Knook/multicomponent (#4322)

Multicomponent microfluidics demo

---------

Co-authored-by: Patrick E. Farrell 
Co-authored-by: Connor Ward 
Co-authored-by: Aaron Baier-Reinio <130484015+ABaierReinio@users.noreply.github.com>
Co-authored-by: Pablo Brubeck 
Co-authored-by: ABaierReinio 
---
 demos/boussinesq/boussinesq.py.rst            |  73 +-
 demos/demo_references.bib                     |  27 +
 demos/multicomponent/benzene_0.png            | Bin 0 -> 197012 bytes
 demos/multicomponent/benzene_4.png            | Bin 0 -> 217725 bytes
 .../multicomponent/microfluidic_container.png | Bin 0 -> 399347 bytes
 demos/multicomponent/multicomponent.py.rst    | 624 ++++++++++++++++++
 docs/source/advanced_tut.rst                  |   3 +-
 docs/source/conf.py                           |   1 +
 tests/firedrake/demos/test_demos_run.py       |   1 +
 9 files changed, 693 insertions(+), 36 deletions(-)
 create mode 100644 demos/multicomponent/benzene_0.png
 create mode 100644 demos/multicomponent/benzene_4.png
 create mode 100644 demos/multicomponent/microfluidic_container.png
 create mode 100644 demos/multicomponent/multicomponent.py.rst

diff --git a/demos/boussinesq/boussinesq.py.rst b/demos/boussinesq/boussinesq.py.rst
index ac093bd556..ae6cf751b7 100644
--- a/demos/boussinesq/boussinesq.py.rst
+++ b/demos/boussinesq/boussinesq.py.rst
@@ -160,43 +160,46 @@ cannot always be sure that the linear solver at hand is correctly utilising the
 To directly eliminate the nullspace we introduce a class :code:`FixAtPointBC` which
 implements a boundary condition that fixes a field at a single point. ::
 
-    import firedrake.utils as firedrake_utils
-
-    class FixAtPointBC(firedrake.DirichletBC):
-        r'''A special BC object for pinning a function at a point.
-
-        :arg V: the :class:`.FunctionSpace` on which the boundary condition should be applied.
-        :arg g: the boundary condition value.
-        :arg bc_point: the point at which to pin the function.
-            The location of the finite element DOF nearest to bc_point is actually used.
-        '''
-        def __init__(self, V, g, bc_point):
-            super(FixAtPointBC, self).__init__(V, g, bc_point)
-            if isinstance(bc_point, tuple):
-                bc_point = as_vector(bc_point)
-            self.bc_point = bc_point
-
-        @firedrake_utils.cached_property
-        def nodes(self):
-            V = self.function_space()
-            x = firedrake.SpatialCoordinate(V.mesh())
-            xdist = x - self.bc_point
-
-            test = firedrake.TestFunction(V)
-            trial = firedrake.TrialFunction(V)
-            xphi = firedrake.assemble(ufl.inner(xdist * test, xdist * trial) * ufl.dx, diagonal=True)
-            phi = firedrake.assemble(ufl.inner(test, trial) * ufl.dx, diagonal=True)
-            with xphi.dat.vec as xu, phi.dat.vec as u:
-                xu.pointwiseDivide(xu, u)
-                min_index, min_value = xu.min()     # Find the index of the DOF closest to bc_point
-
-            nodes = V.dof_dset.lgmap.applyInverse([min_index])
-            nodes = nodes[nodes >= 0]
-            return nodes
-
+    import functools
+
+    class FixAtPointBC(DirichletBC):
+       r'''A special BC object for pinning a function at a point.
+
+       :arg V: the :class:`.FunctionSpace` on which the boundary condition should be applied.
+       :arg g: the boundary condition value.
+       :arg bc_point: the point at which to pin the function.
+           The location of the finite element DOF nearest to bc_point is actually used.
+       '''
+       def __init__(self, V, g, bc_point):
+           super().__init__(V, g, bc_point)
+
+       @functools.cached_property
+       def nodes(self):
+           V = self.function_space()
+           if V.mesh().ufl_coordinate_element().degree() != 1:
+               # Ensure a P1 mesh
+               coordinates = V.mesh().coordinates
+               P1 = coordinates.function_space().reconstruct(degree=1)
+               P1_mesh = Mesh(Function(P1).interpolate(coordinates))
+               V = V.reconstruct(mesh=P1_mesh)
+
+           point = [tuple(self.sub_domain)]
+           vom = VertexOnlyMesh(V.mesh(), point)
+           P0 = FunctionSpace(vom, "DG", 0)
+           Fvom = Cofunction(P0.dual()).assign(1)
+
+           # Take the basis function with the largest abs value at bc_point
+           v = TestFunction(V)
+           F = assemble(Interpolate(inner(v, v), Fvom))
+           with F.dat.vec as Fvec:
+               max_index, _ = Fvec.max()
+           nodes = V.dof_dset.lgmap.applyInverse([max_index])
+           nodes = nodes[nodes >= 0]
+           return nodes
+ 
 We use this to fix the pressure and auxiliary temperature at the origin::
 
-    aux_bcs = [FixAtPointBC(Z.sub(1), 0, as_vector([0, 0])), 
+    aux_bcs = [FixAtPointBC(Z.sub(1), 0, (0, 0)), 
                FixAtPointBC(Z.sub(2), 0, as_vector([0, 0]))]
 
 :code:`FixAtPointBC` takes three arguments: the function space to fix, the value with which it
diff --git a/demos/demo_references.bib b/demos/demo_references.bib
index 652eb1176d..31c360853f 100644
--- a/demos/demo_references.bib
+++ b/demos/demo_references.bib
@@ -34,6 +34,14 @@ @Book{2006:SV
   year =         2006
 }
 
+@book{Perry:2007,
+	title =        {Perry’s Chemical Engineers’ Handbook},
+	edition =      {8th},
+	author =       {Green, Don W. and Perry, Robert H.},
+	year =         2007,
+	publisher =   {McGraw Hill Professional}
+}
+
 @InBook{2015:lmscup,
   author =       {O. Bokhove and A. Kalogirou},
   title =        {Lectures on the Theory of Water Waves},
@@ -309,6 +317,25 @@ @article{Ricker:1953
   doi={https://doi.org/10.1190/1.1437843}
 }
 
+@article{Krishna:1997,
+	title={The {M}axwell--{S}tefan approach to mass transfer},
+	author={Krishna, Rajamani and Wesselingh, Johannes A},
+	journal={Chemical Engineering Science},
+	volume={52},
+  number={6},
+	pages={861--911},
+	year={1997},
+  publisher={Elsevier},
+	doi={10.1016/S0009-2509(96)00458-7}
+}
+
+@article{BaierReinio:2025,
+	title={High-order finite element methods for three-dimensional multicomponent convection-diffusion},
+  author={Baier-Reinio, Aaron and Farrell, Patrick E},
+  journal={arXiv preprint arXiv:2408.17390},
+  year={2025}
+}
+
 @article{Brubeck2022,
   author        = {Brubeck, Pablo D. and Farrell, Patrick E.},
   doi           = {10.1137/21M1444187},
diff --git a/demos/multicomponent/benzene_0.png b/demos/multicomponent/benzene_0.png
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zX!{+}9mg-o>C)yaocm%VucvoXy=ORAtR~OauIv^Zx&_Jobv`__ and `Kars Knook
+    `__.
+
+Multicomponent fluids are those composed of two or more species. Solving the equations
+describing such fluids is challenging, because there are many variables to solve for, the
+equations are nonlinear, and because the system possesses subtle properties like the
+mass-average constraint and the mole fraction sum.
+
+We consider a steady, isothermal, nonreacting mixture of benzene and cyclohexane in
+a two-dimensional microfluidic container :math:`\Omega \subset \mathbb{R}^2`.
+Pure benzene and cyclohexane flow in through opposing inlets on the left side of the container. 
+The chemicals then mix in the center of the container and exit through an outlet on the right.
+We use :doc:`netgen ` to build a curved mesh of order :math:`k=3`::
+
+    from firedrake import *
+    import netgen.occ as ngocc
+
+    # The polynomial order we will use for our curved mesh and finite element spaces
+    k = 3
+
+    # Specify the domain Omega (i.e. the microfluidic container's geometry)
+    wp = ngocc.WorkPlane()
+    wp.MoveTo(0, 1)
+    wp.Spline([ngocc.Pnt(1, 0), ngocc.Pnt(0, -1)])
+    wp.LineTo(1, -1)
+    wp.Spline([ngocc.Pnt(3, -0.5), ngocc.Pnt(4, -0.5)], tangents={ 1 : ngocc.gp_Vec2d(1, 0) })
+    wp.LineTo(4, 0.5)
+    wp.Spline([ngocc.Pnt(3, 0.5), ngocc.Pnt(1, 1)], tangents={ 0 : ngocc.gp_Vec2d(-1, 0) })
+    wp.LineTo(0, 1)
+
+    Omega = wp.Face()
+    
+    # Label the inlets and outlet; the other walls are unlabelled
+    Omega.edges.Max(ngocc.Y).name = "inlet_1"
+    Omega.edges.Min(ngocc.Y).name = "inlet_2"
+    Omega.edges.Max(ngocc.X).name = "outlet"
+   
+    # Smaller problem when running CI tests
+    import os
+    if os.getenv("FIREDRAKE_CI") == "1": 
+        maxh = 0.3
+        k = 1
+    else:
+        maxh = 0.055
+
+    # Construct the mesh
+    ngmesh = ngocc.OCCGeometry(Omega, dim=2).GenerateMesh(maxh=maxh)
+    mesh = Mesh(Mesh(ngmesh).curve_field(max(k, 2)))
+
+    # Get the IDs of the inlets, outlet and walls
+    inlet_1_id = tuple(i+1 for i, name in enumerate(ngmesh.GetRegionNames(dim=1)) if name == "inlet_1")
+    inlet_2_id = tuple(i+1 for i, name in enumerate(ngmesh.GetRegionNames(dim=1)) if name == "inlet_2")
+    outlet_id = tuple(i+1 for i, name in enumerate(ngmesh.GetRegionNames(dim=1)) if name == "outlet")
+    walls_ids = tuple(i+1 for i, name in enumerate(ngmesh.GetRegionNames(dim=1)) if name == "")
+
+    # Define the surface and volume measures using a fixed quadrature degree
+    deg_max = 3*k
+    ds = ds(mesh, degree=deg_max)
+    dx = dx(mesh, degree=deg_max)
+
+    # Define the spatial coordinates on the mesh
+    x_sc, y_sc = SpatialCoordinate(mesh)
+
+The domain and mesh are visualised below.
+
+.. image:: microfluidic_container.png
+    :align: center
+    :width: 60%
+
+To model the mixture we employ the Stokes--Onsager--Stefan--Maxwell (SOSM) 
+partial differential equations and discretise with the method of :cite:`BaierReinio:2025`.
+In what follows species 1 refers to benzene and species 2 to cyclohexane.
+We shall discretise the following unknowns:
+
+    * :math:`J_1, J_2 \in \textrm{BDM}_k` - species mass fluxes,
+    * :math:`v \in [\textrm{CG}_k]^2` - barycentric velocity,
+    * :math:`\mu_1, \mu_2 \in \textrm{DG}_{k-1}` - species chemical potentials,
+    * :math:`p \in \textrm{CG}_{k-1}` - pressure,
+    * :math:`x_1, x_2 \in \textrm{DG}_{k-1}` - species mole fractions,
+    * :math:`\rho^{-1} \in \textrm{CG}_{k-1}` - density reciprocal (i.e. specific volume).
+    
+The equations governing these unknowns are presented below.
+We first define the finite element spaces and trial/test functions::
+
+    # The finite element spaces
+    J_h = FunctionSpace(mesh, "BDM", k)                 # Species mass-flux space
+    V_h = VectorFunctionSpace(mesh, "CG", max(k, 2))    # Velocity space (minimum order is 2)
+    U_h = FunctionSpace(mesh, "DG", k - 1)              # Species chemical potential space
+    P_h = FunctionSpace(mesh, "CG", max(k - 1, 1))      # Pressure space (minimum order is 1)
+    X_h = FunctionSpace(mesh, "DG", k - 1)              # Species mole fraction space
+    R_h = FunctionSpace(mesh, "CG", max(k - 1, 1))      # Density reciprocal space (minimum order is 1)
+    L_h = FunctionSpace(mesh, "R", 0)                   # Lagrange multiplier space
+
+    # The mixed space
+    Z_h = J_h * J_h * V_h * U_h * U_h * P_h * X_h * X_h * R_h * L_h * L_h
+    PETSc.Sys.Print(f"Mesh has {mesh.num_cells()} cells, with {Z_h.dim()} finite element DOFs")
+
+    # The discrete solution
+    solution = Function(Z_h)
+    J_1, J_2, v, mu_aux_1, mu_aux_2, p, x_1, x_2, rho_inv, l_1, l_2 = split(solution)
+
+    # Lagrange multiplier trick for enforcing integral constraints
+    mu_1 = mu_aux_1 + l_1
+    mu_2 = mu_aux_2 + l_2
+
+    # The test functions
+    W_1, W_2, u, w_1, w_2, q, y_1, y_2, r, s_1, s_2 = TestFunctions(Z_h)
+
+Note that we decompose the chemical potentials as :math:`\mu_i = \mu_{i, \textrm{aux}} + l_i`
+where :math:`l_i` are Lagrange multipliers.
+This will aid in enforcing integral constraints on the solution; see
+:doc:`the steady Boussinesq demo ` for an explanation of the process.
+
+Governing PDEs: SOSM Equations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Momentum transport is modelled using the steady compressible 
+Stokes momentum equation for a Newtonian fluid,
+
+.. math::
+    :name: eq:stokes
+
+    -\nabla \cdot \big\{
+        2 \eta \epsilon(v) + (\zeta - 2\eta / d) (\nabla \cdot v) \mathbb{I}
+    \big\} + \nabla p = 0 \quad \textrm{in}\ \Omega.
+
+Recall that :math:`v` is the barycentric velocity and :math:`p` the pressure.
+Moreover :math:`\epsilon (v)` denotes the symmetric gradient of :math:`v`
+while :math:`\eta, \zeta > 0` are the shear and bulk viscosities respectively,
+:math:`d=2` is the spatial dimension and
+:math:`\mathbb{I}` the :math:`d \times d` identity matrix. Despite the fact 
+that we are considering compressible flow, we still need
+a constraint on :math:`\nabla \cdot v` (as is the case for incompressible flow).
+However, we postpone the discussion of this constraint to the end of this section as it 
+involves other quantities that we have not yet described.
+
+We shall non-dimensionalise all of the unknowns in our discretisation.
+Hence we introduce a reference velocity :math:`v^{\textrm{ref}}` whose value
+will be specified later on when we introduce boundary conditions.
+We also choose a reference length of :math:`L^{\textrm{ref}} = 2 \cdot 10^{-3}` m.
+It is then natural to define the reference pressure as
+:math:`p^{\textrm{ref}} = \eta \cdot v^{\textrm{ref}} / L^{\textrm{ref}}`. ::
+
+    # The (dimensional) Stokes viscosities
+    eta = Constant(6e-4)            # Shear viscosity, Pa s
+    zeta = Constant(1e-7)           # Bulk viscosity, Pa s
+
+    # Non-dimensionalised Lamé parameter, dimensionless
+    lame_ND = (zeta / eta) - 1.0
+
+    # Reference quantities used for non-dimensionalisation
+    v_ref = Constant(0.0)           # Reference velocity (to be specified later), m / s
+    L_ref = Constant(2e-3)          # Reference length, m
+    p_ref = eta * v_ref / L_ref     # Reference pressure, Pa
+
+The non-dimensionalised variational formulation of :ref:`eq. 1 ` is then::
+
+    # The Stokes viscous terms
+    stokes_terms = 2.0 * inner(sym(grad(v)), sym(grad(u))) * dx
+    stokes_terms += lame_ND * inner(div(v), div(u)) * dx
+
+    # The Stokes pressure term
+    stokes_terms -= inner(p, div(u)) * dx
+
+Let :math:`n \geq 2` denote the number of chemical species.
+Hence :math:`n=2` throughout this demo (benzene and cyclohexane).
+The continuity equation for the molar concentration :math:`c_i`
+of species :math:`i \in \{1:n\}` in the absence of chemical reactions is
+
+.. math::
+
+    \partial_t c_i + \frac{1}{M_i} \nabla \cdot J_i = 0
+    \quad \textrm{in}\ \Omega \quad \forall i \in \{1 : n \},
+
+where :math:`M_i > 0` is the molar mass of species :math:`i` and
+:math:`J_i` its mass flux.
+As we are considering steady flow, the continuity equations simplify to
+
+.. math::
+    :name: eq:cty
+
+    \nabla \cdot J_i = 0
+    \quad \textrm{in}\ \Omega \quad \forall i \in \{1 : n \},
+
+which are discretised as follows::
+
+    continuity_terms = (inner(div(J_1), w_1) + inner(div(J_2), w_2)) * dx
+
+Next, we incorporate the volumetric equation of state, which models
+how the concentration of the mixture depends on temperature, pressure and composition.
+Composition of the mixture is described using mole fractions :math:`x_i := c_i / c_T` where
+:math:`c_T = \sum_{j=1}^n c_j` is the total concentration.
+Note that by definition :math:`\sum_{j=1}^n x_j = 1`, although at the discrete
+level this relation will only hold approximately.
+We assume that the mixture is quasi-incompressible in the sense that
+its partial molar volumes are constant; this is often a reasonable assumption for liquids.
+It follows that the volumetric equation of state is
+
+.. math::
+    :name: eq:eos
+
+    \frac{1}{c_T} = \sum_{j=1}^n \frac{x_j}{c_j^{\textrm{pure}}} \quad \textrm{in}\ \Omega,
+
+where :math:`c_j^{\textrm{pure}}` is the concentration of pure species `j`. We use values 
+for :math:`c_j^{\textrm{pure}}` that are measured at room temperature :math:`T=298.15` K
+and ambient pressure :math:`10^5` Pa (note that we allow the pressure :math:`p` to vary
+in this model but we assume that these variations do not alter :math:`c_j^{\textrm{pure}}`).
+We will also make use of the total density of the mixture which is given by :math:`\rho = \sum_{j=1}^n M_j c_j`.
+To non-dimensionalise the concentrations and total density, we employ as reference values for these quantities
+their values when the mixture is equimolar::
+
+    # Constants for the pure species (at the ambient temperature and pressure)
+    M_1 = Constant(0.078)               # Molar mass of benzene, kg / mol
+    M_2 = Constant(0.084)               # Molar mass of cyclohexane, kg / mol
+    rho_pure_1 = Constant(876)          # Density of pure benzene, kg / m^3
+    rho_pure_2 = Constant(773)          # Density of pure cyclohexane, kg / m^3
+    c_pure_1 = rho_pure_1 / M_1         # Concentration of pure benzene, mol / m^3
+    c_pure_2 = rho_pure_2 / M_2         # Concentration of pure cyclohexane, mol / m^3
+
+    # Constants for the equimolar mixture
+    c_equi_tot = 1.0 / ((0.5 / c_pure_2) + (0.5 / c_pure_1))  # Total equimolar concentration, mol / m^3
+    c_equi_1 = 0.5 * c_equi_tot                     # Equimolar concentration of benzene, mol / m^3
+    c_equi_2 = 0.5 * c_equi_tot                     # Equimolar concentration of cyclohexane, mol / m^3
+    rho_equi = (M_1 * c_equi_1) + (M_2 * c_equi_2)  # Equimolar density, kg / m^3
+
+    # Reference concentration, density and molar mass
+    rho_ref = rho_equi                              # Reference density, kg / m^3
+    c_ref = c_equi_tot                              # Reference concentration, mol / m^3
+    M_ref = rho_ref / c_ref                         # Reference molar mass, kg / mol
+
+    # Non-dimensionalised molar masses
+    M_1_ND = M_1 / M_ref
+    M_2_ND = M_2 / M_ref
+
+Our implementation of the non-dimensionalised volumetric equation of state
+:ref:`eq. 3 ` is therefore::
+
+    # Volumetric equation of state, assuming constant partial molar volumes
+    def conc_relation(x_1, x_2):
+
+        # Normalise the mole fractions before computing c_tot,
+        # since they will only sum to one up to discretisation error
+        x_1_nm = x_1 / (x_1 + x_2)
+        x_2_nm = x_2 / (x_1 + x_2)
+
+        # Compute c_tot and the species concentrations
+        c_tot = 1.0 / ((x_1_nm * (c_ref / c_pure_1)) + (x_2_nm * (c_ref / c_pure_2)))
+        c_1 = x_1_nm * c_tot
+        c_2 = x_2_nm * c_tot
+
+        return (c_tot, c_1, c_2)
+
+    c_tot, c_1, c_2 = conc_relation(x_1, x_2)
+
+Moreover, to express that :math:`1 / \rho^{-1} = \rho = \sum_{j=1}^n M_j c_j`, we simply take the
+:math:`L^2`-projection of this expression (in non-dimensionalised form)::
+
+    rho_inv_terms = inner(1.0 / rho_inv, r) * dx
+    rho_inv_terms -= inner((M_1_ND * c_1) + (M_2_ND * c_2), r) * dx
+
+Next, we must model how the free energy of the mixture depends on
+temperature, pressure and composition.
+This is accomplished by introducing the species chemical potentials
+:math:`\mu_i, \ i \in \{1 : n\}`, which are unknown scalar fields that describe
+the local chemical potential energy of the mixture.
+Thermodynamics requires that these satisfy
+
+.. math::
+    :name: eq:gibbs
+
+    \mu_i = g_i(T, p, x_1, \ldots, x_n)
+    \quad \textrm{in}\ \Omega
+    \quad \forall i \in \{1 : n \},
+
+where :math:`g_i: \mathbb{R}^{n+2} \rightarrow \mathbb{R}` are partial molar Gibbs functions.
+These functions are derived from partial derivatives of the Gibbs free energy of the mixture.
+It is natural to non-dimensionalise the chemical potentials using a reference value of 
+:math:`\mu^{\textrm{ref}} = RT` where :math:`R` the is ideal gas constant
+and :math:`T` the ambient temperature.
+In this demo we employ a Margules model :cite:`Perry:2007`
+for the :math:`g_i`'s, which in non-dimensionalised form, is implemented as follows::
+
+    RT = Constant(8.314 * 298.15)   # Ideal gas constant times temperature, J / mol
+
+    mu_ref = RT                     # Reference chemical potential, J / mol
+
+    Me_1 = p_ref / (RT * c_pure_1)  # Non-dimensionalised partial molar volume of benzene, dimensionless
+    Me_2 = p_ref / (RT * c_pure_2)  # Non-dimensionalised partial molar volume of cyclohexane, dimensionless
+
+    # Margules model parameters
+    A_12 = Constant(0.4498)         # Dimensionless
+    A_21 = Constant(0.4952)         # Dimensionless
+
+    # Margules model for chemical potentials, assuming constant partial molar volumes
+    def mu_relation(x_1, x_2, p):
+        mu_1 = (Me_1 * p) + ln(x_1) + (x_2 ** 2) * (A_12 + (2.0 * (A_21 - A_12) * x_1))
+        mu_2 = (Me_2 * p) + ln(x_2) + (x_1 ** 2) * (A_21 + (2.0 * (A_12 - A_21) * x_2))
+
+        return (mu_1, mu_2)
+
+We discretise :ref:`eq. 4 ` through a simple :math:`L^2`-projection::
+
+    g_1, g_2 = mu_relation(x_1, x_2, p)
+    gibbs_terms = (inner(mu_1 - g_1, y_1) + inner(mu_2 - g_2, y_2)) * dx
+
+It remains to model the mass fluxes (recall the continuity equations in :ref:`eq. 2 `);
+this must be done with a constitutive relation.
+A basic Fickian constitutive relation may use :math:`J_i = M_i (c_i v - D_i \nabla c_i)`
+where :math:`c_i v` represents advection and :math:`-D_i \nabla c_i` Fickian diffusion.
+The Fickian approach is appropriate for dilute mixtures 
+(i.e. mixtures where all of the species but one are present in trace amounts),
+but typically is not thermodynamically consistent in the non-dilute regime, 
+and fails to account for cross-diffusion and thermodynamic non-idealities.
+These drawbacks are remedied by employing the Onsager--Stefan--Maxwell (OSM) equations
+(also called the Maxwell--Stefan equations :cite:`Krishna:1997`),
+which in the present isothermal setting implicitly determine the mass fluxes through the relations
+
+.. math::
+
+    -\frac{1}{M_i} \nabla \mu_i + \frac{1}{\rho} \nabla p &= 
+    \sum_{\substack{j=1 \\ j \neq i}}^n \frac{RT c_j}{\mathscr{D}_{ij} M_i c_T}
+    \Bigg( \frac{J_i}{M_i c_i} - \frac{J_j}{M_j c_j} \Bigg)
+    \quad \textrm{in}\ \Omega
+    \quad \forall i \in \{1 : n \}.
+
+Here :math:`\mathscr{D}_{ij} \ \forall i \neq j` are Stefan--Maxwell diffusion
+coefficients (note that :math:`\mathscr{D}_{jj}` is undefined).
+Onsager reciprocal relations imply that :math:`\mathscr{D}` is symmetric, i.e.
+:math:`\mathscr{D}_{ij} = \mathscr{D}_{ji} \ \forall i \neq j`.
+Since :math:`n=2` in this demo, we only have one Stefan--Maxwell diffusion coefficient
+:math:`\mathscr{D}_{\textrm{sm}} := \mathscr{D}_{12} = \mathscr{D}_{21}`.
+
+Only :math:`n-1` of the OSM equations are linearly independent.
+To uniquely determine the :math:`J_i`'s
+one must utilise a mass-average constraint:
+
+.. math::
+    v = \frac{1}{\rho} \sum_{j=1}^n J_j.
+
+This constraint ensures that the continuity equations in :ref:`eq. 2 `
+are consistent with total mass conservation
+:math:`\partial_t \rho + \nabla \cdot (\rho v) = 0`
+(note that we do not explicitly discretise this equation).
+We incorporate the mass-average constraint numerically by 
+introducing an augmentation parameter
+:math:`\gamma > 0` and reformulating the OSM equations as
+
+.. math::
+    :name: eq:osm
+
+    -\frac{1}{M_i} \nabla \mu_i + \frac{1}{\rho} \nabla p
+    + \frac{\gamma}{\rho} v &= 
+    \sum_{\substack{j=1 \\ j \neq i}}^n
+    \frac{\gamma}{\rho^2} J_j + 
+    \frac{RT c_j}{\mathscr{D}_{ij} M_i c_T}
+    \Bigg( \frac{J_i}{M_i c_i} - \frac{J_j}{M_j c_j} \Bigg)
+    \quad \textrm{in}\ \Omega
+    \quad \forall i \in \{1 : n \}.
+
+One can non-dimensionalise :ref:`eq 5. ` by introducing a dimensionless Péclet number
+:math:`\mathrm{Pe} = v^{\textrm{ref}} L^{\textrm{ref}} / \mathscr{D}_{\textrm{sm}}`
+and pressure diffusion number :math:`\mathrm{Me} = p^{\textrm{ref}} / RT c^{\textrm{ref}}`.
+Moreover, :ref:`eq 5. ` can be cast into a variational form by
+testing against functions :math:`K_i` and integrating by parts the two gradient terms
+on the left-hand side (the boundary terms drop out owing to our BCs below).
+This leads to the following implementation::
+
+    D_sm = Constant(2.1e-9)                         # Stefan--Maxwell diffusivity, m^2 / s
+    Pe = v_ref * L_ref / D_sm                       # Péclet number, dimensionless
+    Me = p_ref / (RT * c_ref)                       # Pressure diffusion number, dimensionless
+
+    gamma = Constant(1e-1)                          # Augmentation parameter, dimensionless
+
+    # The Stefan--Maxwell diffusion terms
+    osm_terms = (Pe / c_tot) * ((c_2 / (M_1_ND * M_1_ND * c_1)) * inner(J_1, W_1) 
+                                                    + (c_1 / (M_2_ND * M_2_ND * c_2)) * inner(J_2, W_2)
+                                                    - (1 / (M_1_ND * M_2_ND)) * (inner(J_1, W_2) + inner(J_2, W_1))) * dx
+
+    # The augmentation terms (for symmetry we also test these terms against u)
+    osm_terms += Pe * gamma * inner(v - (rho_inv * (J_1 + J_2)), u - (rho_inv * (W_1 + W_2))) * dx
+
+    # The pressure diffusion terms
+    osm_terms += ((Me * inner(p, div(rho_inv * (W_1 + W_2))))) * dx
+
+    # The chemical potential terms
+    osm_terms -= ((1.0 / M_1_ND) * inner(mu_1, div(W_1)) + (1.0 / M_2_ND) * inner(mu_2, div(W_2))) * dx
+
+Lastly, we weakly enforce that
+:math:`\nabla \cdot v = \nabla \cdot (\frac{1}{\rho} \sum_{j=1}^n J_j )`,
+using special density consistency terms to handle inhomogeneous BCs::
+
+    div_mass_avg_terms = inner(div(v - (rho_inv * (J_1 + J_2))), q) * dx
+
+    # The density consistency terms
+    N = FacetNormal(mesh)
+    div_mass_avg_terms -= inner(dot(v - (rho_inv * (J_1 + J_2)), N), q) * ds
+
+This concludes our discussion of the PDE model and its discretisation.
+Altogether, our total residual is the sum of forms built above::
+
+    tot_res = stokes_terms \
+            + continuity_terms \
+            + rho_inv_terms \
+            + gibbs_terms \
+            + osm_terms \
+            + div_mass_avg_terms
+
+Boundary conditions
+~~~~~~~~~~~~~~~~~~~
+
+Let :math:`N` denote the outward unit normal on :math:`\partial \Omega`.
+On inflow :math:`i` and on the outflow we introduce parabolic mass flux profiles :math:`G_i`,
+with magnitudes :math:`M_i c_i^\text{ref} v_i^\text{ref}` where
+:math:`v_i^\text{ref}` are reference velocities that we are free to choose.
+We then strongly enforce :math:`J_i \cdot N = G_i \cdot N` as Dirichlet boundary conditions
+only apply to the normal component of H(div) functions.
+Elsewhere on the boundary we enforce :math:`J_i \cdot N = 0`. Finally, instead of specifying
+the value of the barycentric velocity :math:`v` on the inflows and outflows,
+we enforce :math:`v = \rho^{-1}(G_1 + G_2)`. Boundary conditions that couple
+unknowns and/or are nonlinear must be implemented with :class:`~.EquationBC` instead of :class:`~.DirichletBC`. ::
+
+    # Reference species velocities, which we choose to symmetrize so that the molar fluxes agree
+    v_ref_1 = Constant(0.4e-6)                      # Reference inflow velocity of benzene, m / s
+    v_ref_2 = (c_pure_1 / c_pure_2) * v_ref_1       # Reference inflow velocity of cyclohexane, m / s
+
+    parabola_inflow_1 = 2.0 * x_sc * (x_sc - 1.0) * as_vector([2.0, -1.0])
+    parabola_inflow_2 = 2.0 * x_sc * (x_sc - 1.0) * as_vector([2.0, 1.0])
+    G_1_inflow_bc_func = -M_1_ND * (v_ref_1 / v_ref) * (c_pure_1 / c_ref) * parabola_inflow_1
+    G_2_inflow_bc_func = -M_2_ND * (v_ref_2 / v_ref) * (c_pure_2 / c_ref) * parabola_inflow_2
+    rho_v_inflow_1_bc_func = G_1_inflow_bc_func
+    rho_v_inflow_2_bc_func = G_2_inflow_bc_func
+
+    parabola_outflow = 2.0 * (y_sc + 0.5) * (y_sc - 0.5) * as_vector([1.0, 0.0])
+    G_1_outflow_bc_func = -M_1_ND * (v_ref_1 / v_ref) * (c_pure_1 / c_ref) * parabola_outflow
+    G_2_outflow_bc_func = -M_2_ND * (v_ref_2 / v_ref) * (c_pure_2 / c_ref) * parabola_outflow
+    rho_v_outflow_bc_func = G_1_outflow_bc_func + G_2_outflow_bc_func
+
+    # Boundary conditions on the barycentric velocity are enforced via EquationBC
+    bc_data = {inlet_1_id: rho_v_inflow_1_bc_func, inlet_2_id: rho_v_inflow_2_bc_func, outlet_id: rho_v_outflow_bc_func}
+    F_bc = sum(inner(v - rho_inv * flux, u) * ds(*subdomain) for subdomain, flux in bc_data.items())
+    v_bc = EquationBC(F_bc == 0, solution, (*inlet_1_id, *inlet_2_id, *outlet_id), V=Z_h.sub(2))
+
+    # The boundary conditions on the fluxes and barycentric velocity
+    # Note that BCs on H(div) spaces only apply to the normal component
+    flux_bcs = [DirichletBC(Z_h.sub(0), G_1_inflow_bc_func, inlet_1_id),
+                DirichletBC(Z_h.sub(0), G_1_outflow_bc_func, outlet_id),
+                DirichletBC(Z_h.sub(0), 0, inlet_2_id),
+                DirichletBC(Z_h.sub(0), 0, walls_ids),
+                DirichletBC(Z_h.sub(1), G_2_inflow_bc_func, inlet_2_id),
+                DirichletBC(Z_h.sub(1), G_2_outflow_bc_func, outlet_id),
+                DirichletBC(Z_h.sub(1), 0, inlet_1_id),
+                DirichletBC(Z_h.sub(1), 0, walls_ids),
+                v_bc,
+                DirichletBC(Z_h.sub(2), 0, walls_ids)]
+
+It is now natural to assign :math:`v^\textrm{ref}`
+to be the average of the species reference velocities::
+
+    v_ref.assign(0.5 * (v_ref_1 + v_ref_2))
+
+Integral constraints
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+At the continuous level the OSM equations imply that
+
+.. math::
+
+    \nabla (x_1 + \ldots + x_n) = 0 \quad \textrm{in}\ \Omega.
+
+Hence, at the discrete level, we expect :math:`x_1 + \ldots + x_n` to approximately be a constant.
+However, we have not yet incorporated any equations to make this constant be one.
+We accomplish this by enforcing that :math:`\int_{\Omega} (x_1 + \ldots + x_n - 1) \ {\rm d} x = 0`::
+
+    tot_res += inner(x_1 + x_2 - 1, s_1) * dx
+
+Moreover, the steady SOSM problem still does not have a unique solution
+since we have not specified how much mass of fluid is present in :math:`\Omega`.
+For uniqueness we must pin this down by imposing one more constraint.
+Instead of directly imposing the value of :math:`\int_{\Omega} \rho \ {\rm d} x`,
+to demonstrate the flexibility of our approach we enforce that, on the outflow,
+the species have equal average densities::
+
+    tot_res += inner((M_1_ND * c_1) - (M_2_ND * c_2), s_2) * ds(*outlet_id)
+
+Analogously to :doc:`the steady Boussinesq demo ` we use
+:code:`FixAtPointBC` to remove the pressure nullspace and pin the
+:math:`\mu_{i, \textrm{aux}}` at a DOF (by carefully studying which rows in the
+discretised Jacobian are linearly dependent, one checks that it is
+mathematically valid to do this)::
+
+    import functools
+
+    class FixAtPointBC(DirichletBC):
+       r'''A special BC object for pinning a function at a point.
+
+       :arg V: the :class:`.FunctionSpace` on which the boundary condition should be applied.
+       :arg g: the boundary condition value.
+       :arg bc_point: the point at which to pin the function.
+           The location of the finite element DOF nearest to bc_point is actually used.
+       '''
+       def __init__(self, V, g, bc_point):
+           super().__init__(V, g, bc_point)
+
+       @functools.cached_property
+       def nodes(self):
+           V = self.function_space()
+           if V.mesh().ufl_coordinate_element().degree() != 1:
+               # Ensure a P1 mesh
+               coordinates = V.mesh().coordinates
+               P1 = coordinates.function_space().reconstruct(degree=1)
+               P1_mesh = Mesh(Function(P1).interpolate(coordinates))
+               V = V.reconstruct(mesh=P1_mesh)
+
+           point = [tuple(self.sub_domain)]
+           vom = VertexOnlyMesh(V.mesh(), point)
+           P0 = FunctionSpace(vom, "DG", 0)
+           Fvom = Cofunction(P0.dual()).assign(1)
+
+           # Take the basis function with the largest abs value at bc_point
+           v = TestFunction(V)
+           F = assemble(Interpolate(inner(v, v), Fvom))
+           with F.dat.vec as Fvec:
+               max_index, _ = Fvec.max()
+           nodes = V.dof_dset.lgmap.applyInverse([max_index])
+           nodes = nodes[nodes >= 0]
+           return nodes
+
+    # Fix the auxiliary chemical potentials and pressure at a point
+    aux_point = (4, 0)   # A point on the middle of the outlet
+    aux_point_bcs = [FixAtPointBC(Z_h.sub(3), 0, aux_point),
+                    FixAtPointBC(Z_h.sub(4), 0, aux_point),
+                    FixAtPointBC(Z_h.sub(5), 0, aux_point)]
+
+Solving the system using Newton's method
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We provide a naive initial guess based on an equimolar spatially uniform distribution of benzene and cyclohexane::
+
+    J_1, J_2, v, mu_aux_1, mu_aux_2, p, x_1, x_2, rho_inv, l_1, l_2 = solution.subfunctions
+    x_1.interpolate(Constant(0.5))
+    x_2.interpolate(Constant(0.5))
+    rho_inv.interpolate(1.0 / ((M_1_ND * c_1) + (M_2_ND * c_2)))
+
+and define the nonlinear variational solver object, which by default uses Newton's method::
+
+    NLVP = NonlinearVariationalProblem(tot_res, solution, bcs=flux_bcs+aux_point_bcs)
+    NLVS = NonlinearVariationalSolver(NLVP)
+
+Newton's method applied directly to the problem with :math:`v_1^\text{ref}=0.4\times 10^{-5}`
+with the naive initial guess does not converge. Hence, we apply parameter continuation to :math:`v_1^\text{ref}`
+to find a better initial guess. We start by solving the problem for :math:`v_1^\text{ref}=0.4\times 10^{-6}` 
+with the naive initial guess and use its solution as initial guess for the problem with 
+:math:`v_1^\text{ref}=0.1\times 10^{-5}`. We repeat this trick with :math:`v_1^\text{ref}=0.2\times 10^{-5}`
+and :math:`v_1^\text{ref}=0.3\times 10^{-5}` before solving for :math:`v_1^\text{ref}=0.4\times 10^{-5}`. 
+We can reuse the nonlinear variational solver object each iteration, but have to reassign :code:`v_ref_1`
+and :code:`v_ref` before calling the :code:`solve()` method. Finally, we write each solution to the same 
+VTK file using the :code:`time` keyword argument. ::
+
+    outfile = VTKFile("out/solution.pvd")
+    vmax = 0.4e-5
+
+    cont_vals = [0.1 * vmax, 0.25 * vmax, 0.5 * vmax, 0.75 * vmax, vmax]
+
+    if os.getenv("FIREDRAKE_CI") == "1":
+        # Smaller problem when running CI tests
+        cont_vals = cont_vals[0:2]
+
+    n_cont = len(cont_vals)
+
+    names = ["J_1", "J_2", "v", "mu_aux_1", "mu_aux_2", "p", "x_1", "x_2",
+        "rho_inv", "l_1", "l_2"]
+    for field, name in enumerate(names):
+        solution.subfunctions[field].rename(name)
+ 
+    mu_1_out = Function(U_h, name="mu_1")
+    mu_2_out = Function(U_h, name="mu_2")
+    rho_out = Function(R_h, name="rho")
+    c_tot_out = Function(R_h, name="c_tot")
+    c_1_out = Function(R_h, name="c_1")
+    c_2_out = Function(R_h, name="c_2")
+    
+    for i in range(n_cont):
+        v_ref_1.assign(cont_vals[i])
+        print(f"Solving for v_ref_1 = {float(v_ref_1)}")
+        v_ref.assign(0.5 * (v_ref_1 + v_ref_2))
+        NLVS.solve()
+
+        p += assemble(-p * dx) / assemble(1 * dx(mesh))     # Normalise p to have 0 mean
+
+        mu_1_out.interpolate(mu_1)
+        mu_2_out.interpolate(mu_2)
+        rho_out.interpolate(1.0 / rho_inv)
+        c_tot_out.interpolate(c_tot)
+        c_1_out.interpolate(c_1)
+        c_2_out.interpolate(c_2)
+        
+        outfile.write(*solution.subfunctions, mu_1_out, mu_2_out, rho_out, c_tot_out, c_1_out, c_2_out, time=i)
+
+The mole fraction and streamlines of benzene for :math:`v_1^\text{ref}=0.4\times 10^{-6}` 
+and :math:`v_1^\text{ref}=0.4\times 10^{-5}` are displayed below on the left and right respectively.
+Owing to parameter continuation and the high-order discretisation, we can robustly solve the problem
+even in the presence of low species concentrations and sharp solution gradients.
+
++---------------------------+---------------------------+
+| .. image:: benzene_0.png  | .. image:: benzene_4.png  |
+|    :width: 100%           |    :width: 100%           |
++---------------------------+---------------------------+
+
+A Python script version of this demo can be found :demo:`here
+`.
+
+.. rubric:: References
+
+.. bibliography:: demo_references.bib
+   :filter: docname in docnames
diff --git a/docs/source/advanced_tut.rst b/docs/source/advanced_tut.rst
index 9bf7da1eab..dd3609a20f 100644
--- a/docs/source/advanced_tut.rst
+++ b/docs/source/advanced_tut.rst
@@ -29,5 +29,6 @@ element systems.
    Monolithic multigrid with Vanka relaxation for Stokes.
    Vertex/edge star multigrid relaxation for H(div).
    Auxiliary space patch relaxation multigrid for H(curl).
-   Steady Boussinesq problem with integral constraints.
    Preconditioning using fast diagonalisation.
+   Steady Boussinesq problem with integral constraints.
+   Steady multicomponent flow -- microfluidic mixing of hydrocarbons.
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index c902bb74f6..ba2ea1bcb8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -277,6 +277,7 @@
     # Additional stuff for the LaTeX preamble.
     'preamble': r'''\newcommand{\require}[1]{}
 \usepackage{cancel}
+\usepackage{mathrsfs}
 
 % \sphinxDUC{2730}{\ding{80}}
 \sphinxDUC{27F6}{$\rightarrow$}
diff --git a/tests/firedrake/demos/test_demos_run.py b/tests/firedrake/demos/test_demos_run.py
index be2f038d67..c64b7fd594 100644
--- a/tests/firedrake/demos/test_demos_run.py
+++ b/tests/firedrake/demos/test_demos_run.py
@@ -35,6 +35,7 @@
     Demo(("matrix_free", "poisson"), []),
     Demo(("matrix_free", "rayleigh-benard"), ["hypre", "mumps", "vtk"]),
     Demo(("matrix_free", "stokes"), ["hypre", "mumps", "vtk"]),
+    Demo(("multicomponent", "multicomponent"), ["vtk, netgen"]),
     Demo(("multigrid", "geometric_multigrid"), ["vtk"]),
     Demo(("netgen", "netgen_mesh"), ["mumps", "netgen", "slepc", "vtk"]),
     Demo(("nonlinear_QG_winddrivengyre", "qg_winddrivengyre"), ["vtk"]),

From 7a7d73a2e47bbccc914995d3f451f51c7b0f46a5 Mon Sep 17 00:00:00 2001
From: Leo Collins 
Date: Wed, 16 Jul 2025 17:43:41 +0100
Subject: [PATCH 191/222] add `petsc_raises` to tests (#4443)

* add `petsc_raises` to tests

* Increase permissiveness of petsc_raises

Previously issues were encountered if the garbage collection routines
threw an error. This should stop being an issue when the original error
stops getting destroyed too early (should be fixed in later Python
versions).

---------

Co-authored-by: Connor Ward 
---
 tests/firedrake/conftest.py                   |  8 +--
 .../vertexonly/test_vertex_only_fs.py         | 52 +++++++------------
 2 files changed, 24 insertions(+), 36 deletions(-)

diff --git a/tests/firedrake/conftest.py b/tests/firedrake/conftest.py
index c42e9c8330..eec05bf3a6 100644
--- a/tests/firedrake/conftest.py
+++ b/tests/firedrake/conftest.py
@@ -205,12 +205,12 @@ def __enter__(self):
         pass
 
     def __exit__(self, exc_type, exc_val, traceback):
-        # There is a bug where 'exc_val' is occasionally 'None'. In my tests
-        # this error only exists for Python < 3.12.11.
+        # There is a bug where 'exc_val' is occasionally the wrong thing,
+        # either 'None' or some unrelated garbage collection error. In my
+        # tests this error only exists for Python < 3.12.11.
         if exc_type is PETSc.Error:
             if sys.version_info < (3, 12, 11):
-                if exc_val is None or isinstance(exc_val.__cause__, self.exc_type):
-                    return True
+                return True
             else:
                 if isinstance(exc_val.__cause__, self.exc_type):
                     return True
diff --git a/tests/firedrake/vertexonly/test_vertex_only_fs.py b/tests/firedrake/vertexonly/test_vertex_only_fs.py
index e152fee26b..635b6413ef 100644
--- a/tests/firedrake/vertexonly/test_vertex_only_fs.py
+++ b/tests/firedrake/vertexonly/test_vertex_only_fs.py
@@ -68,7 +68,7 @@ def pseudo_random_coords(size):
 
 # Function Space Generation Tests
 
-def functionspace_tests(vm):
+def functionspace_tests(vm, petsc_raises):
     # Prep
     num_cells = len(vm.coordinates.dat.data_ro)
     num_cells_mpi_global = MPI.COMM_WORLD.allreduce(num_cells, op=MPI.SUM)
@@ -153,13 +153,10 @@ def functionspace_tests(vm):
     h_star = h.riesz_representation(riesz_map="l2")
     g = assemble(I_io.interpolate(h_star, adjoint=True))
     assert np.allclose(g.dat.data_ro_with_halos, np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
-    with pytest.raises(NotImplementedError):
+    with petsc_raises(NotImplementedError):
         # Can't use adjoint on interpolators with expressions yet
-        try:
-            g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
-            assert np.allclose(g2.dat.data_ro_with_halos, 2*np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
-        except PETSc.Error as e:
-            raise e.__cause__ from None
+        g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
+        assert np.allclose(g2.dat.data_ro_with_halos, 2*np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
 
     I_io_adjoint = Interpolator(TestFunction(W), V)
     I2_io_adjoint = Interpolator(2*TestFunction(W), V)
@@ -167,20 +164,17 @@ def functionspace_tests(vm):
     h = h_star.riesz_representation(riesz_map="l2")
     assert np.allclose(h.dat.data_ro_with_halos[idxs_to_include], np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
     assert np.all(h.dat.data_ro_with_halos[~idxs_to_include] == 0)
-    with pytest.raises(NotImplementedError):
+    with petsc_raises(NotImplementedError):
         # Can't use adjoint on interpolators with expressions yet
-        try:
-            h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
-            assert np.allclose(h2.dat.data_ro_with_halos[idxs_to_include], 2*np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
-        except PETSc.Error as e:
-            raise e.__cause__ from None
+        h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
+        assert np.allclose(h2.dat.data_ro_with_halos[idxs_to_include], 2*np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
     g = assemble(I_io_adjoint.interpolate(h))
     assert np.allclose(g.dat.data_ro_with_halos, np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
     g2 = assemble(I2_io_adjoint.interpolate(h))
     assert np.allclose(g2.dat.data_ro_with_halos, 2*np.prod(vm.coordinates.dat.data_ro_with_halos.reshape(-1, vm.geometric_dimension()), axis=1))
 
 
-def vectorfunctionspace_tests(vm):
+def vectorfunctionspace_tests(vm, petsc_raises):
     # Prep
     gdim = vm.geometric_dimension()
     num_cells = len(vm.coordinates.dat.data_ro)
@@ -263,26 +257,20 @@ def vectorfunctionspace_tests(vm):
     h_star = h.riesz_representation(riesz_map="l2")
     g = assemble(I_io.interpolate(h_star, adjoint=True))
     assert np.allclose(g.dat.data_ro_with_halos, 2*vm.coordinates.dat.data_ro_with_halos)
-    with pytest.raises(NotImplementedError):
+    with petsc_raises(NotImplementedError):
         # Can't use adjoint on interpolators with expressions yet
-        try:
-            g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
-            assert np.allclose(g2.dat.data_ro_with_halos, 4*vm.coordinates.dat.data_ro_with_halos)
-        except PETSc.Error as e:
-            raise e.__cause__ from None
+        g2 = assemble(I2_io.interpolate(h_star, adjoint=True))
+        assert np.allclose(g2.dat.data_ro_with_halos, 4*vm.coordinates.dat.data_ro_with_halos)
 
     I_io_adjoint = Interpolator(TestFunction(W), V)
     I2_io_adjoint = Interpolator(2*TestFunction(W), V)
     h_star = assemble(I_io_adjoint.interpolate(g, adjoint=True))
     assert np.allclose(h_star.dat.data_ro[idxs_to_include], 2*vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include])
     assert np.all(h_star.dat.data_ro_with_halos[~idxs_to_include] == 0)
-    with pytest.raises(NotImplementedError):
-        try:
-            # Can't use adjoint on interpolators with expressions yet
-            h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
-            assert np.allclose(h2.dat.data_ro[idxs_to_include], 4*vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include])
-        except PETSc.Error as e:
-            raise e.__cause__ from None
+    with petsc_raises(NotImplementedError):
+        # Can't use adjoint on interpolators with expressions yet
+        h2 = assemble(I2_io_adjoint.interpolate(g, adjoint=True))
+        assert np.allclose(h2.dat.data_ro[idxs_to_include], 4*vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include])
 
     h = h_star.riesz_representation(riesz_map="l2")
     g = assemble(I_io_adjoint.interpolate(h))
@@ -292,12 +280,12 @@ def vectorfunctionspace_tests(vm):
 
 
 @pytest.mark.parallel([1, 3])
-def test_functionspaces(parentmesh, vertexcoords):
+def test_functionspaces(parentmesh, vertexcoords, petsc_raises):
     vm = VertexOnlyMesh(parentmesh, vertexcoords, missing_points_behaviour=None)
-    functionspace_tests(vm)
-    vectorfunctionspace_tests(vm)
-    functionspace_tests(vm.input_ordering)
-    vectorfunctionspace_tests(vm.input_ordering)
+    functionspace_tests(vm, petsc_raises)
+    vectorfunctionspace_tests(vm, petsc_raises)
+    functionspace_tests(vm.input_ordering, petsc_raises)
+    vectorfunctionspace_tests(vm.input_ordering, petsc_raises)
 
 
 @pytest.mark.parallel(nprocs=2)

From ad518e56c9d6451f7ebb5ee1ce1571a1619cd09e Mon Sep 17 00:00:00 2001
From: ksagiyam <46749170+ksagiyam@users.noreply.github.com>
Date: Tue, 22 Jul 2025 14:10:01 +0100
Subject: [PATCH 192/222] remove mesh.init() (#4201)

Co-authored-by: Connor Ward 
---
 firedrake/assemble.py                         |   4 -
 firedrake/checkpointing.py                    |  24 +--
 firedrake/cython/dmcommon.pyx                 |  40 +++-
 firedrake/cython/petschdr.pxi                 |  24 +++
 firedrake/functionspaceimpl.py                |   1 -
 firedrake/mesh.py                             | 202 +++++++++---------
 firedrake/mg/mesh.py                          |  66 +++---
 firedrake/ufl_expr.py                         |   2 -
 firedrake/utility_meshes.py                   |  18 +-
 tests/firedrake/multigrid/test_basics.py      |  10 -
 tests/firedrake/regression/test_constant.py   |   1 -
 .../firedrake/regression/test_exodus_mesh.py  |   1 -
 tests/firedrake/regression/test_facets.py     |   1 -
 .../regression/test_mesh_generation.py        |   5 -
 .../regression/test_mesh_overlaps.py          |   4 -
 .../regression/test_point_eval_fs.py          |   1 -
 .../supermesh/test_nonnested_project.py       |   2 -
 tests/firedrake/vertexonly/test_swarm.py      |   3 -
 .../test_vertex_only_mesh_generation.py       |   2 -
 19 files changed, 218 insertions(+), 193 deletions(-)

diff --git a/firedrake/assemble.py b/firedrake/assemble.py
index 10c1ba2b43..e5adb33a62 100644
--- a/firedrake/assemble.py
+++ b/firedrake/assemble.py
@@ -981,10 +981,6 @@ def wrapper(self, *args, **kwargs):
 
     def __init__(self, form, bcs=None, form_compiler_parameters=None):
         super().__init__(form, bcs=bcs, form_compiler_parameters=form_compiler_parameters)
-        # Ensure mesh is 'initialised' as we could have got here without building a
-        # function space (e.g. if integrating a constant).
-        for mesh in form.ufl_domains():
-            mesh.init()
         if any(c.dat.dtype != ScalarType for c in form.coefficients()):
             raise ValueError("Cannot assemble a form containing coefficients where the "
                              "dtype is not the PETSc scalar type.")
diff --git a/firedrake/checkpointing.py b/firedrake/checkpointing.py
index a9fba2dd00..6a63b1aaf1 100644
--- a/firedrake/checkpointing.py
+++ b/firedrake/checkpointing.py
@@ -566,7 +566,6 @@ def save_mesh(self, mesh, distribution_name=None, permutation_name=None):
         :kwarg distribution_name: the name under which distribution is saved; if `None`, auto-generated name will be used.
         :kwarg permutation_name: the name under which permutation is saved; if `None`, auto-generated name will be used.
         """
-        mesh.init()
         # Handle extruded mesh
         tmesh = mesh.topology
         if mesh.extruded:
@@ -669,7 +668,6 @@ def save_mesh(self, mesh, distribution_name=None, permutation_name=None):
     @PETSc.Log.EventDecorator("SaveMeshTopology")
     def _save_mesh_topology(self, tmesh):
         # -- Save DMPlex --
-        tmesh.init()
         topology_dm = tmesh.topology_dm
         tmesh_name = topology_dm.getName()
         distribution_name = tmesh._distribution_name
@@ -1048,7 +1046,6 @@ def load_mesh(self, name=DEFAULT_MESH_NAME, reorder=None, distribution_parameter
             # -- Load mesh topology --
             base_tmesh_name = self.get_attr(path, PREFIX_EXTRUDED + "_base_mesh")
             base_tmesh = self._load_mesh_topology(base_tmesh_name, reorder, distribution_parameters)
-            base_tmesh.init()
             periodic = self.get_attr(path, PREFIX_EXTRUDED + "_periodic") if self.has_attr(path, PREFIX_EXTRUDED + "_periodic") else False
             variable_layers = self.get_attr(path, PREFIX_EXTRUDED + "_variable_layers")
             if variable_layers:
@@ -1106,9 +1103,6 @@ def load_mesh(self, name=DEFAULT_MESH_NAME, reorder=None, distribution_parameter
             # tmesh.topology_dm has already been redistributed.
             path = self._path_to_mesh(tmesh_name, name)
             # Load firedrake coordinates directly.
-            # When implementing checkpointing for MeshHierarchy in the future,
-            # we will need to postpone calling tmesh.init().
-            tmesh.init()
             coord_element = self._load_ufl_element(path, PREFIX + "_coordinate_element")
             coord_name = self.get_attr(path, PREFIX + "_coordinates")
             coordinates = self._load_function_topology(tmesh, coord_element, coord_name)
@@ -1194,7 +1188,13 @@ def _load_mesh_topology(self, tmesh_name, reorder, distribution_parameters):
         plex.distributionSetName(distribution_name)
         sfXB = plex.topologyLoad(self.viewer)
         plex.distributionSetName(None)
+        plex.labelsLoad(self.viewer, sfXB)
         self.viewer.popFormat()
+        # These labels are distribution dependent.
+        # We should be able to save/load labels selectively.
+        plex.removeLabel("pyop2_core")
+        plex.removeLabel("pyop2_owned")
+        plex.removeLabel("pyop2_ghost")
         if load_distribution_permutation:
             chart_size = np.empty(1, dtype=utils.IntType)
             chart_sizes_iset = PETSc.IS().createGeneral(chart_size, comm=self._comm)
@@ -1220,21 +1220,10 @@ def _load_mesh_topology(self, tmesh_name, reorder, distribution_parameters):
                              distribution_parameters=distribution_parameters, sfXB=sfXB, perm_is=perm_is,
                              distribution_name=distribution_name, permutation_name=permutation_name,
                              comm=self.comm)
-        self.viewer.pushFormat(format=format)
-        # tmesh.topology_dm has already been redistributed.
-        sfXCtemp = tmesh.sfXB.compose(tmesh.sfBC) if tmesh.sfBC is not None else tmesh.sfXB
-        plex.labelsLoad(self.viewer, sfXCtemp)
-        self.viewer.popFormat()
-        # These labels are distribution dependent.
-        # We should be able to save/load labels selectively.
-        plex.removeLabel("pyop2_core")
-        plex.removeLabel("pyop2_owned")
-        plex.removeLabel("pyop2_ghost")
         return tmesh
 
     @PETSc.Log.EventDecorator("LoadFunctionSpace")
     def _load_function_space(self, mesh, name):
-        mesh.init()
         mesh_key = self._generate_mesh_key_from_names(mesh.name,
                                                       mesh.topology._distribution_name,
                                                       mesh.topology._permutation_name)
@@ -1271,7 +1260,6 @@ def _load_function_space(self, mesh, name):
 
     @PETSc.Log.EventDecorator("LoadFunctionSpaceTopology")
     def _load_function_space_topology(self, tmesh, element):
-        tmesh.init()
         if element.family() == "Real":
             return impl.RealFunctionSpace(tmesh, element, "unused_name")
         tmesh_key = self._generate_mesh_key_from_names(tmesh.name,
diff --git a/firedrake/cython/dmcommon.pyx b/firedrake/cython/dmcommon.pyx
index c3ee78600b..db194b04b7 100644
--- a/firedrake/cython/dmcommon.pyx
+++ b/firedrake/cython/dmcommon.pyx
@@ -3818,7 +3818,8 @@ def create_halo_exchange_sf(PETSc.DM dm):
 def submesh_create(PETSc.DM dm,
                    PetscInt subdim,
                    label_name,
-                   PetscInt label_value):
+                   PetscInt label_value,
+                   PetscBool ignore_label_halo):
     """Create submesh.
 
     Parameters
@@ -3831,6 +3832,8 @@ def submesh_create(PETSc.DM dm,
         Name of the label
     label_value : int
         Value in the label
+    ignore_label_halo : bool
+        If labeled points in the halo are ignored.
 
     """
     cdef:
@@ -3860,7 +3863,7 @@ def submesh_create(PETSc.DM dm,
         CHKERR(ISRestoreIndices(stratum_is, &stratum_indices))
         CHKERR(ISDestroy(&stratum_is))
     # Make submesh using temp_label.
-    CHKERR(DMPlexFilter(dm.dm, temp_label.dmlabel, label_value, PETSC_FALSE, PETSC_TRUE, &ownership_transfer_sf.sf, &subdm.dm))
+    CHKERR(DMPlexFilter(dm.dm, temp_label.dmlabel, label_value, ignore_label_halo, PETSC_TRUE, &ownership_transfer_sf.sf, &subdm.dm))
     # Destroy temp_label.
     dm.removeLabel(temp_label_name)
     subdm.removeLabel(temp_label_name)
@@ -4112,3 +4115,36 @@ def submesh_create_cell_closure(
     CHKERR(PetscFree(subpoint_indices_inv))
     CHKERR(ISRestoreIndices(subpoint_is.iset, &subpoint_indices))
     return subcell_closure
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def get_dm_cell_types(PETSc.DM dm):
+    """Return all cell types in the mesh.
+
+    Parameters
+    ----------
+    dm : PETSc.DM
+        The parent dm.
+
+    Returns
+    -------
+    tuple
+        Tuple of all cell types in the mesh.
+
+    """
+    cdef:
+        PetscInt cStart, cEnd, c
+        np.ndarray found, found_all
+        PetscDMPolytopeType celltype
+
+    cStart, cEnd = dm.getHeightStratum(0)
+    found = np.zeros((DM_NUM_POLYTOPES, ), dtype=IntType)
+    found_all = np.zeros((DM_NUM_POLYTOPES, ), dtype=IntType)
+    for c in range(cStart, cEnd):
+        CHKERR(DMPlexGetCellType(dm.dm, c, &celltype))
+        found[celltype] = 1
+    dm.comm.tompi4py().Allreduce(found, found_all, op=MPI.MAX)
+    return tuple(
+        polytope_type_enum for polytope_type_enum, found in enumerate(found_all) if found
+    )
diff --git a/firedrake/cython/petschdr.pxi b/firedrake/cython/petschdr.pxi
index 750a3c3714..a94de11e56 100644
--- a/firedrake/cython/petschdr.pxi
+++ b/firedrake/cython/petschdr.pxi
@@ -29,6 +29,27 @@ cdef extern from "petscsys.h" nogil:
     int PetscFree2(void*,void*)
     int PetscSortIntWithArray(PetscInt,PetscInt[],PetscInt[])
 
+cdef extern from "petscdmtypes.h" nogil:
+    ctypedef enum PetscDMPolytopeType "DMPolytopeType":
+        DM_POLYTOPE_POINT
+        DM_POLYTOPE_SEGMENT
+        DM_POLYTOPE_POINT_PRISM_TENSOR
+        DM_POLYTOPE_TRIANGLE
+        DM_POLYTOPE_QUADRILATERAL
+        DM_POLYTOPE_SEG_PRISM_TENSOR
+        DM_POLYTOPE_TETRAHEDRON
+        DM_POLYTOPE_HEXAHEDRON
+        DM_POLYTOPE_TRI_PRISM
+        DM_POLYTOPE_TRI_PRISM_TENSOR
+        DM_POLYTOPE_QUAD_PRISM_TENSOR
+        DM_POLYTOPE_PYRAMID
+        DM_POLYTOPE_FV_GHOST
+        DM_POLYTOPE_INTERIOR_GHOST
+        DM_POLYTOPE_UNKNOWN
+        DM_POLYTOPE_UNKNOWN_CELL
+        DM_POLYTOPE_UNKNOWN_FACE
+        DM_NUM_POLYTOPES
+
 cdef extern from "petscdmplex.h" nogil:
     int DMPlexGetHeightStratum(PETSc.PetscDM,PetscInt,PetscInt*,PetscInt*)
     int DMPlexGetDepthStratum(PETSc.PetscDM,PetscInt,PetscInt*,PetscInt*)
@@ -56,6 +77,9 @@ cdef extern from "petscdmplex.h" nogil:
     int DMPlexGetSubpointMap(PETSc.PetscDM,PETSc.PetscDMLabel*)
     int DMPlexSetSubpointMap(PETSc.PetscDM,PETSc.PetscDMLabel)
 
+    int DMPlexSetCellType(PETSc.PetscDM,PetscInt,PetscDMPolytopeType)
+    int DMPlexGetCellType(PETSc.PetscDM,PetscInt,PetscDMPolytopeType*)
+
 cdef extern from "petscdmlabel.h" nogil:
     struct _n_DMLabel
     ctypedef _n_DMLabel* DMLabel "DMLabel"
diff --git a/firedrake/functionspaceimpl.py b/firedrake/functionspaceimpl.py
index 00f517c088..988a7b0585 100644
--- a/firedrake/functionspaceimpl.py
+++ b/firedrake/functionspaceimpl.py
@@ -359,7 +359,6 @@ def collapse(self):
     @classmethod
     def make_function_space(cls, mesh, element, name=None):
         r"""Factory method for :class:`.WithGeometryBase`."""
-        mesh.init()
         topology = mesh.topology
         # Create a new abstract (Mixed/Real)FunctionSpace, these are neither primal nor dual.
         if type(element) is finat.ufl.MixedElement:
diff --git a/firedrake/mesh.py b/firedrake/mesh.py
index de786079b5..07956b7e39 100644
--- a/firedrake/mesh.py
+++ b/firedrake/mesh.py
@@ -528,7 +528,7 @@ class AbstractMeshTopology(object, metaclass=abc.ABCMeta):
     """A representation of an abstract mesh topology without a concrete
         PETSc DM implementation"""
 
-    def __init__(self, topology_dm, name, reorder, sfXB, perm_is, distribution_name, permutation_name, comm):
+    def __init__(self, topology_dm, name, reorder, sfXB, perm_is, distribution_name, permutation_name, comm, submesh_parent=None):
         """Initialise a mesh topology.
 
         Parameters
@@ -555,6 +555,8 @@ def __init__(self, topology_dm, name, reorder, sfXB, perm_is, distribution_name,
             Name of the entity permutation (reordering); if `None`, automatically generated.
         comm : mpi4py.MPI.Comm
             Communicator.
+        submesh_parent: AbstractMeshTopology
+            Submesh parent.
 
         """
         utils._init()
@@ -566,62 +568,56 @@ def __init__(self, topology_dm, name, reorder, sfXB, perm_is, distribution_name,
         r"The PETSc SF that pushes the input (naive) plex to current (good) plex."
         self.sfXB = sfXB
         r"The PETSc SF that pushes the global point number slab [0, NX) to input (naive) plex."
-
+        self.submesh_parent = submesh_parent
         # User comm
         self.user_comm = comm
         # Internal comm
         self._comm = internal_comm(self.user_comm, self)
-
         dmcommon.label_facets(self.topology_dm)
         self._distribute()
         self._grown_halos = False
-
-        def callback(self):
-            """Finish initialisation."""
-            del self._callback
-            if self.comm.size > 1:
-                self._add_overlap()
-            if self.sfXB is not None:
-                self.sfXC = sfXB.compose(self.sfBC) if self.sfBC else self.sfXB
-            dmcommon.label_facets(self.topology_dm)
-            dmcommon.complete_facet_labels(self.topology_dm)
-            # TODO: Allow users to set distribution name if they want to save
-            #       conceptually the same mesh but with different distributions,
-            #       e.g., those generated by different partitioners.
-            #       This currently does not make sense since those mesh instances
-            #       of different distributions in general have different global
-            #       point numbers (so they must be saved under different mesh names
-            #       even though they are conceptually the same).
-            # The name set here almost uniquely identifies a distribution, but
-            # there is no gurantee that it really does or it continues to do so
-            # there are lots of parameters that can change distributions.
-            # Thus, when using CheckpointFile, it is recommended that the user set
-            # distribution_name explicitly.
-            # Mark OP2 entities and derive the resulting Plex renumbering
-            with PETSc.Log.Event("Mesh: numbering"):
-                self._mark_entity_classes()
-                self._entity_classes = dmcommon.get_entity_classes(self.topology_dm).astype(int)
-                if perm_is:
-                    self._dm_renumbering = perm_is
-                else:
-                    self._dm_renumbering = self._renumber_entities(reorder)
-                self._did_reordering = bool(reorder)
-                # Derive a cell numbering from the Plex renumbering
-                tdim = dmcommon.get_topological_dimension(self.topology_dm)
-                entity_dofs = np.zeros(tdim+1, dtype=IntType)
-                entity_dofs[-1] = 1
-                self._cell_numbering, _ = self.create_section(entity_dofs)
-                if tdim == 0:
-                    self._vertex_numbering = self._cell_numbering
-                else:
-                    entity_dofs[:] = 0
-                    entity_dofs[0] = 1
-                    self._vertex_numbering, _ = self.create_section(entity_dofs)
-                    entity_dofs[:] = 0
-                    entity_dofs[-2] = 1
-                    facet_numbering, _ = self.create_section(entity_dofs)
-                    self._facet_ordering = dmcommon.get_facet_ordering(self.topology_dm, facet_numbering)
-        self._callback = callback
+        if self.comm.size > 1:
+            self._add_overlap()
+        if self.sfXB is not None:
+            self.sfXC = sfXB.compose(self.sfBC) if self.sfBC else self.sfXB
+        dmcommon.label_facets(self.topology_dm)
+        dmcommon.complete_facet_labels(self.topology_dm)
+        # TODO: Allow users to set distribution name if they want to save
+        #       conceptually the same mesh but with different distributions,
+        #       e.g., those generated by different partitioners.
+        #       This currently does not make sense since those mesh instances
+        #       of different distributions in general have different global
+        #       point numbers (so they must be saved under different mesh names
+        #       even though they are conceptually the same).
+        # The name set here almost uniquely identifies a distribution, but
+        # there is no gurantee that it really does or it continues to do so
+        # there are lots of parameters that can change distributions.
+        # Thus, when using CheckpointFile, it is recommended that the user set
+        # distribution_name explicitly.
+        # Mark OP2 entities and derive the resulting Plex renumbering
+        with PETSc.Log.Event("Mesh: numbering"):
+            self._mark_entity_classes()
+            self._entity_classes = dmcommon.get_entity_classes(self.topology_dm).astype(int)
+            if perm_is:
+                self._dm_renumbering = perm_is
+            else:
+                self._dm_renumbering = self._renumber_entities(reorder)
+            self._did_reordering = bool(reorder)
+            # Derive a cell numbering from the Plex renumbering
+            tdim = dmcommon.get_topological_dimension(self.topology_dm)
+            entity_dofs = np.zeros(tdim+1, dtype=IntType)
+            entity_dofs[-1] = 1
+            self._cell_numbering, _ = self.create_section(entity_dofs)
+            if tdim == 0:
+                self._vertex_numbering = self._cell_numbering
+            else:
+                entity_dofs[:] = 0
+                entity_dofs[0] = 1
+                self._vertex_numbering, _ = self.create_section(entity_dofs)
+                entity_dofs[:] = 0
+                entity_dofs[-2] = 1
+                facet_numbering, _ = self.create_section(entity_dofs)
+                self._facet_ordering = dmcommon.get_facet_ordering(self.topology_dm, facet_numbering)
         self.name = name
         # Set/Generate names to be used when checkpointing.
         self._distribution_name = distribution_name or _generate_default_mesh_topology_distribution_name(self.topology_dm.comm.size, self._distribution_parameters)
@@ -635,8 +631,6 @@ def callback(self):
         # To set, do e.g.
         # target_mesh._parallel_compatible = {weakref.ref(source_mesh)}
         self._parallel_compatible = None
-        # submesh
-        self.submesh_parent = None
 
     layers = None
     """No layers on unstructured mesh"""
@@ -672,12 +666,6 @@ def mpi_comm(self):
         """The MPI communicator this mesh is built on (an mpi4py object)."""
         return self.comm
 
-    @PETSc.Log.EventDecorator("CreateMesh")
-    def init(self):
-        """Finish the initialisation of the mesh."""
-        if hasattr(self, '_callback'):
-            self._callback(self)
-
     @property
     def topology(self):
         """The underlying mesh topology object."""
@@ -723,6 +711,12 @@ def ufl_mesh(self):
         """
         return self._ufl_mesh
 
+    @property
+    @abc.abstractmethod
+    def dm_cell_types(self):
+        """All DM.PolytopeTypes of cells in the mesh."""
+        pass
+
     @property
     @abc.abstractmethod
     def cell_closure(self):
@@ -1093,7 +1087,19 @@ class MeshTopology(AbstractMeshTopology):
     """A representation of mesh topology implemented on a PETSc DMPlex."""
 
     @PETSc.Log.EventDecorator("CreateMesh")
-    def __init__(self, plex, name, reorder, distribution_parameters, sfXB=None, perm_is=None, distribution_name=None, permutation_name=None, comm=COMM_WORLD):
+    def __init__(
+        self,
+        plex,
+        name,
+        reorder,
+        distribution_parameters,
+        sfXB=None,
+        perm_is=None,
+        distribution_name=None,
+        permutation_name=None,
+        submesh_parent=None,
+        comm=COMM_WORLD,
+    ):
         """Initialise a mesh topology.
 
         Parameters
@@ -1120,10 +1126,14 @@ def __init__(self, plex, name, reorder, distribution_parameters, sfXB=None, perm
             Name of the parallel distribution; if `None`, automatically generated.
         permutation_name : str
             Name of the entity permutation (reordering); if `None`, automatically generated.
+        submesh_parent: MeshTopology
+            Submesh parent.
         comm : mpi4py.MPI.Comm
             Communicator.
 
         """
+        if distribution_parameters is None:
+            distribution_parameters = {}
         self._distribution_parameters = {}
         distribute = distribution_parameters.get("partition")
         if distribute is None:
@@ -1137,7 +1147,7 @@ def __init__(self, plex, name, reorder, distribution_parameters, sfXB=None, perm
         # Disable auto distribution and reordering before setFromOptions is called.
         plex.distributeSetDefault(False)
         plex.reorderSetDefault(PETSc.DMPlex.ReorderDefaultFlag.FALSE)
-        super().__init__(plex, name, reorder, sfXB, perm_is, distribution_name, permutation_name, comm)
+        super().__init__(plex, name, reorder, sfXB, perm_is, distribution_name, permutation_name, comm, submesh_parent=submesh_parent)
 
     def _distribute(self):
         # Distribute/redistribute the dm to all ranks
@@ -1228,6 +1238,11 @@ def _renumber_entities(self, reorder):
             reordering = None
         return dmcommon.plex_renumbering(self.topology_dm, self._entity_classes, reordering)
 
+    @property
+    def dm_cell_types(self):
+        """All DM.PolytopeTypes of cells in the mesh."""
+        return dmcommon.get_dm_cell_types(self.topology_dm)
+
     @utils.cached_property
     def cell_closure(self):
         """2D array of ordered cell closures
@@ -1690,7 +1705,6 @@ def __init__(self, mesh, layers, periodic=False, name=None):
         if layers.shape and periodic:
             raise ValueError("Must provide constant layer for periodic extrusion")
 
-        mesh.init()
         self._base_mesh = mesh
         self.user_comm = mesh.comm
         self._comm = internal_comm(mesh._comm, self)
@@ -1739,6 +1753,11 @@ def _ufl_mesh(self):
         cell = self._ufl_cell
         return ufl.Mesh(finat.ufl.VectorElement("Lagrange", cell, 1, dim=cell.topological_dimension()))
 
+    @property
+    def dm_cell_types(self):
+        """All DM.PolytopeTypes of cells in the mesh."""
+        raise NotImplementedError("'dm_cell_types' is not implemented for ExtrudedMeshTopology")
+
     @utils.cached_property
     def cell_closure(self):
         """2D array of ordered cell closures
@@ -1929,8 +1948,8 @@ def __init__(self, swarm, parentmesh, name, reorder, input_ordering_swarm=None,
                                          "partitioner_type": None,
                                          "overlap_type": (DistributedMeshOverlapType.NONE, 0)}
         self.input_ordering_swarm = input_ordering_swarm
-        super().__init__(swarm, name, reorder, None, perm_is, distribution_name, permutation_name, parentmesh.comm)
         self._parent_mesh = parentmesh
+        super().__init__(swarm, name, reorder, None, perm_is, distribution_name, permutation_name, parentmesh.comm)
 
     def _distribute(self):
         pass
@@ -1977,6 +1996,11 @@ def _renumber_entities(self, reorder):
         else:
             return dmcommon.plex_renumbering(self.topology_dm, self._entity_classes, None)
 
+    @property
+    def dm_cell_types(self):
+        """All DM.PolytopeTypes of cells in the mesh."""
+        return (PETSc.DM.PolytopeType.POINT,)
+
     @utils.cached_property  # TODO: Recalculate if mesh moves
     def cell_closure(self):
         """2D array of ordered cell closures
@@ -2272,14 +2296,6 @@ def _ufl_signature_data_(self, *args, **kwargs):
         return (type(self), self.extruded, self.variable_layers,
                 super()._ufl_signature_data_(*args, **kwargs))
 
-    def init(self):
-        """Finish the initialisation of the mesh.  Most of the time
-        this is carried out automatically, however, in some cases (for
-        example accessing a property of the mesh directly after
-        constructing it) you need to call this manually."""
-        if hasattr(self, '_callback'):
-            self._callback(self)
-
     def _init_topology(self, topology):
         """Initialise the topology.
 
@@ -2294,20 +2310,13 @@ def _init_topology(self, topology):
         import firedrake.function as function
 
         self._topology = topology
-
-        def callback(self):
-            """Finish initialisation."""
-            del self._callback
-            # Finish the initialisation of mesh topology
-            self.topology.init()
-            coordinates_fs = functionspace.FunctionSpace(self.topology, self.ufl_coordinate_element())
-            coordinates_data = dmcommon.reordered_coords(topology.topology_dm, coordinates_fs.dm.getDefaultSection(),
-                                                         (self.num_vertices(), self.geometric_dimension()))
-            coordinates = function.CoordinatelessFunction(coordinates_fs,
-                                                          val=coordinates_data,
-                                                          name=_generate_default_mesh_coordinates_name(self.name))
-            self.__init__(coordinates)
-        self._callback = callback
+        coordinates_fs = functionspace.FunctionSpace(self.topology, self.ufl_coordinate_element())
+        coordinates_data = dmcommon.reordered_coords(topology.topology_dm, coordinates_fs.dm.getDefaultSection(),
+                                                     (self.num_vertices(), self.geometric_dimension()))
+        coordinates = function.CoordinatelessFunction(coordinates_fs,
+                                                      val=coordinates_data,
+                                                      name=_generate_default_mesh_coordinates_name(self.name))
+        self.__init__(coordinates)
 
     @property
     def topology(self):
@@ -2366,7 +2375,6 @@ def _coordinates_function(self):
         if hasattr(self.ufl_cargo(), "_coordinates_function"):
             return self.ufl_cargo()._coordinates_function
         else:
-            self.init()
             coordinates_fs = self._coordinates.function_space()
             V = functionspaceimpl.WithGeometry.create(coordinates_fs, self)
             f = function.Function(V, val=self._coordinates)
@@ -2732,7 +2740,6 @@ def input_ordering(self):
         _input_ordering = make_vom_from_vom_topology(self.topology.input_ordering, self.name + "_input_ordering")
         if _input_ordering:
             _input_ordering._parent_mesh = self
-            _input_ordering.init()
             return _input_ordering
 
     def cell_orientations(self):
@@ -3091,16 +3098,19 @@ def Mesh(meshfile, **kwargs):
                                % (meshfile, ext[1:]))
         plex.setName(_generate_default_mesh_topology_name(name))
     # Create mesh topology
+    submesh_parent = kwargs.get("submesh_parent", None)
     topology = MeshTopology(plex, name=plex.getName(), reorder=reorder,
                             distribution_parameters=distribution_parameters,
                             distribution_name=kwargs.get("distribution_name"),
                             permutation_name=kwargs.get("permutation_name"),
+                            submesh_parent=submesh_parent.topology if submesh_parent else None,
                             comm=user_comm)
     if netgen and isinstance(meshfile, netgen.libngpy._meshing.Mesh):
         netgen_firedrake_mesh.createFromTopology(topology, name=name, comm=user_comm)
         mesh = netgen_firedrake_mesh.firedrakeMesh
     else:
         mesh = make_mesh_from_mesh_topology(topology, name)
+    mesh.submesh_parent = submesh_parent
     mesh._tolerance = tolerance
     return mesh
 
@@ -3175,7 +3185,6 @@ def ExtrudedMesh(mesh, layers, layer_height=None, extrusion_type='uniform', peri
     if name is not None and name == mesh.name:
         raise ValueError("Extruded mesh and base mesh can not have the same name")
     name = name if name is not None else mesh.name + "_extruded"
-    mesh.init()
     layers = np.asarray(layers, dtype=IntType)
     if layers.shape:
         if periodic:
@@ -3344,7 +3353,6 @@ def VertexOnlyMesh(mesh, vertexcoords, reorder=None, missing_points_behaviour='e
         tolerance = mesh.tolerance
     else:
         mesh.tolerance = tolerance
-    mesh.init()
     vertexcoords = np.asarray(vertexcoords, dtype=RealType)
     if reorder is None:
         reorder = parameters["reorder_meshes"]
@@ -3394,7 +3402,6 @@ def VertexOnlyMesh(mesh, vertexcoords, reorder=None, missing_points_behaviour='e
     )
     vmesh_out = make_vom_from_vom_topology(topology, name, tolerance)
     vmesh_out._parent_mesh = mesh
-    vmesh_out.init()
     return vmesh_out
 
 
@@ -4665,7 +4672,6 @@ def Submesh(mesh, subdim, subdomain_id, label_name=None, name=None):
         raise NotImplementedError("Can not create a submesh of an ``ExtrudedMesh``")
     elif isinstance(mesh.topology, VertexOnlyMeshTopology):
         raise NotImplementedError("Can not create a submesh of a ``VertexOnlyMesh``")
-    mesh.topology.init()
     plex = mesh.topology_dm
     dim = plex.getDimension()
     if subdim not in [dim, dim - 1]:
@@ -4676,13 +4682,17 @@ def Submesh(mesh, subdim, subdomain_id, label_name=None, name=None):
         elif subdim == dim - 1:
             label_name = dmcommon.FACE_SETS_LABEL
     name = name or _generate_default_submesh_name(mesh.name)
-    subplex = dmcommon.submesh_create(plex, subdim, label_name, subdomain_id)
+    subplex = dmcommon.submesh_create(plex, subdim, label_name, subdomain_id, False)
     subplex.setName(_generate_default_mesh_topology_name(name))
     if subplex.getDimension() != subdim:
         raise RuntimeError(f"Found subplex dim ({subplex.getDimension()}) != expected ({subdim})")
-    submesh = Mesh(subplex, name=name, distribution_parameters={"partition": False,
-                                                                "overlap_type": (DistributedMeshOverlapType.NONE, 0)})
-    submesh.topology.submesh_parent = mesh.topology
-    submesh.init()
-    submesh.submesh_parent = mesh
+    submesh = Mesh(
+        subplex,
+        submesh_parent=mesh,
+        name=name,
+        distribution_parameters={
+            "partition": False,
+            "overlap_type": (DistributedMeshOverlapType.NONE, 0),
+        },
+    )
     return submesh
diff --git a/firedrake/mg/mesh.py b/firedrake/mg/mesh.py
index 446e042b35..5924d76c7a 100644
--- a/firedrake/mg/mesh.py
+++ b/firedrake/mg/mesh.py
@@ -5,6 +5,7 @@
 from pyop2.datatypes import IntType
 
 import firedrake
+import firedrake.cython.dmcommon as dmcommon
 from firedrake.utils import cached_property
 from firedrake.cython import mgimpl as impl
 from .utils import set_level
@@ -130,37 +131,27 @@ def MeshHierarchy(mesh, refinement_levels,
         else:
             raise RuntimeError("Cannot create a NetgenHierarchy from a mesh that has not been generated by\
                                 Netgen.")
-
-    cdm = mesh.topology_dm
+    # Effectively "invert" addOverlap().
+    # -- The resulting plex is to have the identical data structure as the one before addOverlap().
+    #    This is algorithmically guaranteed.
+    dm_cell_type, = mesh.dm_cell_types
+    tdim = mesh.topology_dm.getDimension()
+    cdm = dmcommon.submesh_create(mesh.topology_dm, tdim, "celltype", dm_cell_type, True)
+    cdm.removeLabel("pyop2_core")
+    cdm.removeLabel("pyop2_owned")
+    cdm.removeLabel("pyop2_ghost")
     cdm.setRefinementUniform(True)
-    dms = []
-    if mesh.comm.size > 1 and mesh._grown_halos:
-        raise RuntimeError("Cannot refine parallel overlapped meshes "
-                           "(make sure the MeshHierarchy is built immediately after the Mesh)")
-    parameters = {}
-    if distribution_parameters is not None:
-        parameters.update(distribution_parameters)
-    else:
-        parameters.update(mesh._distribution_parameters)
-
-    parameters["partition"] = False
-    distribution_parameters = parameters
-
+    dms = [cdm]
     if callbacks is not None:
         before, after = callbacks
     else:
         before = after = lambda dm, i: None
-
     for i in range(refinement_levels*refinements_per_level):
         if i % refinements_per_level == 0:
             before(cdm, i)
         rdm = cdm.refine()
         if i % refinements_per_level == 0:
             after(rdm, i)
-        rdm.removeLabel("pyop2_core")
-        rdm.removeLabel("pyop2_owned")
-        rdm.removeLabel("pyop2_ghost")
-
         dms.append(rdm)
         cdm = rdm
         # Fix up coords if refining embedded circle or sphere
@@ -172,20 +163,30 @@ def MeshHierarchy(mesh, refinement_levels,
             coords = cdm.getCoordinatesLocal().array.reshape(-1, mesh.geometric_dimension())
             scale = mesh._radius / np.linalg.norm(coords, axis=1).reshape(-1, 1)
             coords *= scale
-
-    meshes = [mesh] + [mesh_builder(dm, dim=mesh.geometric_dimension(),
-                                    distribution_parameters=distribution_parameters,
-                                    reorder=reorder, comm=mesh.comm)
-                       for dm in dms]
-
-    lgmaps = []
+    lgmaps_without_overlap = [impl.create_lgmap(dm) for dm in dms]
+    parameters = {}
+    if distribution_parameters is not None:
+        parameters.update(distribution_parameters)
+    else:
+        parameters.update(mesh._distribution_parameters)
+    parameters["partition"] = False
+    meshes = [mesh] + [
+        mesh_builder(
+            dm,
+            dim=mesh.geometric_dimension(),
+            distribution_parameters=parameters,
+            reorder=reorder,
+            comm=mesh.comm,
+        )
+        for dm in dms[1:]
+    ]
+    lgmaps_with_overlap = []
     for i, m in enumerate(meshes):
-        no = impl.create_lgmap(m.topology_dm)
-        m.init()
-        o = impl.create_lgmap(m.topology_dm)
+        lgmaps_with_overlap.append(impl.create_lgmap(m.topology_dm))
         m.topology_dm.setRefineLevel(i)
-        lgmaps.append((no, o))
-
+    lgmaps = [
+        (no, o) for no, o in zip(lgmaps_without_overlap, lgmaps_with_overlap)
+    ]
     coarse_to_fine_cells = []
     fine_to_coarse_cells = [None]
     for (coarse, fine), (clgmaps, flgmaps) in zip(zip(meshes[:-1], meshes[1:]),
@@ -286,7 +287,6 @@ def SemiCoarsenedExtrudedHierarchy(base_mesh, height, nref=1, base_layer=-1, ref
     """
     if not isinstance(base_mesh, firedrake.mesh.MeshGeometry):
         raise ValueError(f"Can only extruded a mesh, not a {type(base_mesh)}")
-    base_mesh.init()
     if base_mesh.cell_set._extruded:
         raise ValueError("Base mesh must not be extruded")
     if layers is None:
diff --git a/firedrake/ufl_expr.py b/firedrake/ufl_expr.py
index 259de72b4c..bbc8a0d933 100644
--- a/firedrake/ufl_expr.py
+++ b/firedrake/ufl_expr.py
@@ -348,7 +348,6 @@ def CellSize(mesh):
 
     :arg mesh: the mesh for which to calculate the cell size.
     """
-    mesh.init()
     return ufl.CellDiameter(mesh)
 
 
@@ -358,7 +357,6 @@ def FacetNormal(mesh):
 
     :arg mesh: the mesh over which the normal should be represented.
     """
-    mesh.init()
     return ufl.FacetNormal(mesh)
 
 
diff --git a/firedrake/utility_meshes.py b/firedrake/utility_meshes.py
index 99d687449e..6540611752 100644
--- a/firedrake/utility_meshes.py
+++ b/firedrake/utility_meshes.py
@@ -33,6 +33,7 @@
 from firedrake import mesh
 from firedrake import function
 from firedrake import functionspace
+from firedrake.parameters import parameters
 from firedrake.petsc import PETSc
 
 from pyadjoint.tape import no_annotations
@@ -375,9 +376,14 @@ def OneElementThickMesh(
     plex = mesh.plex_from_cell_list(
         2, cells, coords, comm, mesh._generate_default_mesh_topology_name(name)
     )
-    mesh1 = mesh.Mesh(plex, distribution_parameters=distribution_parameters, comm=comm)
-    mesh1.topology.init()
-    cell_numbering = mesh1._cell_numbering
+    tmesh1 = mesh.MeshTopology(
+        plex,
+        plex.getName(),
+        reorder=parameters["reorder_meshes"],
+        distribution_parameters=distribution_parameters,
+        comm=comm,
+    )
+    cell_numbering = tmesh1._cell_numbering
     cell_range = plex.getHeightStratum(0)
     cell_closure = np.zeros((cell_range[1], 9), dtype=IntType)
 
@@ -474,10 +480,8 @@ def OneElementThickMesh(
 
         cell_closure[row][0:4] = [v1, v1, v2, v2]
 
-    mesh1.topology.cell_closure = np.array(cell_closure, dtype=IntType)
-
-    mesh1.init()
-
+    tmesh1.cell_closure = np.array(cell_closure, dtype=IntType)
+    mesh1 = mesh.make_mesh_from_mesh_topology(tmesh1, "temp")
     fe_dg = FiniteElement("DQ", mesh1.ufl_cell(), 1, variant="equispaced")
     Vc = VectorFunctionSpace(mesh1, fe_dg)
     fc = Function(
diff --git a/tests/firedrake/multigrid/test_basics.py b/tests/firedrake/multigrid/test_basics.py
index ec80c1ee4f..d4161ebca1 100644
--- a/tests/firedrake/multigrid/test_basics.py
+++ b/tests/firedrake/multigrid/test_basics.py
@@ -60,13 +60,3 @@ def test_refine_square_ncell_parallel():
     # Should be fewer than 4 times the number of coarse cells due to
     # halo shrinking.
     assert mh[1].num_cells() < 4 * mh[0].num_cells()
-
-
-@pytest.mark.parallel(nprocs=2)
-def test_refining_overlapped_mesh_fails_parallel():
-    m = UnitSquareMesh(4, 4)
-
-    m.init()
-
-    with pytest.raises(RuntimeError):
-        MeshHierarchy(m, 1)
diff --git a/tests/firedrake/regression/test_constant.py b/tests/firedrake/regression/test_constant.py
index f95e0a3e2d..019e982356 100644
--- a/tests/firedrake/regression/test_constant.py
+++ b/tests/firedrake/regression/test_constant.py
@@ -200,7 +200,6 @@ def test_fresh_constant_hashes_different():
 
 def test_constants_are_renumbered_in_form_signature():
     mesh = UnitSquareMesh(1, 1)
-    mesh.init()
     c = Constant(1)
     d = Constant(1)
 
diff --git a/tests/firedrake/regression/test_exodus_mesh.py b/tests/firedrake/regression/test_exodus_mesh.py
index aa761c98fe..bb2cac177b 100644
--- a/tests/firedrake/regression/test_exodus_mesh.py
+++ b/tests/firedrake/regression/test_exodus_mesh.py
@@ -27,5 +27,4 @@ def test_sidesets(exodus_mesh):
     if exodus_mesh is None:
         pytest.skip("PETSc not configured with exodusII")
     else:
-        exodus_mesh.init()
         assert (exodus_mesh.exterior_facets.unique_markers == [200, 201]).all()
diff --git a/tests/firedrake/regression/test_facets.py b/tests/firedrake/regression/test_facets.py
index b50eb95b5f..ed0936527c 100644
--- a/tests/firedrake/regression/test_facets.py
+++ b/tests/firedrake/regression/test_facets.py
@@ -187,6 +187,5 @@ def test_facet_map_no_reshape():
 
 def test_mesh_with_no_facet_markers():
     mesh = UnitTriangleMesh()
-    mesh.init()
     with pytest.raises(LookupError):
         mesh.exterior_facets.subset((10,))
diff --git a/tests/firedrake/regression/test_mesh_generation.py b/tests/firedrake/regression/test_mesh_generation.py
index b7cffa6777..4cb7cc109a 100644
--- a/tests/firedrake/regression/test_mesh_generation.py
+++ b/tests/firedrake/regression/test_mesh_generation.py
@@ -247,7 +247,6 @@ def test_periodic_unit_cube_parallel():
 def assert_num_exterior_facets_equals_zero(m):
     # Need to initialise the mesh so that exterior facets have been
     # built.
-    m.init()
     assert m.exterior_facets.set.total_size == 0
 
 
@@ -421,7 +420,6 @@ def test_bendy_cube_unit_parallel(degree):
 def test_mesh_reordering_defaults_on():
     assert parameters["reorder_meshes"]
     m = UnitSquareMesh(1, 1)
-    m.init()
 
     assert m._did_reordering
 
@@ -449,7 +447,6 @@ def test_mesh_validation_parallel():
                          [False, True])
 def test_force_reordering_works(reorder):
     m = UnitSquareMesh(1, 1, reorder=reorder)
-    m.init()
 
     assert m._did_reordering == reorder
 
@@ -461,7 +458,6 @@ def test_changing_default_reorder_works(reorder):
     try:
         parameters["reorder_meshes"] = reorder
         m = UnitSquareMesh(1, 1)
-        m.init()
 
         assert m._did_reordering == reorder
     finally:
@@ -472,7 +468,6 @@ def test_changing_default_reorder_works(reorder):
                          [("default", 6)])
 def test_boxmesh_kind(kind, num_cells):
     m = BoxMesh(1, 1, 1, 1, 1, 1, diagonal=kind)
-    m.init()
     assert m.num_cells() == num_cells
 
 
diff --git a/tests/firedrake/regression/test_mesh_overlaps.py b/tests/firedrake/regression/test_mesh_overlaps.py
index 2466d1a328..ac766b4fad 100644
--- a/tests/firedrake/regression/test_mesh_overlaps.py
+++ b/tests/firedrake/regression/test_mesh_overlaps.py
@@ -62,7 +62,6 @@ def mesh(request, overlap):
     else:
         mesh = UnitSquareMesh(2, 2, reorder=False,
                               distribution_parameters=params)
-    mesh.init()
     return mesh
 
 
@@ -93,9 +92,6 @@ def test_override_distribution_parameters(overlap):
     params = {"overlap_type": (DistributedMeshOverlapType.NONE, 0)}
     fine_mesh = MeshHierarchy(mesh, 1, distribution_parameters=params)[-1]
 
-    mesh.init()
-    fine_mesh.init()
-
     if overlap[0] == DistributedMeshOverlapType.NONE:
         assert mesh.num_cells() == 1
     else:
diff --git a/tests/firedrake/regression/test_point_eval_fs.py b/tests/firedrake/regression/test_point_eval_fs.py
index 91a751a826..6c4a9e837f 100644
--- a/tests/firedrake/regression/test_point_eval_fs.py
+++ b/tests/firedrake/regression/test_point_eval_fs.py
@@ -217,7 +217,6 @@ def test_point_reset_works():
 
 def test_changing_coordinates_invalidates_spatial_index():
     mesh = UnitSquareMesh(2, 2)
-    mesh.init()
 
     saved_spatial_index = mesh.spatial_index
     mesh.coordinates.assign(mesh.coordinates * 2)
diff --git a/tests/firedrake/supermesh/test_nonnested_project.py b/tests/firedrake/supermesh/test_nonnested_project.py
index 9439e649f3..798228908d 100644
--- a/tests/firedrake/supermesh/test_nonnested_project.py
+++ b/tests/firedrake/supermesh/test_nonnested_project.py
@@ -18,8 +18,6 @@ def hierarchy():
     mesh2 = RectangleMesh(5, 5, 1, 1, diagonal="right",
                           distribution_parameters=distribution_parameters)
 
-    mesh.init()
-    mesh2.init()
     coarse_to_fine = numpy.tile(numpy.arange(mesh2.num_cells(), dtype=IntType),
                                 (mesh.num_cells(), 1))
 
diff --git a/tests/firedrake/vertexonly/test_swarm.py b/tests/firedrake/vertexonly/test_swarm.py
index 555351cd01..96c4602511 100644
--- a/tests/firedrake/vertexonly/test_swarm.py
+++ b/tests/firedrake/vertexonly/test_swarm.py
@@ -16,7 +16,6 @@ def cell_midpoints(m):
     `midpoints` are the midpoints for the entire mesh even if the mesh is
     distributed and `local_midpoints` are the midpoints of only the
     rank-local non-ghost cells."""
-    m.init()
     V = VectorFunctionSpace(m, "DG", 0)
     f = Function(V).interpolate(SpatialCoordinate(m))
     # since mesh may be distributed, the number of cells on the MPI rank
@@ -46,7 +45,6 @@ def cell_ownership(m):
     m.locate_cell(point).
 
     """
-    m.init()
     # Interpolating Constant(parent_mesh.comm.rank) into P0DG cleverly creates
     # a Function whose dat contains rank ownership information in an ordering
     # that is accessible using Firedrake's cell numbering. This is because, on
@@ -158,7 +156,6 @@ def test_pic_swarm_in_mesh(parentmesh, redundant, exclude_halos):
 
     # Setup
 
-    parentmesh.init()
     inputpointcoords, inputlocalpointcoords = cell_midpoints(parentmesh)
     inputcoordindices = np.arange(len(inputpointcoords))
     inputlocalpointcoordranks = point_ownership(parentmesh, inputpointcoords, inputlocalpointcoords)
diff --git a/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py b/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py
index 18906ec924..9970b41a1b 100644
--- a/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py
+++ b/tests/firedrake/vertexonly/test_vertex_only_mesh_generation.py
@@ -16,7 +16,6 @@ def cell_midpoints(m):
     `midpoints` are the midpoints for the entire mesh even if the mesh is
     distributed and `local_midpoints` are the midpoints of only the
     rank-local non-ghost cells."""
-    m.init()
     V = VectorFunctionSpace(m, "DG", 0)
     f = Function(V).interpolate(SpatialCoordinate(m))
     # since mesh may be distributed, the number of cells on the MPI rank
@@ -121,7 +120,6 @@ def verify_vertexonly_mesh(m, vm, inputvertexcoords, name):
     assert vm.geometric_dimension() == gdim
     assert vm.topological_dimension() == 0
     # Can initialise
-    vm.init()
     # has correct name
     assert vm.name == name
     # Find in-bounds and non-halo-region input coordinates

From 8fb6f56e0b9edf477867d9c69176f51233993a8b Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Wed, 23 Jul 2025 11:27:36 +0100
Subject: [PATCH 193/222] Test `ReducedFunctional.tlm` (#4448)

---
 .github/workflows/core.yml                    |  2 +-
 .../adjoint/ensemble_reduced_functional.py    | 33 ++++++++++++++++++-
 tests/firedrake/adjoint/test_tlm.py           | 32 ++++--------------
 3 files changed, 40 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index 536565acbc..348bb939e1 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -207,7 +207,7 @@ jobs:
             --extra-index-url https://download.pytorch.org/whl/cpu \
             "$(echo ./firedrake-repo/dist/firedrake-*.tar.gz)[ci,docs]"
           : # TODO: Remove before merge
-          pip install --verbose --editable git+https://github.com/dolfin-adjoint/pyadjoint.git@dham/abstract_reduced_functional#egg=pyadjoint-ad
+          pip install --verbose --editable git+https://github.com/dolfin-adjoint/pyadjoint.git@JHopeCollins/tlm#egg=pyadjoint-ad
 
           firedrake-clean
           pip list
diff --git a/firedrake/adjoint/ensemble_reduced_functional.py b/firedrake/adjoint/ensemble_reduced_functional.py
index 6a2eaccd7f..fc17f2b1ed 100644
--- a/firedrake/adjoint/ensemble_reduced_functional.py
+++ b/firedrake/adjoint/ensemble_reduced_functional.py
@@ -209,7 +209,38 @@ def derivative(self, adj_input=1.0, apply_riesz=False):
             return dJdm_local.delist(dJdm_total)
         return dJdm_local
 
-    def hessian(self, m_dot, apply_riesz=False):
+    def tlm(self, m_dot):
+        """Return the action of the tangent linear model of the functional.
+
+        The tangent linear model is evaluated w.r.t. the control on a vector
+        m_dot, around the last supplied value of the control.
+
+        Parameters
+        ----------
+        m_dot : pyadjoint.OverloadedType
+            The direction in which to compute the action of the tangent linear model.
+
+        Returns
+        -------
+            pyadjoint.OverloadedType: The action of the tangent linear model in the
+            direction m_dot.  Should be an instance of the same type as the functional.
+        """
+        local_tlm = self.local_reduced_functional.tlm(m_dot)
+        ensemble_comm = self.ensemble.ensemble_comm
+        if self.gather_functional:
+            mdot_g = self._allgather_J(local_tlm)
+            total_tlm = self.gather_functional.tlm(mdot_g)
+        # if gather_functional is None then we do a sum
+        elif isinstance(local_tlm, float):
+            total_tlm = ensemble_comm.allreduce(sendobj=local_tlm, op=MPI.SUM)
+        elif isinstance(local_tlm, Function):
+            total_tlm = type(local_tlm)(local_tlm.function_space())
+            total_tlm = self.ensemble.allreduce(local_tlm, total_tlm)
+        else:
+            raise NotImplementedError("This type of functional is not supported.")
+        return total_tlm
+
+    def hessian(self, m_dot, hessian_input=None, evaluate_tlm=True, apply_riesz=False):
         """The Hessian is not yet implemented for ensemble reduced functional.
 
         Raises:
diff --git a/tests/firedrake/adjoint/test_tlm.py b/tests/firedrake/adjoint/test_tlm.py
index ff6fbe7a12..14148a233d 100644
--- a/tests/firedrake/adjoint/test_tlm.py
+++ b/tests/firedrake/adjoint/test_tlm.py
@@ -54,9 +54,7 @@ def test_tlm_assemble():
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     g = f.copy(deepcopy=True)
-    f.block_variable.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, g, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, g, h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -80,12 +78,7 @@ def test_tlm_bc():
     J = assemble(c ** 2 * u * dx)
     Jhat = ReducedFunctional(J, Control(c))
 
-    # Need to specify the domain for the constant as `ufl.action`, which requires `ufl.Constant`
-    # to have a function space, will be applied on the tlm value.
-    c.block_variable.tlm_value = Function(R, val=1)
-    tape.evaluate_tlm()
-
-    assert (taylor_test(Jhat, c, Function(R, val=1), dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, c, Function(R, val=1), dJdm=Jhat.tlm(Function(R, val=1))) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -113,10 +106,8 @@ def test_tlm_func():
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     g = c.copy(deepcopy=True)
-    c.block_variable.tlm_value = h
-    tape.evaluate_tlm()
 
-    assert (taylor_test(Jhat, g, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, g, h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.parametrize("solve_type",
@@ -170,9 +161,7 @@ def test_time_dependent(solve_type):
     Jhat = ReducedFunctional(J, control)
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
-    u_1.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, control.tape_value(), h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, control.tape_value(), h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -224,9 +213,7 @@ def Dt(u, u_, timestep):
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     g = ic.copy(deepcopy=True)
-    ic.block_variable.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, g, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, g, h, dJdm=Jhat.tlm(h)) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -255,9 +242,7 @@ def test_projection():
     J = assemble(u_**2*dx)
     Jhat = ReducedFunctional(J, Control(k))
 
-    k.block_variable.tlm_value = Constant(1)
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, k, Function(R, val=1), dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, k, Function(R, val=1), dJdm=Jhat.tlm(Constant(1))) > 1.9)
 
 
 @pytest.mark.skipcomplex
@@ -268,7 +253,6 @@ def test_projection_function():
     V = FunctionSpace(mesh, "CG", 1)
 
     bc = DirichletBC(V, Constant(1), "on_boundary")
-    # g = Function(V)
     x, y = SpatialCoordinate(mesh)
     g = project(sin(x)*sin(y), V, annotate=False)
     expr = sin(g*x)
@@ -289,6 +273,4 @@ def test_projection_function():
     h = Function(V)
     h.vector()[:] = rand(h.dof_dset.size)
     m = g.copy(deepcopy=True)
-    g.block_variable.tlm_value = h
-    tape.evaluate_tlm()
-    assert (taylor_test(Jhat, m, h, dJdm=J.block_variable.tlm_value) > 1.9)
+    assert (taylor_test(Jhat, m, h, dJdm=Jhat.tlm(h)) > 1.9)

From 7fe6ff381f75a336ba2a7baa2b9135350a7911f5 Mon Sep 17 00:00:00 2001
From: ksagiyam <46749170+ksagiyam@users.noreply.github.com>
Date: Wed, 23 Jul 2025 16:33:25 +0100
Subject: [PATCH 194/222] ci: use ngsPETSc main on master CI (#4457)

---
 .github/workflows/core.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index c2b7aae6d5..c2a65088ee 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -191,7 +191,7 @@ jobs:
             : # because they rely on non-PyPI versions of petsc4py.
             pip install --no-build-isolation --no-deps \
               "$PETSC_DIR"/"$PETSC_ARCH"/externalpackages/git.slepc/src/binding/slepc4py
-            pip install --no-deps ngsPETSc netgen-mesher netgen-occt
+            pip install --no-deps git+https://github.com/NGSolve/ngsPETSc.git netgen-mesher netgen-occt
 
             : # We have to pass '--no-build-isolation' to use a custom petsc4py
             EXTRA_BUILD_ARGS='--no-isolation'

From ec176d342e543519d4d25d491b2a4a921b63e20d Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 24 Jul 2025 14:47:26 +0100
Subject: [PATCH 195/222] Fix CI timeouts  (#4458)

* Do not use 'mpiexec -n 1' for serial tests

This was causing a hang during MPI_Finalize.

* modify timeouts
---
 .github/workflows/core.yml        | 8 ++++----
 scripts/firedrake-run-split-tests | 9 +++++++--
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index 1e671b3474..fece42b1fd 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -233,7 +233,7 @@ jobs:
           : # Use pytest-xdist here so we can have a single collated output (not possible
           : # for parallel tests)
           firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/tsfc
-        timeout-minutes: 60
+        timeout-minutes: 10
 
       - name: Run PyOP2 tests
         if: inputs.run_tests && (success() || steps.install.conclusion == 'success')
@@ -245,7 +245,7 @@ jobs:
           firedrake-run-split-tests 2 4 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2
           firedrake-run-split-tests 3 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2
           firedrake-run-split-tests 4 2 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/pyop2
-        timeout-minutes: 15
+        timeout-minutes: 10
 
 
       - name: Run Firedrake tests (nprocs = 1)
@@ -255,14 +255,14 @@ jobs:
           : # Use pytest-xdist here so we can have a single collated output (not possible
           : # for parallel tests)
           firedrake-run-split-tests 1 1 -n 8 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake
-        timeout-minutes: 60
+        timeout-minutes: 90
 
       - name: Run tests (nprocs = 2)
         if: inputs.run_tests && (success() || steps.install.conclusion == 'success')
         run: |
           . venv/bin/activate
           firedrake-run-split-tests 2 4 "$EXTRA_PYTEST_ARGS" firedrake-repo/tests/firedrake
-        timeout-minutes: 30
+        timeout-minutes: 60
 
       - name: Run tests (nprocs = 3)
         if: inputs.run_tests && (success() || steps.install.conclusion == 'success')
diff --git a/scripts/firedrake-run-split-tests b/scripts/firedrake-run-split-tests
index 3a2b450640..f082f934a0 100755
--- a/scripts/firedrake-run-split-tests
+++ b/scripts/firedrake-run-split-tests
@@ -35,9 +35,14 @@ extra_args=${@:3}
 cache_cmd="PYOP2_CACHE_DIR=\$VIRTUAL_ENV/.cache/pyop2/job{#} \
            FIREDRAKE_TSFC_KERNEL_CACHE_DIR=\$VIRTUAL_ENV/.cache/tsfc/job{#}"
 
-pytest_exec="mpiexec -n ${num_procs} python -m pytest"
+if [ $num_procs = 1 ]; then
+    # Cannot use mpiexec -n 1 because this can sometimes hang with
+    # OpenMPI at MPI_Finalize
+    pytest_exec="python3 -m pytest"
+else
+    pytest_exec="mpiexec -n ${num_procs} python3 -m pytest"
+fi
 marker_spec="parallel[${num_procs}]"
-
 pytest_cmd="${pytest_exec} -v \
             --splits ${num_jobs} --group {#} \
             -m ${marker_spec} ${extra_args}"

From 5f2c9c25787869d676c12b5a00b53ff461209241 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 24 Jul 2025 17:38:52 +0100
Subject: [PATCH 196/222] Increase permissiveness of petsc_raises (#4449)

Previously issues were encountered if the garbage collection routines
threw an error. This should stop being an issue when the original error
stops getting destroyed too early (should be fixed in later Python
versions).
---
 tests/firedrake/conftest.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/firedrake/conftest.py b/tests/firedrake/conftest.py
index 5f3b3a921e..7af7a78392 100644
--- a/tests/firedrake/conftest.py
+++ b/tests/firedrake/conftest.py
@@ -204,12 +204,12 @@ def __enter__(self):
         pass
 
     def __exit__(self, exc_type, exc_val, traceback):
-        # There is a bug where 'exc_val' is occasionally 'None'. In my tests
-        # this error only exists for Python < 3.12.11.
+        # There is a bug where 'exc_val' is occasionally the wrong thing,
+        # either 'None' or some unrelated garbage collection error. In my
+        # tests this error only exists for Python < 3.12.11.
         if exc_type is PETSc.Error:
             if sys.version_info < (3, 12, 11):
-                if exc_val is None or isinstance(exc_val.__cause__, self.exc_type):
-                    return True
+                return True
             else:
                 if isinstance(exc_val.__cause__, self.exc_type):
                     return True

From cd75c21902fd2e3fe94dd6c36138be817c27216a Mon Sep 17 00:00:00 2001
From: acse-ej321 <89605848+acse-ej321@users.noreply.github.com>
Date: Fri, 25 Jul 2025 10:44:52 +0100
Subject: [PATCH 197/222] Acse ej321/gmsh demo update (#4326)

* extend demo with gmsh python api examples

* add compare image of generated meshes

* swap 3D cylinder for 2D disc, and improve variable clarity


---------

Co-authored-by: David Ham 
---
 demos/immersed_fem/gmsh_demo.png       | Bin 0 -> 2719657 bytes
 demos/immersed_fem/immersed_fem.py.rst | 259 ++++++++++++++++++++++---
 2 files changed, 235 insertions(+), 24 deletions(-)
 create mode 100644 demos/immersed_fem/gmsh_demo.png

diff --git a/demos/immersed_fem/gmsh_demo.png b/demos/immersed_fem/gmsh_demo.png
new file mode 100644
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diff --git a/demos/immersed_fem/immersed_fem.py.rst b/demos/immersed_fem/immersed_fem.py.rst
index 42db8688d0..bffadc08e9 100644
--- a/demos/immersed_fem/immersed_fem.py.rst
+++ b/demos/immersed_fem/immersed_fem.py.rst
@@ -1,41 +1,74 @@
 Creating Firedrake-compatible meshes in Gmsh
 ============================================
 
-The purpose of this demo is to summarize the
-key structure of a ``gmsh.geo`` file that creates a
-Firedrake-compatible mesh. For more details about Gmsh, please
+The purpose of this demo is to demonstrate methods available
+through Gmsh to create meshes compatible with Firedrake. It overviews
+the creation the same mesh, three different ways:
+
+* Constructing a GEO file
+* Using the Gmsh API
+* Using OpenCASCADE through Gmsh
+  
+For more details about Gmsh, please
 refer to the `Gmsh documentation `_.
-The Gmsh syntax used in this document is for Gmsh version 4.4.1 .
+The Gmsh syntax used in this document is for Gmsh version 4.4.1. ::
+
+   import matplotlib.pyplot as plt
+   from firedrake import *
+   import gmsh
 
 As example, we will construct and mesh the following geometry:
 a rectangle with a disc in the middle. In the picture,
 numbers in black refer to Gmsh point tags, whereas numbers in
-read refer to Gmsh curve tags (see below).
+red refer to Gmsh curve tags (see below).
 
 .. image:: immerseddomain.png
    :width: 400px
    :align: center
 
-The first thing we define are four corners of a rectangle.
-We specify the x,y, and z(=0) coordinates, as well as the target
-element size at these corners (which we set to 0.5).
+Relative to a central origin, ``(x0, y0, z0)``, the rectangle is parameterised by its length ``L``,
+and width, ``W``. Likewise, the disc is parameterised its radius, ``R``. The target element size is
+set to ``dx_rec=0.5`` and ``dx_disc=0.1`` to generate finer triangles around the disc. ::
+
+   x0, y0, z0 = 0., 0., 0.
+   L = 12.
+   W = 4.
+   R = 1.
+   dx_rec  = 0.5
+   dx_disc = 0.1 
+
+Constructing a GEO file
+-----------------------
+
+The first thing we need to do is to create a Gmsh ``gmsh.geo``,
+which is the geometry recipe for Gmsh.
+
+We begin by defining the four corners of a bounding rectangle. We specify the x,y,
+and z(=0) coordinates, as well as the target element size at these 
+corners.
 
 .. code-block:: none
 
-  Point(1) = {-6,  2, 0, 0.5};
-  Point(2) = {-6, -2, 0, 0.5};
-  Point(3) = { 6, -2, 0, 0.5};
-  Point(4) = { 6,  2, 0, 0.5};
+   Point(1) = {-6,  2, 0, 0.5};
+   Point(2) = {-6, -2, 0, 0.5};
+   Point(3) = { 6, -2, 0, 0.5};
+   Point(4) = { 6,  2, 0, 0.5};
+
+.. note::
+   Gmsh tracks most objects through a combination of its dimension, ``dim``
+   and an integer tag, ``tag``, which must be unique to the object. For example,
+   ``Point(1)`` has dimension 0 and a unique tag 1, while ``Line(1)`` has
+   dimension 1 and a unique tag 1.
 
 Then, we define 5 points to describe a circle.
 
 .. code-block:: none
 
-  Point(5) = { 0,  0, 0, 0.1};
-  Point(6) = { 1,  0, 0, 0.1};
-  Point(7) = {-1,  0, 0, 0.1};
-  Point(8) = { 0,  1, 0, 0.1};
-  Point(9) = { 0, -1, 0, 0.1};
+   Point(5) = { 0,  0, 0, 0.1};
+   Point(6) = { 1,  0, 0, 0.1};
+   Point(7) = {-1,  0, 0, 0.1};
+   Point(8) = { 0,  1, 0, 0.1};
+   Point(9) = { 0, -1, 0, 0.1};
 
 Then, we create 8 edges: 4 for the rectangle and 4 for the circle.
 Note that the Gmsh command ``Circle`` requires the arc to be
@@ -69,7 +102,7 @@ Then, we define two plane surfaces: the rectangle without the disc first, and th
   Plane Surface(2) = {10};
 
 Finally, we group together some edges and define ``Physical`` entities.
-Firedrake uses the tags of these physical identities to distinguish
+Firedrake uses the ``tag`` of each physical entity to distinguish
 between parts of the mesh (see the concrete example at the end of this page).
 
 .. code-block:: none
@@ -86,12 +119,192 @@ you can type the following command in the terminal
 
 .. code-block:: none
 
-    gmsh -2 immersed_domain.geo -format msh2
+   gmsh -2 immersed_domain.geo -format msh2
 
 .. note::
 
-   Depending on your version of gmsh and DMPlex, the
-   gmsh option ``-format msh2`` may be omitted.
+   The ``-2`` flag indicates whether the mesh will be 2 or 3 dimensions. Depending on your 
+   version of Gmsh and DMPlex, the Gmsh option ``-format msh2`` may be omitted.
+
+Using the Gmsh API
+------------------
+
+We can alternatively use python commands enabled through the Gmsh API to build, save, and
+read the mesh into Firedrake from within a python script. This allows for parameter flexibility
+and improved readability of the mesh generation code.
+
+We first need to ``initialize`` the Gmsh API and create a new empty mesh model. ::
+
+   gmsh.initialize()
+   model = gmsh.model
+   model.add("gmsh_api_demo")
+
+As before, we define the four rectangle corner points and target element size. ::
+
+   rectangle_points = [
+   model.geo.addPoint(x0 - L/2, y0 + W/2, z0, dx_rec, tag = 1), # top left
+   model.geo.addPoint(x0 - L/2, y0 - W/2, z0, dx_rec, tag = 2), # bottom left
+   model.geo.addPoint(x0 + L/2, y0 - W/2, z0, dx_rec, tag = 3), # bottom right
+   model.geo.addPoint(x0 + L/2, y0 + W/2, z0, dx_rec, tag = 4)  # top right
+   ]
+
+Then, we define 5 points to describe a circle. ::
+
+   center = model.geo.addPoint(x0, y0, z0, tag = 5)
+   circle_points = [
+   model.geo.addPoint(x0 - R, y0, z0, dx_disc, tag = 6),
+   model.geo.addPoint(x0, y0 + R, z0, dx_disc, tag = 7),
+   model.geo.addPoint(x0 + R, y0, z0, dx_disc, tag = 8),
+   model.geo.addPoint(x0, y0 - R, z0, dx_disc, tag = 9)
+   ]
+
+Then, we create 8 edges: 4 for the rectangle and 4 for the circle. ::
+
+   rectangle_lines = [
+   model.geo.addLine(rectangle_points[0], rectangle_points[1], tag = 1), # left
+   model.geo.addLine(rectangle_points[1], rectangle_points[2], tag = 2), # bottom
+   model.geo.addLine(rectangle_points[2], rectangle_points[3], tag = 3), # right
+   model.geo.addLine(rectangle_points[3], rectangle_points[0], tag = 4)  # top
+   ]
+
+   circle_arcs =[
+   model.geo.addCircleArc(circle_points[0], center, circle_points[1], tag = 5),
+   model.geo.addCircleArc(circle_points[1], center, circle_points[2], tag = 6),
+   model.geo.addCircleArc(circle_points[2], center, circle_points[3], tag = 7),
+   model.geo.addCircleArc(circle_points[3], center, circle_points[0], tag = 8)
+   ]
+
+We then combine the edges into a closed loop for both the rectangle and
+the circle used to define a surface for the area outside and inside the 
+inscribed circle, respectively. In the ``addPlaneSurface`` function by 
+convention, the first ``Curve Loop`` defines the outer boundary and
+anything after in the list is treated as the boundary of a hole (or holes)
+in the domain. These need to be registered to the model with ``synchronize`` 
+before we can use them. ::
+
+   rectangle_loop = model.geo.addCurveLoop(rectangle_lines, tag = 9)
+   circle_loop = model.geo.addCurveLoop(circle_arcs, tag = 10)
+
+   punched_surface = model.geo.addPlaneSurface([rectangle_loop,circle_loop], tag = 1)
+   circle_surface = model.geo.addPlaneSurface([circle_loop], tag = 2)
+   model.geo.synchronize()
+   
+Finally, we group together some edges and define ``Physical`` entities. ::
+
+   model.addPhysicalGroup(dim = 1, tags = [rectangle_lines[1], rectangle_lines[3]], tag = 11, name="HorEdges")
+   model.addPhysicalGroup(dim = 1, tags = [rectangle_lines[0], rectangle_lines[2]], tag = 12, name="VerEdges")
+   model.addPhysicalGroup(dim = 1, tags = circle_arcs, tag = 13, name="Circle")
+   model.addPhysicalGroup(dim = 2, tags = [punched_surface], tag = 3, name="PunchedDom")
+   model.addPhysicalGroup(dim = 2, tags = [circle_surface], tag = 4, name="Disc")
+
+A number of meshing options are available. In particular, the algorithm for mesh generation
+and can be set globally or for individual surfaces.
+
+* 2D: 1: MeshAdapt, 2: Automatic, 3: Initial mesh only, 5: Delaunay, 6: Frontal-Delaunay (default),\
+  7: BAMG, 8: Frontal-Delaunay for Quads, 9: Packing of Parallelograms, 11: Quasi-structured Quad
+* 3D: Delaunay (default) 3: Initial mesh only 4: Frontal 7: MMG3D 9: R-tree 10: HXT
+
+For more information see the `Gmsh algorithm overview `_.
+
+When writing the mesh to file, the format is determined by the file extension. For example,
+`.msh2` for Gmsh 2.x, `.msh` for GMSH 4.x. ::
+
+   gmsh.option.setNumber("Mesh.Algorithm", 6)
+   gmsh.option.setNumber("Mesh.MshFileVersion", 4.1)
+   gmsh.model.mesh.generate(2)
+   gmsh.write('gmsh_api_demo.msh')
+   
+We close the Gmsh API kernel after finalising the mesh. ::
+
+   gmsh.finalize()
+
+Using OpenCASCADE through Gmsh
+------------------------------
+
+Using OpenCASCADE through Gmsh, we define higher level geometries such as rectangles 
+and discs directly. It also has additional 3D capability, and integration not illustrated
+here. Please see the  `OpenCASCADE documentation `_
+in Gmsh for more details.
+
+As with the Gmsh API, we ``initialize`` and start constructing a new mesh model. ::
+
+   gmsh.initialize()
+   model = gmsh.model
+   gmsh.model.add("gmsh_occ_demo")
+
+We first use OpenCASCADE to create a rectangle and a cylinder object. This automates
+the create of points, lines, and surfaces. Both objects need to be registered to the 
+model with ``synchronize`` before we can use them. ::
+
+   rectangle_obj_tag = model.occ.addRectangle(x0 - L/2, y0 - W/2, z0, L, W, tag = 1)
+   disc_obj_tag = model.occ.addDisk(x0, y0, z0, rx = R, ry = R, tag = 2)
+   model.occ.synchronize()
+
+To create the whole, we use the method ``occ.cut`` with the option ``removeTool=False`` to 
+retain the disc interior (which would be otherwise deleted by default). The ``occ.cut`` method
+takes and returns a list of tuples ``(dimension, tag)`` as do other functions such as 
+``getBoundary`` used below. We save the tag of the combined object for later use 
+and register the new object to the model with ``synchronize``. ::
+
+   punched_surface = model.occ.cut([(2, rectangle_obj_tag)], [(2, disc_obj_tag)], removeTool=False)
+   punched_surface_tag = punched_surface[0][0][1]
+   model.occ.synchronize()
+
+We then extract the boundary from the objects.  We extract the the punched surface
+lines along with disc points to define the ``Physical`` groups. It returns boundaries
+per entity (``combined = false``) or as a single shape (``combined = true``), and 
+adjust the signs to reflect orientation if ``oriented = true``. The boundary operator 
+is applied down to point-level, or dimension 0, when ``recursive = True``. ::
+
+   punched_lines = model.getBoundary([(2, punched_surface_tag)],
+                                        combined = True, oriented = True, recursive = False)
+   disc_points = model.getBoundary([(2, disc_obj_tag)],
+                                        combined = True, oriented = True, recursive = True)
+
+We set the mesh resolution using ``setSize``. The choice here is to first set all the
+zero-dimensional points to the background size, and then override the mesh size for the 
+points on the circle boundary. Another strategy documented in the Gmsh manual is to
+identify the desired points by a bounding box search. ::
+
+   model.mesh.setSize(gmsh.model.occ.getEntities(0), dx_rec)
+   model.mesh.setSize(disc_points, dx_disc)
+
+We parse just the line tags to create a list of physical group tags. In this case the
+assignment of the lines was done manually through trial and inspection. 
+TODO: add a function to automatically assign the lines to the physical groups. ::
+
+   punched_line_tags = [abs(line) for dim,line in punched_lines]
+   model.addPhysicalGroup(dim = 1, tags = [punched_line_tags[1], punched_line_tags[4]], tag = 11, name="HorEdges")
+   model.addPhysicalGroup(dim = 1, tags = [punched_line_tags[2], punched_line_tags[3]], tag = 12, name="VertEdges")
+   model.addPhysicalGroup(dim = 1, tags = [punched_line_tags[0]], tag = 13, name="CircleEdge")
+   model.addPhysicalGroup(dim = 2, tags = [punched_surface_tag], tag = 3, name="PunchedDom")
+   model.addPhysicalGroup(dim = 2, tags = [disc_obj_tag], tag = 4, name="Disc")
+
+   gmsh.option.setNumber("Mesh.Algorithm", 6)
+   gmsh.model.mesh.generate(2)
+   gmsh.write('gmsh_occ_demo.msh')
+   gmsh.finalize()
+
+Compare Meshes
+--------------
+We can load and check the generated meshes in Firedrake. ::
+
+   meshes = [Mesh('gmsh_occ_demo.msh', name = "Gmsh API"),
+             Mesh('gmsh_api_demo.msh', name = "Gmsh OpenCASCADE")]
+   fig, ax = plt.subplots(len(meshes), 1, figsize = (8, len(meshes)*3), tight_layout=True)
+   for m, ax in zip(meshes, ax):
+      triplot(m, axes=ax)
+      ax.set_title(f'Mesh via {m.name}, # cells: {m.num_cells()}')
+      ax.legend(loc='upper left')   
+
+   fig.savefig("gmsh_demo.png", dpi = 400)
+
+.. image:: gmsh_demo.png
+   :width: 400px
+   :align: center
+
+Illustrate Features
+-------------------
 
 To illustrate how to access all these features within Firedrake,
 we consider the following interface problem. Denoting by
@@ -125,10 +338,8 @@ The resulting weak formulation reads as follows:
 The following Firedrake code shows how to solve this variational problem
 using linear Lagrangian finite elements. ::
 
-   from firedrake import *
-
    # load the mesh generated with Gmsh
-   mesh = Mesh('immersed_domain.msh')
+   mesh = Mesh('gmsh_occ_demo.msh')
 
    # define the space of linear Lagrangian finite elements
    V = FunctionSpace(mesh, "CG", 1)

From cac442c64724883bc37228b9f6eff8599fef2dfd Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Fri, 25 Jul 2025 19:58:41 +0100
Subject: [PATCH 198/222] document varying extrusion by changing coordinates
 (#4385)

---------

Co-authored-by: Connor Ward 
---
 docs/source/extruded-meshes.rst               |  24 +++++++++++
 docs/source/images/morphed_bathymetry.png     | Bin 0 -> 122644 bytes
 docs/source/interpolation.rst                 |   1 +
 .../test_change_coordinates_extrusion.py      |  40 ++++++++++++++++++
 4 files changed, 65 insertions(+)
 create mode 100644 docs/source/images/morphed_bathymetry.png
 create mode 100644 tests/firedrake/extrusion/test_change_coordinates_extrusion.py

diff --git a/docs/source/extruded-meshes.rst b/docs/source/extruded-meshes.rst
index 8540b194b8..363df0a6cc 100644
--- a/docs/source/extruded-meshes.rst
+++ b/docs/source/extruded-meshes.rst
@@ -181,6 +181,30 @@ The 2D and 3D hedgehog-extruded meshes are shown below.
 
 |hedge2d| |hedge3d|
 
+Changing coordinates
+~~~~~~~~~~~~~~~~~~~~
+
+A common use case is that the bottom and/or top of the domain is not flat (or
+spherical) but is given by a spatially varying field defined on the base mesh.
+In this case, a straightforward approach is to uniformly extrude to a unit
+height, and then to create a new coordinate field by changing coordinates to
+conform to the bathymetry. The example below shows how to do this. This could
+be combined with :ref:`interpolation from external data
+` to incorporate domain boundary data from
+any source. Naturally, the change of coordinates can also be varied to produce
+a non-uniform distribution of the mesh layers.
+
+.. literalinclude:: ../../tests/firedrake/extrusion/test_change_coordinates_extrusion.py
+  :dedent:
+  :language: python3
+  :start-after: start extruded change coordinates
+  :end-before: end extruded change coordinates
+
+The mesh generated in this example is shown below:
+
+.. image:: images/morphed_bathymetry.png
+  :width: 100%
+
 Custom Extrusion
 ~~~~~~~~~~~~~~~~
 
diff --git a/docs/source/images/morphed_bathymetry.png b/docs/source/images/morphed_bathymetry.png
new file mode 100644
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diff --git a/docs/source/interpolation.rst b/docs/source/interpolation.rst
index b079a9e352..ae20e5f967 100644
--- a/docs/source/interpolation.rst
+++ b/docs/source/interpolation.rst
@@ -274,6 +274,7 @@ interpolation:
    :start-after: [test_cross_mesh 21]
    :end-before: [test_cross_mesh 22]
 
+.. _external_interpolation:
 
 Interpolation from external data
 --------------------------------
diff --git a/tests/firedrake/extrusion/test_change_coordinates_extrusion.py b/tests/firedrake/extrusion/test_change_coordinates_extrusion.py
new file mode 100644
index 0000000000..9eb445352d
--- /dev/null
+++ b/tests/firedrake/extrusion/test_change_coordinates_extrusion.py
@@ -0,0 +1,40 @@
+from firedrake import *
+from firedrake.__future__ import interpolate
+import numpy as np
+
+
+def test_extruded_change_coordinates():
+    # This test exists to ensure the code in the manual works.
+
+    # start extruded change coordinates
+    base_mesh = IntervalMesh(16, 0, 2*pi)
+    # Make a height 1 mesh.
+    unit_extruded_mesh = ExtrudedMesh(base_mesh, layers=10)
+
+    base_fs = FunctionSpace(base_mesh, "CG", 1)
+
+    x, = SpatialCoordinate(base_mesh)
+    # You could set this field any way you like.
+    bathymetry = assemble(interpolate(0.2*sin(x), base_fs))
+
+    # Now we transfer the bathymetry field into a depth-averaged field.
+    extruded_element = FiniteElement("R", "interval", 0)
+    extruded_space = FunctionSpace(unit_extruded_mesh,
+                                   TensorProductElement(base_fs.ufl_element(),
+                                                        extruded_element))
+    extruded_bathymetry = Function(extruded_space)
+    extruded_bathymetry.dat.data_wo[:] = bathymetry.dat.data_ro[:]
+
+    # Build a new coordinate field by change of coordinates.
+    x, y = SpatialCoordinate(unit_extruded_mesh)
+    new_coordinates = assemble(
+        interpolate(
+            as_vector([x, extruded_bathymetry + y * (1-extruded_bathymetry)]),
+            unit_extruded_mesh.coordinates.function_space()
+        )
+    )
+    # Finally build the mesh you are actually after.
+    mesh = Mesh(new_coordinates)
+    # end extruded change coordinates
+
+    assert np.allclose(mesh.coordinates.dat.data_ro[:, 1].min(), -0.2)

From 0415ecdba21807cdd2c9c3dd89001e95d27ae62a Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Mon, 28 Jul 2025 10:10:06 +0100
Subject: [PATCH 199/222] Fix firedrake-check on macOS (#4466)

---
 scripts/firedrake-check |  2 +-
 setup.py                | 21 +++++++++++++++++++--
 2 files changed, 20 insertions(+), 3 deletions(-)

diff --git a/scripts/firedrake-check b/scripts/firedrake-check
index b04d943a7c..62b7618843 100644
--- a/scripts/firedrake-check
+++ b/scripts/firedrake-check
@@ -4,7 +4,7 @@
 
 import argparse
 import logging
-import importlib
+import importlib.util
 import os
 import pathlib
 import subprocess
diff --git a/setup.py b/setup.py
index a43e399fc5..96fda7db38 100644
--- a/setup.py
+++ b/setup.py
@@ -14,6 +14,7 @@
 from setuptools import setup, find_packages, Extension
 from setuptools.command.editable_wheel import editable_wheel as _editable_wheel
 from setuptools.command.sdist import sdist as _sdist
+from setuptools.command.bdist_wheel import bdist_wheel as _bdist_wheel
 from glob import glob
 from pathlib import Path
 from Cython.Build import cythonize
@@ -250,9 +251,15 @@ def extensions():
 
 
 def copy_check_files():
-    """Copy Makefile and tests into firedrake/_check."""
+    """Copy tests into firedrake/_check."""
     dest_dir = Path("firedrake/_check")
     for check_file in map(Path, FIREDRAKE_CHECK_FILES):
+        # If we are building a wheel from an sdist then the files have
+        # already been moved
+        if not check_file.exists():
+            assert (dest_dir / check_file).exists()
+            continue
+
         os.makedirs(dest_dir / check_file.parent, exist_ok=True)
         shutil.copy(check_file, dest_dir / check_file.parent)
 
@@ -269,8 +276,18 @@ def run(self):
         super().run()
 
 
+class bdist_wheel(_bdist_wheel):
+    def run(self):
+        copy_check_files()
+        super().run()
+
+
 setup(
-    cmdclass={"editable_wheel": editable_wheel, "sdist": sdist},
+    cmdclass={
+        "editable_wheel": editable_wheel,
+        "sdist": sdist,
+        "bdist_wheel": bdist_wheel,
+    },
     packages=find_packages(),
     ext_modules=extensions(),
 )

From 201ccfa09f859172692b167d761b2b2681f7b045 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Mon, 28 Jul 2025 13:06:51 +0100
Subject: [PATCH 200/222] Avoid mpiexec -n 1 for firedrake-check (#4465)

---
 scripts/firedrake-check | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/scripts/firedrake-check b/scripts/firedrake-check
index 62b7618843..89f53c1261 100644
--- a/scripts/firedrake-check
+++ b/scripts/firedrake-check
@@ -78,7 +78,11 @@ def run_tests(tests: tuple[str], nprocs: int, mpiexec: str) -> None:
     else:
         check_flags = "--quiet -p no:cacheprovider"
 
-    cmd = f"{mpiexec} {nprocs} {sys.executable} -m pytest {check_flags} -m parallel[{nprocs}]".split()
+    cmd = f"{sys.executable} -m pytest {check_flags} -m parallel[{nprocs}]"
+    # Only use mpiexec for parallel tests because 'mpiexec -n 1' can hang
+    if nprocs > 1:
+        cmd = f"{mpiexec} {nprocs} {cmd}"
+    cmd = cmd.split()
     cmd.extend(tests)
 
     subprocess.run(cmd, cwd=test_dir, check=True)

From 6bbb0eee26f370c698ff703d1db0d30d6e8bd4c1 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Tue, 29 Jul 2025 11:23:17 +0100
Subject: [PATCH 201/222] Add extra guest info to Firedrake 25 page (#4472)

---
 docs/source/firedrake_25.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/source/firedrake_25.rst b/docs/source/firedrake_25.rst
index b6c8c45f7b..9d27b16412 100644
--- a/docs/source/firedrake_25.rst
+++ b/docs/source/firedrake_25.rst
@@ -64,7 +64,8 @@ Conference dinner
 
 Registration includes a drinks reception and three-course dinner at University House, University of Leeds. The dinner will take place on the evening of 16 September. 
 
-It is possible during registration to purchase additional tickets for the dinner to bring non-registered companions.
+It is possible to bring non-registered companions to the dinner. To do so please purchase a ticket
+by following `this link `__.
 
 .. image:: images/university_house_leeds.jpg
    :width: 80%

From 9cbc0a691998df2d8199b334ff01ff21e2829806 Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Tue, 29 Jul 2025 12:02:35 +0100
Subject: [PATCH 202/222] test for early cleanup of adjoint variables (#4451)

---
 tests/firedrake/adjoint/test_assignment.py | 49 +++++++++++++++++++++-
 1 file changed, 48 insertions(+), 1 deletion(-)

diff --git a/tests/firedrake/adjoint/test_assignment.py b/tests/firedrake/adjoint/test_assignment.py
index c4b9fcd467..42ec11f492 100644
--- a/tests/firedrake/adjoint/test_assignment.py
+++ b/tests/firedrake/adjoint/test_assignment.py
@@ -3,8 +3,9 @@
 from firedrake import *
 from firedrake.__future__ import *
 from firedrake.adjoint import *
+from checkpoint_schedules import SingleMemoryStorageSchedule
 
-from numpy.random import rand
+from numpy.random import rand, default_rng
 from numpy.testing import assert_approx_equal, assert_allclose
 
 
@@ -240,3 +241,49 @@ def test_assign_constant_scale():
     assert min(r["R0"]["Rate"]) > 0.9
     assert min(r["R1"]["Rate"]) > 1.9
     assert min(r["R2"]["Rate"]) > 2.9
+
+
+@pytest.mark.skipcomplex
+@pytest.mark.parametrize("scheduler", (None, SingleMemoryStorageSchedule()))
+def test_adjoint_cleanup(scheduler):
+    # This test checks that the adjoint does not discard too many checkpoint
+    # variables. This is achieved by computing the derivative before conducting
+    # the Taylor test. This extra derivative is the thing that would cause the
+    # spurious discards.
+
+    # get tape
+    tape = get_working_tape()
+    tape.clear_tape()
+    continue_annotation()
+
+    if scheduler is not None:
+        tape.enable_checkpointing(scheduler)
+
+    mesh = SquareMesh(1, 1, 1, quadrilateral=True)
+
+    V = FunctionSpace(mesh, "CG", 1)
+    R = FunctionSpace(mesh, "R", 0)
+
+    u_0 = Function(V).assign(1.0)
+    u = Function(V).assign(u_0)
+    r = Function(R)
+
+    r.assign(2.0)
+    u.project(r * u)
+    r.assign(1.0)
+    u.project(r * u)
+
+    J = assemble((u - Function(V).assign(1.0)) ** 2 * dx)
+
+    pause_annotation()
+    reduced_functional = ReducedFunctional(J, Control(u_0))
+
+    # Deliberate additional derivative computation.
+    reduced_functional.derivative()
+
+    # Choosing fixed perturbation
+    dtemp = Function(u_0.function_space())
+    rng = default_rng(42)
+    dtemp.dat.data_wo[:] = rng.random(dtemp.dat.data_ro.shape)
+
+    assert taylor_test(reduced_functional, u_0, dtemp) > 1.99999999

From 23490c39a9382d9de33bd64db826c019b354bcbf Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 30 Jul 2025 09:27:28 +0100
Subject: [PATCH 203/222] MG: Support Cofunction in NLVP (#4474)

* Support MG + Cofunction in NLVP
---
 firedrake/mg/ufl_utils.py                     |  5 ++++-
 tests/firedrake/multigrid/test_poisson_gmg.py | 14 ++++++++++++--
 2 files changed, 16 insertions(+), 3 deletions(-)

diff --git a/firedrake/mg/ufl_utils.py b/firedrake/mg/ufl_utils.py
index 6a89bb2eed..a4dced7ed6 100644
--- a/firedrake/mg/ufl_utils.py
+++ b/firedrake/mg/ufl_utils.py
@@ -33,7 +33,7 @@ def __init__(self, coarsen, coefficient_mapping=None):
         self.coarsen = coarsen
         super(CoarsenIntegrand, self).__init__()
 
-    expr = MultiFunction.reuse_if_untouched
+    ufl_type = MultiFunction.reuse_if_untouched
 
     def argument(self, o):
         V = self.coarsen(o.function_space(), self.coarsen)
@@ -42,6 +42,9 @@ def argument(self, o):
     def coefficient(self, o):
         return self.coarsen(o, self.coarsen, coefficient_mapping=self.coefficient_mapping)
 
+    def cofunction(self, o):
+        return self.coarsen(o, self.coarsen, coefficient_mapping=self.coefficient_mapping)
+
     def geometric_quantity(self, o):
         return type(o)(self.coarsen(extract_unique_domain(o), self.coarsen))
 
diff --git a/tests/firedrake/multigrid/test_poisson_gmg.py b/tests/firedrake/multigrid/test_poisson_gmg.py
index a50bbdee63..202aee131a 100644
--- a/tests/firedrake/multigrid/test_poisson_gmg.py
+++ b/tests/firedrake/multigrid/test_poisson_gmg.py
@@ -81,7 +81,7 @@ def manufacture_solution(V):
     return exact, f
 
 
-def run_poisson(solver_type):
+def run_poisson(solver_type, rhs_type="form"):
     parameters = solver_parameters(solver_type)
     mesh = UnitSquareMesh(10, 10)
 
@@ -93,7 +93,13 @@ def run_poisson(solver_type):
     exact, f = manufacture_solution(V)
     u = function.Function(V)
     v = TestFunction(V)
-    F = inner(grad(u), grad(v))*dx - inner(f, v)*dx
+
+    L = inner(f, v)*dx
+    if rhs_type == "cofunction":
+        L = assemble(L)
+    elif rhs_type != "form":
+        raise ValueError("Unexpected RHS type")
+    F = inner(grad(u), grad(v))*dx - L
     bcs = DirichletBC(V, 0.0, (1, 2, 3, 4))
 
     solve(F == 0, u, bcs=bcs, solver_parameters=parameters)
@@ -107,6 +113,10 @@ def test_poisson_gmg(solver_type):
     assert run_poisson(solver_type) < 4e-6
 
 
+def test_poisson_gmg_cofunction():
+    assert run_poisson("mg", rhs_type="cofunction") < 4e-6
+
+
 @pytest.mark.parallel
 def test_poisson_gmg_parallel_mg():
     errmat = run_poisson("mg")

From 00a6d3af76aa66d0017a2469f0c505b192dfa8c9 Mon Sep 17 00:00:00 2001
From: Alberto Paganini <20994366+APaganini@users.noreply.github.com>
Date: Wed, 30 Jul 2025 10:12:08 +0100
Subject: [PATCH 204/222] Demo created at Dartington Hall - Shape Optimization
 (#4307)

---------

Co-authored-by: David Ham 
Co-authored-by: Connor Ward 
---
 .../shape_optimization.py.rst                 | 109 ++++++++++++++++++
 docs/source/adjoint.rst                       |   2 +
 docs/source/advanced_tut.rst                  |   3 +-
 tests/firedrake/demos/test_demos_run.py       |   1 +
 4 files changed, 114 insertions(+), 1 deletion(-)
 create mode 100644 demos/shape_optimization/shape_optimization.py.rst

diff --git a/demos/shape_optimization/shape_optimization.py.rst b/demos/shape_optimization/shape_optimization.py.rst
new file mode 100644
index 0000000000..69c0776451
--- /dev/null
+++ b/demos/shape_optimization/shape_optimization.py.rst
@@ -0,0 +1,109 @@
+Shape optimization
+==================
+
+Shape optimization is about modifying the shape of a domain :math:`\Omega` so
+that an objective function :math:`J(\Omega)` is minimized. In this demo, we
+consider an objective function constrained to a boundary value problem and
+implement a simple mesh-moving shape optimization strategy using Firedrake and
+pyadjoint.  This tutorial was contributed by `Alberto Paganini
+`__ with support from `Ado Farsi
+`__ and `Mirko Ciceri
+`__, and was written during the `ccp-dcm hackaton
+`__ at Dartington Hall.
+
+Let
+
+.. math::
+
+   J(\Omega) = \int_\Omega \big(u(\mathbf{x}) - u_t(\mathbf{x})\big)^2 \,\mathrm{d}\mathbf{x}\,.
+
+measure the difference between a steady-state temperature profile
+:math:`u:\mathbb{R}^2\to\mathbb{R}` and a target steady-state temperature
+profile :math:`u_t:\mathbb{R}^2\to\mathbb{R}`. Specifically, the function
+:math:`u` is the solution to the steady-state heat equation
+
+.. math::
+
+    -\Delta u = 4 \quad \text{in }\Omega\,, \qquad u = 0 \quad \text{on } \partial\Omega
+
+
+and the target temperature profile :math:`u_t` is
+
+.. math::
+
+    u_t(x,y) = 1.21 - (x - 0.5)^2 - (y - 0.5)^2\,.
+
+Beside the empty set, the domain that minimizes :math:`J(\Omega)` is a disc of
+radius :math:`1.1` centered at :math:`(0.5,0.5)`.
+
+We can now proceed to set up the problem. We import firedrake and pyadjoint and
+choose an initial guess (in this case, a unit disc centred at the origin)::
+
+  from firedrake import *
+  from firedrake.adjoint import *
+  mesh = UnitDiskMesh(refinement_level=3)
+
+Then, we :ref:`start annotating ` and turn the mesh coordinates into a control variable::
+
+  continue_annotation()
+  Q = mesh.coordinates.function_space()
+  dT = Function(Q)
+  mesh.coordinates.assign(mesh.coordinates + dT)
+
+We can now implement the target function::
+
+  x, y = SpatialCoordinate(mesh)
+  u_t = Constant(1.21) - (x - Constant(0.5))**2 - (y - Constant(0.5))**2
+
+solve the weak form of the boundary value problem::
+
+  V = FunctionSpace(mesh, "CG", 1)
+  u = Function(V, name='state')
+  v = TestFunction(V)
+  F = (dot(grad(u), grad(v)) - 4 * v) * dx
+  bcs = DirichletBC(V, Constant(0.), "on_boundary")
+  solve(F == 0, u, bcs=bcs)
+
+and evaluate the objective function::
+
+  J = assemble((u - u_t)**2*dx)
+
+We now turn the objective function into a reduced function so that pyadjoint
+(and UFL shape differentiation capability) can automatically compute shape
+gradients, that is, directions of steepest ascent::
+
+  Jred = ReducedFunctional(J, Control(dT))
+  stop_annotating()
+
+We now have all the ingredients to implement a basic steepest descent shape
+optimization algorithm with fixed step size.::
+
+  File = VTKFile("shape_iterates.pvd")
+  for ii in range(30):
+    print("J(ii =", ii, ") =", Jred(dT))
+    File.write(mesh.coordinates)
+
+    # compute the gradient (steepest ascent)
+    opts = {"riesz_representation": "H1"}
+    gradJ = Jred.derivative(options=opts)
+
+    # update domain
+    dT -= 0.2*gradJ
+
+  File.write(mesh.coordinates)
+  print("J(final) =", Jred(dT))
+
+.. only:: html
+
+  .. container:: youtube
+
+    .. vimeo:: 1083822714?loop=1
+       :width: 600px
+
+
+**Remark:** mesh-moving shape optimization can lead to mesh tangling, which
+invalidates finite element computations. For faster and more robust shape
+optimization, we recommend using Firedrake's shape optimization toolbox
+`Fireshape `__.
+
+A python script version of this demo can be found :demo:`here `.
diff --git a/docs/source/adjoint.rst b/docs/source/adjoint.rst
index 5dbc237ebe..2a488c4d4c 100644
--- a/docs/source/adjoint.rst
+++ b/docs/source/adjoint.rst
@@ -159,6 +159,8 @@ The essence of this process is:
    derived automatically by applying UFL's :func:`~ufl.derivative`,
    :func:`~ufl.adjoint`, and :func:`~ufl.action` operators.
 
+.. _adjoint-taping:
+
 Taping an example calculation
 -----------------------------
 
diff --git a/docs/source/advanced_tut.rst b/docs/source/advanced_tut.rst
index dd3609a20f..5fa8f360ed 100644
--- a/docs/source/advanced_tut.rst
+++ b/docs/source/advanced_tut.rst
@@ -30,5 +30,6 @@ element systems.
    Vertex/edge star multigrid relaxation for H(div).
    Auxiliary space patch relaxation multigrid for H(curl).
    Preconditioning using fast diagonalisation.
+   Shape optimisation.
    Steady Boussinesq problem with integral constraints.
-   Steady multicomponent flow -- microfluidic mixing of hydrocarbons.
\ No newline at end of file
+   Steady multicomponent flow -- microfluidic mixing of hydrocarbons.
diff --git a/tests/firedrake/demos/test_demos_run.py b/tests/firedrake/demos/test_demos_run.py
index c64b7fd594..daaadc4e15 100644
--- a/tests/firedrake/demos/test_demos_run.py
+++ b/tests/firedrake/demos/test_demos_run.py
@@ -49,6 +49,7 @@
     Demo(("saddle_point_pc", "saddle_point_systems"), ["hypre", "mumps"]),
     Demo(("fast_diagonalisation", "fast_diagonalisation_poisson"), ["mumps"]),
     Demo(('vlasov_poisson_1d', 'vp1d'), []),
+    Demo(('shape_optimization', 'shape_optimization'), ["adjoint", "vtk"])
 ]
 PARALLEL_DEMOS = [
     Demo(("full_waveform_inversion", "full_waveform_inversion"), ["adjoint"]),

From 4924a49147838ad91a867b9385ac0cd3b3f97950 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Wed, 30 Jul 2025 11:52:32 +0100
Subject: [PATCH 205/222] Fix broken Stokes test (#4469)

---
 tests/firedrake/macro/test_macro_solve.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/firedrake/macro/test_macro_solve.py b/tests/firedrake/macro/test_macro_solve.py
index 2f0abdc921..454e04a285 100644
--- a/tests/firedrake/macro/test_macro_solve.py
+++ b/tests/firedrake/macro/test_macro_solve.py
@@ -144,14 +144,14 @@ def test_stokes(mh, variant, mixed_element):
         Z = V * Q
 
         a, L = stokes_mms(Z, as_vector(zexact))
-        bcs = DirichletBC(Z[0], as_vector(zexact[:dim]), "on_boundary")
+        bcs = DirichletBC(Z.sub(0), as_vector(zexact[:dim]), "on_boundary")
 
         zh = Function(Z)
         nullspace = MixedVectorSpaceBasis(
             Z,
             [Z.sub(0), VectorSpaceBasis(constant=True, comm=COMM_WORLD)]
         )
-        solve(a == L, zh, bcs=bcs, nullspace=nullspace)
+        solve(a == L, zh, bcs=bcs, nullspace=nullspace, solver_parameters={"ksp_type": "preonly", "pc_type": "cholesky", "pc_factor_mat_solver_type": "mumps"})
 
         uh, ph = zh.subfunctions
         u_err.append(errornorm(as_vector(uexact), uh))

From b948d8ffbed55e52225e11d3ce6c24a8f7aa47cd Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 30 Jul 2025 16:03:39 +0100
Subject: [PATCH 206/222] Do not set different cache directories per job
 (#4460)

* Do not set different cache directories per job

This should not be necessary as the disk caches are now safer in how
they handle race conditions. Using a unified cache directory should
improve performance.
---
 scripts/firedrake-run-split-tests | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/scripts/firedrake-run-split-tests b/scripts/firedrake-run-split-tests
index f082f934a0..8876490cf2 100755
--- a/scripts/firedrake-run-split-tests
+++ b/scripts/firedrake-run-split-tests
@@ -31,10 +31,6 @@ num_procs=$1
 num_jobs=$2
 extra_args=${@:3}
 
-# Set per-job cache directories to avoid race conditions when multiple jobs attempt to compile the same form.
-cache_cmd="PYOP2_CACHE_DIR=\$VIRTUAL_ENV/.cache/pyop2/job{#} \
-           FIREDRAKE_TSFC_KERNEL_CACHE_DIR=\$VIRTUAL_ENV/.cache/tsfc/job{#}"
-
 if [ $num_procs = 1 ]; then
     # Cannot use mpiexec -n 1 because this can sometimes hang with
     # OpenMPI at MPI_Finalize
@@ -57,7 +53,7 @@ set -x
 # * Uses tee to pipe stdout+stderr to both stdout and a log file
 # * Writes pytest's exit code to a file called jobN.errcode (for later inspection)
 parallel --line-buffer --tag \
-    "${cache_cmd}; ${pytest_cmd} |& tee ${log_file_prefix}{#}.log; \
+    "${pytest_cmd} |& tee ${log_file_prefix}{#}.log; \
     echo \${PIPESTATUS[0]} > job{#}.errcode" \
     ::: $(seq ${num_jobs})
 

From 0f02acee8707a28053a557840c5d7a3fd44c02f5 Mon Sep 17 00:00:00 2001
From: Leo Collins 
Date: Wed, 30 Jul 2025 16:07:14 +0100
Subject: [PATCH 207/222] Make `interpolate` function avoid using
 `Interpolator` (#4432)

* changed `interpolate` function
* take action if passed a `Cofunction`
* change inputs to V; comment about adjoint interpolation
---
 firedrake/interpolation.py                    | 55 ++++++++++++-------
 .../firedrake/regression/test_interpolate.py  |  4 ++
 2 files changed, 39 insertions(+), 20 deletions(-)

diff --git a/firedrake/interpolation.py b/firedrake/interpolation.py
index a5704231de..ff2cbb559a 100644
--- a/firedrake/interpolation.py
+++ b/firedrake/interpolation.py
@@ -24,7 +24,7 @@
 
 import firedrake
 from firedrake import tsfc_interface, utils, functionspaceimpl
-from firedrake.ufl_expr import Argument, action, adjoint as expr_adjoint
+from firedrake.ufl_expr import Argument, Coargument, action, adjoint as expr_adjoint
 from firedrake.mesh import MissingPointsBehaviour, VertexOnlyMeshMissingPointsError, VertexOnlyMeshTopology
 from firedrake.petsc import PETSc
 from firedrake.halo import _get_mtype as get_dat_mpi_type
@@ -150,13 +150,13 @@ def _ufl_expr_reconstruct_(self, expr, v=None, **interp_data):
 
 
 @PETSc.Log.EventDecorator()
-def interpolate(expr, V, *args, **kwargs):
+def interpolate(expr, V, subset=None, access=op2.WRITE, allow_missing_dofs=False, default_missing_val=None, matfree=True):
     """Returns a UFL expression for the interpolation operation of ``expr`` into ``V``.
 
     :arg expr: a UFL expression.
-    :arg V: the :class:`.FunctionSpace` to interpolate into (or else
-        an existing :class:`.Function` or :class:`.Cofunction`).
-        Adjoint interpolation requires ``V`` to be a :class:`.Cofunction`.
+    :arg V: a :class:`.FunctionSpace` to interpolate into, or a :class:`.Cofunction`,
+        or :class:`.Coargument`, or a :class:`ufl.form.Form` with one argument (a one-form).
+        If a :class:`.Cofunction` or a one-form is provided, then we do adjoint interpolation.
     :kwarg subset: An optional :class:`pyop2.types.set.Subset` to apply the
         interpolation over. Cannot, at present, be used when interpolating
         across meshes unless the target mesh is a :func:`.VertexOnlyMesh`.
@@ -182,13 +182,15 @@ def interpolate(expr, V, *args, **kwargs):
         some ``output`` is given to the :meth:`interpolate` method or (b) set
         to zero. Ignored if interpolating within the same mesh or onto a
         :func:`.VertexOnlyMesh`.
-    :kwarg ad_block_tag: An optional string for tagging the resulting assemble block on the Pyadjoint tape.
+    :kwarg matfree: If ``False``, then construct the permutation matrix for interpolating
+        between a VOM and its input ordering. Defaults to ``True`` which uses SF broadcast
+        and reduce operations.
     :returns: A symbolic :class:`.Interpolate` object
 
     .. note::
 
        If you use an access descriptor other than ``WRITE``, the
-       behaviour of interpolation is changes if interpolating into a
+       behaviour of interpolation changes if interpolating into a
        function space, or an existing function. If the former, then
        the newly allocated function will be initialised with
        appropriate values (e.g. for MIN access, it will be initialised
@@ -196,21 +198,34 @@ def interpolate(expr, V, *args, **kwargs):
        then it is assumed that its values should take part in the
        reduction (hence using MIN will compute the MIN between the
        existing values and any new values).
+    """
+    if isinstance(V, (Cofunction, Coargument)):
+        dual_arg = V
+    elif isinstance(V, ufl.Form):
+        rank = len(V.arguments())
+        if rank == 1:
+            dual_arg = V
+        else:
+            raise TypeError(f"Expected a one-form, provided form had {rank} arguments")
+    elif isinstance(V, functionspaceimpl.WithGeometry):
+        dual_arg = Coargument(V.dual(), 0)
+        expr_args = extract_arguments(expr)
+        if expr_args and expr_args[0].number() == 0:
+            # In this case we are doing adjoint interpolation
+            # When V is a FunctionSpace and expr contains Argument(0),
+            # we need to change expr argument number to 1 (in our current implementation)
+            v, = expr_args
+            expr = replace(expr, {v: v.reconstruct(number=1)})
+    else:
+        raise TypeError(f"V must be a FunctionSpace, Cofunction, Coargument or one-form, not a {type(V).__name__}")
 
-    .. note::
-
-       If you find interpolating the same expression again and again
-       (for example in a time loop) you may find you get better
-       performance by using an :class:`Interpolator` instead.
+    interp = Interpolate(expr, dual_arg,
+                         subset=subset, access=access,
+                         allow_missing_dofs=allow_missing_dofs,
+                         default_missing_val=default_missing_val,
+                         matfree=matfree)
 
-    """
-    default_missing_val = kwargs.pop("default_missing_val", None)
-    if isinstance(V, Cofunction):
-        adjoint = bool(extract_arguments(expr))
-        return Interpolator(
-            expr, V.function_space().dual(), *args, **kwargs
-        ).interpolate(V, adjoint=adjoint, default_missing_val=default_missing_val)
-    return Interpolator(expr, V, *args, **kwargs).interpolate(default_missing_val=default_missing_val)
+    return interp
 
 
 class Interpolator(abc.ABC):
diff --git a/tests/firedrake/regression/test_interpolate.py b/tests/firedrake/regression/test_interpolate.py
index 45a2d9c89a..b6594373dd 100644
--- a/tests/firedrake/regression/test_interpolate.py
+++ b/tests/firedrake/regression/test_interpolate.py
@@ -403,6 +403,10 @@ def test_adjoint_Pk(degree):
 
     assert np.allclose(u_Pk.dat.data, v_adj.dat.data)
 
+    v_adj_form = assemble(interpolate(TestFunction(Pk), v * dx))
+
+    assert np.allclose(v_adj_form.dat.data, v_adj.dat.data)
+
 
 def test_adjoint_quads():
     mesh = UnitSquareMesh(10, 10)

From 520dfbabe82c9711d73d29b5674e60a7a2910f16 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 30 Jul 2025 16:20:56 +0100
Subject: [PATCH 208/222] Improve install docs (#4461)

* Improve install docs

* Be really aggressive about purging the pip cache as this is a very
  common issue for users.
* Mention that macOS needs Xcode.
* Add venv instructions to dev install.
---------

Co-authored-by: Josh Hope-Collins 
Co-authored-by: Leo Collins 
---
 docs/source/install.rst | 56 ++++++++++++++++++++++++++++++-----------
 1 file changed, 42 insertions(+), 14 deletions(-)

diff --git a/docs/source/install.rst b/docs/source/install.rst
index 85505a5c4b..6e3d08e078 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -49,8 +49,9 @@ Prerequisites
 -------------
 
 On Linux the only prerequisite needed to install Firedrake is a suitable version
-of Python (3.10 or greater). On macOS it is important that homebrew_ is installed
-and that the homebrew-installed Python is used instead of the system one.
+of Python (3.10 or greater). On macOS it is important that homebrew_ and Xcode_
+are installed and up to date and that the homebrew-installed Python is used
+instead of the system one.
 
 
 .. _firedrake_configure:
@@ -177,6 +178,25 @@ install Firedrake. To do this perform the following steps:
    This is optional but strongly recommended to avoid polluting your system Python
    environment.
 
+#. Purge the pip cache::
+
+      $ pip cache purge
+
+   This is also optional but strongly recommended as some cached pip packages
+   may be linked against old or missing libraries and hence will break your
+   installation. For a lighter-weight alternative you could run some or all
+   of the following::
+
+      $ pip cache remove mpi4py
+      $ pip cache remove petsc4py
+      $ pip cache remove h5py
+      $ pip cache remove slepc4py
+      $ pip cache remove libsupermesh
+      $ pip cache remove firedrake
+
+   Noting that this list may not be exhaustive.
+
+
 #. Set any necessary environment variables. This can be achieved using
    ``firedrake-configure``::
 
@@ -197,8 +217,6 @@ install Firedrake. To do this perform the following steps:
 
 #. Install Firedrake::
 
-      $ pip cache remove petsc4py
-      $ pip cache remove firedrake
       $ pip install --no-binary h5py 'firedrake[check]'
 
    .. note::
@@ -293,15 +311,20 @@ Missing symbols post install
 
 If the installation completes but then you get errors regarding missing symbols
 when you import Firedrake this is usually a sign that one of the Python bindings
-packages used by Firedrake (h5py, mpi4py, petsc4py, slepc4py) is linked against
-the wrong compiled library. This is usually caused by issues with caching.
+packages used by Firedrake (h5py, mpi4py, petsc4py, slepc4py), or Firedrake
+itself, is linked against the wrong compiled library. This is usually caused
+by issues with caching.
 
-To resolve the problem you should first remove any existing cached packages::
+To resolve the problem we recommend removing your virtual environment, purging
+the cache and then :ref:`attempting another installation`:
 
-   $ pip uninstall -y h5py mpi4py petsc4py slepc4py
-   $ pip cache purge
+.. code-block:: bash
 
-before re-running the instruction to install Firedrake.
+   $ deactivate  # needed if venv-firedrake is currently activated
+   $ rm -r venv-firedrake
+   $ pip cache purge
+   $ python3 -m venv venv-firedrake
+   # etc
 
 Unable to configure PETSc on macOS
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -367,10 +390,9 @@ To install Firedrake with SLEPc support you should:
 
    $ export SLEPC_DIR=$PETSC_DIR/$PETSC_ARCH
 
-#. Continue with the installation as normal but remove slepc4py from the pip cache
-   and install Firedrake with the ``slepc`` optional dependency. For example::
+#. Continue with the installation as normal but install Firedrake with the
+   ``slepc`` optional dependency. For example::
 
-   $ pip cache remove slepc4py
    $ pip install --no-binary h5py 'firedrake[check,slepc]'
 
 VTK
@@ -551,11 +573,16 @@ should be followed:
 
    $ export $(python3 firedrake-configure --show-env)
 
+#. Create and activate a virtual environment::
+
+   $ python3 -m venv venv-firedrake
+   $ . venv-firedrake/bin/activate
+
 #. Install petsc4py and Firedrake's other build dependencies:
 
    .. code-block:: text
 
-      $ pip cache remove petsc4py
+      $ pip cache purge
       $ pip install $PETSC_DIR/src/binding/petsc4py
       $ pip install -r ./firedrake/requirements-build.txt
 
@@ -584,6 +611,7 @@ package.
 .. _discussion: https://github.com/firedrakeproject/firedrake/discussions
 .. _issue: https://github.com/firedrakeproject/firedrake/issues
 .. _homebrew: https://brew.sh/
+.. _Xcode: https://developer.apple.com/xcode/
 .. _PETSc: https://petsc.org/
 .. _petsc4py: https://petsc.org/release/petsc4py/reference/petsc4py.html
 .. _venv: https://docs.python.org/3/tutorial/venv.html

From f056ef6c338adeaa4a573012e92bbffad35e19e4 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Wed, 30 Jul 2025 16:24:45 +0100
Subject: [PATCH 209/222] Document solver options (#4434)

---------

Co-authored-by: tcalderon-space 
Co-authored-by: Pablo Brubeck 
---
 docs/source/solving-interface.rst | 45 +++++++++++++------------------
 firedrake/petsc.py                |  2 +-
 2 files changed, 19 insertions(+), 28 deletions(-)

diff --git a/docs/source/solving-interface.rst b/docs/source/solving-interface.rst
index 4be395d60a..411bde92d3 100644
--- a/docs/source/solving-interface.rst
+++ b/docs/source/solving-interface.rst
@@ -271,9 +271,9 @@ precondition the system with:
          solver_parameters={'pc_type': 'hypre',
                             'pc_hypre_type': 'boomeramg'})
 
-Although the `KSP` name suggests that only Krylov methods are
-supported, this is not the case.  We may, for example, solve the
-system directly by computing an LU factorisation of the problem.  To
+Although the ``KSP`` name suggests that only Krylov methods are
+supported, this is not the case. We may, for example, solve the
+system directly by computing an LU factorisation of the problem. To
 do this, we set the ``pc_type`` to ``'lu'`` and tell PETSc to use a
 "preconditioner only" Krylov method:
 
@@ -290,7 +290,7 @@ iterations with:
 
    solve(a == L,
          solver_parameters={'ksp_type': 'richardson',
-                            'pc_type': 'jacobi'}
+                            'pc_type': 'jacobi'})
 
 .. note::
 
@@ -728,32 +728,23 @@ solving with.
 Default solver options
 ~~~~~~~~~~~~~~~~~~~~~~
 
-If no parameters are passed to a solve call, we use, in most cases,
+If no parameters are passed to a ``solve`` call, we use, in most cases,
 the defaults that PETSc supplies for solving the linear or nonlinear
-system.  We describe the most commonly modified options (along with
-their defaults in Firedrake) here.  For linear variational solves we
+system. We describe the most commonly modified options (along with
+their defaults in Firedrake) here. For linear variational solves we
 use:
 
-* ``ksp_type``: GMRES, with a restart (``ksp_gmres_restart``) of 30
-* ``ksp_rtol``: 1e-7
-* ``ksp_atol``: 1e-50
-* ``ksp_divtol`` 1e4
-* ``ksp_max_it``: 10000
-* ``pc_type``: ILU (Jacobi preconditioning for mixed problems)
-
-For nonlinear variational solves we have:
-
-* ``snes_type``: Newton linesearch
-* ``ksp_type``: GMRES, with a restart (``ksp_gmres_restart``) of 30
-* ``snes_rtol``: 1e-8
-* ``snes_atol``: 1e-50
-* ``snes_stol``: 1e-8
-* ``snes_max_it``: 50
-* ``ksp_rtol``: 1e-5
-* ``ksp_atol``: 1e-50
-* ``ksp_divtol``: 1e4
-* ``ksp_max_it``: 10000
-* ``pc_type``: ILU (Jacobi preconditioning for mixed problems)
+* ``mat_type``: ``aij``
+* ``ksp_type``: ``preonly``
+* ``ksp_rtol``: ``1e-7``
+* ``pc_type``: ``lu``
+* ``pc_factor_mat_solver_type`` : ``mumps``
+* ``pc_factor_mat_mumps_icntl_14``: 200
+
+For nonlinear variational solves we additionally have:
+
+* ``snes_type`` : ``newtonls``
+* ``snes_linesearch_type`` : ``basic``
 
 To see the full view that PETSc has of solver objects, you can pass a
 view flag to the solve call.  For linear solves pass:
diff --git a/firedrake/petsc.py b/firedrake/petsc.py
index 7bb84dd1bf..0a169c89cf 100644
--- a/firedrake/petsc.py
+++ b/firedrake/petsc.py
@@ -440,7 +440,7 @@ def garbage_view(obj: Any):
 _DEFAULT_SNES_PARAMETERS = {
     "snes_type": "newtonls",
     "snes_linesearch_type": "basic",
-    # Really we want **DEFAULT_KSP_PARAMETERS in here, but it isn't the way the NonlinearVariationalSovler class works
+    # Really we want **DEFAULT_KSP_PARAMETERS in here, but it isn't the way the NonlinearVariationalSolver class works
 }
 # We also want looser KSP tolerances for non-linear solves
 # DEFAULT_SNES_PARAMETERS["ksp_rtol"] = 1e-5

From 0d0bff46f53ac86be1b51ecc72ae74f8adf7ffdc Mon Sep 17 00:00:00 2001
From: "David A. Ham" 
Date: Wed, 30 Jul 2025 17:37:03 +0100
Subject: [PATCH 210/222] Dham/fix notebooks (#4323)

---------

Co-authored-by: Connor Ward 
---
 .github/workflows/core.yml                    |     2 +-
 .gitignore                                    |     1 +
 docker/Dockerfile.firedrake                   |     2 +-
 docs/notebooks/01-spd-helmholtz.ipynb         |   173 +-
 docs/notebooks/02-poisson.ipynb               |    71 +-
 docs/notebooks/03-elasticity.ipynb            |    73 +-
 docs/notebooks/04-burgers.ipynb               | 94391 ++++++++++------
 docs/notebooks/05-mixed-poisson.ipynb         |    94 +-
 .../06-pde-constrained-optimisation.ipynb     |    69 +-
 docs/notebooks/07-geometric-multigrid.ipynb   |   171 +-
 docs/notebooks/08-composable-solvers.ipynb    |   255 +-
 docs/notebooks/09-hybridisation.ipynb         |    57 +-
 docs/notebooks/10-sum-factorisation.ipynb     |   224 +-
 .../11-extract-adjoint-solutions.ipynb        | 55165 ++++++++-
 pyproject.toml                                |     6 +
 15 files changed, 115992 insertions(+), 34762 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index fece42b1fd..df79bb4d8e 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -144,7 +144,7 @@ jobs:
             $(python3 ./firedrake-repo/scripts/firedrake-configure --arch ${{ matrix.arch }} --show-system-packages)
           apt-get -y install python3-venv
           : # Dependencies needed to run the test suite
-          apt-get -y install parallel
+          apt-get -y install fonts-dejavu graphviz graphviz-dev parallel poppler-utils
 
       - name: Install PETSc
         run: |
diff --git a/.gitignore b/.gitignore
index dede58dcd7..d80bb6cca2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,6 +15,7 @@ __pycache__/
 /demos/immersed_fem/immersed_domain.msh
 /docs/build/
 /docs/notebooks/.ipynb_checkpoints/
+/docs/notebooks/tape.pdf
 /docs/source/apt_deps.txt
 /docs/source/firedrake.rst
 /docs/source/firedrake.*.rst
diff --git a/docker/Dockerfile.firedrake b/docker/Dockerfile.firedrake
index f9e2d4ac62..c2ca01623f 100644
--- a/docker/Dockerfile.firedrake
+++ b/docker/Dockerfile.firedrake
@@ -25,5 +25,5 @@ RUN pip install --verbose --src /opt \
 
 # Install some other niceties
 RUN apt-get update \
-    && apt-get -y install nano vim \
+    && apt-get -y install graphviz graphviz-dev nano vim \
     && rm -rf /var/lib/apt/lists/*
diff --git a/docs/notebooks/01-spd-helmholtz.ipynb b/docs/notebooks/01-spd-helmholtz.ipynb
index 021ce7fcfc..418ad21308 100644
--- a/docs/notebooks/01-spd-helmholtz.ipynb
+++ b/docs/notebooks/01-spd-helmholtz.ipynb
@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "# Code in this cell makes plots appear an appropriate size and resolution in the browser window\n",
+    "# If the following line fails then the user needs to install ipympl.\n",
     "%matplotlib widget\n",
     "%config InlineBackend.figure_format = 'svg'\n",
     "\n",
@@ -151,9 +152,20 @@
      "text": [
       "Help on cython_function_or_method in module firedrake.utility_meshes:\n",
       "\n",
-      "UnitSquareMesh(nx, ny, reorder=None, diagonal='left', quadrilateral=False, distribution_parameters=None, comm=, name='firedrake_default', distribution_name=None, permutation_name=None)\n",
+      "UnitSquareMesh(\n",
+      "    nx,\n",
+      "    ny,\n",
+      "    reorder=None,\n",
+      "    diagonal='left',\n",
+      "    quadrilateral=False,\n",
+      "    distribution_parameters=None,\n",
+      "    comm=,\n",
+      "    name='firedrake_default',\n",
+      "    distribution_name=None,\n",
+      "    permutation_name=None\n",
+      ")\n",
       "    Generate a unit square mesh\n",
-      "    \n",
+      "\n",
       "    :arg nx: The number of cells in the x direction\n",
       "    :arg ny: The number of cells in the y direction\n",
       "    :kwarg quadrilateral: (optional), creates quadrilateral mesh.\n",
@@ -168,9 +180,9 @@
       "    :kwarg permutation_name: the name of entity permutation (reordering) used\n",
       "           when checkpointing; if `None`, the name is automatically\n",
       "           generated.\n",
-      "    \n",
+      "\n",
       "    The boundary edges in this mesh are numbered as follows:\n",
-      "    \n",
+      "\n",
       "    * 1: plane x == 0\n",
       "    * 2: plane x == 1\n",
       "    * 3: plane y == 0\n",
@@ -185,12 +197,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[31mSignature:\u001b[39m     \n",
+       "UnitSquareMesh(\n",
+       "    nx,\n",
+       "    ny,\n",
+       "    reorder=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    diagonal=\u001b[33m'left'\u001b[39m,\n",
+       "    quadrilateral=\u001b[38;5;28;01mFalse\u001b[39;00m,\n",
+       "    distribution_parameters=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    comm=,\n",
+       "    name=\u001b[33m'firedrake_default'\u001b[39m,\n",
+       "    distribution_name=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       "    permutation_name=\u001b[38;5;28;01mNone\u001b[39;00m,\n",
+       ")\n",
+       "\u001b[31mCall signature:\u001b[39m UnitSquareMesh(*args, **kwargs)\n",
+       "\u001b[31mType:\u001b[39m           cython_function_or_method\n",
+       "\u001b[31mString form:\u001b[39m    \n",
+       "\u001b[31mFile:\u001b[39m           ~/src/firedrake-pyadjoint/firedrake/firedrake/utility_meshes.py\n",
+       "\u001b[31mDocstring:\u001b[39m     \n",
+       "Generate a unit square mesh\n",
+       "\n",
+       ":arg nx: The number of cells in the x direction\n",
+       ":arg ny: The number of cells in the y direction\n",
+       ":kwarg quadrilateral: (optional), creates quadrilateral mesh.\n",
+       ":kwarg reorder: (optional), should the mesh be reordered\n",
+       ":kwarg distribution_parameters: options controlling mesh\n",
+       "       distribution, see :func:`.Mesh` for details.\n",
+       ":kwarg comm: Optional communicator to build the mesh on.\n",
+       ":kwarg name: Optional name of the mesh.\n",
+       ":kwarg distribution_name: the name of parallel distribution used\n",
+       "       when checkpointing; if `None`, the name is automatically\n",
+       "       generated.\n",
+       ":kwarg permutation_name: the name of entity permutation (reordering) used\n",
+       "       when checkpointing; if `None`, the name is automatically\n",
+       "       generated.\n",
+       "\n",
+       "The boundary edges in this mesh are numbered as follows:\n",
+       "\n",
+       "* 1: plane x == 0\n",
+       "* 2: plane x == 1\n",
+       "* 3: plane y == 0\n",
+       "* 4: plane y == 1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# In the notebook, we can also use a special \"?\" magic to pop up a help box\n",
-    "?UnitSquareMesh"
+    "UnitSquareMesh?"
    ]
   },
   {
@@ -202,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,24 +283,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d2eb055c34424748a6b45c26b90b273c",
+       "model_id": "f8bf43b8d9dd41d8bdf438014a9ed13a",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
       "text/html": [
        "\n",
        "            
      \n",
        "        "
       ],
@@ -273,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -306,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -340,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,16 +413,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since we know that the Helmholtz equation defines a symmetric problem, we instruct PETSc to employ the conjugate gradient method. We do not consider preconditioning, for now."
+    "We now solve the variational problem. Since we don't specify any solver parameters, the default direct solver will be used."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "solve(a == L, uh, solver_parameters={'ksp_type': 'cg', 'pc_type': 'none'})"
+    "solve(a == L, uh)"
    ]
   },
   {
@@ -373,24 +435,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ffe3d3453f32451f8d872fc6d0d31764",
+       "model_id": "4170784e89494cb3ad93aa23914c320f",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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p5+pqRL/xS/9feulDr6HtpS87lN9xt4NpK5oeeM1r6Ld/2/+NCufB2z04Vrjdbn2FQsFQBpPChHe/+930Yz/2Y19uMoiI6KN/6s93QXLjLnNxv/LGx+K3IVRvTLQN4RniaeRjHukQf3W10WdF8N7YC+lTnAjtIU7qxnLs6+q0+3LbFdOvbxvxtg3amZglvI1+MPG2jfJzmoW30c/zabQxbVdXRNs2/q66xD/aIe75x6At4iF8SV5mOl29hNqIb7xRI+7XGGamRhYmDR8rQ6F8o41OfEXX7fN0akytcb8S06nRMHYwpFG4NwNILHdyZYlO1MtYPURfvGaV+U8if+v9MI6cfJrGY7lTF2bnOiTc6HTy8adTo+vrRu3UlYN2atCmxWm9Ia7ThXEE+ec4rP/6i9f9RVfFw65dccErDQUmS5OyI8+49otPkzj5i22mNKRtRTpDm4HeSINo1c44QOzCatwkMQB4I4Jo6SzlpjgzHChvwfo2y5fREts6F4/tcaSDmLZt62xg63QIP2Zm2ja735Qn8mAbksaDXfBUxt0zQVovs11to2zrhoth5NjYwvF+mbYdzDfCV1ujjRsxN+o/V924YHHNx417F455srpiXm50RSf616dr4nayP+pMwMUNJiNhiae9e4rlLI3aifj6i3QS5uWZFDlG1E7UMM0xO5+/7dZlae10Te36mtp1oyZlTz3cNM+oL4ZHXhp5pRxl4dE+hk/A1zw/QL4R4g/kOVRG4kM9Z9vYo/VMGtZVBpNCofBiRBlMXsR4+OGH6dlnn3Vxzz77LD344INL7xIione84x30xBNP6P1zzz1HjzzyyD2jcw991++mOjdtBOG1sUPjJyNGjM/ziNKhUm+nRg0y0UiC19lYQqFdOhOHI9CIGndhhC0qDWf3dDStxYZ7/tOpG01G/49dMxzLK18rm0pp604zhJuGQTpchIcfRRovj/d6tNZab0lyWV5Wihp1pUW8RjQuUB2vmLZtTKeT1G1kWU8bNYayDeKhXvGi4dYCzQAhjGyKb1v3wDld9zoYKm3Qmf7VmnVadgKF5uHF04nrxSWOWhiAXnjbxEtHxtSu1JiYGzW9DmJ1VHwpgh7rnSsTh6G/p+7L8iipdGt3fZthGOFJwLzee9/cULDO5uE/AeH5upcito8sj7UXBonJ5Xjh10XK4HXZsJwZpjM4MNAh++lkdtqjRSNbXidmTXbj6EakL1dkt66ynfp8UstT3DSUjkp+7u+Wjlv/k7cJ37Gmz9Pn1UbwJYlpNAzyp1OnAfhC9rs12Ip/qeJ9g/dufgF9pHwQoNbH/HoU3oj4NEjfiOhEGq9hol4Gu7Ob14d526gbbsezJvvV4cHIW2v9nWhEjZu+J/ocEmhaKDPlkcFEQwcHOqQ9oeNo22fyFY6Brzbiq9vdFSFKKoVC4fZQBpMXMd7ylrfQ3/7bf9vF/d2/+3fpLW95y265Bx54gB544IF7SdphsHyqUwFrSCZMJnGKocKFIc/uNf9DYwZhuPXwLCfh11ZW4URSokKESowIm4w1St9UTkXi4F4/a2KfQ92RWB23EBdlXhWEmxd28yd15nouT/gDQdiNFYTbFL/OO5ebu4OixhCz3bRSgwGxMyo4Y4lM1daosakaYlggYhrZR5ooMKOsKiCBuDE/xFtBlQsQW9V01EjbtngL4fQQ2nRKbCNJDAiaBvOoDeONs+AMAd46NipsqZuwFhMD5ckrEEgf09yHcyK7t//lCvWkOJA9tx4vIyotmYGm5+c8j2sbe5wot8JbiNx7aVHx3QYvErlC2D+ryCukvD23yWAcsntagOw0D0/x8VXXKHkHuAUe1aACKE9zXfGR8vImQWAzR4ut6jmKRl3P3tq4ytyDSa5f8dkbKxteCeN49MV607IpLw+lCdHGxJxhZBgezDQohc1siCPXmMdSHd9PLcvcDZD44JiImlgdlLj+RsU0ZXYyLzaiBmkygNYgvG4yJ7fR7ZPlZTJGoeOfhKVKMaLCmOd5x7gy9+bEWBGeExO7tMZN5QuJX+q/oYxLCszHGaFJnt/aiDItw0nm95SvcDFq09dC4f5CGUzuI3zuc5+jj33sY3r/8Y9/nH7+53+evvqrv5p+x+/4HfSOd7yDPvnJT9LP/MzPEBHRD/7gD9JP/uRP0o/8yI/Q937v99Lf+3t/j/76X//r9MEPfvDL1YWLwcO5gUEgUQ8TjmHwEnFhUAyypTWoMCQeJmab6JqBfOneMiNMkOaljM8XyxDEkwoxanwhMoNFpkDITRQYnbQXtAwODaZEQFqjHW8TTuKya4Y8jbXRS7xN1vJlLLcniyoNLB8SmwnFQJ+Wj+RkDfBO2tQmD1Uh8TbR55p5olj9mBZHGKqwKQhd3JipXTU6nbwi1OBxSh2NFnHQKMc4+cJLMKTbMCQ1L+D7r9sQUkWymcIWjC1HYV/WRyfHGpw2rGKTt4no9A08Usi+7WEepBMJs9cx9FBfY8bp5tiEjTW713pl8MAwsoVo2EXjWO4lsvYqyWOTa+B7rZHaSLSvhAjjRkdwYBLA+3c6jdWHoYizUYews0EBbef/wjxmIOQIOLm5qA4jXo2lShHWE+sMaUhH22vfJqV5m9D8iIB5sdZvb0Zzk0Q8IiCOLc0eTH9/m7R/xaSeNqcTiVFD20djHYb1t5fnMJKJ3ZF6N1KekhpKdPQT741GSy+SWCbGT7TA403bzrxeKBiwm29D2076XygUCi82lMHkPsI//sf/mL75m79Z72XZzNve9jb6wAc+QL/yK79Cn/jEJzT99a9/PX3wgx+kP/En/gT9xb/4F+l1r3sd/dRP/dR9daSwLoEhE/DHjdcC3BdXyUP74Z2/aEyZDSeeDre0BowyaICxa4jbmLzhhlN6VKsFyd4+rIGAp4MXB3NvoHfSXJ5mQqNRQSjc3khr3UGjsaac7Rtnv6Ai3UU4MbGgcJ3Rcky+MwGXRZjsuwO4OruIz2EEQOkcCZOyRBYXKdyY6Lqh8hB1lFXaaB11iZgGRDjdAA06xMTciDemdtIOuHTaGvH40DsZU8YH4oZx0AFzRLH++yVH6K5Owyun2VdVAk+dcdU50eR5aZdN8cHR2JmiUUeYPElaoNflWbweiDCW/ZVnU2TUGOK9RliZEMwdsJqYwcMaccYOMMIqD9J4GBegZzLqJuFYdpU/sDV7J8BYgwW7IuffN18xhXB2v4BvavYMOsIkbsLm0NhHfZ8Rp9GeqdObD0PZg833uTKWjTSmrXUvEFymY1wNnofwtTZegFHOCDful3aEmbht1Bg8SxpRN2REhkHKWJjNwKPp6Lk2WWVH3Ijv/EB+b7dhNFAmZPP80jBjWxg2zsE0jLrobULeQ8N5eSiPm/O5oVylQRR6q6ARWss1Gdu5vRu1XTgM3U/uNus83W59hULBUAaT+wh/8A/+QXPXTfCBD3wgLfO//C//yz2k6t6Cr7xwT3bRsMkpXsmIpyQs4zMjRmK0SP9U8ArKhQiWSKSrM+aPdXPyN/K5teAg5YvcrGkSRiPDYqB3bRzsr41mbxOGdFSwtIowFrn25JvCariROLF7jxMfRsV2zkMa7rFzvOsyaMRMYgM4UXcXF6HxHmCQvHHfIFbo00dJpPM5Mw/p3h++urkNCbrHZhoEs2yceaLr615nT2LCr6s6BXGdEhgz4vQRBQbb1mxSj1TTzEjCaB0Ze5rspmUDCy7scS8Sdn3v+Zp8GQ7148jOS3PgWWgu/xQYQlkY7CLA99i9asBqduJyL5RIxRQHX9JnQ4qv63C8RDJerc3eZGBEsT96a1+4J5aC1/BHGA5NNOr7iI49ttf8KfKqOHx7eV23WdttjYbzw4Kj7Fn3FCsmbkTEed53r+2bNm8nJtvbRMrJi+Tn73SPRTCNk/wbq2LX3IOFBymVstEqRlOjK9CAbTORrp/RKhi6La6p16QbMzP5vMjvXJjgd1/a9mH0eewecGT7npBxDSIwosCSnczzJAI9Q1aY6pIxaJbeRw3yJHTs1VsoFApfCSiDSeFOQy3wKuSyDxN1QUSPJZAw9ftxgk6PpzxPItyqYUWvK6+RIPCRCR9a/5SGcZLXx3mlg62chOUL3zYRbfXwXhtxoEMc3k6uy/I3JK+kO3OcF34vkbUwazRyoGAqCgEqDsn3fs0xdRPurdQwSIi8TMMY0aA0yu0ShcJ9bCwjyXdS47QIeJS44k7BgUdO7JbTxH5i2G9uau3zUAwaMW1bn2+n8Sz9x90xUuwJF1pafOYyH8i8HbQ7ooew2WTmPUTASANGMZ0NMEB+qOPAZyYlm6NeKVgbRmK5uExozquDYIOkYzPzE4nuMfhe0+AHRO7dBwOx40HKu8a7oQYYyWdhzUP71/Mvf6AV2JTwWGaypZU60kaD4x98rKlL+AtCmmon0tUbyeNYI2MsWRZ5RzWuqbEG31p7zxIDqMt7LKzt4QtG3dtE99DQySAvILta/AI0oMhnCy2G8DhmuPPJ6zy7s7g2nZ/GT/B9Znt4MJ/TedGgS9tmRyWfRjnZYEbkh1MLYRrW7PVvXwO+JtyJqHsUdE9FOuZtQgtPjpVtLGCq64beJkfbKxwHX9Gt72Fy6Qq/QqFwHGUwKdxpMDoxTAoBmcJAXteYw6YkqMiP8UEVQCUi/vG5qxxtPBlXsji2444xDivUTgwqUelpbZQnElHW2TZk+YLVpGkE+bL4+dMtFlLtBgTvS6SqVd7FoOtTYVe+K/hg0CHrr6leeXupfO/S2jBI+NJia/siKBzY7k1klmkkQkXR28QrZpZZQ80/IitphhfJb+vXx4iCMtU3j+3tbWOp2Revp+HuF40T5Qk3x4XO8GiTcINVb/qQZTUINVwxCvo8l53iYiV+zLMvuvMafRgTKdtQp2t6mo/vzRnAK2bGj/4P63XwE/JX4wcEtHr+hhzOqkaeQlYO28X2PAuCPGas8QZeH1YWsve6GyF9jHXOAN2XDO3x4c+LM8wb7NP5Kg+3O/FaIpIDm8RYORXCOQdx/oXcx3JohrdJO32RiFg3ao2n6VC8V8MwDBQO2h6UF9DY8JrIuWLQXhzU35I4tbwK2QwkG29q2zgq6XQ95Tu+KWwMz78NhMYKJlvOSH4PER2Tce/2FHE8ya5TGkHdg0FPJ/HI2IzTflJvkyQcgafsFAqFwosVZTAp3Gl4D5M57D08CAQXUyqkTCrL8fovephgWMR4TiptjbwAIcpC/EJ6kI6zf631L6LytWJPWYhy7K6Mm1WUaS1Ba5bxv9HfjPH9byfFjuYzAU9owI6eQwvBRZmRTabmqTXYqFVnAXlVlUgUDy/6J2Msw0tzNDHR1phOeroFaStqKBkGkfn4W/my6d8ZTo451eOJxTDF1tq2DfrH2cvyxVSpFCGeZS+VIMiDMYenfhjFGieCPaHgbiVMKZCKWzaq08CKgoaxTKaQSp9VuZJW9SSdsNkrZY9tX3N3PG2M3WQYIZvXOr4SwxDnvE18/JQnUBXjeCfV6UYc6Jr4Mjt61KNErmQGahfP7PbUdGOWD+ULww4byk89Ctn2WdgCCWdjS+EQ524ngvYMJUjcMKq38W7qy0pgrdmI2jV5z6jwnjK+R5FEfHcjHZFO7jvutr6csp2gFawA3m3zlID6NDyu8iIDP3LGE8eThsHvJRvR9cnmHRO14UnSw0R0GkbRjaidxFDY7LQvohEGZpeMDhPbz9bJGysmb5OFJ4ofxfOeKEtvE/V6ueW2C7vgKzaZ7bbqTE6kKxQKt4MymBTuNPQHhYNysQpLuY3M0+PIiTmL03PwLzp+7F01DEt/vAFGlANWDxOMM08UVppoisM0IhVUo+A+/YYe/Pya5kFBln0+EbpPRHSF7ey1d5SWFXXyiexq3GGbISynnogRwCkslwt88kg26raDCahJx/ubyJeiCwwBtkc10xncP2HPE6gAZXnVK5BOMn1CjTBwz9y9TRo3uv6ipeFpnzhNtKtZO5MyMfrkvgwTxaNUJAr3L8m+gWopqANNLUbQbIAimCcaJwM23PG73tbn1nSssnvOYZ7DWKQGEjQ8QP6VZ4fyH81D6zzOiJx5iXh6eDxIX5YpGq11Tkq89svTtfxboJ0akVMsIs+A+5uyk0nph6TxnIl26sb3+Uz7Lhl1fCYzMKrBLm8q3z3pKHFePbf4QYAYTYiI6ItiYgFimdw5yNiB+LvQ9p5boI83Yh77VLVrnzUYRHQTWH0xwm+NkiK0UeifvLxEZg3lvrcJMdEXvzh+LsZoa5iAG+Rmjl4VEB/4V2QLHPc2id4mi5bQoIFwSjMMid9I+4y3CbadHX2MPXYdKhQKhRcnymBSuNNYe5j0gBkHMEwWCF9XNQ6leJDv8G+1h4mLVyGI9V8nNDage+oc0hvTlIgR3qxtMmOJETsEvQZf/HBAUGsybWrVOKTFwTGBMYdKZ8QqdGf5Y/1WuiXh1b1BJM4r8sp1DzOEo2qMFHG4x5yyTCCTD0Ft8KVVRp9X/rvyuPg40Sua/gO1s++qU012TthhaIOJ9GNxfErRsDCrqeBtcur3HCvbmPgU4uCVs9MgvLFCDBF6soUUHYL9iU6EJhJTl7zitHpWfobMx2eq8YPmuMZDYWtEfGo6I3JlBluD2eGMvDYgk6Ek5NW5rO8/GW8iu0p89h7PhhF4JtHAor3BK5ZBXkgz65jYDniUDBrEQO2vIzyMyS206+jLJncwNHWc0ep48Yc13JCf98UtwjmBkw163fsPdJ6abb21bnQuF2mOTJXJ3zu7npurV8R0nRhYRq8YjInJhvTn9eiQw1n9iNQKKw9a6GPhyZBfaZcoYJA4LyDcbSf2oUGu6m0i+5pIAlt1bSlHsHuekROZ/QSWFS68TXr+fGnMapnMEU8QZ/xAIwp6mxw5+rhwY/C2DQPd7dZZKBTuDcpgUrjTQJ2bnUCyFw6KwKXeJmzxZ4VooWvnanmlTnTZ5SktFfYXYf/FGdoEf3aGNkCcHE0O9YVNlVlDRD9TEpeSfGt0am0nS2IVOAAU5fN08zbxKn9uIIkUreFUtilaHt3WTB3qHzGHgCrCOTX1LneC9BlXWs5umuxtMqJxk9egR6Ubwzaj28rYc9UyQ+lnUYrwnsXb5ETt2jSx3RM/nQ6SCfxCL3RUvwL3DWc3PS1pCO/g4eGOH3YGJe86jk90MqroI/G7OLhZJIPnvE0GuWqEcQ97nkap4SReCd5xfIMhnoLRBHiCq04jXS2+Lcc/Y71Gp7xZTHZq1NnrDh/1f0A00NnC0dCXImWjwHJTUpDcMSdoPF9h11vStaybGT2soRa72+fV4Go4HGb/gPnsDH40T+qp5ZWnCYT1eJdG5kcnph9b1uOVdHvTfN/TNw3CkIZuF+5B+ReJ0SCJA8RiTEmeuKQZE5qMLX2+b0Qv4bFpE7ShRmFehudNXzMEI3qjydtEDBqplwfHcVfKF63lJ+BE7xXnbRLTDyzRKRwDb3z7xwrfcn2FQsFQBpPCncYlS3JwP5Npb5ORd5Jis7AoBmJMGVKxKCIY9u7rc7x9LjW61CYyjDaunhG+yZIcM9D0mxYNFnExvtNJd4wfiRQ/xOUzebrk3lJvk9v/YTdDjpgQZsOJ5JN/wyBgZUHKZRL9t6c1XyzI7ZZlx6skNnE2IU/TEc0EaFX81wVTDxPZfyTmY8wiY9A9QcTbpMmmsI20TSfEj8FhYjhZJ3Zx/ldSVRlTRccUgHmPE7uuTGZoViNVKOLjBR8hZ5TpSg4eiTuX9aHYW40NRhFnqCDhMxT4CVxtIgAfQF4J/ELGT3kXufDqJB3vSWLhSAeHeIL46W8Vn+XjuLeJPbOUoxyxr3hdf79os4zt1GYF5QK2djQr8pO0XD7dISOO4qB9ZOjPVZgEFrSwvTnCT6+JXZ0tbcNPgGgpxLjYEfJ59T0H4yMyJT3mC+qwCQNVsyUzmy1zeLE13ojbaSy/gQ2rr/pvV7u+tnrh2G2lA8M73RJSdImo8uieavzEGyuIgtEDlthgGxlPjQaWyRsOjDP9lfLeJtp2Ei4UCoWvBJTBpHCncW7T18Nh0qBTBszAIcrBeQ8Tp5wQVrzOi2VU2VMBj5SeldwpnYgKCHRKI1FGJCJq7URMVznNq7D7C5YAIYdBDlSBEb/2SVKbjTdLLAby0N/m7xsK0YIu6jVCFbrR3udYk0VNde9f22CM/dDohrDXjaCMfFOcjSiHRM8geMe0TSkk8zZxj4ItBzym5ibMSEDdZFiJ7DhlyW4ivTz6jZnoiuj62g8ILg9qMpcpP10BzQ6ZCSJ+G2Vms5GNCnDjVnfFIZsUi3mArWWZT+EEHT+QXYlmt+uJ1Zhp4Phuw8OKy3CmdPdUWe8tfvbvsOq0EXhGYKyBNl3YGk/bc+2qYWY2KhOTvxK0EdPwGttQZzKGtOz+DJCFhOi9Mg0NlDiWB5u8AUlE5Gw2OxV4nncebQojz9N4Hny24a5NnaOx3S6IXXG9hcVKx3YjGnubrJbn2D3UgQM4yDZXnezaLM+42sE9TMRXRF+87nxxuPbJMK83fZ0R2UAbTXcW0+BdpNzbJDF6rAyDe54g0VCSHW2sBpRTu7jtwj62K771Y4W32vS1ULhnKINJ4U5j6WFCpEKqF9DJCxwYhjK0MfhRo7Buf14pwLsRCt4faAQ5V9a0TKtHO6dKxtHrZnVsizSxCaBNgUj3Y4Cms6ewfj4UNt+bpFR5Bl1rNW+TZMBfsNTl1NsRE71NBkmq/B6tF2u1u2X5cXKMLJnpyrr3fZhrPEbJ9MdkB1xIvWzUMXaB4H3Qj6QjX0yQsmAIY62MyW202qwhJqar4W1yfcIvr3LB44R3LEAw7pMJguXL8Oj4ZgpT9DZZr/WfW4qtW9q8NIew/tTbBJUh7IMbDDJDSMbD8ji7sFaFhhCXxmx1s43EdHXP3N4kZ9NAOoG3og0Gy7phH/mmvw3veXH1eYVd4HNw2PWYC1jo8bt6YFDO8WjVF8rFXHmccEzE4g2x627iCJyQ98vPajC9Wl294WGnGMt0YK7H7DOB2cisRjnEy1oVRnqwQZlr3oCZtq9pFt9r3PT3osW8TOpt0uhEfN3j3PIY5aN+SU4EjrTnNHPaWW+TJIzjr/GRoY05JGlxQ1jpk9yn3ibJ8p5CoVB4saIMJoU7jZvtYYJhdoK+6SbDlCFGF2f4sPib/PGF1xhntGIGmjO7QZFsTN6gEoqohk0WCcab9YPYT/Z5Z0HQqhFvkxMxX6V5bo61GtANJNZW9k3TidfNck7+BG1WtlWgVeHWaGERtkciE/mjfdNPjkn3hKaYhgIyyPn675C4JwOBTHGne1llSoYW8CfseB0lGgj63ibUmK7xZArph5xYs5x3OAi41l82JOx1NFVm2tBrQn3md0845LHHjISH1lXB6BTDgIBbP6aN5HayNN8HqDsYdGc+JfH2YC2ek/zRmAFfu4EvaGM6+aHiRZzMKTWaIP0WY9fAuswgl1330mSiwjtFZorWJ4a0ZtXvgeHvkqLKQhmMGOdL7nE9x5tCP+xEqz7Jhu+gzU94D6XYyhiYI85SU9NJQyM8LFh22hiPjZKld0cHPyKjDseWqW+CDkdyufe32XzJrYA9Xj1ORo9YlgkSXKl7jBAZf9m2Ts319XinxIhkdOAUWK+EZTC0RS7B4G0yKtjb24QO7imy+GnG8rEuZ3xmGscsr/MXjqH2MCkU7i+UwaRwp3FoDxMVkue4ZXjk07/E6eHcKTkYXp6ko2I9EYr3TqBsHLxdeKIlj4vpe3kgPQo3l/7Gsv5DopyowIiCqaMJx0zUy+FtEmmlM+FV3CLcv4U2Mm8TKdxA1BOhP+3sIg0gFog0rZl83kTpYavS5OX8PpKzk5YtXxeB3A0NCsyLcdSmIK8ai0aiLLXJPniLS7d4m5xOpljwyHFWt2Qi2zOkl21jPHGdTWOGPVVYH6d+nR32MtwM03yjcJ+GVRz7ofe2EX2/3Qa02/hyS2LYCZY2vYb3ZoyRXBnyOYMw5ONYj1Zu4aRq3zQUM56KeYORxKVDRvdMke8EbxHHHhZpKVuLPGU8kjFZN+RN2E+4TmEcLUg/G4aRln9ba3Cc/PQY3DNIeRfSHeJd+uBsp+HNdsU4veQNw57l88KHzdjh9jlxRhDIw0zqvdeuYU4mDCEF5J0GIjJFAXqbEJklVAaA8y7HLka7jtCd5QlXZibaOh1tfICQudHfdaM366H0D81TPa/xiintoLfJCnt5Um+VRt7bZKQSepuMPO4o40KhUHgRogwmhTuNW9vDZBEmwnv0LkkE3vAXBfFL/qaydgEawVNE6fPxFjaZUY00kk6Qju04Tfjck4jwn6hMiIsVLT5pEeneJucFvTzs7lmMIqzxDfK4ck2O8Ty6JGc0MEnhcw7fjsRZOeb+FTaOyKHhX0veUxqOi9KQnKKDXz9V6RYjiNAleckEeaIG3iH4xbHnifNK7WLXRGrk0DmazQ78aGypmWBvT3L8G7xNcLmEqx+tH2r1c63ruDT5cixLb2iOo2bj4OY00zguFN4EffBgfICX+JxhBA0kmu7qCHxNrshw3IhbnOcjSCyE0TASDCJIR8y25IkU6jp3lWej/40w0/RF/2KcYwpn6u7eJkdogIbA0skxLXAz5Vo84tlyMU7OnfYXpxj31uVkL+pGSG7jecq6P7E6Nhq8THZm2sa7nRk6Vm84hfhV2awTPLelm4FI+WyOHwgLP2DqBlulBJ4OMxFvY3XSybw9+OiSHLmfF+s1SJN/NY2J+ETanhg01su0IM9iLJ2HiZzEE+Lx3nmb0LrewhrM9+BYYb7d+gqFgqEMJoU7jWMeJgQKA5EYPkjCI98yPCpiqxwMJ3I17xEMX+SFomK9tMfQsohGZuDwf1mcKSEzPXNerWeTDEQi+MpYLQpd+EcH0uAZnBq1LVdqDeeE3aR+jEPjEYlxR/6SdqNUO0pZGkfJds82BGnNPcqmgv0LQEIrh/h+GFM/nvQ0IkWpS0/JwXsRykOdummq2EkgTERg7JD8TNsV0WkI+zKMiy5AHKsCOB2rOcYznqpjSmU3ULXWTNCPBo0zwMcpBhnUhieDzYhs8JXYtnzAvSGwp3kc0nnpRrCuJnjl3RXrYKxa+Ahb21l7bG3ZHh7+2jQcjBt4FZ67Sp+uRtPEXmi8V3jC+KU4x97OFKVGfUnW1pTPHfqDqSD37rokE/naeXgWlTI7l1f7RYNhKN+CVGZqbdNS/h3bf8N9HIV4ZCghbhuW2NOpO50QW+f0o4JkH4O4EdHJ5jExqxGzG3HxOuaZbgbL5M545hFHg47r07z0L3QTTLFEY1khqxFZoi1NTywTY2wb9IzNq+IzP+JFkqYtjhx2c6sBH6Km3iaFQqHwYkcZTAp3GvkeJhg+YhABY4dcknC/TyTU8Merq5GXX8+VdxIxRhwJb2m8eaj09NlzhUVStC/f5/4Iw6J1D7nQ9UEUS9SoI0SdQiHc/5n6mxKADYa8NKVPtyjvL0qxZjRxU5bXrFrSMi3G2b8g8840ZWOVfZDFeEhrBB+E5Y8lCyeeJXK1Z2XNiNInafb10Z8c49tnjYCTdKjvbdLapl8nd8Fklh0xwGTHaqLBYuTpukbubXLcaAIPYwr6B4XGFO/9st8/HLdozMA4y8tzXnxmwcihYXjvtaDWbV4r7ipjx1KH1SttcVYe+oXeJmd5y5G/zWhfbxILCu/5p6DjOzGBSxFpHe/aOrM3OrpXjDHe3jttZspj17hFUtaypWGOOeyHA9XzRV6h9xBWVCbxsvfWxNzGpOCTGR+IaDKYoJF7zJ8m+aY8IS8+h5C3tbGfCm/dQNbaQQ+TmbfENJghhB5sytGYuvcaeNGlBgwmZ2zeI8ztjRIM1J2y8axl6eELemG+clF7mBQK9xfKYFK40+ArENZR+HeKADmBfjcsUskqLO1Cm8v9SUQgO5Anjac5nmL+jS0dT8ARLxHm7ta5k4d5W8SzeZvIGB35WKSSGl7trk1pHHKQtRe0FGm+jXrs3gwhEpalLpZ31MHn83qaGjGdqNGeO6vPfxZq/FmljVq5t3p9IlMACMItxENawz+JHvdTmVDnEXHeyslTRddr89WQtN4EGMBGWiO2TW9HqY2Z2tY/DO9iKCy4xaApZ/PGg0zkvsrK3iZtvHPibWK0noPvddZ3GSM7Trjl9KhGhBs60sR/5qU0o01exI2yPMpgHZhOc3B9VZbJO3mjLwmmQwr7EpNhYwv3ypJ6eJO4bd84Ml3FhnwUK539Eiyn1MyRLF5eyJnrZUxBOSWHsmEDI/Vec5Yqi+s6cYjPwsykp+Pouy15QnjMb2Ie/PQ69CGOyYK53SieRruLByfkwnU6erx3df/K0E0iG3cm9TZhYGxgFnGjJbww/hKZt5oZJogjrx2pjYf3mjyjY14ke5g8TMBA3cfML9nBsS1vk0Kh8GJFGUwKdxp8FY0M5BWMVZgoNbIczduoqQBFZ/44XA8L2jt10dbXt87Gje1yw0hIz40rw+hy+MmgGLhIS5OzclGKRW1nhDWe8/C59DN5G8uyDTYDBF5bFx77lZO0xVXDLc3TlWcYCtRBzoAxAGUOi6yhHDOZYiSPbygSvWLzIrEDQYbxQfY9cHWIwN+GwI91tOXeIl0ZEQ3lkg6FWuB0Hm5g9GDSEycUy+ksLvMhTlQFLZfsqAJpphyxbgo6L3OxdtL9R4R2zcf2nCCfXXz9br7oO0DaDkGZ1JgD/BcIDeXP1LV1D6M9A4kZPPYNItuqjsQA0yft6hkDGP5eCNj/MYFtGoZIwhslaeG6rdJDHNal7xk1Y0g04tC62nx6zEtjLxNLI8hL9s4Dc/OGFUFkdkfiJP4oI4C8LM9dLCJCN9ZnYdmnqaGRQHtiRgsxF+i+WeP3oe930uy9IDC4Kkuy9jSUdG8vTSNdN5j4dKK2GKfM9LuL8L5kBmqXl4BvFc5iu2Larm53vLZTjX+hcK9QBpPCncZ2tQ2dxwTyF+JVQlm4V+rC6H5+I++RGE+X5Dd5SzfGZCaizfISQ90c6B/9kXiU2qHrChRyhksnb0ytazekxothVPH3cxozU8O8aTlJk/hh7BEBfQidh8VkkYWP5M2L08ZWPn5VJJJve6ynIPQvbYu4UZF9veRxkoBtkSieLkysS+NTPUGA8umODuJEpnNK4kZ0xUzX10MlYPiCDDqGGB5sHbuMUx9123RwzOmGHhiRAMu3bWaMspZn7xDz4NgT3ActMiQ7fedtPIOTLReyDYjNICJp1LIdUBrQ2K+iXPFIUzf65vcziTURhsAI4d7ViWdJFL7YBO//uqwaEpSXGJ8UA4Ole1pW+5q4clN5KSeeDc3TmFkJJAn/2P7yRHzI9mfssL+Q6FinLIhpjr8kvOXxXWdnPc1m06dtCq87jVeGRu87zW2VHoDR/dlsRM1bCNPhm6uCvJFj+XBeZx+UdrrW92XdworxZcaUrKdJ/NUV0RfFwwXToMlk3HOPkuY9S5pd3eDJZB+MzXnc6RKWnmcvLXLE/bECOL4WPf/I3Z8Lx7HB03DSja1v+uP7FYpaklMo3F8og0nhTqMrNnIzC+0SoQJ0FpayU9gE7OWSn/FZzxs/ZqOHSccHw3Quj+Qb6tegWd2G9WoGFQnPeTGeXZm8XRV3hxtulCwReZqXnyDt8O9589aKL5EcIKLqeTUilOP44TI+qKSq6QseKPouYiYyOz445r9EfhWlLjvhBjU0pnE6ggj76CrORoAZXnqdWqv+0wjd4OlkG7jiMcJqfJLNEYkIvxa7fUziV2SJ38PYzNHtAaBdHyExiLiCQWVwz/KclUqGV3gL8DXlSZ5H9WCvMzUGI0+CcT5Xh7z6UE1iEJH4WBe8nqFtpUGGgS0/Qzwzmg/na880nuVi0465LLC1+MdSonmjy01xoA6X5K0Z01Wfx5kmLwbzeH9kXMf4jfdNXpS+bG0YQ1iMgNBonNpm+/RHlUM680atnSi+QWtjSBZ/gOFlUAMCD7vRRrydxoa83K+D9ykbYdkLCeMtvVvEaGz+iryS/TNlhtNs7H1TozDxMm282fZ7cCFvGVu6uGU0xN5wstqnBE/fSRHKFAqFwlcCymBSuNPAr60mI5tgsru3yQsJE5nywCAsjfj4xwevtFOH/TUit0krkSmtqwpFxOLkeqhR99dUu2BpHdLJ0xK1hr20g+0L1afWhbwGdPQ/vJcwrl/Hb3OWPodzOrsBpLnya+HwiJUCn9WcSajdmHRvj/QEnfEO2DiMP6Zpk1fMfw6iA/Bo+9Ry3Ujyouu5KFW2dAcMKbBXh3XHtCyGIWbtQ1zcourFUCBG/UHAx7X10vGs+xjHRF2parHtNtpB9dwrMkymDJ1rJ8YxRMweGjyl9XEPYbimeTV+XRe+lZqHQjzwELv6fHOZxVVfs954HIv4JwYO5ArTcptLWA1Qq54ukb5Fv2JfZCprmPPmfbeF//QXnNl2V5r2NtG9MQaX02cZZ1Vz4cAxgPi+r8iJrnU8/WaywVtL3isJE9lSOcmDSnO4ddSMtrunC+7TEpHFX5I3AXP3+Dj1M8LmdHI2OHgC6TUtOzU5+h1Ps9ETccSjRJmmbViLBozRBqYZJ9qhQxh6OEnHGUpI5mLiYdJod0PX45tmF1bg7R4cK3zL9RUKBUMZTAp3GupiKMrEZl+BJP6me5vsKiMShiN4N/3yxNauCu3s40GlwNAyf4hXO4mkqXbRIC+BwgDSevqXldn5C8IQkyiHKKqJ4pt99Tr4JewMVA4UQ5l2QFIxLMEz6WfoktRt3Jxwg04i2qj1E3K062z2LCL3IZzlqrWboMnauYQGlirMuID61K6wfAPYPipDMOdxFUFaX4ko7FvHOcwH9h2HadXLMZuRZcRoHUtvk0EDivgUY+U9kZNyYl/J4vRjKmMcepvAviNDWew6Jsx7jSOrbKHUSHs6Zshz4N7zJ+AHTk3J5zZDHC/yoR1a5pcGecpgEzKNA97iKsK8RMLHPKvxvYnGkZCYvrqzidhIm/6ALOmn8zY5iNTQcxFkrkMdeA1Vp9UnZfbIsHp6SPvdxNAcvE9CGN9Pfc+bp9B8fBbUbDy8yaK63fIynMSd7S0OSH/pejeZeNuonU7D2yT3KtH1ke744eGyIXUibRzzgvF4jFnj4W0CM0dG1ozKtgWspmktfnvYmD7DeLC2DYYS3H8E7+OGrre2H0qhUCjc5yiDSeFuA4Rzk0+8oEOa5bLwpARMCoHkt/uN1t/GPN0v5K+RLqpHGsTIQiKY2maoqjKosUbi948TXsU7rYJZTxmRtq2T2FkYOxEkXRb22Vd/BAKnPha/t0lEC+GWhFdp2X0TGhqWlMtCUF9ODC/6HoXJ7m3dLqOxI6ng4rQeOfQay45ftTlQgktuQJOMus507+ZUVwrU+EQ09p0Ug8VIF8PJEP5b8DaZDBoHXlav/LJ+hJYTIvBLqzPP6BGmHOo6YCxEfjTuMw+Rm6cTxPurZMDXKN98l8kVwVgwIPJu/nB17zq7sJsDMY9xuBFtXia+rtDRKd5o4dEeztuJZWF4RJixxbMiHMFVGCek8NFTa8LJfaHxld8Mr83m9HCJ4jHNu6eK52S2zwh4r3Df7FWMtc1R4umz1JFHeUb+Uu3Pemuwz1NZ07J6XzJ+G9vdY3BYXPZXInh48L4oTyHlHW7vKY5GFYZ9TqwOP8eAZql7G3wpeJvMhpAzackox5GQDXp1SaMNNzlzc7JkJzNLz+PaDp24U1ij9jApFO4vlMGkcKcxeZiAwL8xmaI08lwSPmRUAQ8Tv+7/Mm8T68TBcJaMwrkIz9omhbbF+IGDCRWdCwejCX4PZTpRa1ugST+ve0IdDiiSe+BRR7K3ySQw0o4ofZQEUQ5EYnYVQOf3ZHYV+JOHeI4kmd/aRLRGkBoX0gqP2Qt6difojy+MQ0Nyp8SIUjFuRGFgSOtJsN9JizVju/6pqWcJzKWVtwkNRVHo80tmcK75eYd04NhJLt7MCJUpEPkynNE2jkEy37VX8G47QweGE2PIWaPJYUMLwZV9WLIjTyG8Iv0wHzEOeRekxzdB/6AtrQKMIpoGedy48uIva8fR2xVTqVJYS/pO7RkfbwjZnsW9V2Fu6Dvi3ov9Zs+RpL9duhG1vWPy9gyLgD57QqMDMpf8NYO8PtEZFVtmAsgG+igny+F+v8ZvN219c9R0rk7XkE9WFy3KRANk5hPnNn2lYbZS77a46St4uSVmk/melRxtZ5PNp8FQQjveJiFt2VihUCh8BaAMJoU7jT2DCVFfmmwC/0LZyJSHVTirB9uGus57mwQaUAEJBokYVtdoMXxIWPNsJvhN9Qz5Fk7mzbWHnb8Y4bSQDQQ3WTMbpX0Mx2syRklsC1ksZ7OvdFgeC0k4E+YvEvhYTwYQr2u0YugzkCYTBcjiUKUPJGT0NBvSbZB9ktaaG425POo2GcIYmJcKCM9DUdPl/9qsff11tiSGIfavLSgrVj7qYHg6RY/rAylfdNO9TXRtv7QWdxqx+KzrssxHlRhcfsW0422S7miSxmHDphSHAQrxYrxgsuVBaLiY8wOvgXdxNoL4/FO6ttP62I8+6FXKUbN8YtCQdFmSxQTtCi9CPgThwDfUSDaxIDFwiDFlMccXfG2K0vat3GTUO1fvRZDxxG7b4Mvc3q2asexNaCDfyWE4wWU5tvmr/KfmRKJF2LhaRlh4FySNmcYiR/JqvmTPrDKIWG+I0/fCt81Etvm5MHbxwghLcvo7u1Hjk5WTZiRvfI72MtkYoZGCCbxNyIy/kN/3bG30TZIhYtF24Gs9y8iTbKi9e3oOphUOozxMCoX7C2UwKdxpZF8yUSHw90T+Kyj8wKOQs1I8sC2V59i3DcoLUd/XhIhsyQpbmWgcsWUwUJdUn8RPSgXEWV5SxZXIG1eGREemVfAinOWZ2+6yk/9B5nbqRpP4O81ExywTL+RzVUvK36LAEORNPPqTVZg3UVE9B5opPhLXRsAJutGIk7WPaeNxNGiboM0VLvs2i5VZGBVshqxKBQTkRAmsEstoEF4tdBpSupsZSyxf8DYZlTb4V3p93LkcyWIfJwLoKdZIvm5Xia/x3AzHsTG25m48X9NCaEA2PsZQ6ZyeNYxxwBsxI/IlTOdAp87JrCHlTvnfaEOuRESy8e4RhczZdBdtAClu4MXLRKJNiQ1trF6mC9lOYjqwNDGsBZ1X6FkcFnRTUkah0fM276QxOAAF03AS3kOb7qJ3mW+bFh3JGOY0ghYnO2EHOnVDbR6maDm2mew3TvqtTFesw3ols4C6d4Bg4uTGD/XeYHZ7m/je4QasBFQdWvRH08Qg4GHyneMUn7BRad50cxpiL61QKBReLCiDSeFOI/cwMcEkGjBEqb+xt4nkFzlj40Ntm1jixHMfdtL8uXASZ8QOAUruTcxq6v0B5ZUMrMPXN8VHOkY6M/flEPq38DZxV0RM2xH79iRCECDF16SRCZ5yL8juz6LN5ax5/BrYLyJXC+mwLaoThJ2HPc3hc9jG8z+d7NSYQJzrA8e4rD+a3qZ/MZ5h70O3CeRYGtOb6opAa5amY6PEND91pRWnpHo1Q97B6G1CZN/GUZE74Fyu3kPSQz2JAh9ca/YlmUbbepLFoGmhGBHly3Swi7bchhwfM54jY4F5Zz50jrdlPE7aXNOTjBn59ljLkXumagjG+L0/GB9Ea0S8DeUyYU1oeAMi078pSsvanETWNM2ZXdovAxMtjB/dSHjiRlfA2froWFj4X3xPd5Vp4D2R/+gG1sTuXfJcjOElHfHincLDWwEH0IV9cNr4dRjvW0u8TZB4F3+Ak2tXoE/ybGU8hLGplwmB54jQ1pJnFa6uL0ZiPHFGn1Gj5Uk6DU4wsgNyZJxDz1ft6w8Oz9ka4VYyytf6I54NJVNYd7Au3ATlYVIo3F8og0nhTiMziKB0rnIbxG0scqeUPR/e38/keNv4EQqVBHc92u/xh2Ha5H5s5rpBGvsv3U7WI1NclvKsk21DghpKoHMUliShtcB1xmUiL2V6zFsVYluDBrjv/VJfjuV4ChnnxWvfbxVqidUgIOkqQ4vwHbWoxpaIjadC9oqyrE82H88Kzqu4M+14NcWbfPSd2Zqd0omGDlVCknugo8v/XpDXIjKuzoDSczINB/5kbxMbb0/zysPEO8H3+sXkpmXkOV/1uk/tRO2U+a+c8VZJehqNsKKk2PvIyF78uGJeSMd32XnZUYjXor4NdL23djvdakxBWtQoAiO8eBX9UppoAGHaVmmNAy3ZFfJT/pfyO+krs3EVqKNB3eSLHIZsytrrHZuwKi0ttCv58Au/p93dH+RsEU5pn3rW//zJOJ6BNQjLexgrm5emkBlxSbwsJMcIbBvRqel+G0bt5dBNpYM3Wo+0thsRqeDQ2GwjbAZP3QyW7USdJg9DIuYpOfefID+JAXU+SYfJ9oiyGvLfuTjyZkqWcYV7McpJ6sLbhMhzzqM+e4Vj6AaT2z5WuJ5JoXCvUAaTwp1G9DCZlYQgSI+rN5qgQuDDjPUEZULbv0nb+kWLVbAQjxBVMoQWNYxA/Go8dsPQN6LE20TaH+2JYMxyRoPlmbQG7Sg8D848TkSsW/UBJcooXZ7/sU9VA/YlmyP60r/YGMvHP73HtG6kMjGS+yWYhXiKy60aRzvuFW6G2Mnb5DAiPXaf19jGSTrjebMpVm7/kpGmXQiTluMRP4ybvPb74LZDjXns6eKF9+hD0o0tuGltC8/AFAPsdVzag5vRNCbaaKO2jafYvLeJHmeMhhidR/ZlWFv17GXiT3YJvMrltUmw520yGVeg61i3pgOB7DIhPUaLXpFebH+Xr0UvvUW4EfE272HCrn7kUf5vioK4tGlIbKptRtqTYQW69l5tS+a0bhnz7oiw2Nsk6fZN4Oal/n4No2azN0YmMSPNcgmNH1rCBFVob8fgCa9cmTvz3kIc0h34g3mokXGPRsTc9ysRLxPp0+pKTAuPFE9Rm+6DKbVR33dMPT6Ah44S/bekhePSw1ImMt449xpzoPGIJm+TPhZgtFM+N+JvaMQqFAqF+xFlMCncbajBxCsPIpVGQT8qCrOSsQqzF3ZQAUmUD6MB27R826ijQTuplO4+iWbh1R/QD3GM9crRqwxO1SxfDdk2vUMafSXuj0ddhGNCIngGAbENoYtxmQ6M3YS1mN9CeH0vIqb4VL9AtJ3vmg2vQ8AH6XPvu6yIstjj/d6b0pTF6yNsEJ8oJFMjIK/PuqynvEXDhgjw+h6a+ByGwt1jO30+zTS5/CTCv1ka7LhPeUB2goQYSbJlNZna1YJGh+25tkeq7UtjRKLBZvXVdZXm+Rl53qT8K+djfh7hsprZiKEX5JNQh381Qxtg8DK6mtLlPfLI1eX5y5qdIatBA0gWlnFnVk7mr7IprK/Wsx4XiW2PepAOsrCdZDI9Sl/vOTCRqMwdw+sE0qK14dTGClFvjXXvxY5NyurOX2V/1SAeB4OcHjNbpzkbVKhNe9uoG0sbO3r85qJjfxF3kk40O8S4VSfbnCosYvA2JuNHvHEfcJkAarzFKwU+w9hFR05cvRKPPjcjM/cTeJpUO96zcc3NP8H4QUTRyOz6rnS3sFyQ9YQwa9s4S1yyw24ckn4XdsEb03Z1hFlcUOf17dZXKBQMZTAp3Gl4l3JTIFYKRv9/kc7s0i4/VhjbZghqwXAPJ+mg1D2FaQpzGob20NihNItQC9I+dZdi83WnxV9M4/WfaReOFjOmbEAffvtKpHLF6mvhJfACK9OJGl3duLY2BeZ0NdigfNql8LmrKGwnpKcNQFkmfx+tC7rNhm5gGCo9omssYFMNpWMLbxt3vYpEh/CGG1dclADGeo1I3ETV1AQOOXpDri3t0Lxx5YruOEbY3tx2dEof793JV6D7Fbhjf7BNGFDgV8g7kNdIvD1/NKBA2pQvuWLz+LrE9tO2knawThjx9ZVdiVj6XB4M6ubC8Y9CEWRZSfa5aHhWWT8bUeo+kb1jGdBYKcGFIQTz971NeOxtcrOmz6LhVa1T2vbWMnPwDZHwofi2k/6OkPHBw/XLs3I1OmOnpDVHg/Av3NuE7QrvhPUjiV+RReF5ieECjyxnct4mndSMIxqrOYep3Gi7zy1rm5hybxMwojQbxBc44QqFQuHuowwmhTuNSzZ95SCsZMaW1nz8sT1MrM3cGLOIG8W2UUblNjCC4MaJDP/mxhXxCiEIM9Ema6sp5Ol/egwiyTgwmfHInT1s4YU2IYKcxbMr2kJ+Ilp4m1AIJxLXnl0lUSZgV1Gjsw1vkxsIdCtd11EOtMAep3Yle659PTzMBfyyuKt3WL9yNQme7qCHkzQtn/QnCtspOS2mNP23kX11Z3gusX9umwOe252Ig7FlHWsmUGlIB3t0Dr1NYAMCsq+wUHLynIk0JMYXJn+csaz/k3FQZcImRxxyNHhwuGK6vNMWt/CaI+RrwcMkxPkGQtmsnaSYbw/6MPiB8knsHGfLaOSvAS/BJTeL8HgyMsF4tMljtB37JE+K9pvzex59cyfnAK2SX7xNUt5ygN8ENrnLChnpoma8JpSb6jzT/h49MpYyc/tRwz1l9mmA5Rrh/UlbG+Pn+Z7nc4zl8DQblyvrCfA9NVxDP+AWeYLaniVem+ax3IaVz8nSGb2Gbu4tPzMK5V/jEdYrO80mepuMmWfsmHG8BXn7lnP+IY1pZ71NknDhOGrT10Lh/kIZTAp3GrseJiiY98wgQHKaLieMxHpVKpY4Z0hBxWDOa21YW/MyHdbN8M1IEtpQoT2K+dJZ8dpgq5pFrLU6GOrBI4pNhvMROmaM8ezzQv2ePl+nE6M50ETobSLFdz5P7f32h2oaRKrg6xAMC5TlSYqkhLDL02jeEBbHQkVi0embqRSXijccb0xqJmJKPLFFoYNnnegZ01Yi1r0QiSNnwjs2vjHTyRmNujKsmzgOAZ8b+fmgLTLI8l0pNuOVlVNBnsk2ZJQR12UTVn96dDFl8NY4p1SRLfuRDRlZ9xRYP81JqXDswfMoIzvjJxbPU36fz7JyaI/IDBuLclndaXt2E2wwOi/cPa/+0JjCLj4NbxCW8ZgMKmzFOFQV/rYVWS2Ui/fa9+bYZ+QGE468+MM44vk2vOASKzQtlWfAZKCAcJN52iB9/DWIYyLZR8MZP5sLUNhoI9ABbS/yuF8L4CN6msy6aFrbHEIDAbDSkNu2QeHRp424ncJGsIOaQZD7OVgy+t4Xvx+ImFuZzNsENoSF443tdy41X/WUwPPQPKLLaqZamBq0Tcz7e5tQ7WdSKBRe/CiDSeFO45yHiQnwKPCfTxdh45y3yd6xwi+sbZG6ozQfJXtUDMjnUUWB9J4tQtSFQQeqAVaXlLUNaLGNzdHXN+/crA3929zeJniKT2NXqXmbbOBtEvu3usowiqC4yhvierD1001os0374C/eu/iWp2EeheSVfupEkxag8TO6zVRxbgkC6r3KLo4PJ6ElPPsjbUc53wvGIQzajX4QJlJh25TrQSsTcXStmehibbXL9ngiDpFobXoCpygTNJ4BnKSj+wUk/dobAfQwmZf6BHUFnhGenuP6YYPheAbGO8NGyIfxyJN0eCdexUl5ystnNI2MWTo0Sa5AZDdgoFn9RaxP1EFuZp4w+mSUf0FHHbG+YT2xZkobzy4QKfMWfybSA8L2JlnL7AmjPeANapSQtpFvsCyTORG3RpueBGPcqZMtRpCmYZmvGLb8UkfvmNGIRg5WGoiYnKcNGk4WPIYl3VXLZE9R7i2DzsBtG4ztBGzHmwu8KcTHYRQabXnwESIkneGVbupt0vAFQl4Ez2YPgRt4iuVY4TgS8fhhGRGZS+Bh6fYmTynIjSxylPGUdtDbpHAcvG334JSc262vUCgY6u0q3GnwJn/dRZS2OY5DHE1l2Je56nHb1RDCN6aNLdx1/e4u2QX1cdoMy73FcYgjjmU2X2brcdtm9RGb3OkVBA5hjLN4gjjaMJ5gxY2VVam4MbXxbXU6WSbRaJjmOAK6Mw1IgzFvuhRiNQn0H4wIYU7yiqHIwqbAHm2XunJ0Jg+svxm6lnfn1y/YDZUVX830xzvh9N42q4zjbUpFo6wzzv4V/nxdO0qstmUK7SbGNm3EdwbbiPVQpIU8DTbvzJPAxY289lzGuwIdmt/rGEfk3qcQdu0wBxp1Ark6ld6zfwxzw4f1vcLw6BsPIpjJ+Fp8pu6lhVpkfOIDgEliHiDWn2kc3Dyw6+5Ei8/AvRmLMBOUR1LlXUg2cA1/+O/8bnG418GG9i04kbmDZRaeb12zU6aDDd4Ic70tpHujRFZ+/sPj4ae0LJ57GVdOjPJCkysS55bF8XJCYBo8a8gqXeqygaUR2RXf/9Wfn+sU7jGdyL9j3OUZ4GsEdaz7n/Udy83ph9omgne1cL/ive99L33t134tvfzlL6dHH32Ufu7nfm6Z9w/+wT+Yzulv+7Zv0zzf/d3fPaV/y7d8y5eiK4XCPUN5mBTuNoI0YsJ+v5qQIumWZtl26mBfB7s66OyxwtrmyCJ10Bk6iWFvE7cAmq1c9gftZDKzGghAkNNYqFrL4Jdx/FsJuUnaLOCOAzBdneBxIvmatL90iM867r6d0gg3ztLtOyt6pDDROBlh1deRq+XtuQHXj7M+vS/TQW8TG+b+pc6vAt834ki9lge/IXuPl+gcbXN+4/5RFvd6kcue/SqKw00ba9AwhB0F/fhh3phOp0anMWbqDILPRCqX94r8V9geJ/s38Fitg6fZkMXBV14Zc3fUL4yjth363KbQfMSmnNgjJ6dEbxNuvczyJB3HJ4zmjG9wkpdXeTXe7h1fQ14A4z3XNbgHh3SoH+liXxHksfRzbznCWMh54xyOa7NHECqEK4wBRwLO3Jtu2p8qZpW2N2h/6msa38hxUx5vt1YOnGikiTFXnoEtk0j6Lg2u3nV8OZRheY+X/AUahvdxmo4YpdF3ZWqq5Twn43RsXe+8Y/Aafcf7+ebg5LJvEG8u4AekQTz6qOAm2uYQw+SPILY57ihYjjkLwyLnjcMEcf2qfIbGSTWptwk8mMUpOc6nRY9cb8qThbdJGgEvbcu2CzfBXdnD5Gd/9mfpiSeeoPe973306KOP0pNPPkmPP/44ffSjH6VXv/rVU/6/+Tf/Jn3hC1/Q+1/91V+lN7zhDfRH/sgfcfm+5Vu+hf67/+6/0/sHHnjgYtoKhbuEMpgU7jQu2vQV8qGg79OtPkvP6yM2QX9VZxrn6tkpO/LxJnJnKJP9Rck+GDFwPXU/RaK3OW8Ky75qimGQup0hJYmb8tFQhmd6uywY2m4q/dJRqBzoiizq2K02OZJxzrJTme9fa0PlmT552j2P52QS/rl+i+Bst1ZsTiMi2F9ADEOjnJDmNkM8DzTKGLkojUM4bm6ABgRqauRIe45Fp/LklAs9mlMGBOPGSUHeiAEbwkrtZ+X9oB2OAezGEhnXHo9HGouLPO5tYm3j0anGF3iMkV4WBpFsmc5UVvJPZYzvGDvCOkJdSfvarmVztFgRn0frOvMX2U36HLShBjzIwp5dxb1N5jdTJtCUh/tYqSED6cJMMgfZyDnE0c6/AJYCz+hQfbpO7TziUNvYjHGBPDrUeE9jTqnizX2JSMorIG7aPCnmw9OqPLWNyJahcDfY+A1UY5lgAtXphPPK4t1yHSY1DthKojHXto3odNLfWOn/HjoNYL5onhXj4j+Na02NJtJHOUlH9zZxfHfVLpGbnf4nwtcvBd1eK8CXTsbXCpfhrhhM3vOe99D3f//30/d8z/cQEdH73vc++uAHP0jvf//76e1vf/uU/6u/+qvd/V/7a3+NftNv+k2TweSBBx6ghx9++GJ6CoW7ilqSU7jT2F0Cs404cRPdRj53b/nsvsfRuMoPl4QJ2qCN+5rpjcPfZu6pcmTvNpbk6H0X5DjJJ3SJBL5B/0iE+90/n68LbqH8+MQ518tTPAqhXiOYHkiIZhVPrQwTYWwg3rkFo9B2yTKdSFZ2f0ax6K23/mV053PnrizIRGbBkOHx+xtQHM6RtrFPuvTPMNPu83ofIJm+2n+hO/nLlFhTKlvehmhTTEQQluUR2za7e6//yOYM2b3No5E+iNTnDulMdr/pMjvfv/UfGB5Am+YkzJCHQ1nemLYN33GCfP4PX4tpDkqAiXIDbE+0OevfTyFx2WboJykPwbwMaWR8ENM3z4M07cB/fgYT8PwkTOJh4sM2MMOIiV/c2VXv5veURwcOb+C56JDFVqXtBVtrlr6EaPWQFz3XsB5/7ZtQn1rfv+imiEVbFhkz2EMfkXEy7wy+dnUeydnDL6ERGFsDPoF8hDh6SiJJnjZ0GjS6hnMFtK+eGW5JLu3+yXvi7y2OsA4y+o1vkr1PIru439i9dimlEetnrB94AqbR1Hb6iAp3GF/4whfoIx/5CD322GMat20bPfbYY/T0008fquOnf/qn6Tu/8zvpN//m3+ziP/zhD9OrX/1q+vqv/3r643/8j9Ov/uqv3irthcKXGuVhUrjT4CvcPVIUATZZRxUIhgtr/kyxYMjv0/tVT7MhMX4cr2umkSG4T6uTyc9JXKJ4EYalHpOOpAliUydEaBIJx7nqgwBrnijWRoM2oyDqFK2FsCoNMckXOp9HXMob5YKp3nO4H/WhjjGVWdbJ476Nf1ntN1g+wuVZXJ2Xhyo+TRWfTm6zPBfA57byOb1z/cykXhErxU1KTMk6f/CLpn5epsxnx33v9a/CHplztAxnk/ffH7jJgw70NrGiQhvHWheIS6c4iYVaE2+T3tTiJB3gKcgj5P2019reZeRBjPkTvsKhrJTZ9zBBfhDrTdrFNlIaR2SjQ6vv4vIbN1h+4IC3+PCsFDJk688Li1q/AjkZS4P7SBL2GfPbqU3jXWdRwVMuRfabJ3n9Bq68yIN1yHIdW7RBwUIT2mwQlnjJj+VGuMl+TIPh9SUbVq9Qmb1hGo8sxN9AyQZlII5tBInIjgh33iYzfCuhTb2N7Vg80zCmkJzYNfIzdYPN6eRfliWbAf4QMg+Woc+38xeryDxSJD+cpJP2+nzbzqsF2tb6IY3ZuC62XTiOe7np63PPPefiH3jggXRJzKc//Wm6vr6mhx56yMU/9NBD9Au/8Atn2/u5n/s5+qf/9J/ST//0T7v4b/mWb6E//If/ML3+9a+nX/zFX6Q/+Sf/JH3rt34rPf3003R1dXVptwqFO4EymBTuNK5e+hL7OkjkhXfQukwhMMkfhfl+cVpAqAPL9OuGJ7mE+vMlN+v6CGhRWneMNrxtphCKpwoaL8RbhTfIAwaO6YsT5hmeLeCRovH6la6X70KS7JhreVu4F1rQ2NL7l+dri7K8bSCc4/jz+bAKfxgnAm7QZsJ4W/G+50Zr/XCXvrwG27F2Ge8ZBFx3WsJIBx2dMS/0tA3DhezJ4rYIGWvK7d5ojDL/NpWF+5DGV0StsfW5jcMnWtOv1NQabY311Jkm4RMYl4ZA3baQZ9RJjWgb9/gn71FTJUyE8qHujD6rkjbG0sU1XweWUWUGytiSKW1q0CyKoxizWqBH6lFtRffCkfyu7Z2yDMZBImEFno8onwBedtFynFCP5N+ikB7bDPmh+kBLoHmiG+gZcdtmy6KiMWOD8GTw2BbxIbzt5FGvKu5KHrBEK0fIllq4j+ly33bq6PebHFnlh92zMrzSkbzCUOZnyYz3TJ0Pyzt1IjQaSQUsy8yISJR028OEYYlNZxYsjEyJk/cPCd6oydHg4sk3/prEoRFpvIus+U40mAr8QRw1Ij4NexHkYxp7imyD1hPR6XrsfzLq50btNIw84/3nUWcPQzsnY2CadtqobUjrYJ7y20uDPzQiNeKS3FsaqeEFGDMasAZ9OrY0lhuCMatRHz7a+u+XxrfeR2lblxONstxmuqw+qD+kUWt2D2mFu4FHHnnE3f/pP/2n6c/8mT9z6+389E//NH3DN3wDvfnNb3bx3/md36nhb/iGb6Df+3t/L/2b/+a/SR/+8Ifp3/v3/r1bp6NQ+FKgDCaFOw2+2ky4UCm8/xMNIpnhJPcIsTKpEUXKoGIR8unHGZBMpzhmkTWtzpCOdTq6eeuKtWUI+reJuzYyfCAepe7FHxut3asEa8M6KNSF9Epdg141oMB4TfWNOBD4+hicSIwnLEI/tEYMYYxP49Zh1yslHeadBEU6bbIXySjCMU9oClrkkOZ0DGxzKon1Mg3/KzBaUPjjM+l66i8Rd52AiOh06o1sWkeD+kZ4KL9dV/DhE6wF2JI2rW15Dwm+wvc6vIAuHhz9nzYGUeOccUMUADFe0HhnQtwYz0ZCP/XNI2WjQ7KlHJ0eUS4YlAomUWBEkRRWpfpD0H2oCd8Ro4nxAeQfR4wfPRrKu7J5GfTagOCizLE4pGMvTr1bKBpNyPhOCONyF+RiWVhJy/4YrmxeUFquxZsVPM/wHDEv5zdvlZjm7l1ZnQvnqYllYl7cC0PnHYEXmPIqtv2F3KajRCTzVL04GOKwEoI4Ap7fj3Q3z5hG1E7UwMjB8MIYp5IeIAcjqAOMDe6PiNFCK3OpW4P7e3dqxJvRwGJc2LpBBGnrL6/8DUMKjTJoMCFyYU3vDIh002oaxikwuKq3zuBlrHX1/qn3DhhiCfK4OBm31vtDJ2i7jaePdIEhhIiDAQfakTQXRz5f4RDu5R4mv/RLv0QPPvigxq82XH3Vq15FV1dX9Oyzz7r4Z5999uz+I88//zz9tb/21+jP/tk/e5auf+Pf+DfoVa96FX3sYx8rg0nhvkUZTAp3GuJiaDo0KhhesRAJ0/Ty2cPj3BIepzToj1mQXnfKE+/XqXVrdYt82G8eX2W1jvAlGGhDm4TmZ4K6Lc7uw59pKupcYUaT5pQZzAOtkuSOceTq24PQ2FRJjrWKKtAkXWX2oKmqwE8mtGozJmy2sEEisyj7J7rGHf2GcmEnCJB2yGpmX7+W7YJyFC05DJE3UNDir6nBYQ2nBaaIBg1xNDo1Vm+Tk66BGgJ7o+E6om+mC3dPAtKPs6tObDoQQ5EbwrjqZuPLNuptblhHWvfaACF/UlAkDuaMvDOig4wTIHp/CRQLqUfyj0U4qjiggmHKjLXTtL9CD29C0Zg3zZb4IP+Qd9heywNLc1ZliZG17LC1pnldvtAuQyGsJ1uOI+lmg2Y1ksU5NGYCOdOv8pz9sLPLTnwNYW+g8A9nwCHzQpnsOHCPda+axaYZ28TnrXzEcjqDChPwmtgXX2Z+2yPvZCLe6NSImE4EvyakZhR4p/r0ZU0n927SeNWglmQTankoTY0cYgSP3Azj5jxd8w+GDG0I/2iuZ3hq0ulEfLoeNo9NPUTaqQ0jSzeWzPE9jdqmHifeGwaMCGA8EEPQyttE8jheMRkrsridNEnn0f7ga52hw/LBkU/4GQWjitDJ0EYsa8O9nJiFLzEefPBBZzBZ4WUvexm96U1voqeeeore+ta3EhHR6XSip556in7oh35ot+zf+Bt/gz7/+c/Tf/Kf/Cdn2/lX/+pf0a/+6q/Sa17zmkP0Fwp3EWUwKdxp9D1M4J5o+iprwv2sQHjFwisSI+OkUGi+TLPwmgHUCULiTtueth0ag3eLyslDYsf9RRos12kU4kX90C+78LfSLEAbYKe6jOOCm9C+uTyxPo1P/phg2U78I2mbxlIaIhHJd0UyjvoEKmPnwZPAZ2U3Ak+MBHLqjZwU4QgJCpFuqwGkqSGAYvqY67EuoQuX6rDJ6i4c7zFMQwcgr170uvv1dGLanAHnvIeJGR3ypTj2sdY/H6Nt9jbRODmFZgymyfJNT65pY1BNERAlTwT+8PwgD9Mw0pxMWfAKBXq/sG970Ekkhhahza6i53vYUhV5zs4wcYFBxK4+XeuDr5qRFc3xSE9s2/O9mR7H4oiYaAuWwY2Hku+MHcCD2CoxzrEOQ9X5H0P+sZRG2d0ud1mn9ed5ibLILr/sNOKIh6v0PhpQ8pppMka3WKHcOX4tmYGJDr6je+80ItkfZG4InyvwKxcv9RI5bxNq1Dff2IIBgpRRWPyIQ0MLkzdahLI+3LTLfDqReoecTmqHESNKm+J7Wr/pTFOPR5Y/GSc3HXjknb1NaPA6MQwb3zC6O8+wcvrxxBlO2OWXJ4+Mh6mJJZzUAKwGX/Pk82lk3xRGHEPThNdLXoHCPd3D5BI88cQT9La3vY2+8Ru/kd785jfTk08+Sc8//7yemvNd3/Vd9DVf8zX07ne/25X76Z/+aXrrW99Kv+23/TYX/7nPfY5+7Md+jP7D//A/pIcffph+8Rd/kX7kR36Evu7rvo4ef/zxm3euUPgyowwmhTsN3uYNouzLZIgMyoUJ77MSEpUOjNOy8uPjhPxEw0jrx/ax7szjZKaLJu+Wngd2ZRDtwkRSRrLA0wZIChpCrlVAITR4NMzTmio6UkKX02C7kwcKxZslzFgyriKZDsFbNysd9KgMr4o1kR0vi2IkfIVVrTaT9yzGvE0andpmHglCCxo20NsF1+yMMWLog41I0xb1EYDXRBtlvaqgKsMZTxOtNUdIwm+60dvkpBaXMXaNh4UF2+jh+K04xbydxvDk6HXZkpiRqoK9KZomvI/3AwV8SG/6j9RndTNOMaFBDELSP2kTlAv0aPH7sCAdc9urYzh1bwFlMzOPkHcfecsRQ4mkb65O8uHA1hgS5/YyGhf5JTpd5ajcg7yZ1Xub9DJoXFmFKedrkefZy6th1acjK+Ng/uVQDTNRtucJW7INr/FwnBc6JrochowHC2Hg9TFZXckMeH56SQNgdJH3aewx1dqJNj7Zs9L80gwaq/GJWQymW8tAyCBXSjtvk2bcovfijAeKWCpCWa+5Z+FxL/uCna77kcCbLcuhE9CwDSOOc5M7jc1dJR+kbQQudYDO2DS+Ce+kaGilYfwR3mLGWcmj+8xgnHYR2nUGlX5lIucZo3xw/IbZT+wqLdaZpBXuK3zHd3wHfepTn6J3vetd9Mwzz9Ab3/hG+tCHPqQbwX7iE5+Y9r366Ec/Sv/gH/wD+p//5/95qu/q6or+yT/5J/RX/+pfpc985jP02te+lv79f//fpx//8R9fLg0qFO4HlMGkcKcRPUw0flzX3iak8WvvkzN5V0tyeI7jEDcbRZJ8C/qcsYZ8GtM4NhHlIqhj3sMEVA4nRe9oFiEo+ivzCMsNdzHWHyMpNHGIMw2iad/PaDXWMaAXlsE4TQOFNTMriXECv7v5b7U+/ixEcRLaRKOIy2xUqfFXUXZiS1F/NiMPW3lnlPDYZtH9+B/ncXgkqb4Kp2Q/k412PUyo7XiZnJL+MKg5jcCzhLqnFKGSwGokwy/hbRT2HibGK5xsr/mhbY1j2HAXvU1EWRnKX5O2waDT7LFp/fps8+coz3ff26T/s2tUAZ7iWBeTephEtoN5p7hR2NdlfVh6pLBva0vK+Lri3iYYhsbJh9eeJ3t/bQ6zUDHG3zXHns/M7HL1ek5Qrsa+F21Rh7FBZzInz0kGf2QfGw0bRBTsuL2vuuRMYltGSnOX82AfbMDW9X1Db5PuveL3NhEXuG0YGuwoGB6+f9ofx8CkYfAPZCLWuk9EfNXntXibnBrRdhr2mGFEOTVqcUmOWBY8MyM6bcbspnEzL7XdfUAY4yzN+CDuN0LgHdLjdMRjHbHM8KAhYjV4WP08e7VA2+lynjKYXAZjardb5w3wQz/0Q8slOB/+8IenuK//+q+fP1oOvOIVr6C/83f+zo3oKBTuMspgUrjTWBlMNJ1M8NjdnwSUCa9ImIBvSshInzxMokbBIX2u76ZtzxsN+Ha7fDiEsK4FdcFPfoSnE3BA60hPuEGtRP7mM0BZluUQfAsWPd61R9RcWeuDfo10v+0iyPPuPSpgTS0QKovH2lRZwKac2YExfiHwhWimrvidhpJOSRuZsUQyZGaPyWEKm2XSZRzOWMFDTidRNbKaI0EJFssJgtO8TpvTyU7P2dvPJNl9IGtkTS6RU+TESKIGBzWSEDnvEo2Lhgp4a4IOwSHs43q5sV8k4VIg/RosygSz7esgbQAd0vbSYAITM/I14Q9ojDWWYXzCb7Q6e6IENjmxNKjeiAo8K9apOSeDycgjcbp3i/XVt4vmTHwLbG5lXiXrvU3sT6PScFiiAxMoqQr+5ndnL58NaVgoI10FS0a3z4zlO8pHYHL7GofCy9OrbuNptDKkWiQT8RW107XNqakELk7M0pHGgIlF2AiYtwmZMUK5WuREpGHdoNmVGXkyI0ezetWDRbxNrr849voAb5M2PFtGMd3b5HSatldpbSMeS3Vme4lMdjNCTHubqOG9X/tjNuMIBz7oDCI6P+BXLGVAMr+6QYSvNmrX/dQk84xjv+mrlBvh2vT1dsDMt7/p620bYAqFgqIMJoU7jd01mewuhBsnohSuykQWR2RKAPu4eQ8TDDuNYKkBLNs5k49BmFy1zUPbRDHF5FB0ZEcZlnfVaqWLrRxK/g3SzVOEVKgzbxO2PE51EDpRi3FaiuXZMZ40/YwKQqHKgg0GooW4MGKgGS/HxHQYpK57XTTxxNgXVBoEspyT4iXKkVoOuuJu02x+irJkyKsOqGrEtPEnG7SS6RRtCMwnGedmbfAVEZ+64eTcfiYnsOqkXiaJhwk+OpJhkDT84irzollGle9VOUCZfo5bhXEpkBkqhhHjJAremJOJt8l4YM5Qov1pZwRb9oFL9jaZjSmzwWQLHiYYdtcpzHOY9VYz+zp83NrDJNTTe556myQDRWNUxl/uYTIjvth4bzTk/NK/r9bOQml0FcR3vd+LkaZhrn224qvmqeYRzTGnzUVl9JAg+5QQmkwkH46GH/8snG1BO7E25dtEk7cJcLXuLZgcM6wdwD9ahGUpKUVGRMQvGRvCirdJNwzxJp4tYxnLODo4MjM8aYZP4UlIe/jCiUGUwt4mzoOEJwMFk3mE2DOR7sEPzJSeX4Wv8Qk8VYJhxNPk25zSCoVC4UWIMpgU7jTOeZi4vAQ/8Gr8mJULVB40ERQRjcuW5EyaBFyxDaFBo6AsKDNWVWw7Wey/aL/Lm9BvU1VG30CFGMqHepLseZgMutBThJnUCILu3RbfQOMd5SftRWhGRCGcXT242Swqn6A2Q00yGl3gxvGhkBfDSw8TIStJZu5K4LV+5RtKuT6nIfTGFoMW5nOgoo7VWCHpU1cB9pbidK8H9ESJf3ub2U5LfZjULibeJl23wA5Z+NxSoVP2eged1XQLMyR6b5Nexj6+214OfdjaKMMkSxrk2GDSuv2Ye+OHpfW9TRq1E540gRs1yvz0ruryzGVZz9kPgexvIl87bzRhe8WRVzDwJSKog9I4YFlGTcqOOJQNNEkasrVYB9QtFGbeJnFvEwln88+NIiv7GWHvUYLLcqx+qduocOxSaOZm/E0zhnElH1YvFo1rNrZN5iA8n7C3idyrHr6YV7nHjsQIgaNevR9LP0+yREbmVTem9KYZqgwTpElwMdHhEaFRkkj2NjnB0pn+1yQucK/GdqKN8yhxe4mMMEtd8lxDHudtAoaSE3BbPXFGFyVC/VJP/LFo5Cy/MgAyTtHbZDAv8fIQviZ59B54CkEeF049TfyVG1G7akTX41cD91dxbcgPidVdS3Juhruy6WuhUDiGMpgU7jTO/gBwfuuEMKdIkJMq95bxHF+Ss0hrbUGD10TSuEPeLUCrityGlQqRqxMAMJzEk2y6qDY2CkTjisQ3yWc0oarjxsyVj/2yKFfH6KupTjTaE0Vit2eBnhB/TuCLx09YAl0R0YmHR0XWwIhr41jhmCeqNPq1WU6I0aExywCTGDtEm18STntPPNsy0ask63J8JR9icw+TbD8T/NvO7GGCA+PmdhuKwxgcPSmDiNSziocCMgxG6HBEZI8zOh+1RZwU7EaTXrdsp7BrOFH3eqjnHFubhkW0S7mzOZDyrhGRsa4jHiZTXKSJs/Yy2nxdSw+T0JaF7SExsWcZZtlw4bjvCSOtCngn5OGokb0Bf4L2aOVtIpnMGBY5o+unI8G42Ziw2gfJJH4WyCbnyoQvzkyA24ICeMlmT5ORl/sSnHNcdTkuCc9U/qbdlnek2ZinplayON30lYai7vMRNWqb7X2CR4Gx1i+n/VAwznhvk24UsL1UdFkO2eapWo5gf5Q4DjK3cFw0Thhc8DYh3FC1z4/oYXJ4D5Mxt9rCC6VPeegTtM3Ejq5Yznm8FAqFwosQZTAp3Gkc8jBJlOT+Iz4ECjCKmNwsQr2X0p0SwNFgAuF4zeLU4OLbOXKs8HEPEwyHvU0226uEuyvECJ/bw8RXG2VoDJr3CPt7IhPiGCIJPVakLIGykmsFzTQ0E+7ZDCdGr5OKgXq0TjTrmH5Z3bE3aIfbTibWbqRyoyi6i7YyZWOphMAms9sYv0a2BEbldwYdgJL78bf0MBljpKoL52Vlml1fm5IHEjkRzfuZaPkze5hM803GEeaUfJWV00Rk3jnDh9cZnOc6wTMbZo8Rxz5dT1ySulg+uIe2VxvEQtsLDdRFo3EBh2byNrG83suEHM9TQ8YB1pVv6Loqx2fKGj27Hibs+7nnbaJsY/pzNbir9zAZvnMuTsLgVzfGzbxZmqvdldNImpBSxUQy0XtZ5GbN6mtkRrcMkmdcY75dz7lInzL85lN50yUqblwbhbgQXhHtWAT3d0vfFfstMG8TIlmK06ZNX5szfoiBhE/IdQjCzRiDGAbUODPvbULXXxxeJba3SRt7mPhjhcf+LhqOfQ4cvdl81uFqDXhVp1FPgyOgGZlhSNNHsvAsYbw35qZXMcC06+baVm85Jtj0lZTXlcHkMvB2D/YwueX6CoWCoQwmhTuNI3uYTDfsY9Z7m5DTAjimb5bmrlncBYYUPc3DaQygLTD+kAaNYlU35FvubTKkeRXbJg8RqQf+wj2LkWJoC6yeJpa1iYFm9LVLaWKkGXkmVQf6R/HWL+FhrRfD5s+Cu7ebmiVCJpmwLEInW96zQEIc+pr/jXrfT8TD28TPX+1DlJ2TSnmc0OKaBbcIjJMptDZurI8ePudh0tWIQLC0B22Mj7Jjmc7NPUwakf8CjeMG8RhnS3TQm6NR3NfENk60GmRKaEyDdwQUAuUibvqwuui3E6sysjLeSNt7e5icYWsaWu1t0seE3RXT1cNkh51dEudZ1GJpzvjnnIcJhpHtQS3ktW0KYWFVs5eJB0yuqaoGbAnD5OKTN2Liapod+xZK2JHnwXdO9zYZz3u0uUknA8/Q0Zle03nclIdHJZ6IdFmhK9UN8NxOxPoLs3oGUDbxMFGeDQxd3w2It2cWvU2EKtmPJJxcI3kWm752D4pwbDHDkh+1Ko+y0dvkdOpyidYF+XunqcU9TGzUw0Cs43BvE9lculfvl8eofCNGC33czV+D4aSFe7wyEbEcPzyWW5q3CdlvbOLxUigUCi9GlMGkcKdxyR4mq8+2+AO/e5IOhWU705IcCMd6srQzcbttu0+xIK3PWoSrA4gZCnXot1QX5Vsn3Q8ksrTJ1yB4jzjnOQIytyiPfjyS9oj2N3oNfW2YDyuBZ9Isi1Mb9B7a3/0SuycLahqMMfUPlNfa757PTrFors7s+zEOvw07CM4QJ4aCbrAa3ibY8Ua0tWBMEWPHKd8M9gR9Or+h7OjOwttkaZTJPsROgeQWDRzkT9Lp1zGmzmhh8zDK9k634BhmV++cPhTDLT9JB/URpe2owSREcEhY7W2icZAm4XiscI8nF5exloz1MERm4eiB4hznsP0Y3mkT3uzRlj85x9L9HPRsLu7OtEqbdnGiydukkWz54TvnWFlkIKDYOk5GhEc4aRvQFV2+okPRB0eXWjR2PzfpPjDAsxrQ4Bimyytxwxg/NoW1WvD3YI4PzU+Q7Vkwn/+pQW+TuLeJ9MJxokUY4rhBPd2A4g0z0duEia6v/SawJ+SKYzPYUyM+u4eJ0MQJw5O4RnFvkx5ny/16N2yvJcfYosEkGFNY6t3Jy611Hn19Gt4mbG1KPn3lFg+3kKL2MCkU7i+UwaRwp3HpHiYr7cLkBPwqCwqF5EOPkwsMJofyuDYHVaokeM2EL9j0NY3TWxBkx6fWRrYsp7Ftm6gn3Cy8S2jkbXFJz8in3iYjrt9vJMsnbONWO0lHl9pInQBUG2L/tD/kDSnusM7xFbbrUPhFuLl/o3qVYkfYt7Q5wxX3vQf1JJ2RL54ekevPgSqRTZmSuJ5XZWJebLjKw9vjfOPxfIpk6c48amIY4avR7+BtcvJuL33jl0gOgd52Zsz12Y1qejuytwn5Y37BiBFPUp4+xlIwvshX/mZp2Gb3cmG6upL+2rKc/lyaPSuob4W9ZJ9mdCFPYcgoYZ0XiZfH7XmboKee8TUl7QV5mGBYOTqEyYWNRcHeJ5JFJoCLk3DzYUrCAa21bojSPsQNZLE5METwnGY9AG4mCqzMOSQ+VGRedYP/ZePDNpc5eY9jXrOnNOPXjYjoevkkXB2QIc2nLyXDZIb9f7RAbtToe0OB8WO16Ss10qV10SNlhHv9sweKMhrxNjmdqI1lOdSIWtt6/EaJ7YDnSLCPTHnY388n5eA9OQam1S08S8x4I8YXeDBJXiYiGqegyZiaJx8YrJZ7fBUKhcL9jzKYFO40+OrqTIYzEcntvESHgqA/hPy4JMdJtzEOrpi+k+YVnFD3DfcwcW1CeIif42tzUFomiX5E8PwNtsHX6gb5el6jxzaLHXm1ChTn4QhiojnsJGWk26d1Iw70E/sn/R4CYJcT8TSKpvvxHVnrv4QqFYnKMNVvSrzg0Mc5FrkdCmscEY3nK0YT8TaJxx5P3h4c7uEPjSS5l0i+BOjEdlDESY0i/tDQRrT0MHFfmyVOApIPp7jqMzaH0EiB156Wt4c6iF+mE+N6h4Uu//F2pLnjh6Ui9kbbBeZkPpNOFJfokI4HOR6jWzO5fElcKOfjZjqsDrY8zmizYGuLdl2vV6xPeACbE4AaSYiO/fFeWDxYYni0wWYYkUly5tFOfZwTJNgnjXnpmDItG7z65TY90fTWPjtTvtbm5j0Pau4iN95m1Ae7DUOCvbNMWZPnMBZ3jPLjzcINYYHvdwMJLrU5QZNjLxFlWGHT19bzNOFu3POwGlu68UO8T1j3TAFvE+lqa+ZtAl4X0wbijqnJvQQIHx8Zs4r3g8sPvqa/aWFJjrYyeZZkhhO46glvg7gGVx7Lgjay04FGe8L/ysPkMvBG92APk1utrlAoAMpgUrjTOORieEa5WN3OCobf9PWSJTkS5p20VTnvbSJKTVI2ag2rNhbaRb63CXiYYH624HQssPSVfVrPyyJHm+A+FBhNI1O2d7ULsAtMolhME9lOw2FDWMnZQhTg7B4mqrMsK1gKjfLx+UTjIx3oP+u2QyaS50UuXo/4pD6m5vnQ824Mho1R0nmL7DwC9DKZyphkPlWiniZbbx+9Tdx+JisPk/DYcDoHW4p/9lhMlQkaj57NyBGeY+5h0js5LcORKy/SeZTVvU1aaJuPKdVn+NoqfbW3ie5hEowYWFdkRRYX9iYJ4dzbhKgr/EBL5t0S2wr17BlzsEzkbJGXIYtTF6QYLw2iBwrmJQzDvf2qCPuxZjn0AZvjNjWfpzVI62H5ZXQsR8hzc8Noi3MIja9+HIFgZLBQle6oohZb2XjV8kVPOq0ykIIUuuYjXyfrWxtcxsYGGQveB28RakTs9zXpvTnRvHjwRJOZl09EfDWW35yIr09WfizJmYxUYV8YY0rs7+WmhXsZC2pmpBn51HhBoY3VcpsQ15/HIm+4MlE/Vr014tNJ+ZoOd+EwatPXQuH+QhlMCncax07JORCZ5BFBgYN0rgrC0sNkde3hzAAyayGJhgFKDZ07VlgvWT2RXl9WRXz7HAt/Umf+t1rRT2ODU7f6v8mGsJvlHccOWzmKn4bh6UhffH+cGgFjoMt7NDzyhLHwxomWhBK05U1SkBfxNpIxCV3t41TVONX0/aakUrEZAuT5yskTc2anFojeQKY/6B+bZ/s27jWN8B4MMgzpNHubxLYjsr5NtyijJzqHzG/ZdFYfv7igB93BjExytS/77kPtaMTiGMo2/TiORisZj15Xb/vIl8DcqMKH8hzhayk7Gv8w+fge5t28q3LI1zIPk6y8CydtpmEy+pCd5X+r/UuytLYf5jmNWjMr6QpTWoO+Bh4VeBdDvFXDWALC+OZ4zuM8VGCezrUgsaNdZwAYb3UbHWhZuVAdvoOYE9NcuOn7i0tYG52IOW7WSouwbNQqrYoRpOebNnEdZS1+GFqknMgIp763Sjc8nCgMM8HgLjod8nBSBuKY0Mgx5oX0U+uXsPA7vDJce/k25QlXrZPH3ibw4xDbLhQKhRcZymBSuNM4fkpOTNtLtLIqGoKCodfJwyTTCExwk6u5UNOU5squPFGYju1hkpU9V0bJUpE7lAlaBpG7b9gfuR/5oidKg/5Y2R7XUJsZyvbZr+4wpCLXW9gMQdKsk7nl2ZDQJsJ9EDqP4Gy20zwY8LcNg1E/SWfsJQNyslXflObmEtV5HcqMOJa+SOmmgyFjsuttghiVLL1MsoFJ3PFzb5MuX59Wp2hMygTEE/m5kOh1Eo/LGWxPEjblINahz4EhLuZjq1/j7LQIhmI82uv3TVcG3OSUnOR2iuQQwjGQ11s9TBJ2cYEz3c3yb4HCnD15dhnDse4sb6a8OX4GLwrHdCbvYRIJbp54zYt12hyT5UHYBEkx7L+81U3mh5iGuSvJDHkkvxoDbY5pbY5x+EFAHRg1d3bx+3nnRNtLZBPak3mubJdjvCwtaUp8N0iOzFCmwSMgFkMF7ra0c6SwvuRoGEm8UEI9bdzveZvQ9TXRts1LcoToc3EXlONxFW+TNizcHMvpYHkjiD8lZ4xzWJKjHzXw93JcuZGepEOnVgaTS7Ft3oJ8W3UWCoV7gjKYFO40znqY8BRI0haRIRj3Nrlo09fbTOuNz+kr7SINr7QNHxaZ2itwvPzD/ULcHzPoyiMvc/BChmU5mnf0j00pl9zoPULErm7nWRK8UtzJOq4OMNqogjA2vhvt3xoSw0GWRb7spVP1UDsgD4tG4eKGIsJ+Q1g0bmyUn5JjaoPMmeaOEUYVxf5CXaNdNbSweJswjf0RJyS6lDMOTXFsN852MuJl3vQxMC8QV3/z1yNx3ivF6sb02dukzd4fCfbZ2j6vm8YO22M67mGyiMtYjb5xCavCrmYrDQ+ztEhzDDuaWNs4722inO18Gmd5MX2uxyvuZGH2953uPB/j/cgXuJ4fM6UiWR4CTa9ZFBhe5B8xOLqKmieLiIg3a3O1ESi8s/kbb/kc3wJGoIYX6FD/LRtH/i42fWVqupcVPhvhZI18OeVwo4ztWTJ7mzBR3ww2YzDxnY9x2T3Rfhx4mzCmY3hiZN5wsnd1e2a1/Nq9TVZHKRcKhcKLA2UwKdxp8NV8eso68wvLp0KkCB9HluToJcaF/UyysjsaC8sylZWGgu0m5ad+ZnTorYjcpnA7GuHPNnoln08EdLb6cVPYtZP75tO0LGnZTpv1CdSLcOyw79u8AwDGY1pT+8EuzsmDbXmzjNqot/vFmNhGQrAEsD6DlsSRCsUYJ0Kv2HBkDw1TDaLRA40lq81e0UDCU5nQkakO8Ta5TvYwafrPYvhaEgc3U1EXb6f2uCypccS0sb10i+Mz6TS8TTjVnxDn5uJlm8Yy0MO2NZMl5exj/DPHxZNwIB+2ncRtYqzBsiHvKnzWqyQL956HiigJtzle01uel0baMi8YPJSmaIhojk7HsUIZG5M1Z8N8bVK4wTDR5C4YK1rIB3Hg77Kbr6N7m+jGqpCVyXTunsajT7J8TvibbQIrJ1xJPqmgwd4gPDyD1Ntkk41eOWFcYTK1jVo76Yk3/a9vFtvaeJRqHDmNLqG3yejnVfc24S9+0U/YowaUiINeKX1MG3WvkJAvuzJZmTN5NR9b2XhlIvM2KRxC3zT6nNBxeZ2FQuHeoAwmhTsNvro6o1ksb5ZRLnJVBNzwdzWAM8aUeITxUa+TdnUFP36p1rKuby9/qo2AEtNkU1JLiwaNyfiB9REYLrA8fOJFQ4qrisjyigpwziDiaLQx8AaWxbhM9fUN/tbGgUv+1kaI7G9SXJjGqQuoZZBD5imRZJvTIRNz39Zw8aKQVxGyykh7IPdqHBn04Uk68SPkngfxSva+NH5OG14vbU4/5k1yLG4vneiFsrV9XpdXbfu5aA2RBY1/Mvbh43hmeSGcsbxtMy++JUu7STi048PDQDU8gIBzRU52y/fI2maPECMxpHGb87HdYz58zuk4UH//ZubRdNmIT1m/RPOcGoYM15jkErNuOBZYDDXIqI6EM2KW+ZjsGHlJMG6rXBWX5CT5MM6bha2seZuQ1dVO3XASobwS78/EuX5lcSNCXmtpX8J4lcF3V4brOm+6x8nUBudLkQqFQuFFgDKYFO42XvJS8/hY4bx2sV9op/LGoJqcNZLspUWNY08bYaIh5O8bRI6EL9E8TAlqPDx7eCM94YZYaZM0CvEtxDcOabRBPkgja8/kN6a+yR9ZO428EQXGSNdbT/coY8bvtXavQ3FESNcI+GMmt6nL2fKQ1KXd8cFO+myCqi2FQuHV+qpx8PznuFGpKs0dG4OsL2oBo0Gh0Tbmo6oSUmZ1T7mhxXmadL2mt5CMS0vGa1K2IJg45GCvXRxz342gt90mGtp4APjFdDaq8E5ZogYdiGW7sWt6OJ7mXaZ3YFnPDt/TfSEg32H2FOIylrNih31v0BMtT8tZ1RnrTehJw1I/xCnbIX8ssBpT0nALcS0PSz7Na/fUZNyFnBb6lqSxz6dpWnD2SrGRBZ8Qfe3ByiBj0sDQk3lF4KCqxUOSYE+VdDqP5TkNqhGdG71FFl4kTV9qHv3x+aRd5oS16kawPJbPNP1rEofciseyOpdXGFsSRydSwxPmYyZ/Ag8+vEb4CKYfqBin9wRxkSnCWBAT0/gNXXmNgNFDl9wsjSB5uenq8hWOgLft2CmQF9ZZKBTuDcpgUrjb2Lpac9ZoQuQl62VaErFTMW9baDvRBnYMKG2Ki9pHJumz/fCBAcC3lbWb1Z+El+lY9+ZkO5PhVLQ2WWnU4Za6NDIjS0yDOnyap2nyKEFZPtCc3zPQDP10KkaSJjJ6BjdX2pxvNc/aKn1Eo0zc7Cu8GQhMG/FGg0byLLwhYV8Zz4kY5Yb24ZfRxH1O9vcuOQ1azGl9vU8KEegg/k1zuoQqXSE+1SmwX6JfLbotJ+lE75B+7R1Ze4xYutXnr2kcd9XG7Zu0wJqtJWWPsTXiTfoM473HkpRm0gF3XiZQbi9uY9LBdW0nbV0axv6uWOY25qXjOgz3IdwBE4/JXtYpTCHcZnobqdHE0GAD2KgMN0e/Kae97fUz9vXou9T8jNGj3aNen1ohlEgXn89gnsroXIdNXCe+NcIzP3O1udA+v2ZaHQ3chlHDTr+RNrxfXW9F9mUBEzBvxO0Umt5Id30SJuIMCTJ+4QdhN84/sfnNTuLGXLLjhyXPYIZwZfkoccYwcszLpFAoFF6cKINJ4W5jGA5U/DqnYKySDvu/e7TRfottZ/7mk8ZhVz6QB+tpDBsNmPZmBKMWtdQkMq3hWFnzMCGTrfSzKcinLPRaU40gL5EXKHlSCQKMzihDshphFjKytpe0zYsw0Uy7KoKJQSQ2uCesewp20axl1RQmb5Om50hAXC87n5rjbyzOFKGop0jTcW+TU9rHTHA3oLHlNBE1QxU6FcrZpTUXSPp5Bmd1KhoeNENB9QYS1rie3wwoQiumj1Ijzo+R1d0sgqTdHUbkaH3heTpbm9s+ayzBPKD4px4pSIuwDseamk5Hed9TNrZDy5GwZ4OJAQJo1Hv2+VnCZP3wRha4j38TG+6z0fqC9xCm2IewjEfHBHjHoF/CmF/nJPV5uaGpwzFbqNVt6spQF+Yc/CphPO5Tg3qRwDNF44hUPfrqT8hpOjYT3xm/T+m013Ky34l4kOycfsNiQIlHFSdxOkb4R+aeJ3NKaIm0ZfRemmcVRzSWyMDeIuJdGK7sGJqNs9vXTIx3WJagf4e5cYGIiDd2p4bdVp2FQuHeoAwmhbuN4GLYf6fPf5XVzHsRR35b2NYid3kCBW6R0nY0hOyK+VZprt8MMswkffv20/BeWwlNRPIpmIjMSOH2FxGjDlvctPzG5eX9vJrPSBA1wIwYbPfQd7v3aX4fE+gnXjmJw6t+ZeXkD7K+QFnRT0UTUp23CQ8RFr4Sm6JgWoOpFFmcVY9xTkwGpYWY/ck4jHpB01NwdDnOSD9BhZukY1nRK8KaHZmO2XKVzGiyindZRtq5R9Tp2/M2kThR5ExnWHug5HGzUeOAtwm7i/U0VMbLG4OTq/mFeZtk7IZDPRLnTsnRf1pavyN/xeIijWfybi7v4AotLLeZDB+4dCbOor17MCil4Qbv2sjfsD84oZvLJ6/nTM/ykfs8YvQLBacVJPrb440p9r41pSjlYWCSVXMv8rWxaaulDUYB9Oh8aL5mR+i5l1uZG9HkbUJEslksO48QMag098zwr6+GaSGe5CgwGI5GalhRmgJzkDi9beE+7f1OnLXBbRhN5FniVZnjmSscKezoLg+Tm4HvwbHCXEtyCoV7hTKYFO42tnnzNJMbZ2XhLC7NvzDY9KqCVrGjMTRi+7roNIJMS2Bf50ATab/1PE6gPRTe0X5cP2h4uOA9m5Y7yjiZmXuMqBoqY7Pcx+Uv9pcdxIlXHuOn8tto2B0nDHK2CwN9eKsRi7SocHdvk0b5QbiUCLYxnXbT/dGf2mPKvE1QWWdHa4ulQ9wq5PSJcTOfpJNu/JoK6+hh0nbKBsUdIW1PChwtFDsff0SlkHisB7+ST97sGDcSGgyc6hYyiM2eavR2zzdtHbzigLfJOTZ2Ln2W03niayuvkR02dzbvZNyA9qZlOpBpl32t2szqiRul8nxi0vyy2hu1e6Tw9Oe5Xt5vMZIAV2o7G70GLuqvaJTJ0sISnmFIbo3GVtfs501U3GM8Jyfn4ENwLYIXXCPvbYLvFvY7vFf4XKMN4Rz7xd+obujo/JzRa8Rxq7gg8UTurC/0QnHGEikbLcHIWLCzMHgt3rO/JyKzkEN5t4cJxLn2m23ymxk6GK6QxsLDVmWytEKhUHgRogwmhbuNcxb4e200Sdo3OQEE/MzzI1zbTtpUnhMPEwkPGa1lbaZaxSJfEG59GTSYDDFT3fibGS7GtZlfuu8r3g9EWc7VtVIHgLZpyU2a5j1TLBxUDr1fqSFIA3vtOfZiER2ryRB3FrAOmqaQ7W3il4kRKOSgYTRXY4pmxUdXwZNlCOjxu2x2Co56mpBXGZZHEy+IUnpkyON4XRi/QvrIYOijl4h6m4zG5KstfglX/QHqV5KYDrCs2eMjzbU3n86wuNRQlRhsMjYicS4txOlbFFjP5GGiaaa0MVR82GgS2Jmve+63RTFtmz2w/b1Nxj2yiN39TJqGp36kfWpAu1eop2U3euM4a+j76kXAumROzx4iZjwMsxjikZ6sNQ7U8LBu+PclepcMfxO4lxfPzC68aHGmAGkWvmnGmPlo4KabxeIxw30vM93rpBHxMIzoOI181E5Ep5XcAsaJhgYstvjVvfYjcvOEu7tb7SwRjY1ez3mZqDdJf2atJXkwLyGthUO4B0ty1l8gCoXCC0UZTAp3G0uDSfhhuMRwcslvSuLhgtXYV1mJDNrFpEUc0T54XpLj6iKaNJGYnoZ3tI2pzk3LNsxDKFAyeKIMdaKJdtDjZ+d2nz/+zUtypE3UUmi0jeFFWlA17AudiPpxMvh2vGohQ9Q8cSHPTbAvaporO1H0NiFQJMjpEbmRRJSPkB7kb60bi4yv8ae0r3Nrh7xMkiUFLhloa055I9MBpHkJRvJayBvTQlC7zWD8QGMJY9xQ8EK+HraHcX5JTsSYf2cMJ7y82Z+R+3boHW+ThO0csBVrODMc5CwoP0lnyh/DI5CGkS4i0rdf20Zvkzhb/P1q2c7qbzJshHgdi0Y0naQDr1ZmOHHjqPMvSaMwFs4oEzf+jPwk5yZi3BhVQDeBeei9N3QYz5q9TWYe1KwB9aiA3yTovyczYYauYtnb5ETsTrYRGnF7a+950rjZccKEfzQvyVE65EEKXwiGk+hu4zx7kjiMT5pLbqhvFgztYx3plefrXplCoVB4EaIMJoW7jV3JPhEcDhtNDuY74+Fi8hwqNqCUT5I7XJGOGBc9TCCPisKpAIjhRfvY3kL7sBNuCO4hPY6LJg+tiOc49UKR+uCvKd0LEV/34vDjNnmUwDjk3iZoJGGfRqZmmOjHIQ7Gak8oxXE5IEcut8JlIlvjL3F+vwvc22QkD/1gxHEgY8i8Pg6VJxonJzS9J/A22YIaYX/zSTpmJGm5l8nOF7Eof3cDhteItA/QmYNDPiGWG90W8vOlOdK4KNtMqHM6bxOMO+zkJs96z2iySNprY28pFAYyb5M9NoKvPCVx3mCBeW1xmXqYhHG6kbfJVG5ekoP0eG+T83+aOQ3bvWszhjl7/xtkDHXgZrvO6LFn5AFlPlzd/hzo/QH1xs2L+8vRJzymNJcBfzfw5SHtsyxtI5KNk1d7m1jl2QoUT1dCz4jXd3qyPUSuZsYRt4dJ3OBVWwkGk5QDtUBf03GcTgHU8T0Tt2rLDZAwnXBPNIyE40VbWYWJ8qU50bhTBpOLwLwR3/KeI7ddX6FQMJTBpHC3sePh0ZFI/kc1kiNKy0GDTZdlUOCHwBGvEyf1M+0tyTFFDrSEKOiEMnM40zZgQDa/JCcqUkzg1ZF4m8Q9Sebvr4b9JTHodRLjcK+UcddMSPdpQ6wUmXD58H372Q4E9kU10YZXVZ6RJXeTudnRjzQUHPBscHubNNFHWMtanCdmNj14ejKlSafaMBCoUSTtY7KMJ/yt9jYhacfbcfp0I3+STqIHTMrHpATm3U5tYNLUvrcJE56eY/nsOWHZwwYTIHLPcDIpW3n3FIf3GuTz3iaXeJhkS3LcaTsYJzeh30sjyoFwXJIze7ywtm2vNdASlt3IEh3lbBgOnC2CcYJxiBsTzoxJkpGIncWgzy8mmiwJM+fK46aw8Ef0NtEMzQbGGUMWP7srdy8MsVV7dm8TMJpQiM94aGavcbYbebTynmbeJq1RwzjgXo22wXflb+xrcorUuIdsP0xyPwwz0zKdyQiRxKU2FPYZoqcKvk9Nnl8wfIRlO8ulObUkp1AofAWgDCaFO43JkyFFIpIOoeCsXnImQztrsMmahrZv6HViX96DWAv5WlrHUJezNhw9gTa9jLrDpq8ERoopDow7zWXB03IM7gQdR9fOkhw0VnBIZ3Zh64P1D8dqz0AjffRtr+K2ELEjMDLRrDxAcvwiGLT1pptVyhdgUdwSb5PWxW98rtPpoM21Al4pBEIwO3l4ihuZU6PIkOtlu8QVdlb6K11uKKRZFgE/1J3pGLR+Mok6shwnVOBmw0gv0KDxvY+xK2QjZa9T+OK+V8/Z9JkQTm6c2mXa5eQYx7FMwu76a98Iss75e0uhDh5xPLWxCrv6HctLTKCLOJmb/dVGo4nGBqwfrjcKzWHZvySmR0ufcsEx9fO6Yn4i24yDpiuDEWTf20QbDe8kw7+R0uZiGjAjhvKyX4nwrcnbZLzYjYd5uPn45fsQeK6+x8DTuiEYjQi5t4mLE4bqlvDY2LVtYQoGJsYuzpiN8zZhmplGjEvy+G5HBtp8ksxnZGzKtMZYj2v/nZD5AldK6CzsY+Pb33Ok9jApFO4ZymBSuNs4uodJEt+GG+mu8nBOszhrsMnLtyjErT69rj7PpktygijMIR2E2ebyh7xLbQNpsT1MiND40OOWXh9NlGovZhNbW04hZRDukBasN/YFrwxqQtPm7Z4Dfdof640XLn39O2dcWDrTULp412ayl9jiF8FE/WjS4lBkerwJtuptwkaddG7/+GHTOZpvrAdNlleFXfQV8TY5acEZG6EBxf+tPEziaGS1tyHot9B29ExZxaV1Hmjb723SXN1525zE5XPhPFebPT4uqmggY2vpaxDi/bHqa/a1iks3fWXfxuz10SO5JekXhudNX0N/UMFnH8dknE3D8vozqTFG7MGavngWy2V4mK5lG9DT7+0mcNtAN/b3HDfz5ZoaUim+32D0UIbh5j6TGrmMstWvJekpcqDbY9vK51q+IezagywZY8f4OiNrxMPTQiZjvrdJyzZ9hSGQTV853fQ10oJMFuI4LH+J3iFZXJxkbXUTnoJa0ke/nZfJSGN/9UY1KHNmPhcKhcL9jDKYFO42Dnt4zOKSKYNnTpzYUy5u6OGStn2R7/p6DxOLzb0+3KfRc0YTLO/CrOEWyrZQh6kTRLSh+N61BVb1Yuv37NWO3g9rL0JlMpHpMFHkaCXJvgtidd5YEtUH33a2XKcl6T7fGPSVb7h2ZN3HdRkv5LZhNFF5lmxw5pN0mmtzX2nxs8vFMemSHN1PBeI27vHZaTl7XiZ7b5caK8JDj0aMdG+TMzrGVBnlxpJdD5fRuJ6UA5VIWK9slbrXMsEqiUPohfC11YdIXtzY6z+3fQlby40hBDM2sKYRz0TA184v0Tlr1JH2snYW9NgDjT3AmdFCuuOOIYyHgg+FndfpqsRinoaGlL22zoC16rysM0rMzNj830KlOwYLe994GELwWUF7o+vAwr2NYM3UcucMfExA4vm9TcKmr8OglHqZpJu+sgVJfcUs3f1oCQPNOrAwvLh8YR7GASSph/3YofEjW6Jz5Fo4BN424sNrI4/XWSgU7g3KYFK427hk09dFWpcxhjB5Xhs52P5lbXsHDbZUDldJT/cwCWI8ahyjXMMyGyVCURZGLWK0gUtyOBhBIG80nrg+Om8TEA1VMmZLH3Gr0238UpwkT6SxES1P0mmUtOPrzowi54wmDcZG9KsJC4NKSzNrIVdZV6OG4qR7m4iy1/Oqt8lo006JlC+noVZmuwfZHuOYTcg3I4R8naXc22RUsLVgTBF9YFrrPxtE5EY3tQ15berN3iYro0cGDun4mmRfsJumjbkghpOg6OkV69phH1MSr9IOepskWLG1S9u+1HBxyMNkiu9PRp9z0mZW18TaiGiLS4Ky5TireGFZ4/nFnZr27v3M8pzNaE44EUMYEU7SifEU41GZB+4V6ZCJylleBv6g7clFeDjyMqZps9hQtMENskbxKHGeW9i98E4t9zBJG/NItoQBvjL4qfBMZV79z5ZHBoPJ1FjWeDBSx07I3ihE3hiRueLE+s94LOokbpiHIK7ZoJTB5J6A78Gxwrd+THGhUFCUwaRwt3HDJTExTUK7X2Wz6Iss9nm9ads3WpLT/1HxaPKUoOG9AeVSb5OddgVu09dRF2gg8bQaFzfy4gkJqscOQ0kDzUINGajhZFfXh2BQgbSW0Yr3WEcyhudwzgPFPa+WCaszdnPseIm0sSGsxY9UJpr2NpFcyZKcSQlK6PbCPYFiYcoNU/A2IVMjTpnSkLyLUdS3rOtx0LjRb0lBelO3/RDRwo3qURzyNHwG49rEiCD3pHbDGHfOvnGMqxlhl/K1Pbl615jjG0/b3mVtwNbcG7Pa+PVMXtKxheft2WUom9WHNLVlnMa7zZ73FOMF/TGuRbqAszlje/NlnLFl3Ovr7w1DGZebOTd4TCzzknvXY/978wzxyWRyvMNKZTF2Ws7IMehb720yEYuEpTzDLDH4sp7b2+SkcWpcaI32N33F/mNfwT8Hxt8eLhhpYr9WvzHutrmLG4UWn5PkhftGxjzdtQwmhULhKwdlMCncbVxqMDmjEfXf+jOu7Be1v9v4nOo+L4tABFeiLpA6jWbWQNCbYRJtOVHgxZOY0Q04tmvhuOlrZlg4Fod7m2xDWR/LcIipi9f750k0qG4SydgH0JATVQa/fCkaS3z5ox4mZ/OigD/RjH2cLBaS0O/hE6idVjDu9GuzfJHd8TbJWwnqCtIF06IJpRziRizPG8Jih7ehj6ARZfUBeqVuJSrDlH+5t8lOOytwkscUN19vGwnNHo7q1KirpEpb0sZexFx+9vjYwx5bu7htXnubHFmSM7Egjbd577lKj7c5GPchIWNtIRw3nfWbrQpaQlOb6xOvAzYOwgzcjPteG6v+OcNIWLoyj/HsV2ekx7LZrG6QL+detLimXI9lnvuBt25Ird5olHO5wdPUK1FjeijbEJakrfEA9tZsHkAjMI5oXWA0sYdOjU7EjPuaiDEDxm7a9HUQJqwhrCfythBvGJM4Y7aQ2QZip6tHjDch3f04gMEGLb9y1a6UweQiTB/GbqnOQqFwT1AGk8LdxqE9TM5K9PvJu1VfqNqc0zYE0S9/8jxZL8npoWT/Ej6QDvLgXv22ZCbWzaGMj+s646T2uNOOTI/tbXgPkKQddyUylaBLzqmRphFNz04FQNayfhnTMawMJ3kcniwwjEYJ/HlOeyq13yARV8If3dtkPmrYwlG9meNIVRki0S/mvU3sRIUeH71N5O90oXwnR/dO8UAnTkun1Jk+FnpkaDGxhTjMOylnBN4moEtAu0LNEcPGOVYy18Da9rnqN171atR0pvycLm3vG07SJTmhPv82s1cgs3jH9wgft2dT0P6xtrG8vQ8Wz+QNHWBBY38/0x/i9FXFt63tp0e6OdY9LaRMueiUPuZ1ntcbTYi8zqwbluqoIa+EYXHzx44sl3jlaw34neQWfR7pZDNOLlmndG228yh9WF6dTjRRfjq2wVeitwkRjY1gZz7vG20xjkmP97XRRsMFUDkMGDZeHB5CAtd8YGxxSY8bWKl/VLJrOLmQmRcKhcJ9hDKY3Id473vfSz/xEz9BzzzzDL3hDW+gv/SX/hK9+c1vXuZ/8skn6a/8lb9Cn/jEJ+hVr3oV/Uf/0X9E7373u+nlL3/5l5DqG+Kmxwqf0zYU5ySsG3wBOCo47BlNbnRKDuYR40VIJ3beJr4JlLjDkpxglDjiXRINJxo3hGCsXmTCbA8TFcB5Zaww4xAK6ytvk7hEBzScUUdC95m4PS8T722CNYT+Z0gkfCYbPzxCuFffVJkzb5Mxdg3ysSNHwxldpuQ4M4wZXRiSJSvsbWIVzD3fe73bFLAqpvFqaRCW6PSC06sZDR5J8qoI6iqEcdqupUX94giLOMJF0jyiP+1UcI6truo9n0dmp73fyFpS48SBpTeH87p+s2vb1WPkhvpaEh/jAjeA+b3ax8TSLZzHxfqBi8C7FLkkEemmsUfguZmnxxT1NqUbrWFvE9Xtj52LM6WO93RVGnkctjeVQ6a21/nALzhLi2GCtvT35kRMGzkvjEbrJTlaZxybsazIMT18gcTAMSoAvqY/ZbseHgk901gtaEa6xdPFebk0fy0cQu1hUijcXyiDyX2Gn/3Zn6UnnniC3ve+99Gjjz5KTz75JD3++OP00Y9+lF796ldP+f/H//F/pLe//e30/ve/n77pm76J/sW/+Bf03d/93cTM9J73vOfL0IMLcdNNXy/1DFmgHTJ+HNIk1m2gS7l4P+yckuNUBvZ5MiOFSVQgomd7m+CSn71NX0V4JF8uWyaUxvGmAlgTP/1ooAm9mGVYMRB4FSicK6GyqSj1Gg/ypztieHHdjWucpM9qEBGrcjV7OuwJm1GytXvvX0LkvU06beL5wUOZxSU6ppDEVtAIQ24w5zEecSbLk5pWxsCvvE1Wxwrvdv9MnknPkbYP7ifjquXzekCWZww9EaHhBNTBHQ+PPQrPen7EiEWXV/Xw8uY8W+MQQqONeZisDB5CbBYfuF7aprFOGlXp8hvIdGjT17hsJ6N5p5wpxAs69WovjOdmEGafd07P4qVOpDsYOQjTR3wwkFi/cYlIuluT/UaozUbe82SeBz6hdOsyH3iM4+UCc3AfjbFcptM52oF81paftarzh8msUXEPE9zbZPz2obFGvU0aeptsi1NyMLhIb+E+5okGjrEkyOZChuZ/5KS31ukRLZMNuCfSg8wUDCcsDLA8TAqFwosYZTC5z/Ce97yHvv/7v5++53u+h4iI3ve+99EHP/hBev/7309vf/vbp/z/8B/+Q/p3/91/l/7oH/2jRET0tV/7tfQf/8f/Mf2jf/SPvqR03xgvZNPX2zCa3MRgsqw6r6sr8ODKzjwbPlAyhTgnXqKE7WgH0TnWO3mbiGaz+bKhXIybjSdIVx5nSxpQeAt07NSRe4qMKwNNgb6pDv3baX8vjon8aRBYt28/epuYYkA59iR8IvXk0CrRi4RoKHbz3ibxa+6NgMqDPEAXJ4Yb9koGWFC2hSVkZdpwhhA3voQaGLQP90yE3iahirnNFl4NzBP0m5hnerZttD3m38WGD0x7AWWP1DGxmnN18zrdGU1Cu+6NGQ9oWiaiZe3hSrx/4+yF8uUCfe6xN1dPFjcbLjwNGKf1J3ubnH8oOHHXxhaliSGs+YCOFvuxRsbhVjR2W8LC4DLGzhsH1zXa+za/7S4N+ceI68t0uiEA38FpH59z6/eWiUxuDxO1io54GsdAwyD397rvbdJOO6ZgJmUK7CP9b2J8F0bb8GOu9y0YsjxaqC7MNTWUaC+gQ4FfqtFG8jT74FMeJpdh2y7cI+9gnYVC4Z6gDCb3Eb7whS/QRz7yEXrHO96hcdu20WOPPUZPP/10Wuabvumb6L//7/97+rmf+zl685vfTP/7//6/09/+23+b/tgf+2PLdj7/+c/T5z//eb1/7rnnbq8Tl+LsHibntIdzIuOeWkZ0fEnODbSLLFYkw9TDZBLjQzykp54mWXmG5das2Zr7XEvTcpvJqySL2zXYyN4mIHgxpd4eLpwaPM4ZRJAGH2/GhJ40qwIUymM8O9kVNxltIZ+ndbTMXfQnIli/niGR8EcU+nt0gb0TYmYTMVqQKhcsRgxrfa4/xqmNA+JdXO+DGAMsztrv8rf1uxHR6cz7gOOLRghJRWPRguyp4m7QkJFY9jilA9vJVkCouhHp0GkejhJd4Gz6ObZ3JnEvnZc3O3XzIh11L9o7Vjgqh0hn3DtEEI0ZebzMUyK+0bHCu3ljfTTeu83ekU7PzEkcN2AIuz6fSx9hHQNkZg3Gps11RoPHUHpzbgV5R760DhuY0WICsDM4/gMvhm76KoYF4D084rrB3DhAN85JuVFrmKxKehYfyJz2MJG5J48WCjF3WnRvk1RsAGPHqMiRIg3xXMbli5u8jnvbVyUiMivk4RwI4FAvvMASwAYatL1rtCkUCoX7G2UwuY/w6U9/mq6vr+mhhx5y8Q899BD9wi/8Qlrmj/7RP0qf/vSn6Q/8gT9ArTX64he/SD/4gz9If/JP/sllO+9+97vpx37sx26V9puibVe0ELsGDqge57SHVdtE5zWTc/Wc0y6WbXMQcOQCYjxDXs2aiP+ph0egAwwZJzpvMFnuT7KMO1OXyHCicWdqzMoYMrUB6SC8a/tQrYn7jRolRqq03v04r0pgnIev5xTahOp3pj8mnw13mZ7EvKIeJulSlTnuXK4G9cc4JBi9TbYd88EqJRo5pjRUZJJyqki1RttqbiRlpjjUO7J8gQ4zJlHqNIQ4wNVeQPm2y9Z4eXOmbj6T3sZbkeQ7vvQGjQNz+agwCivp432ieEpOz7OOO99226dzPPhsxUIL+SmEJ2Wa13llUlm2pkYWO1/mHJq7uvc4ll/ypVjHEZ52jP9Y3DoN989ZGjTP8NSUTg2PBwneJtqY+03ecu6J9Sy5014ZyKOGE7h3xnLopPNMIcoHIDArVz6J0/im1zKZHEc/Ret2x+u26ysUCoYymLzI8eEPf5j+3J/7c/SX//JfpkcffZQ+9rGP0X/2n/1n9OM//uP0zne+My3zjne8g5544gm9f+655+iRRx75UpHs0K5eQuddPc/8SNxwaU5zn4mOSlhHqj4gusLGCJbbDABKUWog6f80V2aVntTrPFEkPqZBntuKYzJhFL8ckiwrEXdrjenpSbxLB+OAiqls4VTA1FJZvZTHqQs3isOWHmVvvPZhODmN356fPyJX9wWANf89ftSoUwfmiavTxtU/DlmFL0K6CcCm/J+JC+NHjVKvIR2Ljai1U1jS5KF1LhSdc0L68s2F17t/IIV5TkSx2owNZWS7fKpAeYiecVS/OMpGjlQ3n5CzT8ittc1EesoR8BvlAOyyWhml0Yohh4KqfLxnd6Hfnrfmhpys7szbJfBpdtxX2acYTVIe0mB/o5lbBS4S43zeibvoHJSleBRqCnyxxRZ8fUhLzmctf+OrfieeCDIucppOs7Gy49Fhjydu2lLPNC9HEo8SyzvSGN671ogHfzWPu6ZtKI3jGbh8UH+WT3gu5lP+OnldNHfxPJmsPng2Rk+oQ4Ls42K/0Wsotj2TlvCGlaFlVU/hGPgeLMm57WOKC4WCogwm9xFe9apX0dXVFT377LMu/tlnn6WHH344LfPOd76T/tgf+2P0fd/3fURE9A3f8A30/PPP0w/8wA/Qn/pTf4q2hGE/8MAD9MADD9x+B26AE1+BdnFUvzgizZ+vqU0/Pl86o4l6RwzjwVQOJfkRYcpbpnmMepNyU55Yb2jvpgaSo14pYjSxL6QgjDcQ0tnLi07tQ00AgrNSgM1nCkekwMchbF19rPgCyGkLoJIQsa7Pl66pezqTGkdwi0UOxpS5t8m4k5iIxoaNbMqLfrWlMSOkm8w+Dh5Aa/aMVnH6PIJRKIMmT+O7/2Vz+TjCELTWnOFmKqdjvV9/etxw0rjaBo8xtUO870ieaDDpd7OH07m6DnupuDJNxxGNTe5RKItrPt2xqnyZjiqGU9yKPlHyk/1TiAmX4WRxnt69OHhXwVOOce7G9x74kVs6OHVk8KsG4QUyrubpaLrnkM+fV4bsbi/vieUEGHlCYoyCfUIwTX/zgb+M/NhPl+Za94YI2SRWPIdaC8YQasrvzOgxaIF7/3scrmhI0d+3liy3bMlg4bKomX7dFwfriHVKKFoeWyNusPgx/Y3KabI6sjZXcYVCofDiQxlM7iO87GUvoze96U301FNP0Vvf+lYiIjqdTvTUU0/RD/3QD6Vl/vW//teTUeTqqu8L0lIJ7G5BjRYqUx41nPDu7U7k3PacclnbN8ijQo8KaidQAlAaVxWBvEaxZ5Do/zj1gDE9VUv0vu+15+v1cZmqkufhdsbQA/2eBPMhAKugDwpANHoQLepIYfX1XPFbrld00Jgihi5O3JOdchTi/FKERkQn9VbRNuUdGOOmYfWuwXhRCGCHEzfWSIuNivt63Lq3iSgvbgxYpqb3LsEaxfUdRPnhOQVxcSq3fcPJKmniZZBn/zmH22G4mT1mLE9s6qgakZkpgq4805RTejZ+L232MBEaGmUKd/Z6LutPjAc+fXCqod+b4RL+HQrqxLlW8cb9fDOhn9k+IjTK62ko+t7M9Vvf8F21OmbMca7fbLxkUEFowpB9nYJZxT8hNcA0GMHm8kWe1+uzMhzq7FWKKSJ4jQS3KNwCY545fuiaGFdbqJvxdx0MIcE4IhV6I8lIVx4VnlnIoyPNJ60bvU0a2TvAyjOt77hHCrbbicd4P0atJYYTnJ+NrF84iFiGyfd9mgiWz7dD1Oiq0y/eJqFtP8ehfhi7hNi57cJh1LHChcL9hTKY3Gd44okn6G1vext94zd+I735zW+mJ598kp5//nk9Nee7vuu76Gu+5mvo3e9+NxERffu3fzu95z3vod/3+36fLsl55zvfSd/+7d+uhpO7jMloMQlGezgn4e/XsDaY3EREuPCHzHk79HseAvRUZ1C02iJeLA+TyrHSiABY5sTih4B0BtXFKetGT/xea19bF+3zBic+oKA96ALZdFYksF4wTDQv6O9BlZdJqTngbXLDOKF5Y1uqYt4juPzHaFM6Nd5K2TDgV2qrEcd/qIwje/A2aUEtG8Oi4wHGhMw4lCF9JeHI0Azpu8d0eZnVWzz6IerkTYwmR40oc53nOds5NnZu1PP5hjreYj8dl2dR97l0aLvzD6ITfPknbFnf0XY+PrzP6RKbiXdm5mAa+m5CD8TvxkE/kSu71rXf8L4588R4oxuZwu4oj3SDYRTfUSjZXByq3pOpRuuf28L+RMPxkd9FBm8TNGCQ/q7LiPT50XTjaIK89q7gnPJLcnyr0SDDxHzqBnuSH5HWDc64ieqYEGKA681avNTp2/QGExmc9caoTQdvon3wXZ8bjBto1JAiq3dw0OA8ciZ2E40xe4aVEFcoFAovYpTB5D7Dd3zHd9CnPvUpete73kXPPPMMvfGNb6QPfehDuhHsJz7xCedR8qM/+qPEzPSjP/qj9MlPfpJ++2//7fTt3/7t9F/+l//ll6sLF+HEs1GHYXMzogvViwuMJnsGE81zNsextuaswWBCBO692CqDdJsYTVy7x9Kz5TWYx+Q0azt6ieRiYSoqBiR5mInbOKZR16nLv7LMI7iVQ/xx4T6nxsuU+94mWo6TuCyf1AdCMcOgbtT7bd4msQ+gWE0KDyhSU9e8YuVj5V+vvp0Y1DKhZ0wB3eyVoZ5GOEVGOR+3Ut6bKi2RwplejR/HjMZezjWs4V8l9HZhl+4VnKSOMMGO8IkWFME9ql+I0WTlYRKooZXx5oW0rUsiIJ8sl3Bxkom8ApiZVzNDRZaem2YTo0lszxlesqVAYTyjornAxo1OYhBlb7YgCKMSn3KR4QUROVtzd3FKAp+EOuVtz/YoUUNAayHn/EzDYLhRYmI6yZg26BPwajsmV/omL5zkDc0M5pOZadf93kZ+8zbJluWYpw8YGuSK+Vx68haMARajy8SpFgYQMVbPHc7vM7uxLWnC9sHw46o73lZ+XzgE3ujW9xypPUwKhXsGbvfDuozClxXPPfccvfKVr6TPfvaz9OCDD35J2/7U/+f/ncabUqmq+w72JPp1ydMFPz4LEek8LWfzSf+CQu02RoX80WjCkCb5OKgJYXlNXG4zq/KgJjjX45XhJYtb5wHqSfs/BFrbcC+jx8YgoxnpRU8XTec5rtOY1DMUDV8XXRyn4Tbndv12yjtRnPXNCE36TiRHClOSFtXInE4eKjTWGfI6AwHncU2LYpNrtNxoErIsI3Z/3BZVTqpMI7ch7F69R/Y42YM+kwMDtGe4eOEGE6ToMqPJ/iNt4c7PtdWGsERmbFnFn8sb+723REdZp5Lj2zAWNPnMBb48G1Wy90u8TSI97m3nVWk0IMzpfiyWnJHk7fYcBtOg3oZxq7xREU+eN4G9IfY5aYMIjQexnRWHPdZvqUPT4N7kDGVI2u7ksXFUGjiwISzCG0128i6anozTzttk1LEynGTttHA/8NWPfmtOwD3Gl1NOvQRC5yf//A/Tg6+43b0Cn/v1z9PX/D+evPNjUCjcjygPk8KdRps8TMIPdPA2WWNPgMnTjniYIE7tfEuHd3gEg0n/8GQCWzeEtLFEok0GD7uEb6rs8yRqBrRocaa7rZT1EW7sjRW67mXnu2/zdDVI9eHxVZCJ8OvYnK//Y67uTVPM68SXi5S1Key9LbC0z3vJkptVeq4QEYnCF5fimGLZhodFOibchvv5DHzavT/jX8a+wVdnqYfHvBRDDUMdIs+zrzd+Bz/7NrBVdnzJTaqGHMaUf9BwWnivOB0ikhj1mpxEyG7v71FvkzNVzvkuNJjszV+r82DboeZpWRvTGOfmWVksnxkqJLw0bmC+fSNC75PUw5Au/yy4JLIzyJMtG5T3pFF/t9XLhm08jIKmG8YiB9aQGpKTdLesD8LOK8+4ZCzveWFzKehvl73PceZk/h/mbSL1jDoHP7N25xHUtPG7gC3F3w8MIwfX3vCJxGjQvVgajO3I5bx9mrbUp4Q8g2MGEzsNOBgt3Esyz0eXpnlxDp9/CiPjmIMnSzp7/HAwzCyMJoUD2Lj/3XadhULhnqAMJoU7jT0vDxRSiYiOeZtY6XO41GAiAsR6BwDJd6Btp4bH2GYKMonAhUJLomJEw0VcdsMYzkR+LI9qPIQha0v6mMfFmFn4ViVCdYIhqKd7m5hSjxs5RvE/F+6jImJyOB7VmCn7cWmOUTPH+d6e2evD645EJMoVmnKIVExO9zbJWlp7mEgYN+l1LcEpH2KM8RvULuLIz1KvCB3AmZN0GuTDai8T51G98m1tYUNYrRsnzKK94zTAUxIlWzTrAzzrCO/b9zA5lxbn72V0IAuY1NuhHF+Nvp5Cc9H4kBkker6WxPm5tva3grC+8CdTQjPvkUUc0jwbOUZY7ALU+RXT/t4muPmy2k8krMpr4HG6DCoYqJoZTTDd//bowiBIE4YYzTrZ7Fn9lmD/evjE8lsGRhL2fSZqsERl5i6u98nyr9gTcr266pTwiXTpj4xfi237dinkOyqFEBH0248ODE7oG7Y93++9wSm/5bEh7NRfqNE93B0jSqFQKLxIUQaTwp3G7GFCZF92ggChwlXHC1UvLjaYQJWtnRObzrWdqZoW15XiMQ64twmbgmwXVJN5Plo43bgVaAn0zvuVzOH8RJw5LirpmeglArV8cVRBfjzvpbfJGLJJkA8GEGtnfiYquOuXxcu8TdKv6K7+OW5ybdd+Siui9M4n6RCpLqM90jEJm77O/fYmla6E9cEKKguZQUa+is8btWZxEZd5O9gsO7e3CbGfC8eBikeiAo7KzNvEvG0wqxvmqH8coMEpl/o82/Q+ZzjX3+0sNeeMJmvOdn7T11gX9tO/LVdMbo+PwNmIaLFZ7CI+zhb/3mEZU6U9rcICAkeEuJh/ps3nwPdbWjzkbSK1Th5zq5CHmgPgEVzibRKa9/1ZjgLez78dTPaBxDaYbu4UMBpHELfgaSON+98ApCY+S975d3PeJvq7Kifp8BzfXBst/TiwB6U5W3YDE81NNU7y+gJT+iQzQZFed/c2cXzZ8R6I1MrKYHITMG/Et7znyG3XVygUDGUwKdxpXHa0L0pv8YvMCuscNzKYYLXtnLfJXtuZwUSuyTeyIbgsT9IhctqMfcXmOe8NPExiXneSjkiysRxjnRjnoYo7odFCgme8TcJXVFdnbCeZUkxkwrkaa8xoFBUJr9Qc9zBZf/2O+wRYmGg+SUdKpEcBk39aWrNTFL26ocu/gJK4IaR4XeCGnupKH+NAED/2fgao9rs2xji1t/k4V9VCqckMLbH8lXi7jL1NWixAMJ94TcPRttWToMmY3txocs5Idczr52Yn6fh+2XyT2e3fFvE2GV4Xk7rdC673/gh5JyPKnvruqbTGQt2ZYSY14sxjyhQpsPfskLeJvEGDNbk4COUeINEkktDg+JX3NsksFL6uaQAm3qcGG8L+dZzkdDTH1UjfeyZ8B8BQAb33JoSZv+LG3DOfHl4X45QeomxDWAwHOi40mCiPaP2fOFLujiVjnmdHqjgb13gj520y5rzPtm67UCgUXqwog0nhTiM7JQdFL/9lJwjHh4wmL9Rgcl47OO9tkrUdhUEMe9dgJ1aCt4nJbJnRZB13yEiiSmAsa+HxrUq/Di+NJ9Nmqx6pIC6akriFDyEy9TZpSPcwOCX73mQybv7cWDRXUF9GvUNQTw0ydOkeJ/AkhHS71VZsb5Mg/DMpPfZlFmsw8460N3mYDG3MTomILvpieABvk/GPitxNHrl91ZbxudTDJI5Pr39+QlorJ3E7Mb0IjgdWwzEjNbf0yKvhk9HjQNuRs03xTIe8TfZ4zZFNX48bTebWjhtMjHO5XouCCuN5FbwupifCUckkil4fMT3jcjbMsWxUikfWlrTtlOi8bc2GtDZ2KY3Oe5vQgVC+xwiMvdldoMy83xD2BQ0TBO//xPPCNPL8xTNqv2DQe5sYRTzeAeljg3rE2IO9xv76sBkfw2+ohHn0irtxULxe7DSuprT4N/8GBhPsuNphst/+8/fJm+BilzITvAP6EYJsLCxbCwXLYHIj1B4mhcJ9hfLfKtxpNN7Sv5O7v0rir6ht/e805cc/XrZx7I+1/uXfltF85o/iH89xIy9pH5iIN2rb1vuu5Wj8sRpvCOM0jKJ2/OvxKoaOL+u8SoewtCHFXHvMZ9vuAjGE8Y8hTsZD5bkGFIz7ZoLx3MM4EoPythipNo2uXpkatHM+zv5OIa/FdcVshCGfdHjjk6ZvdKJNw22k4ew5wcw6Qbsn/dsgbmsQhnxSl2uT+5dZbXPc96/mkgb3N/2TdrYTbXyibdS7caMrCF/+d1rUc5ryXnGjl2wnumKhAcKbxc1/edtXizSMv9r634omGdub978bW8/nE5nf03DkmfFogzmheYvt93autkYv2ca7PPKTziN55wnm2szBSPMtlLzm0/CdlfrtZvSBzLDAg1Ud5i0YZnuXkWvKuIie6r//DwpbEjdxiLl1zd8snZJ0CvWR1mvXrJcTH5+eCPJwcu3IL8yJ7fdPuRLPXMzfb3RiphPz+I3GsPzuGzez31cMb9QXsL2EGoGMAb+9+Du8//t9wd+o4xpklxNfwd9Gp+1qyD1CV7y/ocwE4dP2Em3T1b3bdvaBq5CBt+2e/N0E733ve+lrv/Zr6eUvfzk9+uij9HM/93PLvB/4wAeImd3fy1/+cpentUbvete76DWveQ294hWvoMcee4z+t//tf7sRbYXCXUF5mBTuNPb2MFnFT1/SxtcnH3+k7UuOFT5T80g+dJKOy9GSa/hWBF+43Jcy8DaZlt4whPUi4ZAXwlF1wP1QWsjfsvh4cs7ilBwEfuHM3MJtr4wuZh/Z2yTfxHV+Kn60RYy39nFz2ZgOWyQC5eeX6cS+u9GxD5tKFebJvU0IxmkeVSsfVUXSZStCt4WJxMPGejfGsPm9TexZ9zmJ82WpuF4K+VLcsDeG4+9+fO/kLotnF2zNP8smWdpccg/rfPNcsZN08t5lsZcfK3wEs7dJ1rZ/JnEuWIvyFd/miM2UK4YTi/ylhxtRthFr9GaaZ/uFaY2IWP3oeoR7FFBHMhiaFYuxpfRuWHjlbdJLrDdxFc+V6Xdx8EodX2Mvg9fYyBu/mt+AFq4U4mKfLRzfn5yrMaG3iaQ3kmOWdZxg7JCamc8kOzJxFkvwL+u/tvn0+H1J9jC5TNJYw04QmkeyuQHG+Zq1faHMZJWNsTj5Igz5bomFF748+Nmf/Vl64okn6H3vex89+uij9OSTT9Ljjz9OH/3oR+nVr351WubBBx+kj370o3ofl7f+V//Vf0X/zX/z39Bf/at/lV7/+tfTO9/5Tnr88cfpn//zfz4ZVwqF+wVlMCncaaxOyTFVzccJWowXff2Ck3ROFztgHahVDCdnc2cKWjCIiFjJTYXdzGjSs7SgRIB4dOmmr2T57bACDmkEVEC9uPwmGm0WI4JNtJYYLUj27MC8Q8zf2dtE54TWNEPjG43TYbxAztTSzWVzkfXYRrD4bTeMns3l5vPF9vreJkQmPi/GyfXbU9NlYlTC/L+SZrWM8YFNGpv0uNGoZ9RJosQmA7WLc9J5viFsWjJtO6qDUO9UONSoPEaUvzGWMM+OYc+INm8I29vcm3mRzMs0nIvys3G23GDiZxsqbNNM5zYU5Ghi9HubIP9x/I2bi4sGFE+X0YJsCc0EnncGEwJ6uYBhJlVCXS1GagspTIM3N1DbRzurvU28rivLVXY4m1hf3NMY6a1BG2g6yHjGzDvO/Y7gCOlvQxgHP6vlJB3rnfLgUdbkgsm0qP9yErYllpHL+X+1bsZyGLaaL8FZrsak88BhxE95E1wsMzn67CQd7aM+nLKY3AjijnbbdV6I97znPfT93//99D3f8z1ERPS+972PPvjBD9L73/9+evvb375ohunhhx9O01pr9OSTT9KP/uiP0h/6Q3+IiIh+5md+hh566CH6W3/rb9F3fud3XkxjoXAXUAaTwp3GJZu+OgFoFT8EjCMizeWbvl72Y5XvABDrQuXZttvsObxw6PeWQPWC1NskboU3U+B/xFvIkyk2orBkgm6i7rvm5rXe82igTDidbqP2gy7wmuIQvE1YegNGlmmqLOYOKMKOOoan0kBlSPY2QW8Tr27tbfqajjbhnMielKSbtwmMY6ICoiLYQmzqWYKzbSisUZ3CmdjIvmS3kR9H5hKcfcNYMpmXjSB/uutWZhMJ1oepvh35Wu3H61jbuSdLVP+i0YFEvz2EyzxMLn9G4nWRv8t7Y+7V7fP/mrfJbHTMwudU+Nzca3l28sNpNZ0FrGaHb52TML4Zyk+aV97F20QKoW8EhVBuxIi/J8gFIK6/5IQm09nbJLaEiKOf3+cjTeSfQqf4pONs7ymGsrcXTff6+8n4W9rLYJ/stybEEw3D/cl4/Rgn69llssChN8wex6IkMPqDrRySmbB9XYZKqbGmcH/hC1/4An3kIx+hd7zjHRq3bRs99thj9PTTTy/Lfe5zn6Pf+Tt/J51OJ/r9v//305/7c3+O/q1/698iIqKPf/zj9Mwzz9Bjjz2m+V/5ylfSo48+Sk8//XQZTAr3LcpgUrjTOL4kx4tuu/Hqvh9FuNj2TdaDXiYoEa28TTKDiVyHeIdLcRiMIyJAohJPHLxNQltsbe6pDbNgy0DSMaOJJDRm4iaeJutx84oFnpAgykQwhMR4FTQb4VfneRblT8EUjmD2aM15nThjTYhfNZN5mLisK6MOY1ownIQyG8WTdDiM6R41jSgYObyyRaogolqBX51FLWk6B639i5XxC8B8ovi59dLWprE4z11G4z1eThCa59bKYLKmJPc6gXhoEyImXGow6TXd5DnNnG31RduHYY4slubgv+4kHWiOF9c8DO8V3E/GEYZwTJNyCQ0rbqO8xRm8EvMQEyw7HG9q6HfkbMCplILULKJHpmM+oG6cbpVQ5e58f/eR52Zhq3qf/Z4IJ4veJrL59Ll2/XgEDq+eQvGzg/8E0fndRr33J6vRGOK5IUhoO1amNwG5WWPhPi2Vxh3javZ+WPutjCYvBBsT3XDPkd06iei5555z0Q888AA98MADU/ZPf/rTdH19TQ899JCLf+ihh+gXfuEX0ia+/uu/nt7//vfT7/29v5c++9nP0p//83+evumbvon+2T/7Z/S6172OnnnmGa0j1ilphcL9iDKYFO408iU5K+Esqug+boofP/aZOkN0U4NJTtk5RJEDvzRPQrv2P3qYYLgNpXp2Uda9TXglos0tTioGz08B8zZN2v9O278W8vKJSqwdShB9NSTDFHRKiNWB3ibn0RUKGv0NxhrNMAV1nxSLN5obuLqf9TBRpQBHCI0mltcE6OaMKUTsNo30ao9XoiYPE1BCJg8TJrLvueCJoi7+oJSReZt0pV6e3c2E7eMKvP+GfFwlcSOdpmFMajQZDca9TRYVA70rKg8YTaTNHZqOnEx0ZHyPPYNIw6rMTKvOPG7dsDryrRa0mbcJ+bpkqrmo1e9Ckua8RfKwux9MA80JGdbGjDllz3i08jYJnMqFPVfKTCmeUjNsn/M2WWPfzBTm72I/kxboPDHnxw8vno3115mMrIwa3VbzbDYZifEK+faNT8k5wqHG71q2r8m6iluSmTBSjh++aVcL9wyPPPKIu//Tf/pP05/5M3/mVup+y1veQm95y1v0/pu+6Zvod//u303/7X/739KP//iP30obhcJdRBlMCncal3qYnMuDwmMPjLjEcHLcYJIpUPt5ztWGyq1d2cc5F+B+7YoFCMPqUdG88Kfrrk9i/9D6vbB3gWM65+nRHMCQR9fucxx9w0qxQMXB7oK3iSgRTew34kItEVGt8FgpHCg0213wNpHHxL6cDZLsDxJ769v3cdFAMhtS9MsfBYMK4zyYdiuhqHj78SVw1oiu7+jFhMqctWR199gTX2LAmHHYYII0t9Di0nAwq5a+shbyooFialjnmXmbrLEwu8zzZ2qtudCs5MD7eNBIdbtGE6EhG1d8l8Z9C2niFQj5xOPC4s175gSDsFIEs3fHIrL3CihaGl+ier/mLxM/GRE+t1fPxRg6qe2JtwlyJwrhyVxgDNJxAeBmw3CMJutoOgC62TjE3OtEfW8hjeNT4XlOSAneui9Ty3rtGkjD89IbP2q6tBM4HYaF0zV93ntGz3M4WI5JT5xDHt/T9jjIXtxBmUnbkZ+xYTg5RnlBcA/3MPmlX/olevDBBzU68y4hInrVq15FV1dX9Oyzz7r4Z599drlHScRLX/pS+n2/7/fRxz72MSIiLffss8/Sa17zGlfnG9/4xsNdKRTuGspgUrjT2NvDJFcuTHXZU0RBDewRTLrERUWlwz9mKyrO58swUxqvUXGza/euljEIxgONNSG/jS90K0Utp900kfmroc/bhXwr5wRgyGvy8kwFEy03Vo17XWq6Kh5D5JclI7C3SdwQNnMtHjoCUUuUj/AYNF0VlkGB7LEAe5voE5mWR+EojRGCjk4j6NKgDMxrc9/ucRs1ikoBti5XG2sOaTEfmEYagYeJUNJcPsYReAEbBl4uah5VZLwSnincLYzdrJ4l85hXuWO5bB5mzyprp03xkSNuC+6UURORU368PmSpXm31HDlT8o1vrTwurN9X3OgU5m02e+1+nu2+ZPP3Dfvi53t8Kqs57vpm9orAi2Mfo1GSKJ6kc9LesDW017q50FFs3fb5kLqC0WA6SWekN6gXWlzdz+HknjmwaDQTsnqbzO9tHIT1zJvkghFrQ4RvIprM5fe0EdPpIJ/JcWnZaUPYZfFblpmgPTEW3bL6X7ghHnzwQWcwWeFlL3sZvelNb6KnnnqK3vrWtxIR0el0oqeeeop+6Id+6FBb19fX9L/+r/8r/Qf/wX9ARESvf/3r6eGHH6annnpKDSTPPfcc/aN/9I/oj//xP36j/hQKdwFlMCncaVzzFfmf8ZXKkcX7H/ZM8bF4VFzbUCov+fm/N0YTU/pjGDxGWohX40FYstOSskTw9ayN8l5lSI0ge+npqTtZ+ZDeCGg3Ac2PhYSjIo6b3Q5Bvvn0GE8wTpm4OLeJ6uioh41SbefA3iZ+j4C1mtsfzcJYImW0qn2jSnwCs3eAzQxTyZj81/0kH5vCqWU0P44F5pPrCxOyjy/NIdP58GYnY0vrRnUpi6eQnrWzNlCuRmNvlI6WaSQz9ziOju8lS6RArVa6pvmjJ39l8wqMce4kHf/v1ai/n6Rj8xhp6SX9vUXGJ5yo8uzLxxFf/eZIHfLarvgcjbHIvEqMf7O+341YDWIn2nvWc4v4HJTqRmFvEL8QJy4vtCdAwQPIRi0fmdlYkhpOxg+WN3ngb8+mLfpfKX+1sPAoIvxVIbL56WKno9TxScjobDtv+DFczBXlNxzXgi5qnM0gFNLiyM8pmeEkli3sg7eN+Jb3MLlJfU888QS97W1vo2/8xm+kN7/5zfTkk0/S888/r6fmfNd3fRd9zdd8Db373e8mIqI/+2f/LP07/86/Q1/3dV9Hn/nMZ+gnfuIn6F/+y39J3/d939dpYKYf/uEfpv/iv/gv6Hf9rt+lxwq/9rWvVaNMoXA/ogwmhTuNU7tayB2ZSLFvNDknvGL86YBaldWQt3w+71SWvWpJLhy8RC6M99T5a9MN7IzWWYjNjCO3l9cbS1YGkFGSKXMMSaEl3ZdIaelE85waaXrkJPrp7D/FeQ+T8a9uROvNJtwydSJTJRrofW1Owzoapnkji6vXjZ+pFhxyZupGzOeVBx/G5yxltljthbjIYALIuMKxmlfcJa9r3c56Bl1qNLnEmHK+3x6XGkLO1qdeBy3MwhbC0noWNr6A4VzZJvWCkDRsNSsxv3vxHsLj5d0zlFh4MULeRhGSnHkiDa+wjWpXv5Sc3nmjBybPKZiQcUe5HjCU6BjsLXCy/FN5jWXtM9rH9n0b+WyYoc/rMJH+ytx4/zNf28XgOErnal3lv5ncVOaSC8Bb/7vtOi/Ed3zHd9CnPvUpete73kXPPPMMvfGNb6QPfehDumnrJz7xCdrAEPNrv/Zr9P3f//30zDPP0G/9rb+V3vSmN9E//If/kH7P7/k9mudHfuRH6Pnnn6cf+IEfoM985jP0B/7AH6APfehD9PKXv/yF97FQ+DKBWzuqbhS+UvHcc8/RK1/5SvrsZz97yM3vNvFP/sUzpGJfIgWs1ZrbiVeB4pD8ctRocsBgAqeamNAG9KyUZMwzbQgay2b1NCEg9DlZQiMtcxTFFl4pDOFVXpU/537ziE/pDvGM/cj6ntRPROEEoVgbT331Xhc0DCvWkTl9XW9OV6AtUObynfNEAUPLlO90gtOKEvVxcdS05uOMasgHY4ppTg2+4XrumxpNiIi4nfS5Ha95pUIcV0dk7x7x9clwTml6Iemq9h4a85WidD7fXA6NHHtzZoTbIj6G24E8BOf2uD10FoYTtrQ9td2GcMdwoiezzf3WGtvSLNPDLX+zNRwMDq7fcIJQxg/nvrakjzucjyEcqOv9zvs11Trly+/zk3Tm+9M4AShyrHSEOY+fKD03Flq6IfkX48ZGEyI6ndbLHe+1zPR//V2/8wCFt48vp5x6CYTOZ/6f76QHX3G7BoTnfv036OEf+PE7PwaFwv2I8jAp3GmcVMDs7qZRvscvjbyIx7QYL2lrUX+khiMrL8FNBB/8XtWNJn4px+wC3YZAvcoz4t3GeHsQbwhKDAgYZtW+UGwkmsRRqGjfE0Vvm++3eoeQLHGBnnDoFcqtWOVIw41YJ7iTdMKY6n4ktB5fWKZDMR1SYpuufbtxeZ3qgLadZBRBB5oQ2+8rFhqMMSndRASnlMgXVAmLYpsNuD1v3dw3zBExCKrCd8N37KaqxTYMJtOGsFpvVIF9i1mJ5u5W1NlzXRlNznmUxNSYn5M4gXgf9DE/P3pHjFLHDFc2C/pX+HkZxDTP2pk8VqOjJgtfSS5ZppPQbkbVtdFk9X7upxHJUiOjHRYaMYSJ1UNM87g9QZTp2mvD61doo9Euh/dYcqv3HikPsD5w7sWnY8M4mZI8I0t4x2aT8KAPWeGCw0Vy2iJMxNRO7I5FXs6lhnxtXpqDYZxtMSz9MNmBboybGk0abcMj88shMxUOgVmPAb7VOguFwj1BGUwKdxrXzSSuLp+1foSf+13IBYB1PE25liIAHG+oX/Av/k26ucGkk+AFNblOAhsTKKYxj1+OYuqUF4uiwNQ3vMMvZZkBhMKxllgD5EXZ+ky/efQbBc6gKgzlIzFakPUvmhSmOjJoJjtBaBrTBhvJOjE8th/KTSkSl4n8e94mEHbxvh6nngRrkmsTvVNOJ/UoieqC1OqPWrbanCqfHHUaFdk4r3Hzyy8FuNnWoI3OcYu4w4PFx7tZ7dihQdtnwvyzMnM8XtKO8D2zde3TeitGE4bZ1K5HCdsG2CnttDaPwBoOq48oy0nz8+mpTY4gZpzBoS9jUq98OzotTZuY+x/rZDJvE1rQvDYWTVxkh+d7sBqJxaZrBt/Iwbp1Q/Ym8stCYTTVAOO4tss3Gz8a/KbOy4qW3KvFuclpvjmNlE/JRrQMnKzFcJvj/e7erD9Je78l8ePBmV+cJW5qlEBPJkoNJ/dQZioUCoUXIcpgUrjTOLX4c85Dhpm/nKx/6LP4+YtJbjAJbYtQeVb22Wstp9LlRyNBI914z7xNTDifvUok1oR+TEGF3K5zmgjPbbSLCnBUIZTmxdHCx71PLL+aQJhso1Yn5pPbw8SJb+7G9yQqGUvwNp2k45QHkKVnRcITolTHYZwgStBQkvTEIxzxsONDgzD5J6PSfShD7krTPZ9OarmQUffjSGRGoyyf9J18GTcPGs1K6mjlRvrF5cI7n/wsVqPJ8gU/ZjQ5l8bxk/348r/yNrGxPRZ/Ls3nYxjzc/kzvrlOn9vyc6Mpv9g8f534kZ9XrJ4a2ezyxju/Pa+9j0TjRBl4kePvQOe/wgMs7ai6HlT3KaPf2DZR5NUoLHXMCvg55T22z4zHD+MohSfQaBw/bzXOc9+bXc6r0luvs8WRye81DHw+crjQu90Z2L1NjEpvpIK+sx8H432jz4wz2e+0Q0QUX2/gbDvU3R5Orh3W34BjstEq/qDMVDgE5o34lvcwue36CoWCoQwmhTuNaDAhGrJtG4YTQsOJiY1rdeey+DnfkNxEWN8t+MKMJq7skOlRqDYVFb8NJnv4cxtLKmYhCMVbH+9F8P7vSbRJZxBw6kVLBOBwluiuQBsEeE9pFItHrmE1mZbIqDUlGC1oT3QN/WYibmMjXHDr1nHHjWh5jndGBlHKkjHIFKvNHQ8dRwjCo8L8K3hzp+jMSt8INzUVGE6NiE7kl+hI/SM8KV4YZht+pTrOsvVbcTNvk8sEeH220LboR2vDyXmjSZytc7shotEwiorRZjacrOo8R0uGhCLtd2/6+KAfW4oz8oalW9xYT+mSfts4JHOqZfHwr+tEDON9p+OKuW9zfc7bJNs/SKuF34MdVT7G4yzyVAU+Pi3jIZ8bjAlrQ4V/nhs3/W1Fb5PYPo89QKx34be12XIXWvYHR6IbS3LVOzGahN+T6Sk0CGtZqKeFcvDzPRuMoO/JeDvjsJsPsScrhDl5AV+71CyRyU10kueNtd17malQKBReDCiDSeFOI/3hb+SMJPPeJrN651NndTmL08YCnDHh4r1NjmWOlHeheKgFLFQ4P4xRrrmQGhAYhf7syi4ufnfr4b4DwCFvE6S+odHkTF6lInMX918BnZjv9jaB0lAtEygWvBJCceSlnb7jQ+5tAsYYd5rOXOtKhctoUENBa6P1littOHp7m762WR3xYTCY4NKzRkSwMapXT0ffm70TOvJsOWxvHaVUqVsr2+DTcn7AboxoMCHyzwm9pmKunFt4Q0YMWcak36MtHXbKjSaz8kjT2KJh6yjUUCFT4R4YTSaezKwGRPlq3xrubeJ5ecYXqC3iBzJF3Awsja5YudowECyMJvG9Ur03ey/Pjcts2mhA2eRtMniBWyoyhfZ4y0yHbJ9w1ttEeX0c2Rh38EVlIkJDBjDq+FuQGkuWm9/6sq6MC7OwNUIjuJ8/PeTC+lt/fsQzscXT2M5lekGYlzBB2om130iTPUPPN+KztZ7fQ8b8lYCNb38Pk9uur1AoKMpgUrjTSA0mlKgRrX85ifF7X7BjbC7czmqPF/mbfUU/9Ft10GAiij3N1+htYl++Mm8TVHHnHpmhKPhzNOytKDdiKCBSb5ODhpDoLTILvOtxWXmboEnDUcpjjKBvWsuwlKxEvTgDXNvMRLLnBcw1R0MDxTO2sStfetE+HgnsvE0a5KMsHA0oIR76NofDdQwft6YTDw1GPctQrRgMJw3fJwbdQEqL0rA2mCC97vCiM7hEZMwMJlM9LY6uR24kmdMOUjTV03QDSV/vXlu8iLfI2dxid/b1ng8aTQ57mYC3wnRt4/2k62HAYFBXUZk3Kudw5OTeMIGmkMnIR9yPkw9G8KCuJ9zNp1u+tVovJcw8Zn1AruX6psehU8hBuqWL7y3QwnH+2KPYxrIfYeept4ny+qR15woW0qelNxKG+BH0J+TM8z0Ln0tzT6vNdKCXDc4Iu/NhezuzsOdX+8h+PY6WOY+V3KTQfs+/ePPcXlGymm2FQqHw4kMZTAp3Gqc2r8kUo4HqsSO+n+rivU1aIgC08EOPoss5cAio2jL8fG96/HDEJBQ2MYyw9l8NJe2It8kQ2puPn3vfoAT0j9r4+hrExWjZmZQVEFZlqC4YBxmL7Lum1caQaygfQwnw6kcm8HtENTToCUS8DWVD9vhIjFSjKVds0Z415NUJGy7/zLpc31Q56nX7Z+c3hIXRnjyMIByX5CTHNxMRUbq3ybhvIofHNBkBTBtxu5rFrPbetmqxNph4uqw/c81zX49Qks8+30l5x0b97lQXrImnuL34vPm8bTMGnx/RQ0aTeJrSWJLDyT2O+ayiEsW5tFb18AcB62hghOgxV+NlP7VRo/uNSTib4/l7qvw0EI5voNGE4E7efOXjjEo9e+YaHyG21pL5rAPG+rFhz9sEPVyUL0o4MPn9d9UKTmYnZzQJaRTThMLQV9fvVbx/N0595SHMpRaewYhVb9Z9T8LLDKViEDuS91i9Zw0m2vSg1mXP3iT/C3ZJ7woL8Nb/brvOQqFwT1AGk8KdRrqHiQQmo4kIYo02Bo3S/ey3Kc7iEzEgETTXbd+e0SQqO3GdOjENL5Ah6rMYTczkMYt9Uo7CKFgeo86rZE7I5egYfiI/fCujCSvxs8C7J17PoqnFwRhALWjomJ7sGMtUsZgUZVvyY0dvtn4k6p63SWgXvNoPY9oYdIprzgiyVC3AgOLzLgTgOOfxtmtVZMpb76Ua2MaeB35G4YAf9TDxfZloTO5vgj0Pk9W735IXfKUkLeNTewlooDFukNMSgfjStrUyzIkTGBgb01AuUUPewTmjiZ+jY/8LOPGmjSU6Ont1znhvEzUgYLj5eGzJwvhWomIoXjW901e08jaJcyIymFyVz57OWuFGb5OVJ43wIRkke+dzU5xvSUnuD1ipPeRtIi3BZrix9UzlnkYG+LP/MW1kS0riKMF9MNL4NuQ+pLUkrQFbI/IfJhy3GuEmpWdpQvqd8Yg1TG65Ha62b7CZ5ofsB5fubXIDmalwDMxHhcXL6iwUCvcEZTAp3GmcXZIzGS566JQeP9zTji/TIUKX4iiM9hUqqGKL98dQsF/Ib1fodzB7uDhZpmGbjjbdD8C+WIIIyEx+yUcLLZnhwBlRYJwbxDXZ22SyQiQqQ8OuzQMUv2zG2lZx1lcZAzN02BdkLLdQLDjMrYlSaIO3ocWKpL2zJKpNj3SixzWqQvm4aZa+jgvpMiajs5MnimuvTW3LDZ7CowUbGBt0zEzpdMq6aGZDS3LK1JklOefi5oUpl2HXYEKU0qcGR57nVKa8pTTu7GGyiuu8pS9XeUFtT82LQcTXJnH6bGUevACh3CtfK3XT4hg2FW20ke1V1PO4MGyOGjh1CIlBwNrV5XpQ39Xo66lh3tgP1fFp3hdiT5U3TuV4yjQCwrkZwuTDwFxWKuxsCAaKhjFYOecRbxNpaTQdW2+xDQ3D74DrJWbWXZtCHwKXY6lnnc+3TVDGqMTlTEREp5P0uxfYm29QJfZun9lHmhQ4F14YDnuYYJkT07xEh+himalQKBRehCiDSeFO43rPw2Rx3+NMGdu2+GOPAnT+9USR7YbPs2DELgONPR9urltM7vRQF8M9CnI0BHsxoPg9TKIij31y6q3WiNRguEGqc1FmonHmhBusOIKxpdk0YxSv4vwCJDQNzWKnE+iHdGyu7R7T1g5glMANKjEPNyZqJzWgRQFavpJ7D5WkcehprxctNs3tXeLjxgjueZtEzxSsh2T00CACxpVIi2gXYixq5E8IgllJFJ/lZXuYHIl/IYL7eYPJotxI2/+SvJO26l5WJhpNtO3M/TrnhlPM1P6CqwZDik63GzI2U8jEIIJjyM7wG+OYrqlv+OkWCi7DlIZHnUTdiwEZty45hKUyrY2TdMasRR4/GSHW95kKj7wrcCzy5onI86QPi/1MprYiP49qedb2eW+Tuc1VaKYm40bKIolIjCZE5DYwPWcc2TeyaJXA1iMn7BRn3ib+t6/RnpHqyJKcnAWk5peLcRODCRH1DWGJJsMJ9gd/gwsvANvW/267zkKhcE9QBpPCncbukhyQ8TjJIKLN6SReF1kL+deTpDULtXAvX5qCJt2VqpF2oWyRL8kxVbMrsZZTDAB4Coz0DU0LvURUmKPATE4sdt1Uxdh62ZxrNnibaAXYWjSazGk9tO9hgsYSHCt7BKaIOAVofI1Fo5FvYxZ9nWrB8TmLnrV1SrKTdLRQp6clrTgDhdBykzj3LkTTVCg39RO1CV+nqVFIq3aJ+HQNSr1/ppNSDGmX7GGSxcvdTUX3swYTol0azYA2Gxz8fMzm1VRb0pnZcKGzp50o8zaxrLMymDcf2wi0wolY+vZJ3gVjWz6PuIcJrjUcTIT1FBR7b3SzU3dS03kDQqSGUcnVbIOHDN6gb+hQlqk1721C7DqYLTBc3WNKNFz7uRK5m9GZeZuI9Xz1Lux5mPTfD4mw0T3ibWKDmfNsT4MnwF4rKKfxbsK7vU2IKIxKnP+MN3PbLiKMcPhtP+dtInmjieOFGRPwt5jzB3oGe6fkaJ7dxDED0mrOyUyFQqHw4kMZTAp3GtFgEkXhLE50UxNi7Mvo7HIKRgi9z5HJDkzktOil2tT2jP9zzVFHmwThJnoMLslBhaYdPkkHhWSlyI07aOFhnbXuZ9JA9NTlQPEknUFb6Hc2cpd7mOQqhswNJ4IOATyIpSO8ml1wVRd4G8MmUrWepCOGE7M/aAlQFmzuYruzB4jGMWlhHZ8RJ4qPzAfpD/Ytnr5jGkVuMOEkTubzJDSfxLtIZiGoF2MzT/+cndaUYFbysrRMYTmKQwYTon2jiVI0tz6/WbvU7Me7V4/t30bO28S/uYtFOVF75Rg/ZqDGc8jOOmWOnqTjGwjzQK6qI9ryLpn4OvXdUppN35Vpuciy/bBkDHi0M8iMvil/Qm+T5Dj5fQ+Tef4gJRnFFnfQ20RYk+9VD6+MamhkYUtpRMMQu+9torQ03NMI0lPFHTyVgEY3StqPQPd06k7klNF7JYS1zVgv5rP3jQm9TfB98vucOB4b2j2Hfc4iA3EZZzsdzL+/hZS8A1MpOiozFXZQm74WCvcVymBSuNM4nXIlBKWSoEP0YPMFNElOlAmFTGnNxNap+n7PIJDKUg9Is9JduLg+DUHjgCwzqRWqmExivlfKWdLQaAL9DsK3VzJwJIK4qnXnNGibokwzkfc2YfegZgH3MoHwvAcKPjkbJaURHl5UuCe9tGUzw3u48Kizj/mOt8lQvEAmX/Z8mm+TsWOO88cP52qEjfh6bi/bSDeEDQPZTkPGZ30aZqzxS3LWXyonbSrewC0qLMIfjs2n7XTQYEJ0TrugJiSd2V9kty5kKhiHjSTxfW+TLW37vDrDy3o5KrwTfQwOIufHPFsC6bgJi1LclN/1/1kNKXJaS/cAOfV+RwU+Cfd7aFu0f1caORslRhOiq3HfX2P245W8Zzmi4Tp57KB+R75t/CfwcfVk87wlPSVHhryRo1qeSLtwbxPsQMafJ+OFPt5o9CAzQJN/Zt4TxkYyC+O91o02iKxd7EF4KKcTke1T401dUpPMo0tNCPtGE8lx/DeyJacLnm9rkTZ4PM5yC11GV6FQKNyvKINJ4U5jd0lOjAu/3ajsy40KO40WG5ytW5sU2P8/e+8as9uSnAc9tT7HM4FogsHMxSMLG4hEEHEsbHk0AYQEQ8bADywgMgHJwUJGChnJMBJxHMKYXMC5gDVcLEYJchQkolhcFJAc2YJB8ytDIgwSApEEh4sD6Aw2ljXCCE/Ou4of3VX1VHWt9b7fPnvb3zletff6Vq++VvfqrreqVnX18kz1NV9mnKl6FR6jctQkdxszC1CckNLE8k/GbjCz+YvlRLnpbUU2seHjZAqXLUj9ko5fBIa1iZR+ryJFa/JNuZ8T11ubGIN75EekjIrreujbYTpemfIh5EkXO3Sf41C/PEqKY5bfbjE6IdF022LE0yP/5kqeLA4cigjpWGFdO9a0Mf5mcWV0iJU1q0Aa43Bm2t0pTJqIgiev+UcsHx62MAFwT2Hi/VLcPyXjsKqmHAn2R/Gj7bHGatudjcna/CFVLQXrGi7Khjv9XhQmaY+bzClkwmooL9L6gylOxjgrxnawrCxdw9FqVtoBQRuXY46NhrJDWDuNZ1qbiG7ld+cRpUmmTv1PQlCHNW/FvVcQ5boCMhWv9IdSROZvSlATszbxVxhU38sGDcxtet+1xtvvBscTVqTxyDS0qQMnoA1ODamx7a9L/C6+BJiG3adnCxrPTKc5LPm5g+f4MHkE4/XDSsXqgmfDJuN63XVecMEFbwQuhckFLxqepTABnqE0wWB800k6lcXLj0u7i1KkMEyN0sRZ8alQOJIv6l5qDtuHV8dWUI4YRjrJwwRqT0v1xbaclS2v4m3FZZZxpUJhHQ99m7DQVb7sOvN6viUnxZmyiPpwbG3CYkgH98zj+zgecx8FOjWnWpvwUZ6dOGtz5CzuVBkigPjXYSWLq8jH+dN30UYxsjh9LXlWh7QzqPvsYL8N4q7CpATbiNp2ReVEiL+vMDmmB12Ezwdaf2sqDpQvJ5RtmShrfCgr5zif9bt7Sku/CNiLlUsuY8qGIEhHbReFiRGlFCc0rBLzx/sVaR4Hm02ClZatMJpVLFpTX8fqD07LYJYVsYafIFC5xZHGB9Ym7WlLcIrd/fIsv2OrtYlX/kC/13nqb0KQFBL5HU0rRS0YzAUf1ia5bNuf2qkS59NKG8o2K8zUL78/5fwWbpy61hOijtJaRc6seqefsf536fnwaJn8rnp4dEvOWudJHsB/6IJUHL/pCy644IL3ElwKkwteNOyNaelgzrHwC/7YKUcsfkZGXm2UJlyGWbTcmOTkYGr5uMGqcBFDbzLhrVIlmD7uk7gDxFmf9VFJTTH5F0mMK9VFX0tta9KytWTpLQnSQs8eNfrBp9FwVkWMr5qQfKCxeCULE7f46JQlnbXJ8XfYGLPcwxTnY1xFi3UEXZFivk2E24/8yZqE+pbilEfoIG5RHnTKkprHwvU53m3GI4kSjkMULTjuOoRTLxMC8r1tCwuaLe71cU07Upo8W2HSRh0JwzZUjwovThya2lDSqgLDosI2YqyLvu0W46P6AXL43JRJ2YUEqzPBndNiPmAK5z5PphbS17M7N41FKI67rdN1i05uncfTWsqKW95e2Pk1MRrnShvz21T6faYQrKt/GS21H4ead6VuCZ+2zXXlSxPuUsYQDALP2CqOrU2slpWiTLwpcXk/Z0oT8G/q1iu9mjYXElbbZPJF86OOpM0BANAddOR1pmxnyrozOKOGz6rnjoXJs0lqSpg9PuKZLngMRPD6fZhc7+KCC94UXAqTC140HB4rXLix+ttdfZikPMSzO7OtoUBoWishuMDMjUX9suY/PUln5D1yVeCVlT6ZqfT44mcFp5BAp+a44CCmqDGe1JQvQ4nSW5h0I3Eo7iRxKMJsbUJiTHOSTvT/GRYmJBhVxU9vbeLibGHvo+dpPmnNARr/UcLeQedMVueLyL5N6nacTrQocYKD44Epb3v8MJdTmrskwhw4fWWpYd0OtOaXro5y/PGYrPNNPOr0tWRrhUI9fOjldDyiMOnQekysySspN746F87px3EnecrENdrSWZvk8bvXxjJbVhSOTtJZ2q4volKNVXkReSKv56ETqERtHd5mS9Z2peES6YlORD9iC4LZixFNoeOHfe1P5YZCi2+To7nC9nhHtDP6vea1v0HxZuswa5MMGY+lvRmRMc4t2TrWikFjbXLW8xUdmri6JvOpOTm+o56VMtQF3y1fSXTe66dOJEqbCLdgj1oIt26drvAIJXkkT23tkS05S733SGpt0XmHu01d0MGZifE7qfOCCy54I3ApTC540fBKp+TU58ncLCKCwj9O8RfF/JtzKiYkBjyHisJhEShqHZPNNb5R17zS3F0m8fzM2M44sygxwX8KBG4JIDK3b9yxMPEGqyAvjnP6BjkH2Vj+MbzGYFlOO0mnG5lngAIirPAxlO9bm6QZUxmORVMCGthIjC1Q1LZS702QUCRrk8Mv0NqMRo1bFBm53xFHygx/fvwbaBJJquKizgfNgUUhopr82xwrTA4UHofrscevrSc5NX1nxwo/ki8JwGbR8BynrxYPrGndfC3xCpxam6SRPFyLZknRlPGykuc/U6rQ6KbyrJgwidWUvUa0u+coqRRHdMYUwCrJuihT5bjzZsIljer3vOlktFDe2Lwevk26flcYfWAyk0hOerC8rBCu5ai+9IqOV7sreaU0xz+QRtedZplFTYQ3or9spcFdMVx4OfO4Z4VIfW+lLI7KruUq+UjlWvLV4UMP/s4jzU+MK7ifwYOU5aF8/L5fSWHySvmk/6264IILLniPwaUwueBFwyM+TBYhUpo8jeArlH88h7bClBcsVi51mmwz/5DYDRaSEw6gL4WJp5zWHrbdoRPSiw7DnRMSJzf4eFIJCPyLJ7tNDBUCKxJmP3xgcpN92L6uGjtPfyfXnr+Onvk2OZHpluForE302J9JHyfgtyaONEN+qwDiozaNporSSUmzHbLgSXFDvIBvTwqpIbdVLDmchdfjuCzscJx6d6SpN1mYNG0vRxmf5FnwKHfh+g658SahGaNXVpoAY26+hmOFH8kn58klVxO/0C8iHp0So6Fr2eIjUaaCZENhSW5OqraqXNGSf9anS/ykQEUZ0d0XhUVT5iiPkTI7QSiTUBskPmXG8lMuEVcM+PMkwE5ZbOvMpAFPQlt6II0T4Kgv1BzRI+U8tDUnU3KAqfpibeKnCa00jKFvez4lTXC1LcF6ks4svSOP9QJF+VDT2m1UmoutW2caxUnDP/RrMP0YLyj2Tmcpz5w8QU0eVyG8LqXJkcPcZ9d5Rnrv1HMpTp4B2zau113nBRdc8EbgUphc8KLh7ZssQnSVD5Yf6U5h0mTs5A9JKdnctFOYHNZ3iAexpCfpbK1puAvZCLPj11QXxUluKuVh/PmQCkGYly9Cr1rmLHYEo8ps/PzrgguLAIPJVsIhTOL3ZiBXYCVMilPrhzoOeq/MDInuxwKtcplVds9Ch5nl9yb3XZlNb0hSKYBl+w0pSvo4VkKcxyW/JvuOtJ+ozXuiKHkgT1XSeBv7fuKU9UDRUeLbefpIXXrWdi3+qFhzntffsAvBTY6OkHDh0/hjYpnmYCKIJwqQh3E6L+MUQIDYtz/zsNPX9AwiEM/I41Y8U3A0epDSa9+bsThKS5Y0Na7Po+UknUIRDoCUI9Mqa6UnFurqG+X9o0Ml3YjoLpwLOEV/KGx47yqpBsOLyUXvT+TAkuRIaQKEUm7mt7aXUe4ULA3pGPGyxGccVmsZhZHSx2jL61KYGOzvtO2Dhh5p/zlk8oILLrjg3QSXwuSCFw23+clmIwWCM42Tn+PfaFMIJH4axBRJiUOTj2NYsJw8cAg9ILkgi+MVj8o0nuI52xUZZs4Q+Ic+E/DsuOCwMgk8q68SbmzUwchQJwo3Ppg/828ywmZ2vrgYnM5kk9LAR6Zj9HEgAGwQ3SE+3itr+qbikqxoPm3mQK3lQ0nALLVbyrT5jsuO93XrfZR0J+MQl+/tpzojbmH5teK8R33B7Wfut1qP3Dk1Z2m3+FCJcZjKpVf0ZXIYWSWcBlbrEiIQDzZzv+0erL/KhM3LV2XEfFgIViGItZAvqixeL6iK1VUWJOf2NsTpTsK1ti+5TKpSFCqbKz5kEjunL0b8gDidxuPmNhvKY1Mq+3EiWpd+MCy/Jgey5lA72pt44ti6pHY509OcZ5Mdu25zvMXxK9SnrN5Q1JznvUfVZn6n6xRneRSl7hLWrg0Ka44P2j+2J63ba+z9dnViUX54nkQKqrIk15OsU5nsLGVKfV1eQqRX8NAzgH23ifdLrzR5dDvQaZ2vRtYueA5cPkwuuOBdBZfC5IIXDfuUaRSAbPn3wPQAHOHJGnmI92KeOeogBUGVUfjBfC8IJye+uPmappi+Nbim1XNFVda4abMKtk2RKjaFCMhXCeOgmuUYkhxcDn7wd7U6bB0CywwL770XV+IALISE/H3aYStHL0SoILPi0QL6uCq8p/txXG5DvZ76viOO8mvF0SZag4eWPDMcPhn2pW+vZkmC1bKji2PFgaXtSu+IRDCe2Kk/rMyp9Zex4Tr23Zs5V5rk+mrssqL08IGiu+04d3B4h1tzarqoTOVBXbDo4zi+tlG3XmgTVyDogTHvpITp9jHO0Y6kmuecuIy29kETt6dE19IWRh+CsBWD0yGalm3TR31O1ClvDXS6yjgI+UWK9XUujx63zTSdh7l02TG0cmlMmrZpZbctd/WyhZPS4DNFDxs9eMeVEHmEshLlWE6MsrREErjvetA3zdQfpW5NcTm8/PaVunIbR20zVCVOxUcfVmA8QlkeynM0CU7QaOttIh+kfhdccMEF7zm4FCYXvGjYXRgDhKxNQMw1K0lOLTqQf/A71jbxFIuMYl8gQ2ZJShvJ5Zk9Z7ZswRvHvL8A0F0OrU3c0mP23Vpa/HtQfzPfxLYgK35TPBpO7dBYm8DGvHoLiR73LfTcW8Rus40dgn2O7crCMots4eO4HF7jgs1nxUPIjjH+C74kxXVzYOnkwnkq4SxDmC+WINBGTEltMjffx6HLZ4qDJFXMG23TCauiUieNVaqn4Ni1bUoLt7jQKgpVOGDZH7VQYdhPypwKGI9IEndEC8d3+lExZQUrSVjAXBZoF18QlpKX42s2R8eojpT8zax2GlwHqhDCtq4Nst8AkcXaRFnJo0WRkqjYGrv0iyOUUsxBpyqStYlk2sk7p9hTh60Gp/9Spgw5ha1YbVDsJqhLpkg91SKrkUUxcJLX03N+kuVPqCjKfDqwMHlQgrZfgl1x0oeob7XiyBQ65WGSQrixhYkywoaD5MQjy5E6hzIOORBtEz5e2X3FySPDeS/PISm8467pdbR9wTNANrz+Y4UvHyYXXPCm4FKYXPCiwSxMjM8WALscbNGxB8k/7C4QFL49MTtS4nJ2Ypwn42Z7ylk2IGaqfi1d6pbMlNX2OtZqn8x2MHojp1mwZP981KJYmrTIHPVbqSpXfVRrE5gyoXFF6O+iKnAkBPECq9AzGX7ybXK2vYZrebU4q1DnUKnn821Q0Ma6wzjmiGOFRcY5lxd7Xt6JInO/Nt5FbChc+7N9nyzbZQhnpfSlz1jHobS1WriwxKGxwCceY60+X2myjsv9MqntDnZkBcZZfUv1d0QLUpgk4nZkbaIVly6+wamzMFkWGVnGOaEjpYkv8onn0vZCbYt8SHhbGbG7FGuTODnJFSe8BUfJaTMR2YzKaLyQ/KzAUOQT0oyuGZ2cPyaeBlLm0LadoLe0Zr2i5n3N+yZjeoW1SUeZ1nCGqix5RKCNUVF6Va0Cw39HOkp5ToXrZkYnbzKV7z5wiVqM5/yaDuLzunzcwoTGakdMEE236MsSn9vytJIv6WJtTj1o2nnvHd5Nvz8JHq/7Hpm74IILLvgVApfC5IIXDW41z3y4ADc4v33o22St7CD+qG0Ks9IlKQ8Q8kWvYLG6jr9cHrUtzaVarE0ozsWEyav7WTCKeeTh7EkaBz4hJ/c7yx/Guo5+j/dAJ+AossWLpbhc0whueXS9XB0HYIMJ7jLNjM6sTZLiSZq4crdw52ME0fXIywKkctv99HpsyrFYsYMP6DQJYxEv7ihO+rhGieGKA6UXDqDms0lL8TEU2taxKpdKvdW6ZeKnM+7YeeE6lxR1TO7kv6cwMRRVH3MO+xxJxfK60C8+V0NxUssUIuMKDe2VE9aBezOQiJEprBw3i03VRH35dJojHI6eg7LJVE7ptkXcxKtuyTnsmj8f0ZqMx9i6OPJWa5MhWA/FB5/zdaAeamM4LW/WFB9ygU7rvdl/GptM1XI3+zhp01fKEYqDIA9GcRrFiTI+NYx+qFtkJX4PdNCG7NskRqvb5hK4PBbPSotOeWKZkwNmI3U+59buscVIJa/e3da6Jfr1DErx7PyPHvz1KD/0XFwveBDkDZySc1mYXHDBG4NLYXLBi4bd9/qDFAQAJPbRV7+Jxs84i1qY6sTkzjRpGKOlTitfBYdpbZK26Tie9HzATB89L0AMDlubsDLHaspbdOYgHJzhetQ281N2ZO+IM0Z6DIax2EOemU+ilM9YSxpsE8JKS+s5NmU8ysvtvnOC47zq43xrGFXaQVZYaHoW62Rt2+O7OG5z5fZJJJ1h3qJDGWucRpocxS2KliOFSY5TYChXkiYNRVFRyjbtpfdetgElBZQJsHcVEZpCeZqflH1EYTJx0dPTfLi5B8WLqpSYX97F4/bBTFerEhM8AVSLiTRpE/F7ECdSmkSVRNiUUiRy5vdDdKbi5EWKwkRlVC3T/xJZmxhtjSlT/Irw78LaoRzbEHhXzjk9CV9VQKXhlhb1+nyTKn/mjYi56fhN22a5HWO7ivU7r+CqRMljeJQ337l1HjCj16ClWZQpwoK+5HpwUAZOKeOaSpmkQIAthVq+Kis6PyJUpqljcTTbkTUL17mhRvGqYqn51Wim2D1lzatYgTxa5Fl1P5NcXfAa4XL6esEF7yq4FCYXvGjwLTmToTM+2hUCmq1NmD9MQnZSXFA6MbupDFYmofpaZMUJM9nsGDbJ7EsDOZiSxDejlCu+A5plydMWDHhYluSGF4GCmPOFa9JSxvjrOf6dKTu1OnERqFul1LZJqCpw9HMfjPoGUx5sXn++PxpX01joD8VCv7UGMz2LDJUbro5kcxrXLW6nPttxRYvlG6OrqqtyItW74rGIMtVaZeHiTSqgO0gwZsdCrTKExKpOOUTSh+yrhQmShQlS+BgaqeU4Yvbjdlojw1CazPCpadijEggTNrtLLDABcJtrq/tqmB175LqAnHaXiW4p43y9SjhSXi3PXFclZih4lS05o3+2P4KtTZ5mmeLbJD0XtH2+0pG8Pgx5wKLYUMws1iZE90BpR0dCjxz2g1LyaEYyb8kcSiBrS4UpPfAcygYkitXcWREhCQfPN3Fldc8S1oP4pc28JWfZopPa5T6UvhmJQpOHSQq3zWVKOJeVlL8uh1R38RN0iEMlqWdxD8q4D1KWxy1MasVnZO2ZVV5wwQUXvNfgUphc8KJhr3LF/GN72FmBAiC5AEh8wOCzHRo1wRJXef5q+U66AgylyWiI9TZVFAFtnzlv+4hFWTG/7bI4hIVtl1GSwYpfE2YIc18lxyvcYkQB35LiYolxoWxtgtmeGCvNLRxziWeMmRTObvi60KWqZez0bDyPQH2MWDZUrovmgL9vVgYscV2+biRoq0t6R/bStCgQphhRlRePxh1amFQOn9KV4yPcKmLQKZNK26UuF9YJX11wr8D51vgFHrQwMfC1vN/ZovOI0qSxMAHgitIcv2PxbcJtVE3uvfhlxsVc9wDTW6IpSSsdxCDX9ZAEyDjtk47MbUgynOCKYmzRmYvPTs1yKkBNZVSS6oJazHjxlhyrnx3OhnNtG4NBYHlMqLm8RheCzr8EkgRls4Qb1iaCXRU7JH5XopKkMOjunDfu3G8puInTMX7pZxYlAKYvl5N0fjF21xonjqGqKYRBdWQ4VKikjDEGh1Ye8w+R6qzs2HVhIlLfUl0132y7OXjssO1n/DQ9kvXZChMD9unyim1f8Ay4nL5ecMG7Ci6FyQUvGvxj9mQwE+tnW0I4bRy8kLaGOm9aGewlQ4Yqcy2OXCUYIMB4rGJtUuoczCGYN21RsOf+GqoLjuutTcgxYdbupEHpz9Rp8E5ZYosOJk7s++DIt4nXlrRPuZ0jWG0M2Nrk2LcJ9+ksrlqDVEuOKqwnoZyVDlaX5jqd1a+WLKYEoav/LrvDhDG3SKlWLjwhrY0St/arUZgs0gTdAZKo1vHk8u02pFQ3nRte2hgCa8S5MvDU2kT9712lyTMVJgYycTj1bXJPCjpQmADiVmK+Xo14CZo9791aIkUJr/lo/KhXkaaMk9EYxllK0YbSScrQ5J316FDEhK+jqF+m8HpsbUL4dUqKs/dQ0wUxx1w5ElZ0QwEtSNYmhkPVQ6wUk8BboKfZAbER2IcSQcKnC5fqVl3qyEG6nW6mHOP+TOzdy1KY/dQcHpPbdDdTsbPT0sjCBvZaEmVb+1K33JQ83ZacXL6gTe++Uzq0ipIlvonjdkp4bfs+3Mv3jrbP2Mt6RbJ2wQUXXPBehUthcsGLhn1qTJYtOXD+EkpxxvTuar5NCqOlhVE1eaVhWJ67HTSssMnapFGMOB6S25SD8BJZ5SBicHY/UQL5CykJVpY93akOl4tAY6QmJBA/XeJc8WKKLBNe1d7D+tW3tHTMjFvOZSuPeDk7fnjEZhZ6Hds+DpamR89Haf6YfGXmSZUFpdSLbksOxYlLEhnnepJOd4KPv19XJhRnsA9tyWnikgRgE5rqgM2tHJfy7Odth8IF4LF3q6Z2NLntHJez3Ttn81w6EOD8JJ0HrGFi0REBs3hfoETwtFqb8PhUAgGsWoQDdLVJKHFepcK3jQSelZL1lM1O1BpFxS+dyk9xBcoGnZYmR9Ym1dnsscWIYZHHIWaPraSgZ5bBXwvR0mWLjg0KtY2pTPO33CgW83aMWdOM22auXRU77fGMWnoFytoKnGBzWe8fP5c4m1JBlg4UFMr4rHhmKKrgQu/593PXXH7te98HS3tlC5NUjgk659XAo9b9mtp+Vdj3ZzIuC+iptclrQPECIOjf667zggsueCNwKUwueNFwvCXH7hoMNUIJIWK+TXR+lM0MVf1ZaeMKZ8C/RcvHyapsIMbPcVp6d8x6WP56bTNhGrFDlOQPv4ZliVmbRJ3FlmQywfVYYrZGlhQg2cj7JC6Ar75NTBxRZ6qH3EcV5JYahUgZl+WkEINtChzqdTzfwoRAsgyYypW09CxwQdzr1dp2CP5VlGHMXH7W+swsu2WYfS/1df5OVgVG3RZjXHxz53z1vlhslPRad9v2Sf0U9j7ctT8/mU8PnZLzgHjgZnDPYFarhUkmanNdAq7wEL6bGV13hlCHQ6MIWbLcIWgUJxbPuKf1UKV7or3Cz0y1ZiIJEdmODou1Sd2S40oHV4Dknj++JQdNXFibyFyDVl+yLLT1OIfTa5OMCWNmazbIYdC4QePZ2sRiuaSkmnK4V2QovZu0HJmCaKnT8rC1Sdsm43Z8rXnICeyc+/68UraC95rnVRQVa7pZm+R3tradw49amPS4aerPcyEdaXwA52RtjmPhvS644IILfiXDpTC54EUD+5c8vJNpvMdP/sasTURW8/mqDKh8wZlSpW6ryV8LMzNvnBB9KJxlQtIWZMbriEcJ14hDRbBIP9Qu+zbp2uza5dZzWhLPp+xGfky6uIhMQoEsTl+jpQd4vbkVa2XBYz95+DZ5jtNXYneTRUn0v6aZ4F7TIqqw2FiVGqUfqhjqMHpWped5Wo5yOcO/P0nn2CJmph8eK5zxTvejvKmtkj89z7gDp6+vHNfCQfqjW3Ie/ey7749LF92WHGum82eizX0HVLZC15SWk9Vd8W9wrPh02mKKS6QtfSktmoJKYQx/oXK2HYetVsYADCuNkj9Zm1TLt0RW5PzdPbAlx5Um89l+M6wbPlRSe5qdYJeGHWco8iluVkv5fRGc+TapVKoqF9a0Wm68Nsn5aHiUn5W31qQD0Nd6E5mTFOdGdIi2OR/PsUX5MQcokZ3S5N4e7XtCtrq6aID440dXb267z/MYSZvjyRPrGfAwWXtGfZfS5A3A9gaOFX7d9V1wwQUOl8LkghcN9y1M5n0yF8klgAafPRhcnT6xgsFjpQnxqJ7OsCgXKgOlxESXZwDpo6wrTyaXv7bdn5KzEWZDeVLzrb5NNgG2jdpQ8Y+YwzpEcv+TBUPuOMephlAQwoP5GLAxCx8AcIXLlBK8w9H7Y75MKdS8LFh7APs2CaHjAfawlzBSM0dWJykPRaiyaBEyms1A6wY7eIWWZ4/LCFalztFJOjFUOv8XpcqhD5MiVYwO5fuSt9yllC/SiTzDwkTb9g/iHoFnnJLzLOkCKBJGU7hTmNjdhCaZdOCQ6Amgt6lQkLycbOCFn0/wOVOU1PiyTac9SSetz2V1NDjsALYoJ3PrkffEVMVz6071bYJsDZKfLUduM1OduTYKWVLulmKxNvHXMdPcwmW2HXWtIv1ySjO/M882V+7i2yT6rYh8/b0SyvV9xJRhyuA/EpG3/AgmC5NumuPeKTmEg6LktWFbT9LhsolkUX9e2cKEwyXvztZkZ/mOlDVtWA/i5xxb1u85PMc10zsiaxdccMEFv4LgUphc8KLhYYUJAMgU/mua0n0fyoMj3ybMQCxb+vU+w1DzdM9Wd/Sp4AL4iRDe6XntAsh0uDqt8h3/kWfVbuwAtFqbMIM5H1rZyuupo1PGy8YYk4nWODozW5tYf3tW7ZiBk/KkRSir5UPQq9YmR1YnrYNWflYqr6WuajnClh6que1StooUoxalvCReuCQwXShSch4OzThwn4vVR3rWcqd+r0qSLi7uCkzzrnU8okxx+pray/XFnK1tHeC2QEl4rtPX5yhk+KSNs7oOtuTw3Z3Azug2LwDd2NqEacDxegt8cIBvE1fyusLA+mOWIl2TyfSOCJseWJnM8FBF5OOHoUcn6Yz+VvKw+MtA+C1ZlAFOJ8O6xOrvrE2CjFpe5P53Y0tT5MyNsdclMRq9tUmlWv09x42KtUHZLBy0PHs+/g0pfbjbH8oXJCzb+fHbE06nH6mMb47bGR/KuIwDk4yjMLerdPocgNVHTGnvTljJNOp1WJs8siWH4TnZL2uT1wdq1nOvuc4LLrjgzcClMLngRcOuzJJ1dwnGUxH+KxR+lK47h538xu1mShOuaYU7VukP+jFpZKMZt1ibcNtI4kS62Ohyb/MMxr76NoEKtg20baZvk2WGykwFk2gV5BNxQq6L/f5RX7Y2icGLljqvDAnDKmA0COYaNgC3OeCZVV6VCY3wD6xKDuXyOtEKYZ/Z57Rlh6wuVlctmq5732WllBEJHEf6HNNp955wqiLDIz5MOP0sH4+b5eN9dVwf9MTp63Pw6dLQQIl8kwoTrr8VlgtRKJYlh3cmZpnwAbcbdHi6zmvgjiPlaP8OwWN8G6ImFp+sZgoVUQTBoy05wz/HdPo6lSHjFnmEyox4sjbZtvDx4WSlEuil06hz4pC+1e2T1g8cn6TDW3Jy03oYktJ2N3am5hl4SHOoTe1obb1JT00Vh69B1Ocym9TEn7ueVYpWqJjm5w7HpdwRGWraXdILqTgKe5HT8MB1J3peyc5ztuQ8FjZrk/tr+dlk7XnZL2uT1wVOw15znRdccMEbgUthcsGLhuc4fU35XI4YTEYtp7dx38i3SWUc7v30PGJNcmRdsjwXQf+s7diKM765BoNdsc8c5S4Yh2xItjYx4URKVbna8bTGmcjA2aofE+Ceb5NQAZyxb6F9qU5YGaW1hnnmhO6wk3SsNb6n8afhW8SLmmZzzcOkoFjSrKyJCSEyADnN22Nu3cQH1TwWphhJFik6hbY9ceHLSTqPbslJcVVqOUqnOLc2adruP70uaeMLf592KlE1oK9yrPBzlSazneX44U5hgrE2k8A/72pptYxSGZHh6RrA1IxaY3iAmjV5uK9EtNJ6k5ScijlhOxLgha5ZlytIQoEyVlO2Lgn31bP8bSgNz0/SOXp3JPnX7KwrsDE2indgbWL0gbfkZNVJWd8UF0s9FDQJTxr/0XPFrmitTfK9Cv0WnJSKBfOUh52rlrr4eOJEd3O8381KoiKkgM6xNLKmOhRPsdbjxZgezKukXxFu+yGlRcX9kXBt16zJeFw7skR9Xklq449lCed3dSQbP9fCpOL7KFzWJhdccMGvJLgUJhe8aHjoh5z4KQuasmLcBzMWVidxvwGQYm3CYgZDJzjfszi5Z13iz7kbHj667LtEa2Ey6zexois/rE1o2wzJC8esPcUJkBwuloHzZMt6x7dJsLuPwbJ3vhTsfZbM3ut+0BBz8PG8uDUkJcEYO8qnKOGapl6/0FaoeqywKz88jrbfWB1gXyXUB7JqGUNjEtyeu21WM49YjnB6G3e/rAKr0uQVnL5G8fN8S1yFhxQmTdkl6pxICbAeP3ygMMmOXomI1bSjMna/3dAeP+wIdViWfij68p12JOWlPo59NqWpKdwX65HO6etIm1YUlB+yQWQnC5SwNhlKk+n/JBHV2nGmeFL1Ef5DYjNOJCtJYGobNdyLtQkLvAtlB+XRnEdmPl3jLMmFdQn6vh+eIJYGvyfsM00pWxov4W07TNhnOSMxVJsiNjoSxTp8TmNQcPK8GmUNJ9Vc1ilR40fEs+pBuOY9CnM5jXljaUxaHrVu4fBdPPX4RPNX0QMTCs8row9MuQt6kKBTr7XOCy644I3ApTC54EXDQz5MjJmd+Yx3dyZGMIX1kbjUMa1N7CSdI8ZhZeWe8Vzko7Nni5LmimOFcaoQiUuwNVyN+TURgVubkOjgKEWxkepxHggT8YiWUBBIZD33bXLm2QRtyhmjpoe1CYZzyXkSjYSYICVbOg0n2PCZFs+Hab49TNu08VqyuGAcc507wg+K/PHZnsun06xgGePtp+xEY8RhswRA6Q9bk3D7a17huuec1EcsTLq4V82flBYPOn1dplEzrx6RNkywEsFYgUBe7TBitd5rPl9Y464zr6SyphgsJ+kcEriDFdXFPyOvWrw0ffDL+iRBBKsCpclvW3iU0uU2Jn89SWclGCuNOI6JFAFNtflusm+TQeeUfpeaEYn3NnuSpGZZ89orR5NXIHjCam0CCKpvD6BRfnif2GdJojqpHJet5bzsOYHmDnkZIw0pbGlEUtJJOiLYdcXjORYmHFZKvBsG0viaA2SlRg4VJmd4HZUp5Z0/InxexcKktv/8wsck4YILLrjgvQCXwuSCFw3mlf5IYQKYkKBLmsyH2K6jXolMYaNu5WGHsItgURiCxBOKy4CHzwDF8TPLMmryQs99xFkRxjl1jKmWMuUknam0cCuVaW1yyuA+AuryG5ypT3HAPd8mq28PeK7UNym9bMrZ+28Rdfxmuxae6QJkod+fGwewB89JTOHhTWnq9cK0HEppHqde15g/SvhoNODtRNl8ss/0rJC24bzmLTmLZHFQdleIKQ7auqOsFlzkoC3luAU3TDtyCj8C7ZxsIh8QOMTzFcJDhMudvDKhOLImSQRvWtK5WYURl1vvEHbB7Ijg6RrXEbcUL6mECZJHOCh2iG5UbJyaEzSCT8kZ+QcRGGVExv4AFaujWpt0/St9t2ELpHx4/ZWxJUkp44oUUoJoGieuuLb+wDtJ7XN19u6Dxj9mbRKdbEdmmmIq501bT0xJhNRxnWVd6eHX6t66pkW/S/8cg6ZOIg0PKSBm4F64I4FHdbCVy9BJV0XUnfpSWJ+HZyElKNjIfwABAABJREFUr2phkvvxKoUvpclz4HL6esEF7y64FCYXvGi40Y//qhChO+LHOu7BxC1pAPxrINU15Ckdx9mXH5/6U5TY2eAlT5+BYJD8uTA8rjSxO0zRgano0ANnr5aHLE/aOiTqMDzd2uSIzy7ShJhslCWGiIsOcZwJgx4nVvNkfA81JsYiZ3TO2AOlfCU2OjERlJrNn3WGi5DjQkL0Iz8PjjbGgp6T5cnsx00xFBfzruSPZNqdi5ovEo7jcus2HqvL4pTzTEXJw6fk3Et/2AIl0vVGffIi99o5TnMfJ127LA3U/XP34NG8r5KvVX4c391n0EGeVuGyz20T2xNaZaw7kk1IEpGTHAeOpziOz+RivDLbJuSESQCOcyUI59kor2JsxzHHr7bAh1LE/ZbMMuPUnB3YnpDdZRuiEnenX4T0tBwxX1n2DLBivbMuaZ6XMYdP4+SG28dcI6OPuTrmKhJlNSr13whfLnwY/chDKxFGhCMufqgS9dOcHssw+tZZqZj1h85nRZAk32Zzku5jlPI9ElfxasKpX9znVwhT3bc5+M9Xlhje+uz2Vcfv9ztSeiD355e28AUXXHDBy4VLYXLBi4bbbZ8KAKwHQwBZXpg/1qviJBhhtnRv65jtmh83Nqmuyvt7z69ShnHemvoAcoVoOAK989f6nOqgchyvI7Li5ULDM6Baz1ScFCbQRLIcXtLGPwJ529JJPaZ4MFY7WZgYZ3pwOk56nmLGicVKPFs4lB5Q5O00ruSw4qz0sLZAkkJxDNsqX9Tb19ttznOlNq3fhPddq5Ln59N2s38nwdBJTF19JEFojessTXTH2A704Cx63coV14q6dNwTrrt3Xe9nZSZha7eKvMOtNw/lFcXURk9ivAGykwJkPPNqnTOcrvo8ttjZeLZldoz9jDIdQN99702ehb4WAkfpU9eCUAJF3rBCk1Rff3bZEQj9BfU359hRt9HQXRM1c5xSnqRMkbKkctlc1wjvRoL48sZqX6UNG5XUNnz8fCNjsk5JQiRjJRUH4a5MV36nfTH36uzDeozDSbkb7p9ofg/eicLlnSprfkXB5cPkggveVXApTC540bDfJgs0mXyz2vA7NCtB5he2EQL8BINFKWJ1aaswGXUMNcH4WMknvoz70fMjeY7KMDO5y5AtbCs+iQczFIVXi5PGv4nJaAC2Wb6zVOl8mxhzbWMWzk3pnfjXz3gHIezWuBlIZjiAYsOO/UBhdMyNyeFDMPL36thlw4a3k58RdtR6Kh4k5UpRlnAdWuqwZ5ewjN3fZ/udKIAmLvolS3oHNA7bBn37bZp8JgHQHUDPqd/L20kWdJ8Kk8h+0tZBmr5iOQGw7zcjJPfhNVqZmI8D2baxLgphi/VSidN8u3VrTrFSacvPfD5btk5xIs1YSLotD9LE+WOmU3PQJ2F7wunJN1Ohsjh3ndtvQsli/bdykSeuuSY3jW07GpYjw0qDLElg74jjxjYTzmNTOWib/yqBTzzaZB/KaNnIue2d4fSHZoKK0doo4ZZuVHiDQmTHDRuqL5NMGcT7ktI02tDy7N2l/CNPhH1bkgpXjuTAVTlMaZxuFWYyu9Rr/YCO34/bLdZaxjfqWMhTCS95T+oIhQlVdK/tk/A93BKetMxut71MzOfBpfi44IILLshwKUwueNEQX2oU22TwJ68b98mPDX5RXZjwNCzySLI6CS0JNRz84qh3Kk0sMpVjPvUo7ux5xiVfE/NhMF4ylAezQGylqdYS1QFsTmfD7L1Jz3l18W1iMr2YgEFxLBzlOOrww2DMuxZ+74D5k/PapX1BPaiOzUqbnyjDJ1PMGg4sR7L/Eg5zBWyhorkNFy6Ca1ZFsjax9IgbAgFbnLhCa0ogmixOSBLhONtusO+EX7lTX1O4U57cS7f++Ck5oAXZlOUxWdpZ677ry4TrVR1Hgz6iNXlNShMT4nTfp9JkrjyzSJByZG2jGFm23hzd2/IYY7EVh7A2RjXObqvWk+Jrn5s4Jr7AYm0irujYptxtlCqfC6akNjZ/J1Craqf8uQ/jiGeFbk8xPpp9Klk97qcE8WPhParTpdDAFcRpxVDSB/0c5e19sTcToXo11cXt27bIsxk3qJpi163kXR3AxjKKDurEMVExXk5JwRL1BGWcxwZ7PB8jXLYsaT+4SnUu9bRpUd07sRY5Cp+V6ZQ0rxZe63monJHV/RlWdAUepHQXvCq4heFrrvOCCy54I3ApTC540cBOX2+3oUiwrTLd1hwVO7GgKkdoWw9CIWHCSJIxwL87SsoVnRaPwTiSDqWUQ1Smd54tLvGM4r+nqmitTZJIcCi0BPDJOu0WniUv+pN0ZlskTgymkYQM7ioNZeH1QxCxuBiageU+FRfZ2mQVws74BE2NnrOBQ3jcZpkb+cQI5QiLJ66M8PDIqwBZ3Bw4kU1IDq4+1WXC/NyusyhFoKg+SsaLsOc9x1VxopYZAzCY7AeUHSn9TClxVnaPsAL3j7bwMe3aznF6UDbVM/s6HvdXlxKe+UlWabyhNygEsh0fG2zPVUGi/Ox5xl2bumJbyMyz39x6YzU2OVhUr2PrzrZBbrdDa5OBI6uAQWnl2ZQNcpC+PG+Q/Tb6b/5R/NjzSYX49QTiWClVryIJ6jhhznsRDBu6SUNVnjgDEctRa3qsrflvT8RJykvC9lTYmO+qsDZR72SidJPQL1t2PBynzjFo/EDk8ShWO0EH4pfCrUtmvmyFAtLzzh8bRsxImf0QEV623XAsdaYfZZS0DyeyUcuVOixv3mmYB+m0jhJmZ9fPwXOfg6VM219BcfJMsnbBc8AYutdd5wUXXPBG4FKYXPCiwb5AuzA+75gWHy47qN01FB9KSpXyRc9PyRm1J5aa70j5AbkJ7PhhAP5x3vFlRjsxbefPFhfhlVPZ95HBrE1cvJg4jGjxU3A8ztKFjiW2dEpj6/b6uzu+2WrIZZO5FQmxwA94sReizN53cfAHFkUAuMVQUsys0oNDy9fxeAaWXc5UT+AwBanF2iS4dPHGz6xNCENXUuR6VtTFpQRvY17ZHwnCMsny8ydVy5/K0IUmDZPJZ9z14M79eETJUsrFscK5fBqRw/qbMidKll5hEuO4WK6cwZJF76SXZCcUiIm9C9S2yTiBSMQNp1t1BEimd6C6612jbZnxKnWLTiFup/EHebviAgB7tGdjZdYm5JNEXKmRt9iMpUmEKwYTotPBbUfYSrham8gk6Gp94i04Wh3A0lA6URsDazmjz2ONWZZNzCnrjuR3gCxNojYhwkR0rInL0y6omTvnFmCba2VX8XSnWtSnlHbq24TumsvVy9pw5Yja0l+35USlkhoKpQryHZTWtS1w/bKPUOqv1b+GHy0TpEbX/IW2pHpOwovS5BEck9NZXdp+FC6FyQUXXHDBgEthcsGLhrAwmWKz85PhXwQuRFS5YDKtB7LFckrOUj7q9i0onq5TcbH6NpnoPeu5AluYxDX20O8CPE1/YVO8KEAqieCZx3iOHiGM2nvY1cSPuAynbG0i3vdsWB1Mc1VW6EmI2EkXFFQ2QBU7dmxRDToGMA0p9f1RPyaSsBVANrc+YP8jUoTrI2VJtDfyV7GjZ+3nJbkaPxPDZOjUTc3PJMCVKdBDUZqMdxtbgJb+JM7+wfSF49dkYRJZIj2KdhJLrZ8ElbP8HE4WJqDwA5LCkkXvpHPzRDTMH4UIZJ8z/2lzRQbK3WccETO2PFEp6V09HtZQQsts2/yqDERL/pP4TgNcv64bTvtoZ6Tu45SofZ5m4yeUTQUIbP3atYFcXyOUB70Chah7E5bpY5atTWzo1i050REcPGcqRoPhmeaoB+VTU5psUZcVwdEKtllwRtcCF/VW5zuWQTMURsPNIglwZZuV1qKIKb5QltOVtIQLqbNhirUaYxPlyMKEw5jlHrUwyWRt/l7rjNOoQnN1h4qImreUY7JS6wmlbpQ4ra+EO9yO8AwLk67tZ8KrlLngLlzHCl9wwbsLLoXJBS8ahtNXwI4IduXF/Do4GG9zCht8bXxo1fjiSMza4Z3kGIOQNcSZLBEZO+e3bG3C+Y+eLe7s2XwX1MuUHLd97NLfZFqLWB6dMkMWJ5byQOcottRTnm1bzGJtMvvGvk2M/w1hR0tcDz7sxBPP1z0Y99mYf2Uv0MqwkhUKD6gPcnEX3swfyBR4rOPleTSpMTCJe+/uCI5eKYxQ87jiYsbHlp29POvEQeMYYioXz+WI4g6E2t5PrE0Mf3+scdTfkp58mBCHb0LN6BtoLGm82jZCEDrL4+9t33M7htcipTwID36SzQoTwImPS5NkgXFgJcLWIHqUdnDXklcNBRHo7QaV6tsEpBDgZ68sxw2kejl/9snXFlu3qAJ4WqxNfBwkW4+M+DhZR2cZ0WyRAivje/vmmiZrlPu+Tcyi7hF6VmK1xhXipjr6kE654DGdvztl/LXWd0DaBDVZsU1l/44pYBcqWS0plN6zYRN58m+g1dVafAjXrZQ2PgikZ8fJiEDg5JdibaNcFRTw445rf71/HUmbf5QydmO2l8FTKtApgetYc70t2as41TI7PSfFEClOHv8ZfD4dvOCCCy54j8G14e2CFw37ruulw0njvg+Ba78p9rf3mT4YoXT3vFZ2fljeD+qnS5u7qmLfd6gqbjfF7bZj1x27jvoNx+7Zvtovz3uEdeLYXfsefbrdgLdvs5+T+cuXLHG7X3JQhsvma6fyt11w25l5NsY14piR9bh0xVfDVEYjDamszGvDrp06yK4VouydS86uDYotGE5Tnqk5WY1nS+cwDRKljetIaWWDNUVbH4woO+Vskh4ktYvUTlLM4Ojl24SbuM16VWP+p/tcC+O+jzuHOa5cKNeIv/kCVVuotxtPvnzPE3ONO7kcf7vfAi8lfPS2v6HrVu4jvNv97dugNc3l8VrfgR6W8XG/lWvfgZtCb+OO2w369i0r6oygOjGisVwIlKK+24inusq7gI628fbbUSYRQVME9pe0ZSrO1G6Z77Lfhn8TWzy2fmzhpWciZoXo8fob6zeozEKN1O773P5n9KCkn6gDury1TdQ6NONi/fMkw8SbIXpf/IRkOFBsHGDueqR0SbShKL9lEe7L5S7mK5CODxn59zf9zmsfbq+9+f22JeM8i005TUvElhdP00SG529+0NwSZv6E+KR0n20aH7TfnnHd4ZP4uuBBsGOFX/f1CvDDP/zD+IZv+Aa8//3vx8c+9jH8+T//5w/z/rE/9sfw9/w9fw++5mu+Bl/zNV+DT3ziE0v+f/qf/qcRJ1uO69u//dtfCbcLLngpcFmYXPCiwffiGl81pUn/KGtfZjEFRyFrE0SZfJ870akeh/IpToH4ylja1WkFooJk6aKzoH+opGfnO/nZ6mRe48DCZH5zBZe+3WR8kN26/OtJOvGTKsdWJtOiIH+kFWyqSS9x28W36Ng7MN8mwGD2BqYzTuO9cDc8LvYCwL9g2ngJZ9yGIC4aRZaXWKNeAzNn1iak6AA63ybqL97FDLXvroZLUi/Nay/PivH1nCQzJYsSFOsSmHUJP9fuk3AEilMF6hjp/AYs1u5+Ym3SxQXO3nTpR02vcek5JtRx3Tp77UNNY17jkoPbgzbt2dq+Bw9Ms2RhYuNLViDsgFT2HWqn2cReK/j2nFQunrXkTfeUD8kiZdA8I4BH1iaJeq1113Y53iw93DRu9Es2gbpzJRn08EmztQmdmuNTwCwznC7tWL8FGa57qscopJajiEWPfJvEb036jQgChrTmvHWngvM5cuQti3ONpa1FliYelBRPvysHkGqyV0f3bQZvsClPlIrMbNQwJrrNeSMPLdFM8nJYc7mwKumtTeilL9Xk8kfL0MaYyAJkKjoIl4JX6n9H7kq5xcKEEoMc6VI/p3dtdDj2+ZSeKaxa+mMJjxC2C96L8KM/+qP49Kc/jc997nP42Mc+hs9+9rP45Cc/ib/4F/8iPvjBDy75v/CFL+C3/tbfit/0m34T3v/+9+MP/aE/hN/8m38z/of/4X/ARz/6Uc/37d/+7fjjf/yP+/P73ve+X5L+XHDBm4JLYXLBi4bdTPanwM7KCiAY++zjRMahC2Cnr/PudWgwnlz3wufXfHDpM9qe+aaDwG0z1QSVAfElLPOxooFlGeMJm2vIFKEYct8mO/D0FFnNwavDkISw41gZ42WP3gfKVh/FNKcHnrYQA1TF++5G7CnOEbLXS3EpKcbPKxcfoN2OAZbAuey4T32/B9nXSRjAK+E5BIZ5jOn0bRKCVOmZpq5MmbtyujPBv0obN69egaR468tMI3Fh6SEpVkZ+sxiBP4fFSWTjtgSBjrngvevbJHHlJ+ntlpwsIYz2acxYuFlwiPDh1hzO0zp9BQkWgZNWyeYM7mS7tyWHlRjj5K9xkg6eSCmidJ8Ly/xTLESrOoGlMNNOo5lK5UWs7S3mF49lt+fQcWwGwpSOxZnrwN3iAd3It8nTVyE7fp14+3a52VfccfrabMmBUJ+JAvpJOts8ycbotq+9cffh5a62fedMNTwbgEzlxTxJx5VFPHeFfiuiDlbiVFAqb0rtWh6gU3Qw6bWXFVqWGuWV0kFLjWo/v4pyo45jGp+1zDq78gB0FibrlFR/XZyeSBjVx7ig5PV86ZScKKDgcMwRzX/aurt2CslycLLKimiUsD8SLT+aQBe8dlDZBh/xmut8LvzQD/0Qvud7vgff/d3fDQD43Oc+hx/7sR/Dj/zIj+B3/a7fteT/D/6D/yA9/3v/3r+H//g//o/x+c9/Ht/1Xd/l8e973/vw4Q9/+Nn4XHDBS4VLYXLBiwY+VjjkAoV9TXWLk/kFcAjwiv3tYXUhlG/cNZQTCrQKFKxyhU6BJmSQqbQRQEwRoAoVYL8Nx6h2AoRyfcT48EdaLW36SZkH13bAfJu1ybad+SiZip0TBrstq1Pm6LnaZG0is1PLUcMafYuO+y3zbUpyR8VTEe1gmmibxc+x1BCIHUB2osjffk14iKdgTsyUfgwSO4fNz5pqiWOFGJ/CkU9On48sTqLCZPAlHvOpONZtUpr4N9iiSIkr+z4JpYJZhMx6ZpxYw9aalnsbh6iby8905fxdHKYwIFjrPpQwVulCva+WJY8rahy/l7twnMcFKqHFIOLrxWV5D8+ZPhwnZWuTch+0RKL+5q7KbfOd6xtxjgedXLPAg3Hua2UHZNuR7N12QLeRKJBxatBUngjepmM4fcDg6lvvj1C7TL1m/mLVMhQ14UR2dHaD3rE2Ceud+V5KXDxby7b2rETMDVe2TtoUuq2xFsczC0LqTctSC3I+fxWSYt0Czn9YidLZaWYSJ+moDWc9LafGld+0wx+YO+DKC0zrDwBxks78EbGhMDIx0/jEHWRs09hUZYr1X6Fped8Ld3FsYeLJjnsf51hSha/S9gjrWneioyXseW39n8AjpO+CdwV85StfwU/+5E/i+7//+z1u2zZ84hOfwBe/+MWH6vh//9//F3/1r/5V/PV//V+f4r/whS/ggx/8IL7ma74Gf9/f9/fhD/yBP4C/4W/4G14r/hdc8EsJl8LkghcNrDAZARMiNMkJ6mkjs0Bx02ECvZ2cpJN4xpKGFKcIk3eUtksaFDcp1ibBKTsLyXxHVUGcWZgMWSJbmBxZm5yehHPSxEPlNGQuu1RlWHts6v1wxlkO4kjurbyajZuPBx8RY3zz1MDs88vrkziLfwDHDGEtZdVbAitwbFpAZH6N1QNrE1IWceupMaUILc9WWHNSEuSHcCWmXDHFBitA/JmlBLp3bcIN5CmMeGemRFk4eHvJdPcbxTUWJr2lRy7nzewkKD637VmepCuvg53ORpHa/h3J4USp0luYDNrmTkiBUJTMfAoBdrY2yeXbu7VDd6EwK2aGAoEE7FkHH+uuIkNpU/vaLd6jOLcogT+H5YlMq7VpgTK36ohqa23i+Ux5gkmLleq1vlULkwNlS7ajM2uTDTrPW69bcqyEUbK0zjHfW4rNYfHfCHV8ZCoEx30H5KnUijh1iKCTd5OFyfybVCxG2AT+AWDTKLlYm3jHJdfsS008y3LVZbGSHPo9tArz+JlSg6lT9C6eKrlzRUwQjQURp2uE6yEZKWHP31iYrEcNa0mvcTX/4+F9J7pVaVjK2yhN3A/J8e/kBa8BknL3NdYJ4Mtf/nKKft/73tduifnZn/1Z3G43fOhDH0rxH/rQh/AX/sJfeKjJ7/u+78PXfd3X4ROf+ITHffu3fzv+0X/0H8U3fuM34i//5b+M3/27fzf+wX/wH8QXv/hFPD09PbdXF1zwIuBSmFzwosG/QCc+V43LBzC+5CW+cZ5kIAKoKPQmSanAsgqQt9bktFFpVbCYUqQqSQwPpXr3Xd23Cbgrk69zlrl5Jpb+jlKDmcQI324CnR9lV+WGewFo4UyZMkUIoJaf/OwOQKtvE0ymmxy1NDUgRgAH+UKgQImzMd5nqW2t6i50FiZCYc4T7DWmMDMdT5rVheUn5jc5kAQQSguENKFIA9Mrf6iNTZJEItyE8hYcSi9xvWTBeIy4UI6wbxM8bm2SJI/pr8Gj1JqhvDMf1zvTXc/jc4LbjvDZUcO6P25hEkqUglNMkhUOlCZHCpNqVSIzT7U8AaY1HVubKNXjdGvWf29LDgvqQDp1x2idb3kUGU5xF98mRMB8XTT9F5k+TMqWnM0sQARm5SHu64SUKAfWJmOEij8Sp1gzT2NhImKraV42DrPNyD8cs7q1ybO35KBkOgrnZ1v/qreJC6mziabKUpLrkCVNSol1m07uRjp+OGFmaU2PFjpj+QuJaZzI2keJ/IvBjUiq232eUN09xMvq1MHqHQdOfZsck5UDC5PqP+Q4rtb7nLYBTKvA6GUmvdFmao/T/eEVfkQveAgUb2BLzuQKv/7rvz7F/8AP/AD+lX/lX3mtbQHAH/yDfxB/6k/9KXzhC1/A+9//fo//J/6Jf8LDv+E3/AZ80zd9E/6Wv+VvwRe+8AX8/X//3//a8bjggl8KuBQmF7xo6C1M8j3SWfkxv+sLQrkhCtniq2z4K4k2Kr/P4ewkdlQweWuYU9PxPJlH2yKi1vYoZ7wIW7YcPXfXNFpB+Ec8sTbBON3maSMFi1r5+R2V27Jx2KqzWExFycDQXC/m9OwFgK1NnPXScLarUP+aGemVa0a2MrEXNb+GMk/HYsOO8cX2vrVJgY4/ZL7Rwkr5meMVcWZzC4keQNmqA03Kk7wrf17VEoTY+jXf7nXpNOWPbQA65jgpOgT7GFDGkSffggN115QjpECxeGbA71t8aHzNTEJCzl/rzM+T/VfQl1Fuu8OH2iOnr2MoTvA5a1sVyQlRgnX+nVuYKIVH2lD+Gm0z2iOQaW0iT1vUN+9antM9xUX7ADvDNjpWnclOAVz21dqEu5oImkfGWirOWl3xICNt+FaZW3JmvG77cLCt2libWF7z0lS2sRy+j+kI1ta2mLrY1MLxPNDO1ibAuk2n35LTEo6Zmr0kgWIsdoOt3h0qT7S6gzJk+hWEq2s1VT5/A2DTWHP82LRk89+ovTVT5j09N1QtXZHPNwoe5gkNi8QzK00c+TwC66VU99G8GOGhG2RaUEiYNcsoIAznopw2+TSVaRUXFS3C4yy8G/kueB0rRxD0jfE7UPhe8LLhr/yVv4IPfOAD/nzkcPVrv/Zr8fT0hC996Usp/ktf+tJd/yP/+r/+r+MP/sE/iP/iv/gv8E3f9E2nef/mv/lvxtd+7dfip37qpy6FyQXvWrgUJhe8aDCnr/y11LfkzAS37pApiEg4wHNjFOOldrjFh1fZyS6J8V9kCgDF6WtiOsmiRBD+VahtjGxJ6YPS5uGYgBUYlrlnxLGPRm5K1iZUXvTAEuOAUWKnr+u3kZUdb61NJvPm8pRSyYJLbMVZ+fIkpJm05fvvxYX4XR63NumcviqFOY/9Hc3byRsYgtsUbNzqwgpoEjWoD0oCgcZlrR9Zf6idiAMaVLK+cMZZSx1z4GpbFYeSZzErd8Z+vsR5PG/0m9uKUfZyB05fq3VHZ/GxSDBcVmr8AS5zS44LEK4ooXb9/RShg/EgAeORqXZoYYLYpqJGY9xibrYrYT3iCpV9bF0J3ybwu2CNUwpDkNJltoHUfqSFtcnEqbU2wQENMyIp0XezMAHhPxUx2IcyRMV8p8y8sk2U2drEO9M4fp3hdksOK6SYsoZaON6qOJ0Zvk02f3/LSTrpfbMWAudh/+3QiRvPaZtzQ2kT1iZGJLlhpl6V5hCtJgXEaLbDy37bbJko1TqVO7wc6b4AJbC/EV9+XoFEghdlBREQfk4a8gnOyJdQ5jghRw/CIlZ/b23Sx+kS5zTCw8dxfkttNHUe4XHPwuQQzyavhR/8Db3gQWBa/TrrBPCBD3wgKUyO4Ku/+qvxLd/yLfj85z+P7/iO7wAweO7Pf/7z+NSnPnVY7g//4T+Mf/Vf/VfxEz/xE/jWb/3Wu+387//7/47/+//+v/GRj3zksX5ccMELhEthcsGLhmph4kK2BAOXlBSAW1a0ViYIJmh1CBs8Qf0Quzh9FYE5nRSNtPgibHibT4D4OrdtGgIPog+JwVTi9ctlFiaQ/sScFHZLER1HdgKL8uDQwWsTvx2VM3xNvPBn4/MFT7PfSnEuBFoc1V+fPVbjVIOQMwShLOG0DdDd38F9kBTW8ncISgMz858S79aKTwRMcWcOYTP3i4VDRg2jcP+cJySJ9G125pdUVd6WI1zN0g5LHoZr1J2tSsJ7gBurINaGK0OydJDHoXH6qkt+YuxP893xbdJIGabcqcqYdLpEKZ8EiyYt2wb0cKQwyYSIBH2g2aIT5VVNSbwNxcmkV4BttyECCirrzUVbTl9JIbMQxgWnfWpjpcyvPBJe3y6Qzc5kGR2X3XyMzA2DU1li/VErJxuw7fFsc3az8RBANuRD06NdVtKY8iU7fZ1jSnmETvQZipVZZjelyeZ9ljkuab71i87TxOhJyZM9jQRFnBTIadM9WOlqIp6ewnTKlTyG5rxvGNaJvnSt1CTuhqHONKZW3ZVBsj4DSM/c17X++23p7PtOYb0X1qxgSGQEaYg8vJC+UTiTvxlHN28jlTXaRmWpyja8dwpersvpHdNRom1cn+Fty+mC9xR8+tOfxm/7bb8N3/qt34pv+7Zvw2c/+1n8wi/8gp+a813f9V346Ec/ih/8wR8EAPyhP/SH8JnPfAZ/8k/+SXzDN3wD3nrrLQDAr/k1vwa/5tf8Gvw//8//g9/7e38v/rF/7B/Dhz/8Yfzlv/yX8Tt/5+/E3/q3/q345Cc/+cvWzwsueKdwKUzehfDDP/zD+CN/5I/grbfewm/8jb8R//a//W/j277t2w7z//zP/zz+pX/pX8J/8p/8J/i5n/s5/E1/09+Ez372s/iH/qF/6JcQ61cDvQVTCRhzryRoaGH8611JwGA/I3MP9/wqm5QvyGxZMJEhDGQLliGQRB06lQGT+UkWL4qbTiWGhJKnyhZnHx6qguPoxJwhICgJCIqbDmZ3K9Ymi7XIyslS20NIETlyDturOm67TPlHi6IjSrDMkE7TMZxcwTIFPy+kXoFqVOjvX10kOrU2qViLt5X/2lxQD0/hlP6O7g0hEnv1bZJPzzkVJ2QarOfPrylPtEs92AC2DLHyI1wcwabOU90FFWfASXGyhncv0Po2oXvrw4TbrXGpPOdt4mhOZMmGBIR0rHDUs1qPVLwZtzXNcDhax4dbctx6I+JcyeHKDPK74TRE5wf5qUx03yZUDys+0Ndr6SluPgNYnNDaFjt+B9naRHObCleCjEN3QikB91cy/ZdQXDiBxThuOPYjhtJGtbU2SQqTJcw/LPOiU3O0KWOrfWpVpmXJam2isnQcLZ0mZa9rewXTSoYIIf2WRBHFMJsEkm+TQoPZwoSpF+wdzzYH3R3PIljaDro8zxZS24JIv2N0ak4iSQdhmzq2bvIpN6kbuWs+56QJl3aYpKVn8TV+FF58m2B8yDmz8liUHjPRyEbk7eIoL9fRKFe8KLcDwEjwvTpaZYlHMX2buf10L1zwTmEqdl97nc+E7/zO78TP/MzP4DOf+QzeeustfPM3fzN+/Md/3B3B/vRP/zS2LfD8d//dfxdf+cpX8I//4/94qsf8pDw9PeG/++/+O/yJP/En8PM///P4uq/7Ovzm3/yb8ft//+8/3Bp0wQXvBrgUJu8y+NEf/VF8+tOfxuc+9zl87GMfw2c/+1l88pOfxF/8i38RH/zgB5f8X/nKV/AP/AP/AD74wQ/iP/qP/iN89KMfxf/2v/1v+Ov+ur/ulx75V4DWhwkzdCY0WHDevYwYQzsS2PLDuKdNZDgXpHaqhclROzIz8BdCq1uoTeMppTCjItQPErCMP33MyuTEwsTCmyJ8jugQTBDKg8XKxHhwbhelbUx9DKf784F/FIx+b2xtYrICiMGz18OTwTLQGDozJzXTKjLwTvxDviLt+3kwrAB4Sw4JD65ik20qD8y3yGBWvaZk2aFxgaxDOH7GiZay0ZHYqgP7Th1XDFmRIioOnD6VLtbMkKNmvIsUmq1NZh7lTf3MhR9syQmBhwUKZu6zhMHMvjP3Vkfn28TuZmHiQ1ElHxYqWIDQksa4Bk6699Ymq8JkhKu1B+cZU9/CvHUHQVuAoUDQ4R/k6HhhrXFE0NyPykyTWac/z/aDpo67K6HlNnAw4mIIGnh/sw8TW9phybEFPReEEgXmjHVuyxFAsTttHYoT61dHvKhSmAKCqV+kS6gGCJENkqxNZHYnW5vwSTrZMQieGY4Vlzb8kEXdrlOpyooqGmwmDVKe80OmbiVppqvTyW12dOwAsfk30jPFoUtrmFrkBhM5kpU0Yay/VB+SvV17cVOPWJgE0QnkBXPMywAxWfE+oIQJ96BnkSmUJvC3ncvetzaJU3JqeerRooDJlnYZb814m7LzgvcEfOpTnzrcgvOFL3whPf+v/+v/elrXr/7Vvxo/8RM/8Zowu+CClwOXwuRdBj/0Qz+E7/me73Fzuc997nP4sR/7MfzIj/wIftfv+l1L/h/5kR/Bz/3cz+HP/tk/i1/1q34VAOAbvuEbfilRfkfQK0wGIzni1Jl6OMPuMnWOQ3z1kynJiwI3UchuSgbbzw5v0NqJr66TaUztgoQL254zWFOSRRbrF8dnG22HHNWxqjQuYJb//ok548utuuCwz33xnbVJru+o7TNfJh0uE3SgosXapIoTpzAz2gfFKDMjZ2WxFX8qyqZEprphx9gms0lu83zU7yF1J8m+KOkOsW06rpQ4qEe7PJ5A8TNMyhO3KlFSEE2LCpnKGy/TihV6kq5I+9890IgdJtizbxNj1l9lS05h4FerkCi7WpvYxKEyTZ3rscLxrBptprQFjxDmxjuImdZbmJgSMCsw2MpDnfbFF++ksJg0R0WguE2DCT5Jh+bRokyhNg9w6PK68gbFSe2N2kZtd64FPh7YFSEjzZ23Svg20Xm6jilWdCpBZCclyvQdFNYmpPBotuR4X2g7TqtsqfmrQqWzNinHn+UtNupDybH2m2GnrXlZUrznsobFPnSDZtWWKKNSiz2dNXoKuturO/RnTPWNpSTU2qNwX8mRtxBRu6YA9AZLnqayrAsWX/9nYT9mGKEatrbZ2uRZFiZUYCnHfVqUF2s5qjbq8bZ1LUd/jtvWE/w1eLPHfrUvKKCucH29dV5wwQVvBi6FybsIvvKVr+Anf/In8f3f//0et20bPvGJT+CLX/xiW+Y/+8/+M3z84x/H7/gdvwP/6X/6n+Jv/Bv/RvyT/+Q/ie/7vu97V5yH3jp9JaZ8FR50Mnri5cKPCQklWspNxnSbX2Xjd0dXHydCsldqg5Uj49lx9y07OmWjiZMJ+DdMq4tgABOP3lyrL5PBVD9iYWLhDYqdrU2MCYyqm2seXjf7sJe0nFeWshY2k+5tC6HAQWOcaxyDu2eg4zUzwz/FkUUYCGuTdJpFYjh4c41Q3V14biMwicbaquEx0lPI3tO4RG8rlw/n8t1fw5IPKW4RWcjaZAjy5OdEo/7OisXrLekuOij5NqFhhqL4NsG0NqE+PWBhku/jj29HoHxjed3Jlyxd8MCxwiUtCRkxRqsVCo+fKU5sUxjhmRQmmNtLNMV1liBhiaKpTN0yo5Bxko5Miw9LR6nX75PeEXFToqcQ88nkCyzKoOAnAtxusT3IX8wkWJthMlfAblYpltfyEcWaRxHrRpRlJyew2z58NUnn22Sbxh95bSh2iG7R7Dw1Rx2LfEqO+13ROJUHLvx0vk18RZS274QVyOZ3SjSsj5d5Qla8I6sq3rvyeyDgN5RhfnQoS9Gek2+TOTpB0RpFiGTy0lExx4iGrsu/UM3pu0hPLFxyz+5bmGhCgBEPulJ9m6yn5CCRNSYXftM5boviYtLsVFa8Yq4n2ie6lWjY7DVVtli0JJy09KGzPFFc1iYXXHDBex0uhcm7CH72Z38Wt9vN9xYafOhDH8Jf+At/oS3zP//P/zP+y//yv8Q/9U/9U/gzf+bP4Kd+6qfwz/1z/xz+6l/9q/iBH/iBtswv/uIv4hd/8Rf9+ctf/vLr68QzwYUr4qFhygiTVUnmGHmGKGeKjDhdYvJfEsoS/iIrAG4yhBqz+JjVGS+T5JjsR/HYAkUpPepb8w/fJtNR6wP8R29lwsw5hYuFiYV3Gws1axPFtsmphclh22qDwQx/LRnpVkdYm1D2mS1VkYQFEwgjLkzgWVBgTpKExfl+FBt23SEyjiBmpreFg+Ftw0VGzOEhwA3Oum67QVyzUB5GXRj37N8EyJUUscWEWjp6eAXKT4JBRcE+1arn07DaAIV1z4LA7PepM9ei/EhKillGcZDvrF6eWObDxLtsAkItT4JESWNrFBYsujQfJwOhSS/rdpesMInwsDYBTBHrSuGqsABCIbgDtk1HEXUdbgM6ypMUNoHnYZ59CvDTKawTDZRjhYnOpu06Ew31eLNG4SOH92l9YtYo22w/rE2GcRf1846ViZgSZNbnbXu8I4vRU3JiS9YmYeXSk0TX9RLdMAXvckqYEom18bLpQ+ENil2n9Q6ewArUw5+W2S7XX+9n6TZ0m9qxtpLpWBdOz+u2mxT2vH29/gqr2UVzBYkSauhOGEDZfOi/L9Xa5HELk/FHT+JmUa9srUMpD+VPbR/kTeRUT/LWcsXaZI7VpTh5BqRTrl5jnRdccMEbgUth8h6Hfd/xwQ9+EH/0j/5RPD094Vu+5Vvwf/wf/wf+yB/5I4cKkx/8wR/E7/29v/eXGNMebrfdv5YGb25c28rfD0Z3cHGsyPA0ALxNx9NpWw2gEDrBxS1OmEcGp+dnuODAdQLZfwpSuuEFDEZ726LdRyxMhvIDvYXJNsasszAZKgMbPLM2URdEShN+1Z/lc4uUeWmpU7MD1l0BeWIRILOnWTyYHK+EMsAZOIm0yAMPa0mzMbeiCzOOJm5J12fkbbqy3yaOZvHAF22d4Th6To5cax5Yei7n/k9uN1dshBPUkVdV4+jd0l6O35vy89p7nLAr9O1bCF4uECQuPjHvWZg4yHfA1C913/YphGpU1SlMSlqqd8Evp9W6OO3IH8nirJXDrpwwmmGKibV8r0RRih+JVUmTnp2WneSxNoHWwsWyqSkujHjteUtOOH0VuIIiOX21fNskOHTkcHIYi2H1sU2rFZEY+22bL8AsP0jRYRK/00OmXvV59WuC6WPFtgYN2Mc8fwoaGyQm5oNOpfWQzdXXRBevFm9TbFqbWB6T7+M4YsXiAwQl7HM8TfvDdDR1cJot6dvYIxT5uL47zzhMa/qTwiPttlteviM970vaQXh/Xvnb2+rTxpa/z0Efv6BFTKc8v5clGsRl6T04rQHIgs/K6JqPKgv8ar2EH73gpT+GN8+BC+6CwuxtX2+dF1xwwZuBS2HyLoKv/dqvxdPTE770pS+l+C996Uv48Ic/3Jb5yEc+gl/1q35V2n7z63/9r8dbb72Fr3zlK/jqr/7qpcz3f//349Of/rQ/f/nLX8bXf/3Xv6ZePA90CmTbFtsj1MytZZivDn6YFBbzk93IYnmMiwQwxfDJ2sO5ifrpbJqQKKwukADB9QdzaqbsOi0eAld4G4YzuP0S1l2AjZUfWMLBv5Nwsx3nf7ge8CkH6tEbl4cuH2jlLD3VMd5VLmO9F8g89HHUoVHXxEwobUvd11wvwmol1UF3F5smzvPt+nuQxNZEOAzOMa07GL+TvCVsc1A2gd5ui1KDFRTLyTYHeV9F6cLHBWNXKKWxEiQpTfaoJ5ct+TuFC1t2WBkfdmPwSUpIjH6OZ0FgETooftk241IWCROtoNCnrW0fWJh0aarDH8m2wU/VmvNPfR4ylbLwVPaWZ6cgEuhEffZsJEhzn10hbXRt3IXoH07a9mGHk4/Z7wa/fR9bdFTmbpWx+M2ZK2CKkahs4BVXIp1LWVBZpLJCiHqfTMEDtiaRVLcmwmH4BoWYg1fyIMWJKnax1b85BQC26W9mWsxMnJTi0cRbWadQlAfYZt6Jv40LhJaDDMUCTPFAz9wvy4tIY8o2Mvd5AWDbBLf9QMGxPGvg0+TFWdkuzfHJCo2d2+PwfhDfhPc7eWxRvKovkxF+tbKZljbPC30jesDhSksLXdWDOi+44IIL3otw2W+9i+Crv/qr8S3f8i34/Oc/73H7vuPzn/88Pv7xj7dl/q6/6+/CT/3UT7kvEAD4S3/pL+EjH/lIqywBgPe97334wAc+kK5fNpiczz6FqsygKAkiDdPCDJTGFzl3iHbvsn8k/Kz117pq2/XL1OzW3bYnY7ZHmcyR9OEzZo+4yPOLqrW962vx4z3iXTozePebFnog6QcRFBPwGM4eS9oZqDdJL/LhwXvVC/CtEsuAjIc0lotkwAqJkoe5426ScZt1EigPHb1V5TdMw3V3ZpQOkoQw1qj62oOjqH4HpVt+UHoIR/qsCzuFPd6sZvY2LT3vXN/+QJvzFdx2Vx5xG1jC8a44/uzyMk280wSqr9ar/H44L60JLe93LVMuu+1TaYZSj/mVojmnbvYVsydN0MO0LkbnHIUh4ri6z5OyftZTqkDrQkvVXD6j5xZd1v45+XosTu7l45TA1XdnNuV4ma7jmbOloTlIYz9SZ9coeDB+j035XF8Z//ht7t6z4c0zugnburmHF1W97zoVLGPOh1XKjJ/WK55HNW3tSfF36UrBzeqZ7Y72Aq/FUmZHUiilcKeH976U93jBXVDZ3sh1wQUXvBm4Vte7DD796U/jj/2xP4Y/8Sf+BP7H//F/xG//7b8dv/ALv+Cn5nzXd31Xcgr723/7b8fP/dzP4Xu/93vxl/7SX8KP/diP4V/71/41/I7f8Tt+ubrwLGAmwBgOcNzIRIxMMEUuAITklepaBYn6HAxA3CczY57xteIIGHM4CsSdce2ZwmB2lDLtOzNQdgmVo3BigXO48I+PjT8AE4H33famU/3Wdi6wPHMXjxjijCB//Rxxw5rDhHNqN90lCwKetooGdw4iwiCPm/c/tX8U1sB1FZZm2tJXyudfkmfkgbWJz7ky4Jr+xZzz+ZyarooGWlOzXCgO9lgjZxYjtOTGpMGCf+57FBCg5DEhFmuc1ev3mZ5OAeKyXRw83ConyKAn8sWzCSL8XAWpo/mdaAbTE1D7KV9V6CDTxeaqc6UqZFDb2ffpNDPS1jI2QfLYHl85T5q3+w690clJk54U/ch8DvGf13ZVCrRrWuNmM00pSXdz/kwNGGkr5C3XU0DQkZlUalRt6ykXVgp7KaMFpmGRib9kS5Do11E4WjcjmKGcUiJiE08p/dP1rgfxh/PerFx43ZXBjGnCyom0/BKlS+PXvPc6FUtHl/mgNs88oQk35VuIRr3VpOD1MVxpb9CB4CPyej9YepTPLwQeRtBGe9OHlLWNnfLvSAphmP+p+D04VdhccMEFF7wH4dqS8y6D7/zO78TP/MzP4DOf+QzeeustfPM3fzN+/Md/3B3B/vRP/zS2LfRgX//1X4+f+ImfwL/wL/wL+KZv+iZ89KMfxfd+7/fi+77v+365uvBMUP/rJ6LY1nGH8QUrG7VPs2hVTzdhWqG1AqqFn6ltrkGQGQORidvchgNBnGIgAweL4q1BCxqBgUwT5mC2hsLC/CbC+pgwHBmli7ewxBPQjcIRRP3WrQ7vtQyx9crljsoQ1mm8KntKcfSVdXFimjQnRSR5tPO2zeoZRZ4FzTD4Vq9F8Mj9GHkq14yGk55lOw57JvmUTIxvrAEXbAhxVkq6ioYFdkT9SSlBgoNn0ZKPG9a5bh0tDZxO03M9yvnP0pa+a6l3ljlKm3F6lOZNG91SCLnRGNsoLJ94Pi6R41c4TtMUiiU5twsaHkQ7fAvOzFNprlfBo+b7g0ihYRN6rm3RqWwT9S2VQV9CMM20mBQKlAZI7s8h2OCbHxibczsgT46f9TBrYextNfUB/juwtpexGnWPY5PZF4zL6LLW0L21HP8YBDngH4PQ9miY2KGoqIDyvmvaPbD5c9dCkecNd7KQtAO9XD8mVEhdkYtSmdXBHyi6cJRt06lJvzPNQZANpw2WByU+kak+jutj0h0kLR60NLbSs5JPPWXFeSl3wcNwOX294IJ3FVwKk3chfOpTn8KnPvWpNu0LX/jCEvfxj38c/9V/9V+9YazeDNgPtADhoNF8jRhPjsnGTv8VfswmgqUPFj/UJgm8Hm47vl+qF+YaNJQaIlS/AjqdLKpOwQD+TEgnxofZfWvPTtixCFOcPC1HykRYYV8QJfhdEXT8bMfjsDjQi1talDdnECqqGvuY+sFGRrArsNHRzUtO66KNGXHa1QdKSsP6PTbEEcZ1w47dPRDkvkT4WNRY0xzf5UVobNHxch2Dr6RYKRKDM7Ozb0seusDKD0cBLiB5W1HG3KIgqmisMkBWJjWO4gk/PoqYFQ4xBLlMVZTkcif52jpzWW3L65IWAkb0w8et4OGKIjTit5EA53uPfJuMfBJoL9CJ9pbCIa7X2zE6tck8An3StDL3FTh4PlGi8jswpcq+j8enp6nAyKqefGq40YCqyNSlWVfp2FxjPEixYT8tuE3Ctq0UqpTOIXsPPZkttZDF2hzr8Q42r0B0+k5R66fED1GKtyE8/j1IrcePVEpVNeXNscDlJOUdgvsT04mQz4fcCCsfglSENYhbogCo5CXyNPXwu/d4paaXRg8bWWhd6cOR9ibRmIh0MpHygKbuQp/XuFx3CVMZTY0ZiVKqksO1HaZrtSMXXHDBBe89uBQmF7xoqCaeVQjwkxNhvHLmCPO3WS+FxDFqDU4Bgr94ziKmINHJ1I44q1mikuk00ZUmd9joKl5IYv6FEBi3XcfxjWZtQqzvKK19/CMqClB3ePhdOUL93kEn3XBz6TneTG2ia5rHIqo2R4ua3ochKTREudv0pb7pfI2KZ1LMKDlsXeRAtldSj+F64t5JBkdc98RnDrrqXixJcv5os9ZtQSuTcZ+Vp6tuwZDuq2ni0aMfWnCKMLfZ4FeHyPHKAoCVycw/rdsaVwUTTi9Mf1aUcLkorFrr1IzXIY5sYRJ1xNyZ1h22xPepaBWb+1FXrGS2NmmA6cdBnlwXy9zV2oTLhHIlleO1582tlDfGzwjZCAsUuu8Q2Zz2TlQYs2hr1is1rQzBY0Rv1iP23hSsmM+jxJUqI5miEvh67ejxXCuujO/f6PFbxMN9XPGKUQw8sxKDp/wD1T0EQdcUw9lsIUPI4aANioeOK6bgQpG8HXMIG1YjWd/cU7P9ID5dRfGw6BqYxng+BN3wMPdHD+M8qtAnesEeXpXDQmlRLuOc+7PkJZwueAzUHEq/5jovuOCCNwOXwuSClw2FU2MBw56cAZgfx/h8CVeg2M2/xunJD3xVukTWvKOmihtWbMbxtpyKz2m7cKZQrVKldszaZB8f6rf0NZR+MNOPJ4v0NfYxWLGeI00yg+D4escwx1JVXYgTmCm/CRwFw2UrT1fpq4AAug+UnAMGMavMuVdu+UGgrHbahc2gaA/erio7qARLBr5SWqnkoG2WQbzP0HJM8LQImRYjYoKAKVrIp4hym45XtKLUmqbWNQsYncIiMfKMfCc8WH05T1aGZJNzbp+Fhtye0mtfcfb6aMxjxVc1BILQVGsT2hZjJ+zAdLVBtsBCfxLC2/aDboIwcTqqAw+zNkkn6Uh0OXbhTDoljBBC6WeVGgK2pnUoTaAAnrbYmmZYmkKJ96xI9MC77GB4oEDGK+IMpakpAkJxAknva1Ej6Vydd7fkpJdE9Y6F5KfmIFa75XSDDIsuuoN7EHMrxwFD8a0A3lb1tpd6K1HQNalm7y7Pb5ru1Km1Ei9rpKN71vx83HhGfsxZTQ1GXWs4GjsORwvxEH5KYsU7zaB+LmFoQruqnxlngJZ3oWGrsmPiMR0f5zY4PRIO687IXHDBBRe85+BSmFzwomGV54zZjLsx+snahBQjorZNZTD64sz8bMBMu52vnnXzV2D62FjZZ7M2yebv1QNAiCAdY7ikJZ1N4AcEPz70JhJb72Um+vablTHHbEcgR+xpYtqWDxYFRZaWjKHvgYX9c+ixDrxsLPf5RZ7EsYl0o6RpGl7VYc8D3TbIfiPkSj3vmIFcGfshFFZrEzoCeConaM/LuPRVr+ZohImTcr32bAy5B6P9sFyh4VLuJiVM4SPWvzHsiaOnNpo4rtfKprai3Kk/k07QaNpIihSro1PYEFHr5npeqbpam+C8fMCReN8varbF43Z83SXfJjOt6AcY934BTHqxY5jIuR8NBeZR6p62D4IuTtStbeqJUL1Nz3I3g4IEtihx9uQqDAQhVE/zeKWyC819HvgvhP2QqWQ3MLZFNMWNu/v3Omx+/rIxvh5vaeOXYcN4Bb6sDugYL9+ah5f1IcxM9ks0ToC5b20yrEKEnpHDj1yaLxtM5XW8IBvhXqFC9SkV01IF07SZvlqbaMqS6M/MpF1dyLSs0saFlvHfpc6mrtoG1X3/hV/A8CZOtblOybnggjcHl8LkghcNWn6F/SsoM4wjIZQWivG5zLfF0Ne/5VedhGz/zbc4YixbxnQ8BGtd2GwFRFgEOcIBh2nlw1fcJD/bkX9PTyxMVMbY8p8z9JyqTVxKcwY9fy2TtlBWKr2KWFHFGoViV5mvW31cXHFjX98treJ/iMeZIoXSRABsgN7Ab7EXSc7auwOESrY2WXFUVEsTODMsJee49Ry/WQ/xjqYYYhvocbkyZGfFyCyY/JVEvpTHhQdj2uPZHkG4cDkpzDzUrCK4f9TGjEtKkvLFFEs7RXhgvBbcQfmoPW+rvFAARNlK7BzrOfCyBY2qViBWa6ci5fYeoT6reoCtTTR8mxBlW1pimsxNZYks0Y+EvUr4NvmqzetiHUks6/k+igJn5LMJfKxaWqkB92e1NnGlicz6BVO5g/IWrV98IcJq+Bk9UernPgQgiarOqFJC/5mwDNmsZzf8TNHF9JWe6+/RQ1BwlU3i1KmjjigHpB3SM7KWFS/Nm3Ft1FHZVYmbw1aW6RbRNL9pTkOlYV5TIlsotG6tlwKVlhW6ppQnSOWCQFLgtPQMhe5dcMEFF7wH4VKYXPCy4eDzlqkhxDb912L7dJopIVpW6w4WAzK7HinWFis8siWIWa1QBcnMYtZvzL05T5SV10xOX40fJD6eLUvcJF2QesXOWNNJA40odbjdlWWHygRLkXlSHeE7JcknqfkxfodNaxYK2vB0ojsitvkOdL7rPBnE4g7OED70//Bc8G0SfVN55jXFjz5Tpk+W8ezWJsWQxKQB9S6zBIHSBgrDTvhac4gyfLRkKBJindSlqhRaVQHWjjfiAgTH935FCAdviE3bqQyin5GU42o7Ga/jtLCwoXGiu+oaV8c85nfMkDWOXFfPE8LM2oQpUpSd65uoFs++ozm/nr6Tup3X4mJtYpTTaDLXUwhBI9DxOBl9MWetAh3OWJ9GxzPpkaWFtU+PgRoxXYDeiM7+oaiJfK2hJ3xHZIaGTyj7NkduOGPd3DdBLPVW9dnGhdWGwGj0qDusOfjEHPU81HMiEun5HmVLR/PGpQfPbG3iJ3avJMvXX3ICW5rp2l0vXe93R9bG1a7VyuSovK3rla2J+c9prbICQZNYeVNJjb8rDhvd4rCX63DQ5Z1rG7d09YJ74Od7v+Y6L7jggjcCl8LkghcNB/qSmovEA7Momc8q09oEwbtqKC5GvFINSHVZ/azOUCC298z0ggHV402ML2iKUK4k9Uww96N+SsDAdSgEZD7OFmaF7tNgMk77PqxNZFZhW3A4fMrEH0QzU9+8BleKMAPnUdYVrrDgcPp772kd5ztTVMZJOopViVU0GXLUD2+ju/dp7CbBFAvuM8Tup+FHYP2cG8e8ji/Sy7i4MKALM85M+rmypgggqTinWxvUnks9Vm3J00Bi1lPEZNRJOop2QRGZ+Y+iOR9Xm+oC4VrLNoqXRXjRUgal/jJudQpG3KryUMpg1tdch1EBEC1axvegxU4tbOFcX7Y2EXN+4criPEczSBoTz1bGiV2g+H3fB+ZPT1mpIlE3uTPxEXzIh8msS6munNeCO3Sf9W0CashW5FJkqa7FgYR/CtvgiO6LuX20Z78jpASZ8Xcpi54+egu+dKGA0sSDoCpbYMcSHzReSUhZNmNURKZ5C9A5eU16jmYt1/rSNddsR/a6+vq85yObyE8lRQ19iDhrVpdyI6x34x45JSdvAbqXRwO3mmc2TCTtgkfhDWzJuY4VvuCCNweXwuSClw1HQhVW8fHobtYmqzl1MNPtHnBvm74oJmVHpCdlicC3AlEBwLamI5ev3yvX7s80Nm9nc3gNpJlx2W8AnuwUm8ycx7e8fmwrHIk/XCjvYBJHJp2kM2s7Y+VXTGdY/RXSGEcmndYmN9XxMdoQL9gLjtO6fntoKmKWeigsJskyp/yO4Q6OMoWjfSrQsmRAQoRx5UXiKLAwv54vFB1hbTLS05Y3an96F+px4TgEPrzFxk7oadMR6a90/DDnpXDqA3L7CRcAYW0SbbOypeKVFDHo36xYFmkcwQaVObA2Yaq00hp7alo8WHerb5OFWu1RsPrXcIUeV8xjvby3HWOLG+gEHcq/K4DbUJoka5Ogf81OytxGQsaixFN0LUkjFWM1CFJ5l9ZGKqrl4vp1CUuJZ2uTQVsOfrMI5SOKscQl+lnqKfE+L5ux4Z69LjBF3Dg+WJZRrDrXpNBAzVyRpB8SPbnb20gamkBgVb4QvUDc+W8eKbfPo9SgHZqzRj3cRydGMQ4WqHlOT/+iwtmKZIxDb42yIHfBBRdc8J6ES2FywYuGoy85LUN4IH2wbxPZwqdJCAPEAk4+aYSz+KAAbQXR0H6QM1mzOnEbE9GZNaxABh6xlWekUxsghIzZ4a1Hs31hrlm4dNxVbZsOb8Eh5crB2FK3c7CTI6JbS5wz/zMxnLTeVcHcwy6Vz/qY6dsEwJPlo9NyuNX732CfgyflnUoMY1qPlFOPwcIJt8+x/f7I2uQZF3QIaFrK1rpgeed9jjVblvSSTlSTutOFQc3SkMRbsYqYkc+Fq6IjlD6Bk9b8VCYpTaz+IjwkZYiG0BF11Pd2upzQ0qcSZwXC2qRagRRJOgalaV8O09Yamu07irFDjtqMLTpUq4+DUJjiqnlJobUyfZsAGIqTeHFBo7xHRH8e9GGSlSZAN3ZO44sS5mCH6DFMVNgSRinM2opxPP2cmJKXVFdtB2fUrFEjRfyM3DB889afZXbGy0nd8sdJeHnWiUGaM7lA1cXevfQ5ZXgzYaMYObjStK5T3PIgEAgyQUiVdgHKm2jRzOODp56XG095aYAX2pbqnjHdSTotrbvgURhKwHfGGXR1XnDBBW8GLoXJBS8atD6U34PEW4OExmrdgVH20NqEKl8Z5nsQipGQFWQyuABv0vE691kGdBIC55gd0qLFcYZFTAkUjLWmz465+3EEcR1I25N/r4ePg9BlMcaUsbXJq/20E1dajxNO9Y6/u6r5/23nzl0kDjl5fravkpSugJumv7Jy6Plwbm1ScxMHT5cdAey+KBtJI/LUO+YRG8ZEszIllDgWlz4HJ2Z/jV/32M81YhrORWlC5UF9sbg98i2CSicMFMEgK1wIH5JkWImSlDaulUVeMIq0eMZylpjjakL5pF9mybaPhNW3iXoT/vqFVyaCNpliIrVj4aHgdXph9AZZeZN8mzRHt2uiNUy/rM8WF32zo6rHKyYFiFubbJCpMVKplnOa+nsOB6qjdunydkxu54io8ELCEg5brBwOTBT2SzLm1R5aMp5H7xCqTpb7zhjbvBnLNJ9YU/O+I7B1MK1N/CQdWkZaG+3J2rGSZM4vuyd6mTpiv/PcM6VsEW4VtkQbMmnqFCJMiuZ6Y0Urv5dEX2JGerihV4EHpd/JG/G2/gMZ688FF1xwwXsVLoXJBS8bmDNgBQAyn52UJilz8WmShAiyNqFzg0MHkTnQLHRw422ryG4TSW2SrE0UoK+y0S9Zu8/yh1L9Gi2aliQJF7DtQSPOXLrIxGphdDJXnKO6z5AlTjluRpiTUmYOXcFTmk4C2GE4+4OJ94gUTyY8s7/qfX9MwDiSIDouXec8jLTsOiVY6sbAfL3OPmG2eQMv73bCFVglh5XLzcwvt3MwKqUKrX/PPuc246kUz05hDf9FWcLxlL8qSNJXUC1CCsXHTSmt1LvUkcuxouSs7U6VFnFMDapilxQKlua0BClnpUYrzBZP9HpHDmHHejs5SYforbWUhEkn2hr6T2hJQ0xmJTxtYe06wvMIIUXS+zymGPXcwHI2enWMwnkNPSjGdiKhd0V5j85bLwTOrWX8qPuJfOmIqVTEaL9b4JC2bcG5eb28BnoMm9UfNcevlmW+P9BH7RzmJXrh86mY8hySlsNL4960mbKVy3L5M9E2V7wU/H3tOhkJJUOQMKIhJZzrY7pS+sA4BsmCvyWmT00dmfauOI/bXjqYOnnBg3AdK3zBBe8uuBQmF7xoONySMwXCVqJu+N3lVBoF3GQ9OcYgPv1AmG+wCUHB0SARR8i3CVbFTUjVZMx+1LeDsHB8khYy8qrDml22zrdJZO17SQzYMhC65E3tNlndXL+paRX6avjIXwPAAsuI37BPTnKTrKwQwE/SsQspHEoWLM8gIQeehpSXRyREDH56PtTJ30u6dpqH7uhP4blzsXPVJD3sR3n5PsqYb5Ow0misS3jIDp4Va5rSs7ZlSXrQuUpJCEiCOzH8fEIOSTN0IyGiEShIMil5qF3KWudAxBXVia/tkWNdAzK2Qs2JdXSSztEK7w82tjS4xVrM4HPfJvkkHWrbx0mSn5LRx3DWrbsOHcgusYURdh/VyA7opsBNAb1BzNpkNiGkCA8p8UhNZUFbu48qPrJ7cMhjVnsLePWm/MhhPhkNKmPY+F9ps3+Th03fj+e1Z4/2e+kWkRLD/BwgkoB6FbAtXvVk8kUXDH3sbkqO7t6WmzNddGn7kIbRuC006iDOla+UFONEq9nyLTSLG6V4Ct91+FrjqRNJccx9uOCCCy54D8KlMLngRcM95msVGbWJbe5kdTLq6ZUehw1mLNF9AbavYNVtIlfC1iYpX9qSgyXsSogm7DwNCzi078et2u045AfgbjYzo+e8irWgDf+sNY7pfJ0wuUqp9+Bf02lDiofH4UA3cZgxDYFLnfe++L/DxkuWMccEMvehr/l1vRDh6uA1taNNPST9aMlUksEWJP41lRj91oIEUW49ajjXVYWLYO7JxJ2FglNhQGNtaQn7rQgdJU8VeI5E97SeD1QZVXGRFCiKA2sT4GhRBvVZIVOwAz8pArTWJiAdrvmgsDERWpQ+TpLTki8T+F00LLj8+HiyNnErtKXLR4SJMs5BXH4HkhI9qUk8Rk0rvT1ROl84CMtpeNqUeEgxLQeXhdiB5LAexPszqYX1IN7zj9OJ9lTvM+EeMdS1avtJUx5Gzv/ohTvXXK/dPWiCvRsrQzShKCeSgoNpj+WhuKBV1i9d2kjjQ7SlrbuLfyRPGx94JQu6Cx4DwTMYj2fUecEFF7wRuBQmF7xsONCYMEvJ9y6WLTpaFYox5dOcObYz3OcARh0sABsHz+0sG4HilBdKNRnBcnkDNBShjMgOYp3dVyqmRfji/iL8Jm7b/d/tTsQ4ysdwVIZFjH3HsPzYnslw39OLtRhsk7ndXXFz1vcqTpylVTHCpsKaNs32pxBo73C5ary9aJYQumeszyIYJkU3lhR2Z8BLKWKm5z0xxqREIckkGPaTuwl/IS0sfZVAu4RXC5F8ok4pV+b86hC2LBZHSTNOVXB4RLhoysdY5fE90ClSKKsyKlULxSOQFCjF2oSVeBlY9dLDeug6l4w8hmuiekbYTIHj4ybNu5g1qvjWNrFthirjpLGNx5Xo5Dxgx6xN3LfJM32Y+DQ9uao1R5SneN2h2J6hEObZcB6WEr9BoboP90GyYQw228vZfYanEnW8gjFBYmryL98gkqoI6xEB3EHtfAUJQ69IYq6/06sZKWBOqw247U4SwMvvcJif2/ZZX2xIThpfonRNsVWlfUbKYW3KVNzMcmUpOUlSKrvQIytTaNmsKJX3ejWVUe/7Ja1fcMEF7124FCYXvGjo2HsA7NuUM6885uTV1csYk07S9YGD2EcYgBBBhLUvlJYR607ScSZYjAmJdl1AhID4G29HkQeC8w+nsyaAkKiT/JyY4kTwxB9EG2AmdcljApnmMVSUvEI35XzizHcWwppwyrOeA5LD1ZPMDEs4UEQe8oNen6XV+1kacdqM2H2O/f69oto9+xd9SiyfXP00DlasIKeNrRE575IG+u6q5PAVWtok/J1JL0y9kjhB+KevnSjxqOXIqgSNkODDfWBhUgSIaNoECBpXrtPa4vxn08qhV2F06g2OW6xNSukjRXDduHMEYxrJHI9sW2e4GCkwSqhQiCk0PKdHUFykqW5zulpf7L5DdHN/QYNmGDWdSgrArU3kobH2nuXHtWfRI0GJX4niWAs7oA/4FqAXlmisE7iJn+KubxP/HSl07VApVslRgxqa9FS9RlxD7RZq1j2jeaZlacs4VWDzzMiJFdKjy4qe5WnL6HpXgI//5bph9yBRuR8lLspoU5bijD45bav97mgQ0bQj+ubxurbb4LDSuB1SJ9wFh6DYoHjNPkxec30XXHBBwKUwueBFg51qowgnh8nnh2r4CHEGNU6giV9zJGFdm7vl07kHvfWf1fDUkh7oa68a42vOGRG4KQkdzYk6+aNkr6GQgr20PTtBPiXp8G0iAnlakyVnbavJPhZ68G+wTR3Bmo1LjsKqMBMCMVOCdEd5bu7emolYfZ/s3TwCr5bXFFo6N+XPo3z3ee+eOd/h81lZ4ub3A3zTgohnZvhRwsH4G6cfF0eNjPMbeUovDDvhycqIVOahvCNf10bqq1L0QZqPm9+sfQrXPCxNEb4y6Ziv+0kwMg0bY1XpHIzGUBqM4inC2kQn/RR1ujaPyiovO9vJrSQn5upqbRLCuitHWMXB2x/TlhyEzkRD6F3uU9+Qlva8r++m3HeF4gbZNoSG41DjG2MIYLVMUTCRaIdoqW+EdqijGZerE9ewWX9QGHM8zDBH0YcH6lNRw/uyOtDTxyXtaG1wv0b7M2nPvkaq+6M2bb+fty1r6OgxWXuoo/cGoHaWwOe9KhYlqd1cSUGKloVerXFeppRlpUfdprOEj+7g+hqcUcIKdBYqRz7nLlhBRQ4s1d5ZnRdccMGbgUthcsGLhvgBJsUAfb1U9OwvUqmjDTnnYAqEzCZPoUBJN7B8Zqs2DfPu3L7ML7DmvyP66GofhfueGB8K7cuipDCAKXgZk2N+ScJ82hQUkT+jM4shCS2NMuP0p3h2yyArkXI9XWWGo2C0LUJhTEUOuvjurg/lAz37+0DhynEQTs94Xt6jtJOxPYZT9d9DoLfdcdEphejyHFJMcvDaPPdpe3q2utWP9m0UIPPZlQ7MpDPDf/Cs6MOWd7/tpK/SKI8ijHCY8sRrU2rDBIcIt8IFrYFFWZLeYuO/REkhQimkMmnfs61tYCe6Q5SN170nUZ5KNyyc/kZdmfZMJcpcY7ZNRdzZdfTf6Z2VkVDAmGrGyh17x+Y0xFo7PPr3KA7R0RQ38QDi7Yj/wfoOjPBweNJyJ0hNGEYMp+8QI44o95PwcF46fg92G4rm7gqIo7SDsl5nl4fXXLkO014T7Hu2AGnv+530Vy2/Ex1gmkZxRiMiS82XaUlHXyzO67C2Jdc5W+zpG7DGe1h72oamf9TWBRdccMF7DS6FyQUvG6bwJZvMr/LVJ8n6RfM4BzPXJCikJ25aB7O+cclg7sOSJH9zBYUZtwH0TZYcsVaBV/f58XWDGZhTHoYuLeKKGEO4dNoNH3Lvk7kAcEUD6LkqNvwyYQVUCEkeqJflNceBq8XH6+fElncuG4ZAt4esoubIVmeZ7lmfl7dL2zbgdptMp3OzD9xLXC271LXC9rRhf/vtrPhQhU7LlKo8yYoUPVei7KSMWdJMciLhCRF+VauRnH6cV0SgN6V2kMMsbGgJv0qeFJ4WH9sGbJV+9aGASs/iDde4Dtwx6rZ5qUPrEIo1S5FIy5t4KpWzkkEVAey7YyjbU5q+nQWJ36XcNchnsubLw7QoTXyboikV0mUwfQxx/CHhq2m12hEfDpcF2AInTe/r7N0d5JP75d110Wx/TMVJjXSGyfqm83nCY6QlT7bcyWXNv4hh9zDo6eNDdW3bhrff3sP4zq53qiTRsYbO8iRMbd073lru3KOuZ9o8HeeTTaD7fkCnGmUJh1Mewx1EewmDlEd7lC44hOtY4QsueHfBpTC54EWD/+bvGjwuC/3F/4iAmOoH2+jUEJzqbZf2XMyYfklGRToZZEKrMLUuZFh0x+fq2Huvu2KXDdtmTD/hqoS7Ul80Kg5GeCDkDLoxQP5n5rcKQ64LzBlHHrDZ9TyI/UtgVzGtaEDoja0yQ5KiLtG44vH42WJhZRnbEAAggO5I1iYeBk3KYEJTGJTHn7nhA+ZYZPhmwK0bGSpSO/oaQGy+jL6E48Z5hQsSUjywFHJ05TJH6V4fLAxi0mMeBV70xbV7JywA8KuxCxZfhJyZMQkVFI5bgxOQ+lDzrmFMB0Lii6FQMgC25Sa9LFdFrHBOzVK+2w5sElt7yFm11TQwXk++uQdH/lDGFiJMmjr22li74/zreSSwb1ucd92hssWJOPOdDXq3Q2RLcwb8Prv1at5j029KYBkj2RG9PAKmQjr1eeWDOeebcCs1U41S8NuoYUnxzSTH3GKqwG6EkV6NNqWO0ioZO0of4al+MzJF19kzLSevK+HD+bR/tstHRPdQajhu3f0sTUsdJxet/05x8bAVidEiSnMywvSyxMWYlLYLHozDmgeU5/G8F1xwwQXvRbgUJhe8bHDJwgQ6QDad1iaZTbQjegXCbk4WHpMdi5KO46B52rOvxM9DgnEn1jrv8LcGnKX21HtCh0qIG/s07902Kw8Yo8w8Cj/L/Juf81B0zxZhfLXtmDCLj7Pvnwzif4+5qKXtlCBQbFD7MnvPc6Oc3bn3D+AiMo4E3W8Rr5yz5Oc05+gRnH/l6I/S7M3KBuhtZUILDknxoifPR3iUSwBgk7FFp27NAT/v+Rm6PI+1SmnG+FM3BkorZ+5oeqYQHkII4DALAPQtNzH19asuI2OCt/Jr6JUoHGZ86X3kOppXZu9CALNUgwB4CgoChBWJ0Y0ct56aw1Qtr601zmsy833/MJmVI0BexT2lW2FV/ES8zUe1o03YzIy6YU6w/ZQZ9Ui6A+G8gxSIS5q1O+PN78/2NEwwWkK00tglo1L8A5okAbDZaTYQ6JbfyjGl7usaOe/nBcaQPWFYm9gM4HoMmMwY7Uu0SKP9/u0ft69NgbaOShb5ei4IhtXFrRTu6nZ6iP7OGJ/k09QIiHYGrVJQnZrDVbHiL0BLmJrImiaju0QDDQ+qL9GxJZ7wTO01OHH6BQ+BWWG97jovuOCCNwOXwuSCFw35xx6T2YV/GTTFRTIvuPPL/XhOVnBM4WA3TUyubfkGa5oYao3VJi1CKWoqh2ZdqjqUF3v4bAzms56IQ4IMm9CrYZjbc+YIwqd6piOKdxmW5GmYGTQrnR7hnbrxD55Y68hn3Ue9rw+l1vu4RPYxVsoWH9VJ6zMgxvLRsgq3Ulq54nk7iF/uzwTFFGAbZYhLMaV6frYsVpaVMqBwEkxKPUlQKMw7CQXalU04xLMe5iv1utLHmsw43t1qg4zjYZqPH82pG4biwmlbUA4tIQZWYlh1q5oiezdZNizuc57KpGZ0DFmoZ6IESDndAZepKlpVdQfRqgrc5hY4bJBt9EEwrU8Q7wgWtrtXr3Gndto4fxdW3pQ2bMqeFaw+ehpxnm/RmnbqB1Zd8cjYoBs+veKkqsaiplcTjtwhuiue4Lqmhb4zybt3X1taglMXfAhezb2uPZe8KcYHFd5K4xSt3DsydUK6urSsPGUqroG+58lxQe5KOtd1EMfLg+meHtE1w4rWV0zzLj4G/ri+Cy644IL3HlwKkwteNswf4ZZ/UnXTh2M52pjCY06v+47opU0pwwy6VdtZm9BJKasflf6bpXUzixVSGJH5vA/FydNXAX5UZZaEIv8MC8ff+5jJzDNyvGLsIHiiZgVDJmktdBQIJ4sZHmP1A2nF+Cj8dFr4dXFsWiyUDI964YF7h1///hcQmZNs71JrZqSX9wqKnaVtq8u34yjiS2lRhhxKE5iMe59vFSVGGFPhSPy692exJtEcHz4EzCpEU7h+PS02JwVna78RDoqg8FxfKHOgkSJMht5YEaspp5R37TI35ar0BFGCwkX9YhlIdyDzb6ewOZthx9YnQgdVhSNY3ecc30KAT0cGK8KC0O46R8iVmwo7sj1NHD9iZj5bBtcO6/Ctsj01o0aDU4hcHgkexNzfvBhsZKzvZkH3lPJEXWYlyO0fEe9HIGZQ+jk7IV9HaYvCst4bOmDdEK73Hrq+Hun5YdAUTj9HVteZJ9x79wNPuayocFrlNCPyVmsTVlCsY5rjlPuwhFlBoqmebiuP4wcOZ9ziRvEeJtwueAguHyYXXPDugkthcsGLBi1cVWLSRZJvk87aJKtGlGqg+yzTCQDJusHVH7OJ3RNc9eIcocJ9myQfJx2Da0UULqULayxsDHYMgQLAflPgSbFNJjp4HfGPhi5TediUMHkgtQkn3pwHH4NH3IBDa5O1m9wA4bQWrcOy6IJMdbClMfNXSGMpzhyz8oPR6sMsStjEEkC3ORNui9KLrXHi+R0qLBwX61Azb30wXz+nOpRsAt1MoJ2M8VSeOPNfnlNaUqw0ZVnIwEG4MP6hAAGU8wAezgIKM/101fws3CHyvxZrE6C0iVDA+jUnkU031dgqImTfYUerL+qTLu7s/Ra6phrGBvtUQMigae5jZGmx91Myq1wJjQ3BVHbwWJlfE7x9g3xVUVx412jRO7JzgU9CIK7Ytjutj/KO6SUNlPd96KBbaxPKPwlJ9OwBasZZ0nFihaj68V19NVVlkyhYmhd5woXaJXyeCEaTYfBRaqfluHTnZHJF0vl4uNLk4HoDZG2spfkDpZjWJv50dqfwpENnvkzSnAPRAYsj2mJKCFfozvFJiuGyXpKiBY0Fi+c3hKhtxqe0E0GqeaFlWdESjb+JF3bBBRdc8DLgUphc8LLBf7hJHJhMM8dB1feqVJEygD2I9DlWyHnH182CkcJN6eMUH1A7XNNJm85MqzPOWaxhBn0oi3YM1wehrGG+xcaJ8DRRSanOwsuNO30fFssXDLBiWJts28oWa6DsbRDaOW+JCia9+5odgqCiflkvIPMrrklmdr8Dav3V0rofi7pBsQ/Z1qoFh4fQxgoUzmfPDwF/2jU/D2qSRGLPH7/fbVtz2F5Qarq3MlnjiHEv5UB5EuPtzHg8j7mnuS4g1R9CRfk62ihQcnqHY0JnvopOcGB8uY1cv4e5X2m8s+bRVRHTv0j4QQ1liVldhLw8FbNVA0hVuyJVMBWv0XaoPjRetknUgLfp22iQFcFOR7z9OWdR1Co6T6rRiTMoPJUf7tvkqwSyA9jG9hyvFxinzkwnsBD1e3RB405jvfjMSQRQ4ceTPR1Zm5BCoyq8HgBzWsuKDjsWfvgGAsLapL7MSfdgJ9FkClDzHkPOw9MEaKZoU/pu9RTUEvY5CACbKcvoNbwCHBXltW1Kjuhg+S0mBF0h0t1RlCldvoqD42KrLO5BInKax+lBXKP8yPU3tNAQOVWclDC9mNP8FzwLVAT6IN14Tp0XXHDBm4FLYXLBiwb7fc6sDQkNdNd9n0faztTJNCfRQDqBNX+jXaGqKjjOfJvAOc8sJuSTdKrrxWCEa4i41h3AFnz+/EA2nvfxhfBpwzDj574JMeNW1k0ttKBBbYr1L9Kt9yKBJTA+jAoVRykaZU3pgIxjeax+UI5+/sdRmKQ0sYyLQqY71Dmy1vD6ZgqGppwixdha2z14Tt5alBQnHdToZzfT1K3wdaVix1Vq3EkisXA4i6WjiWuahwFn7lM4M/zO9Jc8iKi4yvNxunodoPrs7gL9fiwsBJ4zPO9xI/wp7AtHbBHVuHFX1bG2SVY3Csh0q4srrzHilGs6pmuLtQm1drxejuoe8aqTBqu68+4xhuLKj+Hb5Db3/z3F9px5d8uR6gTWt98QnaMtOeLE396lWMJ8QfN+28f+P7c2kVxuxoXxjdHTo1E/mpySwmO+8clJhlt5S0Xp8qg/E1dyGaoaTW1QhF/UTP14ebTxB73jvBWUCi1Km1K2fQ6yk8J8eV2NbmSMx5zL5ajhw87Ztd9JZ4UE0QamAd0WvqCBa5zRFe67l+/iUlhznTyez1CaPHLqzgWPweX09YIL3l1wKUwueOHQMN3ECzO/zExFWJtMxp++jLoZt+SKWjE2aWwOnC4KJhM6EdhyG77X3hlbZ9dg3GIw3sTFMg+iRVGiPB4aH0Y3QBFMvuW1qrU0HQ5qZ1UWX/ifOKhCYWIZvw72bXIMefTOf9pbFrvES/Cmh5W9Ahe3FBEEs8tf2gW7CMSOIF5Ehu5+FPcMcN8mzBXfLfTq7SGKytz/loWKEUgn6nSTqOOpTwSKTvAwUT0rS9TzKjXEhupJeqB7Frg0stY7LbxOqFkEIGuCwzFUhIfB2fsRYB+Kgm078oqkUQdvyztqIc2bO85kp3LVLF0szZUqpwqU3C8FyMIEQyGzzbAqpq9X35IY1iZfNa1NaPxU5lYWImx+JwUIp6XxL78B/mz1T6K6HbFJ2vxu5HGto7G+4bAYsXwDBfNt8lWer1eQnBK/FuXDhDlvhIZpWTZn1ehJWs3XZbKfopqmB9frhKbjpjxp707l9DAPrC9Mo6h/QVbs7UfHPC4RDy+QKtE6v2G0LBTOTMNahU1CrLGa63DgMhx3wQUXXPAehEthcsGLBvtRt4+KLBeEUMDfVycDqruL9VIY+/qFtN5T+0upKTSOwMQR2TJCi7tX/zJqe+znV9Z5WkHW34yW4gjCYOSi/1YmhAP/MHpTbNuObeOvn4azTpPNsDxRD5vgANYPwIaXmdgqXNkHy5sOeWZz7QriQylnlhAP7O3xeLPwYYJB5tu1xAluqniiblR56PQlJxDKwN89m/D0LaLj2+xptewMuM+g64Wj59l7955Y8e86fQd0Mv4mBmhmlNOIiwwFpOji24QvVw50fWOGnhlz4v2D4af6SGC469uEwms9FTd+jnvGI3A8UpR4HuRyScCweoUvep8+vcy6I+67krWJ8FvuaVt6xRRnzlG1pKxxQcuGv6jV2qRT3KxbHzHrL+Pt79MUM0LjTx283aBTIyu6JWuTcAo73oX5qIgXZCjw+iGFChML3tNkaftt7j2s1ibRjiuXDkbc9TChf4oII5ds+QEZo7jvUwF/5FflMWWJEAJGYYW6bXiImpusMU63po2FmpySlxU/FXo7OuYML0l/NbRWmbSghO+iQBlGe/O3ELkNGwBdMWruXlnf3BEtOouD9dviRkSrdAHFddgV+uhxFe8UpxkPlLA/6mHcBY/D5fT1ggveXXApTC542UAMwjE7yt9caS+8FRb7M0tM7tq+Zp3tcMji+5HsXTcHkYKmIK1ep3PJccKDIHygsBm2KTcU7oBxMDlm7QAXwETDt8kwstnyWE2O1C3RQTIDhdNWWJM5SA/iw4oYO6tm33VYm1SHtDSqHNKUcsSBIvQ5/gJMwBhjZA5hzQkwOAs9S65yCa8wRcE5voyxyhRsVLDLho3PzEwcvb+0/Mxagjvt98oPEnFDQ0GSQPPcKmcanJc+1JaF5s6oW8GMt1mdUBMFjTQuVTBghl+jnVqOnxdrEz2oC4FXxmfFb43XYQkBxsvGn/JQt/i1p4gTqEdqq425+zbpDt62XGmWLnGiJ0qVudhXlUecFrZam+RWjhzC+vG8CiSUp4JkKJsBV6AYQgr4uepftTlVs1N2TIntxyHz++Y2li05M91MW7CHcsIJm2JYm0ic6b6ALGOZ+n2YOAchBp7oqrqiG7gB8lVExE3ToXDriCWMCN+lbozP7I8M59qmC7XfjhhCfsNBdMebv6PIoXXT4sQ0HDnv/ZXTNVae3ccNN6CzbcE+133dqvPo/RFUsu8RiwPRjEyrLENSpHghzp+yp3tV4i7WJl42Csb4NwpgNDhecMEFF7wH4VKYXPCiIVmYlLQkAKDwPxzejZE8Pz2iTSPNQfBWsz5M5nz6CjFFR2I+5lfZ1QEiiyEdTsbQSDB3AH2FnOlu7TLwUlMOKfD2DXjaFNv0beJtsLLEULFWV97Ly1Q+sGFDncm9KfAEUvzwSwGz12koCjz2tmrxceAEOXxdrueCHoTp2Sx4prCW5LJa/GEUzvrP2QTANv0TNPlfJyNb6hJsgOwwnzKD+UYz7ApXpDSKmhRipp8FCJByhAtym+jDi8KmNrBgcifO1p/qUh8rRaqAkbYyyb3L6FYXN60qNtDR6tnyC7AZ1Myjw1PBYqyzxxKOU/fZZNYmtZUI51aGvE9jwEduTUWUb7FRmTtTNOJ2Ad4e1iby9ETlbYCJLntbRIOq4tLfDf2CqB48075H2cq7mQS1V0EhOmzjpE1aHDncpUFv40meyvIKS7wTYvpAeqR5762LJjhT2VcmK0vBns5ZS2wp+VB1Z/AwSR3aIidVOLj06Jorpip22zhS7jp6busXT04XJ+2xe6uo1dROpk2E2xE+lM/bylUVnB4b1wsCLh8mF1zw7oJLYXLBywZiIo5YUQ4vcSzoK+g0m7WM5T/64V/ZyYM6Zh4lRQoU9EV4pqN+wS1tOGdCPa9x3jf72hpZRTV8m5hgYydKgE7caBEog2CPy++x5GiNWMVqbVKrq1WyooZfRbaIIYuchPPoo4lvqmjwbfpWU5Y5EOOWxEJBsjhRyBDsdmCHYNOda23u9+CoXDDF8cziTLU4eFVQqsL6rLnd+Zx8ZhTlyOobJOI7p6/rVhtOL19IjbGvjD4z9ElwKELEK11RVoDoHwsQej/8alDe6fz87yfpkDh+TFui+8+jqcUL00xwgwxSsbiF3dLKeOd8rHA4fTX6G+/R6fcs49sQ93GizPZVT/4OeEsOqxBGxdY2yvhrEBfDl51zC6djPO9TW9QZmySP4s3IL4M+6xceXcuXRz7wuiFMMCLPO5pW7WyINKGjrRNtbe6aSq5Lh+OpBV8/RZeapvxz+zjqklBsasQ5nXA6kvONxkrjvvaJjljiklY6WulOKe5j1yg6Ir0p34RVS3FGE8f5PA5GReLe0rgm/oILLrjgvQiXwuSCFw2Vtz3i8Ov2c7b68C93ArI2ie952aT9FIMCeRuQMmONlZ90axMXwCVt0+cyIfRlFJKsWp4V8BN1hnAQzPftBmxPwGb+TkhQ8FMnPDyZzNzVhEtVGbV5JwzfJpp9m4DECZZHqjDRQuJoCQss8ewTsh6EwUJ+Ej20mQka8eL5gum2uTA+NG8QDKsL4DV+83mEIZ0DaMLjypnfrzqFNccvAg/x725dJZi+TYrSZGehgipUamxpxNaB5jIAkrUGomx7ko7dqvBAwnm+R/16EMd12drL/kwIR+Q0G6fza9KJ6S9musyZ9/Btkk7ScWsTWq+01cNf506LsMreMuvQqN/wzhYlZm0yEto0qno81FOrgo6IC+LkSFYRJ5kA4QxWZgu3G4AnOolXfV06XXNnnmZ1hgYDIgr0q7Cqnay4OYQ13yZHvx1rfF6JQvHhOnpJc7o9400ZK08H7fVtPx+injxVRvzhUkbtZ6BeE1J/6xF2Nk1nV+SkDVN88PVI745xnHPZrAehTgvWeyVrhUY1ca2FSInrfZuUNI6Lxog4BT3kuxoBW+Iob4OHrUtfuanuCx4FxRvwYdJqcS+44ILXAZfC5IKXDcz1NMyWZ8t8fVKGgI70rebpJuTFXvFSLzGdwUdNpnxhqHsz9qQgcSHCLFB6Nlcb/x+1+8qhxFCveQWAziNCRz83Vxp1CNQP4dUsuo6U85dNXlcY6fg5t6+V96Bj+VkmGgKWhsIMiC/SCP8aCvXxdBwPEZDIVMNeueQ3TfEAKenmSTbVn8NdB7Dc2VOguZc++Y40Kfi8MlPbvuwzPOfc5U+1MxxM+nx2qadakFB4ET7u5C15kiBQhQeOS+ia4ILTfJ2S567S5OF3YHOts92o97G+gVAEVsqVl/n9LTmJptnCszid9MtmtqqRlFQ6MOZu0Tj6e5Bme47EdhvbnmOn5OyzhW2EFeOYLpnvWhSz/CxrSPH7B4hYaeAGfrZGp28Tf7ayO5LG10cRVI/wsiyDrkiaW8siNjZEVGF0c8QPetdNpvymxzu0t0pOX1PumF9cQ54nUU4w/HykvrUUu8fuoUzdUuHxq+TlgRa7UNSdK1JuwN4TObpOa7/cyyzy2mqfcj7lnJHm85NsPgo9sTUYpEcpn7b1B5613VgTns50jcsq1fDIb9oFF1xwwbsULoXJBS8azn2Y1GMti7oiCbJcziufX2yz/xF7BvIHLxYCKjbUarqvIg5C+CDfJrVmcaalKHJqnD0Da5wVM0EE0yGsCmTbsQmxzq4IiKFrO9hCZZwnlPyq5vogrE2Eiq9woiF7Rpr517hhDMNTkV86FPJz/65HBcwBk1DKGpw5NjHCKzv9KrCy5eUu/ue5FT8/T3kW2ea02umLKGBfagczryFk2ACZ4GEFjHGnNOW0UneuixUqZXmQwJGULxpttdYk99JH72c+pbbV2/N+yr1ragLbuIM0o2nu24TeCYiuEQr1NY41KU78TmkZ170HPqu1CbXQKkw0kLL0er6tcFqEVfeB6k0BbJAnXtVUlucYcpZ49srhDmoP8877rsA2FCki3chqukJ9FW21af4OFPH2Il5sDaGzNgFWPI7iHk/j3m1Tx8WquKSPOrqWdO3zcNuU9jyKVhr0xVuR4KycJ1chEGwC3FTXLtC10rAyODZuZR10VieJxqHWf9RW7lp3T7QRa37l/IhV0VrE8Hhd8BBcPkwuuODdBZfC5IKXDac/woNRY6UJM7v52yynrix//83WmMEj4PKEapHl82NVq2DBxnAMRmTi4tqW8RAMje3fFxckkgEMt2/V6civ6csltV/4n/oRldOCyZesN9E1b/COBWdKb/OntqJFj5Octoot8T6XL953ITq/nv7RpBlCFA/ZxhYH80PQdfI5oMs0O0FfmJO/32itmJ7NMipLy4LWrMiEeCArRxZJ40B46IQGyuffP5evwnndapUMWCAiHDq81vt5WtrGs2fc/LVTfx6Dx5SDiX6YDC3HdK2rdY3LzmQXGgUliy/qX2ttMmvgcQaF2YJEMbYwTofVfgSWWYwkaxNLG2GdCiXbJimueFGEBtzm1pzLVqEi5+Fne2f1mU7YURVg62jEEcTorLY4TZrjE/GmhFSZvlVO2jucSW4BWTLNHwMR+r3x6ge2ovNUmedQ1aLTQv8YmRtLGtkAvb0K+Wzo1dL2KREMkroXy7ZJ57Jyg8J0n68OwIFi1weclgqldSdxRboirNm0tBM0yvJapW0+m2NN2YwcLngGqMgbOFb4UphccMGbgkthcsGLhtMj+oADDvBIBTGFPW2Kq6L6NgFAx/smpJD3uqcor3jEkZDMFixK+/y9sPkBgO/VH+1Rs4Zx4uXjZJ54HgX9ecoD4syQYN+H8LFtOn0ClO51XSaUKs8rcNTiWfKzixhT0BEZ1iav52f+SBxghlugumHHjm3VqhxX08aJc6hnyhIX2GSjLQTh7NI53fUzZWbEgXjGvecJ3m0Z/hZu00vovbaPrpq/e0Z5lhDcvcg+GPoO1cSLK6VZWYtRpGFJX2I1Co31SQJE+UJ6YpjextT8OXfE2XYJF6ro/ajNu9NLQxllPkJMWEu+Teb2s5LfFKdmbZItTMIWjyHiLMRqFovN1nwc5yuhWJtEXTEGIeXBiFuEy3jdtTbhenab49PaJNVV1otPKCIG/nIefG4Im25mbVIJ6emgU5w6wT1TlpjCWaDYVLG7tcm54oRh0Y+kO//N80OmIl9kWO3tO7BT/lPw918xOMYyWWcZaZFBUm9MM+61/QqQpsjEJ0jq7rTMqUlHr+ucxnpnOtbHxbz15dLRIB7fGm7vWSHi9JXxrt0gfN65M+sLLrjggpcNl8LkghcOIZak01xSnGsniG2P/MZ7+sfEJm793nrwCczTtISWpKiXfJ5UhnQp5FEhtnAB9lMC3OHlgeCvDA3qkj3vqkN5YRbdLFfUtvi5MOSJYRNua/WXwM/qSNdhOODAeK954vZnfHZmMzBTc/iYRQOeUmp9Sv5uaOA1vhyrDbb3L9pUakai4mBKReJdnPozuS9IrDORX37J7GtFltIPQ1fnnWcX1EUwvsLvlF6EhhRu0pqvoWl8qc66FYjjktB+cq0n/PBdm3hdyjmaLAgdvnOG/H7yUj9UCdN9xu9UnfBsJ+CF0LSdYtlhFLWdMaD1v0VO4UFp3zWFWSnSKVQaJcrYnrNF3D4IWzgEpxcCqqN7JkWMlOfoP/k44bsOaxNhWjQHa3n1RvrY0TY5yHZnsMKZZ7zYEEhGv/xOCCFw5MNkhBtLRw9Xm0x+TTo/GITy3lqtF06e0T3b+iKaXfMuRnQaz4uedK5bX7PIazOe1eu3CrN6dSq+JcroXOfcL8YTXVqiEYZjJGQ6RtNV52ikPIbdxE/jyXtS8jIt7dulfCU/TBFdO3vBXbi25FxwwbsLLoXJBS8aOrnYIOKYvStOLpmFT4qLIlokhYIOQXb5SpfrRQpFnnU7UPPkgnLBw9o2c/Pa+XondLK1uTmy5TwFW4vTYZGy3wDZgKengx/dpbMRUUtkC2olQa2pVuFnylRFUmb1iFk0PzP2ShEn4gCTaWfhRAA7NcisjHZ6h+bbJDCuZvWBXE6rQu3JthwUaxMA0Fszh2jczu5pbTAnfecuwNyI37ba9fmVnusLne0KNuhtD8abhYQqEBDa2cFhzVuY/pQeaULtWJ29jxJWhmi5P5pPvR3v5E74yb1rrps2jtNqXJOmZm0yt7ncZayzZUmOM7rVUrY1h1ub6PgkT+Nv7xaKZUvOs555Ww49D3xuEPMvUrtdFx8/E80cFmHiz66ZPpv/5oxWNuApOp3FfiMqiuGXhNN4cFYfJhw/5r1MlBRgpWT6HTumNl1aKL6OyvHinvRXhp5Kz9paftDzr3lqQUq+A2VjNaJr0eyAaUxBclEG8MloFCcC921i9GikUaVLnNGzQic4rSotqJ7FsqRMG6adQWM11bEonhmnmq9JS117SAl8wQUXXPDuhEthcsHLBmcOkLe8WJw9uDDb8MSsTJiZnbGfCoPOnaEfk0rlVpZqjVldImY1S/cNrz7p/HRoPEwZDmDKIEmg5IEpaW1ZY76RlUU6FSgt46p9v8+ZXGPUQrmyjKpYPbXWelk1B2ln5br45NWX4uvnySVOI/8MD5nH4nCSN9JcKOV26rMePHd5n1OXy2rq76gepRvHA898O4Vneuu3w8Jcflc/etfRW3Dr+xyvJvoU85mUF9RfrXWB8FZAde+nSrr0WXG9RUrFL8b8HFbqchb3kNXJjuTU+qztNVfnz6TQzIRdzgHM9cHjl/yLzGI8P3GWXuoBhlLD81L6rtP/SVkbKM/LqTkRHkqTZ5SlZyX889SxsWvSFERHRz4uo1xm5s1p6nPO85SltcS16bPOh/Km0UqkL9ZdfjW1H13e9FzGCty2xu+J7mQRMd9BolX2XjDpEzXizqB9DFkhS2WtLo2R3xF4BpnWJS5NMzU8o3DN52mOK6VRZfu+e6Wa2i5lmSYit7/m14P2aY4T7hfch+HD5J7i+vl1XnDBBW8GLoXJBS8atm2DAr4PPr4SCsWNOwTuc8R9j5h/DLqPrPnu9XD+LdclpWzK680d4HfYPpePPsiMk02wbfN5s69oEnm28WVLPM/w4h9hmWmIZ4rLdVFZegeC8eXOvqIJXdv0oZDSDvM2z1t5JpFrm+MT92Gh4XGCeUwxsGGmcdxROSovkNmH0VOd+wcEQ5ger86lAB8PsVfZxdNlKTKPkK5pIyyQbQP2WzDRLhxaI6bdoGdjTmefnFfNUmt+rmkQYNscD8Wt+J8IC6wG8Xf2vMncPqEk6JhvhCnkSwhBQFhmWRosTGmYAqP5DwKq3yB7p/NobROkpk8fkj7p6tJq3HScqVanNPfZR2yjh0YP6G4GAV0a5rx1mrHcuY6DNFsERNdSPkRbHY1L9c5nx4voHdfjadsgWIP+jMUv2zZxGnGyUT7hMtNKZCtlNipjtM3Lz3xb/u2wfgRhM0LUPC+E7STvQtiiH2oTX7a4MC2uOL87nRn4exoiHrLNtBlfyotsUG8Lc7YNq7q5IubR0EQ7RMb60ImvkReZcZC5xsI57AjPtLLAt5l/n6vZyc6hTF0IKuXVkm9R+C10zV7N3LZ0w1T4zzYqDXTLvxgLVxhY3XxHc59psmEq7nI3iKqBrV3dDxp136hYoncWSw56XRlqlqLQMfcBUv4wiqTiFO9sSgHhF/SY+mJxSx/U1/0FF1xwwXsNXq+L5gsueN0gxuhmRn8RCLZewEj5THFgCgKrt1w1bjBBuU5WAARaQkJ4yCTCYUyBHlSGnzl+Mh/x0UYQuceNS0fK7AMoKz07Ti7UrJdVZmFmuhDJEzSXwVFeTX3kvLLUaf2OlJXPDiad+VOP88vSOC72Dx/uJRbxL7zekmAwp55dadwofoY9PncljW2U2SA+O7Ym01kFc77aPzl5rnOe3kcIf2UyPONZpKnj4BoyHq2zZQ3C7y5AU1q3fnO5swvR/mb0hdtHuXM6ynPc88I/7vdW+pcVipHGrzrjVOMj/9mV3jvhG/1gobzi3sVF2v137svG15gvDp5PHOeLByVc8i7pR8CSrK7Z5SDv8vQKoEN94BTI30vUPOJNiUvUS2b8zJPiC9XDQuVq11iDar81UjLxxEIZ9hKfrhw/HiUnlytef+TMtKqWqTTsYI3Pf5t/LOC2aH2h1l264uuFu1biuJ8iLV2L3/WGzuEeTZOS1oxDGqueVqb3fRSXexjrkNL4Bee4Cx6BYcn7+q9XgR/+4R/GN3zDN+D9738/Pvaxj+HP//k/f5r/P/wP/0P8bX/b34b3v//9+A2/4Tfgz/yZP1P6pvjMZz6Dj3zkI/jVv/pX4xOf+AT+p//pf3ol3C644KXApTC54EVDZX4AuHAn/kD5mwzGLMwKI2N3cVphGqz9+FIoYCHhWLhoBIlqblHyZKZMmG+Ovqbn+g3uDpAJ7xHUOubuimXMgrF/EB7iqwarv/OHf/BVTNI9zO8IFO4ULmdYx3vZJYsdxyLII/F3QODWP8t8XLn4Z9wfv+Rpgzw9+Rf9hxQvj6Y1AgBMuNjCz0QW5O3excX4SNN3Tq9xSSE6cdieBPIk80M93yOMdAfda7njC9PqwS0f0jzJd/u7UiP+90jafKLx4PcQdI3GEzZOFrYsksgsDzEovExja9M0ykZQ6tIRihBK5/e7NCRNuFyQXB90OtwgpWaLR9Sr5XnF67jtsR7Uv/5Xwi4N5Xo8/gGYaI6fn+rs46iu58VLFyemIIw1u8wNey6vi9OP2pCm0Uobxvo2q0rQ2ocZ+8BoSpTNOAcdkuaZ4jx7WIjaukFq29aRrbFYa9xJT+MEkZptRq90LWhrT4OzAobHge/ntDyUQRe8m+BHf/RH8elPfxo/8AM/gP/mv/lv8Bt/42/EJz/5Sfxf/9f/1eb/s3/2z+K3/tbfin/mn/ln8N/+t/8tvuM7vgPf8R3fgf/+v//vPc8f/sN/GP/Wv/Vv4XOf+xz+3J/7c/hr/9q/Fp/85Cfx//1//98vVbcuuOC1w6UwueBlg+RLlq/M22TEtsmYDMZga37Inx/X8b3iDBfA/LIxL51wYbyUNHFHvPXKhEGMRS1bJIz/T4yWludmXI/STl4F4B9J16u1AWmgUf7cg2hzdnjW4c2aNcoR/39mBn4KAplm8CrN91qx6s/Y+ee1Nye6t+vKtEWSeOQ52TPltKYMM+2mFBgSFrJpFAkYx2kHz+mK9mVIVcHse7gIAM7Qd4w/CQmLgIClviq8eLsn1ibHwsJZWpeXXu9GOFv7fo9tJbSXrCEgNQ4Hcfze5/sGkrATolkQikiPeiTNH8pX5msWMst0NAUCLQ1Ffvalcfb8LOC+zPBz/C+8SttSwnTal2ijAnYdTrGIOQRWpDyCjr1dTfXSyHjbB0ZyD8c5KUjzbb14OmUScfR73CwDeqXLkIPWKUgJwP9SmtXV8QTStB20ItOPlYZZG12+U9pW6Ewtd0jXiPY4TUPFF+nOdEPK8+F1wYOwQV/z9Soi3Q/90A/he77ne/Dd3/3d+Nv/9r8dn/vc5/DX/DV/DX7kR36kzf9v/pv/Jr79278d/+K/+C/i1//6X4/f//t/P/7Ov/PvxL/z7/w7AIZ1yWc/+1n8nt/ze/CP/CP/CL7pm74J//6//+/j//w//0/86T/9p9/BeF1wwS8vXAqTC140sGLEGJlg1CczQ/Kkx1m4Edbk7Mc+XeecmTH7kvKDGKCOyWDh4ZjjO8xCjDRG80cjB+NewhKlcjQdp/PY4Oxz7/rC7XYorJ04R8Pe0yw68oSx+drtnlN7lviQKmzyT9OAUJpMBMFKlBwfaf0Yr2l1/O4ICeliQQDrWJ9dJwtDtmltIhs2KRYnXfiR53Tl6cPKjiOGn9eX95MUJPfW2rEQQ1drbcIXsFqg1LST62AsaML7vYZWIQtLv9o+0fxI06OuN1mtKBy31F5U1NM0fkezrM0FEC23Dh559Oye8cDzq8C+k68hOby/M8hvVPhEG0tX8feT6YKsIV3Tjlq0sD1vMk4Hs209GR5TzjycJtFmFe4P89N1aqnXk8+ePopg2zZsT8j+fDYpbdZ53MX18z3TtHklutbTt0S3UPMetVXom8/UWSeoDVe6xreHpc8+eNEXNLgeKW0ueAzq9uDXdQHAl7/85XT94i/+YovDV77yFfzkT/4kPvGJT3jctm34xCc+gS9+8YttmS9+8YspPwB88pOf9Pz/y//yv+Ctt95KeX7tr/21+NjHPnZY5wUXvBvgUphc8KJh+SqyUfhQuJgMSzwlhhfCz+s/T2Fmy3BJjIhA3HFfMP4NNlgxJX4r4QjH8fTyj4JLjYVv7DjSIy71DrNDCIaQ09SwVH9iuv0QfxUDWw3YuYLqwwTEQNz1YSKrz5Mc5q1YtkVIMi7x8ka9lRkFCvNduiFNenECmZ07zru9EGaa782fVhiY7U+vuYkRftpCoLAtJVYmOexBeN31+jj9+EoCAzn9hCBtU5KJYxaCIm2N6691rLBalri1SZTp70j45zFdr+w3BSFMbWPLQLRX50VX9xonpzi0gxFjX/riY4EiwIEFpnXsu/dQX4oEwiPMR50IAuozHnh+CIoKVqZ1S9XWSJP3CB7EQ7S0QaeVtBsOlalQ3oj4ypcapbNXYr5VzIfKMj08v8W9Yvc9990pCVAeafOm5WHlKzI8h+Zx22JrHrmt+jzqz7/Rh3lt0AodPlUGo6db/boK/qajcaf0FfD2O2uThBOXPSOkpZ8X/PLD13/91+PX/tpf69cP/uAPtvl+9md/FrfbDR/60IdS/Ic+9CG89dZbbZm33nrrNL/dn1PnBRe8G+A6JeeClw3OtU3Wypm4Eg/6ofd8zAgE98fMwXIXVqoYI1Xa5TYoLNSmtPXBObuuDDMsC2N2wDgNC5fAb7l3cbImhUBwzJBaNq5aQdv/hSIZzngoXfFZGFGp8SYuxDGmkmrQWq3HPw6cv5QVO4XgbcKRLX804pd6H7F7aXAVAZ29fJD/gfuiZCPGWIE4HSHCdoKEbALFE/D2DXaCAwR+igPETqMZSMqMG/LvfC+zOusGf5Hkd+ZnR2wA9pzenaQTrypOo0lDQ8Pguz92Le3amRM5zpQzdkSynYjjp97oHKJ5Ss7D8uqsPtEyF2qYRhzd+zinccs9aEfQMwtH3kzfxNNR6lriQPmJ3jKdawnbsvUq3p9IzKVcBdEqBDwSThCI0138pKOc502DAroD2zammOhU5M40H9tydHAdBGQSUWlkWhpCVejQbd6SxYqkOmp8D5GWhk5BTrQHjOUtM1njZ8jyUPnaYqpac1tjaon3zRofp8NEx8f0E8gTcLtFpfbbavQn4SL5OfUxzc8M/g5qx0r6+ttVTrBpcqw1rVE613Wc1DPHYVc/Lclb49OEJH4ajn5G72F0wQqHzubfYZ0A8Ff+yl/BBz7wAY9/3/ve91rbueCCX4lwKUwueNHQKUaYoWdlxkgKriYrI0CMOx6Lyzx0G2b8qpBQ214YrFSm5GPBo+PWKN35+i0EiVz9/JqWUNQinOGVPhCl7MQ1eVvEQArjZeUlng+bPtEBKBjnA27ubg/ul8nCwkRAngDYUcDG0M48y9GVVrS054/BrkaCBWfcNjlXbMNRpQ82D4j4s5TnMQ9WnFTpRWm8I1NGjGNGx30DoE9PA43b0GSoCcx21KXNyxnnup4pCLuiRby51KYJyKb2Mv3UOOk55lH0yvp7HLe8BhpizlOnWeAyX/c+ZNpAuxMVHhAfeL0bnUg0Q3KepBCJMU6L5zDuIA/TEnAb8SwlX1LAoMQ1dVQlzLrFgPtU0kwZZRlMGZWeqS/L4L4qjPfHcyjVW5Usi9LlUcjlfF4rMKT0p7l0XZUAuDAbmALwfB0O59RtTTXlidJqdDQ1yITrU2c1Qjg5FkVzw47KE6UTzKNyGxoFFNctjTqiW+iObG1foEYjaXy3behm9x3ApqGolVQ4aFZRqqS45oc0o2jH+toTHfc7BzX93JGyKfXvgMalIah1UZ0QDPx1YmH0mnGmOtZ+HMdd8MsHH/jAB5LC5Ai+9mu/Fk9PT/jSl76U4r/0pS/hwx/+cFvmwx/+8Gl+u3/pS1/CRz7ykZTnm7/5m5/TjQsueFFwbcm54EWD89V2JVN8OMNt5q6gOGbmgyGP55weV92Ta/W7M1iQSSxm2gzbHXyXbebdnPmXVNfMs7R5cKG/7It5hgM25g1wN/xxvZcdSqN35IujZClpLvDfyf1qXRb/6++bL9kwJOmK2Qgv/k2WtIp7Jy3IOri2GIA0n4/vj04oyQ5da3nbsvK0AV/1lLffbKWsb92hesrpUOs+eKCuP8976tsEKWzbW5CbW2lGWXMVp4qX+zY5PRGniysX1b9Rv9jh9MZpy/w4mlcc11/3pgNOwnmqklKamvUsHslx6/s/Imze5lRdrO2/ZiLWLD0AUFWon2rzmturS5+HQVlEnaHTY0PP0u4hQfMDNhfPaj8bi/sqmqL/GDdak6jT4RESdi/PA2W2TfBVT2U0JD42jDvxAMJ5iE7cWVeJZjU+jZy+geoD3R2P5i229Ku/PG3b2pN00NRxTCPFcb7gPrxJHyaPwld/9VfjW77lW/D5z3/e4/Z9x+c//3l8/OMfb8t8/OMfT/kB4D//z/9zz/+N3/iN+PCHP5zyfPnLX8af+3N/7rDOCy54N8BlYXLBywb7ASbm28PirHqEiVsXKy8RznfO1zVBD7K2IxU3514ksjNT7HH0o3bw+9Ztv0l3Pxoxp5XeQ3QyMvOrlfjHL2maP/+xranLM3897D+ywT95HTalfo/vqpnFVjqywbqyzw998fFVSn0dSK7XT+CRUEzYsadawsUOwaxNML+Iq51+4Yhq1NngdCoHzX6078oHeeIs83Onmi25PRse3BjVwXPZBtE+Qc5tNbB+zaEQAHiy+q19KuvWJfbONYbNh7rvOG+OEZDtzjbHz6xNZLRbLUvOvr56qn0x9pGI3GtcmLIrdOChgO6dsbwub2vpH70CDnuaC0uWictKlJPIn+/0Wpu8iWYxsSp0bbE2Sc9H9VB+Txvhig+v4aifhDCfKLS1i4f2HlFKY06D0kmx6J6pXwB6elK/37eNU3tcZ32mMQGgtnantYkmK5KwkhhLek2zMK+DNmzr2dbSzGBKkxsQZiUexj2yRit3xSp1uIQE0VbqieZ83g+JfoDi7OfGMvuWIB/L6HsUDWsT3cniw8jhrNTHzekj/LfB1o1ZjIyhm9sYSRHmdkwSFCvRmciByNFbjByNuYFbiti7FuoUvHOO96DfGZs6l6LNy8Lk3Qqf/vSn8dt+22/Dt37rt+Lbvu3b8NnPfha/8Au/gO/+7u8GAHzXd30XPvrRj7oflO/93u/F3/v3/r34N/6NfwP/8D/8D+NP/ak/hf/6v/6v8Uf/6B8FMOjsP//P//P4A3/gD+DX/bpfh2/8xm/Ev/wv/8v4uq/7OnzHd3zHL1c3L7jgHcOlMLngRUO/JacoPJzfKky6x3KZIhwkjp3qEG+t8OeSql8FHxIKSFiIJmZ5F4hK/yitH5DS6MFdVd15JMsBJ0VOQUpYSpjHgxnU6Bvl07CtuNfuIRJNUQWwq80BDdWLcaQI/tD8bGiq6RGWT3NYKSwCbALd92BaDRcfC2bLiQlfPrl2LGmDgylEsAFy67N7AyxBEIO8WOiskyMYbfceMYWAbYznkwD6NilEZptK4UXAprlS+s5qiFA/lG067tskCxid+iTXbd0Wiu2wGLWvChEeSsG+6xCGVNIdCrrmAPKz1dPQgEybQMoJpHK9soSUG0JtpDSrt4al4MGK5xyf6SV1g9r1153o7tlFONHlp52RxLwqcwKf1krpIbij/FDDQcK6CqUP9fmBtl2YrvHWqO4z3XAzZYlOwV+XtAg/ADP7ogueS3cTYNe8rdIGpKEW1G7uk3ip3G5Kl+zbpCpBOuWAK3loWSVdwOwIb/txP0iTuJnfEtuKOKy7dtzmuh3J842IzvakkFRWcnC7oW8a9YcS2t79uOPwlYWuRb2DoWjh+zrmvYJlVczE+xWo7i31859TimYfKBc8Bm/Sh8lz4Du/8zvxMz/zM/jMZz6Dt956C9/8zd+MH//xH3enrT/90z+NbYvNCL/pN/0m/Mk/+Sfxe37P78Hv/t2/G7/u1/06/Ok//afxd/wdf4fn+Z2/83fiF37hF/DP/rP/LH7+538ef/ff/Xfjx3/8x/H+97//nXfyggt+meBSmFzwsoG/WoJ/lSl8pHzwcszMU/FSLjPgkgs585/jPP+sNFDI7KOF2u5IwX0+t3fYN9c7Figi5WvcytWkn9YD+eAQ5ODqoKv7LH/Fr8l3VDSYysiUbRQqasdpx1ixokVT/HiBtkXnNplyIdYol0l+TyZG+TniQu9TuWqaky4cIIRy4+iTdmbWIbAJN+sg7r8umpCcKA+GrmbH2KJzA/S2+5x2Q5e5XnQDZJ9hobpTX1eRqzL4gKzWJv5uBH2tZSI2X7BT/jmGnSNYFjK2ba61/WwR1bbj7q+0o1EU4e9eclooCJCVF36vSg1qYEEiP3v7k/aA66vPXRzh243NEu1zYlI6o8VBGOf2GJrGRtvq8Fcp+hQezGftQRGf6p8LXaHmncy7zWY/vcdPZoP7A6pVpuX/LOhpoZMJjRUaNFCWvHctrFKL/dhbzCbhIqSWBcZ8qPPIpxGRsVBoIL3uarTDJxSxbxOQbxNrj8ktO4iV3GiZirnBqq6QLX7DXomula1jnlp/A3T4chEbQ8lKE2wDkUSHy08JuDpc8G6FT33qU/jUpz7Vpn3hC19Y4n7Lb/kt+C2/5bcc1ici+H2/7/fh9/2+3/e6ULzggl92uBQmF7xoOFKCpK+vHD7Ln/JmixM2L1+/mILqz3FnViRHcSxUeGU13vJ213aSVi6ZXM5qtXIoJrYfflHDpXT3zLU609Uw9wyMfn2uaVYfDxnDroInIHNzxOQ9F9gYWdKnwBlWCst4Uaq3mUP8K2L0wr4s1pGrrKd1TklGqyOMwWnLtDbRG1xi4KMw/DnqSO/EpCLjgi0u2XNPfDTuZnIO2YAnAfQ2+wZA7EQd+9I6w4L0VT33PL+kZrQ9j1ubGHoIIYxf91Je1rrX9tZvrDXOX5HUk3RwfllDpnCdf6ryQfy95DC8h5E3046zOG4fRZH8SLrNHa6vsXRJuIciOvmmgcUFYZMlTXIaTFDeDpCmMTyFA0VW+9wIpq/br8kJiLW578OSjWf3MTlfSN4aTqupF3yNFMDIwD2CGr8lTTUpQqRgwpnKdGUdVfymxIrPDVDnraEi/dvpXhZvjqqTyZ8AsgmeJE7SGfRjjhZVG1qaPD6ZxnDcVJbY9hfleiWfEKYIZ7WIUe+6V0YhveGUpkTGLXW2YzyL6LCaVKmUcN5f8ff0VzqoytwG/HrrvOCCC94MXAqTC140SA13ggSMabIkC8tSJscXpYkx515RZryzEIBgKKxEwk2iqDOOq90Jm9pzc6c+TGb5Pl+OMw6XmctIC7y53OsG54eBcKth7SJfz673JO2IgRzoxHfb+61o1LlczAozIzmvaW2ieqMZsYr+d4G2c9gxttaTjK4x7DynrS0gzEFmnBiTJbkORR7AGVa/I+aVT9wN2PZpbbINaxO9wfbCQ6ZiBRymLvJ4mnJmohn77uHCBWA+VeTgJB36di1wxZHHbdQ/VmQIhYFQBtE2moHPxMFN/efcVkzfJquQFENc6BW9gqO0I8Vwoh+2rF1pEesbUugC0bTcFtGrlE7hIGRJgRLKkNIPSucrK1BmNRvQKla6u62LMhaPy3DyjHu5mFiastBfxH1g2cb8Oq/P82hhThcA5NsksFQKPU7hUp90/LK44NUUpimBsI+IkUpjX8nTKT7FhiKTgHHPyanmuiXHfBT57/JUbng5VgAItU8VsVUIW5tInL28zLXR9v05YCqi1XKE4vwEmwO65i1y3L22g8g1qt9FJQJgsTYZGc0q5YJXgZeyJeeCCy54DC6FyQUvG4rSwhl9igvmvYsXqiYz/lkBwmWNIST2IylSsJRZt/cE7keKFpY6GD/Le3gdKU8euFdu6NGf1+f8DPs4HlyPMlhnPOdz06p3C2Yww7UsIwpPDR8fvaJjbY6EuMmkh/KBPpHy17m7A8ziCAe5IOE3zS7sC6r3vufuaZ3xJ8d5NLDwl1i7W59IuZGHZczjr3oC3r5Bd3FhWOeWHBE0fhsmgicS75FDWCgOrE1y3TEPjr67chznrtuqWFli2wLEx2Y4jJxZO6sTH/8IL0oRDtc8Jb9TLKZt/n6tLVnatHeBUtaemb6xMsXj53O1NuHykYfas7sbiQgW5clhXsQ0lzK+vFTurau7667mreIk3d3q4nF1zTGEs1BvnnR2kWdMpnAG22n9HnD6auEZMZb0fF+07H1dydidAgyHsHXIz2h8omRHmQ2PEm3bc4yUxkjUiuG4i703Im1ZT6K5PfHsQ+lSlDabCLDJ8G3CSAE+SK2z27av0XBYmUi0y51YrE3ooaFrqW0gv8Qyf7tthzZg2doEw4pu0oPXMdMvuOCCC94NcClMLnjR0AoKHD8eQljxmwRDVOP9VqWGEU5CBiU7HhyPo7y5fal4p7jchyzklPt2EP9w/swg5851zydgwyYRfk5x/yibyhbutHkH6/hHvDfd5OmZ+Hvs/Vl+4qxnWNs8NkjTt4neJkvNKjlD9Eh85/cVcYzWOuzcNkhakMC5vjMxpjqSwyHhZOjnGvF9734fa3Tw5RLD8DS+vKvu8O1hMhwcHn2J7XfsryMUAsI93yZr3Z3QiCWuiBAC8Ab+9et6KKfEfZs02Cd6EjRJHkkr+RZrk3VqzHtYpS20rsaPiKJ0ppo4nnFZWsx3V86VKylb/DrLm61PEu2oHX8lMKGwCph3ygBza1qXUiETqawQOSBy5W3GHC1UfVGuvFM4oJEyXXe4XuGxsTqmuKatWfP7QWWI6UFoREYjPVV7cyfMLp/iNCCqPDQPgAi2p7E7SnbStMikRIWudTTGKg0akt1WAyCHsDiwNgm8dDrlPXrv9bSd5e5H25HD9HL3F974SbngeXBZmFxwwbsLLoXJBS8aemGisSrhcMpjTAxIMIj4tQ0QX0/5WaigOvxLZxIaJMW50NDgvFixSNzbC6eyxHF+GIs9744Af457JtzjkmJIjqugLToPySMn/F73edNfHaWpvZuHOnGESs99SxdPCpUhDGxQ3NwVTYaOtSZcF4EK3kZWDRAOso2BZqHLJrrjxtIHx5c4vyPmjJl1CFxQULNwsXa2bcoaGttopBPqRv8FfNJEzUFbcZCtTUbPTXECV5p4Oao78q/vweP8ddx3urgYuPOr3LlWapHoTEvbKOy3Mzpm9KjSp0WhSsNN06GGa77AobSJoF2JHi71NEQqCC6yg9e+/fy82tHV8X01kPP7kdYW5odDIU7Y7sHzv9MvRwnPfXqxTezZVRIubYs5udzjNSsUG9LvWtPCOWqxqrz1hiy6tQmRRT42WNKrmjSizCHHkq3wbH2R0sxOtvESsx47PGS4LlKf+1KVd4cd7yzXIm+r2pXRcW6vo2tH8BwVYP3hZGsTmDK4eTcXXHDBBe81uBQmF7xsKMJE/MJTuMnTKTBGVqHikjiG/D201M+CBMXl9uuX2ZxWcZAqXDAWi4BT7h0D3+avYSoK/mJnAsczodbbtMFZO1Bibh+BhdEzXr6RZTrVg0Kwq2I7Q6ptNzjD+8WYi4zw2NoiALZ56MLtMRRkqgEWxnT90tfuKt9mu7sCezDAMT9I0uB4i5tCRWzXMeWPpq5KjeO7yLA2EaSTdNb+LOc+lBz9yRExwtm3iWBsj6lCRWcP5MMNlNSswpFoKRQ2YlY25XvxZmOF6RSWlbw2biVc0+bzmXIl6pFSTqKs0ZtZiXjFFC8UD6JXRqck0ygnR1JxzciLcMupGzAKZPn8Wdbn0b4AsqH1a+IEgft/cHmdhGulsd1zekFoQXWHyBO1Ye3Vtgoeh+1S5WUCyVyTauZMPA53gWZ/9V2ymhgc12Fd0x06HNF0JDCC5wsvd1FLVvEm0zadOQx93U00Pwcd0fKcR7LSWIGE4sR8mzzwg8YUzGiX0degOvVQ9bA2ER10bqFrB46IW9LMD9TZIPUSpJ7Gy5x3y6Zx1PHD8+0CYPx+XBYmF1zw7oFLYXLBi4bEgM97ii9CwxFfKxSRwsIZTDiYzH0RQqxcCAhr3JEvk67NnAYXSMI3yZ1LzBKlfmN9xt2UO8YFeZ35wtEzv6sY+ghTXka+PgPBzHX15H7n99pUteC0pEl8IHM3CIetz/fVcf81fOfUDFHN/WBO9AySvXjtYY5vLU3U6phOX80/gX2xdAGOnMKSUDeY5g0q+2S0JZhmBVQ2iOx+ygPSfVi4qNUp22S0jzVlLCicwRh9VmjQGrfeK5K1iSt1HmIu+3ydWofjWneKFuDjh2ndpzCChjBdeZU8nkb3OsUXRQdKfsdR1nRQPNO/ktcnfXNletDnibxNmbK1z7fr6GsW5WzN2Lqv87c8KnasZlQxRzVHgceZn50+cVpS9NAYAQAdtcsHAHdoMrWI3wLk08fvxsvchifYZotpiZWX4MOIMjxOSqkAK1Ck5CO8w+2HeFzcDXke6qAb0RErP9exEBUS6sbMo9TOOF58mw6fO22NJI2FtZ5+P2Yn/EQ1KuM+TlK50bhtizyb7afroCSGUsTmD9dd7gKyoLvgggsueG/CpTC54GWDMfrO6bBS5EhZQow6EGGvoos37gr5vj543Brb51ufAr9oLvrn/ZmnRbhSxO5H8c+5b6ti5DlSRS8CNPAAD5XLmkKBmU5FfMGM7zJi8V4ulxUqK1ZWmvrt6+Uj/e+OE17CB0VrPrWB34D9Bj+m1Lrll/rlJ+RQXDopZBZSVKadKwQg4fNjgaYrzrfz3fh7vptQ0dx5bcm2DYXVvjt2Qo2X765F7oo8gE5BrZaZaWDfJlM82pVqvtf3bFmS4lzxwvlyu/aE6RzWToBxc3pSciz07Iy2ndG0hr5x/+76MPH2JOH4nNN1ICD8A68x3WX4mWEFyCaQQdjoeeahcE2z51CkbF43nwR2nw4F3T19TuE7d5r0mUpVKidNWpf3KC2e54uYcYpQCb9JWAdXxlTHbY/khWQZ2ar0bsap6jx6tSWFKd7J2gE+PX1mGpjpBtIdTdyaZsrYbQP2vSs38fDthICWeha6QVRNYA5hkbYkmrJKtadrFesuZd3Uow025K+KU3WuQdtuecFDoJDXf6xw93t+wQUXvBa4FCYXvGgQY46RhYukQPHfiMKwj0KIW2/pseQ1RqIIB51gYnFeVYdrictlAweO27bxxeohBcirKlCacgNOv0U9D2S9pInzNLgcFWEc+Lo9S3tmWUwmVDA5cbs/EsZkFD0OOTzziYdB8ZbXJwO9A5R4pSyy5lOLXxUNxy9H13Cy0y6CgR3N60f0ykwZaet99E9R0wzdWHOslmBdRGBGuQT+RTlLAixqrHUBGAK37sUZ6xHcOfZziTPRhseeBCoSrNoteQ8qStY8UceZo1gFQk9ndZfwquh4JO+B8oXpp2D4stmmYoQVG60SZDtMa5UpXl+u39p+NarWrYMDOE2mAXNFzkH43vMz0nSutyGckdLh4bs+kE8O00+Hp67Lg+F8tgie6MEcFu3phtFkPx3I8ZoUxyw+AAylJ+efVE1G3DBY0alUMFSqYtq27s3GtCC7dKB2ZuLpY0fUUVhRE3A8c2Nus7PZo2l8vhKmMl+OSl9wwQUXvLvhUphc8KJhk80FDABFwGgEB88DEiA4Euu9iyPGvwo2JqD4veJAcau/kpzvsD7pRS8XWvh6LpyUM3xN1iCZ4/7zlp+fVXY+D8Y+GMCEavPe7nW/Y9IP8/mZtHCngfJIePK+I2xf/SI8+sDhcokA2wbdzZ/JkYggzvgLxy1lpPTziA2e8U9PgL4NNxU5Wif0MlwA5Xvs9nFZgO8QzCM1ZjsY/Qbtww8lTLEOcSXQDLMDwsTKrz3Om2NCEJGnaW3issv8WmrYjOH2d+KnBRndUVKBzDRfsRJKJROQHAsfDHU6A4m1VxUlECpD9ALpvpZjulTjQrlA8ZUuMT4JF5nRpd5CJ1d6aHelNkBl6PI6ae4wMeA0HiufoMgwcVazZJlKm6k1fuD5FfNOohgzb85ntZNUHlBret5HoFCHiY4d/btQIDcLe+weW+5kWliNerheDg//IrYWCEs5UJbkZQxbvW1PS96US4a7pNtuCpyl4rNGKW5VeGQrFI+OuPnaoUWNau/RrDR00glIOibY6Y4gti0uyuMedZnrs7P0cAooiO1LqZ6iQLEjjnFE52YsWc9d8DjsEOx31v6r1HnBBRe8GbgUJhe8bBCZzPT8KgsEgz3T42YMO8UTV32kdIksheGnMmJVUnqWWRoGnwWUkuZ3EgA4jlh/L3/XV4k8eIGugzzWuAt4HN88u+wU3UlhKeHuufZZEV8Cu3otz1Eapx/JG8E0MhNMDjtB3CWF9SDe503msXvkCpIi23AU+UAvTSiv8Z6XbiyuHcK2QbBDb6YosPuMn/XYkcHjvkEnixaqiygXJhxS7hSek1D8NJvJmJPzQx6BddvLfTjzh5J8m/DYppAJClzy7DyK+0KwYeb9b2gPJOjPEV06y5OeE12iMt4HUpZQLKd7fZZPIt4Ih1CapDRHlhajzDGUcNgMktH9OSjdONqay02FNvXT2vI0uubbDP8QB69lfa5z+Ox+QtUSQeryHNV31maHvCyxQ6Y3hQ2vqbq+unimJTOcaEwfHl0NRaNPnyPCTSQ1R8rS3cbNyQL+u8mZvaoxB3wly4gzZYH1OnyDYM4hU4xooO9/NHTONg/d4iRG0amYWluRvijENSp8zLOTN05UihTGpjGBpEf/rV91y8sAa3T/glcE3o73Ouu84IIL3gxcCpMLXjQEb21MM+JvERAeEi7wSB7keOLVpNTDf1HCgXqTfu93LWk2Gkap5bef8WNZ+Pg2WYiBKtW3RWedrSzwnAvwF29fyvwIXmfoND5UG0M646OqCI+vepOZFfUy1SeK94u/0vkZnWSdcCpWnIWPgJnzDdDbjD0qp6FR8muOiWuaKO9DIJgb8POEN91HejfTFH0bVho2R8cdA4lNhoPTrFkEn/npa2MD7LMzKyxYOWI9MTRM9AhBop/QqwKkpKW2V8sWtn3JcVW4zNJGfus1B41JmfNjDTGd6KzYShhrPaxEXvNEeROUeKtOGxazkBEvn/ogWcHKYVpYsz9MTEYDvrUG/LzlZ8jyHPWRCpmHLzU/LUxEYjYkzW8pxc9y8rzQr+bypnRYXRBV52VsQqzNnbqc62ru03ql3VBmGpHoTAKoDAvO3f0OeBYFZBNsCuzk8PhQyVGe9aQ/XOZMaWJkDUBy5Jr0MEKt0PibQsMUOU4LaI4lA5p5gg23b9ubnHqY0vmAitk8CMe0XP4+WI3RNvIhZ0CpM9O9+rKPpsEFF1xwwa8UuBQmF7xoWBUZqzCQFB8cvqNAYab9OC/fhfAocQUv4baoTuvDWp+ktJXftki547Pk5NrupNOVeNP5fPRRlhnjU3iAye7KDFPgLFoMlFiUMCEg8qxhQlIqwh3yNhC0rca32dg/skRxVvIsLKVulPvEY3L4ykfQJjwlx0kT512SiFv6p+lRgLFtQae1i8BxiBNxpkAgiEmRvL8ipARLS92MOFYDimRGHsbC0+fMTpnhJu4HE3AVBOoYwIUeSSfprIIFhxhY7LFmsoiXcuTmjT7MQYjZEDSJaQrnqUoTU2xYuYXuLWHxrXBBk6Ieo0lM4w7TmRYWfBJdpMv7pFafxN1p3jpeaexMWVIJWEvYclVhbdII5A/Tqwc1CE3b7uuoI6x36yYJ+PFG4dvFTANgCuF6fyWCnTFfUBBzTkpzu1mSyaHrK7a1tm3TTLxQzL9pRWaV2bDS8KfTgQruwmEMpUxkCauUOM0mKl9s5pQrzNRw6enB9Oh8kiyOWrlDd+7P1JVd8AAMh8avdzRfd30XXHBBwKUwueBFw5l1iIpmJn6mJeEiCQKWnAURkj6WuGphsjC2hJf9ZcEmMfdNnONchIAqVLAShb88lcod51aZ4mII37s4aq+0/xAU3F+V9xbv5mDiVXY2Tli7f9LM/eaNHWQRPr4i0jfGSPNXoMR7Mtc8S3VCQWl62XKuc16RIuD422KuIJjr3Id1FFgakNklU9gI7LOpCNz3iG5a/JRMFlxkHBW8yzxFOJQY4454cfwSl+UU236qv5ZQsRgTn3p5+I7PrE/4zQokrGmoXLddJ4kQpkgq49q99kXoOFBuxIIr9MRuHm5o2UG5THvE43VpG7F2QWGq5izvIT1L7bKCw8rr9DES+aTc+yu3c543X+kknbuQ3/lzxMcjFe7wYWExa11KA8nUSU/TVru5oViM/EbneSZzDyMsB/FBK3ie13BH+ESmbxM/QYcomw3vUqitasGtK8YQGwyNLjO9rm2ph5XjjwfLw2u0ANPnUmttAsS2JYCozmrh9sjQ+PHDXkQOdS1+F6zbbA76fLaj7YILLrjgvQiXwuSClw1VsKJfaZme1R7bioMcvpd3ETYYpTVv/YJaLUr8RnEJD25ytsEfS1mwCF8mq+PURn5Yx1PQZ6RrqaPwav4RrJZ73UyUv4z4GtexjW1faxVYM8XjiUbDFDYTj3hbxGGTPxMtG8Lv+jPxvCtG401vgO6N2M84Z249Mepa8x71E0nwl206ecQGvRV/JCKF0+Y7Yn5B6NAdKWm0lhz7Y+UE+zZBl3Y0A0ixdTRLUl3brC1Zm1BL5By2YtGpVHpgulKsR4IwEB2hRcnjVtKOaNqsOqdROznOMhM+rKBhnJNWxdqkvIxAR1j8GsQjuZpoFvnBMi6V5bw5m6T2ci6NOVxb6ojdO7zMPi0Th4JucQ77PAewB1AVA8kqLOLntA+6QNsg/UQUzavqHqmbnSIfSSOnz9sq1NehqRWS4K4UdzJREoLZ2oTWrhAVkrAQYUs31i7wKxk/F+JNTfUMyM5k/HXLuIirufMmHBv43MdE6628WwXSGG0xSN1WHJmOtLmBNEUkN/3AL8oFJ8BKztdZ5wUXXPBm4FKYXPCiIfPjnTAg/rN+aG3CYWKGs2ASjbFYyh9nU15vx372BgNVlR9RN9XLcSyIWEFSiARrjVozBtM1Gb1FARKCgStZahbL1lyJ4ZQmL9B/CaR+34MjXrZpnrozYszZqNR6CqP8KrLFwugrs4+sOLHWrUTEhzgU4b5nKK1VVpQ7MpysDl/4HWuk6bbEPwS5fe+RSJxw4yfdWKYRiC07wXy7YsG4dz4OAuiFv8mhH1ub5K04gAlgGu+Ku0GS3+oLSKJJb36+22RtYl2IPvk2Fm9n4lEFUpIw3H8j4wYsCoyF1nHY6q50TpDqyPWBFm63xnNdRKBK/YxfwbUqYZh4Wh0LVdtyvMRqCfpFzl7LvdMSD58rvdPXBZiIPETYHoBXLDuaHlY2MX4Fdx7jo+fDBsbaSyfU2FoFULUSKxmhAeb7q/YVYci2z9OqFLHR8q5lHqP2nLZn49sm06cK6clmZT6Mk2bJpDdGx8Q1R0xv5q+EALKYaoCsMiTGXQ37rB5xp8STdvjv/Ol4V3VV1/ZEdj8oluIUCQ314bjgggsu+BUHl8LkghcNy1fSKkh4WjCMwbNnZjIrPZp6KC4JMimJBRDAHecZM2O8banbed+KY22ayx9cSQFihVxnUpUtzdXLGQ+ViReD4Jk5nfrZZD8MH7HewciXrTIYTKDJ7zE/uvLHbGbHK3Z5YxzEJDISvomZprAWZYF4+ACZhDHhV4+UoNNpjnvJCoaeiU7ISOmDlbC4bXLNEOhtMvimoLBqRDJX7ccU28uhr7RlXQdG2d+IP4usJ+k4/nn7yzrE3TfYPHbWWmoTMhzCAocn6ZzZlnSveokrCpARJW1a0Aceu6woSTSOwl6vLzxJ9SdamZ5zvQLOE51i2pBpXUk3XDcKUxzrCKDU7kwIRQ1l9OdRgXRp6WqcOVWE3LTCBzfucuf5nYL7NfGIZ9R9kte22KWmaFxnmlrfbaJqv2YCXqXfmpQjtjCqH6DU4smCSklM1g5bH395qWX/D7yauRGywlB6R6ylben7UOjGyb2DDtqou7XJVIywNaMpg92Xk7U12w+rQO64lNYZLR3HigOJrq2+TcpQNKNzwavD5cPkggveXXApTC542VAVFiY4eBwx7/GnKDqK0ACc52FpgBUeS3mOY5bE2KB857R6r3myE9ccDr4+h9UYQBNcj+6OK5fPl1dtMgP1uVO0vBYe+gBYZOkaUdUh7lC3+N7GPwdNKudf6qagkc9C4QaqVclRuPZwRW5x/DoFPFWzNjlGOneX5nVVsigPENeSvz67wHpmbZLiNkB2whsxgWp/J8Zm6p/HGwcn6ZDwA3YI2ylQVjaf66jG8WmUTn2bNGoT+zqLDIsSay4ySeNDNKijPyMzyemSy9Qx5vqbetqylgfwE1uGrjCUGGbJAZmjn2iSrHFOWI6IjJWRjD802jXFyczDlimVzLbromnTM3O817cD8tTV1MDrEyMFCt136PYUSoV55xXaPT+CZi3Hisex3jTRTf7ZYOfPRl+PXIHc6yPnqtYmhXLd61Kbp45H7ndgG0cPYzk5J9NPcqdq66T8yNTfTGs5b6kkWjPH161NSOFsJ+nwhp7UY2V8Kb04xco+Xok+bZKtTaiWR+4XXHDBBb9S4FKYXPCi4Z6FR7fNxXMSs57zkeDQCAxZwcJ3KXHESnk6MVVSyhfByOWGgv/KbPXjsipUMoN5elmZg/QQngK3LEtoVp5IlHGrGq6vhFHCETlZWgHGF0D+VKhI9sB2hLAcM+lLG3fGVqxd0Ek8bood8V4XnSgRW3DoVJ1GbG/ZzQ4vBZVHjIk9bwLdh3+TVKhUGsdXcnKPg1vFWG3+SNskzNpkKi/G1hldjUuEBG1uMk2KoxeSJDQPki4FnbVJp8DIwkYVE9f2m3MrRvw2y5+cpMOdbEY4DYH3n9a+xYUyhOLs2RQFvOYrTWrTcn1WYVK8WB2l3dFVtmBhBUYpU+hk6j8RkaQYmUFzylS30iS6UYlIatoqyuVzPZLKK+eZkeFbRFyZGO0eiZAnUHFCgyfHGfVwS5ejiqvvifvQbnXpuqO8WLvrPrTLm+V6W9+EgysQOOMBznaazjECx4+alArZ2sRysvJEp/ZWbHuf+XTxsHUg+m1qkUx9ghYnqxPw8K9UqLU6SfVyqKpZ2CJvdExcaWI/caur2U453L+MC54LhZt4bXVecMEFbwYuhckFLxpYcGB+XChRKCELGpKYn2D2ozwlg5IjrrbBzLsAvcKGvKvVClO/pPl5i/bW6+jrVS4LTCbLzC6ku59fS1SNkxKWiPOfba7vgTYf+60nppS7bAyqANFtpXSlIZisikxcPU25qtRi3bed0HXBmRlJwjNtzTno55Eiw1+2SQb8nVFDsARwaG2yHMFzDGes8EhjZn/4ifATdOYLyXeEnxLKW99btEHixXJc8MBAZt1S/ItYOOPKwkbXp6pg6sUMEF73rE1y6+voiT+DF42/x9X6g/PxNhWuJuhQpmtdHbYeo8xq0UL1zbSN3qmVTzS14u94ZDrcgVuKlFxczOa8yjZpocxxELiSxXFDqiutVevnQkzF00VMFzoehpWNYjgaF8or/XMl3negXXc0NbArdNsot91rDw8WVmqpAbeY45zmsLnQLtPhTG0C+8dY1uIBMdGaVAaAf0vU8StdMPJObfdI9GD+jyoC7NuEK/EcMuJ8Zw6QnRUvSpxE1eI+/7jiQupvCNKYiyAdsR71ZtVwjNqITW2SoiWX14HPHd8mebfW/Xl9wTFcW3IuuODdBZfC5IIXDUfbZs7S0skbJCwEc1XCwhlzuPvqFMlC9a/1jrrj62T39bdz+sonTXhkHhXHKVua8BVfvPhUnXS15UgGAGWuYUzG7Yj/BoY1+9anPyBD5KoXZj0/GyebxOuGx342PKprEOOcBSFwA8kWms0uDgc2c6mqkzG3fRFQykWcNIDs26QqArrOEHPu+Bcxnz4Au7KChYNtbA3yve+NY9f8opDqrxMh9tG3o+Hsv0CGhY1aTChHAEnlWMFUZ4IuT8fbeVjsWaxNFpGl1nqgKZuvPm+JoWFhGpXonZVtaNkBXTvzYVIIXTlqeNx1qQNUN1LeqkxZFAnL8V4gNyICV4B0Fif8RiTeaiwvs8DpCBsCaRoKdUUKv5psHwbA59sBSX4WVJH8+Nnox46xxa3EH9bepWWrKQcBzFqM48LtEB8i3PfhOcC1tOdJ8TRzuie+ILt2Oyp377VoExq/mSO0bcC+l3VH6DjmhaRlstbThZpm1M18mYDiAFLeki+TvNWGO24R5kTWcMyUsap7zdpkSU2mNqD3cNyzCy644IL3ElwKkwteNqQvnTPgfLxEUhISEIJEFClxx0oTrju3T+n+WJipBacts6eljN9L+FiZwVYmzKj04VDI6KpcwTMuKeHZj25rDj+nseDrAA6zyAPFhb8Ajo9lT1DAv6gNMSTKKzG9i3gSCHWMKX0V5AIKmbysOBu8crPPAXE8RwPM7NJcMsWJkhVAWxf3kuIOrFCqQFGFOpdeXBBGWJDY/vhNZxwpZ7ahbDjWnK3KCwBZF8Mn6ZC1yejVatDev4dYrwczIKVF2BQnmP0AfYjPuPflLYIXiq3VHCeULyxBRm3C90qjiIbJYRul3lSO4yO9bJBBZ1HixKD0kZWxrgQyhcjsGAXBqt6wppLAoiWSoKtLP7ga4utWVCLAtGyxY34FncUH318XSKpSpm+TV4XjlS4eUjBJCGrmykrkVdLN70dHYa0twybAbeaxk3RS4dIh0qkdUt2qoDpapatvk1BUAEHS3BLHiqeuZEV+bjGpYZHUUiLZks1pe7PJ0Mi/d1CpBaaZg1CNm1m3hJXLWMOa2o4x67blNC/ggodAsQzxa6nzggsueDNwKUwueNFw/5SaEAZanyIl7/h6tAoEWfGxCgdFRKAwcnluysOlDxL4sbzocfaQmH60z2fOYbluM9XMipZaL6jeRt5onydDybKC5CbYOWDlJWvTHaS0KpNIn9fVFWqM70E9TbsdHgt+s8uj6/HJc/jWlclIF2sTr+lMvKgdUlfCrN9O6xYPJf8iR9YmHR4cleMnf014xbs2xZA/bwLdMYVKOIPvd5mOaq0d6cZawJYqrJRST7a0cZe5D2tVWJgqrBfyRkRSfyAkjxB+8gFIYTXmPg3ak3To3dC4RlwiEKE8sD4SsViVITRwvs7XtMPjiIFpuRHlUOvscCxx2eIvKKXPR7V2Ka5VbkgcAbxR2OaRYLxfAdjSRLdMdU254tYl3UruCJm9sQPFSyhNeJjk2NrkRPhv8alhx4vwnX0aQrGt0QfbyA320bYlpzPpJz9NsT4nbq9Jdh4KjHxyDl8+HE4HZSn/HODSR6pgC/OriHU287nyDCksSwvruLbUaY6p+YSK08mMtoBK4XA71L254W3bSVT+/tSd+vLOYrY24Rf+Dl75BRdccMG7Ci6FyQUvGpyfFGJCnId0NjzCzMAvAoD/wWKengvn+pJcwHUdtZ0FlLVtxi+EpeCL+btqvYQOwEzfX/9/9t4+5LvmqA+fOVc0IbX5xdTEGBGD8Y8atFgMphYs0gQNSsFiQdvUl2AjtqYFo0UD1pcEjbEiviAN2NpSaqpQW6htSdWIpaWiYhGL1UClVlETFbGPGoh5rjO/P3Zn5jOzs+d7vtd93fG6H3ee57rPnn3fPXvmO/M5s7N1OIMoRBSsTYayoasQkcLpvg/hoN80yI13EfdnhHpFqNykQYnxU5oIznoyywBciAmfWcy3Ew5AqIUeQz0HnQoYCadIFbNdkI6fG+OpMUWFlMXd/B10HKknDGnWra5UbUy8dwFc52Njot0nRIaJgUWSlDHvLX6PjSqCWpsITDqWy/BV2faBLjpzCEtCE2uTWDfqJj5XPWXgV76oUVlznuFxNcCS+RBDvP4TqoUbWG6H+So+m/oU+EbJGeDfgpMxxqe+QXcyGJMdx4YfEyQESEKt2D9wBAv169qOjI2LOqZcsQhrKHpV0lUXCNEEYthCVy/g6drvazbswYPF6mneL3sXdK0nFnsJ0smcKNxM2JQardlx5+FDQK8XGz7qQMiCjcawg+4M7XpWA5TBqUk8Cv4cvNWAcTGQ2YEp76W2byA0jBd9p2D7oe3AU30g2cVrmAfmqSnE2pJzd1o+TBYterJo4mFg0UOm7/3e76WXvvSl9JznPIde+cpX0s/8zM+cKveDP/iDxMz0uZ/7uY+3g/dJxdfI4a9/LQ0nMKjnzw2+3nYhl3X/PPX7LgSz3qNTQUNMsuBdCOeEYbI6XeAnyKfjy39cxPkfl/E8DXs2EGu6Tm8qgA2D/S//xzmMzcQ0VLDyH0AX5x7/ifSjPC19o3YMbxcubfw0OHO92JF0lYvxbT4qj/itG/UX1TBPTObCRDpskbs9lCFq78W22byHDob1Et+Hep3D+4Xr3JZzeh+2ra7H5qRov3jXh9NSynvSTrQ1uG3tL9RDdh+r4OGPJvHoXNTDFNvZNuJtXk/mUxVvG8dIIY87c67a9+dsbzzyoYtzXMdh/7Fd50ctLJBntNiA5w6+U23sKY4hH2N8GD/Uiy+2LnLNU44fy+Y5ymtT+xDHwfaC0qGmPnu38zt7KQ2pdUFIaA98wXkKD3+xJHteibWGBgXGmXLx0MvZaMfR46PnUHZGrQQz07bhL1pYSOm/nDohPW5HijD0beAdpO+2v3E8NMoxDqeSFWTUZej3lEfITBv7uG20voStb3k+xlnQOhjaiXnVSpIl/v6HnNh/a3vRokWLnnm0LEyeMPqhH/oheuMb30hvf/vb6ZWvfCV953d+J33WZ30Wvfvd76YXvehF03K/+qu/Sl/91V9Nn/7pn/5B7O2j0zVbcohBxAABBC4pPworuRy0jwJdEGgm/bD2ctnUp9R2Fo6CYBYE9jH+Un4KJ+Z42kzAGXSGIhykT7h3QSocznlZaJ1SrTZwmeZSvn0pC0cVt/u7yHVVGRRLc99Ym8be6tfIAT7JknQfBXMCdTxdv/KGMgz2JvahcCPiPXwp9a5WapgUd7pGow0KU/weSZDOxP1Ekx2sTLh/RWX7eDm0D/tw8pdZIZzrvi2C4b7nam3TgbVJNX72shPFjQm24lA+zth7rNYmk/MogN8Af2KPQ75iPGLgYR7n2xFBOSvD7PkDb+XUXuyPXzjWpwk9TncQRCfWXgxBtwBwh7FBPL5hWBYeWVxD3mc4jXrsK2q+1rkUH5iW9zO8HTnfsGKvp3qNzuu0/LUpW0mxfi4S2oAQ0CXxeOcHrUnpfjFIyN/tiz2IYHFOK8sw0WBVYqDGubFbsRCJsx7DwYF3D2/MDaaC3xI0zOHQl8yjIQq3wPR7W2Sc7DfS8zVrE/gV8F4n/lVOaTwFzEr3vEfWJn7EMqEP2kVXUPUR5T7qXLRo0eOhZWHyhNF3fMd30Otf/3p63eteRy9/+cvp7W9/Oz33uc+l7//+75+Wub29pde+9rX0Td/0TfRxH/dxH8TePjrNvvSiJYgK00xbF2pbGvFG+gWIaEv5N7Ivivoa6FeU0AYd/rUqGMJkFiwhj311hng7KYJM8gyKTDUfhHIPiCjqR4THsDchQ3lTMPO4qoZD2L+mpSGATsJeLsrZ1nsTxzjl4RRH8cpUx49zBI1I77WwW5uA2OLiS46LIIzGN+2gV6z+DASEzgzmtMVhNY0DGIlxkjC+KGWPXqBcP2WnWVZtqCMX6yH3ebwr4SnRNEhHAVoXhr0g/s6OLxUdv3CgnFd5DRTg9qx548HaBMuN73oV73/xfcd4T2tjbm1vN1soh9fZX34X8J43jvepPmNnzHNLjGIe8vxLjlNeZZZ7XTHeONQnqT+64IzXDuNN7Q+WJz6Xw1g2HBfwd2tT5wT4eu9L7oKtydA05iSPLdZLeChqmTDjf5oth8nDiE0IuSNnIRrCYlmF2t6JY1eSpWLL8epsR4a0FpbQF//NwN+ky3/DUgltFPfYBgcjOrpW/czdccpcTguMv6ubvo/Kz2xgFAc2GbvzrDhGr9Pjg4Xc1q1sdK1DOeeB87/R2s0aAR5IkL/3tfPUbav5mgO3ixYtWvTMomVh8gTRH//xH9PP/dzP0Zve9CaL27aNXv3qV9NP/dRPTcu9+c1vphe96EX0pV/6pfRf/+t//WB09d4oCAwmTfjvu+fhGA554hfbuk4t723gvZdJXzexbFHubFkMHsk7UeACQCcI+zEcK41hE/qJukAI8diP3CdKfSLoG6U+ZkAm1YvEqRqNZZZJWiYmP41lTE0GH10xYPv3Ikm63ol6H+3T91FlPV3SPS6k3iG3vsAOwrGSJNTOjdxTbpivqdNXaMt8GYBiRwxfGfXLp2Ip/Z1rZ3R2XybUTkmtZp3JFM785TK0qR9j7ats/OqZy7Rq0SKlfurZN8qMWi06j/FbLdnYKVibsE1o5jseNj5xIs14GGN+5GGo7KRm4V3FtNS1lI+LfLG9Md+EgYU5rtLY6sMP5O044ZYWz2+BPmO9YawVM+O+9DnF93nj6PQ1Mz5fkxy7HiwCxnF1zng8dmvjRF6cX2ng7ugjaEJSBc3UIGUV95UQ8BG3HBvY1LRRju9zbvtCWWubFadSxoRr8hxxCMkQlvKodOq/w+JWNlStw9xSfcqapVkUtE+6DjKnAb5m3cu/Kdh3r8t+WxJPzDYrdpIOlmM2axPmuVXeopqWD5NFi54sWoDJE0S/+7u/S7e3t/SRH/mRIf4jP/Ij6Zd/+ZfLMv/tv/03+mf/7J/Rz//8z59u5/3vfz+9//3vt/unnnrqTv29F+Is+CRBHKIxDQX2awEVPa50KtdP+3OmbS2CfZ2MZWjU+8pVWgGIWNiGUoApSl0gPP+VqOcnOvzLwlpFk1NtrXR9M+Yrk/Pz4Th+3aoSnsesDyDgchZ4FW+SOh+DYNza3ojkVsXuyQBDIbtvesEIJVSCdqqln7ywEe23LgCbohMVEdftxSvqa1csQ09iD0ydxzI14OR2j0p7GkdrQGxbRRyH58lHXdoWmHySjr5v0uu11qo55BRf90+P+nTTeQRh0oGhW+u37Glm8LEHnoFpmZ9gXmdk59IAcCmADr0GnhRA47rOoW7LGh8g6vQejlYzMS3n43ke83fCPr7etuh8hIkFGtDbuDgjYIFl2PoVABds+L6oYkwH2WpvR5kzaCRbJEOc6DFFgBOwxbFVpoARS9uWtStaeEB177CTifcBC5I+PrT/YzZMlpQZz3xUTduV3q6M4cjVJBRqbTPt+yWgR8uyg9HA8nS7oh+f3sEIIdKjf/XetpZqfiHvYVacE4oc1kDZTucDoo/ZT9Tx7TjSrLzEHeMuWrRo0TOVFmDyDKY/+IM/oC/8wi+k7/u+76OP+IiPOF3urW99K33TN33TY+zZedo2NqGii+2DQO/6fWE9gsqDJg2KQS9r4XaeHpqYDgpH2casH/O4qg/6wWf250oCWJjw3MLEy7myoWGu6jFdI52mk/SQ6n5QAC2tW4nksinsX+malNqqE/jr9+CXJG+bGfJSsZWGCOpQFdc3lXBRRwh3hcCOFpUY71tzPL4p2DmP96oSsy3eJFwUdRly+f3wBZJRkW9CeQugtckl6u0GSZt80aPArB22bkGaCu2q2Qzg3HmFw0EJj3K9QKGV4iQdGoeB9ZoiM2u7Z2BiPzKb8CmAb5OubFoaHNXZIpC/6DvJ0zQHJcj6OkuL/Mn5z2iGj/mwnpzH+zQDUUK+1K4VGP5iPOcwaZhgnBjPxnwQ6MYtElinpDYCYz1kcFUclPVBDnnU8qHiKnY/SVf+cFg2hxm5p3M25JSE+SXWqXEtQwcmKhwB2BnhfXjL6vc5c6+axvK2xuHFR+MPw3cOlPhp27FyD0ssV/VS28brEVUzU4G1NpN6pLlFsB0/HE9rqsYd46tZHfyZdF4a6/d0h6rXSTl3oeXDZNGiJ4sWYPIE0Ud8xEfQzc0Nvfe97w3x733ve+nFL37xkP9XfuVX6Fd/9Vfpr/21v2Zxe/v8Qs961rPo3e9+N73sZS8byr3pTW+iN77xjXb/1FNP0cd8zMfc1zCuomc96yZ+vUiCewhngd/yuzJgAj6GQd4ncmVh25KLH9AponIy6Qep0H592QYUAWDRFQBmdScwATm2SXwKbwd5bE84+3fVcOgJzfUGDzs4MtVDino2UyjP/fBzDqQr9uFSull9qHl1Ejw5t9d152xRovFZZ+BKQjbaSLq2z7KTOVnpX+9YdjJ1hbkBHaIATLvHMu26+z0JEe+Qv+dlJtqbU1jahGi/Jd5bubb+hGhvYRJq5XV+drF2hLeWZnHSJe291R/ipIMl7USZhh111bBfrT0S8zVjwEQAo46vaqFPUK/e26MQIen9Nr82uU+qp1jY+6f959Cml1E/MtTHwvY1F7Uw5UPIq7hOUz4FaSWQAtqbpZV8DXlUbNP6cU/5eNvaPOpLP/ghqeL5cjxz93OyTeKZaLuxNLF3HRlbPNlJJvEWRyfzMRNvN7rYyI4yUqCFD8LpmV/OO5ZjaueECRPtottpnBHFUGFbxdytSsjXdMHfMNp4HhGpZYQ5kO3sZ+/zsXECY5Q9nbhXby36s6F/zA1g2LStznJEhHZDpoQ282clJLsCU265YmFgYVofNrhJTFdrDX3HfQuTH8XsR7Gz8R3RybV0AITJt0I5D9R0r1dJ2997p1q6pPZiW143w5Xqcpq3/76s7SDX0a7r6J7rXLRo0eOhBZg8QfShH/qh9Cmf8in0rne9y44G3ved3vWud9Eb3vCGIf+f//N/nv7n//yfIe7rvu7r6A/+4A/ou77ru6YgyLOf/Wx69rOffe/9vwttWxfiTPPpFIRxENZJ5XVGWT4qDaZsTNK0OOgVKTtRV1wZEtk0ZYL6YYtLkmVzHHSB3LolgybkQnEKH1qZpDCOa/gDWVxBk2le6/r8lzrmPbGFBzooeG/P2mtG64hKXJODNE1nC+Ma2rogupPtDe9CuIMhXdhkCgoFsZo2kwmhWn9WU1q1oHWwfifskngHHcQk9r0r301FkKQysKkhGrdbv1g2yO/9aFYPvX598HsDV2QXom3vAMJmmgKTkGytP7Rv/UmA5rCrOrN1ICZrGORx3J+kWtyI3vdZM4FdgYko+Cs40ZQCcguacHUlKaTpLGwKAu0m+NfgTWubQXkY6oV4LxPz6rtqY7YlEcGO4B/E+A3yE56nTQAT2uCFYvbnTnqyTGRUQrEuIirzYdsls2NuDnhJ32vnCPpm6G6PPls2JrMKwfwYH0AEUuaV/mCqWR2pipclbzdzN3gIzidyO6Q8JJdNIIStvf5+QKua2+6CEx8Z8p4JM2NY+fne3nlgPfa+Ab9zBtqVd6ZL7jF8reFPNs51z6sgjggeiazcBe9j+u4VGhdM3AXytaxbb3PfmTbLz1CWSbYO7HSQugrv/f3OAEn+23f/nXC/KtR9vEjHGMCZbwAkiCKw4vdmYAJspvERv9oK7n3WKwIwLL7FJoM4HMAQsq1BzltHwIWMXy9atGjRM5MWYPKE0Rvf+Eb64i/+YnrFK15Bn/qpn0rf+Z3fSX/0R39Er3vd64iI6Iu+6Ivooz/6o+mtb30rPec5z6FP/MRPDOWf//znExEN8Q+VthsQ7kkFC48yFdTkc/bcAbzAMLkSgWGrqlW2DSAMkOkBHHQDLD9vu+q3lWxtG1ijWwRAsQI1e3B7aIrBcRh1jFLGN/L5DgI1JvfxHIEh1xIK3WJjTXWBsO+CHQGg4d2vZDmczXafFRIm4o12Idpop3HjDEN2VwTCMRZV/irM2Bu1NlHLEvx+2oVauI9qAtyrVC2T9OFeAY42btpvibtiZRYlXVMQBW72rQMnau3SQBbeukImYK1jGkVP0zicf1UwiNyaA7QD7kK9aQs6PtcWYlqVlyi0q+hW25O/dQVpD+CI9ot7fgdqGECcVL8pIBrvygUjn6L0fjMHPtUuyC/0XT7agoNlkEcxcbBwIGQ2I89EfnnQp8gHY3yoa9Nxt3vAavp4/E1XUATnyctCHv0LDI0gDOnM1N4vuO+8A09IIibPc1Qn9MqtOyjG45xABm6v+mOnDCrr0c87dSC2zwGzr1Hb4mLvFTwH1ijNOKcwQ0wRoCDnOqFA+K0byUGSDg8Dnx/z+R931rYL075TBz/gvd2RB9fhbdbv3Db8fqM1Rhte+620tY8sCoAIhjLRkkTvxcatDmnNQoWQLXr7+975EbuFm/ZHf2uNv3HqW28X21FQRdtbdJ7WlpxFi54sWoDJE0af//mfT7/zO79DX//1X0/vec976JM/+ZPpne98pzmC/bVf+7XR5PoJpm3LPwAclQuKwj0qECEM8qrCCwN4ksoxtJ31h9h2roMO2p7EBaAngjV6LxStTaKlSazjkSxMwh8AIdZ/iR9tUzg2ILEdnoThz4syuX+RLgykz5uVeCA53R6eDPk43cdae9+5zcAerEu6ssxEEUgxLQPquhwO5szaVS5EdAYQQhyU0D8RMuACPlcSkdh+9/g5lCZh8Yek1ibtqJemXHUQpYEYDWCwNCLKFjHi3eh93seH1ZW1aG0SAQsiCpYfTZlI4EQFZBTAiYTH7Onc+9isTTxNFRjvI/X+ed9sHosy2HZ2rGz5jsAGvT9KI4I6/Br4lP4+pPK2JOEeARgEBE5t0ynirG2oX6zuaDEisP1GFOgp49nK+1+xPQY4TBtqjBOSlNe76gALFIG6fZdL7gfMkVUH1i0iBiR4k/hSVGG3TIk2dpNyE0WWSXmX1se2RBkLd0BF14fokJDVxemyuMztfFui12vABgMboub8NLAmTZeObQhYekCa5XeWFAAO+1nf725hEtqu/hBN0b6xbrNtgzHLDabmOJWVd2MaKzvr2yHZsVe4ol2iPouMDZsPlL2PutrOg20ShWvL2ttJebGdRYsWLXom0gJMnkB6wxveUG7BISL6yZ/8ycOy/+Jf/Iv779BjJB4Aky5wqvJkErzKq10gZshtAn9UAAagJdQztzDh3KVQPgEnVR3MRXsuyFOX93M53brBPcwhTKTWJq0MgiuzMCUFAP4Iw9wkXQiHL8MwFitm+gJT8GcS6p20aRfxG4kCmR7P6l9CTXIkPb2BUaALx/hq3dFUPkJJSeKnrd82XyJ2ekDRdgMm+oBVCbY+xPY9HElPSmjKWFesupDqKoWOAFWCZnDuW3ooSvLWnv7RJNzvg7VJF5j7H+++LccAlG6F4iDKZnFWTqQpFD3fOHix5hGIQD8g+gyq+xE8IdIvs3G7TtIsEmgiN1sbo+zQBpuCwbN6h35wiKsAEyLy+dW7/oJlIGR2+k1MA96DPJHZNcaKqSFIfACGDEBKyj/Nl4EWYnt9sKiVDRwOpgXmLAaGmzhGZXrdX4r0e/NrsvfiG9blm+ZmTJKll+ecZ8wvyDiZyN83bMeTbUXocoXaYLUMaV7HXJOtrE1si5vyM8st9lPQ7nQMEjqtLJjE27Z+9UQFiaTXo9wrEowmTcxgZVJRmgi0OEFrk9udyFGXHp5YmyB8XfcZGoNhsFXdf9kMaO2WbfoOKKiiacZHiNSqxOa/d0Jgwg3ghenSZ2HbgDYFgdRijzsg3tLR+gWvsbKYNzS46BStY4UXLXqyaAEmix40jRYmSiB4DKCECuseGWVUSE9AhanNTaZO9cZw1HlqK5YgQw9txzjsxxysceFt7tQVKqcYnlueHP3JGLYqfUtQ2Pmjgl16fGN9Y1vYYxTeXXSPX9LyfGrDKjZyqBFVjCjhlfJeyjZCU1qSIZTmXocgKR7DWYHuYI/pU5zF9WZtEgAMFfa7MIxbX1QlEU7eC4XAv4g2JkTZGqU/dG627E3V2WBbjrahW3b2nVjVmq1v5+l1C+3N0iC0m6bDANFobaLzWXw+te6HrTOQ5kpBi/MlDHV0Ra6ld+1QuAMnZMrhaGGijaf6oAymz47utlWqQBtR5F8DOBHTAk+bACa1hQmG2dY9c4zPAM0AxuQ4rB/bTi9t47X49oPdFhP4Nunvnll29LByEGdAzgwRqIFJFrg3EKOXF50/qJtT/8I7jO0FinNS3ndtOmwJNL7j4WZhSKSgLBG5st3LObu6Tntl5a+6BRBeN7Y1rH0nw4ED8Oi9P+S2emytP/5W2aavRxtif33iBkTgfkZ5602oo4hDx5gqXuxXWJugO6aplUm2MIGhNhaQrE3I+VPwIULsHyfAEsTkHmMtnhefg6VTZkd9XLs+d9/i4wAKgzVfeyARfPH6iPwj1qJFixY9E2kBJoseNM0BEyWwNim/iKrQnYR4j4pKB3l4C19b6WLY267CNWgDzRIqJLzltKzfoDF2EEf7tbYqmfs28T+LwquF0bJFWxNvGvWIoL/4Nh6cq0yckxjjmvQuQ/5omG7z0deBO3eM84MqitUPaZEgTs395Tb0oikRXZoEi5Yu5nq/inD+MuQ9ac/okrUJ0U7j90+ycNiOk/OY1E9J4t9SPu4L82l3AtvrYwNppB1HsTkw04Rv1CagHzL5RmsPBoV26boVCvgKKjk4ggqHptkD0seown9OJxqcxrII0Q2RtE/RwaIFtwnp/n/GvnWlL2gvB4CJj5oiX4OXP1ttGH/T5ZkRXY5ABx9ZmGDcNkmv2rbuxb4iTyNmd+4Q0eUwNgWLmNnW3PRkHARENgiXJ9bAn43F6xGd+P5niuhWMUIiBWwiY8tt9Xzx8QLwQlGbTqnR/4jeKRjp6Qx3gScTrOcLtBHRzm3biej8mIbNgWV5G/7sA8CibRdDlFR1CztYcWhpAn3tnG1ShmJHQtnIJXWp3O7svOnAn0kGaMq2865oYPud/VA8laaDEsZfWrodR218S8EKBDi8/gCY9DI6JOYY16xNxA5T037ZdlD9zQnAjvJeIgRwWr+PedqiSPpzeN91Llq06PHQAkwWPWi6xh+L/bB3wi+cDJEa3S6Q7rJfl3s51RXDR8DJAMZ4V6Zx2Pal7UAI/FAAR8jBES8Vwq4T1BYmNBTtkhoqPiqMmgLlgntdJ1/ME4ZEXqf3pd2rqmDCInZRmwnCZYpPLaA4HCojvIAVDSlgtBFTs7LYUtUSxjuqM7nlPO9tS44PJNwz0eh+0E/N8UfWfYVoNj01J07qJNytRljLu08S4mc1vyZqbbK1PKwYCPUjhnvb/oWUoC71YVK832hdofc6JAAzTPHoC8EtSBDE8HsHauAawq59BIsRfY561LedpOPbbcxU3r4SC8S7UmGAySEQDLygjxkt1kqgIjElZkxPFioDaIHlJszNlivyS2CsRZ+IFKSFckP9HNKJFNCJtmbYvECzIT5wtIoYKtgoQL8C23KopTVLDu3vRj6YiY0cAihTzpq25KR7UUalqEImjAc+Z8N7RIWJSeiG+5GnoeJedeKv+CBQIff+wrvbAwooMrny71Mwwr4OjOA9giRSW5lsPOLA/Z3cdU33IW5ExDcN6933y/5MLlqZAJu1RybjfeMVDkqEk2tsrggAWZzXdu980NvAMKY7OKPzzeZbRQIIw8DDGPhaeKyOv2MfFi1atOgZSAswWfSg6bKFCRJam1gUHVubUFQoyMPF4RGnw+3eOzHqIZzKxj4NRxqDkhDy6bgLqTn7NnE1ZFA3Ur9dt2ph8Wys5bCoS2uqF4a5YKgT6rU6QJ9IxWCsUX0adQkuRuJjlHCvuSX0fthKE9I0IBROhmA1X7+d1lHf53DKO4AGUVFsgm6TzDk4fu0KNkqyqnLMnL5OtuSQ6F52VEN6nmBtAluDdulH9LpDWvd1AtrEvrmmkekgbmptoltnIO/gFFZ9y+Q2UPrva620DOnzJ9tGfNvnVCB/AdTc1cIkPmmo20AK5R/+Yg1+RiaAiYM1MX8d1xMK3yac81mQoapUX7UlZ9K2WZtolR2IMFCEwcKEcIwEzAv/YFqZ3FGrLgkrh/dkz5IE5yE+P8lxoR8wJ8bojJPZfahD12GlAZs1mcfpvyNXo1THOdp6+7daFbP7BLKK2de1Zkv1VFx5yAdbdJjbeyrio999UFopkcyAFf87PMWG0lYf7q9/x8X2vZ2kE3vr4ek2IG07/37DzwZDVcZRjQV1XkXuVyRveRG8d9YU0mKdvvyyFYtbnrT4fWf7ndAjkHXoDOVt0tB/zfXL7E817cT6eeNe61y0aNHjoQWYLHrQ5McKnyUVFi5YmwSLjonVyR0tTCJYkK1JGNqZx00tTHwYUCd2voMjqAwMaAXZ+Cq9IoqzojPS/1DZUwkpPyNRGTsmpWyhW5acVA8wq+Z0T9qzLrTtTLSF7TBEHO4ZBFUfl/W6UNRNuIVxhaEbbUTcLQ+Y7EQDlWabLo6SfwxnG5Oo+Kj3BFB9+rCIs+iv/exxXbmI9r8o2hOAGTEvgxNZKsGZaG0i5tx1jyAKSazXQIRmrTFOOgjqOQ5ehtHaRIV3AQHf4wJ4gfwhW50cWaEoCLP1LUhmbULJqoRS2BWPyxYmxZTAmLNz1WxNYpfMqHrccKxwYCoVc0lXoboPml7F9fbG7UBFGxDWOnBrSlRha1g4zpwxPGdKA9NjCtt4qFuc2HyhtUlmXOjfJLXpkxS7BEFXpN3apD9FB4y0fp1/LYevRFfKa258QJOtFExCN9T46i7eqDnaJiLcrhO2cmgyW9ecl4KPKcZ6MM4zH4ATfTsO9gfoIqgxmQ61NlHg5MyJOflvS5XjmtV7wjju1jDd2qMNH7e+kFvmIHsy9tYmoWRZ5PngpxHKeh3NxVU/frgvqFxe1xklq5dlYXIdLaevixY9WbQAk0UPmq6zMEE6sDbBMAirFu4FKh8mJtNPwoOuARVwDoeyoOgQHVuYpLqPrE2CXB/+slIBNag+wG0uzP8IeQUtHdL6fLIBNiCtDY9wLlkNvQo6cnQka99kIU7S2MZtML1miTNFNJsTLBnBL5MkLXZrguTejtpgFER9BEOL1RTNt+RQiqMmbBeAhgxOX9U3gQCAQeCQVccD95i315DBGfMzQU83YbqfjGOOZhuWBHO5EW9730k02XI3k75NgaTS2kQtPlzKZ7hCvYLgF4wFyptjV31CEq/Nt0myNkngjTuHhfpPWZhQ8d6Q1000tzYZ7p2nEfPxKTnGh+ZpWv6wDUsCxsF0YGGS+xTTmToAx9wcx3amE/yZBB8mbU0OoAixtRG25HCEM7F+IS8WD7nSOim1S0A8Xq0sefsa1vc5lNFUgTuxUs4V9B31f3MtJaVTbpBsKyK7Mm7PNJfprwYDU/MyZPNIGNfv2cbEZrjQ5rtvgTkYxQwYOTw9R8DKhI0VhrK6zG5vw0O38JGFy57YWphiHRvcE3kcWpuopYefpMP9Ojp9FWIHoLCe/g9ut1GWqPyEe/52Zdo2ov3WrU2Ioz8VJqLsR2XiMmbRokWLnhG0AJNFD5ruDpgQmSCKX5NNnmUPlwAG2dfQIY0musaFfEHRTwBJbvuShUnoJyRCj2lUxeNcGiCS9IxIUQri8CfTfDmeT/4N8xfimqTr44Z7oq6wpe+pjHlwXsi+GHvsZAyswmDKaRI/jpW7w1OinW67vxXULmrhe1BrkjUFCrgIBEgHrdQvQwloUP8Wy75NhjDPxMJEhGBrj0r1QrblJ+QHa5PbvVtgtPay01cWBDaKOc9j1ziiNCe+tnA7jKZJByxUYwj3E+sRfyRen2BaujLOyb6bMjL6T1ENsoVPASY6uElU9G0C/CSDHimNsw+T8JJ53AB2YDoqzar889jmReuWo7rxSmT9zlxHcQt7ixC4QEYSrEnYt+AoNwvACraj3M4bEuxzaMvrj8gzRep5jTPAdGq1urp1TGYdFcAdXwMSSji/uWhhEhtM3WyRW8+yU7c20TGYAg/DAkQAQRyGWrW3OmW6JceAIIgjPgI3ZAqmXLIwEVI/Lak8Uzixp7O10tpkn1Q8WJjYbwjFtQpswebPrDpwG07vo/GV7PS1z57yVapYD0N+8vIKvPRlY1ZOTD5eiv6aCOOIwJJv0VmC7xD3WueiRYseDy3AZNGDpkcDTJTQ2gQVAg+4XA5CGjQ9BUUsDPWE+lP4bNuFhUkZPmwrCs54Wg6bWQYKjGJ98L/wHTaJlmpRkvIx3kdj9VR5jEtRRDnOFeTWVYlp4uNXIdIEdhwqXFFPKFea5KAq0jlTFhiZmpi/E84xQ2uo4FzakkNd4BbIgEY8dl9am6jzVf0mms68LMNE5iRWOwPOWluvVCWBE3W6w07ab639ptiLSYiifdO4TBesS6q4bG1i/kZAQzA/I9zLcao3WJ+4gsJDWi7T/Q3cbA0sou6TwTUTyN/rPAuYEDnzydE6XqJgbeI8ha28YxEFYJLy4jXXM+Q3JileBvqhipclTP2nVAwut6tjy/zcOdC4hmvOVTGdtsyZzLpE38tgbZLCqiTrukdLFvhDTkXajoYTai48T2vjVxeo3uuQJ/P0U0qUhEuk+IuC1iZdq05WJdAnWEemy4f5pr501L7EXxWLUz6drU16BZskMEVfuT07inVupZ1QV9kxz2g50k9abifp2EQdbBfa4kQKjNuCOl9wb7xc74uTdPS9b+8B8gH4MdP6cziwP9/i01mm/zRavjbJcqu/ob4Nx9ib2DJYtGjRomcsLcBk0YOm+wFMiEwARWUlKRYaNjl+tiUHy0LakC+VidYmSbnAdCaa+k/J9XLdJw9XQnQYCViXgO8TzWKn4WDfXHDG8FEZby56Gsh/kSYSGGs9kM/mpJXRRydeAJ63/pM37BTzg7L/MIepHOoupkB25VQUeEDlA+CTVK1Yaq806UBEFK1L6MjaRAtVjl+pdvpKQuZ7JVuZaF3kfk6wDDG1B7DvRLe3bQRBs9j60cM4Xzh4jglZHwxxOj+62gWUB1UqQJkAoMOXpAxXU0JUE4CrKyhFfcxmgeNWNNpfsT5cBZjYOItoG8LEt0kvG0CMM8cKm9VI3EpYXomoOT+g0E7uT+OpJ7fkXLBA0XZE47n7G9kgrFty8lYdrTPFCViKGGjR48xBbG/P8vYuCpGBVtGNwDgeSfdhvrVNDWt7Oa+t36gV+5vj8WedvorOy5A9RtjrrWs69QTBk2GbRnt5fCzQAoc8Gif9nfJlMjhsJSLhbu0R2qrfGQdJRvi46I2XUYewEq1NSiuTPZZHQCGx8TDuADrAvVq0OBvy+5YeryGu/+NXBEnYLRd7GvykmLWJnaSzd5ils/rYx2EiFx1QW39X/g6cqHPRokWPhxZgsuhB09WAycXsHJWLXkaVHauC620xQbYvwij8XAyrElG1PTuh56j9S21RpT8c+TlJfxzvI6ElyszaBERrqescdu8HKxIyCdx1F1QM4phtfqT3zz6bxQFU+viQCDmyc1hWjSko+VjbRuYLxLQJbFFSfq1rolRYFf0LLJzc0xQ3D4wASTthIwAgM6evAk5fFQxRAMWsRQjyt/qjtQm1bTrd0kTzte5L95+SaDiJCEYvOY4gPuax9rqS1sziXbpHK5JwtfUZtAxTJojErUeK8gaW7LuVBbv41qer+dpxfu7jRR9NRAqA+rt/2sKEsNxBPkwrfJtweCH5hIXJdekKiOFpOcpxxkmCPJV/E7vfCK1JGuDE7b3B+1TOlyDWleaoJI5XBRvIlx/D0sf7biNwof4jTZYhNM83pviWDe0oQyfRNwkMaaiz5GchrscCaMKMAInXOlh7cLqHPwRJaiuRegtQcyzeyu0dFKmPNI4zZqz/4J7I46p7g8hE+REbX/KtMWOdwfhEDq5MHUDJPk36nEsbvOy6HQid0LrV16JFixY9E2kBJoseNN3JwuRiERc8RksO/zLLBWgRZPf+T4xjj8P0FI4yNLRp19yvou1UzylQp40c2gbpHP+0gGrfSaZvekJ0CNssVPxLe2yHKXhn7Wo4V21msjhJY5KURcYxmmAqprdu0DcO/bkk3tNQJnRA95Vjn1GRZ93SkvbrhwnIlLUnbA9yqGTbVShitzYRRsevWkKgwiqs4xBCsCV+343WJeiIsk18Xzfb5r5N1GvA1hzDUlIsWnEppj/N60G8giJubaLdge00YSlKfaVoJZIBlJyOV6aufHRQKFikCNPVFiZEFxVvH06yNmH3g9GWYWI2GJ4AJ3yQVl71vcN4JjIfJlNApIrL7cb00rcJY/tMl+ZOa3MQjSJIYe8s5O8vHsBU8LpnhlowUG2PU9qRhQknSFl5mRC1LSxak1h/jkcuEIJnkF7LsY6WYWMAIGRvvEaQw3lvI7eWIs64F2QDXqmPpse1tqO1SbAWORg4WpkMZawD4+wpt+PNrWyib5P2t+/Hs44WJ2fv29gpnaQDgIjgby+wM81n11a5cbmB/emM53LUfk/6STq0Yx4HbRado11otIq6hzoXLVr0eGgBJoseNPHN5BSNS+VO5svHDzdZvAnkdzklp13YZXqr80LY2mkxlYXJmTrOgSZsecOH0PIP/JdwulICUmArjgEpQ5gMVFEgI2/R8R4G1SCNWVJc8mfCEMY5UF0ZThvQRPQvEhUJbEkgOahJlGvxvPCEOnixSwduen9GWVMbQYUoy/K+bcdOEOkgCSl8wQSK3d4UVpTshSbhudNXEnX8SqmunaQ7eUWAhUjA2oTc2oT2QTFL0xnno/qCeZiXo98SomBh4nUMGoNfGa4VMNKVBdBa/Mrdt4nWYQ/7DltybGiXy7XmJlt0mJ25HAAfXMTlfDwpS0SDAyirb2ibrX+RUVXMK7cTy9rbEBhb/6McpiEdt+T4PVt7ti2H0bqkXQS6RKxvXzFveSxwj3wGfZcI5I/hXJe3GTnZJS2KO6s6zjc3IHAwSbfQKBtCDplXrsU1hqylPc67Fvw1oW+TnDnv/CvYmmKWtpVm6/eBDdo9ADIM6VRbmyhtlQ+TdJ8tTGb3JZDC7HwNj/5FtlZe1WE5j3nVNw0Tme+UzrvGw8bU+kYtf+RgfSxatGjRk08LMFn0oOmuPkzOl+qCAQAnBloUVh5nwlP/I9fUgWBN/4chglNaGc51V3krKScI/11SSgqBVh4sTHr2dgVXpiDQ+wA8rF/pRyUyOYzV2AAWpLnQcl2YVAVCrQPCSTpdOFSZu/RhQgTCehL7ZczrU1XlVdWlbdHZhWijvU/xVJ04EeMpfWpimzZXvr2mshDxQunerEzIFf+D/N4GpA/WJrf9NKFi/dnDqQYPmhNN4jAenof6MhG18rC6JV5N+IetN3DFrTlEBCBMLIugilm77DtdZWEyZCvKHUQhcKIOUqvjfwdFXt+dwHxGJjRsuamuuigZ+VpRd1X2YnrMyz1vWD0IlJCGN7+nlA5bb0I6MzXAs0O87KAK9zJ2D6DG/FnHsRhHYwUgOIapPUOx3GTAjfJZs2QChOHyUlPFO/PA1FtlNVV5AmuTvu3vGIIGix0LSFGmx8HPUH+LSYEZBZIPrU3CQFqGqZVJNb6AVjeqrU0aG9kzP8/zdsW9v8v5nsnA636PWwVrCxOwMxTPZ+X7MN3HSRv7wAbZ6xIR2vcz62xRIGEDpu6zzsdFv/d7v0d//+//ffqRH/kR2raNPu/zPo++67u+iz7swz5smv8bvuEb6Ed/9Efp137t1+iFL3whfe7nfi695S1vof/v//v/LF/FH//1v/7X9AVf8AWPbSyLFt2FFmCy6EHTozh9ve4HPDpRI6K509eT4aRbHIZzX6cn9Jyt76hvmNeUKfijo7/ojwTry2KxSth12VyP5hWvjogGHyaEz0isWU737QJ1cVSjGAGb1HZFScRPxJ4LtICgIHQBMzxm7m3rZ72DloemTcHBqVdVon9phjIGpIi269Ymsu9QKTaxkTBagbRTc3y0eEpOy9/G6qfykHRfKKLlJfg2qQGTYj7En9e5OIhP1ibMKR8AJQhyZGBE8w1HBidNooEjrtp5+0KyMcl+hYXJI+ZrQ42+Ta45JSfH8V3Kbd42XXT6imG+kLdoq+cJ1iaYJ1iGcIgzvySWJVqZGFhhTAtPzullGHSWXhduVktcqd3by5msTFphGxveWz7xC+ZlcaDhsm8JhlDKe3xb16G8VuZc7SKxK/LIA8FYjLJvk/aPgxvgfan889N2JBwj3P443ae6ersGtLBam3D3Zx1napg3ZQtn76d5mLJD2LJRYFXK/kZwBK7689H5mMWFNM/DLCfW2SIktWi67zofF732ta+l3/qt36If+7Efow984AP0ute9jr7sy76M3vGOd5T5f/M3f5N+8zd/k77927+dXv7yl9P//b//l778y7+cfvM3f5P+zb/5NyHvP//n/5xe85rX2P3zn//8xzeQRYvuSAswWfSgads4yMXX0rWgCZELmBVocSbscXxVOQxvwYEiCJ58ORzqg8KHYSIKyrkmYJiIzFIAy6tOQejPhAi33uC2HKvH2sBw7I/rSQlIAeCEU14L2zijFJHVEvsSZ50DhV1iGQuhIm1d4hDHRRavU7zXrMcPY/Nal0vB9pHTupKUc05bdCCP+VexPGBtshHVtus7heNtukStp8CEPLZNJ5UhtcuJqgrd3BBtG/HTT8cFm23TMf5M3DSvzTapM1jJ+WfXIm4ATY7SQbtgIuJN5/MscyrylUXr+mxV9y/RZ7bk6MvEDDX4ywhXIgNSJml4DdtlyjK5H1fmg/zadynX1DhXzhXa38g1eEx3Zyme3o9TCfm1LzaWwLUogCCEvAgAGeR0Q30E7Wl9cnKZSQgeWpkURaqIjdrU7BXigqYqzqb07SziKPHAvpaESH2bNEVfLSSiHV31l23rxr/xJJ5ohdK4Wq5DrU1u0xadR9mSg+WZZ/fuRyWX0d8Mv6r/k2Q5YmUkleUEqLitpYFV0tbpAk2emfRLv/RL9M53vpN+9md/ll7xilcQEdH3fM/30Gd/9mfTt3/7t9NLXvKSocwnfuIn0g//8A/b/cte9jL65m/+Zvrbf/tv09NPP03Peparn89//vPpxS9+8eMfyKJFj0ALMFn0oOnm5hEBkzuWyh+5S90CGsh6goe51B8u1blt/lW4aufO4cM+9K91krbZFKrC7L5RFpouqR5jXd7fvFHGFU/CdE75GfKHechgCvaysDLR7SgwCg53Z6grL35LeAyu9srXi8TqQb9o9xwl8Jw+j4S2xNpyQEPHlYAOdPya8kzL6B83AMVPyHGAhaQDJ3Unx/7LEEhZpIiDNFUyiLx9DeM1ahf9ynC9lDddhzZcqT1F95TPxq3hE5YiXKWhAg9Mhcu08LKS3NyQbg2KL+pBG6f6ME9X/Xy3dCYFbcyChPSeLM3j4jizbxNiSvUQ/AFX0nSiKwCROK4IsEzmaqivbZGZgwMRXDjibZa/s6CjepwX4DqjrnD73A0sN7G3SbYxHTIxm3vpMn+0jasq09Gy3Rs4YmP3k3Sys030QUY0jufx3HNg03PAhIyVcc/HEBfK9imwa1FHrmvROdqJwV7z/uokInrqqadC/LOf/Wx69rOffed6f+qnfoqe//znG1hCRPTqV7+atm2jn/7pn6a//tf/+ql6/t//+3/0vOc9L4AlRERf8RVfQX/n7/wd+riP+zj68i//cnrd615Hd/bztWjRY6IFmCx60PSsm0f/EX40C5UodB2BJbOwlT4RdgsTIpGdpqflnK3vbD+1fohzyxE//cbjZuHRyoQgbPGaz/KCBYpAORuHQN9Q6fN7jxtBF58TiePFunqmJkCK16DtCjrAhUVln/uwx0Ior+sWJE0a5aRu8aHbJ7pjw+qerH2IU2sTH4B1yXV0tvGHV8ocwXIHNMTuRfbk+JWJeAcrE27AA5RxyxOQqHmH/GJ9FgNP0ndtk+CLOJPcMY48PsTp42hziJUxtRM9ZNAwdE3MT8EhormVSc9Xltd83M3ozwrMZbYi8mR1za+NLpJ0hbAoEHICXDmVxrgFBdP1PYOrXXLcpbJFHIPPD2bSY4XztptWp/swiUDIlsASHuozYMTKdl8p3QxCtE/6dV/rFoogCszRfGsOrvJiq0+/wqufiOHPo45+b8flxVTWo6n9XWwsybc0McbZowYLCQPsBPiXjifHQcchbgNWg45bHdwQ2vp6NA6nZWb3/S8DLcHShKmdkEXOW9wSBIYH7I3h+ciZ+9S+DYl11rztEUBpg8osyW7VGjGzPcsXyw9pi/7E6WM+5mPC/Td8wzfQN37jN965vve85z30ohe9KMQ961nPohe84AX0nve851Qdv/u7v0tvectb6Mu+7MtC/Jvf/Gb6q3/1r9Jzn/tc+tEf/VH6e3/v79Ef/uEf0j/4B//gzv1dtOhx0AJMFj1o2lSeLJXM83RX0GTj0aT7LHARFPJKp5iWIWpH9LaBB9AECt41jFNR9auNm0zpM3GY0z2EG2VJ20X4M1YlVlfTbIiTgkui23Ay+NA6a/0YlGPsY6k5xHxMpEdX+kjElJ5Y5xjvwEJsqlaPeSiTfU4EPcAEb83j7dsrkodo9xdeIiZqzhpBLeBsHUKkW258G5EeW7zbSNTfidbLkrYd9YTW3y6AZ9DE5nBATVJ8VPhqZjEZuylvqoFCPoEtPATtQX8ubc2pbeRbG6zP+ixvKvPxiTxFKb4Z256AHVKlEVF2Hislk4tx5j8FQRNjSrn9Ku5SmSId820dJCOmcTvNZnGBIwm39yDF2Uk53YGsQH2xLId+DBYlMEUImszv6zQc+1Cu4EczPsjQt0CSgpzrKLgbvKLI15xt+/sWgYEWn8GCizysHFAv1/lk3jAYwZBj3yV774tzydpPyk72MxaNAfGefSTB8OzsPVK6b0NW2SUBH9LXjsDaQwBE12nFyvTo6ul2nkVnCa2B7rNOIqJf//Vfp+c973kWP7Mu+dqv/Vp629vedljnL/3SLz1yv5566in6nM/5HHr5y18+ADf/6B/9Iwv/xb/4F+mP/uiP6B//43+8AJNFD44WYLLoQVNp2nqlvER3KwLtd4EOGg+y+oWwtX0irHLulo7odWWYxzZO1Hexnynv1n2VuKwKWjuP95esR2InEAGoycsUW27CmBCQQusRUGpTuWCRksqZ2NyFTBGmjSKAkYV3CfGzBQrKQqgDFBrrk37hi9YlnKTm+EFV02L7p6EDWDzCQiQ7sVp/mGqgPe1xKrn7Z9z5/Sy/fu5l7dtEipxJlo+cF8cPbYv3VxU7Ox44aCzpmvPM8toXdG3uLHDCh7cHkZG2LbWt66piEBVDYzg2llMalsGXlUkY/KfYcs1WLLm+3I+c73xZ4U2Zkj9TsxghODrYxxuPGyZ/36F+gaZgwJEp92trmilYm1hY2yzSKHA4V3z7vWA7qAxjuq67gx/SqQ8T4FuzkmMM9KojH4O1ifghzB7Xyk5P2uk3HtffKYn5cHlk3yZ7qBDHMH9/EGzZh07FfFajPpLUqYwDB2y16NU191anrhMFTkTjycGOvo7c6ofUCI46RhL6O5SlmL7oT56e97znBcBkRl/1VV9FX/IlX3KY5+M+7uPoxS9+Mf32b/92iH/66afp937v9y76HvmDP/gDes1rXkN/9s/+Wfp3/+7f0Yd8yIcc5n/lK19Jb3nLW+j973//I20jWrTovmkBJoseNGUlQr/Q3MVi5C5lwiE9l6xNDsIoN0/jsD7UM/Q+KVWnwZBZX3IdRbjpBV3olcK3Sb63GqL0dOwHJYbDmDSM6ECX1MLcB/RATO4FdWagozSch2xtYl/ZyAV0D6BWhX3SXEJxl5dK3HHmNA9akkhWdnCbjgq8dg+1W/WtnlpP8AKt7Y3GQzrbcci1c1c9SlQgjiwsLHDkJaRvqScihH5jfM4ZOi3H8YPFj81qjqS8TcfAHGQ0Nr9UWpsM8SfKZGak+e60b/tOjM1VurLtibXJI6cFBJxNAw0WEZkZItMs6z5iqpOyxCR6Dm7sUeJUkcIWHWIAWPS+pzMAHRbudQx9IsIjhVsUbl/xNO9fGj+WIR7SBjJThzF98ONU0JFj2FhXLKXPI1ibcGsT+ayDcWI33tsqzqvHOA0jT9XYwemrvv7K+ew+gSSdjpzGYj7k8NnahCAO7/G1uXSvI6rudZmFk3T6XJghpi4FUrCc3DcJzmP8aQlpVR8WHZOecHTfdV5DL3zhC+mFL3zhxXyf9mmfRr//+79PP/dzP0ef8imfQkREP/ETP0H7vtMrX/nKabmnnnqKPuuzPoue/exn07//9/+envOc51xs6+d//ufpwz/8wxdYsujB0QJMFj1oyhYmSHcBTq79edqyQkdEos4i8SsxKKg57DI8KN6VbO9VxhN6rDOugPHJtut+HLfdwsnZKkcP/KiAQyZSIIO7Au9WJrDFBmV565wEHcUEYxw/dWEe+5zuByBFRXtO93nsWF8GfrqQqdYmRPE5loqHSqReS683r0Dprfo8MpGZTLu1ifdfuuSat+TECqr7CWgAPaTeNncJebA26dYi2usMjjjI4gBLtD2uAJXUV43LNssmqR+qB3W9w6TkSjFbf1ol0AFX0yhkPIYYrjx8oq0Zlyt+d2FsV+ZPjJWJKftbOAQk1CKD4CSdwHQqRsSxTr2qFR+O+8hipGznIH/It1mavWe6mplJfZG01c3eZ85OVnFLTp+L4REkRkuU6mjXwD4SeIRpIaz/pFdfIEPe6hO5Wue5JIAZjmvI2klZAqgiddmWNDKiNuwWRmsTfU+CBYo3gKVT3CzkYVsKepJO/z2zjYQDe6h5pYMk4mULqsQWtDTJwMmj3Fc0/MqwK9OVtYmfgMMGbo/HB8ffo1YZNnLcp0VPLn3CJ3wCveY1r6HXv/719Pa3v50+8IEP0Bve8Ab6gi/4Ajsh5zd+4zfoVa96Ff3Lf/kv6VM/9VPpqaeeos/8zM+k973vffSv/tW/oqeeesqc0b7whS+km5sb+pEf+RF673vfS3/pL/0les5znkM/9mM/Rt/yLd9CX/3VX/0nOdxFi0pagMmiB01HgAnR4wdN6vZd8EAF+JK1RlPm+VTeLXXS5W5pzuyC4JnqgAJVGGV4bAbr2LBMDxl4JKZOtD9O9+T3Ti7MzixTXPXIbaewJJAkWVRkSxxTYNn74XUeSXkRsCmtTQzAyOVU2sS2qhY1BZWZrpAw+9AY11tKI6Jm+YGn36RhD+OaLTCitlWBfbom1iZiYEgEVCRtx1EARRhAl7wlp+obgC2ht7NPs9D/U3EzUuBAx4LWJqaJqkbhmsWhk1jVSs725XGDJgVj467mxS069o/XHxgJ+JmAuPDSYP90S06qU7RPvTJ7X3K9VT9CnDVcpsuW2ifybUKgByKNnM0SKIArU45Wv4G21AF88vriWPAeLUyMQ2E4z0M5Hk1DPjWOHqcwD8D7UZdtSYGxeGVmJRd/R3VKsx8n52Vn+NvYogEoDJYsvd/ZLs7/0tHCnQ2csTKpgBTth7KTs9YmeK9xFy1O0uPg3v+WNrc2aawMfluU3ZF/MBGw7gnYyQJMrqJdip+/e6jzcdEP/MAP0Bve8AZ61ateRdu20ed93ufRd3/3d1v6Bz7wAXr3u99N73vf+4iI6H/8j/9BP/3TP01ERB//8R8f6vo//+f/0Etf+lL6kA/5EPre7/1e+sqv/EoSEfr4j/94+o7v+A56/etf//gGsmjRHWkBJoseNGXgYCAQLM7qC9eoIYftM36VZYx+5PDggwNl056PiQhP0qnyTXUXSmFOcaEeiXUHa5NK4cW6p+pGSXFLDoQHnyTYR059DMbpaU5SWq4zXWM/BARFFY2hjYnSoWVdaC+yzWyszX8JdaVItBuQpkoE3sMgTKvg3g8engOa8nqx9lJdsjYhqxXVBfgWq9txLL+Xo23yjVbIxkRWNAMnmlAWjjTNW5XTBQPfsytrkxkwgtehDF1gVoXm81gYW4UEOx8Z+NoASJyMS2mSt+RkjbwzXBEq6kj5p+kpb1UGuYD+huCLCZYlFsvU79H6BNrTOMIzsTrXK9rFrTmCadDfKk/jQex5qjElLieTq7fRc3sHetqcRl4+LsDjN059NLVwtDYhInHfJv4QZiBJf08yiMIxCy4rK9J/z+yIV21LYr3GsljZXwNfdsxvbC3xV+uOPrMeXwAnV1uTFHkCoKJxOn/sHRisTZR1pTgvB9dU/8JLrqP87O+rzsdFL3jBC+gd73jHNP2lL32p/04S0Wd8xmeE+4pe85rX0Gte85p76+OiRY+TFmCy6EHTJQsTpMehW1xu3wWPc9YmciG9bjco8CE/m0xXpkPhKjyrV53OJrEa9A+XXlV07/pEvA/1RnE+5ougzIyiKXgCBhRMSO21OPzhzj/iOa3K66pGazNZeBCR+RTBYqw3YLFBBObyILiGulxiZfJtOERwukqX7G37DkHdMDV+7/0YVR2PaVtyVO2QeB+sTeIpOWIqh27F2WE64dQcaHVivE6xk3pfnKSTP7XmuKN4TLP+kLWFT5e5sDbBBYBWKVhvkedq65GrGNvJfFPGxvAv0UXfJj0sRVyVb7AwCXmgDL4fA7OCuFz+Yt4tjBHBhnAvUnAJZJ6wPHt//b79SRpWpmKZWRziFtN+EPKRGK7arfyOOFdTZ7wU3onpaurvZ6xzVI6mAHlnTII19A8QlW+Tntw5mJiVQ2YTYxyiD9SP+43MUZdHtqPzv/EkHedkUlqZ7Ok99D6JtangTXWSDgISiONJuodhB8pcTSBgvwnU5yNbm3g3YQrVfxuk45XiGBYtWrTomUYLMFn0oOmihQkS/MCf0RseNU9W3PJX2SCn938GOT6FSz2jahN9jJiQeODbJLdz1DfsR9WHXkD9h3gC3KO/ki74GpjCWUcSyHdp3PHcBDaQBACevGVHpbpUv6ZV7agaMe0HtecclGMCyMyk1FEjch0a0yqRN+UCQXrwbTJYm0AtWaK+9IIMNt+qvPgg1Dkgpy02zSGswJy4yqFfjc9vyaGoUfa1onMeRnBklw5K8EDDPPizzJqY76gB4MPa9ut0a841W3Iquk/GdogEcwhdtDa5wsLEAZNeOzIiigCGd+CoXqhjdoX8kgCbcBQyxilfIu4n6yjTyr5NtjFO78n9oYzvOTiFLfqjwz46RniYKzrIO7mWcXoRTC/+2N+JGR0ms3QAQ5vV9V34NjEWDp1ztk7AcBPHje/CAC7AFh21NtkFynm1oZZ0+PpAWW4J7eK2IE3PLIoTuwq/GzG96uKMq2FmHebUt4nEsgraS6ozXxedIzE+cb91Llq06PHQAkwWPWiqnK5mKn8igpZ5d+ILFiZc3OWvspcsPoLI3m9Kp68hf5N8Yn1NoAltF+1daru1X4EFEMcxzgilOlM6Mnk9+S/mH5VpPOkAhUfLH2T+7PB1FheF2boPtYqhpaO1CUqymDPPKQj56ZlJGKeCcWzPSPXwytqEEzAwtxjBUZa9IlBbgtA9nqTj23A8jig4fdVOk8+xDO93ktJRI0MAo9frzw2eZmVRMgEb6q+ilSYCD1jHg9Ymiir1a/timwATG9ugttU0s4g5w9IugSYlY4MVPL5cplBxqN8iojbH6aplNiiX6yFyEBLzbJyeX1F/bqeMgzQbo9c5jnlLR4B7qr3J4k1JmYd6W9AOtKHxsy02tqzIK8T7ePoOhZvyWOE5R09xOgihgcfHaunoZI5o2Qez0rV1MeBfeai/u4O1ifR3Cpi8WURA9Tj32XKC7WSxOGLkfxtR26ZDhbUJKysL9nRDvv3SOxo67n02Q0Lk9SfYWeZjg8EizefJ2wFrEynmrmdWvhZYW9HmokWLFj2TaAEmix40nbEwmeoGplXevf2Lesek0ACaQOapfgHtTS1MLsUXQM3QdlUP1/WBupTSB7WGIiAwAT44p8X7KLrP2pM6vkurwdokfQLl/Ek0nwZ0qR9VXxkUvdLGXsv5pLpw2dUFkDabXMpQlyvnCmFoHAq2JgSDdKzPQlw8HpS5PK5jgAXjqPWhsDYRPXZYHFCRfGqOEHFpYRJmAjqp0rkqURNrk67o0aU4ryq2KalNzSgQF7QE0Cjw6plS+0nbmdEkT+U36Wo68GGSg+ENz5paZjLcV9jA3Pp1cPqKYT5Ig/6esTJJfRrrSVtwekwGTZi4nZ7d1+J4bHDPyuqnJKUDQJOp5nrWFC71Fn2FtcloYULpmrn2JF35de4M0MBTsb4ApqT3mYy7tRY7P2vxus7A2oRhNApE5ziOrSDbsteT4rWlub8UP25XDLxInfVlVQ+rtDCZZUfOqmPScRPpFky6igZjO6KIPafHq3FubUJ+hd8XY29McWkktrnoMu30GJy+3m91ixYtAlqAyaIHTWd9mJS/1SaBzDKcqPdE+xMxsjV9DXBCLuiUFiZWTob8IR6kxbu0je1HcVvzIDDS7qOAJqYjtr9u8kwC95LSz/kwwcEMDmJzWVa1CPsI7hitwZkh61yaQUewoayCGCEOBHzvWgrlHviitVAX2EG07+359ij7KttNcUxQDhL6BYX9cEuOjuXoJJ3k9FXn3rbq6B8RzSxMLFikS76f+DYRyNMiq8Gm+z5QKcpYfNKWqi06pErYGH/Vlpwin7/eyb/INXTCh0m2UJi2nQAUIZqmUXFKjW2XKeLCWx7Gqs8ArpinAlOIutNZ4FzDtpYLcbj8hMm34EC/AnfzuDhGLsbt23QCh0WgJHar36d6CqsV7xP2MwMn87jGUg6AkcNfwUlCApdtS6kBG/6ujSfpRA19/hMvKY/yTUzqYIyA/Urvh55IN/dtUsWPFibYHENkZTBmvyx93ERubWJzAxXm+zjquhNp+D4l2I6CJQCoNPbfIv0XMOZbtGjRomciLcBk0YOma5y+HqoORzrTUZ2PAtj0lDMOYQe9IltGQCO5rdLagykojVNrE45lTLw+cPpaxhd1kemFSSpM/k0QXZmekpPDciI/SNGtGewHpuNcS7ivgZEcl+YqWJsQ2cQI1GcCZkuIvhQo9M0gHglV+bSCmTmaUWtFj7olpylLUcB2/wat0SNrkwZo6Njxj+jyN7GkCcw6o1ES16ONoXzWlgkmIr0wR/EZPCmAkaYXPgJgUvUFeu7WJuerIyKi7WbWWKyfD9IyYANhA9MymHJkYZLi7K0KeSjwtfPWJniFt7a0xOCxD/3iJ+mAXxPGk3O8eNxm492XdCWOq1LbxP7hz5fjDBzukS8MC+Ls+ghKd2DaZMhroRjPtu2MrnNzp5DX9u0eTODbRH93Ou8SzUdE/fQt0bLKe7DW9MEA2CLMWfelwjC/FP2BDb5NKFWUKP9+V7kc6Bifsd7rHNhvBfYRXpfKsPFi23hvv09+jy6X7KqPX53nAitegMl1ZN8P7rnORYsWPR5agMmiB01XOX2lOygO91jfNKsKvUlPmoElQ1xqJIn2Uc/oaRaf6wEhMOss2E7xHTjlqQCU1Kdwkk59Kk4OVwRi62AlgmGGz2zBskSFcCvhoEXexV+rTyNAMoy3yButTcQnRoV81nI8Sjpmh5132Mc86L8E25MuRceTdFS1KGADEPAdouk5UbrWR8rqcNfrt/vpSTq7ay3SO7lPTsnRiRW4Dx2WGA3SfbQ2OdAqKrJnQnX92EVOmSrfJvjS3xUwIZrmt8dyrbXJNU5fMxM4ahuug9LKbW2EupCJEFGw7mDIF9K0LrIXiUOZirFpHRmwSdYhPW8V51G9DlyCZmnSORmDU1cAaYbrANhwHRf6muc2gysc8mIdF52+2itf5dOTdKR09lpxqcMVeWAlIt0hrMc7wjGepNNzFVtyRtYR0SXgcnG5SQdSiA08mFub5FN0iPYkuAw8d+AtwJeR/QHrEOBfGbed4btEMKfQkcTOfYlLymttUwDjfduS93ltyVm0aNEzmRZgsuhB0zUWJkQX1aFGJ/Qmq++g/UtVVOnll6CkGzBd2pIDaakRtsjar8OQn2fhCCQ4WIF9kLEcqhom94vq86pOUNyWE7fopOHGto7CYXzp+6hKeqH+6nOMiry5juzodbyWeVmF1zh53pVRMWlPD4RjbYUp4QS6798FbhRgB2sTk5IrCTvf8JAchequTuTtRiZIMwntxLyZxN1246BaQZMtOez9SfuJgi8Vho77OZnel7xNp/r8NlOoLsUlfyoeJ5RBkvbqJeDkngATS6YrQZNLPkxOtQ3rM7x77IuFPV83nYttzbbZFPeVjxF1CDuerlPXkX2o1KfkYB9auIzbqLU9aOnFshrec+9XHTeOZQQ2UlmB8QxLtoaGM5dPHHyIc/BmfCciP9X6c1zSyIE5MYm9omYxomGB1itrk7qV0MPDn/3OxwxAsa5FgCXwHA83OwUAAO6xSURBVJsLGh54frty29W91Wi/GcCyke8C38F6wkwX7D3/DgzD0XZSvDm6ldQ//CAAfV50jpaFyaJFTxYtwGTRg6Yz8v+QJes2j9T+8S/QpbpnfZudpHNoYcJYX5OgZpYo0dqi0+aS0iWrlujDZEyfp0URVi1CIljRpSz9G+4vkM3DCJy0iHIXfs8rYR6PLUxinnl6lTdvDRmtTXBLDoce08m7ft8f9cy3SWVtMvojqCY+KQuYFfQe7vUT1k9ERBvpt9dgeZNAqbHNqh89xFT0XQqlRag5bcTnUEj11QuatRXMNAA2GA9xQcMQV75UczkFbFxiZFlhhbYvVC9XACZNgZxVyKHkdJuOXY+35IxtcchTgia8FUyyuKb2zwAjuS9DmWTpohYfTBu5tci5X6Dpyhcaxyw+D5KT+pjDlqDURuLGXq7Xc7QBsYV9/fh2v/Cm9qoGblWMsqU1AJYSL+vhjv62GvxdC9tdh6OGK7YFjMKCuu5wi09/9tLaMz7nHJYOfZhMRml9Sawo2xGO6dSfSx9mBsnTK3+V3lxNCdy0uej1KluDe73et3XvM512Ydrv2SznvutbtGiR0wJMFj1oOmNhcgfV43SGS0LA3dvm8ouw3uK4B/0gK/Aov4Pkk9sNjlGDgMOhbQwn1WXeByIaT5zJwElvuKepOM/hXobyHModh1mjjkCTILcHEXGg2Xgu581tO0hiG14O+2BamIvSvd/e/VahWXpQtjbpaVoumbJHbaJCREx7MWsVHQqxjcKsJxg6OPg2oZ24OCVnPCYgS+4jRGQn9FBK10kSqKCDJpZ8aFnTI8Jgc7/yRJDHW5S+XOJACtEdtIsizxA1qedINyU62Os441ZHoImXY7oAmhwAJmY5EdLTWz4wvOTrxNZm0baNAd7OwTKl/SNHcbmPxObjOG6p8aVYHSt86ajhUH8Rp8+kzXnqs3CYpmFMBfeM+VJfi3QEWHC7H9KUY0IZrFnXAB4h3B6r9KlHaxM2vubzEbnp4VtmzUc+mdMhGwX+4JFDS5WFCd5E/g9AEG5DSukIXrCNu7VdAiy5fZiYgT1Aec5lyH8ezPEr3pOz80WLFi16ptICTBY9aDrrw+SMHnFOeEplTrR/R9XHhXhQMA4tTIr+oOzOOZ5THvLTVFAqsu03UMFm5tBkGce2MziDimnKE+JBartGzJrNCRFlCxbOYU7xsIVgPLL4nI8SU/PkfN4mgLMJvyqsj/v/AywSMQBrAaxGNK8KtKZsiH8pZXXUp8oGKvN125djPMVCtk0HfJvQ1p0yom+T7W6n5BAqB9X6ydJ7a3c4SYeGQZFVPovPZWYWKyGv+HoToVEzO6BTDOhkXRfLHDCXWd5iQn0KCt8m+ZQcZBaBiWVGOMlPqr9CflX+StO50YdJHlNtVRLbHeOIBCz4RHnskYVJsQwE4iMAgnE5L/epA+5l8zTjSDA+i+MUV5WtOKO2575NJNVUE1Mc0cjZImzSOR0TqdPRNsUtHMDoYZwEMfiL6GBF5g4ah/xXgZzgELb3C61NsoVJNVLs3+w+9Bgq0f4IEQ0n6dDIljLl/lzMg789/rgdODlR36JIa0vOokVPFi3AZNGDpns5VvhSvqN6z+grZ+s6SMkfnbegxMdKkjhfxls5Tvk07UgPgfQgHmM6itwMeSz2hM+T0rdJLgNhyX2AMLj1VxEXRVq1dHAARcO53xWNefIz8enIqklVTlypM1Vg1gM0Ti9SfdieN/kV0WdrOjxTdBY7bb+Xnug1UZBHtSOWgak3IV99m8h+YLyu0nnVuI27Ev1Ns0n3MsVCwpiDtQgEKo0kMAgEe1AFYgds7Ov0Sa5xL4yt0MwPj/8KL3+IrRx7ji8DhJCxMU8tOuym38tBmjdQ+/oI4aE92JKTyxRxkuqu2stxugXOF3saRzkensRRaXGCW4MM8AhxTBGsmV2P40SKuMjlY5y/ntCngoSoBA7ttQIQmAAU0CzcHbIKtM4ct+hcos4TGe7zK4zbjTwOuDLwNeMd0jYjDsNN7SAQZHmAb0XOrGuB4vz2OLQ26VMxtJ/ZQGZvulxzhgCakPdd5w8dwi5atGjRM5UWYLLoQdM1p+Tci26RMp7N+8j5QEdjohoYCP0BMb3Qb2ZHDReqQBCEdJ/2OQsT6EfobwZTUnvk7cRPkkLZUiS3QxzTxvaOyyu4E9Q6kJqjhQnmzUBI+h7blaQ6b1VO2/SvkpFcEtc99KPe23yRCKARrE4o2dUN/wLZQQ0hsDbpLd1xS47Y/KUtOuR57AhKdnDIfJuUejuAHaqIpGQiSbsOZJzE4XN3C4jkZxzn9JQPk0EjSQ+Hc369bRrGVSfanAZXHrXOI2YJymuVYShazA/TaOER+pIBizFPBjBynhJAsfWq4AMqwKkeeCencTAnMQ7maIOTdBREIYvysYS6MT2DIjBHeS6gH/7WM6EOPXKvPIbUticPTkYxH/YjzkW3/jj85D2uJmc57gurPb7WEQQZorVJH71am8zqx7jA1/x3wLif+YkRZe9kp+aQgxT6W8a9T0JEe1r/VU+sE/nd6oFYxkeOGDKWaWxRZ+Iy5faGfpb9aPFhi05vNksMi45pWZgsWvRk0QJMFj1outnk1I8/0cFPNR/eHtAIElzd9l3yShd5Kl2E432T1WpRpbTw4CKf/qOCEO9UnpIDEbOtNkPfijZL6xSVysT7PdZVtZ3CXf6cqgEmn2IfPD2qvWdWnpYB4b7IM69Py+VSdU11nqO8RR7XDWL4rm3nz4smYevDEAoWL9Adoa1uO9cR0qmIy2UgT5bu1dqEXM0JlI8snrYL8QOiRB5vUTpX6Bj3BJ0GQoqXu6AIShR1lLfjUxrihyqrN0H7iC85e364j/3EdmLckZNYIjaHEjuNFi4XnbpeHZfqBr52xuKj8uOCINHASbhYqgSKLAlJ8KhRcVWY74O4CKZgXGo75NtTm1D9AXvF5IthZTG9dQNUSmuTkcNPc0Bj+vgqixTjhPb7qRYm83YucrWCL4c8TAOuK6RgSuOqwy9gGk9obxanLL6qi3zezXBOhIhuaNGiRYueabQAk0UPmm42eWQP7HctuoF/hTPI/RVqzcUMzAIfrTmWM10jAw+p/gJIKS1Bku6yTa002v2g7xTflkoLF4QFuCjb9Qk38dVxw7aUoGwOZylAnShGHsVip72X6u+CQ80acogk1NeFVSrzVj2FvHwDAmeHYbiX7Mq2gkume5PYF1DqZcO9SfN4NoR0vV3sPur6Ymn2dXi4937M84jVh2Uszu6ZdpH5yTdWDEEIwoToADY81Eo7mLzIUniSmb30d4nPi43dEucU3ygzTRTRCxROyCn7zJPbqAAOGWYvFLYNWmfgankbDDKZxKBs1kIerwMtL7Dvwrn/bHmqModxh/1NcbaOJbyL3Jmd+vkxfiFqxVDzkIrT+RJ3/mhtJNW5lY48auROEMdC7cStyE2rTX2Srm2a9qCx+/PLR+QqrwArNZtzsd+G8ZcDP6ow1OPlG5jQyymPtvqU33J4HSIUDvxziAMW1Xlp23EYx4f5vW/939Cu5uF43/PlOmIen5M2Vo+TlHl482WMy6xXciIX9Sw6RSKFz/N7qHPRokWPhxZgsuhB08Z7EGzOaARndIszSkrYh3xSMDil/JzIx+wCYt4HjvoLh3iZxGP+JFpxkS+In36f1IE6T6gP6uGiXFDuYzw6bXQBvuVkgTCJfe31rSuFGM8gRkpUMqoZAJHTVJYY7ypMyFNXPK0v0856AgyoJUzkFhEAUEh0cajHYiYxnqLoz2Wa9dOO4gVnsdpz+4wLAJKhLX0mQp547+ob9q8rLuYQFikqPoP0rv1vulca15n7uPh13B5XaRYyvrzDFp6kYXCKoz4T+lx5rPI03QFJFk5HcE0ZW8kcvJ4qkodAatvHXc+TapIKTECuPFGwnMU01clQhv6OflIGSxXoxjxuvK/juDO2vftsAvUbmYtylbRMQz7gacOzK+PwBRo5Udz+MnIn94s0jvM06elYgQf00100LNAzRp4r5ktl9JUh47rITfcxKlCiVhhi7ZPxTQGQgvs/tuUIAAJ9pYejnQnSO1+TtBiqaWx9yr/Aku6JbA3m8rM7lSFIQTL/ecyvEgU+ml5JqV+vC6/cokWLFj1jaAEmix40mbWDCibJudmMKnn1RFRM5yiGPIpgcG1Z+47aBbTS2gSklQBOTOLLNgqJp1ArICJ+V2T4p96mU8MQUbHK4qAHdtE5OLAqAcEWxN1ekUu5KFfXgt6ofYiL25S+uxatKAAAknTR3gheYItNcWtD0pzqLBQdIQoo+dgCACjkYEsY31Au9dSUEiHmvc+xgxtE6jw1AR9plD5Obye6sE1zoMrTEXAyAUbqjXvHcRWPaHrTGeDkcv1H8UH31TaP9b6a7gSYJJP5gbcOjYTLEF9G1f1yCw2d053Qt41f2PNjnQYqaBQTpbIzC5PSmSsBJ5OiPPBd4HiW3tYLj+NOcfbe8tbegWRtQqT+iOIy19fXLe4kpGOrooPhFJdyOf/ObyP4JSLjPB7H5D6S0izM4sb3SDqLYQKopIVNYc+98NnPvk0I6iHIj2Rj6HsB20cIPY65t2+P2h1DG4AGvkqsrgtIQVgt5jvJYqD/fiPww53ZnK93mI0EbkDjuTM+bj2+icc2NG8VPwCAkvIuuppEeADT7qPORYsWPR5agMmiB01bPqBPzWavOZrTi17XNh+JA9eJCtf+jGF+lbP2+Cl9lIvYS3AZP4hjEwuTqj8gufFOATQ5BG6imoH1zPqr+YWa099dGL4sJmsTDQvDNpNB5TiIza1W487iem4dFQ0ED8bxjHlnbXO3NoH21ZGreHk2M/LKwiOrFg5e5LQ4Xhy3HkWtio4CJ+rw1fulTmR1QTSFRExg13q9xfodbpYyEzBiBpp0ZW7I39OqMtM3uGtScYtObvsoPvZr0G6ICmYkhNvAjokPby/VILMTcvgeQBM+SKP8pgsRb8Syl5Yco68SrFMBCXhCg7+TCG6M4eSPg3XVUno+I0ATxpGdzc7iSFc8+wksjNyFDGvllB+3ddlbA69P9SYd/UIiUKHjOW1tcsc4mw3eTQOPfJHA+gEtTDhYRQy/FsHsZHzOw2YmdqhGDIj2un3upQMZWovzGHR2WnK1YuKF9P3ysWu8LWcSfNTtnYQ+Ua8jLf0407N3tL/fki1ZCr5WxQ9tyFBs0RW0nL4uWvRk0QJMFj1oGkGL9qUo+nU4D0dcA1wcAya9LwdZLrV1TV+IiG5YGmiSCtdqgdRgCAhmFp+qrP2PYDiCEK263F4NjIw0Agsaq+GtmzWrIDq1NunASo63EUNfJqp6EYM+BBzqUICAU2gYyDCeIm9q2r+tEu1duR2sTbokb1YkxO7kkCFvqUlxAyWG0c7GTaS+TUxdVBDAHpiCEw7KmAaIA0xxs144vlFYm0TtAYc1KmlH22WOWlYdLAMnHNukS/GzNqthswMWPMkypSvQ4ClgAv2qgZOR95QRPIknVHbhjTBgEJlFBB80TsI99mngaiPokesJYS/vVmvYX0732CbOC8TBPLjyj30UW1+avb0eMC80rp7juIpL18BFZA3H1iY+5CstTMI6AmiE+4cQKXg5E6HDVkthAsu2+KYjXBHXWOZlsayQ+3xpLKutwRG86kADw7wZmEKTvkRyn78Hvk1s/BiR5ndIv8TV4ElzG5hu07EcaVEF/ElS3Ol2Fy1atOiZQwswWfSgqQYtYK+xKWDnFYazOTlbt1R5giJ2uebzbTsJ1K1Hw+4WxUP+JlsWIjTHfDwUrPtYqRfas1inpLZG4EWVA4/LYr6noZKh7Vy0NumoCsbTEKqVjktPZ24crvUJec1ZgchtH68XBE2EsrVJu6JvEyK1NoG1QmL79b3N3Pfhmz+kwDNkFdt3SlAKuUoDY7LtBaLIQ68HtIxC0QoTpu3OtujYBElKOVJfLov4+V1q7cNBoanNWV8O25wBHHD0selXZ+kkaLIfASZWF9naGldy1TEugmN/os0OqLZmPbVDyYFbwbrF9mCxBKbGqS9jfRjGvu2MPh9i3jOn5lT+UFoboID3NnyLTnr3+qsTXJBgHUGZVUswKpbb0Vpz+4sMMDyqNUnZJITaGNDaBHg5E4CHkMLULSRmnHz45Sh5bxwhbDkiB2v8+OBeFrZ9Dlty0v3FN/GSbxOuQBGu8w73VetjByVtr7oLaLIAk7vTLvfv9PW+61u0aJHTAkwWPWjaOIMWJm72W7c2OUtnP8ZetjDJpH2dN3BWAUIZOe/3FhK66YJjVjnZC/X7EbTwe7G8M9GtBjWgTbAsCenpo1aoo2jT9ZoRjtAvlULcrU16HGs+IheDifzkiTgZiK1VcAV+3Q8CtgrO9i9bOKsXETyIM4pqQf52PEIC49Pa+0S7Sw0JPkqmPktI4iykPFHdGH0YaLj9uxHTTubLxqxaek39XWQ8kcIeOqV5OQaNLJeuo+JrtVcBaxbzccqL7U8ZQXrvmUjohlj2EFdqEZlnFP07ogBQgXXMKb5xkrEdWpjEzhDREVebK3BzJd05W3jDOy8jvunvukAVnMr29jJwwbO+eIuaETkcgh3I6fYeb2/qcMqOx81hMm9T/y2V9/5ODtYm5Mo5ch8p76AQUPwtwXDmRd67mPd6kKROH1X+Q2uT3p3aXjD2FJV942zRm266FnwbrRjRwsX6kdv2Prjza7I6ZjOTw2htMoAmmFsO0qZtYN0JSOp8KWzRwX4L1JTfLSniFi1atOgZTAswWfSgqdqS46EuyegPd/51P6AzqsX1gAn17u0nW5gTfkfN1gZ27f1Tqwsvm+oKaVLGY7lZ/qF8FtmCYicn267neFDc2UETPWXi0NoETtLxNeKSb4QrtE+1UO6m2QoaxP6h8B0UrDAeF64vfY+dgSf63HdWvRy26HRtqQnrAoqGDiCOzHsvo5Kfej3+e9PrElPsXbc9attH5Nf5e1K++dnaJB/rQJL8YYDUn4GLoumoMsYs0s4KLdd97OxcfT5snCTOCBzNfPoknROgyWnAJFHN1WagyWXABHrkPj2IwNpEUjU6CchUdBUzNMspv4axMoaQc1t/lGxL1wAVodFypIrD/qS2A28R8lNaer7R2kR5Fm6jy3EZ8Mj8I3BCD0P7NCmdt+ZkfyAYhzTGVWAJ/pZQA046XzNebtuSlG+zASHGkcK2u/hcY4vOuXEkzje5czW0cHHfKizwhqZ3PrOAUxY3umRTW97f9DsE1nnjczqiQmZK/RhO0unx5ak5dBy36BwtHyaLFj1ZtACTRQ+atkNRYLa3Vwbh6S501sw4FbK+HYkTl3rGQcjxqwtQrrzfoI8PqH3QLfTfrCAnZewIJDm8h7k3IV5QT+GQtwZ5xjmvzM1PW5toWIjQ7KUW/sa2rckmsdIIIKC1iebPSkx+dqgM1aM+sjhhIrA2Ub8ObvEh/Rmb2iF+j8/EhfKZ6I09Pfi3O9v1k3QgPVmYBEcyNJqlHxKT6qi1tUl4WFhOIAq1nLFtXAP+VJNmRGTWJhwyF/ziKH5CPnsYmb8PH9CFDHcFTJSjnW+wiA9WGgDaEUXQRPmI+TaJvC2DH02pZbuv8ozvWOyjhIcZ08U6kLffMJTFulJfCkI+ou8NS2VtokBKBguoOJBrVIgLj0m+xsyR+HXWJoMFYGrz2MIE3zH8deugQO/XwMs7sBCtTRo/wVfMa8xvDM6Nf3jQu2wliI5ddX1Zn/qk6NMQsLQs399LxFDvMEukbDv8diOrC3kPQdPJNsje/r77XEFXvHSa0qWfL1q06E8LLcBk0YOmcUtOo7hVwOPwJEAGBflubd+HODC3Njnu2YHyTkRqJ2xqe7A2yQKzB1BdqQGV3GYBjvBBmhGcpKP6QG4b7nMfRjihghi4/3/B2kTDcMrQqFYcKRZVP+K/M2sTFNC1fDKiTyMvFDnte6qNiGF16ZaG6OtEt8uEbTqmeLT72Iv8LATmVdKIdF6FmPZuBYS9lCGunYBDIf0utINy7STFg63VZKIjvaJ675NCCNYmF0GTqr6z24E0zpbXJe830JdZC/lY4SupAhZn3GOMyu9JC7fceQsYOUAge+QaZnIDyrCBB5zy9LiJ01ftjSuBPKZBL/0B8DxuOFZ4JLRaUw3chwXWJjhyUNQRNAlTne6nOB44j7Z3myEdqom89LyFSbbGGrhc6RSWCDeCBV7eeZf3cc5H5xYmPQ7mv9pS6WB8X1vI8+GnTevSmwxsXEMGYgEhcIG//xnQML48aRt/o8a4RttNH7ceP5xL5HEuujMtC5NFi54sWoDJogdNM9BiFoum2WesTY4Uj/sBTHofzqk5RihIgqrrIk+wlnBB7tDaBCrIAjj27OI2nFDPPK/uTUdDY3wUSQ0Jo41icBLoQS/WlLPWJjSEnA4tTKg3HvbFV9YmY9nYIvYH257fmzIncA9tqDtSO10DVDw7TcrUDu7zp6Odg1emYAgZ+Igqh1v+sLfQv5LHukbVKysvR1R+ESVQ7rLSFR9az6RBeK8moEVU48d4iCBXaoHNZKm1iJsZ1lxsu9d1kZscAFF3tTAJdRymHvRsZmFir1VSbfszE95qa5PA2/M9tsdpHQHYoeMBkGMETaBMsh6prEnOWJggOWtBoIHN6sZ9umQw1nlKeCo4pUUX6vXTfzQVrIE2tNWab17r4yS/L+T3Ib23WFmbaF8K5d05DPJqSuG+5WqwGiSfffa5bc+hhoFq3n+Osw3EPii1NuGyRBXSOib8chI78FemdPwwvgNn6ly0aNGiZx4twGTRg6bawkQFmRxHMR5P0rmsYoytnAZMTuQTVzevaXuuavawuDir4p6csTahcXxcZCqF6qBqoKiZ7g3diF8WFeupAZNxjKhcowk2p5T2/7G1iRZM6oXlzJTn33GqDCD400VRtlIbfPUeCbY5ja37eh/VgdG3CZE6TkRIjSgqHgLOEbNqgc86KxZZyQAABo4NbkoJ3jOAfeffhxloQtrDwbeJpQzKeMxT11cpPNN41vZR87vQ9sGzv9i2KvWwlq+hs4DJNdyv5hMFzSxMDFTC9dQWtB+z29ei7PUpNAYo9JuBoSF4kssGDuX5JnUItGf3Qx+qcReU9Hp763RaVKmXyEtxtmZAR8s3Pkmc7oEXtqpCt/Izvh4k0Rxx0G26xAZcpxe+TfQubLeLIxktTEa5wbfWAEgFc4icanbEsNZzty05B/k6cJHfJVt7icUc13lSZsJFGI4fTut+2vais7ROyVm06MmiBZgsetB0ZOWRxbWsYlucKZrnFTSiS/5TjnszkDV7+SQdTK0VexBmu1BV7SgfrU2gbtQxeiCrFUPXaRQEOSjbWRxzRXloT+MzlVtmVGEANZ39aUp/wBq+aG0iKPxCPYU9q/UDFQuQGoNCE/4dNouRpGsUyPPM5LXsnahAk3bHpFvQg7UJfCllk+xjyUgyCRM4ntQaPZevQAUPUBHuqgjcE/UHcZKmoAnTxNqEoA/FeAol9pKFhxRx1gdValPfrreTrvMPilivW+hYHx/qucLC5GzPa65WdOrAhwm2GNbTsI0rWpuEtpC5IVMjTpNUgyf+ahUWKDlsy9cdgzqEkcGWa9a5DwP93rqPF+lDC1BwcuKa3VBP2iDjDBDbfjfcqiymR3uW89t0YutxNokpPOMB6mAhonYEMUMunyfvka2qC6fkYBzD80Z+HPi1OGfj/htj3J4lvObX7TScv2U2RwfbdI5riOm5Fh/nGEek68mtTXBJDz6ZF11Fa0vOokVPFi3AZNGDphq0cHHv6PchgyZEBMecXv6pn/lPuR86PklnOPzDwjj2LsCaUsZhzAIV7cOefxR+amN1z1PfM5FJTShwlaq/Ig2QlyeWDSPBM4RxBqHarE4876G1CQjYpgqUTkDJ+i9pnNaafd319gXq9XkYTbozdAEzBpRmtxeSYtY1Zk++TGzsfTKOt+QwhNKK6k4px9FiH7DuvfsAiCqaXq/1YXJkaUJEYO2R6CRwgbAIh/hYllPekNDVqqz0xXxzZXLMLcPMDXeC34GPad8efUtOSZK5WvU+YarE+WYIq+Kma9XmNSrD0fku64uMtUJroau56/4bUT05rrii59uZ/b0u2z+gxASCOs+cTmvpKvrM2gRes5EHFW2IggWY2314ALuZjqbibLW1ycSCxJ6/QGervEzN2iTzmQ5YpNmb+zAZ5Qe3EElbomzNSed7CgWrP5WKq90FSJjwJlu+e3CQHXlTbq9+ShXvrFuttumgtclhjxctWrToGUkLMFn0oGm2JQfFI7JQdmDpoowLVmRxl8Sa81tyvAenSYgOrU2mlWXxdbZNIm6ZuGHpoMmo+B03WYAlHMXNQW1nCFtZFCchOOlPfDr+bDmMn2Jc0yhM3ENrk3z8aP0FdHze42zHFPtX1OqlegLzTRYVHVubnEtjItp5649C1QX/oh+tbnC8EsIK0My+HhsIA2WDwiHq56Slc3jmR1+hazqbf1xaoNmA+lRR5ho5PtcwV08ACDjpw2RW57wvYhFyEjS53ofJyWfUX5AjG7pqQ4m/KQ2QQ07eFGFYa6L5VYm9aT4+QmP9JluUlH5F4haeqMand4tTfor5zWOTEOUtQ4egRQrH9KbJ65Hq9nb1NaVKfnBYqkhImOtIwBLL05uNrwoACMWWH7Q2wd+bERomyjPqMxd5znF+bqBJ7yT+MniZbA+Gz72KIygTnXLHMUVbGtLnQGqRgz29RHPeMq3FfJvk34ATZa3PudVL23Q4JIkAz+aBrS26gva9/d13nYsWLXo8tACTRQ+arnP6mtOiUpqdwNVO4aDtk6LPnSg0OVqbjMpZEUZri8EpZ7S4EGqgCRFam0BbEDVafsyEW0gDZfScek+ER93mJxAVCIBF0jmag8CsR3PCPFAXavcg1M68i0QKorjQALgN8109hzwePOJ32vY481ABKHdkbbR/ovUKEQ/WJrpFp6ZajYsAC87jfIsOkYJIWsNOzATbVmQQ/ud9cqphrfF2epLORZrznDn0NVNburVJAk3mljWu6h0pUZmzEZGviQvAyd1OJroCNOldqmzo4psu6UpEttUB50HDOo9praG1iVWfQZNs84PxVlGfw0k+m/Qz1iY4z/XcCZkO3I+sRU7BcISuhHSLx+O8XYcFfjj/fYupCfYQCVYnbu0zxg+VwrzVMFGR3ZavhEgPZuBEt+LgFh1X8OuNJl62+j1t2+l0TtHCBP+NwJS2idYmNrZTr8sdQBPtrVTjm1Z0WFtMO5aZ9LfUTtLh43oXLVq06JlCCzBZ9KBp7vT1WHHKAu0s3gWPQqi889GAd6EkPIVTcny8jGF2oc1EZVOa9etjtgcA3yaKlBz0Pwu90zQhmvkzGesQK2NfC2EUPlZs9cjCJNVuPg/OWZuEQSSq4YM6penETGoqP7U2qbVh6MUcIDi0MAFPjaW1Sdq2Vb1FPMxyBkT8vYnHducwqlSqgqjyJ0WZOZ2xKpnlqbfoHL/XMwDkKO24jCRl8BLdTbUxYlfpqibv45Sci2TARmp7sl5QMfV0TYtKMXfQNJZxaxPikSfEa4xr4EadjsBH3ErHgNtG98g2zkNeQ96uFKCFZRiCQTn3OtDa5DIZWMMIbkAr0I3wVISCJUWwNgEru2MLk9lvQ2VRItatMm9wCEup1QowSmshPNvMt9I2qCJPtDZhW/OXwKI5nSjHugb8yYRnXsot9yczaRCPH16gyfW0fJgsWvRk0QJMFj1oqi1MjtUWDeW4mLPHdyGzAk7uDpg86q9WVM2rWAtLyl05IJVCbTdrk16ZKSVUtB3BjCDF9/sZkKKgzajC41dA8S9yhYIQRfMusYu2y2lOksk6JcWqP2+0NsGeZxogAVMy8nxAO1M/H6CWWKddHaz6EmbeJmVMw+ePQlP41spb++pvgnb1briqkftsX7TTzASVlqN6q61rTQagSAfKTlo7PApoQrZcosI2y18Zr2u8l45xR/GxxUqJG3NqaP5OtrgZd4ttxjx3BUyu5mrlOx16Yld/35WhRaVacpiVr3l5InIwDn2bKGhRbpOp3rMZ3AvgB9fp0zrP8JYQju9otm4YttPodhmLwPIjlb8lhM9I4C5Zm+jz5FjOB60WMcfEGEhWJLns3L+JPt69Lx38QOAN+LOIwJSAE+z4YcKH4zwfeduwvElo3J54LV1Vro/7nJXePcpM2H5xks6iRYsWPRNpASaLHjTNtsXUP+mFANdjRxEqxrvDQf/hvytg0uq/Y1lEQTCKCkGuDBdKOlOwLsB/b1hoT8pzVCvyuHK4BlNcgJakE08EY81Xylwg0vsn2RhPqjxAHKPwrKbsrdWNUUAG7WPeMuI0qWWmwapEIExE+SQd3MZDwVLjeEZLkCTfJ9AEVyOTb9EZDddjrWO4UunSvxecwvqarFWbOc1ggcv54gguv5eonM/aq8ATKtKr2o+tTeLTlxSPaRqfWxrUn7RF51EsTM4/r9QhKXwiBFVT9Xx9d4t1KFFlzetMAdKW5r5Njn4nhn7S5A3gPKtpLian6oiWLRo0HsLUtxECKKGp3ZxkAC16Dcy6rc2BYreyUU1/oib3JoY3Oy0+S2eyHhAJ+CdCe5L+DMojVI4tTC5aoxw4hW39E5+HYcQV6cjilUkKqxL0rZP9nMTx4weJc32IVL9hR/VV71Zdw33KTBbPBNYuCzQ5S8vCZNGiJ4sWYLLoQdPGt5Of7UxV/EwVPI5vYtd2Wuya0Z3Km7CaxbFRZDOlQWpRjjDMDhhQyqP9bL5NmEaT3lqF98g868WMciyfn8BYdxZkycESGCfm9RMlIA7/ZTIhH4XH3ZSwWinnC+GmdVywKkkn6QT7mnAkJtFspmZgSQmcNJSmVEGImtIsQ6yvqzpMhBYxwzigh+ZXAkZWhdGs/IjOKATVOAMx5pi3ewSGzNJm4Mrsm24GyTAVVnxRo9Y7T/N0wRsiEdpMGbw73Rk0IcBDiCiv+OiFglJYgv+SKh91xc2BIc/HYdwR1BjHxGGLTlx7ScXngzQtJ+THXlPiaoLvC4/xRIMlh89hf//6O2nvEnv6TE3GNRbfy2h1EX5RTvg2CYA14hs2w5e52GFepuSXqALgnaPH2vOzhhUEgJc/CaLI7Sge0R64+biCz3KriuqyE3mCMces1bndHA0p/itXy0wFQNPlgKWwL1q06JlKCzBZ9KDphm6pFh8qVeQYNJkBJp6GeY/OejhLd1ItWqumUCUfJT1cqRRJvaCsns6UbCtdHJ15Uj0fZnlIa6jBoOjV0EBUK+xf+8IYxf1KFKyVzhpw2XrsNUp5HgV37cCdM5YZD6pnoslshBm1IRyAIZB/KA/tifYdKkIgoVZauAhrD1zQZvu0H+1YYv1CQuetHc6rIGfyHT/t2TM8AkHvmjbSbAQzDjbjhCmNifY7r/Lc2t1q0K0ayJmUN8WwwykeHq0OaEir8zk/1/sCPCn8jQz5ApBS5OvpFRcsuVo4Nhg4OdNp5dNKptN0Wks7TdeTgbj1VpODIpC3/xsc0cJY5RJXu8DFSnDkDNDixw97DyKIMrv686EUVovAC3mIaLvq9Th6f4/zzXM8iswUy18jN9X+UxZVtFPfEn3PdS5atOjx0AJMFj1oupFbU+zyDzaqYFW8kkziMX2249oNFu6mINzVSsWVhI3861UOQVjGeBBnvcbenVHliHG7ioCMs1ao3iZAH6np7T6KqxNBuZtVi6BiBUbPTKBgRFXLFRAKhApa2FcDPdLQbllQqe+zKrg3v3wKod7YszqcCU8/OJrBMV/Mq/c+F3klxrdBj5yO1iMtFMJC6Ws45tJezsLjvJpyd/o1mQntl/NV6bfgpHfMcR1ochw/PpumfVZtH3O2sb16rEdWNMb17oic3FXOV38LbGtj5FnBcikfpwutB/DNtvzM8t1oB4i4mmFXhn2MrkTnd2wGqkzzIdsJ/Ww8KxzfexDvPMzvj3mLohtxrdlMd+sQ52EAgKT42qoC2mQEhDROrP1xxia/J+VvQwJJZJzxMa8Qy26/G75i/NnYOpC4JtwnE4/xgmVTPT2P/+acpfsBTWwl6vtQvuBzTlW3cgIoWXQ1iQhYxN5fnYsWLXo8tACTRQ+aeL814aNZGYyKBwqBGhdzZHV9zDOLU6MGTsL0dXT9jxgaCEt37uFqg9YKghkzOEFEIADVKi4EnTp8o/WKz3kpuPaoGWgSwkGan1mXaOUSfJKgGuRO+lSA9RE2RSNXpl1ThQPCiTaioDi5stDKuGKme90hTES4bQjbmPs/IchLNi7sePXtvA07gjOqpkQVAkOztH6/s/l8GKEOCmNkrgCVOLZ4AnS0vOEQuk69OGNBctYaZSfW84ert39Y8zHeW6vjtSezvrApiNWanRnKU3ousa18N5lX6XZV9u5eT3exn8MVsA2rzIETC7FaQEUIONeGSnJ+K0I+lgZCMNbigAcJhS1LAxjSeUNU9SMPHWEAxmRCnyTBOkRHjjyC09uBLxpW2dMm7lJ6WTE+nf2NNFABwRDS5eFNFdMewBIJ3arbh5KVej4F0g0gCRVOwk5M1EGTdpe4OYT8fQl54DS5KEfgNT8UvgNXq8cwgyou0dbHLPD7Hes9Bk3Gvp+TlxYtWrTomUoLMFn0oInxtAMi98g+AU7Ofac5UGAy4ZcnVS7upGFcJzoxgAAqqIkJfBo/922iZWd+S6ClSdhbUGsPB06yKCUg0CbhN1RZ57O01HaEAbLiDqM2sCjAKnE9BEUFay1E3t5ntDZBVS6EhQjN0KOKEVW3MXYktaQSCmjDoJxOVY/hxIR5HUNaV3p20meXnzeMUeJ4LZxOx3AL7QgcjKFKCZ7TfYEmNl+iFgYZdPC+4ZrFXo75Y9rlEc2Ak3lP6hZz6tFKg5yd19yFrV0NmsD63IOPkcifjJtlgFI5gf4OhHmhVMZiiGANE1HP09tgbTP2cebHJAMkGJ79/iDnVYeiGQRw8EF5LgIYDDnicz3LWzzTTv11j7xS+8MVeJDbh3JFeCTp9YobnSQuhnn9p8PXSKjLksY6Ql5d24JAmfPY8beTklWJDD7CbJJs1ONM6O/3qedySNfJDUosu7etSy294DM4+JFlpkWnaDl9XbToyaIFmCx60MT7bbzXL//ll5NjAeDoi0kWu1OClXFn8NcJCldvzQnOS7u6wETVFp0MloRwk9AtPgInUY0dFQFXUHYVAtlTs+Cc1QoPj7XPVf7JdBClPo/j5GDdUQmpGTTK0ElUCTYmO344g1QhnCwuKhXismJRiP2WcTaj/V6qmdfxxsaGtDGBmPq4LSnDTMU6G8LV+MZR1yDO3dSLu1g87Blg0hNHyqpm32Xn32unaeVRoEI8nCrl9dRcapxXPkib9bEtortv0TlL+Iz0fVUYeLQ2SRAKbOcpV6NgWW8NV+S4WayDE5z7xzolNLx/QlMrlLHMyPVMVWcCK7EE9jIAGARPMdzo+Ps8MYGV4QHxNpykE8ADIZgPWE/hBn+DxMZ9vHyENlHQpABArJHJ74nNh4S8s3Ll0PedHAiOFibaE7UmDSCKhWJ87D1aF43v5AcTNNEPTdo2EQ1bzjD1XmWmRYsWLXoG0gJMFj1owh9+IupSRxdXpAsrw5eTWnmaxXtqQUmSMB8ZduzBeYHhOtAE1GtQml3g38BJa6G2Hvn5IK6F8aF9T71hbg7FzNrEyx8CJ0JUnbozn7VjKMHF+tHqQ8IekKiAtIqkH9+J46vBDK1l6197VdacWpuY0lXBLzRXQo5GLzCbE+BkBoCgpclMlcgqS575HdcIWJtcAx7V23DiahwJ5q8EFuZ0LWgyACbavvQZHZLvAlxULRyMW7o92WNqu8rh67jfXzGN13G1/M75VhCmNudhu4j2S/PJ+ObafVuooUfIBcv2WWAd67jHVRvfIabYaJqLCh8D/l0B034HAMYF3yYDaEE4N5lSP5n9t9WORNecbCfloFVPey+UkSWwZhhJmBG74rHH+jBhVkPeYeYNDTre2hOeXvuxTl1yh+7xeeO2KLexsRTWEPLX/Osxn4nxV+Eaug40GeQmClP+eGWmRadIdqL9nr20Fo990aJF90QLMFn0oInlNscEYdWEl0cWACY//qOu71Yu+s9VoMlZSkKYbTvhLsjedrNtFzcjVFCE5XLe2Drs2xahG3bFmuFz4qi6Yw1koEkt5Fb3s/lICpL1H6AiOEknqyYxbi7eDyJwH+pFaxNbCxl+qUGZSHnuqesnfTZdw4M8BypGVx5ngEqp6KW2FazYQUnJdgBa82AJwEU8tFmFxv70QT9G0KQGTJS64jRkwd5LGR/TLvcnqtA6W/O2Bf71Uk5yqe1C/0JOQHQtaHIys/hF3y076ptbHw6tTbrCjfCAc4W8zhAIiG+Mwg/xOPBsXYEK8Bw0GS0M6jI+GgnhvDEkcLXg2wRKw3S338Ae5vLRUh3b/NhMrU0QrEG+JrFWmy+ZrrahHwFDP3AIS4SzimVHcKT6BRuuDG0f+DbR2sKc9MHKkKdXfIHDU0q9Hm44D5pUgElov5Bb7k1mWrRo0aJnIC3AZNGDJs4QvEm8rq2rUt6OKM2qWBa/smqBwssoAOTd8LErvuWBr9EuThEIX6C8ukSMX8fY+zKIfuNWkWD6XQj5HqqEwxazd2UDhz2q5HN1vRaIaxEM1SIcU1QvWk41dfcvsPh8BeavSifK1jDNRLvFbU2HsDZKa5N0kk5owxSscZxooR4UM0AwVM0SsKKJguzcK8Bx2hghKU72aGUTezSxuoE3bWx9fC/r7kQV9xydfxdHny+5T9yXzaynd9iOU+aMAdaF0tuuezkDaCilXdkHsoV+73xtAM30S35/L5Hj4NHqaCWCFhYDr2MmVMDzLFUqrSuE3dqkF4qrfAKaDOOa5880szbJCrWFWUEJnxerpc/f7C3JKyAANJ1fqkVVqfob68TeqbXiWA7bCb3olVm8MTihahY1rZrp/DRyONYjEA3pKl+k03D8F4ftro6PvSaIPaLwbE++YtdYqNaASeKqQuZQPverJdfxmnbfEs+fNlo+TBYterJoASaLHjRt2cKkgwWkF7gXPQz3lEPY6ud+jCtjBPALbVtUqL0fMSJ8ne5SVVO6VZLXbTWqvNfHD2sfwV3i8K+3CSIZ+BcQFaTFhcabPkzzdTE9SaffC07NqAAeqPKHgnhULECBYg93fcDrYguN9ScF2pWwfs8t7tDaRBJIhcqEzMTpyXrsmb0+JnUKaV0e1BJIu9JxbFBT+mThK7ejjgUrZpiHsDUnjxnKnRbwshp5TGctHvZJvqFb05N0Wt9mysw13ECVGGRvrTPd6oKH3Km3FRR8rhd5Tdo7hS/OPdBo3+VbbWxNMawhWGUAGZLz9F6LCNQtaVXmMMX6OigVuG63PsBtdDTURqB05nzn5iKvmxHqSTZ09u5H0KTkZUDlRwLMzGpFofwKwBidaeEAYsX02dsp8coE/ksk3RNdOkkHf0+IEiAytEkJkNFLwe0EgRO0WYx2J3Erjl7ze3YeKA1dPPGOnQZgpxYmSQrSqbxHmWnRokWLnom0AJNFD5rKLTlK6fOMictCJLwN5WYCQBQ3M1VqBJXHDLuZNj+yHIGn5ICWT9nCRNtUJcOBE+yvi7K2ZULDkDuCD6MQGICWPs4bEvdtEp5FnmlJErWUeWdPoPyqCgJtNm+nFKbhC2wViv0iqBcXjxB33yaq4NG83T5X8RjTcZyluC85vlc06W0JiEw0qDFvbjt/4yVbg/5R1hVXhEzaFbfmZJUizv0RRUDlaAaLsifyHG3JKUET0nFnmilJ8/hpTAWaEBGJ0DZpe1Tpx/ij1jOXk3zXX6FH5Wu+LbBXFd6r/pqKz5laa0XIt3jTWOD4YU6rC9fbCCyYIswIV9wQ097BFOeBI9fy97uCZo7W4PGmQeS3aeX7I4mlmabbYqKlJNRufp86p2uTYTxvnKfYqKU7ix3aiR1JIxbMJ9ZX/B1CbufrRvocHG3DOQJwcP3pAhSKFoJo7xR9xZw9JecM2ViFRtyipMt1H23JKaUgoQD8Ydp123QWnaVd3E/Yfda5aNGix0MLMFn0oGnYkpM1CeEiiUmktjbhUDiIOGX7h19Y0rEAJmSpVPcI1iY8fHaCL1wq6dlXfCbb3863Nm4UakKYqfhSFsOg9naBirtugNYBLe6m175L7+Wl44d5kkZUyoKD6jA4KWwFI/hTbEdS/QDjQsjV27hCfDuKaQddm0BgQKCGoeaukOX24iirgUfVj8Kz61VLLF9vvTnaFOXtVBmCAggKmVoXuTNECfNoijYOhzzv0IcJjbpHViMPyl7IU23JieVH2nc+AE3G0meM1/OawO1bWPutSFv/Q5UR+LzUtlxQ6EprloZEP5IRXVC+ewTjO20KdI+TuGbc0XXxhvd/cB1aXZpPcJb6mlZ+pM6d+6IVuull2kHbo28TGFN4XpWaPnBU6N88DrhPHDNzsKrxcvWazc9MSH9jlC+pBWPLfGhtEhy/pjrHgYeAcTPBWYlxzbOKHJ6kM4YnnC5bmEBcmD1oi/db+A2bbMUJAMN4PQeYjNKFW6qeKH5Ax4DJ2HYr02MfUWZatGjRomciLcBk0YOmQwuT/IOdHJGqJcRobVKUncRdiqm/vrDLX9vdhIoKMGkXlcrZtIR2UUGOTXFo1iZRvajC1ViikFj0Q0V3QxJIv8n2bUNe17AffSZE0yCrWUr9HJIgP6hSo7H4cciFaBS/KcWhum5bCGAeDq1NJuORKpytPIaTbygAHXE2M6hyvG4LtaJYDxQAGybqIFnLMW6JwG0THGrLNgBHVOse+YnM6Pj9O3b6Ou+f7AzKZJ2fi7gjyooSF5WZwio0WJtc1/bxuKcuW7piW7LUE4Rrx9rqVg4M9013znFMLIUCT7D6JL9zevV8rf3I2eK73ebYARK1NkGwaGJtEmqt3rN6G84IA+WNSJHv9OnxFvXoeK7V2PwO1cBKB0IMfCeYh8QDAchSwMW3IsZ2rG4rG7lNBkaIiDZw7Hv4C1WCKhV40q85f/otsuu+N15nawmtTSg8n5EP3Q0w0Ro6bvRIwORdABNtnwIYNKSeiFt0hpYPk0WLnixagMmiB02lhUmQ9jhcUIE0p6gTaxMtdt60lFOQQXCEvllOJrITDK8VLEYhTIiCDxMUbHI8ERPzTuoINyqxlWF6HJ9+iTUBvWf13CCcW7zQTf/ymH2b+AjGcGz5QIjUcEIcsiIRgYqkpNg45lslcl2oolE/VppTik7EkW8Tsi+2s1FeiGMaQRQmWPMCAlNWN44dxHqJHpYxj1mu5xkSSttvUI3tSsag/Uuos6IpWBHq0Zi7gSaXAJOjfpAQqeVQXct1vgz0XRrYTIrX2d07eDCylhGguqoPWqLqhz7Zbvt9LVs7w2vdV8YY5yPLFibZ8gL7juBIfAda3WSgQAAkAmgCJ8oQ/LZAKwqazsATLXFkYYJgSbQOxD7n8ff3q7CuiXMQR35qa88J3yb6DsjQis9A4i7hakcNH8UJ9jHO4AiSTNrP4Iggh0vprJfKt0lfb+Elib9Sp9/7ggEyJDFGXEGXAROiI74w921yrcy0aEayi/HR+6xz0aJFj4fu+J1o0aIPDrHcXvh7ul33dG9xT9Mmt3QjT9PN/gHa5Db97XST7vGPD/9uiUO+W+J9TCfZe18u1Tf52+dp2z7m2+Bex8UitIn0vB5uV4G4/kc7Me209Wv1t8me8vX6aacb2olot/smeAo1UcvD+Z6Jhj8K4Z5HNK8KsBLTSUKarSdCwZ+G9LbdQZrEyF1iZYE8ns6aDm1tLEEIltxz4TBK661Q+mOfoRRHOFuQj2iziZI0Izk8/EmvS/+0DMSF/hLFOG55+iqwVWFh2WgXHv+mq2vso5aR4W+zcMuzpb+i3WldWE+dZ+i/bHQr9ZtyO3uDDvqEf7d4v49xt7LR03se4+Z9Kufj5B/Uezv0rbexX1vvybGnMckQ1+oTan9tvfQwe3z+24s/sWeEeVp9u2ywFjfa6cbWYslBLugrlaJZbzCp4/xdLvgI17zF3lf4oxDWdL86D+nprAABhZwWliKu4udCZRzlPApMWRn/rfB2INyZJI+M1P9SPsZxCEHf/fejte+/5yojsMkK+XpGboA//c2sftu77EAHv//X/6HsdJTWx7o/PchELjON8dspoGbRk0i/93u/R6997Wvpec97Hj3/+c+nL/3SL6U//MM/PCzzGZ/xGcTM4e/Lv/zLQ55f+7Vfo8/5nM+h5z73ufSiF72I/uE//If09NNPP86hLFp0J1oWJoseNJVfStwWWXNVJXteDbLVV1mbzL6axA+djJlju5yFZg5F7Atkz3uZ/GtVa49TH7X+ptS2z2H90yBF3ybtvo07mhSThce4ND6zIMAvqAJXSOtfHG+ofZFrX8riMzuyNsk0frvr7fZPw/hVFXulIymtTXBoMKrRl0evjcnmMtfvVieQl+bWJmiVhLOXyeYSxj+N64FglJ7W6My4vayLFBBJ6UVciBc9SUdN+Pt7R+NXdKLYv4qGlSEHaRZ7/uvn7JScO7Xd38P8el+z15+LyoF9hTSsdZcG4NW+TYreHrxyoYrEa8uRqPXBFWwNn1K0FSF7Z8J7Ypnj+8v9vrabq/yZ1F1is5KIU829TUkOYYlm1iYVZ7vm+V+yQMFH5/wXLWw0Q+SbxZh7gghYi/SrEI8WKBesTaKz1twmdipymwgDtzx3PUkncsYpV/V2JcbpgId3xhzCtpKM89Tz+u8MpzrPUJ3f5lOuWUVEG513+hpvK16x95+S3INrerQo05Pm9PW1r30t/dZv/Rb92I/9GH3gAx+g173udfRlX/Zl9I53vOOw3Otf/3p685vfbPfPfe5zLXx7e0uf8zmfQy9+8Yvpv//3/06/9Vu/RV/0RV9EH/IhH0Lf8i3f8tjGsmjRXWgBJoseNPFe+DAJ8sgRiNH/CRKzbiPhYp9uIQAMP0CVkMCFhOgKpHcRAYVLwkYWdedXU0yFvF5T1uM98XZBvcDaVST2PCgUuqKh9y54q3m65nXfJnmGskox/uKXSsxAWYCN/fG+V+MeAQwe6o3zMPSFqa0pcqBAT9JB3yZnyFWFalbY8DGPZ1/6AqPSpT+AHFmt8DjLF8ABWMtFZ6t+qjUMOsqMioVYviM6Ai3qLo1P8IiuOiXnYtu9fdjKMaMJF5mztQyUZLZG3By5Cg3bWSZeLa7q23F6H7NcZmu4VpyDJUCgoyXYb7Y453XtlWvPeiOC9eWKs9ci8K9zNmszvOEF15P+3kFPWy49SScMcpiIGiwcx38mLqfFPsuI+VqueGcOdtNa8hshB6T7Mzb+o2sceGzZsORKKW+/OR8n8AFj5DoR5FAGKZN7jIvt6u9XVR+rpUrpFDZtv7yKjvPzJDzNf8UpORcZHeF7mdfdAk3+NNAv/dIv0Tvf+U762Z/9WXrFK15BRETf8z3fQ5/92Z9N3/7t304veclLpmWf+9zn0otf/OIy7Ud/9Efpf/2v/0U//uM/Th/5kR9Jn/zJn0xvectb6Gu+5mvoG7/xG+lDP/RDH8t4Fi26C60tOU8gfe/3fi+99KUvpec85zn0yle+kn7mZ35mmvf7vu/76NM//dPpwz/8w+nDP/zD6dWvfvVh/odG50xMJ/d7kb6Dyen+dNgqM27Xydt/RrNVjNvCthtI3z3O6t371qHZhoSpCe/cfDekUYrXtL5NifrWHLarkJkqD9t01HQ4pVE3kQ7be9w4nqGNbsjubVAXPIkgfPyVzfMSzQRiTHeYQv92i0eDdW0jQ1GxXiIaFNEUhq/QVj93RacCIIba0p+Mcbp1ZhhBj8c422ZDOrtaLs5KNKaPgA1+s41/vRzHdLK49ne7x+0j12zJ0a0QR/mHdNuuwf0L3vntL9V2nXH7zUE6tH+7b2Hs1VYb/Bvi9jEulN2r+mCbzM71FqK0pQX/pP9VW3Mub1faYNxXbMmpnh/FdkSft5anGCfCdEu+/SlusRnXiziHsjUs7HUCZ+tpG+3s5XYos/MN7QztMRPx2I6+IO2djC/TNC7xi8zRKIwn84T8FylCAYnzpdNfBOIdjIVxAUcYOUWkmvONfNWtiOKIMK4ds95+U/APf9Myl8NZI8ozCH+c0xsQaX3Z/bd/s9/7R9kqI6fzUv+7t7YGmaLuyyZ7k3EuyEyLztHR7rFH+SMieuqpp8Lf+9///kfq60/91E/R85//fANLiIhe/epX07Zt9NM//dOHZX/gB36APuIjPoI+8RM/kd70pjfR+973vlDvJ33SJ9FHfuRHWtxnfdZn0VNPPUW/+Iu/+Eh9XrTovmlZmDxh9EM/9EP0xje+kd7+9rfTK1/5SvrO7/xO+qzP+ix697vfTS960YuG/D/5kz9Jf/Nv/k36y3/5L9NznvMcetvb3kaf+ZmfSb/4i79IH/3RH/0nMILraPwBhs9hLkGBbMbhMn71YpDauojXhdyKxtMievvWLI/5q6+Loc/9+6XmLY+GSQUIrT1gHKLWBdz7yv0Dlwu5Hq8m1dyPH3aHsHksABOEfgQD/2zFEtLTlh8hOxJUiOj4JJ00FdATiT0YMsS83utg/QI91Tw421gHQZkjCxNNFxab77tamyRICML6bDGeU16YHcvbxxCmLY5S0/DZju1P4iX3weOZ3DkpwVqxls9YeBRLY4iSKuhPc2b5MLNwma/Ga9LaA5iPMFLBqY7zSM06zK7r0NKlHsU8+3wOx3J9tVb5c7NxGUI0WiHRYMEhjO9ed/Tafw82EmJ7z+Ib4u85cAfRf4q3vacN9lG9IsYjeYeTdOJ4tN3T1iR9kJVlSW1tUnLHkvpPR1tDRfvI6gJ/NIxkZm0yo4pD9Tk/YV1ydJLO8Yhz3fjLJiWTGfojRTkion2HLTqeSn09Xqb7yDOTW+5wSs4RU00tZmuTRQ+LPuZjPibcf8M3fAN94zd+453re8973jPoF8961rPoBS94Ab3nPe+Zlvtbf+tv0cd+7MfSS17yEvqFX/gF+pqv+Rp697vfTf/23/5bqxfBEiKy+6N6Fy36k6AFmDxh9B3f8R30+te/nl73utcREdHb3/52+o//8T/S93//99PXfu3XDvl/4Ad+INz/03/6T+mHf/iH6V3vehd90Rd90Qelz49C4yk5RA4WpDi4BEWMUwC0YwUuGnCyjUJAEHygja6Ihm5wqp947KoU/QRAAxr2OlA4MU1B+617zZmoK+y2z9/E/94DE+YVwACgxKwCsHsgHvZ2FXbQXNKBEKuHxEAjBCbaVHr70hUCtnnLGrtTBWgEU3HVoECF0GorIERnpeVG1Ylg1ijEmCLlGkNQTnymQYmwunutXanYS6Xqonpsc4g9y4DDFMCQrcf5aKN6k+qBNR7UB51uKCMhbsyrbZmSORlPpkM16IIeEUETLTBuTjnaknOmH5fzxDXi+ccSQ08KQORMHs/n7/mQZzJunt5kpXXME7iHEClYhG0X3DRw0f6KABAruIwsLwsFVtpA2TbOW2ogMQd+4EADlvKgveG2SsxvicB7zIlfKCDGRNVJOhlCqCCNS3EIjFRxYQ7axMOc1s+54nkU2vDy5eHobbqNf6Nvk4qL1uE+Fq5Ak5g/5qniGgPKnO8I/NCI4WjhApzhopzFy+6TYk/iDNfIdY63B5GJ4nOeAyYHdV0EeRJXFZQdFl1L+y6037PTEa3v13/91+l5z3uexT/72c8u83/t134tve1tbzus85d+6Zfu3J8v+7Ivs/AnfdIn0Ud91EfRq171KvqVX/kVetnLXnbnehct+pOgBZg8QfTHf/zH9HM/93P0pje9yeK2baNXv/rV9FM/9VOn6njf+95HH/jAB+gFL3jB4+rmvRJXFiZjruN7/ByJeUBqbF8Nd8oWH1GGqNtOokorl9oe8gRZll3Rde+GntcEeuyDdlwV+N6OOFCh5dpFFccuYNupB91KRDYDPrz1UaEenb5O0rrygube/tXUxfydKFmbHAsQtaBfETdlhlURqn2bdGznHPkn2TR+UB6s+fQ9WJK1icZrKRh2BDqKOIiIAAh1ITbFWU4u4mP9qDsM1isZKAn90PHkOB2/j0fAP8UlHya5f5cij4ALnwOn/WAF3V/bsRct79juGc5Wxh2CJtTfxSNrk1m5MSX7R8l5Kl6IoAnyU32NWCTk9Thtk/pr198b80OFccrDe1yvSD02mXKO7QxcTlJOhFfUj4eyXC2ra17nF61NatDkKhJyB95AZ6xNAo+qzV78Z8LqhWeUi4i3omvBgCqK1iYGupTgw+OKEzqc7WGtCsRFpjW+A8igR1tMkg7B9zVwzsKk6NQk6lrQ5CofJiHpPGhiLV71I7rog0HPe97zAmAyo6/6qq+iL/mSLznM83Ef93H04he/mH77t387xD/99NP0e7/3e1P/JBW98pWvJCKi//2//ze97GUvoxe/+MWDi4D3vve9RERX1bto0QeDFmDyBNHv/u7v0u3tbWnC9su//Mun6viar/kaeslLXkKvfvWrp3ne//73hz2PTz311N06fA9UWpgMXurYL1LEEVH4ogpABdbDvV4Vytt2FmwoCwVJbAJgBsVy11/Td6civ22hyH1M+8qthLApB5pPFXMsF/L1tk3A70J5G/vmCgmOrc9tVMUZLBEcliA1UxcXnNU5YxNS/TvpTQcWzCriguBlzXUwJG6qiVdzhJmsUfCuyfj97pQj3ugY0voFukGGaIjEAKr8vXa38Nj2FEwoLUiOywdAZCjPlC0OJGSo6sa8jDdj3/Da15y/1cdzLtObaVTUbUJCVCbbs5+3f1F1uJDhrMqkxOkmKK+QRyCzjagAIgByaOlg8VH1DWcnR8Z2JCcHVpfHom0TFa+Y0LgEpL+lHBeTxQEfmsYJEbE0axNq751uGzki744CrtC2vs1CAMTO8lcn6Vym0tpEfC4uWZs4KOyMmauFNBtz51VqkRX4KivwmzgcxhtQLBTnumpfrNW82HOczQkcnRTiaARTOMQ5sFH1qdoWdBbs0T4xlBm38h7RHRjbIfHdAROiy4ztsL8LODlL6HPkPuu8hl74whfSC1/4wov5Pu3TPo1+//d/n37u536OPuVTPoWIiH7iJ36C9n03EOQM/fzP/zwREX3UR32U1fvN3/zN9Nu//du25efHfuzH6HnPex69/OUvv24wixY9ZlqAyZ8i+tZv/Vb6wR/8QfrJn/xJes5znjPN99a3vpW+6Zu+6YPYsznx7QeStJkkePyB5nQ/jZsAH3YBhTKUv9CWajkX6h6UuFAte7u8hbYNCAkVsneUiYIlCt7nfFSXY5bg/8Q1Iexnno/0fETLXogr6hXZTspcGRyJyloIV0pZqoNJ+vYM0LxSXUXNJ/o3hmHTkCk4t+JdjEAIxnGII8L1gHnHOFAveryv8V2tkNIIFLyr+jOLn/cTykHefVflsyaZ3kyjQkKd3tprvlX+ZACTwS8HpSVasRLMk1lRzlfWnZToo/pTxNErWfVpjOrvKXvbmIZ2a9YmTCBfGWfx7GNnA09thUI+5QMamyEJf6+apRyRA7EwigSQWC2DCVpNWF+Is7G43w65VMbGvR+uVccgdJwpM84v3FpYKM2bs1DebylsuSQ65Z/k0gk5Ma6uu4EGiatC/tLCJMXZI5sAKsBpQ92074d8LdI9Ayb7Du9B1YfHAJicLrtI6SEAJmfpEz7hE+g1r3kNvf71r6e3v/3t9IEPfIDe8IY30Bd8wRfYCTm/8Ru/Qa961avoX/7Lf0mf+qmfSr/yK79C73jHO+izP/uz6c/9uT9Hv/ALv0Bf+ZVfSX/lr/wV+gt/4S8QEdFnfuZn0stf/nL6wi/8Qvq2b/s2es973kNf93VfR1/xFV8x3Ua0aNGfFC3A5Amij/iIj6CbmxszWVN673vfe9F87du//dvpW7/1W+nHf/zHjVnN6E1vehO98Y1vtPunnnpqcCL1wSK+bVtyZANJmwiAAPsnfuUIclLSLsJX5S7MMeZFtQ+ryWCDt4/CI2Fp60cWXCqN3NWa1pa4XxUU/qWLw/1LoFl1QLxaclC/l5RPGzJdAfsCX3btiyMR+BwBaw3b+68WK12cty+ROAdCYVYLhGDjvZ18oXMAqlRQxq3zRTxcy3Ip3tN7HsNNeCg3AgIIHOQ6e9imE8XrWC9TO3p5tArhGiBBuV7bk9Q/+6cCNzwu+PEI9R7U4R0cx1zV0Z9XVjt24XFdJJLpzTQq6jWzeu/Bf8ldQRMBgJKLVyK874ngNYtlIH32qo28ctyqE27LdpICXPRxzNVjOtvampMRG4vyDwHF2HkK2UTHuPbPGNcD2ZTG3mvnawYQiLffsuJWm2JrThpfCHOOV17Zz+nhU1zj3uLwJ7L9XmTuM6lDYh7LK5g3ggoDF2YikdsRJOn5qzpmcYEDSex7FefWqWLpxQ811I95MQ8yNPH3L3M5iGPpRxP/CWzNuReHs48Cmix6RtIP/MAP0Bve8AZ61ateRdu20ed93ufRd3/3d1v6Bz7wAXr3u99tp+B86Id+KP34j/84fed3fif90R/9EX3Mx3wMfd7nfR593dd9nZW5ubmh//Af/gP93b/7d+nTPu3T6M/8mT9DX/zFX0xvfvObP+jjW7ToEi3A5AmiD/3QD6VP+ZRPoXe96130uZ/7uUREtO87vetd76I3vOEN03Lf9m3fRt/8zd9M//k//+dwLNiMnv3sZz8cdLcLPU3PR6esqFEkqZhRwAFAhMi1YcZ0r24m/rsSwqbIe7lWOJbsFUpvEjQPpnhfkW0lYSHZbnoHvO+DUgTTUemfLtCDaD/rAkwns25rQVkT9q9jrTo/IYXS3F4StPy77g4ObqNIC6ACjOEaEa/KG9Jxe1ZSK3K89juMdyDs5DjpqBII5PE6K8BiUBlGwOVEuQDIgG6B3RSIiO15zbW1S5U3t93V1gsAxtHzPZLvZ0ml1XrxTlxcV3dpuycwMfGGWzk0nqKeRjTkUTaW8+R1OLx1nG7M70TRB43gPBYe8uPczd50fLe3TWJ+4B8xzmsc+dsZnlL1A/marr8e5mKrjQ0RtsfIsFSm3REcg+C4QSm3FqiOs9+amEZF/hiH9bhiP4AlIX7MM1pZzEEUvNrnhH0fxnbmhJwq7pR/E3zBNW2Hh1aMt2LiY704tkm5PlYZyh9RtXBO5BmyPEJ7p+u5/p1bFGkXof2eTULuuz6kF7zgBfSOd7xjmv7Sl77ULOyI2kk9/+W//JeL9X7sx34s/af/9J/upY+LFj1OWoDJE0ZvfOMb6Yu/+IvpFa94BX3qp36qobd6as4XfdEX0Ud/9EfTW9/6ViIietvb3kZf//VfT+94xzvopS99qR3V9WEf9mH0YR/2YX9i4zhLYkIPE4ma92bpnqPkGvyVZC2Dk1CLedM9kn6NNE2HsdCkPFvWUcEu+l3WtZlZc2VtYr9PNiaGbTVC7lyjkOSbbjCKPuGTc7cUEXVq10VE2FffRGXIp35TrAbf965itSsAuXGvcyOhXfvHheJP3pcMpgg8nybfZrDlwMIkxFMfexyNhhxAkVgGRiK9Dk9P4AKU0ZnbhQxMANG9AB10DjRuBC2GfqV6cXuOy/4tEACpoe2ifIq7lDf0W+fxLpYfZ3SNROUBXL1gtWXmbL1n8igfESLi3g/crlK9Gkd9OspTvmaB4laSEji5XMkp0mqYiGTn2toEtrp4XAc5wptYgzrGfTowkbmfc6L2PpXWJkQGdB9uzYk/PNNJ8titt9GsTdq4Zcg3jZMcN7aR44Y88D6HuQzxmVMFbmaEI65HL1am+UbZabAEObEl59y2nRhnfjwiY+tdElgUse47n5ojmK4feqo6rqC7lLvov2T2Ip9honccx6JFixY94bQAkyeMPv/zP59+53d+h77+67+e3vOe99Anf/In0zvf+U5zBPtrv/ZrtG2b5f8n/+Sf0B//8R/T3/gbfyPU86jnsn/QCLUa27TOUbtQYWWwy3ah2e4tL9wHOtCSwBmqfw0FkVSIaNjfQtC3rBpLkl+gbS3DPt7B2oTd3Z/ea1kDaaD+YbRnlKBQPlmbwLiCZ46k82K5sh+B4jPcqDkI3e0kndr4nMgVGdeSogoxAymOidOxuUVd4bP+mH4mDt0kCrW++x7nDEj4FpzL8TVYguXwZEPXLXxNZ5m95SlAkiI+tj3Of2i79w2tWY7o4naYC+mXTnTc9ytAk2qODgj7NmNryElCuYP36FRcyqDOn1v94xYdSy0GVTpxLfqNkcg1N2onFW0E/KGvP7bTpAiAXbYbH1cV5wN3gMS7aCy0ZxysTXqb7sS051OFmp3rhcGVZifV1HW+aUfSIjcbORzWcn1cuiJgIY3jDBwqgxrG272uozJ2NYYG7YhQA4xSfwsAxOpIY6jj0hwERFSgH53UxUmKx21hOGeHdeW4vbBuufPX/yvLTZHgM/VdYmR3HcOiTLLTMbZ1xzoXLVr0eGgBJk8gveENb5huwfnJn/zJcP+rv/qrj79Dj5P2fqxw5bSUiQi36WRtQkVjFH6CNnJB+0ByhMC7EKxNtF5LDAo2F8BNbO4IvHEVg/fWnmybKacZN0JFAXEZrFFvJvL90K0w9dKFSuZB8UPfJjZCA1jSGJNCM/RPFRQmasd0MukpPnMfJU6SH7+l10p9sSu+2FqiRyOnMl0DG+vn8ExqlSCTP1f9Ah7BBp+tudXH+fgwxhSOZf39CScbhfqEym1Aqb6hbeyX+sWxiZvTJXn+6JXeLyEmvV9+PPX9tV36wXD20llKfhugYq5nBtneUG5yP2xxmVibVO21JXF3Jcp0VhmtTQT4W7Tl8HWH3kViP8cQ5nT2r29pA02EqFubiOXx07h6vr6Qkd/VhJBONXvdV5MIMe193AKl6/BR3KwXTspwxcOUfwc6aKbrADkZa3+JfNxH1/4nWo8z/AZMeF5/D1I9hYXHmbhQz8CExIsEUxvjYsQpL5aPaw/b1r7sKb6F/XlfS1e8YxcBE+1OzdcuM7K7v++LFi1a9KTSAkwWPWzawV5dr6pIMRPRLVF2CKtacgAoOgmNeQNNRBmUEcpPsNoH19A555UoYPMMvAnd2Kl9h+3luX32ZqEGmnRF3f2oqELf6+tKrn8nnak+k8GCcjZYlNjXaLdyYT1SmMDpa9+mwyBmMg1N9PAoCLO0RyxEtJOc8G2C22ku541XbF0HjuVsSCkOn2UlUiK4UbTVhXBV0CXl17QRGJo4hbX8VVysY8+WHxLD4YngwROUgZwxPpRNy6q1PUnX8tLW1REdyfNHov0ULxnimfb9JGhyNg0Sg9NXYHPNIWycXwRNwgFTVZ5r+iXYD5/7aouO59CxKL+JabPxc/7r7zdRYW1CysIAgEWe5No3xM3bheLWd7VkYRi3WxOCbxMiEhbIl7iZm54MLY3n2GinnP+ctTY5G+fhqMyjJcYIKkwcwU6sI9izTtad2F84btiWzE60j7Z/uP0o96nqJ0kcdxtPYeWBYQQuCt8mPr7xNymCHrGvuQ28an1CROd9m6S6L9EMMEnF27s1O83nrpxt0VlqH0Ludy6nfGbRokWPTAswWfSwqQJM9Cr9etuvrFuR9EeDo0QXBFu9R80F8qRgjJAxAzoPCBYnkuqFsoN5RwHs8EbUQRKvtxuy761N2W6GIdvvMPowsb0yUbmRECjmIEyX1+U+SyCbbRUqW4l3pXYXoZTR6L19jRXpPl1GMTmUxussb30t1YYWhkc75E3OYkN7MA25zXEiON3Blh3xdJfTTzqFhQjrQ7pH+T/m7/Vlebyah1ynzsWs7VqfIV0tl6xNZHozF+9PfojtxGaRkhWMu7SNxmk5HOOkgQkcn6WytfBu6LpMDR++YnA7cjUERWP+PK741h60PXmGe19djcU0a5Obzf2YKFiLlh8BVKnawe2D0DHkeQbIkL0uFLcQpqODhUnMKiU/e0MBTo465pDe583qH96eocSlGnvH7IpbWaK/D8in23Tgt5NtT1RhXZInOLfnTAvuW5z7thIaLFdKUCJuAcrtRSa2T+KxDQjvOh62eFtmgwVJS6361+rKFiaQLgTr+C7WJhdIrXIzFcuFyadpBE6O1tfx2lu0aNGiZxotwGTRw6YjwGS47kT5JB2heK+XwacI0SACD1+2UZiCe7sFzQdBEpO6OLVwrCT7PdufOipEhweVtQmajiNONP1YnxSQMhnySK6XQGED5SaW7crvpA3LDL0QEMj1y65Zm0iXcfvcSigHcxfWQJFuV+wVap9QTijmsyWFonOqRy6kE+gQPb261/zty9TQhagXDABFBjO8zrDTX8ZwFdfCsP4lPlnQDWI/UU8hsPKY6DTtCmvhwNpEJjcz0f5wR06Z1tqurE1mVU3jlVWk94gYrj3fzjRYm1ScYtiRCH04UsoyO4t9duVR2ZrE1D4eDo1kEMYb83rQZwsT06YgRS98u7Nt0SFSPjOeUmNxksbaO+vs19emYgFivFLn3a1NRBchWptQA03atIAVXax5oOlSs4Z91naRZGUj4XptXOQ28MIxgCYsIdwYT2FtomuB4rVV53UPXM3aAq6mi4m5H8MrNIIgFOKwvcTVxj5c2pIzMKQe3t3SJ8RbvWNcqJ+IZlty8Kr9EwhfphP5jpDgorgNdWJFd1Xbi06T7NeC9ufqXLRo0eOhBZgsetg0A0xUoxiuXdAEx7fJA2n7JwgooJ0Eqb8SEJJAIdgniMN+BY3IhXwTQNhjh34S1NEdn/reea+7sjYJuFFSyjA+i0hT2Q27jFOYps/C2jfTuVQV8SkaZLDh2EzsLgi37MpA27WBgASNYaFLOzvKcv7EtM9pa41rjKCapLMl+EJ62ZOZIgZjpjjPoa8opyNYM+SnuK2m0lOmYXa9AJ5lmMej7T6Teh/F2kQmN9U83903ojrkHbfMnK3GdNP+D5PP4cDWRNmMdCO67LkjrvMMegyvWaU0cewXxmktdrwuF+1C27MxY38r2jt0YZytv9ejtQn7nNmCwjjtAVubHjf2J44hOZsldUJMlC1cwjYhrW2wGrw87pEJbyTUTpN5FGuTqdNX5RSDhUkvF94/f+b2U5feK/xJHccp7U/SlhxqAEnrw04OjHv+wNWQeYQ+w7xksKVyvDoDT+yiZeEFDe2AQ+AwEantDJhcAE709/HCT9Q5usTYJj/wKj5JX9tQYFbRHTq3SEkUJLznOhctWvR4aAEmix40hWOFiUC4VGFYJTlIE24Ci1mbgHrh0g7Um+97KP/2BJSALmgZ4pYgoW1sNYtH2HYfC+s4vArd6a/1CWzZGaxNuhbDqrDjtpzxc/Jk4BEQacNTxYItIXzlxaqtqQohiU8AziGyxrxd8Rz9OW49uIvQDlqmWM0wzzr01Avv5wg8WLnwrDjI3ZLzlj5uvKFKLBZxPyXS20NQRFLHzXeMqGw/AjCVf5E6DstQTK/0i5xXVFDjolyLH+oiHVsB8BzqGJr/3G7tI/nxkmx5UfYUpt22FfSoKltd2P5Fnawp4WOcZmaxV32o296cgc9EqpQyNBaax+lzrLfpVG0P7ZCxsvJvK8oQTaxNWLmBK7wBe1D+SMC/COYEeKQFaYxjGHewnLNnM7OaA6jm4lHZ9VPaFTSxOc1cJ75RZRz+PKVkv4eyfbLsZ7YzpVj3+HJOtwBBdNiSo2UwT/8Ni8cPF3WWY8xxky050OcYjmmizBd/k3o7gdtVbe+5HciH+XXutd6hruuJZ1tyvHuXaS8YwlUVLFq0aNEzixZgsuhhU7YwAaWYuhLOCRzw6227bqjSJJBg0DxASMjyglAUIvJ9inOQQfNsUVhFbCA3ZaYgMG5DTRRMaff+XbbwbaKAhn4dTQ5hS8xkJtwHZKNpJ0zSAQLVVrJDWJUJ0SEs4h/RsL1y+orNhxxWkc+AW5sksMSUHg61UU5PLQvkCeG+5rxM1FCrLTgRrMj9OyJvX3qZIMsD2DIAJyd8m8wdr14TjlsYWtocxMltHwEmx8AJ3Zlmp+TI5KbO7eM+NmdPbRR8IPsz8fT2oHM6A7qQgZHMWrBnsykre1/qTc47zrR9sQ3NzGBl0ttFMIX0JJ3Nv/LbO6mWGDlOvH/IjzgEjBUBwNIdV4uzDPdtovzdrZ3aawgVxJbo4klClakfERFtJHJLJDSxNqETcfW1gRWpzsGyI758BqIQmZIf88c+GNcKoETnYunlZUOL++/VxNoEAYYwnhxXOn2V+HyGsNitz0llsYFAlMAllbmSsXGVVtJB+pl9HgfF7dntff6v8m2y6Cztcvlo+7vUuWjRosdDCzBZ9LBpekqOx4nuMde0fN2JhDf7GmhUWoygEFNIsEPURKAA0IRUyWfMq0AGVpy0M9WK0IKE1dmpHlfCXZHYgqVJtDbxOctbaSrZp9L7UKHQiGqbT1D4JCkqPV9sNjpVrOYc6woDsVuxKCbu1iaeB4eYVYccru6J8TQY2E4D2pRY73SAbbABECnAFGwTVAkPC6Uwe0QPo4I6WIj0BxXGjfI9EU235OS8M10jlONwko7J/wYapTFnHQLbrNoW7Hfdl4EmiUfC5Uxfmesx85N0juoZQZJWR05nWIMWp+8TxyEyxX4Or/mErYWP6RCsrUYQKEqATMUSNY0iCFL9bRVjku7LhYgkW5sQUXVSz/iouEhrPbI4nWtSzlTEeWTg7zw4ffWWTukxaf6dmh1d9G0ysb4YWpu1rEcok/Pp3mX/WZIODEtMq6xN5Aig0b5VnFZSmnRLRfHp69YmWGeb79SOpHEfgiQT5oJxuU78vRbCX4JYP9GB09cqLrdf9OMaOusY4yzaXFqb3KFfixYtWvQE0wJMFj1sOjwlx7WKJvwVeSzvbbM84M0FzcF2PWsMlVAwQxP4MM6rx74psDOrG8CV+K2V1J+Jf2rOHgAqaxNSOd/Cs6NJ4zjGIRNRdCKrypuWT9YmmpaF83A/TMGobJjKE8AmjaPuxNHnW+wzfNymU1+L+RcK+VwuRnVFn0kaBMq9o4qRRjl79mOcTP6CyiLx3sIy9idYmOR+hzFg+RS2NnvYKgWVsrB2ubuFyXHcGbrKN2Keo5LmJ+kM1cFCQEuNHGdhIbM0wbjWVnLZRHH1HLzCx31MfRrYVe91dZKO6fWZOsgwgiTaiELBOV/02iTCtDFYmxC19WU8SMA5q6Y7P0H+k+NEHMR1sFet6XTU+SQdX+PDpA1zdkDThyV9FoRI0LeJz5uGM2fKQEZ0jOrx+T7+/KW83fpDQT7/gcv1SXhBgxWJFi3iCOLYfuv7PY6l7L+GH83CpAwPKGsqZ3F3szC5H8Z2YUsO0tmqB0vfawovqkh2gd/J+6tz0aJFj4cWYLLoYdOFLTnt2oVJPerWpPUiL+0kGyeFBqVqiK9+e4bN+yZRz+PQ2kTU2oQhH6IO3qUSMOlm6b7tJOZpYn1hgSJ0wbcJNp/6ISk5pHldCjiZ00QcvsqV3JUNq6RS71IYFMiZH5Ss+ehs7LLTTpvN56hmzK49D/Yd0rwHoH70T/2WxjOQYgRb5OIfl2GCejUUtuxkoIeI8raYR7YwkVy/9wQxyNH6BfSKg7YOdYmqLydlxkuy5VE9R5YmRPVJOrPyGd8l8rJnLVDUqmPYYUjFuzvpezawi32KaXFoHN71Y3CgbtxBEt9cmHoylN2JRmsTfNfSlpyhY0LloAdWyP5ekfg2xmht4rDJ+VFPKLNE6xWRW5vstJGf5lJtKKwdwaYelccFq0WJzh/mjWkIYAy+UqC+yEEzJ8MxeN68xFiZWmXhQiN4MnX6apcDxpKZSpXPHndV5xWAybWgyiW69uiVawCZQ98mixYtWvTMpQWYLHrYpF9Lhi05FXACV3QAm8vfkm1ViYLXXOCt09kFJ0sniKvv2eIYiiUhZOb0lYnM6at2nXezniEmYtiaI1pWrU22rVtdMPRBkpB+XiDqO/1TXeAQtt+P1ibqTyVuyeFJ25JCwdlvyOHx2gOmnUS24iSd3BaHcJC/MW1wnJpOzukJWW0R7CLKwdiOQP2mOzCkc8iL4EgESoorxauGpz5MIPMQDmNMYaunv11w4kSuf6/qn7UpB3G57yd0gDseJnEy/djapOI4XMX1tRzjxHiXvnYGoOCp6qne2f0RIZu6Nn7WDk/+ECTZyzy1fxMSpsbOa+erGnfE5YPu2/kxnojjwIRb1CDPQmuT8KIjvzqiqtNDevztEWqAt/tHmTmChfTEbMaTcur70d9ITxuYV3VtwQzgGACCfexxAR5GCxT2MiMIksb9OCxMeliIwkk6nNoeTecqRuVtSmjjiOndA2PLdFfTvAWcPBKhUdV91rlo0aLHQwswWfSw6XBLDlEGSswJ7BGAwkR0235ZZNtcocHP4RWFPShEXQqkYCECQe8DubBUxam1yPA5V9WIpD5I9GvSxPF+FGVSQZh2gw2IuPvBleDbxPCagx9b0W5GGVhHFMrn41GJQKFDfCZgNe0mdsEFUpxaIu4OFnOfmaiI5w7KzKxN4jWrGyfTmKKlhvpPse641jucKEOqGuhfPoDzIE38Pmh25K+IC2bYZgHkaNpMT7gyr+sIPB93Vf5IX0hxle5z1pJ9amFSrHGPKFfo4buzyz6AJrhWRgsTInT0aq8B5pNJ+q2zBW0zc7WqqzMQgbypw/hQf384lT51pGKhlUmwMAntjz1tliZtvGhtko8a5qq6fjfG9d+SkC37MUGQpvZt4uv9wmK88NPjDBNpa9y9W5tkoKqyMMHpG5rLaZmPo2WHpsFiGA9LP8HVGMsK+dyT3Vu9+CCBseFzC+2fsRyZMpIZY+scDeveYc1Z3pkPk4JRUXcuPOTJDFHswmHyE53ZkjMytgvpVRXXObpetGjRoieZFmCy6GHTEWCiV4Er6T72BJgYICHxequ+TUA4ngkBpdBaoAjBSUguU8TZZ2KtD/rBeu19ZO74DAIoXTFgtrFgWY/vQAsx8a6gCVqq1MOOY5uMfKaRoHUJCdXWJuYxAGqM89ru8rym+YKyYShCbrBDQvv0RIozxB0cyW1PxHb8FN3XpsvMDPmrdXXdvash0Wks9S0caAUTVRro+7GeUIYHXeMgHLbozNqb1H9ouT5rE+MSnTpMYig8KuyXv+qNDmHzlhoNXxdXOIKFfNumoEWpOg+jqnhABAxS3FF8f0nyFh3tY2Jr7U+Itv4q7P0+sDK7B29NWB8RNWsT2DYDrKLmLCmOWx36VoTXXCBZs17wbRLf9EckHRDlP01CkCq+4RaWIo76CulpDHmNk1iYKG+J8dNssLw+s8541JIE79WSBO+trt3vQ9nd87YHQCR7WpPaz8mxwlOGVjCYs2V1q4qWG5y+Yr+K56Fra2BcI6OzX8t9rxdWeaqPNzlPmyROolvT+1xeWnRI+y7Tk9oepc5FixY9HlqAyaIHTWI//hyvyWKETQlGJbTfCzXgwCRnLq9C3drkSAMqP5tmLeNEnqM4TmNFcCSMX/8KK5RkmcL9Hq1N+JaIzNoE2ivGD3pEiu/im80bgyJnUqANDWVAtzBBZRK0E9CspWt0AQoxBYJCXv0SHOrq1TEx3ZC0I/0I1wzTeAwu+iABZVfTTIYdFWGB3HqyjYJCIQ0cU87/cNtNv6/CQiTifTZtT3SYTLvqH9CPU8cKw6CCPH82TORWFUKkx7Oe3pIztO0RR/3JOkpQ3s/qFXKQVkTW3EOfkwQeowBCqErUSg5WiuXjAIzodgyPa5V1LLgboEkHGY7HW+peRfwQp3PMOe/8JJ1ZQ1Mrk4F0AmKs+jVhJrM2URZUcBYCNTWBRrbZEMr0uJ7Hfk4AHLJ8uij5yLNJPQeHuQ+OH3ZrE53vzqkyz4b73Bzeh20yoQKcelEMPPx8ZC9LFY3cNqedyNt5Ou0dYIF857fkXGJsFUPCMFz7xMjUwqSK8ytP+xPbMgsX2xYEv0GnvVnfC2OzpGVtch2JSLe6vN86Fy1a9HhoASaLHjadcvrar5hvsHNHy5Mu3TmCQmrPPjqERapE3xyXBfkgRU7iEAToQnaId5Cj7VvXcTmAokAIQ5zmd+m+j43Vt0myNlEVZTb8oGkcqAGqvOUyfbi+g6k9g/hVNjfo5KpNptQXmx+JVfb2tEtMdGBtUk+CDGkJSMkWJdB03M6FW3mg+wL5crxWVIZjuRnIYllxuorwoDMchA91ipwX52ZP7WX9A8sOcW3iKt1mqpdQ9Fl4d6evfFEwPSybYzKrouj0NeYbnb7q1cGJCKoQEfEGoGTRN1ueEuNI43s5BBCM5Xq3Lb4CTnDLyOxvswfNdoxw5H54n07S6aCFAi17tzaZ8rOzhPxMeU2II+NhVZz91BxRNbE5zfojB+kK6ui4IxOJwENgHj27WC00uQ914PSGNHxxjQOFe8wz+iyxSsmtUBK3FXGDG13saFUyc/p6mrnNmNoB0yIi2oWY7n5Kjtyl3A6WHhedMw2BIm0SOVnHLhYs0GTRokXPTFqAyaKHTbewH/cANJluw7ELd9xg4uNE8+5928R2Q+M3WRkFAjtlIKMCGldpGUUcVqsCICdfJWlbTRv0QZ6wHUfb7qqGSAdNWpkmYO9E2w1VZ1Rol6KwpV9h2e6JpJ8W0+O73xezQiHp1kCzk3QKgct1FApuHa0/rqL4vIr1Uth3w3v/+1doErBywMNNW57SioSI4hG5s9NwYj5Lg3yatksCOKS2HDHrdbqUnq6HYRhbIYv7WGL4tN6Rw1D37S61boBlJnECC2PaJ4jDdjjdVyTTm1ZDCd5d1i2GvhBVwMhRXHsH5vni9h9mapbzJN056viOwVs19h/YXp6TgA8W+av4vI1G7ysvTDHPfCuOplsd2jezNhnHV468693Bq0mIIxscxunvj8WxPaVueHdhsU371l/SsBXneAjU1XWWxtHyuBHwGByxwouh44j37YXyuYB7PepYPI1vhRqIkbbYdHBj3JKz+5gxP+0hHx3F0976dMaqhHx8h/lCeJIf0uRW0eBU5gJYovMnuQ9V+xXafMlCFrIWN9Oo04xt0WmSnQ6tHO9a56JFix4PLcBk0YMmeRq8GCaB9bqrjNejMv1T9KBc3Pf2m9m9fSpVYKQBIWwn4jDkxwM5PS7+5boU5kj5diLahMy3CdQmlVZVEeY7CDNRPFFZG7HtAyoQRm2nOgtj3q2YoqI/xrbNSghk6LwU8nNOy0f0as2S65jk62FUh8a/nD7xV3JVmt/fwlb4CiQp56DSNyBclanK457rM9YmQ78uWJmUfZa2zJtfEZrSRb0jK15XlG/WCK3xYAmiynkRp+Fr4nL6bux0HDjbPynuKB5uyqlMQIWCFxXtysXEwZCNKPKN6j7UAeUwXlpkHrZ6V7qG0Fqm6pMQg4VHowHgOfXHV+avuL6QK+7RisQ4gZCn9fsA89p952RQPtfn9xrevQ37w3SC9v0F960pPe+Qz/s+cDUhItpJ9lvn9KdAkYqpTRjItI72j9wW/lOG8pP2ByuTI8Y21oHOtUuS6c2FaLmQjn1etGjRomcWLcBk0YOmvVuY8Lb1XSjchdPx2mRUldDn12CNUh073K8m2+VtOncBTar7g8/MTNRMa7etW30Q1Tv8d1JLE+Y+nHQUcYuHOCUV6sNf/8q3iR8/3DukAu5gos/kchvk03KS8lk8kX947Wkb783PBjMR33hDYWJa5jCUaP7imYcvu9FJI8ZtvNMtbRR9mRSWJJp2wcrEZVss1+uWWI41TsgnWCdUqnDKV6afy7txM+TCbSZToCKMMYYLvWFaB1qSY8oZ0KSq+6I+AfXgM7q93efCP6WkIt9drUykP+xNwcuBvUiKY0iL6XU+WJlDvrbiKuDEXq8cl+K5yDBwwf765TnYubM1Z1EAcsT3eLQ4idBwZ1kAsLTylaVK5dukrQXuPyNi7MLjHCBw60SyxTXG9UAyvdl5I5GdNp4DRnnuUqUX8s0z7LzRRrfR8sNeonGbTuB+AVxJYAnWIakOvW9mPr1eoe5lhgZY16xDIE2ImqWi58Wgl/W4cNwxbyRPP00XgZCL4TNMLTOrvd/m+KIv95zGRLTvt/qa13SZsR0XurAsF12mXYT2ewaY7ru+RYsWOS3AZNGDJnVgJvveQRNFAaS4EolaJ4D1xSUAJWgJlRYiRDQcPwx5gq3/JO7ovogTiGNq7TclV8ER34YjMNaZDxOGOLUyMeAo/PVye3feut2QAUt61Kl0d4jm/bAJxkzqxFQltZ6f0H0iDLAS5sS/NArtRHwTs4G1CaiEVt9QZdGOlquabyb9QrugMTuoEEJpLAiAjPFj/j6GXN6uEnSCeOBmPpizzXedN/8dpZGN4VGsRWbhozIRpMFwfT0bdz5/69SRT5JH0S2mtfb2bkmIAZCdW470dynExXy5zGFc7xunLTq6xpEl4vML2ADH+DDWCViiQE5j62yn4hARbKXJXOnYdwlupNuL9JhXBt8mTMrCfJuVxvlESIrDWbxM/em2d4yOLZtsOPgQHpGkz+JmJ8qAhYf2cWI5Ev2XYBgrQAsVgTbaQCQxF5H+MyF4HLEWR04FeTPX0rmccjWxF0V2OEkngUC26IfwBcZ2UMYcr8qk7MCUxOuBF06kSst1FHEmNx2sz0tL90j5PrfsFy1atOgZQwswWfSgyUxbmYnkloTYFYxsZdKl9KDMdx8a3K8q3ctJwIR7WHrbtCH4IIVQK0mgJopWDpzyQHxSaNrn0K19/g/WJvH7KQ+7/yen5gj4OglgwAiYaBnuNvyVtYnJU6Y35BNyVKsrFIuJQoXzvpGQyF5bm0yUFini+WLe+HyY2lfodqJMK2OqA2hNXTS2tSQpfsjj0xRl6DD8nm/v69UEZdVeBSrxvoDeAmEsl9Oxfmxb+oETPopB+Zc6PIxppo9A3j1brle6RVFXrufIOmboK8TteysgeQAV5XkI8VJlnSoW+O6oMtgMwBwUQdamzybGtQo8TMZrvAwF4MPrbjxIT8rK1ibiVQW+JPH2kKq8HVs11lBZm2xDJcfAxGB3d9BJ5YrlSTq9LYVNFFAw/yTkin1oAl5B414iIc6fN9MuGwkJbSStfSr+LD45JTia/IM0No7afkeR96qVBo7JrAItDOsK3oMMukzb19/JXfrwxHiZ1dH5ma07nAYFbyTlDWWhL8jnegdkBy/TM8ZAqdxp65RUFgCTAYy9CJb4Vaq687WoU/pxaKf4GgzlYuTJqhZdpnVKzqJFTxYtwGTRgyb7QoIAyM5+mg0K08W2Gr1GBQIU+ktliRS9aFHYNmnxCHZYnEqDdk8Ql6RbA3SwTSLaG2AQTOfV2oSYSJqDV3fu2vtipv7g9NWAkvbXmmTLm8vGekZrE1MtzKrEp9VkOdDZwk/5oeaFErkKhn1LkU2XzjnWiloLqAMprnTma6qO17cR0S1RB01MI8UekhTWIi0hbtephleS6kvMqluQfUMVCFNKG8LV/bk0Ygon2BCBPA79rMJ3tjAx+T86A455ob5Sh7j+1JxsYXJR4Mxjyp2kIr2oMgAm/aWxXXPJ2sQtTDTs6URSWJvwUAbrM8K2iWprkx5gKFPFC6RjWh468zgZ+94KqbWJQb0CbIjYTsGxOE3n5naJuAEwzDENWdmWnJvs1OBm41P9HWP2teTgAtuNj62Ko4H9VxYlcpAGM3bAJ4niwznKhb8tDTxnuTVu1paSxNwiQ9r8tBxJ4Z4m6Y+J3KpEiFjAoqRt1xHuzl5J8+19jksbu0Mu54wT3m+B2ahAC7tU4zlXzuSWDGScsjaJcXKQNivXwKHeF2SEs7UyY3vLymTRokWLiGgBJoseOAULE9x20wXbZvFBB4CHCpwHJ+notSzvbTfhlrvswe5XBQUpKwcaUbjXuEGVCHH6tZO4tSNERPveBMm9n2azwYk5hN9YDyxQwIEslvU/7YvWAyBFYW2CwxUYBkM4z4Te4FfMSGJ5NBNT821CvW0ZKj6eU4a4+mjiopess9C3yUx8m2ALIkUaytlaf4gDnQLudVguZJv6BUMVitYnHCvVvKGxXM7/TLdgNaP39ge9II8D5facN5XLFiZelgG88BJTIEZyX6SIS21g3B4VClVOsM8zGnUJuZA+pjV20viLshIm6cucR0y3p1PngS0BWJl2nGETXmaF/cYN8xr4SV1xZXU2DZRfscDVGOLwORW6PlqY+F+zStuZ6GYjc7U0ntUFHUjKX+XdqaJdRo4nUlmbOH8L23SIoRdicWmUNI68ztJOx9ppowPfJkJEBdBE2qsTzQ3J7L8VYR3hS8KU0lKFEm9naYGk/wZ3PmzbcpJliQhBGqwtS4N1kPgX5s2Mj1nfeWV02iAdhGeMrApPGBvwJiI4GWxqZXKUltoq4hAwQb52ii6CxifrWXRI+y7B6fl91blo0aLHQwswWfSgyfYCd8XCDDtUmdjZnbLiFpC0JUe/AnodE+AkOYEVCNsed+7hfY/WJkqX7k/kYY1D65huLcI2zhuwNtm6noS+TdTOfe/l3emrdDDE3I3YX5c4NwVTdPLdgmXq24SSDxNRxa3wYTIlk3RNUdAvvE2xwm1F2jvU2MIMevdTKzzJCy5YQVFlE3gdEElaiuC2G0ir5BepowdiV9ykKw5iYbQ66fcYJoL0IjyJK7ratyaFoYb0Ix2i1EOgjj0oFFi+FvQvfIw9FRfa38d2NHxGyTgDitRpurj6+jW208J8296zYG0CV6IxTcOzuBxG1tc5Y4tLli4ttf1jQAnWI0lH1j5U65yzP5IIjtzubODBzokt9bkafZpEcGV6NLHWk+4VqBisTUhZWBud6JjscYHSP2FvAvXEeWiTaD4+tP4Zm6zQpyvIgB7j2UTcfzOE2vmm+gwNcDBOpu9I9FNiI8QXN9xnziK9TvespQwobsPR+sWmiiAunqYT2zEHshoXm/aXiPo7OPNtMjCuirHNGV/wYQL1HYMmED7D1I7yANMu/USpnDSjuzK2RacpYHb3WOeiRYseDy3AZNGDJrcw6So5kyvhKu3vTVSim82BhulVxutBGcZ/ettkQrMq0tA2kf9qGboDv2JVHMZrFdYeEdNu4AdzF3o3IlbfJjc33jdQIZj2pFqQhVl8LqnKY7btPU+uW61NthvQzFQ5gPlFACsPWWe/0rbKjNqDbm2ygbUJEUj7RENjvbTtxZ/k9e/HvmtfhfmNmHZW4IKhpPo20dpiGrag4xWJaaVhOega5ttkGBbHoQarEQxTCnOINwsX+4M5YY+7E3DSA1VYwEWDWnrENiSkYbtn9ZhBL8lx4opM3CIkfj2gu+gWETDxq+O1bQ0yC20bmxVEsEzpN86yGNLj8/My0AlrW5Vp6a8tE91Kb7tn1H7r3ZG+1dPKLBqZ/ozdoLXJjXKbTIFhEBGCJBesTPJWSKLAJXWebzaHPfVdtW06iZ3nOOyfcZPJXFl3mGknJjtJp8wsF+d8Rg7NtDtfB0zMW7/fJ45fjcvZYI1zhcFDPq09M5bM5dRqtKez7NSsTfTFFL+HuPbX81LRRg5TLtvKSQZGL4EmVVwor2kRMGldGHlLyaAmIIkUcbP80y05OtctE52mpYkvWrToTzktwGTRgya3MOlf81jDqkSKHSnLskffJl3QDRoJTbbmzI4XtrAM5URN2VmarxE8Saf1vpCUqzgahF0G0w/Z2czkpasEtIPFB1HbxI/WJsFInUl9nQRbeGu4BlWy81gmJkn1sFS+TUCW1OmUUlexKbF8FuGJepfdqTZrEyL3bYLeSVS5iM+jLQlVG8YOoHLMNqYuZDKZ7xSW9hEvzhnK01g79GGYhCNQo9+qjB+Sdff+0a7+86fmjNTXvOobPe5a0GTQL1K5wcKkJ4byqgSkBs98hL0U5xYmo0JRgSczksnNrEjwSUJUACcOCu87EW/RoiQsbwuLvwFYn79A4W0YtuRY290/kbbNRNmKjodAYmFcxFlbI9dxYMTBhttbbixtq/KPJ+nA5sG5lcne2wc2yMS0KRjRO3y7s23RsV5l/kY08rwwgnLGejkiQoBK7/UdQxY9q2MSVVHuD6d4O32OmCifpBPKORfCo4Tj8cMxLbQETAiBFUTz4roGDoVrWdutJzs2q8wzMaAGmjMpKDO1NrHLASNJ+UYfJshPRv5iYNgR4zoBlFSASeajyF8t8ei3ubo5mvNFp0hEjk8xumOdixYtejy0AJNFD5rQwiSDGQHAUBClW5vwzNqk5y9PyUEFOwMmvS1Ric2+yno51pN0bkBxl1TXLC5JLCbMcfdh0jUXdO7K3YOhoG+Tm2dRBkaYN5LBX8nWAKagOQAYssF8EZNK8Kxzh/UU1iY2PCHHolAwE31+aWpQMM0CZBCwhTYhEtqbPLxtpGbtnj1I2D6eLpwKxqkAHsCNqAQhqem+SDPlJ2ubXcEmgHgkwj1BvqagRwx/uQe6TGVPmQaLkjDIJOSO6y3+RWBEK6isTYIuAfWFZlNey3eVhUlR96HuIOfy6PNKisXQn6BojBTXntTxOXnKfto6ae9RWz9MzeJDtxVaFfZOajmC9HQUsXbAlHUiP0VM87X1HFlgtDaxerSOYfypPWh2hpg0K5MGDAVrEzSiI3fwGiaYR3u66m9mebJT2uojZNs+0dpE32O2waQ4ou60NL1feSaANQ3rgzfapXGzm3AsTGERcKTshuY43BnLM7Cmj4O33qG9L5G+igC0tCWJYybndqOSXXM5ETI/JfhCtHexWZVYHmr53NpE+wDWJzMLk6Jt0iH3e52Oy75NZkwvMZlySw4yvt5mAk2Q14RrFXcpj9YZ4p3XZT53ipZCvmjRoj+ltACTRQ+a0MLETKLtSytHM3IiszYhadtYwkk6xVaceHLOeLX2DD+AdBQ6e9sqTBKjtYmAUDuLw/sep9K7nhDjuEU/JXgn923St+zQ035Op1fg6oA5fdV+c8inYd4VPPHPu23G2k5/lZYlWJsQiZ4Pir5NpO9aRzMTfwSRTEFQSsItx3gLq2+TrD2IT+UZkgSwtOfZ1owk5cRmTJrzRlcZYO2IiuPaHY5yONXTkBOin5Ig/1KwNuntqfAvxMFCxcOxvqhYxE4M+kgfo/t06TkvhEsLjyDIe0IGPUL5ArjI4E2l5wxx1CxMBDJ4UCfMwwINiERAoRr3UZwRvHaNleCCbc9PQRNhMmuzfJLOYOVgfirAQgvWr4V13rTJMq6vlcK3iY4vs66WUMzFgYVJY2thFBZWa5NtO/JR0kGl9J4iDWXF2BcNJ/GIzi/TzeaDtF6VgGzN2qru+G9ZvZaImhXbZvxrksnGO19ogqhWB0la97FOHWCHlWQnztYmACYYl5N0Yg5elZ07o+ktWQLU1+91TYtXQeI/DeHxpiH3VwEiZPzTI3f1KDB1AmtF0pgqZmKXOk4Kp6/KS8O85Dht3+QYKq5VXGSyeISypHwCbcc+aOYLv5YLNLkXEhH//bvHOhctWvR4aAEmix40xS05ik2gwq9pEtKaotEsPvimG3BjWc2nWsFkSw5DuLRoSfbxmkf4tgmpG4AoGRgxYVViHyyOHQTobZqFCaG1yUbMO9HWt++IgLUJkVqN8OD0lQP4gQCJZKevE7DFv8v2Exd2IeKtASfgyyQ6hPXhDT/v0307kYJSYtMqRNRPENJGQrVJlSmk7vHI4ai+tYg+YT2hfXhH3yZgbcKeX7Q2GDiqGEd/9cBReVMhXAVtfdAqFKsWUldU6RXRyiRoIV5DBy8yYIHhQcbH/Ps8LQMjNXABvZroMVMdA8bt6b2NQpGQcO1gz5F8epAGH9QDW5KuqTvfcb7Wlo3YWtdtehXWKxTfL4yzrrGvacvb31V/p5gMeGEiuqXB2iQPlYs4SzNeU//tycJkZm1yeBLOQRNH5YwPFnR720/SQWsT49liwfZO+HPTvCOfawnoH9sqFZ9/4n6SDhPd+A/JSNahg+QLFIbfF4uNRd8NIrA28ZOdhrKhVnzB8IWL6VYHghUs1LYItXzRqevu9wpyDBYmc8Ln4mG3/LibtUm/FhYmYVtf4G1enzW379Bc7sOkfYgbt+QITHVtyeLFL/g2OZ7WRYsWLXpG0gJMFj1owmOFUaHQuCGNdK+/uFPTnaJvk1T28KpaBio02iYoGe3CrkertcnOcJIOCjimkfS4SpnvYr64L5Hsw6Rty2nG6Nw9JzagZiO+zdYmXufgj8RUip5nZwpbdBSsQb8mtk1nC5Ym7WQb6dYmrrXZ13AYfib9mu5zn4Rr00BAqVAht1viyH7b++6+TbCuERRxcb9y8GmPDhWbcHXfEaO1CYVwPJrYW896Rbxn1wEI9QMe6gG1wvMTp7Ic6xlG7P1GNUK3PGHYvlZLDV5g+JyFSftn1EHkgu6i/YRyqd6hDM0sTKIyMwdPQBGZ0SQt+jBx/uK8jEjBkAb++nwreME7E20OKmj/9B0arFAsDeum/r5qXm2nvSeip1QBu2qWLj1PMo2wFcN+H8Z9wcKEKRxkDhPo4dtbJulszU/Pcc62HTyQwxN0yDjjNF3At4nvYgo3w5z32Y4d6cORFBWGaguh5dpFaGOmNOWNBCuokjHxXLj99N1Qm7W27ZMhmx9hTcNxxGDGAB2EO6GwtcZzQU91gYrmB27Uy7afAed8bYsOUSiY/wIzJSunccbXqPuYuGRtUjCd0odJwYyyX5NZvmvaJiLrt2U/2Y5uUbS42Zo64nmLTpHsj8GHyTpWeNGix0YLMFn0oKm0MBnADVUqej7pgrxAmvTrzRbLuqZCURoESQHaiVYtessdP2BTekiVGuIG2CiI4SNL2oUE4URN6lv/d7cOSZqFb8vZwnYd2pt/kmPfJipI4x9NwhAnXofGRTWjsDbRIWu1XQkbFCr4l86EGeayz6MLt2ptkoT1NNdYX2VhgrLqVCfpGUZrEwA1svVMZQ4/qTqOoM7rqlXubZZ8R0lYyj+Bto+fA3N3oEpBJh9k+Szz74OFCSgtVhYF+pSnR3r+ug+5fbdwOQuYuNJR5r9SuZidkjOwH+4ryHhLa8iARfGTdDTewRGJdaWwtUlseTHOLV36e9HbRB53ybfJMHQJLCj89SGEE3Om1ibEtAvRzQYAi2j5zo2wrd5l3rKzWP1DTjg6lA0cUNzaREcugZlVzG2iyEC2qapja6E5sxWZWJscrEF0BjzwHWUcpUmSjmXra1aag1zoMU/vafQvUlmD0CRNOg/HoYr4rk1l43iPXcHFmOcJC/Vy0XdKDxOZFd0h8pvjrN8jz8i8QzPEe+B7yFwuWpv068EpOZV1y9T65UgBP0hatGjRomcaLcBk0YMmtDBpVw9ngEKtG6q05sCv7S3mbeLbJLQDCsAEMOHeTlMcWtsCaQ1U6cqOnaSj1iZWuUt54WQVl/b1xIpBu9h1W06zBGFu1iwaT5q2UWFt0kENcSeyh05fGyLU5OhgkVKcyKPl9AQJszbRMSZtCqY/SbpXhnfyszaaNN2sTW6iBkqmd4Y4m3eIFunjDebdoOLomLgL1nDf5G/R3nhPg5XJ7BSbPBNeZvyDsySg3WhVcuTPZFYxQ2Y/IUcmYT3WdmZtUsehguCJUQeQonwqd8nCJOSFFWNflSWku6JB8NU19yWWwXyX6CxgYu+b9K05HSBpy6r7B2LqLnyUD0W9GOuvdGXjX7k/Aq+r8jGtQ8Sf98S3STkXhc8PJbT+yCfmDOG9vXu3AtYmUJ6FnYWFPs3aD0zoME37GaxNqGcB1o7NV80GoMRAi3RNebWxQ2uTgspHkeI55e2c0niifoRofpjct0m7KBfS8KxVr1woLwcJoTC0jYxpqcem9tb6dh3/A2/YA7PTeAr34R0nz9OeTRuv+gXBX4uS4RCBD5OYz/EOn6sqbiy7wxqatBvaF6sngzWBd6W26rzUx31ysS06RcvCZNGiJ4sWYLLoQVN0+kqEfjC4W3nENCIiNWHvCnuX8s2Phdx2AIUHgCRfGcKVDxPqcVa36eWxHgNwdurSPUehEkEbgjrUZGQ4EljDniZb3JbDvBFtu2/fUQFxu4H60OrFAYemFSSnr30A2e+JWD+0a70/tiWmWZs00KQDNuJ7/KN0DELZkXyWsglEZizGBGeY46goCIShYkGYIqg/Xjso95xUEIWS9sIvy2mxRmV7grnKFYUvkN4XwSxBrbgM0rg64mG5FJYIMgSAA8aC46osTOJHVInpCRgprU88Wyob+0JEpH4fQ3u5rUJ5kHDN/bj8dA0v0bWKQAWRgx1dE3deh0cAi/EVUcuTfPwwhWU/WJsY/xSBuM43rT8MIEnrnLbZrkx0Kx2w6YVobE/n+JKFCXF9Yk4Im6WIkOytkQyQlNtvpIg79Re9NXkbbCdm6fjs9weWQQVsGN/Q5zNcE7/S376etmuzR3xS20f+ELiaF9btgq1VH1DbvgkNcUMv0CGssz4NZ25C4d6348Q8nPJZ/w3cYHi/+rsq5A5hdU4Cw0x/EutWjugbDuH91HihDhCKyyTQh4G5HPkwCXHQWOAzwCyzRQh5fLhCeOr09bR1iaR7IpG9fJ8X3Y12OTbguWudixYtejy0AJNFD5rkViUXVQ7EpU87UQLTiKgL+IxhADP0uEihJqWzARAUr0rhq23QZMhO6ulx3K+mwECaKickt61fwdok/dJZH0CjQrF9524t4moB7+C/hIiE9mRt0vssAtYm0I6NWcOx/taX0e9JUxmKY4vxoE/h/riibxMbZhi+DOEuKkMsivS+RnCfu00jSeuzCLlvEw51JY3HpkG3csU+RcXFTxzpvTSFpykbG0tz3Nifo8umoB50OT6EURYHUCTktWT87gpLxrotFJwMC3taoVMEvUKIsrVJFR58mxD6CNEx56srC/rckj7kQrvFuYBvYAY8ugHgmLRNNLMwkbHtqWXJBd8mE2XWykzYDgIlLcKBEozHU3W4t8nEDVAogJPwldpZYjDACluAhra9Te9vdEzb/nytIhk/LKjiIL5IU1jRAm7v4q20rThbsjYZnLweKhQDIyrTQz/Ff0/U2qS1A78ZVVX22ILXDrJ3U/mJ8ZIWcF7DYRo2zTIZqLUBZZ239ST2k8BsS6iwdcct6YiYNzJLk2RtwvmhF/0BLhWYXjs2GOJ6vs5ZydYa8Cb0WaLvgDMxrZvSn/ifYgvQh2m4z2Ht28Sv53yYzK1LsuVHXZ7LtokobMkJdVxo/5L1iYNHtGjRokV/qmgBJoseNKGFiQr50WpDhVQERsTTu4SpoIY7hO1ldyLijVg/T2bLEA1nzcK+7HqcfwXGdBdUVenA44ft6GNK9atgzETlscIWz2Ma7cGCxrblEEXfJiIuadtfvy+25DBa6YS/Nra005/a9gHtg9cXrU1grP7UIV6sF2G7TFAmuhBH7ShpjfMtBZLqlkJx03Z4iOVK85V863lcgPcy6pp3twVSDnxC0UlrHAqne0xnUyh2SVt0CLboUP0Xq71sYRKem+gz8bbDfIFcr2Wy7hCVBBDmIU8FuLg+AHmZYj4tdgcfJtivoZ1cFg+bKsZ/bGHi/E0dsLYyXc1mIrQGCdYme/OxgTzJXl1KbVNke4OFiejWQoJ2dPuZ9q0r/52/trZTg33cMwuT0crkwMJEw5u5we78vLWn7GuwMtmdxdmfTML5ntpccJ+bfHzxLmzjNrakc5bZDZOBLYGX6ZSJXxnySMgAbferW9nEea/Y3Rnu0/x/CdynMtxP0gHQpI29cwWp/5yvjtzGoV9NFqvXRyY2sWoHx1jWqgVmcsDdGOvX95sm1iakTWdrE8g8OVYYrxLSka95vgBoDHFzS5fLW3KwzdjHmL+nZ164y8W1s+iY1pacRYueLFqAyaIHTcGHiencXTFldn0xARRTnyLFdh7i22bWrfvwmSkoogB6ZEAlaDl6RXAH0sSUEAce9Ohjs3RByVr7Alty7Cjh7qsEt+RkHyai22RwWw6CKP3kA7c2AYuSHcaCf0QUnL5yle+CFcpgbWITacKxzW1GCLqioY5V270qE23+WAVpWzJeRxN692ZZYL5NOLaBzXGIGcn6EKsRSFPtqVmbtK/Ckk7SyeL8TLynk/lVxRBTZvtMWGV8sUIDaITojIWJ+TkhVF/IAEG0NpkCHqasFHGhLCgdVVx7uSn4HEltEx1YmMz6eAY0Sf0ofZtofn02wMfanLX+22vBbHESABJf+/kknVvpdXe+pgBAb8jfD0MWyYAP5F0Kzpi1gSq82B+tQhqYcXvb3/zBt8mxQD9amXh/vSyE9z6ozoP2zhcqa5NY34xGMGKWp9ryI3u0NgFDtIE6WwAewZCi7eD7wMDvOPGXttZ32WnjsT0DrIfOYD0c+6IL2NpCZshQnW6zhJN0Ls3ghLGNZXt9gTe1SWUbizJVrReZWC5r1TrfIoxL/Ec8UUIest+74NtE3+sTxwq3i4R8g8VJFRfqGuu09kOdyD8jI1Sg96juZW2yaNGiP+20AJNFD5qihQm5ck6qMOi9HKaN/kfUrF0cvNjJgQuUAiaASZPXvO1muZH7kJQPZhd4sV9Co0NY7IcBHbiFyMdjn2fNh8lGzDvRxl2I9/gBRCmsTdi2+7iK0QT8bu2Cz6jn8xnTI5DRgmSnDNa0Oe+giebFeRebNpdrg5JAkcQdUZb6FcS5KhKtTSrHduMugqpx68KkjJNZm/QW3TLnmITa+hm+Vpc0r0+I2tctIrIjhou/WOayhQkqGtF0JQreAbC4cEpOu1pKoVh4u1PLFKwjDUx2KtrzvnoZCf0Z+3Jha06vz4ErX4icrkSuB6JCzV1zPPJn0so2oKzhvkJ0q5YZ6CRUoE3gU1LEkfM505mN18E2OAOtK98mvh4zBpv/Rl8mRGctTDS8kdCO1iYK2nnV7U+AjdqbyDGPpoW8TJkLIqm1ycC/MDujXUNOy9f+PIRgy1+69slt23T82RFlvlaHg1tP+2CgvBrjJnXw1tf73ucyuKOmisMMp+hAPg5zM+FMxlu4vXeCAMuMs+V6JOT16QybDOOxxhjPybeJCMXTfZwHBMR4iOvvcYrnxG/sVa3q0B5eOiUnxZUgSmKMyEe9js7XFmpyNYnANq97rHPRokWPhxZgsuhB0zUWJpjPBPYeFwT9fnUrj95WzyQdjGCsk6gbVnBqr//ThXKxpBanigtVcQaidIF272n5E6Hg6S99HBaVHbVS337T/YfsTNyPHNZ4IgRRNpJ+DLFam/B20wSuDMgggANWJvYcUr6msGkZts7NfJu4lYsrk26+TaSSYsObQNhj39svokqKirbUC+jcuUDRIB21NtmIKB1B3CebGeTRTKz1syu1cC9QD17742hbdIQacCEu59uf3bN3zcrE9LK8xbHpBLr0gnA10SncyoShsgthiioFGrxna5PqlJzpCTgePcRFkGISNwAv53yY+KXevjOkh7xxPDvt0EGytVACJmV6W0TOP9xqDcFZP1JYF3EHE9Diw1iqjHEDmxO7UeDGeSm+nxgHZag7aj2hV9VWJq7GhnCyMNHwzt3iRtTapB29fNnCZEbYftXPcVOiW5sUli3K2wx4IjKk1/iE6I9Dz6sFiYiC9xHyF7vd77QRg7WJFFM3DC1NMeD6/XeLkkUMXEnD/Yl1b8rBHwnwFVsvgaQoYy8UlIl/HCplkm7pMskOfzL0S0EXtVgRyCeQ3/OA0stk8sqhD5PIyGpAgyb55KBevTJZ37B85kvOu477M4IpY36RHZ7/okWLFj3zaAEmix407be3pEdlDtpDFUdECobwqTQK1ij2m8/gJBUBAGhb9+5r/e6jJMaFbUMq1JlCwtYHk5WFG3ChFhnUrUUYLEyKLTkE8XZKDhMJ7w2ECdtyOlBCezxZR4U1O364j8VUgi4MJxCkVnOKOElbi8DahHYhuWlSuMuwXUjrYdUp/v/23j/ot6qs/36vzw2Ho19F8EE4ouebpZY0/kpMhLK+JgnpozFTj2gOmqFOo5SBOpKix6LQ0hwtKUbDnGYiHJ10ShmUMMdMRhNhRiekMSJ8jIMyPgSCcs757Ov5Y69rreu61tqfz+e+uc8594H368w+e33W77X23td9rWuvvZad2aCDCZH6yZUUP4x+qquaT3n085E64BCUC1OUyqpvOv+en9GHa900wOZrfmfPYRgr5fR4m1/V4SfCBWW2yILwwYWP8edzDbdnuN9DEzbhHvrp1T0E//k+Kd1frrTT/43inq+1OZVr7fzKde3koUo+YAY1NU5bxmozR0r8kNbmae+HcTCqz7wYY8R4H7RGCfXPd2wxhKjcqWuO1PQw6XM8LTfn0Rpean41O1sHtGlQ87QGnNoeLVvyBmGpiKveYWeYQMVcb4bJDLCzSqK7znkbZ5uISP48yUuljRzrMbwMph+qZMj9IpLlU34OQrj9XQzLMsoqNQ7r73KjZrcaSjRZT151/VyYTIaVfBF+m+dVhnlu1uAE07i4q07vsscQfle/MY2Jg5qXfg4EkbqG1HxejBxi8x1qGm8IWeA/+PSuroOpKzCuE7ZvH+pbGpUDti8XGEWm4nXkUC/v8pIpX5CFBhAjn2DOPk3PyOLTlPpYwUwWMgzIf/c3N09CyP6BBhOytREUxbAaLCQvRDhGyBPBocYMVeJ0bkLSsExyYaWYMSwZvbMO4bJSmkaFu+xuUvMfjSACHQgXv245Zmq+aWqy9RgGSDaalEGLm+WRqtEnoYYVfxumufswO2QfB1mpDo4EpvU2b9RZI8nklaJ/rZf75CShSWv9ygBChyXFqKJ1mJW65Nfl1d3EmZV+k5iu9MOstL3M/NHFbfVy6mwas3OEuSFLmITfsO2GTzN+WlPDZ7OE+ZCNHqp/w+rj4z3cN4Ys/z0siFPrNyw0bhT3MOHfcQ9L4uhN35tVUuoWBwQmwOv5ZgBg8whxStRmEGDq0Bko1CLawUdTvixOOxr/xDxzKi9UalQJ4aVF6wuXdjliXCrW9LnWBS+LFEs6qFe5ams6/h5tITVNLMVKtmHIn6ros2bSVAnj/UdnlBWdOMVY0s+nfMpkrl/ZMEtlZJmtEkRVTl+3Tjbpyu/SSU2YlL8T1bt8Adk7h6MfN89qCV0z68Spcr0+DFUyS67h6C5bTDvJbcLL/bEgD70esxlkPveGDmOcmDKApAWGk8ZP69yJN74AUGE0VCNIMZqM8cUYPcQYRJqZJCXt0Imf/bPxu5Th+h19P2OciLKlG29B2vKpjpFrqfyUkAZGPi3IM9w7Va52jDyEEHI/hAYTsqWJ32T6IcLoI8gDD1UQTJgOkFL9aXIyYV1MaWl8Q5kGmMVEcx55oO/qbRRkAGbAYeq8sOV5YD0IRgNRHUCmEpyccUgVeetfE4jL15xqG12NxPUvdO0SUQNU7CMY/5BPgm+t9Ho9+6UaRw0nkntLTMyFm8Po79QPX0o2fFWlcAKjl05HC21dELd8Cx9miljF1jbMGUYAnzaE29o0R9H38/1sy9XOLgXZh2jaXQwiSw47JrDPemuA0PrbAUUN6Bsnenl03obmsqWXfpFBxOZpy+/5leT+grinuiyemvt7tGJAPy2B8U86myr4ByHXpcqMes8B1bBcRKkapjXLLEe83/is1Fu8vd+d3cNc8yxWoTND2gTGLRP+kGJUXixPDXWf3GIwqlnmH2JkjCsuh5sTTLLGbfvBdEIqnu4Br27tl2Qlp4SMwu+0uA/0Ki+MsAx776wQHdB7DOZTm5qZ+ztohFGNL+bIt3kUjI0RBe7Zbro3h6diBDH3pZiPCdU/JxrXFwkZTgk5rYczmmgaL1u6fh25M/724V6m1DJr9XLtu/m1M0eiXHP5r5oHWQmuYULIoQUNJmRrE+T/OK1aBxE6yLBD+DZMjQOjgl7DVH0UW5hV/sXkmVXE8rbKzTaphoJUBhAxLxi/CXXTeOvbrATkN2NAWpuV9U2i4mvf1qauP2rm/rQE83ZqkHEvTZ3Kn19jmokSrvTar6G2yXdNLE87YiZDXm5wrQ5CeoORGBYHOou0+06YXvZimHJGjLiUoWm8ZLXaLaTq41vdMqrZY3bjpwYytBWrun4wt7kM+r+L4aRbqKcY3lQhdkmWzyqJStvEkMIo5rlcHao4xRxVide2mMCq55unOKar4wer57uBTPPG1Q0YQnlhANQvz+cXK+Aly+gx7oi9ZLaJzgKxsk3UYKyF9GebxMfHPzbGeACM92HuF2OayDYc/TwkD4TdjRSMwdX+oPafsa0pv7gfxtlVdjtca4KtzlQfteD2pU8jEiONlqFhXBt7YsaLGnTs756Bpsm8CS23A2Z1NxuTpDYrGtMFbuZK5wG3sz7aBzz45ULtAqlabu0Bn1+SxeHdcqwU1G+t5ppff7aIN4YMNT1aP01XtjCORzSi1D+uXvSV59E+4xokLrvaTPt897rdXq+x2istrBr8VjFoNDJIgr8amzSftLllN3KOrAS3FSbk0IIGE3LIUAYGg5SvJICqjOurUTu7o4RpHiasDY+j8TpUsAOZ4mNnm5iyUf5HyLn69sb9cPFTiaADmTLbZBZmxiRAxE7ht4N9q/4XLbDf9vj2yoZmzbp8453y4qxici7KvG2z9ythIn4Zj6ZGor0AwdjRKQyOFo6OQn7L+nsRdWBgCjXKstOTY/GhHujF65WZUl4PpM428WMTGW8+W76gGFOsTl+rYcIQ0+lRZ7lUhTtmZlvQd9s1UdDLoyjx9g4xMzEWGUA0TrifncKu4TZKUPDtAEiaND2jiakjvLvn2+sZey30d7k3dckfJNjZHeVZcbO7rCzxBuOe/LEpnNzMt5E1vtQc7WyTXI+eNA2FLSzbdcjoI0Weq+nGy4DaXx1/Iydc/3bwxmPfGcVgFIJLQmdF7VRhKiwE2RrXMsfrLf4SlrN+Jlj3KG7bpt3ZtHERaVwgFUUqaE7xwYD3M0+rk2qrCbb8fCPMOPHPQxRSTXwbhokw86POUxzGmVm6fIpZ1Gl8KaDl5ULN10LVGCFdv+ofzypXpmabmL7rGCX64T4fJ6vCDJMxuvEb2k902rLRKRudsjv5EELI/RAaTMiWxg6c/HBBF/GrM0paQ4gqohNhnb/wTg82Fok6aKkKbDPbxOVjZpsgGDjKcGQBwfhQtPlhGPOazSD5FWVjKAHqQralTdL4T7U89lSMNyqWwxg00+UPrXEJiL1hDQ0CYHKWTYle49frmGsWV4XMx7iGTfht3P6L+2m/2h49gFlec2Rwn0dAx7Y1Sb3kk1Yaa8yKzXbu2fiWv643mKrNoeroY3z9DRSDRz+uevbqZsMk9/3oX73sjJPVZpgAvkhXvFHWfX3UYCMluORllX8TKAv8bLri1xlM9AYpk+urTMRrDS1+EFQGW7C3iLlhBHm2iTV/eKnhJYiVaAueq0ApW1AsJykbTcojmEZHXZ7JGnJQ1m0qE1xi+U11xNz7+vAkyDButT2rU03gJVUyp9Zf2piLaeo19uiQ7/lZyGTq74UvdVF4p+zcj2WmRxZfKf/tyF3fLUmX4HYSMMU4UYqF34L8OdOsGk7K8xjqO3VeFtbEFejCMPp5S0K7tkndZcf6m3jwaa3RwgtDja55hLZVSVbyrYZYI+1c2lCGa690zkG2iZUpVraIyd/LllVllTfKxJknNk2+74yMmq7HRLiVZ7YOZCU4w4SQQwsaTMjWRqwzLIUYZpvYwURVyOKirxK+22+VbSnuGtIMScqg2K5tUhfYG5WtzqwU+8p2YbPFKSlWIUlZeUxDGj/R0dzKf8kN4t0QpOtfEq+E6eWxjmlm6gl481Q38ejoRRJB/aB8HFAl49bZJs1IIvWGLMmdNobkBVrh36Q3Snc1UKx0fVfVa6S2S2xd1K9jeHF1ApYfzRhDgn8KEaSTQeu2RpQ2DZyOXc/+mXODC9NvvfVOvFGl5+fTiCncDgqk5zdhpEEsp94E3i9kHK9ZXBmobERVnoE622Q0OKhBNsdRuRQkGEJ/9ob1qbmHquGk1CzBfN5Y59Hpzi0iKIZkPxPCm3F0e+zS0/aTRkkYhrqTTjABo8qv1j/aaID2CXRtNMmtfFRLgnuCtZrGoGUzX0W86C1vckG5WqVPc4x2sY9sQBEj9nx49esbWXr1WSb/Rbxv7JONUf5w6h/tIvKL/Gju2djWKLA6gqU8ZDVeycHF99G84abWqTGc5HoUQ01PsEW5WFPBGzZQ0qw8m6Rr4Mj+ZoaJS2tlWfYeRMzzX/27nw9NGG0aOUcIIfdDaDAhW5ruDJOsvLezTVT5t4aKdnZHbxDRDw5KOaQqeShf9ANlsJM165k3rtScehPq+2XbT3LiWQSjgQbD6Lc2K/WqM0lU9zcmm2QGVWLiaOZlF5ignDbocCmHlkUAZiWNb1/4pUpeo6PHq9F+XDW2aZxlIymNh0kd1W072bzbogWDi5JX6H8dO5UdYLKxpJvB6t4Thecyx9sK87K3cLg/6rjAGUBcXhO/G3W/5KV3db5rc1+JpBom+XcxjtgFX5OplI1j6tdR6O2gRJ8Wnc2inva6OLf5YRV5MRHL2KA76JBOunoH9nbSqUmlUxdfX6kZlTzNELK0tV3bxD51NZYO8L3EiXILOU326ZadnOFjPNdrlsoOMNYko5GN0aURbD6NGprFVT2VflHxMwyCNTfNI9XcUuvfe6iiV/+TnF7SsZKD5O2DU68Xe+V0pfoYYrzrMlbjU6SzF4fREoaycU+O5xal9Ze0aU9aFmcRKQGYQWQebVD98rvXexnh4W1mmwQBofef80N5hsqrkEaAaVkmQLcLhuYrsJVPzkiiv/MzKCi74Xj/OLul1musgZT/YdIZEePk3niqMqZnNIkzQBqZ5WTUcvdg/lhMG2NMvbsGE9MoshIDZOz7Tc6TELJ/mC2PQshBxOo7RlmKbt3+T8wWgGKUGAmKkpQ8atzpA41fLRdNOX7LQjglzcVbz4FF5wGQobyJ84sGmlUrJC7kN1WG0tO62/eSVWEVxKFF7zquSl/nHzOYAXXhwMkMNjJq8OWX9nXQ7TtLZAS38Wuav6xqE4XXL5XE5dnvBSn/LzoWZtRJkFQ5lpioddt/vczExbS1RvEpIYK8ros+Q/Z50+pkt1mLYPKRCv3kCw/GEkFepRTu+fZyRMu34VFuZLcvqvaQHTTlvIfByCEzcIl1aGUVunWIj3tNU/1s32l9Sn6QEKdTHnp1W7Fv8jG3O4yYaxQ//6rXc0XhstJ9noC4xXe5VmaNIDFZ9R6FqbKcO5XySpm2GhPZNHSNPxvBqIW9ft2cQgypCDYnUszDWvxjb4R4tfMEzTVYeEj55KzcgyqZpN6H+tzVOLF4a3RpbpxQ3+weBDIftzaW+fgbc4EMebtjc8gwuN9F57G/J56p6jZbIudtkEVgdCeYNPYZW/RMmzTkfsn3vvc9vPSlL8WRRx6Jo446CmeffTa+//3vT8a/+eabx0/+OsdHP/rREq8Xfvnllx+IJhGyLjjDhGxp6jAK/q2rvpXKs03Gl3NSZpuMu8kI6tT1rJCV3SVGv2a3FoF5g2b++CfAfpNho41lh510kMssO+mY5Ha7Tpt9LLuMVoDyulOVRjHxBgDIO+kctgbVnEtWyWerMyRcGyZ6v42hBceZIFq1vHJlnm1iZwXVI6874/LVHOph//f1UWU2jUYTrUF8251/mKVk+s1Z0Ak6aDFFu3MSs/lDSZQbWvz8rkay6sjGNt1QZpsUpTuVW6IdNwjcqMvmK+1Pr9PXpRLjWV+Z9waucVBeHofOEetUDAJat+IPF9mFa5rSb2rs0HtSXNwSZp6vaoioGTnjRS05lBHyNs+lloNuOfaCIuz0NREmQJrFlUz6/9fatmsWSfLljLdzqsKh3ON1S+M42yQlKTv2mIfflScwMx6MX+2v3ArxYcmEiYyzTWZrtWJmroktyYnr3oNdwnuzImxWzi/Lm+zvd9Kx90Xf34YbcZDddVaQl9XVIGPXMtFZQL6C7bXc0OyShnG2CTAv5UT7be/3ND2BJ/7sZpvUFwB6CIbWyC9DkUw1brGW1rMRGEmMNBOValklQP5jOQwuCzU29A0fUwcWuFHdpTuMwSHIFfvgWLnjZVY+dxd9DXkG+VTcpU9i2UZuLSqbrMyhtobJS1/6Utx666246qqrsHfvXrziFa/Aq1/9alx22WXd+Dt37sStt97q/D7wgQ/gXe96F37pl37J+f/VX/0VTj/99PL7qKOO2vT6E3JfocGEbG0m5L9VzeMWm6PCg/LiyurJatBYadFXowKqYltzQR5I17zsDjmlnAGQWdBzYQc9bbmlxKBPFl0wG07yCzF/ng/AGoA0c7Wt7mJqaNpeK+O0uE6Eft+5z5BEjNHExG4GJCGPTnHNLhx+nJSdE2ubrFz7TrnRIW24HmW4K1pCzKiq9d2y1Ohh9fxQdLwqZYFOo7P2E2kNg76OTjpXkCrY1hhij9QpsGl4jtFveTOjYvxRz3GgYPrZGTIapd7UofEL6Z3i39bJ+sVBQq/cEm7ctnwxPwRoF08VlVsCOOOuIM2BNMsfbeTtfSUn0s8Ra0PjyijVt5RtalI+Z7Q7tpTuynIu33PjUqFaNoJF1houxT2TyfSjvXbVDpHKgK32FTDMgdmaXVx2eqieOn5Noeuh3gbluaslmfYsFD39+pRrkC0d9uoJdI0JKZ/wjJ0VHvCcSY0Ti+s94Ksdtse6snlJs/36Ix05IR2/vMB2T1i55i9rniYoiYfcJkE1TuQ42UhTDSrheQ7F9dwujsoz0bJQyyyyR58809dBLkUZ42VNlEOmjE5ek3Kt4x4WGU2Cny2b3D+54YYbcOWVV+Jf//Vf8fSnPx0A8Gd/9md43vOeh3e/+904/vjjmzRra2vYsWOH8/v4xz+OF73oRXjIQx7i/I866qgmLiFbDX6SQ7Y0U5/JqCLjp41WBav4D+EPu40PFAVAbJqiZ9Uf05/QwId1yrGfCkHDctn1iMphKAfhbOpUFEA9DzIaTvQdoCqZVUdzedRs78uUWm9cgsj4lm5QJXSsQx2q5dqpv0hXIdeO0reZxeQg5b8SPp6HWJ1eFTeIUT598SV7HeiO95fdqSbBzvQYw+qxUeraEqp8d26nouSa+ppDWq9+uqmBTwIkiZkVLvnQdV6mpnEvuNdcff2zagcxRQZADUf1WWimpjd+nb5Q/6HfKV7G5LN+GabPeN6qtMbtPLPFy8/G0Ta04VLCh2HAkKfwD/l5F3PW/Nvnu7ah/zmMDbP3ce3zIrr0Gg9DuJ7+bD8F0OvYvc/CPed/56d7AIa5ffjsfVjd+q8taQm9KBJ/+rb2SZ2jk2d8JqOMh7nmTVHGIz//dVmj9bV7hZ6BmoSr2Xc8koTf4ZgubXGYX6fG7mWW4+UbRf9+lP6y+fdvj8atfWb/LJeS1Wqg93j5bGWof9vXI9dCczuX2zy3+ZOcfLbPuITnTkyddOZCc2j9B5i2dNz5ARw/BeyULQvKXrXtpHvfbMYBAHfeeac77r333vtU12uuuQZHHXVUMZYAwKmnnorZbIYvfelLK+Vx7bXX4vrrr8fZZ5/dhL32ta/FMcccg2c84xn40Ic+xPuIbEk4w4RsbXpy07zlHKNMu9vZJnYWyJi9HeqLCXOzFXzE8af+Z7bZ1FC7rbAO7rQeuQmuac274DhTQkyieI46qOTA+RxYmyHl2SZjtv7tcq3vklknTWW0FdFPW1PzhYjtnPzmStosuoq+fr6kGmVutH4LILUjZkWdn0OwNr79Drn1fk/h7xT1se+vrQpfB0czAIN9u26u1fSuNivQGwOK6W3RAnoNFbithm3wRL6uaFl8YGJmQm14v4w6ANdodaDjBxHlbmqUqWJQgImfHWUAou1ELUNM4TVc29BLL6gn8XWqOZt6iDuXurvqmzkeRYb4z270BrJ+kqTKk2TvVQnywVwXlT0dA52WEP+vPeaf6am5KzY3wMwyifM+xMTLz8Q4ySKXntMk8+lXjjB+ooO6Q5gvu7pj7ZY+d0mNvRPZAtBFtAEZJ/utKDJ7Mqhet1pU80kTRsmW0migmmkd47dOMNJohTr1pVr7225fXE3WskoRC0qeuhDhD5vZWl6ks+ZWHiAm+wmOF0r1bAVJ42+eSSsURWebpCZN2cjN5aFybh1uXyjc8xtkVOkLLxSdfNE+sf1WizByzso0I+einLJyb5WyyerIMK5Ptdl5AuPnMJZdu3bh7W9/+4bz3b17N4499ljnd9hhh+HhD384du/evVIel156KU444QSccsopzv/3f//38Qu/8At48IMfjM985jN4zWteg+9///v47d/+7Q3Xl5D9AQ0mZGsj1tn7Ij+EqTHFfIujYSIyfiWSAJ263hguVhzQtgYP1LF82Va4hjUDnqB0Cjo6sFV2VDHRN15Ft+z5G+vNIAAGYG0tDwak1slOldcw1wE95bZnJJlQn8uAQJVDU15qc57IIsSz+Yxu+0vTjFP/B0iaLR5ALAhr6hf0+TJrPBqwcve395K/f6VpzxLq+KEfrIup5HugqPcSygr6uRk6tIfENkwjQLkfx7ddZmecifxdRUxOdTCiJzPQAPLAACFtNFjEfP1vO15xxS31l9bfGV+qkUh6+bjMKu3g2Qe6z29suCDvUpWNi8XT3pBaZn/AaqVhKxmtecTvxuO2Hy4Gs650dOW55ofLbJOJfbhMu8cX7+NOOqnZGmYdz1RAFiU1XVkkmxgjRWyyrcri7qgCsftJTU04znCpRoy6k46xOpX0i4wTq9KT/16wNdVd1IRufToPnpULxvCtd6ZLLfAzKK3w0r89+e9BY0xxbjOtTHxmelvLgGosWXCoTKjZi/P3ccTEEe8/eGNtz2ir92EJmNhWuBg3rBu1bl23pjXXxpXV1IVsBb71rW/hyCOPLL+POOKIbrzzzz8ff/RHf7QwrxtuuOE+1+cHP/gBLrvsMrz1rW9twqzfT/3UT+Huu+/Gu971LhpMyJaDBhOypbFvMe2ir+3LqOULwiLBzTYZFVCr2EU1b1oDqDHjaLkNq7NNVL8ZDTqSktFrY00EVtkpiQcAMzFrmdRvjVNR4AeIzPL6KTku5sBsrbyVHW0q2r5UT2o06fbHIj+TjxsB2TfTudIL9760mueYqt4Dtp+rW6AzS6q7vCcXLdOXsqGhhKmG2N8TLRjLsYPrVNJuWLfsKub1UEPh9GwT7auity9U/F3REg81iKSmvDqQRs3IWU7MoAFjXtYQMZ5TyaMMPMpgrT7vYp4z94ZV00GcOyr8ttBmIKHnnl9JFtNKGx7jlioufsZaqeK32k7ozzZZKI8m1/mxdfHl6L2b8kLayPJjDKnbPuuCsJqPEye2y+29Jaa2YnpDTI1S+J2fpyF/gTfrzDZJqbqbMqcEQPLlxzDnnWfqDPkO1CokG0nrnWy6khRl+C9SDK+SQjyxVZnlz98ESOOsulJgNpL7v2cL8Dax1q39J/aijH7js6b342rFrVYRzav5A1/K8LNN6lbB1USsM06wjqPGT9muUpeMkRoOlQeDMXDUjETsbBfNt3V744iRJ7EjzZ+7ONvEy7kaNjXDpOdeaCyZcJcXATSY3Cf256KvRx55pDOYTPH6178ev/7rv74wzo/92I9hx44d+M53vuP89+3bh+9973srrT3ysY99DPfccw9e9rKXLY170kkn4cILL8S99947aegh5GBAgwnZ2iz6e9LaKnKS/ic69neZ4aH6YAL08xk7ZChF+ZdqbjhSDDRlWFLfMtfZJjXMx9D8/BT3hDDYNW1N9tyNk5o0YxzVCJNLAqz3k5wevbSp+SWlHm2L/WjBpLN9Xy6aDaxuHTvooLDagARSjDU5naufL3fRbSfxVx39jC0yv0sdchviArZT+U66U+vf0/t18UxBKktrNHr7ojGFtEdbYXGFltkkOhDIcRaVBQl5IQRO+BVndqgCb6N7hd4MGEr7zACi5BsGCiHc+t+n8FKV/CC7/jXSRZ/zIDPGx0AXSI2fx5g8UyqGDZt7Hy+RmhlxCai7jqHskuMXrPWCp/kkZ8kuL/XxScWgW2bu5SRqGLPyuxpmatnVWGYNKMsJydpecrKollla3kmbJn+4p2HRxTGWGN+Hdn2PYCNazAIhV5sg/rcrOlXBtilMCEcxYVluS+6HaqzpCLkiINQN45bgrmXpy4rWqFHrIvaPUriEQbCt7laZJEaeqWzVOpW2oqZxcnJ9BpONuIehXRC21IscUjziEY/AIx7xiKXxTj75ZNxxxx249tprceKJJwIAPvvZz2IYBpx00klL01966aV44QtfuFJZ119/PY4++mgaS8iWgwYTsqWZWvyp/w19G2a3tXWzTYCiBZYxt44z8gwIq9tP68+9z2v8+iWLwiDZ4KI76ejsi6L05cLNtsKS6yZAfcsrgAzjJ0cinVknOttknhdhnc3G3TaMYUFHHcvVnl5vmFGERL/aV1UBH4BkDBiLRihN8dKks+axmJ9uH1mn5YxruohaMpK+m7RLFVaDSvFXHVkHCiZ9uZd0u+jQnJRLHnQQWAI3YKSSBYdB16up4/GJsoLhw1gYTN5VoS9GEYw9rmefh9Yn1f4PeTk9f+IMzbsJk3Bu7zan6JcI4ppX4pkMWn87S0i64fWkAwjplGfirTS48FIuSyUVUvrLhIcZeGEm1wJbnScLlHovq1EYteyUZY/uiAOpW+LmLcOLTTJ5iWy6zPej+e3qWsRgchHtNdOZJmkGzNynhlP3PKYfvSVh/TWax7oNMOuMrIyVWQuCm6qMqzaVtU203lk8WiOKlTY99yph/bplA4bej7kJ/U9kOgdQ5UG90MYdnu5ybVJZvDQV2aR5WTlmDB/5k5wyI8S4y6wR9YfxL1KufrJTTDamTLHlrtr+Rf3S9DVK/k6HKTImlzvxSQ5CGicfrcyacgPGgAOnZ7QPMVmGXaR1M/PcH5xwwgk4/fTT8apXvQqXXHIJ9u7di3POOQcvfvGLyw453/72t/Gc5zwHf/3Xf41nPOMZJe03v/lNfP7zn8cVV1zR5PsP//APuO222/DMZz4T27dvx1VXXYWLLroIb3jDG/ZLOwi5L9BgQrY2Pfmf9I91X41zM0x0y+HsPyZPLl6N7z/nWKQBRJ3aTmCP4SstNDsgf6Ijxa+n4GBI4yc5Ujoix1FDS6rGFY07pPGrFMntG3XDcdAT6ySm3Fr4RA9M+DeXpR0QjIqW5Lr6nrRao35o48Oqu143e82qOxXjSlWAJQ0Y915erJ82rWx+JPerm1fQ9xPyF1LNHbSEBcr0soFwyoUWI4cu/rpIr3dhMt1PnXr5NFJuVVW+bb6Ncca5/UwV9xw4Jd37qQJf2+Irp0l8iwQ1FRr3ynGbAYapkwnTlGXAEYxrUjJT4wSq8SIbJVqDRjvbpMZFtfFJLCeUXf+reQS3NdeM0adnm3gJ6/tFDQ3x0SrPs+anNSiGIpWRprcE430OYLYGU2LHSDPFoseyc52cfx7IF2mUfFIxP/wnOeVqA1LFthXv1S/V+0XqVRFIub4wZS2WML17GvXvWPfvgHTcYp7vjZiAzZ0h0W/yLkV+OwCRAcn3cK3MgkPc72XxpSyB4tLm6kgRqCpj83NejCmANaqU+KiSqv5aklab1zOaBGPK/nSr4dwacsj9l7/5m7/BOeecg+c85zmYzWb4lV/5Ffzpn/5pCd+7dy9uvPFG3HPPPS7dhz70ITz60Y/Gc5/73CbPww8/HBdffDHOPfdciAge97jH4T3veQ9e9apX7ff2ELJeaDAhW5oNzzDJr9Xb5QpR1fcUlI4Et5POol1y4qDGfdtfvGsY7OCm1K+dbYKUjRsrrjPgzTLJnFXxNYYcnd8vgFpNZN7ONlnOVLye/3RcvRrjm72ZW/tA0apONHnst/J9e72uOqoQ4waAlASz/LZROmubxLK771klu3VhVfO7xqtuO3NlnGGUyjUfdeBqausNRwC0y5FIPz4m3Gk2DrjmQ+xEk4k9uv5VkVdlWVV9G2Vy0AFzaazyv6QNPbthGSConxs01LhFBpiBhLXRqKPER82r5qPp1E9MXOnkISGPmpf/HKe5Esv9imHExguf+CXkxV+lRkrRfLCsbFFhWI3OWV7qbJO4q47/gKfK3mKzzOKnFGRG2bZ9yIZdFGO31E/scmbiWm+yzJdrPox22Nmstm1l8abx7ZHbYP0mOxFj/wx5Et3iGSdS0kxWohs/xtHZJgNmKUsc19n+sBInusv1R5Ve1Y3qFivlxr9ZOqOx5rueo9a1TYu+cEkYL/J8DojONqnrmqxSrp/BFk29tm7mGti6mHu5Gk5Wa3I0wDQGmalLqL2ua6nAyCczw2TK6FFklHNrPppg2l0ilrp27kuyEBkGiE6N28Q89xcPf/jDcdlll02GP+Yxj+nq6xdddBEuuuiibprTTz8dp59++qbVkZD9CQ0m5NAjKPFTcayC31P8ATNsLztMVLW/q5z67H285i1bVqJgqmneBGuYxixhZkHXUlhUVDRO91OdAUnGz07cJzslPPtnhV4GGQcn0z3eaX2p9RJ/24dT/nnYJQMgM0SijtgUo/2TB3Lj29as5pfRWo5T7pms6aU4N6hXer9n2jR60+VfUquG5pzjLr7N2ux7+vs6UIOFm20Ssm4PfWtuB6rhbAwBvTT27BTwOCDIbm/UaA0hNVxK8vqI+Fkm/tGZGEyYzF3ewa8aRvzgYpmRxdV15cFFK1uq0984CTA76LRrJPl4S4oVFMNFSaAGaLOgtquJqIED07NN8kw3LaIrRfPzmvKzqnVN2s+mjfVbM3WbFuegsubKOqSb7YZQTcdkttY/ygDX2JBZlkel321ei5qQ+9pJMdsvS6q4mPaz0skMNV/9ZLErqFYReIvKVPkR8lEjtAzGmCXhgN4UUOOKNOd4KXwe/TjmVlRZOCXY1uleOttEUGYPljRTMq4n76bc6MiuKbfJf/1PGSGEHBrQYEK2NONgHo2G52aYRKXSxO2uZwJjKEFWvlXBL/mJfz0ZqOpaq3rG5RnHMUcePGQ/MWHIs1P85zh14O3OZmzgztpN9pza82RfDQLBvDPTZErpjWG9ND2DSk+lSiZG+14QpV80vOfu1zTmZY/RbjLONlnXTjo5wPandPq1199iuiCh2LowH1AWZ7WLtA5mJ0znv163yae0qSjiEx1UOrCNMzkWKXlLSCs+QnkbHaeaG2OH6PUxdUU46yNb8lhfXqWu6q9tNgOHep6eieLTh3q6//N5kLId7nhLqCEilbVBnLRSP9HZFvWDmCJvRMJ6zyaGyDioTinsKNMhaVvNLLAoL41bADeLrta1trjU0vWpMZ6VEpIpW0o8gRq+BSoLIJrDWLbJsVyTYcilJ6w2wySOw8t/tt7ZOSUmO2JPrLTSa4Eq7cY+q0YYKX76WWEe1KPjZ3Zg04V5xy8zZbRPbcYoVpZHceiUosEInyKE7O/pMJFhbKcNGzTORDrk586u41HaYARQkINOsDm3/hz96noo+VwtFuPzp1dUd0Bxxg39jZX8a3kbSWvr3XFrBxj3ZhhX1nubPJAZBsnyaXPzJITsH2gwIVuaMhsjGTe84t6wwkusRvG3u61onGEog5qidRan8auVhS4g2tSsKMLRPJCqkiyob0NFp1WPDdLp1dOf3aD1lzZd++oyuHUEvN75691Wa4cuM6a0F2vUt3OrdLSjo5cyNz4r5Sm/1UQycZOJ2/5OnbhxbD8Y99R5WBIuYpYKXJSH0dPdcV+YyMc+HgnAPCvc9VOb7B6s35LzMJ6Hqbym8swKnjM+FGW9Dhyicm/jSo5bk62QXrKyWsbsNW4vH01aw80AwdTB+sV2RWNOvR7hIxY1goZQe/GsQaQE5cHy1I2jIUORa1q3auhwFhdD7+luGfMqBpfUSuhxxkdeJDRluSRj7Up8nQWYw1QGSo7XThirstvNSDEiTWe5TH/qONmibiesnEtO48VVciKsuqX6NWH+XMWg5iXduHotxs4y51XcEFRDxFT4lNtYaN3fnnhvdsLizd5l+R95mZtFXrOhRd3F0GCNGoNA9HOexl/j13Z5g4j+HqpcM92xEXne6+qaV6ryLbiH+bz041Jjh5N9cAaXRTNJJt3rbOMDGfuZ6GbmSQjZP9BgQrY2+oc4D4qdoaMzTbx8u49mqOGIu+d046kel3QcMY5kkujkZ79YbA7OIWYAZNTr5MJqMYAq9flH+RZ1Vqch6AACeRCg57TkXAZEcG8xEc+l3VIWiC3auTtsa9bjjwm/6j9uJTqUvobrJe9Oxi3FLSZO312vtvefpQFzmaF8yS85tuQv+ePv0J66Vkldr0Q0XKbjAvkz/CFeCFP9jeCu6XR2s1nCvn3rN5YU48iwolGlc3a1UuUdcFeonmNoDDcxpBfu06VZGr8jt0q/1qPjN+bbiTsVvjBuVpaTMRyUmWYqUYzLjQ/d3A53XSfMtX48KlJmmyD5NZbc7BBTH/v8eWN1KHXJOFbfgKYZsFaexCxXXdI4Gw++P7r1mWi71lTqfZFmWC6iioEjGDBWEXnJ+w/ZkjPO+JAa1iUtakZsVahE65S0Nsp0SNmq2UsfK6lUclWpmkr8EFdiXAlxAcxmkPm8hJubMNQ/yuvx3F7RGGeatDaD7NtXLrwsPQ8bDx9CuLaxxIFJA5N2BX+sJ/6oDw37qtGmpM/dtuGZJD1Z13ETQsj9ERpMyJYmWszjgq5AVpgXLFC4UI8OaRaV3w7d41RxH+YUPjcaCGFdshFI3wbLDCnlNT6sxQTGnQc5JWNrVVEd3Y4kY1wx+Ym2TlBW93MjB1TF3/WOZt7z9z20mFq32p8+T5FclyakjevSTdQMqE2rn0fA9G2uVkJ1mLAU43TS2ppE/bIY/vRzLOlEWg8r5FH6It/Cy40fGi/kv8rRSSO5Es6A4vztgKEq7IuMHO3nOVXhL5mXuJjMr+SFifI68Zp6TKbpX5R6X2SXfoYTYvVmnXR+TOJzyAYLQVkjZMymzniZXMfELSRt0k6Vm681BsGAGWZryfRRiKc/tCh9kMQYoUchkOWUl8PjfzmlmOglz9SKND3E2D2mRJsN63W5vRSmurqUi5iAUq1Uf6mfLlflmmTTmEoIfJi6/cxMG8sfNVwv1H0/dOZTNXLZs62sXj8VovVUtinWOKsWX67/ImOI/V5x6lglTj7KlMHqFbvUnd219NdN85hM2xz64OT22QVgUcOXuku53q1Zj37SdZPVKJ9tbXKehJD9Aw0mZGsT5L+IADPzrlENJUX97MwwSZieRRLS9MKassuOOBLybcOQqv5iwzTnWqrA7wuZ8xaMC1wIMM42sQoRsjYtRavuL/JaF3t1GppZMLZqS6haXkL+RhzjFIiZvpZ1HdT6+cAV/Cyj5pdK4wBxSja67kXjFp+3NWdN13oGYN7UyqQxGq7VU62+GtPFo5tvSnUh3qWrc65QwBLcmC6lOtMlpu8o5NmctvysxpZOuB3BJpureaallIeqtOd8y7lR/AU2+6jwx2tUz/q/fTLFu0ImtQni8ujlXvLJP4shQDBed+S7OYmzg3alVDCmrGYqMck1x1K2ndWRzRBxtkmCM0Tbbdvh0nrUT7tOjZLDfBQrKZn5DNXu4cbO9SGvi8FqxmIL6fSDjsOzHTR/Sjeu8TGLn+mEBqQJ/6n4izGf/gFl/ZG+BRU+LIo/u4D1KnVKCcAaIHOU/ZcPECklYJbGXdkWSsHNCBNjmcvmpDzTpUYRc5SonfP4Y7ntyFxUGJmha9UUeVZlRJVvtozp2SOrxHFnYLzn59j82Sal3q2bEELuj9BgQrY2Yp156vUg5dv14hfjwCvzMZ4vYsl6KDCDBC3bKOajzaC++bQfiPiXk34/Fh8eNebxLa7b1WcY8tR83YK3vp+sSr0YBVvza95RereLqx5mlKLKp26r03RQT1Oa8p/SqoyG57wFgqHseiO6vydynZMapcbBnSRVSe1gIjVZL7VFpGp8Eo0s5Z0zxoGhnXxuwzTcNEt8mA3qF28+V1hSz4UsUWK7V077Uw0d2g+oan4ZEERLUe9sCwuH9lHpJjtQKb9NWbZM62cyq3masNhaZ2zRAYa2uTNwQMcw4vzFVKEad+pJfH720dOfA8JA3c9gk2z4dcaU6Jdq6iA8Yb/rsZcFyI+2Gj/NZ4f9Dxw7ZpxaGT87JNSmys3RypFyvZ1oEZNKai10i+Gx/gkqUVXualprmNEetjO+irEq6S0a1rDq2E9WeRQbTBpzyUv99BlLZfqEuD5arQB7rJpqNAmnbBJOqOI/mQPBPf7291Ur1Raj69f42SYw7r7lyFzh1YhZmPu/GhsQzioL7HlZeMd4URa28vljaMvsHuj7r5TWyF2V3+WZmdpu2MikZQYRGkw2D84wIeTQggYTsqWx71THtTgkK8V5WvjMKO/mrWeJ01H3I/YznymavHLZOmukNYToIGYirFOY1cnr2om5LfqmdciaV0r1MyKrZ8azVcCwjrg6IisKkmDcpQBhpolthzcPtX6xlR1/CX5lIUhd02Ut72YUK64DqNEt6MUpjcu+PX/7xj6NtxcE9QVdfUPc7dt8XhbenDtKs//UyxgFO0r0lNu1MCjdvbeqWu+U0rjGx3yAK1jCOVZdpg7pxukaQVzfCKyiXm7F7ChvVEO/O4OL6TOf3hZVPRJqnTVxvZ7RQFLr7dYuMPH92eRnK2BkhUieZZSfcTWc2cfVipD6RNUY3gjc+iWT2K5vBMh43YG6iKwaD5M1cqTaUXkWiu4EtpoRLz+D2ZA4DGMr12ZAwqwrVSRXytpBy/1qml/6o3M/letuDCcDAAySN0XrWUtG726zaucvx8aVMVO7XudoMJJ+2pWsKIsqM5o+6p2U8tom1Wjisolu2YC7m17MbKLxvmpkihVEVs4IjF1fJrtqEr1/7GyTklcjEIJb1tn+0C69ScVHVtljXcWA2zPGLPW3ZYcjjZdbTJ3K34yl7noN+m4r8wgh5P4HDSZka+P0mM7ONqvONsHELJK+7aJTjQVlJ1X/s0qq3/ibWmkuyYX5ov37NjuSAaBvfCX7z4esc8+gU37VwFLGMcU/GX9B0gkn3YEdvMJVlHCj+A3DaKhySnxPUV/PO0E7kjAaHnShQjVxDLBT0XuLT9qBw9QimLWfF9dEr6weYz8aw47pFptxz5y0EXRXjyR5kLrSvRp+SPRcJfE4kksplTdhxfARzn5gYDLpDoZsIXXWRjVo1HzcIAI1TFxaP5ujlGPLhwT/6o4zTPJ4pdRx+WwTuPJ9e7yfv1fqBbHSQJDlic42yZaAZMMkX5+SMveBESDtPIB4A6ROaJZdg7SzTbLRBNmIUj4lsfUOEqxto216MFJnY8wgko0XaqjRKNU43LRVB+DiojmsaHN2YJPV+IXi9GyTcNlKeGOcaTs2dABqPwJlVly2PZny6o5A+lfESjNJTZZRDBV3bE5ZqwYJY6vnK0rrdUi0bmWqXBmvgX+hUGspqNOCfJbJxllPZcr9JOPfLxnKrjdutgj09wA1YFQBpb0Z3YPz99G9IUME3dkm3TNqtqsdWoaZYZINtFXGCWQuJe927Sjxbj2poTe6S/g6LgfBgAGDDMsjrjNPQsj+gQYTsqXxi6721yQpBoGZmYESZ5vYnXTqEMMrsjFMw1coGzOUN7J2urFVcKcGEf1g80lO7ofSJm2HyDgD4DC7OKmY9qipZkDKn+Q45bSMHkxD1V0Gmzkz1ey11na2SZpSXpeNInpxk/dz3+nr22UBMAfSWlXq7F6j8GYy679KTZryNX7SoYWGTnw2I+60YWz6cUeT0EcTBYTbaDrTdZTdzjbxh5TDGFQwXgGny4ejKSxr4Ua3nzSqlLMNt2lNnrGoGlbf1gJxxkpdA2jRQq69Ok3FFVex3L9aa52pUfzzIHqG8ulNNdzWO6/e8dGvKcR3cwi3+Ws967oqgDUKC9rn0j8LfclXZhTAyko4vzLbJGmZVaaVelgjqpVTYiRcEHUAIMkM9JJvl16aYRCsTcw2aRaHbZvaRZ+BUC3jl6Cz6ctOPBMPa7842++L69OThDIunFSSTjdpA4aK7jGGpVQ/l0qNQOj3VBumPzv1ih3vkiekNMMgc2PYELONsJE5NtycrfyIRhdXT6kyRG/kIlNyxeov42+NHrYcLE7bdHf5TMg8fwMg7nk08mqJnIuyjJ/kEELu79BgQrY29o+w0dV6Mz5kGBWRuP2wjaN/6Ne9nsmSsv1sk5pEhxf17VbNbJnqmYziEt0QjG+CZwD2zYG1Ndjp6kXfHpDfpoX+tOcy48T4pRBRFVLNWH9LXjywLEAQDB4r+ZVCQ4ek4tV+wa6DJoFdV8VugurLWO39qW/vxJChND/UOeiqK5SyepWktq3MNlk17zhOWU+h5nfZSacxhtRBgVPK4xmtf1y4FQhGi9C5cbZJCYv5m/pYJT/GsUOPJv9yNnUsWZj6IuRt62aML9bP4WSLHfrmT/4GqSN1E7kacasBoS9TbK65SLNmkT7ucetxyXbR3mwTiHSkmHf3P4JM+V7Wa1ONFcn1GyBzYFjLi7JqM2vP1PqLtieV/q42XDWkaNzU8a79o2FzAdYgZYZXVzwtw3T71F+bXo5qOLFi2/Vsqj1ga95eZYuad8aUdfWleMckCAb4hVntsd7tsZYzGoNnec0x8yAoAtjZJiLhz1Nxq4DqFD/lRr5Tk5nthhhXwvNbI1XbhYS61EMkFJ7lT4LP3p6XduOiuLlMN8NE66Fhpi6Ta5sscXMNk/uGDNgPa5hsanaEEAMNJmRLI3FwkZlan2ThbBMEQ0c0enSKklQVhfKW085WmZjpUneVEOgshbp98ejXfO5vNV472NQw5zYafwJEhlHLPyyZQU824sgAkdlYfakDX79Dg1UIrfKXIxRDSfLaqgB1tsla91rVjHuGE5gw1YSjpqju5MLG66cawmE5Zoxn3LooZfaXyTJMst45n2YYu9ymnlZ/auJ1q0jh1kyzVEdVnVFcM2he1O2dsmIcMWFJd/EJDY/jlO7RiRO7X/3aWSUmE9TM/CwTMxW++IvJpxov6steafKKZbT10DxrYG2L+LrA5F2aa5610PF984OubQI/2yQ/6D3DhLcX9Ay0qY3bueH9bBP7gWHcT6f+nzsFPdOzytMyS6RGL2uk1D4DMB9v9bXDAJFsGE3J25zUXmSaJUG4lmAJHiZGUjlnbu1B1zbRT+N8ErSey7G91A9LThw3Mkgbkv1GWR4qMlnImIm9r6p/Psxsk6n0q7RlMfFC2NkmCanI9f4Fq/fbdBxfbXOTxIcspVGu5dkmKgP853pGBoRZJn42ihoqqvwp7sHIJBU7UdbYMuOskuiPCf9cTy9oF7iBOnh3sgs1b3WbujaycEH3E0LIoQ4NJmRrM/FHuKjjafxjbY0fAMqOEzrbBFhiZJkoqDfbxBlb7PbBOgAoZUed2k+p7zW1jh/SasqOunWWyHwOrM2Q3JQR+ARF+TYDlhDXK0DqzoOo7p6eyGubAH4nnd5offF7UJOzQ40h1YZlY+mHMmuo3yfVnJJxy2QJZVwE6CAkR7OLDdqci+2idG+qgfbc/2n823e6sO6irOtlKyPDrn4c9OGaR89/A7jZJkXR751R789OBfszOtpzddcBvF/YNcxMsSPqqvE3Ldf+LH1jz27wYGpUEpk2lHRtuX5BRF/34l/utbFji9wS2Akko4VuVo0B9Rb1o+r+8+OfCtuvUc5JmU1izkPKMy6y4VFQ1sIoC8SaZ7xvyElOBFmRUAf+uZtMlYY5kGbDuLaJFU1mdzKbSIo73zN6CYxNpoouY8isGZfydfd1a88xyUL7TAdb+4VUuVM+2sz5la3gy2wWKXXTXeOn19JdzRraXodF4cZ44npo/aw3Xfmbbg0cFvfY9HbbaROIPrPFXTMqsgv1ORCd1dUYRaR9bq1gtgI2GED8jWMNG+KrbrLqnmNfTBzNDBPz+Y2uR+XdUj9FsvUo8qp1N3Jz+vYjHbhLDiGHFjSYkC3NRmaYWCNKnfGR/czaJtbQsegTnW7ZEzvxaJrubBM74AvrFVj1DzlWUajG4NateqJbHDb/0ZQBOGxWX0SWc1bOpQ7IMKSslY8ljwOgqMWhWA10Adky4lFlKgF1tslhdQAQem8R0vzyI6j8ArIJq8vrzlHFmpQRWPl0wRSvY7/it7BqE6MkzVZ8rBh3HDAZ41IuWMw175ao19TkbXXjZmbUAiV6MaEB5l6oUcw9o60zY5XegrDlDOThij83jZpwN0YSV+tg1DC3pJ9hov7mrWlTmIkMI3+aQU8t29bPplm4hklzPfR+Hm+oaASuq4QkpLzGh86is7uHVUOKuVYlWyur/BNUalHu5TqbxGQx+kmqO+mE9GUdp/KJQ8eUY2+1putNrnqfab8N47yDtRkgsJ/hwbTKJW1+d+3AnTRlJyATNgy6ZFMrAxpnsEx5eRrS5gdp7FsE40g1pGq+9nqMi4x7v9iW3nWyhjW9W6z/SDS6+6apu/d7uaEk3lne6JGccJ4SXlMCI/p10kmIY4yq49+YLPsmDj+zA22cKRZUS/K1tHXxhuMVZ56UOnW6pifP1D+hrF1bZ5sEObbIULyg2aSlzD7a5DwJIfsHGkzIoYPTpetuJV3jR3YjYXq2SWdB2KhdJgmqXzCwuNkmWFR2UcWcLu0VY7NSR4oT3FV1Nm8qJSt2RtEGUvkUB8McwAxpbVYUIrNfMcwrTrhXr/ZbcNspTpHTvjHKru0rmUNkhtTMNlmi/FYN1PjnMlIIy3GrsUR7MM82SWu1fc0QZGqmyVT9mqGla42OoYq+W797qrMD2nHBclaoWik7XIIm2UZ0qYX1zbt4mO//J+kp8UFnB/QWy1fGvPm1ynoZJOQE0oTBuWN6O3Ao45smTaeOppL+LauvrysznF280Jc1Atz94j6/KVv6yjj1waxtUofANbG/fF6iOENDMQCbexr+6YI1zmjZqEYS+5kI8jMQZ5mkuNOT+LPkmXLJ+hkjShJg2AektXEnnbjRjuvCTjmu+PK8WHmgZZr+MmnGZbLatU26LAoz4a6uztDir55OIlxph+EGa8aoi1Wncu2tccK40wxStmxqd+pZP7L4bA0G+b6u3WDjSvDrlbGMINzEhox9IGVXOyujVP7kszPKmhktYuIKwic51siBIoiKwUKQZ4XU3whuJ5fCsdoMk9a/NET9UfOLbq5hQgh5IEGDCdnaOH1mhcVag0HDhjWzTTpGj6kB4uaUjRzHK59KnIXcLvpqjCNDGgcWIsCQILOsdA8wbsk1GJDWrEFEK2dGSO4NtVTly46A7Kc47i24eEVX0w8yKttra6WnTeuaXizKMax6blPYtQuS16WdQSQPfmSou/iUYBmV4IQwuNRj2QhHnZ2ZIYLmyosbqGg9295YWGS5Fgtr1G5WFBXp9bCCYcddijLGMgMEe8bYI6OebowcE288J2d22AdEokNMkHRDmpjlrXAtu97/1Sjj6uaKDW+B1c88YzaP4p6k7Xh7Vzp3SnXGGFDWNhnv8ToO7qZ1lrX2vq+1kCKYvPlDZQLGT4RUzrn8ejU3gyzb1JKtrsFk5JGkur2uMUTretaa79itZiafvVWyO1nPLIvrX4FgTkrZjFKqXFdNkryTTn8WXUt9BGsfqgHE+tXuqOavGjdhyJWZRbESWbdVJQqO+n82VRW/mZFs2rK+VIsCaNnTKD65a4s16AD+/oqJguXMFtOr3lTahLplNeBlRfdYFBbKDOmkqYt+5uYTVkmT78vG+LKkDr2+6LmTGm9s3XpuKV2z3GpOLMMwYBg2eVvhTc6PEFKhwYRsaezb2GVrjfgJxlbFzaqGartFEerHd2WkkH8cC4jJS91ZqV+0tgmKWl9a6n4V5bHzVlwVGqvc9N3ZqCGADHMkzCBrYVFY0YG2GNuBoPt6tqtUS2iDPechgcwhWDODi54ybwdWHe1ONbVk4/SMHaN7HOgkyDDPr2X9grQpN3xSxXN6uCrQyV2m8mI2XLqxinnIo2/HXdf4XltIz640Qb7ktW6rlbBxwrgkZcPUONiN94L4n+or5iyleyb868yOMljoplX/HK88SzVtUfSbWtaMSpkoGXfrgU6dXLmujqaiK3Wun1Vi7zl/i4ozDPZkpZdzrcSB8ath7ac51a8/28R+ImjTAJJnxOX+7ax6Xdun1lBzjbUAY0Sez0eb6CwbhFMpQwuvC+aqgcQ+1n4Z29qfLovYO9ljyNdxNvWJTpQJG6C5Rrlf5iJutkmtu/14a8U8Qw5+JqQ1IIyLwY6b1MePCcWUaWX3Ki0057oFWfDP/9m/URo+8Wybx8+V1PMvuUnrTmaWTW82iRNYKidU9gQDsvaUky/O4AGXvtRbi+ice4eUf02JxYCN/FxUf+8ufW3X2ihNFeduDT6EEHL/ggYTsrWxf4R7L5M6YXY2iMDvgFMUfY2TDRqrbDncLS/UqSnbqdtugnsuuw4yrJK7fNFXHQGgrkooacKNcZ0SCFLKMz5U94/52vzFNNQMXkrFbScUJVcHS6YPi1IlZQDT0hll2KDkHDGg9podECX9bEtQ90etKn2J1nOLjenviLhxUk0nJl0atwWVqrjahV3XpVxaZXhValPD/bmOMpedXb3qQLYULFKmlddD3O/VCmrPsujcMWzUAQ6yoq8X0Q5OpNz7zrDh0pp6xAFGqV7nTWzJz+QV0IEudOZI8s9Zu9KE3pvWpGENIyt8klP/M/m6Qszw2dRFZZcLiznYtUxqqJMrrt90ZpiEePV6Jec3RhqyfFtbEwhmY53NJzMCYwBJtj8WuLXfm/hVttWxu5+F0YiomLntjCj2er/dHwuTOFfF2peQzIwjk0WUd9NyoH+XeZPMDGoeWWByXpHmD+iS6CrYVK6bdHbKUsynO/tBQljuKQlhXWP9UGVNDnef85mKVYNtexbbABeGKpes9VjTdQwyTpZNHjl8MHkPS/z1JFaumfoFN1kNLvpKyKHFbHkUstW4+OKL8ZjHPAbbt2/HSSedhC9/+csL43/0ox/FE57wBGzfvh1PetKTcMUVVxygmt536re4/lBlYWEYfDyriIhI/YMV4kzlu66yO/Vw7Slli4lj3gTJsgNGmUNQrKIbxS2DQOYDZOh8IF20Iat09RQwr9zV+sawcAwDMMxr2d08O36o9bdhaTJubfSoWgvSkMvXj8RzvHZvmnh0C2/TdhXyUb+fYRjfyOaBVZKge69ahfWkEW8z0aTFHS775O2VE7a3VPL5iv2BvE1nMg01Z1PQstu8d3suPU+2uvWb6oOFfkP0Cx2UyxAdVEHMvzrQ8scAMb+rDBFIDmvPOT9zHmQoU71tfJj8XL/ntQqiYQtNmNR1DYaJsEEgc5VtPt9uk9HzE3e/xa71YsIbwOZz5HZr/8Gcaz2mDolurUtzT4irxyB6iMuye+utRFohfV4wWhKG2DYAdU2S9R5AlZr6LHn/kRkEMwy9urp6TB0Szr00MY7UOOEzS3/udtcKYR2DVxSi+omnWtKaNknbJtc3vXZru+xd3XlA4KItuZen9JccBnsfL/cvfTBgfMbnAsyr2x6EEHJ/hAaTQ4yPfOQjOO+887Br1y589atfxVOe8hScdtpp+M53vtON/8UvfhEveclLcPbZZ+O6667DGWecgTPOOANf//rXD3DNN8gChWCVsMaNNl7zpmRR+VhH2ejXo1u21LdErfI1cfS1+Ul3HKjpgAqob8kmRxbRL4wuxP0emnC/mp0O9qYvuHvzpn5JsrVBsr5qlMsct6YU91uS7vwz1qV+g78qvYHFEkoDq/FAIPUt9/oq0K/GCtWyW7U2CvxCYkQJ7va3JH/d7NoTvbHEcsXfnxcf4s5a3jD0BtD1nvXD3MH52TguvhhjRX62hjAwGW+1ajy19esOZhCNrzav4I+ar42DJm3/WNbhVR6g+Es4j8H27NsQ/YFe27xf9C+yo+c31DTq11zn3q3RHNL6YXHY2JZ+XrVvekeUWDkvW0fzv48j7nc9kqnXavJpOkYyrmRES7AiFNvCuDCXYH+QwjkGG8PFMhbFiWHSc+c5RPrb7r7WEWzd53hoZRDc853TZ4Oj9YtG2SrX7H1q88VqB5a58w/NU3fKy4ZRNZQWgylnOKzMqH9t/kEI2T/QYHKI8Z73vAevetWr8IpXvAI/+ZM/iUsuuQQPfvCD8aEPfagb/33vex9OP/10vPGNb8QJJ5yACy+8EE972tPw/ve//wDXfGOI/SfVPRkmJqwoF6rkV8UCRQ8wyszQKhrdwcaEvx0kxHirlW0UKjRV8YcdEDRu8e6OQiUi40yT+TC+FSp1jG3rtTUocK59fgARFclyDDLONgkzPuLV79wQXpHrxp0OSxhneoyDsKETdwFiG7B+Esb3sjaPDem1rn/r73Jf2Spr2Wl8Obo+m89GLTqx7ITZLMw20cHCxD90z0P+5c8IMznsWduO7Kv3qVawP+g1/Yvat/V2l3odYOI4d46jL6LdM6n9EK5wHR2N6c0FtsNnMXGtoVNKhcxzO4gz2vTl1pS/la3IZcD5Y0Iu+LJdsxr3Mr/o37v3rZ8MgmGeZ3yYZ2JKFHUPWRC/lGf/3oyHzjYp99gi7PMao8eHWII79lmcbbJa0ZnFz7ktvp1+kc9pDasZThbFkAXnibCELNhMHr0byeVjOyk8eyWPWC0JvwGklHeBqwsMu7/x3SpomI1T/STWq9zgGm+MM+7UZNKUZpvnb5VjWOaGn302lNttfM6GIZ+l/kklhJD7ITSYHELs2bMH1157LU499dTiN5vNcOqpp+Kaa67pprnmmmtcfAA47bTTJuNvOSY02sVKST9ez1gRwxoDSKc+dWCyoM4wSnNI05QNMXXwytH0SK66JY4iYvomPLrr9H2nQFptqT8yMYpmVFSHWrfmGFyaXj+7NQHUT5bF0BDjKwAkmc93Qgp738DYE1KcvCHVrwlDG5aAlFSxNecErBXbQSh8EfEeD0Gr5uE+BVpWXkPyYXpL27iNvl/TaNkyGHPG5PNtz+2gu3dLLj7MM4b8DHYzgDtLNzNTp8Gn8YMXmPUBpHyKtngQAyN7jBySnrvWoxfHyoaevFu17EYmuri+bNc+dNLZf/E3cn7weVSR5tvR9TPX2xqLFrep3396WSG+feX66i3u7gt7X5oHzcifcu+i+pXnqYQlV85YDfMRoKbXT2d88Yjj1viIeX8jpZOV2NIRA214dddZF5JiibbkqfOysAXotkECODm1sBq106pcNOWWTrbPuLRhOXE1/rsb0csQ7SEXZp67ci+aezv0da2qmKra+z/cDN1DFp9tm217rFsXdt4nAD/JWTdFPm3yQQjZP3DR10OI22+/HfP5HMcdd5zzP+644/CNb3yjm2b37t3d+Lt3754s595778W9995bft955533odb3jak3dYt2zJmKV3awQR1Uj2pe/lzCaqcJbiedJs8VtiFpdt/JacoCs0nyNqCo5QhGRWQ2a3VM687bChftuOfupoNfCFbdcwHyTgDJfR9u0nZsDSmGaVnmtwDtlrf2t8i4m02amYidxtswiQ2M6fLvbKwQF16v/mjY0TrFSlps41dQSiSmMUG5KTNM399Nmt71WCeaR5oBMwHmiybYmK5YWtSibtOyNepsnMQ/17WDcsiy8+oxF+WQ62rabe+ybirzmC+qy6LyXcoyCOn1kN6zqT43LlT/r9t5l4U3U8r3lQCSjGwx977KnJSQmvt5uuzaZrtctqmLKVsk1TqYFGPZGGcaiSnTXRwxWyNrlaTuClS/7zK7U/nNjDXb0m8CVNFStyd2zZ66GpLbmPx1gLZLTDWT8c9xhyxe0kzKBlslfs7M2EpcWDybS2PC/ELhmoOOeedA2UmnflyjpaSSR/Tvu91fstgSX+O0lmcPLhIKMnFeJU7nLJJnm6Txb1lkmWhfJSx1fqt7lpAwg8z1RUFbU332q5pRJUWVD/lZccY57yfWCFtueqmF5Lh9oypW89dPbQRlTaLWP7cpATLHyn/LSGZ/GDhoMCFkv0GDCWl4xzvegd/7vd872NUAAPzYW96Ke2+9dfwRlNtktV/nDAP+DYQNe/dg7223YvvOx7RKdVFAl9Qp5r8ozJQte/di3+27se1R/3scEJhpCyXehP/oZ9zO36RDPw8Z9gF3/X+Y/V87Sj45A8Ckq9uIJhOOvtvUeZFbZADuvRvpfx09DgBzuJh81K1h44AmuXPZLjilbloATRoBsGfvHIcdcUTWPXUxQ7v4oeqj6tahg77lzf5lMGXSqj5b3DVsmAN33Qs8ZLud2m0GVVBlu+M3FXeJv/rt3Sf43l3AMUfOzFtxozxbt2TlXpXqkpeYsibCm7iCvXsFu2/fi0cde7hXtqUOJupJilsbISZ+SVH/M3YJgUsugr17B/z3d+7F/z5+exNv1fJqGWKj2Up38hLs3TPg27t/gB/Z+b8A6G3cyrJWxugzZ+plfjT5ZE+bz969A269fS9+5PjttiBTRApFLsi/pPP1jPXWdHv3Cb7zvX149I5tSEhGBFQZVUVMCmE+XhUdnXhWNuWwfYPgf74PHPvwWV9MwZZdz85t2pzyj0Vx9DwIcM8e4KgHJxMvAbCz1qokqfZqdYtJp+sCSalHDEfObyxBsG/vXmzftjaGSZVKECOFRPM0/mK2CXZxjdQz/jbvJAIZ9iH94PuYPeghXug0sxZg3CbORtLkOLJvH4b/uR2HPewYqDGgyAGp+YhJV+VTza+fzrhNuiLb9uzBntv+G9seudM9+3aGa5EnNqz8rvK2uGNYk8eY9/zePfjht/9fPOhHftS0T4OlukudjThbGN+4YdKaes5/uAeHH/UwEELI/REaTA4hjjnmGKytreG2225z/rfddht27NjRTbNjx451xQeA3/3d38V5551Xft95553YuXPnfaj5xnnE8//vg1IuIYQQQgghm80gA4ZNXvRls/MjhFS4hskhxLZt23DiiSfi6quvLn7DMODqq6/GySef3E1z8sknu/gAcNVVV03GB4AjjjgCRx55pDsIIYQQQgghhJAHEpxhcohx3nnn4eUvfzme/vSn4xnPeAbe+9734u6778YrXvEKAMDLXvYyPOpRj8I73vEOAMDrXvc6/PzP/zz+5E/+BM9//vNx+eWX4ytf+Qo+8IEPHMxmEEIIIYQQ8oBjfyzSykVfCdl/0GByiHHmmWfiu9/9Lt72trdh9+7deOpTn4orr7yyLOx6yy23YGa22TvllFNw2WWX4YILLsCb3/xmPP7xj8cnPvEJPPGJTzxYTSCEEEIIIYQQQrY8Sbi0NVnCnXfeiYc97GH4n//5H36eQwghhBBCtgyHip6q9fw//8/ncNjhD9nUvPft/T4+99H/s+X7gJBDEa5hQgghhBBCCCGEEBLgJzmEEEIIIYQQcgDgGiaEHFrQYEIIIYQQQgghBwCRAbLJ2wBvdn6EkAo/ySGEEEIIIYQQQggJcIYJIYQQQgghhBwAhgEYNvkTmoETTAjZb3CGCSGEEEIIIYQQQkiAM0wIIYQQQggh5AAgwwDZ5Ckhm50fIaTCGSaEEEIIIYQQQgghAc4wIYQQQgghhJADALcVJuTQgjNMCCGEEEIIIYQQQgKcYUIIIYQQQgghBwCRASKbvIbJJudHCKnQYEIIIYQQQgghBwB+kkPIoQU/ySGEEEIIIYQQQggJcIYJIYQQQgghhBwAuK0wIYcWNJiQpYiM0/zuvPPOg1wTQgghhBBCKqqfqr661Znvu/uQyJMQMkKDCVnKXXfdBQDYuXPnQa4JIYQQQgghLXfddRce9rCHHexqTLJt2zbs2LEDX7n6Rfsl/x07dmDbtm37JW9CHsgkOVTMseSgMQwD/vu//xsPfehDkVI6oGXfeeed2LlzJ771rW/hyCOPPKBlkwMPr/cDC17vBxa83g8seL0fWBzM6y0iuOuuu3D88cdjNtvayzP+8Ic/xJ49e/ZL3tu2bcP27dv3S96EPJDhDBOylNlshkc/+tEHtQ5HHnkkFa4HELzeDyx4vR9Y8Ho/sOD1fmBxsK73Vp5ZYtm+fTuNGoQcYmxtMywhhBBCCCGEEELIQYAGE0IIIYQQQgghhJAADSZkS3PEEUdg165dOOKIIw52VcgBgNf7gQWv9wMLXu8HFrzeDyx4vQkh91e46CshhBBCCCGEEEJIgDNMCCGEEEIIIYQQQgI0mBBCCCGEEEIIIYQEaDAhhBBCCCGEEEIICdBgQgghhBBCCCGEEBKgwYQcVC6++GI85jGPwfbt23HSSSfhy1/+8sL4H/3oR/GEJzwB27dvx5Oe9CRcccUVB6imZDNYz/X+4Ac/iGc961k4+uijcfTRR+PUU09den+QrcV6n2/l8ssvR0oJZ5xxxv6tINlU1nu977jjDrz2ta/FIx/5SBxxxBH48R//ccr0Q4j1Xu/3vve9+Imf+Ak86EEPws6dO3Huuefihz/84QGqLbkvfP7zn8cLXvACHH/88Ugp4ROf+MTSNJ/73OfwtKc9DUcccQQe97jH4cMf/vB+rychhOwPaDAhB42PfOQjOO+887Br1y589atfxVOe8hScdtpp+M53vtON/8UvfhEveclLcPbZZ+O6667DGWecgTPOOANf//rXD3DNyUZY7/X+3Oc+h5e85CX4p3/6J1xzzTXYuXMnnvvc5+Lb3/72Aa452Qjrvd7KzTffjDe84Q141rOedYBqSjaD9V7vPXv24Bd/8Rdx880342Mf+xhuvPFGfPCDH8SjHvWoA1xzshHWe70vu+wynH/++di1axduuOEGXHrppfjIRz6CN7/5zQe45mQj3H333XjKU56Ciy++eKX4//mf/4nnP//5ePazn43rr78ev/M7v4NXvvKV+PSnP72fa0oIIZsPtxUmB42TTjoJP/3TP433v//9AIBhGLBz50781m/9Fs4///wm/plnnom7774bn/zkJ4vfM5/5TDz1qU/FJZdccsDqTTbGeq93ZD6f4+ijj8b73/9+vOxlL9vf1SX3kY1c7/l8jp/7uZ/Db/zGb+Cf//mfcccdd6z0JpMcfNZ7vS+55BK8613vwje+8Q0cfvjhB7q65D6y3ut9zjnn4IYbbsDVV19d/F7/+tfjS1/6Er7whS8csHqT+05KCR//+McXzgB805vehE996lPuhdaLX/xi3HHHHbjyyisPQC0JIWTz4AwTclDYs2cPrr32Wpx66qnFbzab4dRTT8U111zTTXPNNde4+ABw2mmnTcYnW4eNXO/IPffcg7179+LhD3/4/qom2SQ2er1///d/H8ceeyzOPvvsA1FNskls5Hr//d//PU4++WS89rWvxXHHHYcnPvGJuOiiizCfzw9UtckG2cj1PuWUU3DttdeWz3ZuuukmXHHFFXje8553QOpMDizU1wgh9ycOO9gVIA9Mbr/9dszncxx33HHO/7jjjsM3vvGNbprdu3d34+/evXu/1ZNsDhu53pE3velNOP744xsljGw9NnK9v/CFL+DSSy/F9ddffwBqSDaTjVzvm266CZ/97Gfx0pe+FFdccQW++c1v4jWveQ327t2LXbt2HYhqkw2ykev9a7/2a7j99tvxsz/7sxAR7Nu3D7/5m7/JT3Lup0zpa3feeSd+8IMf4EEPetBBqhkhhKwfzjAhhGx53vnOd+Lyyy/Hxz/+cWzfvv1gV4dsMnfddRfOOussfPCDH8QxxxxzsKtDDgDDMODYY4/FBz7wAZx44ok488wz8Za3vIWfV95P+dznPoeLLroIf/7nf46vfvWr+Lu/+zt86lOfwoUXXniwq0YIIYQshDNMyEHhmGOOwdraGm677Tbnf9ttt2HHjh3dNDt27FhXfLJ12Mj1Vt797nfjne98J/7xH/8RT37yk/dnNckmsd7r/R//8R+4+eab8YIXvKD4DcMAADjssMNw44034rGPfez+rTTZMBt5vh/5yEfi8MMPx9raWvE74YQTsHv3buzZswfbtm3br3UmG2cj1/utb30rzjrrLLzyla8EADzpSU/C3XffjVe/+tV4y1vegtmM7+/uT0zpa0ceeSRnlxBCDjn4F4ocFLZt24YTTzzRLQA3DAOuvvpqnHzyyd00J598sosPAFddddVkfLJ12Mj1BoA//uM/xoUXXogrr7wST3/60w9EVckmsN7r/YQnPAFf+9rXcP3115fjhS98YdlhYefOnQey+mSdbOT5/pmf+Rl885vfLIYxAPj3f/93PPKRj6SxZIuzket9zz33NEYRNZZx74H7H9TXCCH3K4SQg8Tll18uRxxxhHz4wx+Wf/u3f5NXv/rVctRRR8nu3btFROSss86S888/v8T/l3/5FznssMPk3e9+t9xwww2ya9cuOfzww+VrX/vawWoCWQfrvd7vfOc7Zdu2bfKxj31Mbr311nLcddddB6sJZB2s93pHXv7yl8sv//IvH6DakvvKeq/3LbfcIg996EPlnHPOkRtvvFE++clPyrHHHit/8Ad/cLCaQNbBeq/3rl275KEPfaj87d/+rdx0003ymc98Rh772MfKi170ooPVBLIO7rrrLrnuuuvkuuuuEwDynve8R6677jr5r//6LxEROf/88+Wss84q8W+66SZ58IMfLG984xvlhhtukIsvvljW1tbkyiuvPFhNIISQDcNPcshB48wzz8R3v/tdvO1tb8Pu3bvx1Kc+FVdeeWVZKOyWW25xb6ROOeUUXHbZZbjgggvw5je/GY9//OPxiU98Ak984hMPVhPIOljv9f6Lv/gL7NmzB7/6q7/q8tm1axfe/va3H8iqkw2w3utNDm3We7137tyJT3/60zj33HPx5Cc/GY961KPwute9Dm9605sOVhPIOljv9b7ggguQUsIFF1yAb3/723jEIx6BF7zgBfjDP/zDg9UEsg6+8pWv4NnPfnb5fd555wEAXv7yl+PDH/4wbr31Vtxyyy0l/Ed/9EfxqU99Cueeey7e97734dGPfjT+8i//EqeddtoBrzshhNxXkgjnQhJCCCGEEEIIIYRY+HqPEEIIIYQQQgghJECDCSGEEEIIIYQQQkiABhNCCCGEEEIIIYSQAA0mhBBCCCGEEEIIIQEaTAghhBBCCCGEEEICNJgQQgghhBBCCCGEBGgwIYQQQgghhBBCCAnQYEIIIYQQQgghhBASoMGEEEIIIYQQQgghJECDCSGEEEIIIYQQQkiABhNCCCGEEEIIIYSQAA0mhBBCCCGEEEIIIQEaTAghhBBCCCGEEEICNJgQQgghhBBCCCGEBGgwIYQQQgghhBBCCAnQYEIIIYQQQgghhBASoMGEEEIIIYQQQgghJECDCSGEEEIIIYQQQkiABhNCCCGEEEIIIYSQAA0mhBBCCCGEEEIIIQEaTAghhBBCCCGEEEICNJgQQgghhBBCCCGEBGgwIYQQQgghhBBCCAnQYEIIIYQQQgghhBASoMGEEEIIIYQQQgghJECDCSGEEEIIIYQQQkiABhNCCCGEEEIIIYSQAA0mhBBCCCGEEEIIIQEaTAghhBBCCCGEEEICNJgQQgghhBBCCCGEBGgwIYQQQgghhBBCCAnQYEIIIYQQQgghhBASoMGEEEIIIYQQQgghJECDCSGEEEIIIYQQQkiABhNCCCGEEEIIIYSQAA0mhBBCCCGEEEIIIQEaTAghhBBCCCGEEEICNJgQQgghhBBCCCGEBGgwIYQQQgghhBBCCAnQYEIIIYQQQgghhBASoMGEEEIIIYQQQgghJECDCSGEEEIIIYQQQkiABhNCCCGEEEIIIYSQAA0mhBBCCCGEEEIIIYH/H+e9VQUUmZeQAAAAAElFTkSuQmCC",
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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -418,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,24 +489,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "707ca9cbe0594119b8c7468fb0fbd158",
+       "model_id": "ea6880f34a0c496db283bdee296b43c6",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -464,28 +526,6 @@
     "fig.colorbar(collection);"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exercises\n",
-    "\n",
-    "## Exercise 1: \n",
-    "\n",
-    "### 1a: use a higher approximation degree\n",
-    "\n",
-    "Solve the same problem, only this time, use a piecewise quadratic approximation space.\n",
-    "\n",
-    "- Hint: check the help for `FunctionSpace` to see how to specify the degree.\n",
-    "\n",
-    "### 1b: use a quadrilateral mesh\n",
-    "\n",
-    "Solve the same problem, but using quadrilateral, rather than triangular, cells.\n",
-    "\n",
-    "- Hint 1: check the help for `UnitSquareMesh` to see how to make a quadrilateral mesh\n",
-    "- Hint 2: To specify a piecewise continuous space on quadrilaterals, use the family name `\"Q\"`."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -513,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,8 +567,7 @@
     "L = f * v * dx\n",
     "\n",
     "uh = Function(V)\n",
-    "solve(a == L, uh, solver_parameters={'ksp_type': 'cg',\n",
-    "                                     'pc_type': 'none'})\n",
+    "solve(a == L, uh)\n",
     "\n",
     "u_exact = cos(2*pi*x)*cos(2*pi*y)"
    ]
@@ -586,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -624,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -641,12 +680,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "uh = Function(V)\n",
-    "solve(a == L, uh, solver_parameters={'ksp_type': 'cg', 'pc_type': 'none'})"
+    "solve(a == L, uh)"
    ]
   },
   {
@@ -665,8 +704,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/02-poisson.ipynb b/docs/notebooks/02-poisson.ipynb
index fa792caa33..86af54c02e 100644
--- a/docs/notebooks/02-poisson.ipynb
+++ b/docs/notebooks/02-poisson.ipynb
@@ -22,8 +22,10 @@
     "\n",
     "for some known function $f$. To have a well-posed problem, we must impose Dirichlet conditions over at least part of the domain boundary:\n",
     "\n",
-    "$$u(x) = g(x) \\quad \\forall x \\in \\Gamma_D,\\\\\n",
-    "\\nabla u(x)\\cdot \\vec{n} = h(x) \\quad \\forall x \\in \\Gamma_N.$$\n",
+    "$$\\begin{gather*}\n",
+    "u(x) = g(x) \\quad \\forall x \\in \\Gamma_D,\\\\\n",
+    "\\nabla u(x)\\cdot \\vec{n} = h(x) \\quad \\forall x \\in \\Gamma_N.\n",
+    "\\end{gather*}$$\n",
     "\n",
     "As before, the Neumann condition is imposed weakly by setting the boundary integral over the relevant part of the boundary. The Dirichlet condition is imposed strongly by modifying the function space from which $u$ is drawn.\n",
     "\n",
@@ -127,7 +129,46 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[31mInit signature:\u001b[39m DirichletBC(V, g, sub_domain, method=\u001b[38;5;28;01mNone\u001b[39;00m)\n",
+       "\u001b[31mDocstring:\u001b[39m     \n",
+       "Implementation of a strong Dirichlet boundary condition.\n",
+       "\n",
+       ".. note:\n",
+       "\n",
+       "   This uses facet markers in the domain, so may be used to\n",
+       "   applied strong boundary conditions to interior facets (if they\n",
+       "   have an appropriate mesh marker). The \"on_boundary\" string only\n",
+       "   applies to the exterior boundaries of the domain.\n",
+       "\n",
+       ":arg V: the :class:`.FunctionSpace` on which the boundary condition\n",
+       "    should be applied.\n",
+       ":arg g: the boundary condition values. This can be a :class:`.Function` on\n",
+       "    ``V``, or a UFL expression that can be interpolated into\n",
+       "    ``V``, for example, a :class:`.Constant` , an iterable of\n",
+       "    literal constants (converted to a UFL expression), or a\n",
+       "    literal constant which can be pointwise evaluated at the nodes\n",
+       "    of ``V``.\n",
+       ":arg sub_domain: the integer id(s) of the boundary region over which the\n",
+       "    boundary condition should be applied. The string \"on_boundary\" may be used\n",
+       "    to indicate all of the boundaries of the domain. In the case of extrusion\n",
+       "    the ``top`` and ``bottom`` strings are used to flag the bcs application on\n",
+       "    the top and bottom boundaries of the extruded mesh respectively.\n",
+       ":arg method: the method for determining boundary nodes.\n",
+       "    DEPRECATED. The only way boundary nodes are identified is by\n",
+       "    topological association.\n",
+       "\u001b[31mFile:\u001b[39m           ~/src/firedrake-pyadjoint/firedrake/firedrake/bcs.py\n",
+       "\u001b[31mType:\u001b[39m           type\n",
+       "\u001b[31mSubclasses:\u001b[39m     BCFromNodes"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "?DirichletBC"
    ]
@@ -163,7 +204,7 @@
    "outputs": [],
    "source": [
     "uh = Function(V)\n",
-    "solve(a == L, uh, bcs=bcs, solver_parameters={\"ksp_type\": \"cg\", \"pc_type\": \"none\"})"
+    "solve(a == L, uh, bcs=bcs)"
    ]
   },
   {
@@ -174,18 +215,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8efc16e9564447448b8afad54ba59b15",
+       "model_id": "107ba948dc584690b34149369fdce2c9",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -237,8 +278,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/03-elasticity.ipynb b/docs/notebooks/03-elasticity.ipynb
index 15feea6d97..0455f38956 100644
--- a/docs/notebooks/03-elasticity.ipynb
+++ b/docs/notebooks/03-elasticity.ipynb
@@ -6,17 +6,17 @@
    "source": [
     "# Linear elasticity\n",
     "\n",
-    "*This work is adapted from the FEniCS tutorial: https://fenicsproject.org/pub/tutorial/html/._ftut1008.html#ftut:elast*\n",
+    "*This work is adapted from a previous FEniCS tutorial no longer online*\n",
     "\n",
     "Having studied a few scalar-valued problems, we now move on to a vector-valued problem.  The equations of linear elasticity.  Here, we'll treat the isotropic case.\n",
     "\n",
     "For small deformations, the governing equation is:\n",
     "\n",
     "$$ -\\nabla \\cdot \\sigma = f \\text{ in } \\Omega, $$\n",
-    "with\n",
-    "$$ \\DeclareMathOperator{\\Tr}{Tr}\n",
-    "\\text{the stress tensor}\\quad \\sigma := \\lambda \\Tr(\\epsilon)\\mathbb{I} + 2\\mu\\epsilon\\\\\n",
-    "\\text{and the symmetric strain rate tensor}\\quad   \\epsilon := \\frac{1}{2}\\left(\\nabla u + (\\nabla u)^T\\right), $$\n",
+    "with the stress tensor:\n",
+    "$$ \\sigma := \\lambda \\operatorname{Tr}(\\epsilon)\\mathbb{I} + 2\\mu\\epsilon$$\n",
+    "and the symmetric strain rate tensor:\n",
+    "$$\\epsilon := \\frac{1}{2}\\left(\\nabla u + (\\nabla u)^T\\right), $$\n",
     "where $u$ is the unknown vector displacement field, and $\\mu$ and $\\lambda$ are the Lamè parameters.\n",
     "\n",
     "As before, the variational formulation consists of multiplying by a test function in some suitable finite element space, $v \\in V$, and integrating.  Note that this time, the solution $u$, and hence the test space $V$ are *vector*-valued (so multiplication actually means taking the inner product).\n",
@@ -63,6 +63,7 @@
    "outputs": [],
    "source": [
     "from firedrake import *\n",
+    "from firedrake.__future__ import interpolate\n",
     "length = 1\n",
     "width = 0.2\n",
     "mesh = RectangleMesh(40, 20, length, width)"
@@ -158,8 +159,8 @@
      "output_type": "stream",
      "text": [
       "    Residual norms for firedrake_0_ solve.\n",
-      "    0 KSP Residual norm 6.916415658096e-05 \n",
-      "    1 KSP Residual norm 6.192906950760e-15 \n"
+      "    0 KSP Residual norm 6.916415658096e-05\n",
+      "    1 KSP Residual norm 6.552681811279e-15\n"
      ]
     }
    ],
@@ -187,7 +188,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "displaced_coordinates = interpolate(SpatialCoordinate(mesh) + uh, V)\n",
+    "displaced_coordinates = assemble(interpolate(SpatialCoordinate(mesh) + uh, V))\n",
     "displaced_mesh = Mesh(displaced_coordinates)"
    ]
   },
@@ -207,18 +208,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9032e3db0d044287a4c005a22b0c185e",
+       "model_id": "7b4c9aa84ca94d84a168fdbc2c77d850",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -316,21 +317,21 @@
      "evalue": "Nonlinear solve failed to converge after 0 nonlinear iterations.\nReason:\n   DIVERGED_LINEAR_SOLVE",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mConvergenceError\u001b[0m                          Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# NBVAL_RAISES_EXCEPTION\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m uh \u001b[38;5;241m=\u001b[39m \u001b[43msolve_elasticity\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mksp_max_it\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mksp_type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpc_type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnone\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[10], line 20\u001b[0m, in \u001b[0;36msolve_elasticity\u001b[0;34m(nx, ny, options, **kwargs)\u001b[0m\n\u001b[1;32m     17\u001b[0m L \u001b[38;5;241m=\u001b[39m dot(f, v)\u001b[38;5;241m*\u001b[39mdx\n\u001b[1;32m     19\u001b[0m uh \u001b[38;5;241m=\u001b[39m Function(V)\n\u001b[0;32m---> 20\u001b[0m \u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msolver_parameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m uh\n",
-      "File \u001b[0;32mpetsc4py/PETSc/Log.pyx:188\u001b[0m, in \u001b[0;36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpetsc4py/PETSc/Log.pyx:189\u001b[0m, in \u001b[0;36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/Documents/firedrake/firedrake/src/firedrake/firedrake/adjoint_utils/solving.py:57\u001b[0m, in \u001b[0;36mannotate_solve..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     54\u001b[0m     tape\u001b[38;5;241m.\u001b[39madd_block(block)\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m stop_annotating():\n\u001b[0;32m---> 57\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m annotate:\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(args[\u001b[38;5;241m1\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreate_block_variable\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
-      "File \u001b[0;32m~/Documents/firedrake/firedrake/src/firedrake/firedrake/solving.py:128\u001b[0m, in \u001b[0;36msolve\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# Call variational problem solver if we get an equation\u001b[39;00m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args[\u001b[38;5;241m0\u001b[39m], ufl\u001b[38;5;241m.\u001b[39mclasses\u001b[38;5;241m.\u001b[39mEquation):\n\u001b[0;32m--> 128\u001b[0m     \u001b[43m_solve_varproblem\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;66;03m# Solve pre-assembled system\u001b[39;00m\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _la_solve(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m~/Documents/firedrake/firedrake/src/firedrake/firedrake/solving.py:164\u001b[0m, in \u001b[0;36m_solve_varproblem\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;66;03m# Create solver and call solve\u001b[39;00m\n\u001b[1;32m    158\u001b[0m     solver \u001b[38;5;241m=\u001b[39m vs\u001b[38;5;241m.\u001b[39mLinearVariationalSolver(problem, solver_parameters\u001b[38;5;241m=\u001b[39msolver_parameters,\n\u001b[1;32m    159\u001b[0m                                         nullspace\u001b[38;5;241m=\u001b[39mnullspace,\n\u001b[1;32m    160\u001b[0m                                         transpose_nullspace\u001b[38;5;241m=\u001b[39mnullspace_T,\n\u001b[1;32m    161\u001b[0m                                         near_nullspace\u001b[38;5;241m=\u001b[39mnear_nullspace,\n\u001b[1;32m    162\u001b[0m                                         options_prefix\u001b[38;5;241m=\u001b[39moptions_prefix,\n\u001b[1;32m    163\u001b[0m                                         appctx\u001b[38;5;241m=\u001b[39mappctx)\n\u001b[0;32m--> 164\u001b[0m     \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    166\u001b[0m \u001b[38;5;66;03m# Solve nonlinear variational problem\u001b[39;00m\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    168\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m eq\u001b[38;5;241m.\u001b[39mrhs \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
-      "File \u001b[0;32mpetsc4py/PETSc/Log.pyx:188\u001b[0m, in \u001b[0;36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpetsc4py/PETSc/Log.pyx:189\u001b[0m, in \u001b[0;36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/Documents/firedrake/firedrake/src/firedrake/firedrake/adjoint_utils/variational_solver.py:89\u001b[0m, in \u001b[0;36mNonlinearVariationalSolverMixin._ad_annotate_solve..wrapper\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m     tape\u001b[38;5;241m.\u001b[39madd_block(block)\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m stop_annotating():\n\u001b[0;32m---> 89\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m annotate:\n\u001b[1;32m     92\u001b[0m     block\u001b[38;5;241m.\u001b[39madd_output(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ad_problem\u001b[38;5;241m.\u001b[39m_ad_u\u001b[38;5;241m.\u001b[39mcreate_block_variable())\n",
-      "File \u001b[0;32m~/Documents/firedrake/firedrake/src/firedrake/firedrake/variational_solver.py:286\u001b[0m, in \u001b[0;36mNonlinearVariationalSolver.solve\u001b[0;34m(self, bounds)\u001b[0m\n\u001b[1;32m    284\u001b[0m     work\u001b[38;5;241m.\u001b[39mcopy(u)\n\u001b[1;32m    285\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 286\u001b[0m \u001b[43msolving_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_snes_convergence\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msnes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;66;03m# Grab the comm associated with the `_problem` and call PETSc's garbage cleanup routine\u001b[39;00m\n\u001b[1;32m    289\u001b[0m comm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_problem\u001b[38;5;241m.\u001b[39mu\u001b[38;5;241m.\u001b[39mfunction_space()\u001b[38;5;241m.\u001b[39mmesh()\u001b[38;5;241m.\u001b[39m_comm\n",
-      "File \u001b[0;32m~/Documents/firedrake/firedrake/src/firedrake/firedrake/solving_utils.py:138\u001b[0m, in \u001b[0;36mcheck_snes_convergence\u001b[0;34m(snes)\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m             msg \u001b[38;5;241m=\u001b[39m reason\n\u001b[0;32m--> 138\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConvergenceError(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mNonlinear solve failed to converge after \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m nonlinear iterations.\u001b[39m\n\u001b[1;32m    139\u001b[0m \u001b[38;5;124mReason:\u001b[39m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;124m   \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\"\"\u001b[39m \u001b[38;5;241m%\u001b[39m (snes\u001b[38;5;241m.\u001b[39mgetIterationNumber(), msg))\n",
-      "\u001b[0;31mConvergenceError\u001b[0m: Nonlinear solve failed to converge after 0 nonlinear iterations.\nReason:\n   DIVERGED_LINEAR_SOLVE"
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mConvergenceError\u001b[39m                          Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# NBVAL_RAISES_EXCEPTION\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m uh = \u001b[43msolve_elasticity\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m=\u001b[49m\u001b[43m{\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mksp_max_it\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mksp_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcg\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpc_type\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mnone\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 20\u001b[39m, in \u001b[36msolve_elasticity\u001b[39m\u001b[34m(nx, ny, options, **kwargs)\u001b[39m\n\u001b[32m     17\u001b[39m L = dot(f, v)*dx\n\u001b[32m     19\u001b[39m uh = Function(V)\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m \u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbcs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msolver_parameters\u001b[49m\u001b[43m=\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     21\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m uh\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpetsc4py/PETSc/Log.pyx:188\u001b[39m, in \u001b[36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpetsc4py/PETSc/Log.pyx:189\u001b[39m, in \u001b[36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/firedrake-pyadjoint/firedrake/firedrake/adjoint_utils/solving.py:57\u001b[39m, in \u001b[36mannotate_solve..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     54\u001b[39m     tape.add_block(block)\n\u001b[32m     56\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m stop_annotating():\n\u001b[32m---> \u001b[39m\u001b[32m57\u001b[39m     output = \u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     59\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m annotate:\n\u001b[32m     60\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(args[\u001b[32m1\u001b[39m], \u001b[33m\"\u001b[39m\u001b[33mcreate_block_variable\u001b[39m\u001b[33m\"\u001b[39m):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/firedrake-pyadjoint/firedrake/firedrake/solving.py:143\u001b[39m, in \u001b[36msolve\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    141\u001b[39m \u001b[38;5;66;03m# Call variational problem solver if we get an equation\u001b[39;00m\n\u001b[32m    142\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args[\u001b[32m0\u001b[39m], ufl.classes.Equation):\n\u001b[32m--> \u001b[39m\u001b[32m143\u001b[39m     \u001b[43m_solve_varproblem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    144\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    145\u001b[39m     \u001b[38;5;66;03m# Solve pre-assembled system\u001b[39;00m\n\u001b[32m    146\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _la_solve(*args, **kwargs)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/firedrake-pyadjoint/firedrake/firedrake/solving.py:190\u001b[39m, in \u001b[36m_solve_varproblem\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    182\u001b[39m \u001b[38;5;66;03m# Create solver and call solve\u001b[39;00m\n\u001b[32m    183\u001b[39m solver = create_solver(problem, solver_parameters=solver_parameters,\n\u001b[32m    184\u001b[39m                        nullspace=nullspace,\n\u001b[32m    185\u001b[39m                        transpose_nullspace=nullspace_T,\n\u001b[32m   (...)\u001b[39m\u001b[32m    188\u001b[39m                        appctx=appctx,\n\u001b[32m    189\u001b[39m                        pre_apply_bcs=pre_apply_bcs)\n\u001b[32m--> \u001b[39m\u001b[32m190\u001b[39m \u001b[43msolver\u001b[49m\u001b[43m.\u001b[49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpetsc4py/PETSc/Log.pyx:188\u001b[39m, in \u001b[36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mpetsc4py/PETSc/Log.pyx:189\u001b[39m, in \u001b[36mpetsc4py.PETSc.Log.EventDecorator.decorator.wrapped_func\u001b[39m\u001b[34m()\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/firedrake-pyadjoint/firedrake/firedrake/adjoint_utils/variational_solver.py:108\u001b[39m, in \u001b[36mNonlinearVariationalSolverMixin._ad_annotate_solve..wrapper\u001b[39m\u001b[34m(self, **kwargs)\u001b[39m\n\u001b[32m    105\u001b[39m     tape.add_block(block)\n\u001b[32m    107\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m stop_annotating():\n\u001b[32m--> \u001b[39m\u001b[32m108\u001b[39m     out = \u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    110\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m annotate:\n\u001b[32m    111\u001b[39m     block.add_output(\u001b[38;5;28mself\u001b[39m._ad_problem._ad_u.create_block_variable())\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/firedrake-pyadjoint/firedrake/firedrake/variational_solver.py:361\u001b[39m, in \u001b[36mNonlinearVariationalSolver.solve\u001b[39m\u001b[34m(self, bounds)\u001b[39m\n\u001b[32m    359\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m problem.restrict:\n\u001b[32m    360\u001b[39m     problem.u.interpolate(problem.u_restrict)\n\u001b[32m--> \u001b[39m\u001b[32m361\u001b[39m \u001b[43msolving_utils\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcheck_snes_convergence\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msnes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    363\u001b[39m \u001b[38;5;66;03m# Grab the comm associated with the `_problem` and call PETSc's garbage cleanup routine\u001b[39;00m\n\u001b[32m    364\u001b[39m comm = \u001b[38;5;28mself\u001b[39m._problem.u_restrict.function_space().mesh()._comm\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/src/firedrake-pyadjoint/firedrake/firedrake/solving_utils.py:128\u001b[39m, in \u001b[36mcheck_snes_convergence\u001b[39m\u001b[34m(snes)\u001b[39m\n\u001b[32m    126\u001b[39m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    127\u001b[39m             msg = reason\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m ConvergenceError(\u001b[33mr\u001b[39m\u001b[33m\"\"\"\u001b[39m\u001b[33mNonlinear solve failed to converge after \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[33m nonlinear iterations.\u001b[39m\n\u001b[32m    129\u001b[39m \u001b[33mReason:\u001b[39m\n\u001b[32m    130\u001b[39m \u001b[33m   \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\"\"\u001b[39m % (snes.getIterationNumber(), msg))\n",
+      "\u001b[31mConvergenceError\u001b[39m: Nonlinear solve failed to converge after 0 nonlinear iterations.\nReason:\n   DIVERGED_LINEAR_SOLVE"
      ]
     }
    ],
@@ -355,7 +356,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Linear firedrake_2_ solve converged due to CONVERGED_RTOL iterations 81\n"
+      "    Linear firedrake_2_ solve converged due to CONVERGED_RTOL iterations 81\n"
      ]
     }
    ],
@@ -437,7 +438,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Linear firedrake_3_ solve converged due to CONVERGED_RTOL iterations 36\n"
+      "    Linear firedrake_3_ solve converged due to CONVERGED_RTOL iterations 36\n"
      ]
     }
    ],
@@ -469,8 +470,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/04-burgers.ipynb b/docs/notebooks/04-burgers.ipynb
index 002330d865..b52dbf662a 100644
--- a/docs/notebooks/04-burgers.ipynb
+++ b/docs/notebooks/04-burgers.ipynb
@@ -218,9 +218,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Help on function __call__ in module firedrake._deprecation:\n",
+      "Help on cython_function_or_method in module firedrake.pyplot.mpl:\n",
       "\n",
-      "__call__(*args, **kwargs)\n",
+      "plot(function, *args, num_sample_points=10, complex_component='real', **kwargs)\n",
+      "    Plot a 1D Firedrake :class:`~.Function`\n",
+      "\n",
+      "    :arg function: The :class:`~.Function` to plot\n",
+      "    :arg args: same as for matplotlib :func:`plot `\n",
+      "    :arg num_sample_points: number of sample points for high-degree functions\n",
+      "    :kwarg complex_component: If plotting complex data, which\n",
+      "        component? (``'real'`` or ``'imag'``). Default is ``'real'``.\n",
+      "    :arg kwargs: same as for matplotlib :class:`PathPatch `\n",
+      "    :return: list of matplotlib :class:`Line2D `\n",
       "\n"
      ]
     }
@@ -237,7 +246,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 11,
@@ -247,18 +256,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a5b68ab42b0e46b5b001a8d8221e9dd1",
+       "model_id": "abb8422239f5461b9b04ed2376ee279d",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -481,42 +490,42 @@
        "\n",
        "\n",
        "
      \n",
-       "  \n",
+       "  \n",
        "  
      \n",
-       "    \n",
+       "           oninput=\"anim3502fbafac2847afae5512f80c1aed54.set_frame(parseInt(this.value));\">\n",
        "    
      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
        "    
      \n",
-       "    
      \n",
-       "      \n",
-       "      \n",
-       "      Once\n",
+       "      \n",
-       "      \n",
-       "      Loop\n",
+       "      \n",
-       "      \n",
+       "      \n",
        "    
      \n",
        "  
      \n",
        "
      \n",
@@ -526,34092 +535,60352 @@
        "  /* Instantiate the Animation class. */\n",
        "  /* The IDs given should match those used in the template above. */\n",
        "  (function() {\n",
-       "    var img_id = \"_anim_img1418891497c74418ba4cef71748b9a8d\";\n",
-       "    var slider_id = \"_anim_slider1418891497c74418ba4cef71748b9a8d\";\n",
-       "    var loop_select_id = \"_anim_loop_select1418891497c74418ba4cef71748b9a8d\";\n",
+       "    var img_id = \"_anim_img3502fbafac2847afae5512f80c1aed54\";\n",
+       "    var slider_id = \"_anim_slider3502fbafac2847afae5512f80c1aed54\";\n",
+       "    var loop_select_id = \"_anim_loop_select3502fbafac2847afae5512f80c1aed54\";\n",
        "    var frames = new Array(51);\n",
        "    \n",
-       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABEwAAAJYCAYAAAB4lnMNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
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-       "qtcM3dWVb5nu9H2uc9/XZdm2bQsAAAAAAAAhLtMBAAAAAAAAwg0FEwAAAAAAgJNQMAEAAAAAADgJ\\\n",
-       "BRMAAAAAAICTUDABAAAAAAA4CQUTAAAAAACAk1AwAQAAAAAAOAkFEwAAAAAAgJNQMAEAAAAAADgJ\\\n",
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+       "AQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMA\\\n",
+       "EwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJ\\\n",
+       "MAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCT\\\n",
+       "ABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAy\\\n",
+       "CTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAg\\\n",
+       "kwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAA\\\n",
+       "MgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAA\\\n",
+       "IJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAA\\\n",
+       "ADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAA\\\n",
+       "ACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAA\\\n",
+       "AAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAA\\\n",
+       "AAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAA\\\n",
+       "AAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAA\\\n",
+       "AAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAA\\\n",
+       "AAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAAAAAAAAAyCTABAAAA\\\n",
+       "AAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAAAAAAAAAgkwATAAAA\\\n",
+       "AAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAADIJMAEAAAAAAAAAIJMAEwAAAAAAAAAAMgkwAQAA\\\n",
+       "AAAAAAAgkwATAAAAAAAAAAAyCTABAAAAAAAAACCTABMAAAAAAAAAAEKW/wckwc+12qUUYQAAAABJ\\\n",
+       "RU5ErkJggg==\\\n",
        "\"\n",
        "\n",
        "\n",
        "    /* set a timeout to make sure all the above elements are created before\n",
        "       the object is initialized. */\n",
        "    setTimeout(function() {\n",
-       "        anim1418891497c74418ba4cef71748b9a8d = new Animation(frames, img_id, slider_id, 40.0,\n",
+       "        anim3502fbafac2847afae5512f80c1aed54 = new Animation(frames, img_id, slider_id, 40.0,\n",
        "                                 loop_select_id);\n",
        "    }, 0);\n",
        "  })()\n",
@@ -34750,8 +61019,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/05-mixed-poisson.ipynb b/docs/notebooks/05-mixed-poisson.ipynb
index 37067dd030..132881a084 100644
--- a/docs/notebooks/05-mixed-poisson.ipynb
+++ b/docs/notebooks/05-mixed-poisson.ipynb
@@ -103,7 +103,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rt = FiniteElement(\"Raviart-Thomas\", triangle, 2, variant=\"integral\")\n",
+    "rt = FiniteElement(\"Raviart-Thomas\", triangle, 2)\n",
     "Sigma = FunctionSpace(mesh, rt)\n",
     "V = FunctionSpace(mesh, \"DG\", 1)"
    ]
@@ -184,18 +184,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "94599c96801840cbba1fb66f54578004",
+       "model_id": "3703b53e79cc44c1abf4c3c262b49331",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -216,11 +216,11 @@
     "\n",
     "quiver(sigmah, axes=axes[0])\n",
     "axes[0].set_aspect(\"equal\")\n",
-    "axes[0].set_title(\"$\\sigma$\")\n",
+    "axes[0].set_title(r\"$\\sigma$\")\n",
     "\n",
     "tripcolor(uh, axes=axes[1])\n",
     "axes[1].set_aspect(\"equal\")\n",
-    "axes[1].set_title(\"$u$\")\n",
+    "axes[1].set_title(r\"$u$\")\n",
     "\n",
     "plt.tight_layout()"
    ]
@@ -284,10 +284,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N =   5, iterations = 217, converged reason = CONVERGED_RTOL, time = 0.013s\n",
-      "N =  10, iterations = 419, converged reason = CONVERGED_RTOL, time = 0.022s\n",
-      "N =  25, iterations = 874, converged reason = CONVERGED_RTOL, time = 0.137s\n",
-      "N =  50, iterations = 1634, converged reason = CONVERGED_RTOL, time = 1.000s\n"
+      "N =   5, iterations = 182, converged reason = CONVERGED_RTOL, time = 0.013s\n",
+      "N =  10, iterations = 342, converged reason = CONVERGED_RTOL, time = 0.012s\n",
+      "N =  25, iterations = 707, converged reason = CONVERGED_RTOL, time = 0.119s\n",
+      "N =  50, iterations = 1332, converged reason = CONVERGED_RTOL, time = 0.843s\n"
      ]
     }
    ],
@@ -368,10 +368,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N =   5, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.032s\n",
-      "N =  10, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.060s\n",
-      "N =  25, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.512s\n",
-      "N =  50, iterations = 2, converged reason = CONVERGED_RTOL, time = 3.811s\n"
+      "N =   5, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.019s\n",
+      "N =  10, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.055s\n",
+      "N =  25, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.610s\n",
+      "N =  50, iterations = 2, converged reason = CONVERGED_RTOL, time = 4.200s\n"
      ]
     }
    ],
@@ -463,30 +463,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Linear firedrake_9_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 24\n",
-      "    Linear firedrake_9_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
-      "    Linear firedrake_9_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 25\n",
-      "N =   5, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.033s\n",
+      "      Linear firedrake_9_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 24\n",
+      "      Linear firedrake_9_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
+      "      Linear firedrake_9_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 25\n",
+      "N =   5, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.030s\n",
       "\n",
-      "    Linear firedrake_10_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 26\n",
-      "    Linear firedrake_10_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
-      "    Linear firedrake_10_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 27\n",
-      "N =  10, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.039s\n",
+      "      Linear firedrake_10_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 26\n",
+      "      Linear firedrake_10_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
+      "      Linear firedrake_10_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 27\n",
+      "N =  10, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.027s\n",
       "\n",
-      "    Linear firedrake_11_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 27\n",
-      "    Linear firedrake_11_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
-      "    Linear firedrake_11_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 30\n",
-      "N =  25, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.110s\n",
+      "      Linear firedrake_11_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 27\n",
+      "      Linear firedrake_11_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
+      "      Linear firedrake_11_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 30\n",
+      "N =  25, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.104s\n",
       "\n",
-      "    Linear firedrake_12_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 27\n",
-      "    Linear firedrake_12_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
-      "    Linear firedrake_12_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 30\n",
-      "N =  50, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.365s\n",
+      "      Linear firedrake_12_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 27\n",
+      "      Linear firedrake_12_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
+      "      Linear firedrake_12_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 30\n",
+      "N =  50, iterations = 2, converged reason = CONVERGED_RTOL, time = 0.363s\n",
       "\n",
-      "    Linear firedrake_13_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 28\n",
-      "    Linear firedrake_13_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
-      "    Linear firedrake_13_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 31\n",
-      "N = 100, iterations = 2, converged reason = CONVERGED_RTOL, time = 1.507s\n",
+      "      Linear firedrake_13_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 28\n",
+      "      Linear firedrake_13_fieldsplit_1_ solve converged due to CONVERGED_ATOL iterations 0\n",
+      "      Linear firedrake_13_fieldsplit_1_ solve converged due to CONVERGED_RTOL iterations 31\n",
+      "N = 100, iterations = 2, converged reason = CONVERGED_RTOL, time = 1.468s\n",
       "\n"
      ]
     }
@@ -536,11 +536,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "N =   5, iterations = 37, converged reason = CONVERGED_RTOL, time = 0.027s\n",
-      "N =  10, iterations = 39, converged reason = CONVERGED_RTOL, time = 0.030s\n",
-      "N =  25, iterations = 41, converged reason = CONVERGED_RTOL, time = 0.058s\n",
-      "N =  50, iterations = 41, converged reason = CONVERGED_RTOL, time = 0.154s\n",
-      "N = 100, iterations = 41, converged reason = CONVERGED_RTOL, time = 0.577s\n"
+      "N =   5, iterations = 37, converged reason = CONVERGED_RTOL, time = 0.023s\n",
+      "N =  10, iterations = 39, converged reason = CONVERGED_RTOL, time = 0.017s\n",
+      "N =  25, iterations = 41, converged reason = CONVERGED_RTOL, time = 0.045s\n",
+      "N =  50, iterations = 41, converged reason = CONVERGED_RTOL, time = 0.143s\n",
+      "N = 100, iterations = 41, converged reason = CONVERGED_RTOL, time = 0.515s\n"
      ]
     }
    ],
@@ -588,8 +588,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/06-pde-constrained-optimisation.ipynb b/docs/notebooks/06-pde-constrained-optimisation.ipynb
index 1b3cbda968..659239f9c8 100644
--- a/docs/notebooks/06-pde-constrained-optimisation.ipynb
+++ b/docs/notebooks/06-pde-constrained-optimisation.ipynb
@@ -40,7 +40,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from firedrake import *"
+    "from firedrake import *\n",
+    "from firedrake.__future__ import interpolate"
    ]
   },
   {
@@ -115,7 +116,7 @@
     "\\end{split}\n",
     "$$\n",
     "\n",
-    "Here, $u$ and $p$ are unknown velocity and pressure, $f$ is a prescribed inflow, $g$ is the control variable that we will optimise for and $\\alpha$ is a regularisation parameter.  This corresponds physically to minimising the loss of energy as heat by controlling the in/outflow on $\\Gamma_\\text{circ}$.  The regularisation parameter penalises too many non-zero control values."
+    "Here, $u$ and $p$ are unknown velocity and pressure, $f$ is a prescribed inflow, $g$ is the control variable that we will optimise for and $\\alpha$ is a regularisation parameter.  This corresponds physically to minimising the loss of energy as heat by controlling the in/outflow on $\\Gamma_\\text{circ}$.  The regularisation parameter penalises large control values."
    ]
   },
   {
@@ -131,6 +132,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
+    "if not os.path.isfile(\"stokes-control.msh\"):\n",
+    "    # If the mesh is not available locally, we download it.\n",
+    "    !curl -O https://raw.githubusercontent.com/firedrakeproject/notebooks/refs/heads/main/stokes-control.msh\n",
     "mesh = Mesh(\"stokes-control.msh\")"
    ]
   },
@@ -149,18 +154,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "16fa87a623784667ac3d8fd9149bbfa5",
+       "model_id": "ffc57ce7c4b14839b77cad7154c7b282",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -226,7 +231,7 @@
     "u_inflow = as_vector([y*(10-y)/25.0, 0])\n",
     "\n",
     "noslip = DirichletBC(W.sub(0), (0, 0), (3, 5))\n",
-    "inflow = DirichletBC(W.sub(0), interpolate(u_inflow, V), 1)\n",
+    "inflow = DirichletBC(W.sub(0), u_inflow, 1)\n",
     "static_bcs = [inflow, noslip]"
    ]
   },
@@ -279,10 +284,7 @@
    "outputs": [],
    "source": [
     "w = Function(W)\n",
-    "solve(a == L, w, bcs=bcs, solver_parameters={\"mat_type\": \"aij\",\n",
-    "                                             \"ksp_type\": \"preonly\",\n",
-    "                                             \"pc_type\": \"lu\",\n",
-    "                                             \"pc_factor_shift_type\": \"inblocks\"})"
+    "solve(a == L, w, bcs=bcs)"
    ]
   },
   {
@@ -293,18 +295,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d674ed28187246738b21957bd255c23a",
+       "model_id": "fb82400eeda647fb805f79ae9a80af45",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -423,18 +425,18 @@
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+       "model_id": "e21cd4121f2949709d2709ced218a5af",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -510,11 +512,7 @@
    "source": [
     "g.assign(g_opt)\n",
     "w_opt = Function(W)\n",
-    "solve(a == L, w_opt, bcs=bcs, solver_parameters={\"mat_type\": \"aij\",\n",
-    "                                                 \"ksp_type\": \"preonly\",\n",
-    "                                                 \"pc_type\": \"lu\",\n",
-    "                                                 \"pc_factor_shift_type\": \"inblocks\"},\n",
-    "      annotate=False)"
+    "solve(a == L, w_opt, bcs=bcs, annotate=False)"
    ]
   },
   {
@@ -525,18 +523,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3ce9ad6299143f3a616c7dd86c6fce7",
+       "model_id": "57890e0a4aee492c8c6d32b30a7a6a05",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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EEEIIcWrZtGmTestb3qKampqUz+dTjY2N6i1vecsRw+AqdXD43i1btqjXv/71KhaLqaqqKvX+979fZbPZSXm3bdumLr74YhUKhRRQGmL4WMMKX3XVVUecD1Dve9/7JqVNDHt76623HlGuCS95yUsUcNTp0OF4LctSn//859XKlStVIBBQVVVVat26deozn/mMGh8fL+XLZrPqAx/4gKqpqVGRSES9+tWvVgcOHChrWOEJiUSi9FncfvvtR82TTCbVxz/+cbVo0SLl9/tVbW2tOv/889V//Md/KMuyJn0uh5/36aefVldeeaWKRqMqHA6rl770pZOGaZ4wPDys3v/+96uWlhbl9/tVa2uruv7669XQ0NCkz/fQYYUdx1F/93d/p+rq6pSmaUcdYnhiaOkf/ehHZX0eQgghxKlGU6qMHtKEEEIIIY7illtu4TOf+QyDg4PHrO0gXpw+/OEP8+1vf5u+vj7C4fBsF0cIIYSYNunDRAghhBBCzKhcLsftt9/O6173OgmWCCGEmLOkDxMhhBBCCDEjBgYG+OMf/8hdd93F8PAwH/zgB2e7SEIIIcRzJgETIYQQQggxI7Zs2cK1115LfX09X/7yl1m7du1sF0kIIYR4zqRJjhAvIg888ACvfvWraW5uRtM0fvnLX07arpTiU5/6FE1NTYRCIS6//HJ27tw5O4UVQswJt9xyC0op6b9EAHDJJZeglKK/v5/3v//9s10cIYQQ4nmRgIkQLyLpdJo1a9bwta997ajbv/CFL/DlL3+Zb3zjGzz++ONEIhGuvPJKcrncSS6pEEIIIYQQQswuGSVHiBcpTdP4xS9+wTXXXAMUapc0Nzfz93//93z0ox8FYHx8nIaGBr773e/y5je/eRZLK4QQQgghhBAnl/RhIoQAYO/evfT19XH55ZeX0ioqKjjnnHN49NFHjxkwyefz5PP50rrneYyMjFBTU4OmaSe83EIIIYQQ4sRQSpFMJmlubkbXT/3GCblcDsuypszn9/sJBoMnoURirpOAiRACgL6+PgAaGhompTc0NJS2Hc3nPvc5PvOZz5zQsgkhhBBCiNlz4MABWltbZ7sYx5XL5ZjfHqVvwJ0yb2NjI3v37pWgiZiSBEyEEM/Lxz/+cT7ykY+U1sfHx5k3bx4HDhwgHo/PYsmmJ5+1UJ5CKYVSoFCgFJ6nKCwqPOVh55zCdk/heV5hrhT5nIVyvMI25ZXyuJ4im8jiKQ8UeEqhPK903MIxwPMUCoVWLIvnFfKZPpNUIoPngecWzmeYOulEpnBu1yseU6EbOuNDKTzXw3W9Uv6D6wrX9fD5TRIjKTyvkMdzC9dS11zF/h29hTRPYZoGyfEMnlv4LAAWrGplz+buSZ9d+7ImOrf1AjBvaSP7t/cRjgfJJI7e983ite38+88+dCJ/nOJFwHU9cukcVs4il7awchZO3iafs7FyNo7lkM/a2HkbpRS5dA7bctGA9HgG27IxTIPEaBrXcrAtB93QSY9lcB0Xx3YK///GM3iOh+u4uK5HRW2Uvn2Dhf9Pjovnekctn6ZptC1vpnf3AIapoxs6bUub6dndj2EU1lsWN9K9qw9D19GKabqhFec6hl5M03VqWqoY7RtHM/TC8XQN028W/u/rGppeSNMNnWhlmMRICl3X0XQNTTu4HU0r5gdd04lUhMkkMofk09A1HTPow8nZxYuhVGNQ0zRcz0N5Xmm9dM0cUquwuBiKhUiNpYt5mbRPKb8Ght/EyhysrXjYh3lEkqFrWPmp3yJPCEZCpMczZeV1nakftiIVYRJDibKOZ/hMcunp9QVWURtjqHt0WvtUNlQw1D1Sdn7D0LEmfsbHEIwGSI+myzpeRV2coZ7yzw/g2G7p+6UcNU1V9O0bmtY5Gjvq6NrZW3b+s162hv/71h/Lzm/bTtl5HWvq361Dufb08p8IjrJ50PsNsVhstosyJcuy6Btw2ftUO/HYsWvDJJIe89d1YlmWBEzElCRgIoQACpF2gP7+fpqamkrp/f39xx0WMhAIEAgEjkj//md/Q/+uyTd7k+55D1lpmFdL59bJD+FH07K4gR3r95XWJ91jHXbDpRSkxtOl5VJ3TYcsTwRHACKxIMmx8m6mj6a+rYaBA8NHpLcva6ZzW0/Zx5m/ooW9Ww5+Fqedt5hnH508UtGC09rY8+yBI/ZdsLJt0r7HEomFSCezAAcfzEyd2nodJwe6buIzdHw+k3jMRDc0DMNANzSqa6vJtDqlNMPUaWqrxdT8GKZBfVs1kUiMQMhPPmthmgam38A0DQxfYd66uHFOBdPEKaxqtgtwbJ7nYeVtVDGw4joujuXgusVAp+th2w6u5RaDlAcDnYdOpeCnUtg5u5DmHEx3bKcUYC0EchWmzyA9nimmFwKiBwO06mB+BUF/EEt3DqYrheO5KFsjm8pP+js5seC6hQASh/zZPbRLvEOXqxpg8MDopL+7hYXJeUPRAMmjPJgfq6u9YNhPciRV9s+jprmKvr2DZeV1ynhIbV7UwIEyvrcAQrEgiWmUFaB9eQt7Nx75d/54Ok7T2PP0/rLzB8OBKYNINU2V9O8/8rvtaNpXtLJnw/TKPN2ACWsN9jxV/jUChENh9m8q/3t46ZpF+HR/+SfQym+momnlB1cK+U+dJjBzqZl1KKoIRY/9e2VLF55iGiRgIoQAYP78+TQ2NnLvvfeWAiSJRILHH3+c97znPdM+3nv+7c2EguEjNxzlO8q2HKz88d9yAdh5Z9KN7OFvKwsrhVk6mQOlJr0VLWU7ZFnTCm9cc+lcMW9h28Sb1tJ+mgYoXNsrvIHVtUlvZB278Ob60DRN01BKkc9YB9/cHrafrmtoml44p67huV7prbBu6KWHhUPfIDuOi37IOQyjkH/iTeFEcKMUDCm9uS5ME9c+F9oiCzFX6bpOMHRkMFkIcXK57sEaUeXQNA3DNE5giU4MZxo1XQr5y6u9kkgkaGj6+XMp0qzx8DjeT/z4W4WYTAImQryIpFIpdu3aVVrfu3cvGzZsoLq6mnnz5vGhD32Iz372syxevJj58+fzyU9+kubm5tJIOtNhmAbBcHkPC8GIPFQIIYQQYuYZhg7GC/8Fgemb3mNdufktZ+7do7lK4R6nFsnxtglxOAmYCPEi8uSTT/LSl760tD7R98j111/Pd7/7XW666SbS6TTvete7GBsb48ILL+S3v/2ttO8UL0iu52E7LrbjYtkuluNgOx6OW0h33IPLnlLkbQfXK/QD43qFba7n4boK1/PQNY2sbVPorkaV+pbxik0avOINmud5hVpOFGsyHTLXda3Qb4WuYRo6AZ+JUgrTMPCZOkGfCZqG3zTw+wz8pkHANDFNg4DfJFBs9hT0mfh9ZqHPCiGEEOJFxEPhHa1K8yHbhSiXpo7VOFQIIZ6DRCJBRUUF4+Pj0k+FeM5czyOTtcjkLLI5m3RxnslZZHI2luOQylhk8zY5ywalGElmyVsOOdshbznUVkU50FfoRycaDjCezpaaaIUCPlJ5C79p4CtODdVRxtM5TEMvBShMw8A0dKpiIZJZC6PYEaepa6Xlwlwj4DOxXReNiY4zKTSd0rTSvNB5sCrcqh3awTDgOIUOfCeCMp6n0DTI5KxSACfgMxlJZLAcpxDksV1q4mE6e0exbIe87TKvoZJnd/WStx2UUixvb+BA3xhBv0ko4GNxWy3DYxlCQR+hgI+mmjiW7RAO+gmH/FTFwhi6RjQcIBLyEw76iYQDRMMBQkFf4W2tEEKIF4W5dF83Uda925qIHafT12TSY/6y3jlxTWL2SQ0TIYQQs+p3D27l9w9vLa031MboH0kSCvoIB/001sWxHI9wwEcw6CMc8BEJB6iKhwkFfAT8JiG/D7/fLNSy8JsE/SY+w5CHeyBv2eRtl2zeJp93yOQtMlmLXN4mk7NxXJfR8SyZnMXgSIpEIktX3xjpbCGfnXcZGUuTzOSZ31pNZ9cI0UiAVYubGR5LE4sEqauJ4jcN4tEg8ViQmsoI4VCAiniIiliQingYn2/u9QkghBBi7rGVh32cKgG2kj5MRPkkYCKEEOJ58TxFJmeRzORJZ/OksxaO67FueVtZ+1950XKuvGj5CS7li1fA7yPg9xGPPP+mdY7rkc7kSabzJFO50pTO5hkdzTA6nmF/9wg+02D33kHGExmq4mG2bO0hGglQVRlm6cJGbNulujpCdWWEutooFfEwNdURKqsKaRJcEUII8Vx5xel424UolwRMhBDiRUopRS7vkM1ZpLIWyXSuUKsgV6hZkM3ZjKWypaYxmq7RMzBOutg0xkPRN5Qkm7fwGTr5Q0YhiIUD3Pv1983i1YkTwTR0KmIhKmKhae2Xz9uMjWcZHU8zNpZleCTFyEiakdE03d2jdPeMMjKSorY2xjPPdFFREaKmOsrCjjp0TaOmNkpdbZyqyhA1NTFqaqNUV0cIBHwn6EqFEELMVS4K9zj9lBxvmxCHk4CJEEKc4pRShf4qrELfHJblkMlapIvNKnJ5h3zeJm8Vmlsk03lyuULfHtmcjWHo9A4kyOVtsnmb2qooz+7sIZu3mejFavmiRrbs7Zt03nnNVezvHS2tL1nQwPZ9A6X1+poYmZwFgOV6hIM+ggEfkZCfWJkjJIkXh0DAR0O9j4b647cVV0qRTOZKAZXRkRQDg0lGhlPs3NnH/s4hhodSDA8nWbSogZ4DI9TURqmpidHWVk0w5KemNkpDfZx4RZia2hg1NVH8AbndEUKIFwtXFabjbReiXHIHIYQQh1DFTjc9r9Dppucq8paNZbuF0VEOmZyJEVOKzRRsuzDiiuN4OBOjrBTXbcchnbWx7UJnnRN5bcclEg5woHsE23bJ2w6W5WLZhcCIYRgMDCePKOeala1s2NJ1RHpDQ5y+wcSktMXz69m5bxAodEIaDBRGVWmIhUr9hLQ1VVJZGSYS9BGa6PwzHkJBqTPQaDiAz9RL6wG/WegQNOQn6PfJiCziedM0jXg8RDweYn5H3THzKaUYG8swMpwqBVBGR9MM9I3TuXeQUNDHxvWdjI6kmddey/BgslArpSZKx/w6TNOgujZKQ2MF0ViQmpoo1bVRItFgqWNgIYQQc5ODhs2x/5Y7x9kmxOEkYCKEOCG+95NHCIUiAASDPoZGUoUNxaj+oQN0TSwGgyYjo+lSWmkEEVXIP5HP8zyyObuQBijvkLlSRMJ++gYTKI9JQ7sWgiAcHKnEU8TiQbp6RktBkqONG7Z6RSubjhKcOFR1VZiRscwxtxu6hqcVmjT4fAY+n1kYocVnsKC9lpGxDH5/YajYaDiA32/iMw3CIT+O55U6M/X7CvPKihBnr+0gGPARCBRGPwkEfPh8Oj7fxHphbho6weKIKH6/KQ+ER2G5DjnXIevaZB2bnOuQcSws1yHr2ViuS95zyLsOluugaZCy87jKw1EejleY+3SdnOsceQIFhlYYMtjQdExNx9QNdE3HrxuFyTAJG34MTSNg+AiZJkHDR1D3EfX5CZt+QoaPgClf3ZqmUVUVoaoqwsJFDcfM5zoeIyMphoeSjAwVgivJ8Qy9PWN07h0kEg2w4S97GR9L09BUyfBAkuqaQmClta2aYMhHdW2MhqZKorEgVcXASmVVBJ9ffg5CCHEq8lRhOt52Icol3/ZCiBPirDM6iEZjQOGhxbIOeYg87IF9YlUBuaxdWtc0DTQKw7RqlB7085YNqrBemEDTtVI+NI1czkYvpuuaVprruj4p3XJclCoENHS9cDzD0NGL67peGGXFdb3C8LFGYUhZvTSkbCG/ZbuYE8PQmjo+szBCy8Rc1wtDykqwYuY5nkfCyjGWL0wJK8eYlWU8nyNh5dF06EqNk7TzJO08NcEQW0b7STs2aTtPxrFZW9fEk0MH0DWNkOEjaJiETB8LKqoYzqfx6yYBwyCgm/gNk6ZQjFErg6kbmFrhd8Gn6YR8fvKHBUyUUrhK4aFwi4EVV3kopcg4DrbnYnkOlutSFQixLzlK1i0EbfKOTdp2S8dqDMUYzmaI+QJEfQHWVDczmM1QGQgR9wdpDVfg03WqgmGq/EGqg2FqQxFqg2HCPv/J/tHMOsPUqauPUzdFU6CJwMposbbKyFCKsaEUQwMJdmzu5sDeQTY9tY/RkTSe67Hq9Hb27hygqiZCVXWURcsacWyXyuoIldVRKmsixOIh4pURKqvDROMhTFM6shVCiJPBRcM9Ti2S420T4nASMBFCnBArljTL2PaHkWBJebK2zXAuw3Amw3A2w3A+y0g2w0g2y0gui2FobB8dZDSXZTSfY2lNLesHe6gIBKkMBFleU0fasYj7A1T4gzRHYnTEq0pBhgp/gDctWkPY9BPx+YmY/lJtDr9unHI/J9fzyLg2GcciZeVJ2RYJO0/KzpN3HfozacatHGP5LAdSY+xLjjGay5C2bXqSSdxitanzG+fRlUhQH45QF4qwMF5D2OenMRKlORKjIRqlORojaL74OlItN7DieR6J8SyjxX5VRofTjA4ncWyXA3uH6N4/zPhoBtty6Nk/guMUgl1LVjbT0zlMvCpMvDJCTX2MQMAkFg8TrwwRqwhTURUmGPITiQWJxkJEYkEisQDBkL8UuBVCCDE1CZiImSQBEyGEECdc1rYZyhSCIP3pFIOZDEOZNJ5SbB8ZYiiTZjibBRQH0uNUh8LUhMLUhEI0x+KETJPqUJjVsTh1kQivWriUqmCIqkCIikCQsOk75QIdM8XQdWJ6gJgvQEMoNq19PaUYy2cZymYYyRU++4FMmsFsmuFchif7uulPp6j0h9jQX+j097TaenRPoyUWpzVewYLKKhqjUdoqKmiJxwn5XnwBlQm6rlNZVRj+eP4UeZVSZFJ5xkfTjI+lGR/JkBjLMD6WIZvOMzQwzmDfGHu295Icz1JZE2HzU/uwD6lRFK8MkxzNEor4iUSDhKMBwtEgVbVRAEJhP6FwgFC4EGgxTYNg2Ecw5CcY8hOO+NENg0DQhz9gFidfYT3oK9SK8xkSkBHiFKKO1jb4GFynvAFyXbf8gXTVYe1Vchmr7H1PFZ7S8NSx7wmOt02Iw0nARAghxHPieB4j6QyD6TRD6QyD6QwZ22Lf2GhxPU1lOMQjB/bjeB51kQjrWppJ2Ra14TC14TCt8ThLa2upDUeoDYWoCYWJB4PoL9Dgx8mmaxrVwTDVwfBx8ymlGMll6Ukm6U+n2D82TldinP3jYzzT28ez/f1kHYczGproTSaZX1XFuuZmKgIhFtRUsaCmmpaKuPzcDqFpWrGWSJDmeTVl72flbVLJHOlEjlQySzqRI53KkUnlyaTzZFI5clmbZCJLLmORGBslm7ZwbYfxkTS5rE0ua5HPWixd3crWDQeOea5la9rYtvEAhlno+8jnNzCLc5/fxOcziMSCWHkb0zQwTKMUZDGKzQ21UvPFYnPGYtNFvdgU0TD1QlNIQydWESKdyJX20SaaS06s63opPRT2kU0XHtR0XQPtYNPMg/Nis01Nw+czsPIOHNqkszgv/VYW00yfgX1IM9FJv7WH/A5PLBq6ju24lMv0G1hZu8y8Jvns9B5IDVMnX+bxS+fx6eQy5e/jD/rIpvNl5Q1F/KQSuakzKoU7jeFJwhE/yfFs2fknONP4WUXjIcZH0mXnD0UCpMaP3V/ZEcevCDM+0YdbGc68eCn//uEfl53/ZHDc8n4PTiUWBhbHDgRbUsNETIMETIQQQpTYrstIJstIOsNwOsNAKs1IJstQOsNQKo2ua2zpH2AonSFr25w/v52BdJq6SJjaaISO6ko6qqo4q7WVukiEmkiYukiEiO+FWwPkhUDTtGKNnjCrOLITVaUUw5kM+0bH2Dc6yr7RMZJ5i99v382+0TGW19ays3+YBbXVrG5qoKUizuK6GpY01NIcj0kNhmnwB3xUBwqdzT4fSimsnE0+75DP2Vg5GytvYxWHIbdyDq7jkknncWwX23KwreIIXpZTSLMdPMcjl7VwJ0b/ctzCsu1iWza25RZHFCuMGOZ5Hp5bGEHMc71Ch9qOi+sp2ubXsXtrT6Hjba/YIXdxRLKJzrkLaYqlq1rZ/OS+sq93/tJG9m7vmzoj0DSvht79w2Ufu7o+xsjAkaOVHcvSNW1s33jsQNWhplPuCR1LGti3o39a+0z3mqdTrhVntLPl6c5placcy0+fx9b1+6e1T6wiNK0gy3TLPt3PfsnqVnZsOn6n8YeqqIqWnVccm5qihomSGiZiGiRgIoQQL1BKKdJ5i/FsjpF0lrFsjtFMlrFMFst12T86zmgmy0gmw0g6S2tVBRt7eqkKh6mJhKgJh6iKhqmJhGmrrOD0liZqIoX12kiYaMAvQZAXCU3TqI1EqI1EOLO1ZdI21/PoGhtn5+AIu4aGGU6luXvzDnYPjWA7LvXhCC1VcZY11LGmpZGOmiqWNNYSCbz4OqE9mTRNIxDyEwjNzc+5MDJacXS0iQBLYUNxFDVVHBmtMJqaa7ulUc5UaTi2g80bSq0ciscqNVE4yohtk44BhXNPo2aE63plN6tQqLKbVZTK4x59RLdy9puOcptxaGi47tS1OiYCZeVSqvwmJxMKo92Vfw5d17Hto4xsdsxCTa9MuqFhW+XXeGlbWMdf3/SKsvOfjCY5iUSChqYvlb3/qUD6MBEzSQImQghxCnNcj7Rlkc5ZJHI5klmLZD5PMpsnmcujlKJrLEEimyORzTOezVEXj/BkZzeup4gEfFSEgrTVVqJrGpWhEFXhIPXxCGdXtFIdCVEdDlMVCVEZCsqQtWLaDF2nvbqK9uoqLl+6sJRuuy6dI2Ns7xtie/8g2/uH2NM/zJN7u9E0uHTZQnyazvKWelY0N7CiuZ6qaGgWr0ScSkpNbopkjCEhjmT6yvufUW6+o7GcuRd0dZWOq45ds3Ea8U8hJGAihBDPlVIKx/XI2YUhYfO2U5ic4rLj4LgeqVyerO2Qs2xytkPOdsjkLTK2Q9ayyVo2GcsupFk2jZUxNnf3o2mFh9FIwE806Kejroqs5RAN+okHA8RCAWqjEVa1NlIRChIPBagIBYkF/cRDQXyGPGKI2eMzDBbV1bCoroarVi0tpY+kM2zrHWT/0BhP7u3ml09u4Tvpp0ikcjRVxljR2sA5C9voqKtiZWsDFZHgLF6FEEKIucZDwztOHyYeEjER5ZOAiRDihHh0eyeBcKGjyYPVpA+aqDJr6ho52y1Uf1aFUT0mqliXql0XlwEs1y1V6/XUwUkdsu43DRLZPK5XaD9fmB9c9jyFc0ia3zQYz+SwXQ/Hc7EdD8fzcNxCu3tdA7tY7XWimvZEN4KmodNYGSORy+M3DYI+E7/PJGAaBEyT2ngEy3EI+EyCPh8hv0k8FCToMwn6TMJBPyG/j/DEFPATNE0CvlNveNsXM1fZOF4Ox8tiuWly3iiecnCVVdyWx/ZSxd+Pwu+s5WbQNB1dM9AwMPQQlpvG0PyYegBT8+Mq8Okh/EYYvx5Bp7AtaMQJGlF07YUX9KqOhDl/UTvnL2rnzeeuASCVzbOtd5DNB/rZ3NXPA1v28O/bCv0KXLCknYpQkFXtjazqaGJpcx0Bv9y+CCGEODpLGfjUsb8/LYmXiGmQOw4hxAkxMJ7CzB9st1saseCQPJoGPtMkbztoHDL6AYW5rhVHRijm1TWdoK6hawcnTdcwilW3da0w0oKpa3iqUDvDNHR0TcMsjr5g6DrGYXNN0/AZhbymYWDqE8s6ZnG7eOFJ2UOM2b3knHGybpKsO07eHSTtDGF5afJeCqVcktZOFAd/l2sDSxjJ78DQfOiaD0PzUeFrZzi/HSj8LioFlprc8WB98HR6sxtL60ppOIe9AasKrKQ3t6O0Xhc4nWGrj4hZQcSsJGw2Ymp+4r5qFkZX0RZedGI+nJMsGgpw5oJWzlzQWkpLZHJsPtDPnr4RHtveybf+8ASjqSytlXGqY2FWz2/i9AUtrGxvoLEqJv9PhRBCABM1TI4zrLD0YSKmQQImQogT4jVnryQej892McQsUkoxYqXpSo+wLz1AV3aE7swoXZlhlsWb+cRp18xq+TrTT9Ob3UrIiBM0YkTNGqr89fj0IH49il+P4NNDaChMPYRPD2JqQSbCflM9oCvl4eGilIuHh+WmsbwUrsoXa6TkSDkj2F4Gy8tgeWlyro3fiJB1kuS8JHkvS8oZIeWMFI6pVZF1C0Ngvrzx2hdMwORo4uEg5y1t57yl7Vz7ktML/fUMj/PM3l6e2dfH+t09bN8/yNM7uqivjLJ2YTOntTeyZlEzy9rq8ZkvvNo5Qgghpuah40qTHDFDJGAihBDiOfOUR382yYH0CAcyowzlUmxLdHMgM0JXepSMa7GsooHd6Z7ZLuoRVla+jJWVLzthx9c0HQMdNB8Afj0M1E3rGJ5ySTvjpJ0xUvYYA1YfCXuUcXuYltDCqQ/wAqJpGm21lbTVVvLKs5YDkMlbbN0/wKY9PezrG+Xb9zzBeDpHwGdw6eqFNFbHWbOomdWLmqmUDmWFEOJFYepOXyVgIsonARMhhBDHZbkuPZlxOlMjdKZH6EqPcSA9Sk9mnP2ZAfLewSEZawJhxp1UaT2gm4QNP+fWLqI1XE1ruIaWUBUdkekFDl6sdM0g5qsm5quGELxw65M8N+GAn3WLW1m3uNCUx/MUnQOjbNzVw+Y9vdy3fiff++1faK+vQlOwZnEheLJmUTPzGqvR9blTLfv++7Zy9/9uKKxoEIsFqa6JEg75icVCxOJB4vHCPBTyE4kEiUQChCOBOXWdQgjxfNnKwD5OHya2xEvENEjARAghXuRsz6Uvk6Q7PV6YMuPkXIunh7voSo/Rn02igBXV9Wwb75+0b2M4RJWu0xquojVSRVu4kqZwnNZINW3hauqCUXTt2G95hJhJuq4xv7Ga+Y3VXHPhaQCMJDJs2dvHk1sPsGlXD/c8uo0FjdX0DSdZtaiJM5e1sbS9nuULGomETt3hM1/y0uW85KWFmjWep0ilcoyNphkfy5BIZEkmcxzYP0w+7zA4kCCVypFO58lmLFyv0Gl1U1MlPQdGCYX8hEI+giE/obC/tB6vCOM6HoGgiT/gIxAwCQQOLvsDJrqu4/cZ+HwGps/A5zfxmQY+v4HPV7itLHQ/JUEaMfepadZEsG13Wvt4nsKx3akzHpLfK/5/noppGgSn8TfNcY5ejmzWKvsYpwp3iiY5rjTJEdMgARMhhHgBU0oxbuXoz6ToSyfZnx6lN5OkJ52gN5OgL5OkJRrnscHOSfutrWli01g3GtAQitEaqWRNTRMXNy6kLVJVnCqpC0YJGr7ZuTgxbT/a/2tGrXEWRdtZFJ1Pe7gZU39h9/VRHQ9z4ZoFXLhmAQB522Hrnj427epl064entjcyZd//AC6prGgtYZ1y1pZPK+O0xY10958atZC0XWNeDxEPB6C9unta9suuaxFNmuTzVrkshaZjEU2a5HPOaSSWfJ5h2Qyh5V3yOcdrLxN3nKw8g7ZTB7LcnBsF9t2sSwXx3GxLQfHcWlqqWLHtt5DyqpjGIXJNIvLxXkw6MPKO2i6hl7smFs/bFnTNQxdo6YuxkBfopA20UH4RIffugbFjsI1XUNDIxwNkBjPlDoR1wo9igMckaZpGqGQj2QiN5GhRCv1WXRImlYoT2/P6FE/Y+0oHUpWVoUZHEhM62dV31hB1/7hae0DYFnlP4ADNDZWcKCz/PNUVkcY6B8vL29lhMHB8q57Xnstu3f0lV2ORUsb2fJMV9n5W+fVsGdX/9QZD6EUuG55AQqA09a08ezGA2XnD0f8ZNLlBySWLG9ix9beqTMC55y/iMcf2VX2sY/FcXLP+xgnm6d0vOM0yfGkSY6YBgmYCCHEHKSUImlbDGXT9GWSDGbTDGZTxXkan6HzWP9++jIp8u5EkxlF0Gcesg5xf5DTAg1c0NBBS6SClkgFrcV5fShKUzhOwJCviheKt7S9mmFrlF2pTp4e3cZ39v4vGhqnVSymPdzMaZUdRM0Xdl8fAZ/J2qWtrF1aaMajlOJA/xjP7uply54+1m/r4q7fb8BTinNOa8d1PVYsbGTFwiaWzW+goXZuj8jj8xn4fCFi8ZPzc/Y8heO4uK6H63i4rldad2yXvOXguYW35oW354Vl5SncQ5aVglzWLgzaXRxGHlU4vlITE5OGnXcstzg0/SE1BSaGq1eFlYPPTYp83mHSc1RxZXJSYS0Y9BEOH/n2/ljPYYGASTgSmNZnF40GMY3p1dBTFIJi0xGPhzDM8s8TCvmJxoJl5Q0GfcTi5eWtrolOq3ZGbX2cBYsbys5fXR2d9v9dx5lejZHKqgit86rLzu8P+Egnyw9IxOIhqmuiZeUt92f0QiQ1TMRM0tR065oJIcRxJBIJKioqGB8fl1FyyqSUIu86jFt5xvM5EvkcA9k0Y/kso/kco7ksY/ksmq6xebifkVyGkVwWy3MBhc80sL3JN8jnNbbRmRyjIRylIRylMRyjPhSlPhyhLhSlORynKRIj6pveDfwLkeumcNUQjjuE6w3jeSkctxfPG8f1xlEqj+MNoLwMnipMhlaJ7e5HKRuFhW60Yjn7S8fUjDYcdxBN86Hhx9Gq0TQdXQsRMDuwlYWpVxQmowVDj+A3avDrdfjNOoJGDdpJaspkew47k11sHNvLs2P7iPoihI0w66oXsrayg6jvhR1AOZpszmbb3n52dg6wYVs3W/f0MjyWQdkesWiQZQsaWLdiHs0NFSyZX09zfcWcDqIIIV6YphM8O1aTnEQiQWNj3Zy4r5u4B73t6XWEosd+2ZNNOdx4xlNz4prE7JOAiRBiRr2YAiau55F1HLKOfcjkkLFtcq7NYCZNyrZI2xYpy5q0HPH7eHZ4oBAgsfLF4EdBdTDESD4z6VwRn49zmtroTSepCYapDoaoCUaoCYaoCoaoDoaoC0WpDYWpC0WImH55gANcL4/t9pF3usg7XbjeKLazHdvpw3b7sN1eAnoIT6UxjDpMvYaQ/zSUl8bQK9D1Cgy9El2LoOlhdK0waVqwEAzR/GiYKPRJn7enbJRyUDgoZeN4GTyVxVUZXM/C8kZw3HEcb5y8grS9l7w7jGG00JPdiIZJyKwn5FuBX48R87UQ9c0n5m+m0teK3wifsM/M8Vx2JHt4amQ3XZkx9qVGWFfdwfm1S1he2YzxIu2TZnA0xY49/Wzd08+2Pf1kMxYbthSaA0TDAS4+eyGRoJ+F7XUsbK9jflsNoeCp2yeKEEKUYy7d102U9atPnTNlwOT96x6fE9ckZp/UsxZCzBmeUqUpkc/heB6O8gpVrz0P1/NwlML1PFzl4XgKx3UZtXI4rovtedheYe54LrbrYXkujudhF7cHDIMDyXHyjkPedYuTU5xcLMehJR7n8d4uLPfYb25Or29k/WChnXHE5yPi8xP1+Yn6A0R8fmqCIZZV1xH3BwpTIEhFcR73+Qn5fFQFQlQGQ1QGgtIs5jg85ZB1egpBB6ePrL2FrN1JztlPyKjDcrYRMFoJmK2EfAsJ+pYRC16Cz2jEZzZh6rUY+okLQEyHUgrbS5Jx+sk6/Yzb/STtbobz29md2khfbiuesqkLnQsY1AfnUx9YRl1wHrWBJnTt+fdHYuoGKyraWFHRBkDWtXh6ZB9/7t/Bvz5zD+2RGl7SsJQLGxYS852aVb437O2hJhamrbZyxo5ZVxWlbl2UC9YVhnNWSjE0mmbn3gF27BtgdCzNI0/t4a671wNQVRkmHPDT0VpDR1sN89tqWNBWQ2tzNeFTuHNZIYSY6zw0vKP0JXTodiHKJTVMhBAzaiK6/1c//B984YMPobqm0ZdOlnWMgUz6qOmaBrqmo2saIdNkPJ9D13RMXcfQtNLc0HVMTcfQdXQg77mYho6pG/h1HZ9uYOo6fsMoLfsMA79uUBeOMJRNEzBMAoZRmJvFuWHiNwzCPh+uUgRNk5DpI2z6CJkmQdNH2FdY9usGPsMg4vOjS02PGeEqm5S1n6S9m4S1h6S1B6VyjOYewKfHiPg6qAysIGJUEfK1EzTnETTb8BkvnLdHnnJJ2n0M5g/Qn9vHYH4vCdtle/Jp/HqQpfGLCBlx2iOLmB9eQoW/akbPr5RiT3KQP/Xu5IH+3VT7I5xd28HLWpZSFyqvXf3J8MTOA9z99Da6hsdZt6CFM4qTzzzxHdxmshZ79g+xv2eEbbv62HdgmL0Hhhkdz7BoXi27O4eoqYowr6WaJQsaqK4M09JYSUtjJc2NlRJMEUKcUuZiDZP/fPL8KWuYfPjMR+bENYnZJwETIcSMer5frEopaUryIqeUIu0MMmrtYSy/jbH8bsasPSTs/cwLn42u5Yn7FhDzLyDmn0/M14HfqJjtYs+qnJuhN7uX7mwX25Nb2JfeQdzfyog1zpLoUpbHVrMkvojqGQ6gDGRT/LF7O7/r2k5TsIKVVY28on0ZtcHIjJ7nuVJKsa17gPuf2cPTu7uJh4Ocv7SdC1fOp77y5AZ4xhIZ9h0YZn/3KAd6RtjfPYKmaTz25G5c7+Ct2NlrO0gmczTWxWlsqKChLk5TQwU11VHqa6JUVoTlb6QQ4qSZiwGT/3jywikDJh8986E5cU1i9knARAgxo+bSF6uYfY5nMWLtZzC3m7TdRV92EyP53WiaTkNwBTWBBir9C6kMLKDSvwDfKdJ05lSnlGLIGmRHcgc7ktvZlepjT7qTllATp1eexfL4Qk6rWIRfn7khoQezKe7Zv5179m+jKVjBBc0dvHzeEiK+U6fGxHAizcOb9/HQ5r2MpLKcu2Qeaxc1s2ZB80mpfXI0jusxMJigu2+M7r4xRsfS7Nk3RP9Agt6BccYTWSriIcYTWQB8psHZp3cwnshSXRWhujJCTXWEmqoIFfEwFfEQlRUhKivCRCPBU3JYZCHE3DGX7usmyvrvf3kJweMETHIph5vPun9OXJOYfRIwEULMqLn0xSpOrpyboi+7l77cHnLOMAcyjzNqdVPha6I+uIDW8HIq/E3UBBYSNmrkLfoMUkrRnx9k0+hm/jK6i/Wj2zA0neWx0zi3djnn1aygwj9zNS4OJMf41d4tPNnfTYUvxBsWn8b5ze2nVPM023XZuLuHRzbvY8PuHpY017KguZbzVrTTVl8528UryeYs+vrHGRxJMTiUYnA4ieO47NozyMhoipHRNCNjGaorwwwOTm72aOgaba3VeJ4iGg0SiwaJRQPEoiHqaqNoaITDfiJhP+FwoDAPFZZDAZNgyE8g4Jv20LZCiBeOuXRfN1HWf3vipVMGTD5x9n1z4prE7JOAiRBiRs2lL1ZxYiilGLeH6Mnuoye3j8F8F4PZzWTcJA3B+TQG59MSXkR9oI3aQDumLkMbn2yWZ7N5fA/3DzzLw0ObqfPXoOPj0sbVvKR+JTWB2IycRynF0wM93LXzWRK5PAsranjDstNoi516Taj6R5M8trmTx7Z00j00zplL2ljR0cCZy9qojJ7aQyt7nmIskWF0LMP4eIaxRLYwH8+Qt1z6+sdIpvIkUzlSqRzJVI7W5iq2bOs95jFXn9bKpmcLowD5TINA0CQU8BEI+GhqqCCdzuPzGfh9Jn6/gd9vFibfwWWlCsOeG6aOaRj4fDqGYWCaeqFfKZ+Baej4/T6U8tB1HV3X0PVCX1SGcXBd13UMo5CuaRpohX6tNK2wXdM0dE1D07Vif1eFNE3TSk09C/E6rbTfRPxuYllDw1NeKU8x98RCKYh76DbXdQ8mcPB4k9cP+VkpBdO681Z43jRyK4XnlX8C1/XKDk57nke5Tw3TKYdSquzjTnDd8j4UTymm+6hT7rFLNHCd6Z1D06Z3Hl3TcKaR3/W8sn/PamujNDYc+29yIpGgsWFuDSv8L09cOmXA5JNn/2lOXJOYfRIwEULMKAmYvLg4nkN/rovuXCc92U66s534NI9hq5fmYAdNoQ6aQx00Btuo8TehvUiHpD2VKaXYmuji3r5nuLdvI0oFaIvU8IrmNVxSv5yIb2YCWhnb4u49O7hnzw5s2+MtK1dzecdCfMbsNIU5Htfz2Lqvnye27ucvW/fTUBmjIhrirOVtrF3SQjQ094N8ruuRy9ukMxaZjEUmky8u58lkLVzXY3Q8Qy5nk8/b5PIOuZxNLm8TDPjo709gWQ6W7RTmlottu6W0cDhAotiMaCrLlzaydXtf2WVftKCeXXsGysrb1lLFga6Rso+9ZFEDO3b1l53fMPRpPfiuWdXGxmcOlJ1/OtcK4PcbWNaxR3A73NLFjWzfWd5nv3J5C5u3ds94Xk1j2gGTutoYg0NTdyRvmjqOM70ASFVVhNHRo3c+fzTT+QwnTPf3YLr5a6ojDI+Ufw3H4zg5Hr73ljlxXzdxD/qZxy+fMmDy6XP+OCeuScw+CZgIISZxXZdbbrmF22+/nb6+Ppqbm7nhhhv4p3/6p7LeQknA5IVJKcWYPUpPtouu7H66swfozh6gxh8l4yZoCbXTHGynOdROc6iNiCk/+7nI8zw2jR3gnp6NPDvWw/axEV7RspK/mnc6a6vbZqyZ1I7hIX60ZRMDyRQLq6p582mraYmdur8z2bzNxp3dPLntABt2dFMRCdJWX8XpS1tYvbiZqpj0rXM4x/VwHRfb8XAcF9f1cBwP23FxHQ/HdXGK2zxXkbccXM/D8xSe5+G5xSHi3eK6V6ix4Loeuq6RyzulWgwHazMUamMcmm6aOpmMBRysyaAo1PI42noo5CeVyhfSOORBvrhwcLWw5LqH1bo47Lb68Jvsysoww8Opsj/HWDTI2Him7Py6rk0rQFARDzEyVt6DdXVVhOHh8vLWVEfKCmgAhRoa7vQeR4JBH5lMfsp8uqbjuOUHkAD8fpNs1io7f21tlIGBMq+1qKmxgq7u0bLztzRXsb9ruOz8oaCfdHrqzweYMtgzFwMmn3r8coLRY/fRlUvZ/PNzCJh87Wtf49Zbb6Wvr481a9bwla98hbPPPvuY+b/0pS/x9a9/nf3791NbW8vrX/96Pve5zxEMBqd1XWJ2HTv0JoR4Ufr85z/P17/+db73ve+xcuVKnnzySd7xjndQUVHBBz7wgdkunjgJEnaSnmw3PbkeujJd9OR66M32Evf5aAw20RKax+qK03l549U0BBswNPkqeaHQdZ211e2srW7Hch3+3LeDn+9fz38+ex9D+QxvXrCO17StoiLw/JqoLKmp5ZaLLiVj2/xm+1be+3+/ZnVdI1cuWswF8+adcv3XhAI+zj2tg3NP6wAglc3zzM4e1u/o5k9/2cnOA4Msn9/A6kXNrFrUTEdT9Yu+s1XTKDS7Ccz9yjhCvKBlc8cPDhWa5NxycgozQ2zPxPCOfW9iT6PZ2oQ77riDj3zkI3zjG9/gnHPO4Utf+hJXXnkl27dvp76+/oj8P/rRj7j55pv5n//5H84//3x27NjBDTfcgKZpfPGLX5z2+cXskRomQohJXvWqV9HQ0MC3v/3tUtrrXvc6QqEQt99++5T7Sw2TucFTHoP5YQbyg3RluunJ9dGd7aU320dbqAm/odEaaqU51ExLqIXmUDMh49Tux2EuGM734SiHKl8dfmPuPEn2pMf46b4N/HTvBpoCVSyprOf6JWeypKJuRo6vlOLpnh6+v2EDO4eGePua03n1imVE/KfOCDvHk7cctu7rZ9POHg70jrJ+excN1TFWLGhk5cJGlsyrp6kufsoFgoQQYipz6b5uoqw3P/oKAsepYZJP2fz7efdM65rOOecczjrrLL761a8ChRqZbW1t/N3f/R0333zzEfnf//73s3XrVu69995S2t///d/z+OOP89BDD03zysRskteCQohJzj//fL75zW+yY8cOlixZwsaNG3nooYeOGQ3P5/Pk8werfSYSiZNVVDEFx3MZzI/Qnx+iPzdEyk6yO72P3twACTtJrb+alRWLCJtBlsWWcFn9S2gMNhCcQw/yc81Avptnxh4j4+bZne6hxl9LU3AJ9cF6OiLttIaa8Omn3ldzc6SSD668hPctv5jfdW3n+zue5M1//AGrKlt55/Izubhp/vMKBmiaxrqWFta1tNCXTPLjDc9w9Xd/yGWLFnDd6Wtoq6qcuYs5AQJ+k7VLWli7pAUoBID6hpNs3t3LMzt6uPN36+kbStJcX8Hapc0011WwqK2OjpZq/L5T7+cthBBzmat0XHXsPtMmth1+zxoIBAgcpVqcZVk89dRTfPzjHy+l6brO5ZdfzqOPPnrUc5x//vncfvvtPPHEE5x99tns2bOHu+++m+uuu+65XJKYRfItLYSY5OabbyaRSLBs2TIMw8B1Xf71X/+Va6+99qj5P/e5z/GZz3zmJJdSAORci5H8OAPWMEO5UQbyIwzmRxjMj5Jzswxbo9QFqmkI1tIQrKU93Mjy+CKaQg1U+uRt92xYHl/H8vg6oFDLZ8QaYntyL7tS+3h6ZCePj2zm9MrTqPE3sK56MSvi8wiZp04tC1PXuWrecq6at5xnR/r4f1v+wjv/9FNe3rqMy9sW8er5yzH159exb2MsxocvOp/3nHs2d2/bznvv+g3za6p4x9lncHpr8wxdyYmlaRpNtXGaauNcfs5SoDCSTffAGNv39bOzc5B7H9vBvp5hFs+rQ9c02ptrmN9aTUdzDa2NlYSDp87PXQgh5hJPaXjq2Pc4E9va2tompX/605/mlltuOSL/0NAQruvS0NAwKb2hoYFt27Yd9RxvfetbGRoa4sILL0QpheM4vPvd7+YTn/jENK9GzDZpkiOEmOQnP/kJH/vYx7j11ltZuXIlGzZs4EMf+hBf/OIXuf7664/If7QaJm1tbXOi6uapSClFxs0zZicYtZKMWUlG7SSWa3Mg08uwNc5wfpykk8av+zitYgEeDnWB6kOmKmr9VUR80oRmrkk5GbaM7eOhoW10pod4Yqibc2oWcUblIi5vXk5zuHK2i3iEnnSC72x5ktu3b+CM2hZe2b6UNyxZhX+GRr9RSvHgnk5+uWkzXaMJ3nHuOq5Ytuh5B2ZOFeOpLPu6R9jXPcze7mFyeZsnNnXiuB4t9ZW0NVXS1lBFQ02Mpro4TfUV1FRGJOAphDgp5mKTnA889Jopm+R8+cJfceDAgUnXdKwaJj09PbS0tPDII49w3nnnldJvuukm7r//fh5//PEj9vnzn//Mm9/8Zj772c9yzjnnsGvXLj74wQ/yt3/7t3zyk598nlcqTiYJmAghJmlra+Pmm2/mfe97Xynts5/9LLfffvsxo+iHmktfrCea5dqknCxJJ0vayZJ0MsV5FtuzGciPkLAzJOwU43aatJNlfqSJntwgVb4Ylf7CVOWL0RCoIuoLU+OvoCZQScwMywPTC5xSil2pfh4b3M1P924g6ziE9Qivb1/Dy1qX0RCOzej5PvTwr1hT08ybFq0h/BxqtYzkMty5/Rm+uv4x4v4AHz7jQl6zePmMBU4AtvUN8j+PPcWe4RFetXwpr1+3imjghVkTI285dPePsb93hK6+cXoHxukdHKdnYBxd07Adj/qaKPU1MRpqYrQ0VhIJBaitjFBTFaGmMkIwcOwHBiGEKMdcuq+bKOv7H3rtlAGTr174i7KvybIswuEwd911F9dcc00p/frrr2dsbIxf/epXR+xz0UUXce6553LrrbeW0m6//Xbe9a53kUql0F8gQf8XA2mSI4SYJJPJHPFH3DAMPK/8YQrnCk952J6DVZzyno3l2Vieg+3Z2J5Lxs2Rcy2yrkWuOOW9g8tKKcacBGknT8bNYbl24eAaxIwwuq4RNUNEzXBxXphq/HFaQ3XEfZHSFDVD6Jp8gZ5MjpvF8kaw3FEsL0neHcP2UrjoZJ0xXGWjtCh5L4Ohhch6Gj4tgKFXY+oBwkY1ETNGla+KqDmzb/01TWNxrJHFsUauW3AB28f6+UXnM9zXvYdbnvgT1y46g5e2LOQlrfPRZ+C8/3Heq7ln/zY+88QfaY9Wc/3yM4j4yg9GVAfDvHvNObx56Wpu37KRzzz4J7785KN85KwLuHrxMowZuDlc1ljHF655Ob3jSX7w2Hqu/tr3uWrVUt529loaKmY2gARw/8bdtNVXMr+x+qQHKAN+kwVttSxoqz1im+t6jCYyDAwnGRhO0T+cZGQ0w5Ob9jM8li5Mo2k6WqsZGk1TGQ9RFQ9TGQvR0lgJCuLRILFokHg0SDwSJBTyEQn5iYYDhIL+F/0oP0KIuctVGu5xmuQcb9vR+P1+1q1bx7333lsKmHiex7333sv73//+o+5zrPtpODgsuZgbpIaJEGKSG264gT/+8Y/cdtttrFy5kvXr1/Oud72Ld77znXz+85+fcv+J6P5Ptv6BUCwCKDylUCiUUihAoQjoPlJOFqUUXimPV8rrKYWnPLxi3qSTwVUerucV5srDpTD3lIerXFzlUReo4EBmAEc5TPx1K5wVNA5+QVb5Ywzlx/HrJn7dV5gbhWVfMa3KF8VWLkHdT9CYmHwE9cAh636Cuo+wGSRiBPHpptT8OIUo5ZF3+kg7e8janeScXpL2HvJOHzmnl4BvKUO5pwGoDJzFYHE5Hjib3uxGAEK+1QzkdxI2GunOJwsH1hYyZPUTN1ewJ72fpuBiurMDrK08HdfTWRFfQGu4nmXxFsLmzHaim3cdfn9gJ3ds28TjvV2siDbyttPWcPXiZQTM5/8exFOK33Zu5/edO1lR1cDbV5xB8DkcdySb4Rvrn+CZ/gFGMhluOv8iLu1YMKP/P5K5PL/ZtI3vPvAk6zpauOHCdSxtnJmRewCe2tHF75/awd6+EVa213PRqoWsXtCEacyNwGY2ZzGWzDGWyDCWyDKWyJDJ2QwOJ0mkciRTORKpHJFwgB17+klnLdKZPK6nCIf8LFvQQP9QklDQRyjoIxjwEQoUl4M+aqui5PM2fr9JwG/i95n4/UZh7jMI+k0U4DMNfD6jMDd1TLOwbJoGGgrD0DF0vTAvLWul5Yngjfxtnbum+7jhOC7TGfnVUx6eO71z2I7LdJ+CXLdwn3Ii9/Hcwv1POZQqXEe5lFK4bvkvwAJ+k0hQo7Ghbk7VMLnxgddNWcPktot/Nq1ruuOOO7j++uu57bbbOPvss/nSl77EnXfeybZt22hoaODtb387LS0tfO5znwPglltu4Ytf/CLf/OY3S01y3vOe97Bu3TruuOOOGblecXJIwEQIMUkymeSTn/wkv/jFLxgYGKC5uZm3vOUtfOpTn8JfxhCfE19Wd+96mGg8hlYMU0zUnNDQ0DUNQ9PxlELXtFJaaa5p6OiltEP3KUzGIetGKV3XdDTA1E3M4rp48bDdcdLWNjL2PhL5p8nYO0nbewiabSTtHUDh5hIthKfymFqUeOhC8u44fqOasK8DT7mYehhTq8FFoWs+FAEc5eApyHgOtpcn4SgS9jjjts6BTA8px2RfppuW4CK2Jg6wMLyYDaMHaA40UxeIc0bVAl7WsoyF8SNrCzxXSime7Ovmv554FMf2ODCW4KbzL+SqZUtnpH8P1/P45a4t3LXjWV636DT+aunK51STpTeV5EuPP8Ivtm3h8raFvOecc1h1WMd5z5fluPz22R385PGNVASCvPW8tVy4pH3GHrA9T7G5s48/b9jNM3t6qauM8pI1Czhr6TyqYi+svoKUUuQth3TGIpXJk8rkyeVssnmbXN4mk7PI5RyyeRtD1xgcSWFZDpbtYtkOeaswWbZLdUWYfV1DOI6H7bil+aHLDbUx+oeSU5Zr1dJmntneg6FraLqGruvoGsW5hq4XJk0D5YFWXNYmvlO0Q9cL84Xz6tixt/9gGhoTcXVNK3x3aYUNLGirZefegeLGg3kOWT34+6ZBR0s1uzoHj3othwbvD+UphVdmlGBRex3b9vSVlbe9peaYZTlUfXWUnoHxso4Jhd8Vp8yH79VLW3h684Gyjz3x8y5XY12cvsHpjdK3YlEjW3aV9xlOWLO8hY1bu0/oPmuXt7Jha1dZeWORIMl0ruxj11ZHGRpJlZ1/flsNO/d088SvPzmnAibvuv8N+I8TMLFSNt98yU+nfU1f/epXufXWW+nr62Pt2rV8+ctf5pxzzgHgkksuoaOjg+9+97sAOI7Dv/7rv/KDH/yA7u5u6urqePWrX82//uu/UllZ+XwuU5xkEjARQsyoudTWVcxdrpcmaW0mmX+GZH4TaXsXGXsnAAGjmbxbuNEOmq1E/SvxGQ2EfO2EzXkEzGaCZhOmHp3RMqWdDPvTA+xJ97E7McL60f08PdjH4lgzG4cGOKOig4BmclXbCl6zaDlBc+b6l3hk/36+9eSTPLbrABd1tHPdGWu5aFHHjBw769h875n1/LlzL39/9gWc1dz6nI6zfWiIzz/wAPfv28d1q9dw4zln0xSb2WY0Sike3tnJ9x58imQuzxvOXMWrzlhOYIaH7u0ZTvCXbQf48/pdpHMWaxc2c95p7azoaJzxc72QKaXI5W08r/DW2/U8HPfgsutOTIURJmzHKwUVSnNP4Xle4c15cdmyCzUHlCrUbPSUKqx7xVqOXqEmo2HoZLN2sQbk5FoQamIfFKjCm/ZkOl+qsVjIdDDvYUmEAj7Gk9kjr/k4n4dtl19TIBoOMJrIlJU3EvIznpr6oToU8JEsI98E1/PKrqFRXRWmf3DqwFgpf2WEgaHyAyCRsJ+xRPllnzhH/2D5ASKApvoKOntGprVPc0Ml+7vL36e1qYq9B4bKyhsM+KYVMKmIBRkeTZedv62pmkee3DbnAibv+PMb8UeP/ZLPSll855I758Q1idknARMhxIySgImYaUopck434/n1jOfXk8g/S9bajMIu5YkHzkDTfET9y4n6lhH2LSLiX4ihR2ax5DBuZXh8oJO7D2zloQPdLAjXsH1wiDozyofOuoBXLFk8I317QOHt9K+e3cqPntjInsERrli4kI+87ALqZ6hvj8F0ii8+9ggZ2+YfLriY5ucY7Hhg3z5+vH4TD+3r5N3nnM07zzqDkG/mOyfd0TvI9+5/msd27ee1Z63kTeetoS4+878Plu2wYVcP63d08eS2A/h9JuuWtrJuSSvLOhokgCKEmNP6B4bmXJOc6+9785QBk++99Cdz4prE7JOAiRBiRknAZO7wlMuI1cNgrpOB/D4GcvsYzHdyWcNfszR+7qyVSymPlL2HkdyTjOSeBm+I0dzDpe2GFqY2dCFBs4VYYBUx/2qCZusp379Bys7z651buW/vXnb0DzM4kubl8xfxscsvoqli5v6vjGVzfOv+J/j+Q0+xqr6Rt110Oq9Yu3TGPp8NvT3816OPcXFHB9etXfucmv84nseP1m/kl5u3kkjl+PjlL+HSxTPbv8mEwUSKHz+8kfue2c3y1nquveh0VrbNbJOgQyXSOZ7e0cWmnT1s3NWDaeisWtjE6oXNrJjfQG3lzNZsEkKIE2ku3ddNlPW6+94yZcDkBy/98Zy4JjH7JGAihJhRc+mL9cXC9VxGrF6GrW4G8wcYyncxlO9CR6cvt+WI/BfXXctF9W85aeVTyiNh7WQkt4Hh3EOM5p7G9g5Wk24KX4JfD1IROJ2KwOlE/EvQtbn71t7xPH72zGbu3rSdRCqHbsO/vPYKVszwQ/z9W/fwyZ/8noZwhLUdTXzw6ouIhmamA1rH8/j++vU8vGc//3DJRSype259swynM/zn/Q/z0w3P8qa1q3jnOevoqKmakTIeLmc5/N/TW/nhgxtY0VLHeUvauWLtYvwz0FHu8aSzFs/s7mHjzh6e3dPL8HiGeY1VLJtXx9J59SztaKA6Hj6hZRBCiOdqLt3XTZT1rX9665QBkx9d+qM5cU1i9knARAgxo+bSF+sLiac80s4Yw1Yvg7kuhq1eRqw+hvO9jFi9BHSTnDe53XLEqECpNLXBedQHOqgPdlAXaKchuICweeJ+dkq5jFvbGco+xXDuKYZzT2N7CUARMSoImvVUBc+gOngm1YEzCJgz11HqqaQvkeQf7/w9A4MJ5lVU8s9vexk1M9xkpG8syT9+/x5Gh7M0V8X46BsvoaO5esaOv290jE/97o9cumABbzvzudU2AdjU08cX732IJw/08K7zz+RdF5xF8AQ004FiZ7m7DvDjBzeyqbOX1527iqvPXkFLTcUJOd/Rzr+/f5Ste/rZ1jnA9v0DjCUzNFbHaW+qZmFrDQtaauhorCYSntkRloQQYrrm0n3dRFnffO/bpgyY/OSy2+fENYnZJwETIcSMmktfrHOJ49kknVES9jAJe4S0k6A/t5dRe4Axa4BxewhH2bSHV9CZmVxrREdnQXQFGhq1gVZqA63UBVqp8bcQ81WhneDRhDzlMm7tpj/7NAPZ9SStvThuYdQaDZOqwEpqQuuoDZ5JZeA0/MbM/t44ns243c+4PcSwNUDSHsVROj3ZHgzNT09ulKARYjifp8JXybilaA41kXd15oWbqPJFWRhrpDVcOeNNRjJ5i1tu/z1rF7Tw1peePqPHnmDZDv951wM88PhOFjXX8o6rz2HtsufWcevReJ7H9/6ygft27OFfrrqM9urnVkPE9TzuXP8M339sPSY6N1158Yx1XHss/WNJfvvUdn745/Usaqrhdeev4uJVC/AZxgk97+E8z6NvOMnu7mH2dA+xp2uYVCbP3p5h6qtjzGuoYmFbLfVVUVrqKmiuqyAqwRQhxEkwl+7rJsr6hnvfji9y7ICJnbb46WXfnxPXJGafBEyEEDNqLn2xngpszyLjJBm1Bki746ScMZL2GCmnMKWdcZLOGKNW/6T9moId9Of3ltYjRpxKfz1toUJfFdX+JmoCjVT7m6j012GcxCYsnnIZze9iOLeZnszDDOY2YnuF0REMLUBtcDWt4TVUB1dTFViNqc/MsKyechm19jOc309/bhcj+f2M5DvJKEjYA8TMBvrzYwA0BVezPbWDKn8DXdkx4mYlvbkMdf5GdqfGWBRZxFOjXXQE5zOUy7A3keaC6qXMj9Rwek0Ll7UtmrGOSl3Pm7GOX49FKcVPfvc0t935EMub67nxLReydmXbjJ5jW/8gX/zjQ1y9ejmvWrXsOR9nKJXm8797gN9s2sZrVi/jo1dcRF38xPb7YbsuDz67l7se2sRIIsOZS9p4zbkrWdwyu7WbPE8xOJqis2+U/uFxdnUN0z04Ts/gOOmsRW08QmUsREN1jPqaGI01MarjYWoqI9RWRIhHg6d83z5CiFPbXLqvmyjr6/54/ZQBk59d/r05cU1i9knARAgxo+bSF+tMcTybvJcl52bIuenSct7LFNMypQBIxk2ScZJk3RQZJ4mt8gD49SCWd+TQgKbmoyE4DwXEfdVU+GqI+6qp9NURNqNU+uqp9Nfh14Mn+aoPcrw8Q/ktDGQ3MpDdxEDuGWwvTWNwNePWNupCa6gPnk596Ayqg8sxtOcfaFDKY9zuYjC3hcHcVsbtIfan/4KrbOqDZ3Ag80wpb11wDY6yqfA3EtTriftqiJg1mHqYgB7C0PyAjkLDcm3Srk3SztObHac3k2Egm+EPXbvQ7CALwnXk8h6JVJ5XL1jGDaefQW147vQ/8dO7n+aXv92Ak3W49ZbXMa+lZkaPn7Fs/vWe+6iLRHj3S84h+DxGiHlkdyc/fmwTT+w+wIevvIA3nrUaXT/xD/89wwl+8/gWfvPYZpa31bNmfjMvO3Mp9adYZ61KKcaSWfpHkvQPF6bRZIYD/WMMj6cZHkuTSOXw+0xWL24mkc5RGQtREQtRGQ1RGQtRFQ8TCphEQwGi4QCxSGEe8JkSaBFCAHPrvm6irK/9wzumDJj84orvzIlrErNPAiZCiBl1Kn6xesrDUy6ucnCVQ97NkvVSOJ6Noyxsz8JRNo5nYRfnjipss9wcKWccy8theXns4tzycthenryXI+MmpixDzKwi7YwTMqKEzRhhI0bIjBE2ooVlI0rACBMzK4n6KomalUTNCgJ6+JR7cMk4wwzkNjOQ3cxgdgtj1hYclQUgZNTSEFpDfWg19cHVVAUWzkgHrY6XZyi3lf7cMwxknyHlDDOc31HaXhNcw0j+AHXBRTSEVhM26qkOzKMm0EZwBpr4uJ7HtrEB7u/ax+P7u8lmbZ7s6iFomvztujP58PnnP+9znCxf/ua97NjRi4HOv//L6wmFjn1T+Vz98unN/PTJZ/j317+cturK53ycnO3wjfse538eeJJXrlrK37zkTBY1npxaH56n2Linh/99fAv3rd/F4tY6rjp7OReeNn9OddKay9sMjqYYS2UZT+YYS2UZS2YZT2bxlKKzd4RUJk8ykyeVyZPKWli2QyTop6mugrxlEwr4CQd9hIK+0nJ1PEzedvD7TAI+k4D/kOmQNNPQ0XUNn2lgmgamoeMzdXyGUUgz9GK6BmgYho6ha6fc3z3xwuM47rTyW44HlPfY5CmF63plH9v1PFSZ2T3Pw/HKf3zzPA93Gvmj4QChwMEXG4lEgsb62lPqvu5YJu5BX/P7d04ZMPnVy/5nTlyTmH0SMBFCzKiJL6s/7/0VkXgYpRQT/0CV1jUg7STw8ApblULhoVRh3VNeYR2F49nkvHQp8OEpFw/v4DqFtLivlq7MTlzl4BWDI65y8Jh8F9IRXsG+zJGjwxiaian5MXUfPs2HqQeIm9Wk3XH8egCfHsSvBfDrQXx6Ye7XA4BGwAgS0MMEjTABI0xQL86Lyxo6QePUC35MxfHyDOd3MZjbwUh+Bz2Zp0g5vQBo6FQHFtIaPoOqwAIaQmuIms0zco2Ol2Mg9yy9mfWMWXs5kHoED6e0vSl8FgqoCy6nLriM2sBSImb9Sfl8PaX4057dfO2JJ9jY18eHzjuPD5x73gk/70yxbZd/+/ffsGNrD5e+dAV//beXnJDzbOsd5Fv3PcE161Zy0dKO53WsnX1DfOE39/OXPV389SVn8a5LzybwPGqvTJdlOzy8eR/3PrWTB57Zy7J59bxs3RIuOK2DppoX3s225ykyOYuxVJZsziabt8nkrEnLjusxlsyStx3yVmGybOewdZfKeIh9PSPYjovjejiOi+14pfUJoYCPbN4urRu6hq4XgieFIEphecWCRrbs7UfXNHQddK0QkNE0DV3XCunF5eqKMEOjadBA1zQ0DTStkFcDNF1Do5BeFQ8zNJoCCnko/imZ2K4dsj6xrbW+ks7e0VKZS3kO+TOkFTM311Wwv/9g3sO3T9B1Dct2jsh3qGg4yFgyc9w8EyqiIYbHUmXlrauK0jM49QsAgKa6+KRrP55FrbVs3dc/dcYiv88gncmXnR8gFgkyPJ6eOuMhfIaBNY2gybKOerbtGygrb01FmOHx8n5GAKcva2H9tu6y8i5srWF313D5x17awvrt5R37aFwrx1N3/OOcCC5M3INe9bu/mTJg8n9X/r85cU1i9knARAgxoya+rH63+ydE49HCTSl68UFWK/3TNZ2cmyneuOrFm1IdvZh3Io+Gju1ZQPHGWDPQKc4PW1ZK4eFiaAaGZmJoJnpxbmomejFdASgPU/djar7SXD/BnZ+e6grBkb2MWvvoz25iMLedkfxeFIUbyrbIOkwtQH1oBfXB06gLLsWnz8ybdsfL05/dRG/2afoyGxjKbS0FSBqCq8m6I9SHVtEQWk1DcBUV/nknvLPaqSileOTAAVbW11EZnJl+WE6WnTt6+diHfkgqlec/vnQta8/oOCHnGUtn+dhP7uaiJR1cd+EZzyug5XmKOx/bxH/99iFWNjXyt5efxTmL581gacuTsxwe29LJhl3d/OqhZ2msjnPRmgVcvGo+yzsaTnh/NC8kqvgW3nY9LKsQbPE8het5uK7C8bzC23F3Is3D9TzytovyFK4qvJX3lMLzPDylUGribX1hf8fxCt8NhT/8eJ4qfgeoUn6lFIahk8nZoIrh/eK2wl4KivnUIWUPBfwk0tni+sH00vUdcq2RoJ/xVPaw6z/yM/GK13U8ftMgnbXK+owDfrPsvOGgj/HUkU1DjyYWDjCSyE6dEaipDNM/nCwrLxQCVnnr+EGjw/l95X8mE6ZbC6S+Okb3wFhZeaPhAKOJ8gMm81tq2NE5WFbe5to4nX3lBasAlsyrY/OevrLzjyUn/1znYsDkFb/92ykDJve8/Ftz4prE7JOAiRBiRp2KTXLEZEp5JO1+Rqx9JOxe+rLPMpTfzbjVhcIjoMfwaTp1wWXUBZdSF1xKbXApEXPm+rvwlMNgbis9mafpzTxNf+5ZagMLGcwVav5U+RfSFD6dxtDp1IdWETZnbihcUfBfX7yH3/zyadrm1fDN7/wtPt+JGRnGcT2+9vtHGRhL8uk3XI7ffH41Q/rGknzu5/fxp2d3c/VZK/j7V19EdXR2msjYjsvG3T08sGkP67d30zU4xtnL53HBqg7OWNxKS13FnKtVJoR4cTu0phfMzSY5V97zrikDJr97xTfnxDWJ2ScBEyHEjJKAyanD8fKM2z2MW12MWV3k3SRdmacYtfbjqMJbxJjZiN8IUxNYSG1gUXG+kKAxsw96SnmM5PfSnXmCnsxT9GU3lfo90dCpCSyhI3ohVYH5NITWEDQqZuzc4uhSyRwfev/3eeObz+WKl6864Q/2P354A0/v6eYTr72Uqujzr5Fz76Zd/Pih9ezsHuYjV1/E1WevmPXgRM/QOI9u7mR39xC/fWwroaCfdUtbOXdFO8vaG+horD4pHdcKIcRMmUv3dRNlveLuG6cMmPzhlbfNiWsSs08CJkKIGTWXvljnOqUUOXechN1Dwu4jYfeStHtJ2H3omOzPPMFEpfCAHqUtcnax35EOqv3tVAfmE/M1omszX7NgIkDSk11PT2YDvZmN5L0EdYEFjFi7qA4sojl0Ok3hM2gMrSFgxGa8DGJqnqdm5AFeKcV4qjAKy/E8sGUPX737UW69/pW011U97/Omcnm++n+P8JOHNnDxigV88FUXsrBpZkf+ea48T7Gre4inth+gs2+Ee5/aieN6rFrQxFnL2mhvrGZ5ewN1VafW6DtCCHGouXRfN1HWy+++ETMSOGY+J53njxIwEWWSgIkQYkbNpS/WU5nrWWTcETLOCFlnhIw7TN7NMGztJWUPknYGSTmDuMpC13x4yiFq1hHzNRL3NVIbWETAiFHpb6PC10rQiJ/Qt+9KKcas/XRnnqI783QpQAITNUgW0xxeS3N4LQ2hFQSNyhNWFnHyDY+nufkrv+Gf3/1KmmqP//9+a9cA3/3jX3jLxaezdkHzjJz/mc4+fnDvU/xp4y6uu/QM3vXycyeN8nAqUErRNTDGpj297DowyFM7uth1YIhYJMjFaxcQDQVY1FLLotZa5jdV4z+JndoKIcSxzKX7uomyXvK/75kyYPLnV319TlyTmH0SMBFCzKi59MV6snjKwfJS5N0keTeJ5RXmjpcj4fSTc8bIueNk3XFy7hhho4bu7FOl/Q3NT9ispj64BltliZp1hclXT8SsI2zWEPPVY2gn7wHRVQ7DuZ30ZjfSl32Gvuwz5Nxx6gJLGcrvnBQgaQytkhokLwKdfaN895ePcePrL6BxiqBJz0iCv/9/v+FdLz+Hl65eNCPnd1yPOx/cyH//3yOsW9jKVWct44ozlsx6M53jcRyX3T3D7OoaYltnP7u6htjVPURrXSUjyQztjVW0N1bT3lhFR0MVjbVxGqtimOaJ6W9GCCEON5fu6ybKevFv3jtlwOSBV//3nLgmMfskYCKEmFFz6Yv1aAqjKdi4Ko+rLFxlYXsZbC+N4+VwVLY0t70crsqBMkg4PcV8hby2l8Hy0sR98+jKPFE6voaBX48SMGJU+ReR99KEjEqCRiVBo4KQWUnIqMZvRAgbNYTNavx6dNYf+tL2MP25zQxkN9Of28JQbjt1wWX0ZjdiakEaQitpDK2iMbSauuCSWQ+QeMoj41hknDyOUuiaTsjwETUDMorJCbRr/yD/+b0/8c/vv4qaKZqajKdz/PPtv+e8FR28/qLVM1aGwfEUX/v1I/zq0c2cubiVm954CYtb6mbs+CfD0Hiafb3DdPaN0dk3QmffKI7r8eT2A6AUDdUxTpvfhGnqNFTFaKiOUV8Vpb4qRk08TFU8jGnI77kQ4vmbS/d1E2W98NfvmzJg8tDVX5sT1yRmnwRMhBAzauLLqmtwM+GYH/BQyqM4MGNxrqA4Ff4CedherritkF/hoXCLwzi6xWNo2F4KDxel3EPmTmGu3OIQuDp5dwRPObjKxlM2nnLwOGRZOXjKJmjUMWLtLgZICkESKAQ2DC2AofupC6xg1NqDqYcwtSA+PYShBUrrYbMRR+Xw6WF8egS/HsHUQ4W5FsGnB/EbscLoM3p41oMfU8k6YwzmtjGY285gfjtDuR2YWohxez8AcV8LDaEVNIXWUhNYSE1wMYZ28psPWK5Nb26A/ZkeRvJJdqQ6ybsuG0a7aQ7W85eRTlbG5/OXoS7WVizk0YH9xLUY59XPx1AGq6ubeNm8xbTGpIPZmbRxWxdf/cEDfPETf0UsEjxu3qxl8w/f+l/OXdbOWy49fUb/b2zY3c237n6cp7Z1cdW5y3nP1edTWxGZsePPBsf1GBxN0T00xuBomv39o/SPJukfSTEwmiQS8vPsnj40DSqjIarjYWoqIsxrqCqlVUSCVERDVESDVERCRIJ+oiE/0VCAgN885f8+CSFOrrkYMLngV++fMmDy8Gu+OieuScw+CZgIIWbUxJfVfbs/SzwexfGygIaGBhoHlwsraOgYWhDLS6NpOqCjo4OmoWGgoaNpOho6phbCURk0zUDHLM6NQ+Y6WjEdXHR86JoPXTOL08F1o7hNK+43ERwxtMKkz0IA4GRzPYsx6wAj1i5G8rsZze9mJL+bvJvGKgaOImZdYWjhwDJqgouoDy4nZFae9LIqpejL9bM9uZvd6X3sTXWiYbAl2QnAkuhinh3fQ9gIMZBzWRhpY09qhCXRNnYlRpkXamT9cA+JrMfaylZSOZvtIyPUmhFe0bGE1y9dyZqGppN+XS9UD/xlFw8/uZuPvPNSAlP0JWI7Lv/8vd8TjwT5+zdeMqOjyLiex68f2czXf/UIldEQl5++mGtfto5I8NijJ8xlSinSOYvh8QwjiTTD4xmGEmlSmTz9o0nG0znGU1nGUznGUlk6GqsLtVaKDEMvBU+WtNYxnMgQDvoI+X2EivNw0EdVLIzjevh9JkGfid9nEPCbBHyFye8z0HUdn2ngM3RMo7hsGkcuGwaahgRqhDjBcpZTdt5DHw/n4rDC5/zyA1MGTB6/5stz4prE7JOAiRBiRs2lNxEvBkop8l6CpN1FyuplKL+TcauTMWsfSbsXUwuSVxl0zUeVv4PqwEKq/Auo8i+kNriYkPn8RzJ5Llzl0pXZx47UTrYltrE7vYuskyPt+nCVC0Clr4KwUcW8cBPzI/Oo8ldSF6ihxl9JlT+GqU/u56HQMW2W3eMjbB0e4C993Ty8fz+prM1XrriKly1cPBuX+oL1s7ufZvPOXv7x/a/AmKJ5iOcp/r87/4yJxt+94eIZb06SyVn87IFN3Pbrxwj4TN79mvN49fkrCPpPrY5hTzalFDnLIZXNk8rkC/OsRSqbx7JdRpMZMnmb7CFTJm8RCwXY1zdC3nbJWw6W45C3nMK67WA7Lj6/iWW7ZZXD0HWUUhiGjqFrGHpxbuiFZUOnsSpG/2gSXdPQda0419E1ivNCejjoI52z0bVieL4YjNE0DV0DtMK+E+kttXEODIwzEa+ZCO4f3HdyekU4yGiqOCT6odsmHJJWEQ0yksgccb2Hx4bqKqP0jSSn/JzqKiJl5QNoratkX9/IjOc1dA3Lmfrn2tFYza7uoSnzNVbH6BocL+vcpqGX/dBfVxGhZzhRVt4JVdEQA+Opae2zfF4Dz+ztLStvY1WM7qHjX2t7QxW7e4bLOt6ahc08taOrrLznn9bBI8/uKyvv4Vwrx8bvf2JO3NdN3IOe/YsPThkweeK1/zUnrknMvhf+K1QhhHiBc708aaeflN1L2ukj7fTheHn6shtI2t1YXuEGu9K/iHGrlwr/POqCK1kcfyUV/nYq/G1U+OfNaq0ax7PZn9nLrtRWdqW2sSe1g7yXozG0hL3pvWhotIXnsSCykuZQEwsiHbSGmjD18susaRpVgTBn1oc5s76V65afwYHxcX63ZydXLJiZjkfFQa975RkM/uABvv79+3n/O1563Ly6rvHRN13CN37+CDd/7Tf867tfSWAGgxnhoJ/rXnYmrzx3Od/+3yf49YPP8u1fP8YNrzyLay5e9aINnGiaRijgIxTwUVc5c8Mbe54incvjegrbcXFcb9LcnpgX0/KWjasUrqtwPQ/H9XA9D887uI4qvCH3lMLzvOJcFeaHLOuaRiZvQzFdAcorzD2v0CRUKYXnFRqBRkIBKqOF4bBVMT8T+xXfKSpFKR0ddE072MhUMWkZDq47jncwuHDI+8nD31Ratks2Z0/5uebCDumcVdbPIG+Xn9dyHDJl5jUMHcueOmhhO25Zx8zZDpl8eef2GQZZa+rPCSBv+8vOOyFs+8jlp7eP47plB3HytjNlXqv4f6Ic6ojfJDHBUxqeOnatteNtE+JwUsNECDGjpIbJzPGUg+WOk3dHyLmj5N0xUnYXWXeQrDNIxhkg5wyBXsG4tRcoNHEKmbU0hM7EUy4xfytxXysxXytRs4mQWXNKVH33lEt3dh97UpvZnXqWwVwfXbmDb9X8eoAFkcUsi59OU6iNBZEFBI3QLJZYPBeO6/EPn/0ZV1y0nJdfelpZ+9x17wbuf3IXn//g1YRPUNOZ3qEE/3P34/zvQ5tZ0lrHJWcs4vWXriEWPn6fK0IIcSIppXC98h7NCoHD8o6ra5Sdd6IcE+Zik5x1P/vwlDVMnnrdf86JaxKzTwImQogZJQGTIxVG3rFwvBS2SmG541juKJY3ju2OY3mJ4nICT3mMWnvJuSNY3jiHvousC55N0u4mZNYSMuoLc7OekNFE0Kwm4msiYtafkv2veMpjILefPenNDOR6eXr0QXLewarqYSNKR+QMWkMdLIotpy3cMSsdyYqZl0jleN8//IgPvesy1q1pL2ufu/64gYc27Oaf33MV8Sk6jn0+eocT3P7bJ/nVg88SDwe57MzFvPGytbQ1zE5TNCGEONXMpfu6ibKecddHMI4TMHHTeZ5+/RfnxDWJ2Sd3o0IIUVQYOSdfmlyVw1NWYd2bGEUnh+MlcLwMrsoWJi+LqzK4E2lelqyXw3JHcbw0tpdEcbAabkVgLQlrBz69Ar9RgV+PF5fj+I0aYoGlBIwqAkY1QaOGgFFF0KjG1MOz+OlMz0SAZHfqGfamN7MvvYWsW2gb3hpajqZprIyfxcLoShZEV9IYnIeuyTCoL0TxaJB/ufk1fPO799NUX0FzU+WU+7z+8rXEwgH+7t9+yhdv+itqTtDoNk01cT527aX8zavP5Z7HtvHd/32cO/64nledv4JL1i3m/NXzZXheIYSYY1w0OE6zG5fZr2kr5g4JmAghToqx7GO4Kg3F9uOFmhNesQ1uob3uxPDDrpdG4QFuYT5pmGG3uJ+HRhDLG0IppzAVhxcu5LNL+T00bG8ET9koZZeGFy7sV1hXysZTJo4qPNTrWhBdC6BrfgwtiK750bUAphbBxcPQQxhaCFMPF+ZahICvFkMLY+hhIIiphzH1CKYexadHMYuTQQBdf2H1meB4Fj3Z3exLP8uBzFYOZLZheTkcZeIqh6hZyaqK8+mIrKQjsoL6YCu6Zkx9YPGC0NFWw+UXL+dT//ZLvvKFtxIKTd3U5srzlxPwmfzH/9zLB659CU31J27456p4mLe+7Axe99LV/PGJHTz2zD4++l+/oq4ywpuuOJ2LTl/I/OaaE3Z+IYQQM0cpDXWcgMnxtglxOAmYCCFOioy9G8cbL440oFMYQ2DizW1xWSsMN+x5VmGIYM0opvvQi0MLUxxGuLBs4qeuNJSwjgmaWRwq2CwNPewpr7Bd86FNDCes+Y5YV8rD0ANo+E6Jfj5OVUopxuw++rJ76Exvpiu7nb7cXlpCS+jMbAWgxt/C8vBy2sKn0RZeQm2gWT7TF7lLX7KcfZ1DfOmrf+Dmj76yrN+HS85aTDjg48Of+xmf+/vXML/1xAYtAj6Tqy5YwVUXrOBtrziTX93/DA+s38NXf/oQS9vruebi0zh39Xxa6k5c8EYIIcTz4ykNTTp9FTNE+jARQsyoudTWVUxNKY8xu5fB3G6GrV72pTfSl91FzkvRElzNvswW/HqI5tBiOiKrqQvOoy28jKhZOdtFF6cgy3L4xKd+xssuW8HLrlhV9n7P7ujhu3c9xvV/dQ6rlrWcwBIeKW85PLJpL799dCv7+8bY3TXE0vZ6XnHBck5f2srSefXoutx8CyFemObSfd1EWVfe8TGM8HH6MMnk2fymW+fENYnZJzVMhBBCoJQi7QwzanUyku9kxOpkON9Lb3YrjsoD0BBaS3d2Bw3BhTQGF9EcWsaVze+mLtAmzWtEWfx+kw++/wo++OHbWbK4kY6OurL2O21JM++77mI+fuuv+OANL+W8Mxac4JIeFPCbvPTMxbz0zMWksnke2biX+57cxe8e3caXfnw/1fEwr7xgOYta6zhzRRv1VbGTVjYhhBBH8jwdzTt2/1PecbYJcTgJmAghxIuI5aZJ2j2MWZ0k7C7GrS4SdheaFqcz/eSkvE2hs2iLrKU2MJ+64AJqAwup8jdLcEQ8L22t1bz3xkv598/9L1/+ynX4/eXdisxvq+W/PvUGbv7cL+npG+N1rzzjBJf0SNFQgJedu4yXnbsMy3bYsKObRzbto3tgnNvveQqAi05fQGU0xOpFzaxe3My8hioM6ThWCCFOGmmSI2aSBEyEEOIFQimPvDtCxhkg6/aTdQbIOSMM5ztJOb2k7F4sL0GFfwkD+b0AmFqICn8rdYEWaoNLqPa3UxVop8rfhk8PzfIViReqyy5bycMP7uDOnzzG295+Ydn7NdTG+co/v4lbv/F7evrGeM/1l8zaKDZ+n8nZK9s5e2VhqOTB0RRPbT3Anp5h7ntyJ795cDOV0SCW57GsvZ7lHQ2sWtjEvMZqOhqrME0JPAohxImgVGE63nYhyiV9mAghZtRcaus6FyilsNxRbG8M2xvFcouTlyVlHyDnDpF3h8m7QwSMZvpz6yftb2oR0OqI+pqI+JqI+pqIma0EzXoq/K2EjBrpjFXMiuHhFH/7jm/xyVtey+lndExrX8dx+dL/u5exsQwfe++VVMRPveDeWDLLlr19bNzVw9Z9/Wzd28+8xko27e7FNHTmN1WzdkkLFbEQ7Q1VtDdW01pfQSwcnO2iCyFEyVy6r5so6+Lbb8Y4zt9SN5Nj59v+fU5ck5h9EjARQsyoufTFeqIppVDkcb0MnkpPmufdMVyVwvVSOF4S10uB5idp78RxEzjeOLaXwPESOATwVG7SsePBC0nZPQSNGgJGLQGzhrDRiq5HCJv1hMwGwmYDPj0uARFxynrgvq18/9sP8F+33UAkcuwO+o7l//7wDD/82eN8+qOvYumixhNQwpmjlKJvJMnuriF2dQ2xq3uI/tEkW/b1k7ccAJa119M7nKC5toLm2jgLW2qJhgM0VMWor4rSUBWjKhbCJ7VThBAnyVy6r5so66IffHzKgMmu6z43J65JzD5pkiOEOEJ3dzf/8A//wD333EMmk2HRokV85zvf4cwzz5ztopWlEAd2UTigJuYOCg9wUMrF9RLFZbswMTG3CnmVhascXJVAKQtPWSiVR1GYe8pCI0je2Y9SWTyVwyvOJ9b9vpWM5x87onym0UraHsLQY5h6FEOLYuhRQr7FmFqMUKAFU6/Ap1dg6nF0LY7PqMRvVOHXq/AZleia72R/rELMuIsuWcb9f9rCT37wMH/97kunvf9VV6yio62G7//kUdae1sbrX7PulA0QappGU02cppo4F6452Gmt5ykGxlJ09o3QO5ygs2+E7qEEPUMJUtk8j209MOk4py9pYV/fCNWxMDXxMNXxCDUVYaKhABWRIJXREBWRIBXRILFQkGjITyTkx2dIkEUI8eJwovow+drXvsatt95KX18fa9as4Stf+Qpnn332MfOPjY3xj//4j/z85z9nZGSE9vZ2vvSlL/HKV77yOZ1fzA4JmAghJhkdHeWCCy7gpS99Kffccw91dXXs3LmTqqqqaR1ncOSD5Gw/oIoTk5eVQtercdwDh6R7KOWVlg+mB3C8wcJ2PFAuhYCIB8orLrvoWgV5twtwi+fT0TBBK84x0TQDMFH4AKeY5kfDRNN8hQkfaD40oxHHHSls1/zoWqCYN4CuhTG0KnR/GF0LoWlBdC2EXpxrWhBNC9OofQhDjxTzR9D1MDohNM08ZR/sTnVKKSzPJW1buEphaBph00fAkM90rtE0jfd84Ar+5trbOG11G+ecv3jax1i5rJmbPvByvvBf97C/e4S3v+k86mrnzkg1uq7RWB2jsfroZc7mbQZGU/SPJhkcS5HK5tk/MMZwIsNIIsP2/f0Mp7KMp3NH7DuvoZL9A2MABH0m0ZCfaChAJOhncVsdI4kMoYCPkN9HKGAS9BeWg4HCut80CfgMfMX5xLrfV5jrmo7P1PEZBqah4zMLc9PQMXTp6FaImWK77tSZijyv/MYD5eTN5u2yj3fKOPTW81jbp+mOO+7gIx/5CN/4xjc455xz+NKXvsSVV17J9u3bqa+vPyK/ZVlcccUV1NfXc9ddd9HS0kJnZyeVlZXTP7mYVdIkRwgxyc0338zDDz/Mgw8++Jz2n6gOOTy8mXg8BmjFiYPz4kNtITjiwP/P3n3H2XHVB///nGm3l+1dWq261SVLwjZu2NiAqQFCgARwEvIkGIfg5AmYJLQk1BQnwI+WEEISAk8CBEIx4IJ7laze62p739vvtPP7495dSVa7K+1KWvm8/ZrXzJz5zsy5V9d7Z773zDkIBALQSjFCo5TsKO0rJSCccpleihMTy3p5WUNKDYRTTo7oCKEu2GerlJ3jcHaYI5khjmXGOZIZRkPn2cFO2sO1PNZ3hI21c3mqv5NlyUY602M4vuSqqnriZoD5iRpubu3guqb2S/1SFGD3C0dpaqsmeYZExkO/2ME/f+EBvvxvv0c8GT6vc0gp+cnPt/Ov//kEv/fuG7jlpqvQtJdOAs3zfVK5IuPl5MlYJk8mX2Q8WyBTsMnki2Ty5XnBpiYWYvexAfJFl7ztUCg6FBx38njRkEW6YJ/1nPFIkFTu1EQNgCYEpqHRWBVjKJ1D1zR0XUMXAl3X0DRRKnvRXNMEc+qSdA+lEFrpOJoQiPJc0160LgSJaJDxbKH81SIQovStUsrZHF8XAsIBk1z5BlAgTvhaEse/qcrxUBpWumAfv2E8HgUnL5ZWgpZx5hvM03wca2IRhlKZs77PJwpZJplz/LucyNR1iif8u55L6VHSym4NauMR+scqq3t9IkrfaPqccdWxEAPj2YqOCaVEYG4KN/SGrk3p/ZjgTSEJ0Vwdo2to/Jxx9ckovRW8J7etWcTffP+Ris8/3Ty7wI6vfWRWPL4ycQ3a8c0/QzvLIzl+rsCh9/z1lF7Txo0bWb9+PV/84hdLx/B92trauPvuu/nwhz98SvxXvvIVPv/5z7Nnzx5MU7UKns1UCxNFUU7yox/9iNtvv523vvWtPPLII7S0tPC+972P9773vaeNLxaLFIvFyfVUKgWAYbRiGBf3i7V0saz+rM0mru9xLDfEgXQv3blRNo8c5VCmn8ZgLc8OHQWgKVBLZ3aUtdVz6cmN0xCIT94wxcwAnvQJGgapXI5UscCmgW4e7j5EUDdUwuQy0dc5zKFd3dzxzmtPu/3mVy7j2cf3882vPMzdH3rNebUUEkLw2letZP3aufzjlx/kRz/dyh/ddSvz5536y9+VSNc0qqIhqqLn3wGu70sKtkPedsiXEyhFx8V2PIpuaW67HrbjUnS80jbXw/U9HNfH9Xxcz8PxfFzXw/V8dF1jPFfA8318X+L5Pt6L5r4vcU/YbrsemUIRX0p8XyKlnFz2y8ulstINfmNVjM6hsdLNvgSJPGEZpC/LZdCQjNA7WnmCoioSYjSbrzi+Nh5mKJWrOH5NRzMvHOqpOH5OXZLOwbGK46ujIUYylddfEwK/wt9SV3c0s6XCuq9b0MKmA93njFvYXMv+nqGKjglTf79DlkHenlrCRBMwhXwJTnsT2470njNuSWsde7oGzxm3fO7l3T/T5ajSUXImrlknBAIBAoFT+9OybZtNmzZx7733TpZpmsatt97KU089ddpz/OhHP+Kaa67hrrvu4oc//CF1dXW84x3v4EMf+hC6ekRyVlF3FoqinOTQoUN8+ctf5p577uEjH/kIzz33HH/4h3+IZVm8+93vPiX+05/+NJ/4xCcuQU2V2abgFTma7WNvuouDmR4OZrqxPYOd46U+GlbEO9g0Uk6ShKpZXdVGe7SW+dFGagNRmsJJagNRaoJhwrp10k217boMFLJ0ZcY5OD7CgfEhrm2ae0lep3Kqq29awt/+8X+cMWEihOC33/cKfv8dX2bVunZufOWy8z5XQ32Cv/rom3ji6QN8/FM/YuXyVt7zjuuoq5s9j+lcKpomCActwkHrUldlxpycVCmXIU94WvR4+wopKSdp/HLcycc5Hc/3pzRkqef7U7oZ93yP0zZVOVO85zOV/KPr+ZUnLMtJq0r4lbZckVScsIFSy4+ppFellHhTbFw/8ZmZCq/8mTkbUeFrrUtE+eAbr6/43NP9SE4qlaLxax+p+JiXA+lrSP/MrYwntrW1tZ1U/rGPfYyPf/zjp8QPDQ3heR4NDQ0nlTc0NLBnz57TnuPQoUM89NBDvPOd7+SnP/0pBw4c4H3vex+O4/Cxj31siq9IuZRUwkRRlJP4vs/VV1/Npz71KQDWrFnDjh07+MpXvnLahMm9997LPffcM7meSqVO+QJSXlo86dFXGOJYrofuXD970t0cyfTQWxiiOdjAwezxXw9XJpZwVaKNhdEmlsTb+N0Ft9IRbaA6EJ3SOS3DoDWaoDWa4GWNc6b7JSkXKJYIU8gU6T40QEvH6Vt81DcmuPtP7+C+v/whC5c00dxWfd7nE0Lw8msWsn7dPH58/1b+4IP/xg3XLeLtb904q/o3UaafEGLy0R1FuSJNc+MFJzD7HieptIXJsWPHTnok53StS86X7/vU19fzta99DV3XWbduHd3d3Xz+859XCZNZRiVMFEU5SVNTE1ddddVJZUuXLuV73/veaePP1HxRufIVvAJDxX76C0MczXXRk++jt9CHL0PsyxwBJi5aQji+S1Owlo5YM9fUrmF+tJkFsRYag9Voqq+Zl4QVL5vP87/afcaECcBNty9n7/YuPv9n3+Nz/3wnpnlhlykBy+DNr1/H7bcs5yc/38bvf+BbbFg3jze9fi2LLvNhiBVFUZTzVGGnr/F4vKI+TGpra9F1nf7+/pPK+/v7aWw8/XdJU1MTpmme9PjN0qVL6evrw7ZtLOvKbcl3pVEJE0VRTnLdddexd+/ek8r27dvH3Lnq8YaXGl/6ZNwxxuxBhuwhhop9DNuDFD2HXam9pNxSp3bt4WXsTB0AQENjVXIdcXMFbeEmWkNNNAcbmRNpJKirxNpL2fL1Hfzn39/PG377xjPGCCF45+/dyCfu+U/+9QsP8Lv3vGpazh2NBHjbr63n9a9ZxUOP7OEvP/9jYpEgr7l9BTe9fAnRiPpsKoqiXCmkFMizDB18tm2nY1kW69at48EHH+SNb3wjUGpB8uCDD/L+97//tPtcd911fPvb38b3fbTyqGH79u2jqalJJUtmGZUwURTlJB/84Ae59tpr+dSnPsWv//qv8+yzz/K1r32Nr33ta5e6aso0cv0iGWeYrDdKzh0h6wwz7IyQdodJO8PoIsKe9HY8Weocr8paQHehE4CYUYWhBVkSW0Z9oIGm0Fxe2XA7TaFG6gN1GJr6alFOtWj1XAZ7RinkbILhM18sxhJhfvvuV/Lxu/+d9gUN3Pr6NdNWh1DQ4o7bV/Ka21awbWcXP/75Nr7+r4+yfl0HL7u6g41XzyMWOfPICoqiKMosMc3jwN5zzz28+93v5uqrr2bDhg3cd999ZLNZ7rzzTgDe9a530dLSwqc//WkA/uAP/oAvfvGLfOADH+Duu+9m//79fOpTn+IP//APp7diyoxTV7WKopxk/fr1/OAHP+Dee+/lk5/8JPPmzeO+++7jne9856WumnIavu/jyjSun8YpT7afIeeOYXvj2H4K20vh4TFQ7CHvjlHwxnBlHod6Ct7xIQ1dEth+npAeozG4mI7IchJWHUmzloTVTMxIUmPVkbBq0IXq4V2ZmlAkyIIVbezZdIjV1y85a+xVq+dw5wdeyT984gfU1MdY87IF01oXIQSrlrexankbubzNU88d5JEn9vGfP3iWaCTIzdctYuniZha016Hr6pExRVGU2UT6AumfpYXJWbadydve9jYGBwf56Ec/Sl9fH6tXr+b++++f7Ai2s7NzsiUJlDqU/fnPf84HP/hBVq5cSUtLCx/4wAf40Ic+NPUXpFxSQp6pm29FUZTzkEqlSCQSUxrb/kpXGv6yCDKPTxEpS5MvbTy/gCfT+LKAL/NIWcD387gIbG8A38/hyTy+zCEJMu504fkZXD+L52fxpU9OOiedz9TqGS8/LgNgiBBhs5WcbxLSkwSNJEE9iak1YekRInoVYaOaoFFFzKjG1NTjCcrM+MYnv0+iNsab3/fKiuL/7UsP8t/feIxP//OdXLV65h8LLNouW3ceY+feXh55ci9CCOrq4ixf0syKJc3Mb68nHlUtUBRFeemYTdd1E3Vt+8rH0UJn/lvt5wsc+/2Pz4rXpFx6qoWJoigzYiIpIKWLwAZO7LL8xfNSPDjlMr8ce2KcXx6SUCKlDbJYijsp3kfiHy+X5VjySLxyjFfe7oP0yvHg+eOAV45zT1j2SvuIBI7XCdJB4oJ0y/PyutZKwdmOlDZSOkjscj1Ly7q5kaz97Cnvk2W9jFTxuZPKNBHCstaTdfajizCaCKNrYUytEUuvwTDmoGsRDC2CThipRTC12OSka1F0LYKlxbH0OLq48p6V9WVpKMmKh79ULgt1rdUc3NZZcfw7/+BminmHP/udb/DxL7+LVRvmz2DtSp3Eblgzjw1r5nHnb1zL6FiW7Xt62L6nmx/cv4WnNh9m3pxaaqojdMypZXFHA80NSdqakgSs2TeShKIoyhWpwk5fFaUSKmGiKMqMsEf/GNuzEFot0us6YYs4eS5EaVmrx/cOA9oJ2ycmDYEox4IUdfhe1/HtQivHaKeUSRFH+kOl4woNgV4+hwaivCxCeDJFaSw+HSGME5ZNIABaFF02lbcZIIzyNh2BiRRhLGsNCAshTAQmYnI5gMSiRoAQATQCCFGawEAIC00E0bQggsBLLgngS5/BQoaeXIqhQoaxYp4jmTFGizkszWD7UB9Zt0hQN9k3OoLr+1xd18qT/UdZlKilJ5eiKhBifrwGXQi+dOObCOrq6+1y1Nxey0PfefKkTvDORtM07vyjV6IJ+PPf/Rf+6C9/jVvesPYi1LSkKhnhhpct5IaXLQTA83y6ekfZd2SAg0eHeG77UZ7590fpG0yxcW07uYJDc32CBXNriYaDNNTEqK+OUVsVIRy88hKXiqIolyWVMFGmkbqiVBRlRgSqv0hANXNUAMd3GS6mGSiMcygzRH9hnP7COKMFm11jvQwU0lwVb+W5oW4AmkMJjqVLfausqW7lheFuDKHREIqhC42AaaBpgqBuIJFkHJuMYzNUyCGAgKb6V7lcNXfU03d0iIGuERrn1Fa0j27ovOeDtxFLhvibD/8XR/b18xt/cDORS/BojK5rzG2tYW5rDa98+fFyx/Xo7huje2CcnoEx0nmbzbu7GBhJk4yHeXLbEcJBk7qqKMlEmKp4iKpYmLbGJELTSEaDJCJBYtEg0VCAWChALBLAMvSKEkuKoijKCaQoTWfbrigVUgkTRVEU5bx50iPtZOgtDDFqpxhz0ozaeQ5n+hi2xzEI8OTQIaA05HDe1YmZQeqDcTrCzayoaqE+GKc5VMVr21ZRE4hQHQgT1i2qAmEiukXAMLA0/bQtbzzfZ7SYZ6SQoys7TtqxX3ItdGaT2uZq7FyRrn29FSdMoNTS5K2/cyNtHfX8zYf+H8cOD3DL69fw8ttXXBb/3qah095aQ3trzWm3u67HcCrH0GiGofEso6kcI6k8uYLD0b4RxrMFxjMFfCQ9Q+Ok80WkBNPUCVoG4aBFJGjRUp8kV3QIBQxClklDdYy87RC0TAKmQcA0qIqFcDyPgGlgmTqWoWOZBgGz9P+QqeuYho6paxi6hq5r6FppuTTpGLqGoJQgMjQNTRNoQlwW77WiXCpT7fZxpv5/cT3/ouzjl19vruicI/LyI/3SdLbtilIplTBRFEVRTivrZjiaPUDOy5DzsozaWYbsYTJuGlNEeGFsDzk3T8QI4/kRElaUKjNOQ7CJaivOwlgrNVaS17e8nNpAgrpgnIgeIGRMX6eyuqZRG4pQG4qwqKpu2o6rzAzTMojGQ/QeHjiv/V9281K+/KM/4st//b/86D+e5r+/8Rjv+INbWH/josu6JYZh6DRUx2iojlUU7/uSbKFIJm+TLdhk8za5gk2uaJMtOOSLDrmijSYEXkqSK9qMpnMUHZdkNMTh/lEc16XoeDiuR9F1aatLsquzH8fzcVwPx/PQhMA9Q9v0xa117O0ePPl1aBq6LljYXMf+viF0cTyZomkCXRNo5bLFLbXsPjY4uR2YjJtcFqL0RKY4npARQhAJmqRyRYQ4ftMphCj3W0T5Ec0TywQ1sTADqUwpFjHxBGcpFo6vC2irTXJkYHTydQmO39hOxDUmY3SNHO88+0QnxgPMqUtyuH/kLP+iJ5tqfE0sTP9YpuJ4ANv1iAQqfwwsV3Rwfa/i+KpoiMHxbMXxi1vq2NHZV3H8wqY6dnf1VxwPML+xhj3dlf9tWT6nkS2He6YUv/VIb0WxhqZNKWEylff+Yg/X4RULF/eE00G1MFGmkUqYKIqiKKeVddMcyu4lrEcIG1FaQkk6oguIGlFCeph3zPkNokYEQ1NfJUrlkvVxBruGz3v/usYEH/3Cb/L8Y3v5+fc387H3/xvXvGIJK9bP4/rbV1DXkJjG2l4amiaIhYPEwjP32JGUEtfzcTwP1/PLyz6u5+H5EsctlXt+aXJ9H9+XeJ6PLyUFx0VK8GSp3JeytN33kVIihOC6pfNK5bLUCbcvOWF5orxUJikliqSUSCS265VSOeVtpd3kSWUTr0MCAdOgJh4u30zK8rZyzAmxULrZP77txM7Hj78/yXDwpG2ni5mQiARpSEYrfu9jwQC18UjF8fFwENef2l1y32hqSvFCQE1sCnUKBZhKlaJTfM2xkDWleIBoaGrniAando5SfLii2LHsLEwyXEGELE1n264olVJXuYqiKMpp1QebeG3z2y51NZQrTKI2SmoofcHHufr6xay9biGvfGwt//udp3nq4T18/e9+wTU3L+Gq1XNYtnYuC5c0YZjqUud0hBClR3MM1eePoswmF/uRnFQqRdNXPjLl/S8p1emrMo3UVYSiKIqiKBdNNB7BztvTcixN09hw42I23LiY3q4RHvvlTh79xQ7Sj+3j6//4SxYtayUaDzJnfj2Lr2qmua2a1jk1RC9Bh7GKoijTwdCn/vjh+ewzwQ3MwiHTfVGazrZdUSqkEiaKoiiKolw0VtCgkC1O+3GbWqv59Tuv59fvvJ7B/nFeeOYQu7YfY2gwww+++wwr1s5l25ZjLFvZRnf3KCvXzkFogjnttUSiQerq4yTiIZJVERLxELFYEP0CbjIURVGUS0S1MFGmkUqYKIqiKIpy0bi2g5zhIQrqGhLc9vo13Pb6NQCkxvMcPjjAwf19DA6mEZrg2LERDh0coLYxzuBgmpY51XR1j7J4aTO79/WydGkzRzqHqa2Jogd0opEg7XNrSGeLxMvJlFDIojoZxvUlwYBJNBxAIglYBoGAgRACyzRoqInRXD/7+1ZRFEWZFVTCRJlGKmGiKIqiXDApJZ70caWH7bu4vocrPYqei+17eNLD9n2Knovr+xQ8b7JzyIzr4PgeQmqk7AKu9DGFznAhj+P7uL5HxLAYyGeRUuJT6iwyoBtkndKjHUII/u+aG2iOxC/xO6Gcy/hgmlAsdFHPGU+EWLV2LqvWzj2pPJ+z6ekdY3AwxdBwhoHBFPmiQyIRIhINMjicwXE9jvaVRlRxPI9d+3upqYowNJ4DYOWyFrbuKY20sfKqFrbu6S4tL21h677S8ltuW80fv+eWi/VyFUVRXtrUKDnKNFIJE0VRrmjyNEMaSD9bHjryxG3yRXPwZQGBVx4WoTzqwklxEyMxeEiKJ5WduA9IfHyktMtHkMdjJbjSRuK96LgSKQWOLByPL48eIRE4vo3EL8eWEg9FKct18cpxGjnPRUqfcpoBSYS8N15alj66CDPiZEo1lB66CDBsF/DL61GjgWP5Pnzp40mPnGdS8Ar40idhNXE4240nPdqC89k6fgiAJbGFvDB6pLQcnc+W0U4A5ofb2DneC1Jgu6WOJlcmW9k8VLrZXFc1h+cGuwBYXzuHZ/q7Jv8t1tW2smmw+6R/x9pgmKFCbnL9/yzbSPPUBlVQLrJCrkhuPEtTR/2lrgoAobDF/Pn1zJ9/5vpIKSkUHdKZIql0jnTWJpe3GU/nyeVtJHDVwmYKRYdg0CQRC1EoOiSTYbIFG9txqZnCCCqKoijKhVGj5CjTSSVMFEWZEf7Ie/Fd63iBNMDvq2xnYYLX86LC03+7SX0e0t0ypboVCYE895CL0liN62w+9wHNtRTs584eY20kV3z69PURDbj+0Cnluj6HMefU98zS5zPqdJ1Snpc1uDJ7fH8RZdg9+X2LWssZKOybXI+Z7XQVBibXI3otvcUcmtDQ0GkKGfQXetGEji40PBnHlz6a0AhoFkkzji50qqwY86Mt6EKnMVjFyqTAEDoNVh1hPYih6VSZSVrC1RiajudpGEKnNhBjSaIRQ+hUWxFW17ZgCJ2kFeKahnYMTcPQNMK6xW1tC9GEQAiBJgSGENj+RNII6kMqW3K5+++/+zFC05i3rPVSV6ViQghCQYtQ0KK+Nnapq6MoiqKci3okR5lGKmGiKMqM0Kq/jhY/v8cjSv0bnNpcUohTy6R0QRZeHHnW9ZDMHy856ZjipLkv84jJb9UzxQl86RLFnSwTp4n18anBn9xnIhLAkw6Iif2OT5JS6xXQTtoukaUGLEIg0AANAXjSRwgNgYYQGlKCRBwvQ0Pil/YRGhra5Lm0yf1UM1VlZji2yw+/eD9jgynedPerZ+QcUkocxyOXKeA4HnUNqt8QRVGUlxrBOVqYXLSaKFcClTBRFOWyI0TlI1MIYYCYWnN3QbiiOJ1kRXGV1FY/yzb1h1h5KXjqR88zNpiiujHJxjvWTGlfKSWj/eMc3d9H/7ERUmM5ctki6bE8h/f1kcvZ1LZUMdSf4sihARavbKO/Z4zvPPzhGXo1iqIoymVL9WGiTCN1na4oiqIoyoz76T89CMBt77kJwzz35UdqJMPmR3ax5/nDHNh6jAM7uygWHGpba6hpTGKGLNLpPEf3DxCtihCrjpDLlIYr1rRSCytFURTlJUg9kqNMI5UwURRFURRlRvUdGWDzA9sBePVv33zW2KGeEe7/1mNsfWIfY0Npcuk8yboEG29bQeuCBhraakjUxojEQ4SjQYLhAFbAwApZWAEd0zTQdPV42eXMcT18OdFpdqlsokPt44kuObnsSx/Pf/H2EzvhflG5Xxq168XOlETzfH+yPpWyHY8pNIbE90vnmQpf+vh+5fXyfHnSe3I2pq5zVVvDlOozVft6hhgYz1Qcv7CplgbVQTJw+g7rz8X1pj5cu1PBPrmiM+XjXmrCL01n264olVIJE0VRFEVRZtT933gYKSVrbllO8/zGM8Y9/bMtfPMvv89w7xgrr1/Ca3/nRtbdvJxoorLH6C53rudTsB3yRQfbdinYLkXHxbZdiq6H7Xg4rosvJdmCg+N5uK6HYeiMZQq4nofr+bieTzhoMpzO4XqlZILn+3ieTywSYHA8W0oC+BLPl/i+TzwSZDCVxZNyMqEgJfi+xJc+zbUJDveNlPtOKg3dLWVpeVFLLTs6+5FSlqdSssIvBSOBcMhiNJMv3bDLkxMg8oTkB0BdPMJgKnu6t+i0FrfUsbdnsOL4eQ3VHO4fqTh+TUczmw+/uKPxswuaBgXHPXdgWVUkxGg2P6VzrJzbyLajFXaWDtTEwgync+cOBJqqYoQtc0r1caeY8Fnb0cIPntlZcXw8FCA4hTpNNUHg+t65gwDPqyxZUenxYOp19aaQKJtpXvHF/cTNAqqFiTKNVMJEURRFUZQZ47ke9//LwwC85nduOWPcj//5YX7yzw/TvrSZ//vV32X+ijkXq4oVsx2XsfE8o+NZxtMFxtN5xtN50tkimWyBVL5INmeTzRcpei6pTJF80WZOSw3P7z42edPUUBdjaCxLwDQIWAZVyTBF28U0dQKmwdymKvpH0xiGjqnrtNQnGBrPYug6hq6VJqPUvCFgGuiahqYJDF0jGrJIRkNoQkyWa5ogaBrYnl9aF5RGwtIEQoCuaZiGhuP5pW6rRTlGK3U2bRo6t61dhCiPUiUATQgQTK6XB0Mv71/u1LrcyGeyrBzrlRMvEx1fTzQGmmgVdLxT7tK+mjj5l/ATWw+driGRJsQZfzk/XbsjTRO8w6385hfKN8BTaMQkELje1M6haYI3X1P5PqXkV2V3guGAyT/98tkp1Weqbl21kLl1VRXHf+tXFYxKpyiVUAkTZRqphImiKIqiKDPm2Z9tYbhnlHhNjGvfuP60MVse3c1PvvEwi9bM4/1//1uY1sW/PPF9yeBgiu7eMQaH0nT1jjIwmMbxPA4eHiQWD7J1TzfRSIBFCxrJFxwSsSDJZJhQyCIaDjC3KkI0HCASDhAKmYSDFqGgSTBQniyDoGVimhqGfrauoBVl5t2xbumMn+P6pfMqjv3tW07/9+Gl6HJ6JCeVStH05Y9M+diXkpDnGCVHJUyUKVAJE0VRLiopi6cpFafMpfQQFDj5J7yTh90t/bR4fJhe6efLvzyeOGkviisPzesXyqPxTGzXTphKv6KWhjf2AV31h6Ao56mYK1LXWsMNb30ZVuDU5vbZVJ5//esf0LFiLnff91sVdQh7ocZGs+zb10dPzyg7dnQhBTz59AFWLG8jV3SYO7eWRCLEkkVNNNTHeOsbrqY6GaaqKoJ1EeqnKMpL2/lcc5jG1JOwlezjnubv9mVPjZKjTCP1ra8oyoxwR34b1zndl6wPMsvx9pBnmIt6PPcAx9tVnjhxPFYeL/MpgiyW18/+q4krkkh/+KwxwlqPbT9zQonOZFJFaAi9g4Kzr5x40QHjlGVHJPH9MRAmYrLcAH0+tnsUIQyEsBAYIEx8rRHXH0OIAAITTZjoohpb5hHCQhMBNEpzV4QR6OhaAE0E0QniCxNdBDG0ELoIoYsAQgQwtRCmFkYT6s++cnHd9LZruf4tL6OYO12yFH72rUcZ6RvnT7/63hlLlgwOpNiy+Qg7tx9j06YjJBJhQpEAK1fPYf2GDtrb67jng68mEgnMyPkVRVGUi0d1+qpMJ3XlrCjKjDCqv4ERj1/YMaahHqVmrT7Hky2lb0npZ8vL/vHt8uR1XzqEKIL0kXil8skYDyl9QjIDeCC9cszJy67vICkgpQu4k3OfILqWROIipTM596SH5+eQjCGlgy9tdK2elH0QXxaR0saXNhKHgmjA8ccnX6tOmNSLOqHTCTN2Qgd2urAImctIu0MEtAiWHiZhLiDtZgjqEYJ6lIhRjyc1QnqUsBElqMcI6VGiRoywHkGbytAQigLoukY4FjqlPDOeY9NDO7nl7dfR1F43rec8dnSYh3+xna5jI7yw6Sg337aMxUuaeeNbNjBnbi2apn5hVBRFuSKpPkyUaaQSJoqiXNFKzVpPbXIq9HP/knw5pwWk9HH8DL4s4ssinizi+XlsP4crC3iygOvncf0iWS+H4+dx/ByuzFOUpZYrtp8l544CA+zPbJk8dkNwNfsyeyfXawKLOZo7AkCd1cJgMUfcjBMzY0T1esJ6iKQVJ2lWEzMjVFsJkmaMaiuBoal+GpQze/R/nme4b5w33/XKaTleoWDzyM93sOm5w+zd2c1Nty/nzb+xkQ9//E0qQaIoivJScY4+TFTCRJkKlTBRFEWZhYTQsPQLa8FzIl962H6egpcl62W4wc2QczPkvAxZ16Y9spCMmwZMbP8YKTdNb76fvNc5OXxoUKtizEmXhiqVYWzfpdZKENLj1AUTzA3XE9RCNISSNAaT1ARiNAQThA1r2l6HMnv4vs9TP93KxttXEjlN65OpyOeK/PxHL/DD7z7LoquaueNN6/jwJ39NJUkURVFeilQLE2UaqYSJoiiKgiZ0gnqUoB4lSUNF+7i+y5iTYsxOMeakGCiOM1IcZ9RO05kbY6g4jidhf6aH/ZkeBqNpdo71AbA8MY9Nw50AbKxZSG8uTWMozsJoI7rQaQjFqLYi1AQjVAcixMwAUdMioBkYmoYEip5L1rEZLeYZymfpzqXozaZ471UbCBqzsJO6l5gdTx/ghcf3cPffvvO8j+H7Pg/9bDvf/NID3PyqlXzqC79JU2v1NNZSURRFmW1UHybKdFIJE0VRFOW8GJpBbaCa2sCZb1CllKTdPAOFcYaKKbpyo/Tlx7A9iedDX36cnOtwMD3IwfQg/dkce8YHEFLg+QIJrK1uZdNgNwAtoSqOZca5uq6V5we6WFfXwqbBbtbXt/LcQBcAr21fyry4umm+3N3/H09y069toP48ExzdR4f51lcfJjWW41Nfehdz5k1vHygTcnmbA4cHWHlV64wcX1EURVGUy5dKmCiKoigzRghB3AwTN8MsiDWdNsbxPQYLafrzKfpyaXryKUYKOToz4wwXc9RaEeqCY2QcuzQoEmBqpR5mArpObTBCXSjC9U3tNIZjGKpT2steNp1n/44u7vrUW89r/4d+uo1//f8e5D133cJNr1oxrcN+u67Ptl1dvLC9k56BcR54fA++L/nRv/wBVcnItJ1HURRFmSHqkRxlGqmEiaIoinJJmZpOczhJczgJNWeP9aXEkz6u72NpOrqmkiOz0bO/3EHLvDpWXrNwSvt5rsd/fuNRnnlsH5/96rtpbJmelkRF22Xz1qP88tHdPLv5MEIIxtN5li1pxvcl9bUx+ofSKmGiKIoyC4hzdPp61g5hFeVFVMJEURRFmTU0IdCEjqlG35nVnrh/G8s2zEebQsKrULD5/F/8ACto8Lf/9NtYgQvrp8b3Jdt3dvHM5kN8/383Yxg66XwRTQhuvG4RdbUxVi9rY2FHA/W1sQs6l6IoylS53tQ72vD8qe3j+efOHOSKzpTrcVlQSRFlmqiEiaIoMyI7+mEMLwgIEKI0Z6LZfHlZSyK9vslygQChnRCrHd9HBPD9sVKZmNimT8YIMbFc3kfoCDQ8CRIbgQ7CADQEBggd0CfLJTrglLeZ5bkBGAhhAgayXCchrHJZaS6EBVIHoSFE4OSJwLQ+LqAos13/sWGeeXAn7//Ur1e8Ty5b5KMf/DZrN3Tw9t+54YL+n8pki/zigR1s2XGMR57YR31dDNM0uP6ahWxc18HaVXOIRYPnfXyl1HeR6/t4nsTxXFxP4vs+rl+a+77Ek6Vlz5d4E2XldV+WJilftOwzWeb6HlJSmpAgj2+TUH58T5ZH7ZIYmkbRdcv7yOP3Uicsn1huaBoFx50snww/+YVOLgZMg+yJN5by5Lu10927CaZ2UxwMmGTyxYrjF7fU8Vf/78GKYm3X49dfvqqiWEvXKThTu4kOB0zSU6g7QMgyyRTsiuP9Cm7+J2PluWMjQYt07tx1No3jn5WzSUZCjKRzFdUP4PtP7ag4diZ5duFSV2HKVKevynRSCRNFUc7oM5/5DPfeey8f+MAHuO+++6a0bzD6XgLRKMcfJD3xgVJZusD1baBYHpb2hEn6p+wnpY+UKcBH4gN+Oe74JF+0jvQR2OU4DykLIF0kHlJ6gAe4pf2EhecNAA5SuuVy94R1B0/EcdyjIB0kNlLaQOkiyTBXkbW3nfa9EAQwAxvJOzvRRAhNhBAiiCZCGMZCit4QmhZGFxE0EUbXImhaHVL66FoMXYtiaDF0EUXTwhhaAlNLoGvqpk6ZfR79yRZe9+4bSFbYaqNQcPjMn3+Pq69ZwNve8/LzTpYMj2T4wY82s2tvL5u3HGXB/HruuH0lt9+yjGVLmjGMK6/Vkuf75Ao22YJDrmiTLdgUHJdM3qZgO+Rth4Ltki86mIbG4HgW2/UoOi6242G7LrbrEbAMBsezOJ6H4/q4nofj+biux4LmGrYc6sXzfVzPn0x6TFjQXMOB3uGK6tvRXMPBCmMtQ8d2vYrfi9XzmtlyuKfi+Kva6tl1bKDi+HULWth0oLvieIDqaIiRTL7i+JXtTWw70ltx/Os3XlVxrOt5fP3nz1QU21QVo3c0XfGxAVa1N7F1CnWHUsJnb/dgRbGWoWG7ld8F65o4Z+uKBY01HOg79+exIRmlfyxzzri181vYfHBqnxHlPM1QHyZf+tKX+PznP09fXx+rVq3iC1/4Ahs2bDjnft/5znd4+9vfzhve8Ab+53/+5/xOrlwyKmGiKMppPffcc3z1q19l5cqV57W/bs5HN+PTXKvLTylJ4+DJPNLPImURKYv4soikiJQFpLTxpI3nj+HLPL4s4Ms80s8jRRBH5vD9HK4cxPNz+DKHprczmn8KyfFf8UytkbR3/OJNExaGFkdqrejCwtKrsPQkllaFEHGCRg1BvYagXk1QryagV6Fr1iV4lxSlRErJlsf38Zbfv6WieNf1+PLf3k/7ggZ+487rz+ucqXSe//e959ixu5st245x43WL+Mif3MGN1y8mYM2OyyApJePZAiOpHMOpHGOZPGOZPOPZAmOZPLbr0TeaJpMrks4XSeeKzGlI8vz+rlOOtWbB6W/aVnU0sfNoP5apY5kGAaM8N3UaqmJ4vk/AMIgGNQxdxzR0TF2jsSpGIhLC0DUMTUPXNXRNm1yPBC2KrosmBLqmoWkCTRMY5WVdHC8zDR3P8xGitK4JUVqenDO57kuJJkSpQSECIUqP7EEpRpTLJ2J0IXDLjyuUWjOW55QaLU7k4Y5vO7kVwkRseeWU8hfHn+QMOb6ptIgA0AS4XuX71Cej/OU7b68otnNwjEMVJAeg3DJmynUXOF7lCS4o/TtW2gJHSp+pVMn3JefKvRq6RtGprM5+BY/CWIbOa65eUtHxADYsaqOlOlFxPMzMIzmpVIqmr35kSse91GaiD5Pvfve73HPPPXzlK19h48aN3Hfffdx+++3s3buX+vr6M+535MgR/uRP/oTrrz+/7zDl0psdVwqKolxUmUyGd77znXz961/nr/7qry51dS5rQmhAAEMEQEtO67GllEhp48o0np/B8VLY/hiOP47jp3C9cWxvnIyXwvZHybt9jBf34CMZd0ZOOlZIb2bIGSaoJwnpNUSMOiyjlbBRRdSoJ2Y2EDbqSJgNGCqposyQ3ZuPMjqUZvV15+7sVUrJP3zmJxiGxp3ve8WUz+V6Pj/52VYeeWIvL2zt5LWvWsXv3XkjVy1pPp+qzxgpJcPjWXqGUvQMp+gfSdM/ksZ2PfYeG2Q4lWU4lcP1fDqaqjnUd/z/bSEgEQmxoLWWXNEmFgpQl4wQDQVoro2zblErkaBFOGgRCViEgybhoEXQNAhaBkHLJGgZhCwTy9Qx9CuvlY1SmTl1SebUJS91NZQLZOjT3xG6e4H9RV0SM9DC5O/+7u9473vfy5133gnAV77yFX7yk5/wjW98gw9/+MOn3cfzPN75znfyiU98gscee4yxsbGpn1i55FTCRFGUU9x1113ccccd3HrrredMmBSLRYrF48/4plKpma7eS0bpV9IAFgHQawlVeM0ipcT2UxS9EfLuMEVvhIKXYtTuJecOknUHGbe7GMhsKT/eBBomOQkgiBk1VFltWHoD1VYzNYEWqs0mqgJNBPXwjL1e5cr38+8+w6vefm1Fj9V899+fxAwY3PXBV035MZy9+/v4z/96hl89tpfXvmold//+rcybW3u+1Z4WqUyBI70jk1MqV2D7oV66B8cp2i4rFzSz9WAPmhDUJiMsaK2lvirKVfMaqIlHqE2EqYlHSESDJKMhktEQsXBAjRSlKIryYhUmTF58zRoIBAgEAqeE27bNpk2buPfeeyfLNE3j1ltv5amnnjrjaT75yU9SX1/P7/zO7/DYY49N5RUolxGVMFEU5STf+c532Lx5M88991xF8Z/+9Kf5xCc+McO1UqZCCEFATxDQE8SteWeM86VH1h0i7QyQdgYYtnsZc3oZt3vJezn2pB+ejI0atQzYKRJmLQ2BVhqCbVRZLTSH5tAYbCOohy7GS1NmsfR4jp0vHOX3/uIN54x9/plDPPSLnfz9V96NblSeELAdl+/+97P8y78/wcar5/Glv3sny5a2XEi1p0xKSd9wmt2H+zjSO8L2A73sPzZIa32SzXtLj8hUxUJsXNHOkjn13Hr1IlrqE7TUJmiojlGbiFyR/akoiqJcLJV2+trW1nZS+cc+9jE+/vGPnxI/NDSE53k0NDScVN7Q0MCePXtOe47HH3+cf/7nf2bLli1TqbpyGVIJE0VRJh07dowPfOAD/PKXvyQYrKxD0XvvvZd77rlncj2VSp3yBaRcnjShEzMbiJkNp91e9HIM2z2MFLsZs0c4nDvAQLGLA5ltDBZ76Ssef969xqynPriQllArbeF25oTbiRuJKQ0bq1zZHv7fLbzyLeuJxM7+t6W7a4R/+Juf8dn73k4kcuovfWdy8PAAf/03P0HXBH/8h7fz6leuQNNmfoSqou2w58gAW/d1c6BziGd3HmU0XerI8+plc/B9n5vXLWTpvAbe95aXM6epimRUJRgVRVFmTIUtTI4dO0Y8fry/vdO1Ljkf6XSa3/qt3+LrX/86tbWXtnWjcuFUwkRRlEmbNm1iYGCAtWvXTpZ5nsejjz7KF7/4RYrFIvqLnnE/U/NFZfYL6GGaQwtoDi0A4OXlctd3GCz20p0/Sk/+KD2FTsadcTaPPcvmsWdL+4ogPhHaI+10ROYxL9rO3NBcqgLJS/NilEvKdTy2PnOI9/3F688aVyw6fPOfH+X9H7yN5pbqio9//0M7+Mn922lpSvLHd99OMjFzj45JKTl4bIintx3huZ2d5IsOW/f3YJk6V81r4HU3LmdJewNL5zXQVBtXw4oriqJcZJV2+hqPx09KmJxJbW0tuq7T399/Unl/fz+NjY2nxB88eJAjR47wute9brJsomNgwzDYu3cv8+fPr+CVKJcDlTBRFGXSLbfcwvbt208qu/POO1myZAkf+tCHTkmWKC9NhmbSFJpDU2gOUOr1XUrJqDPCsdxROnNHGLZHeG5kO9vGS5OBTlFqVJlVLIzNY35kHh3RDjoibQR01cnsle7RX2ynpilJTf3ZL0z/8b6f09xWxcZrz90pLJRG0fnmd57iez/axJ+8/zZuuWHpdFT3tOfZsqebh5/fz+MvHKIuGWVf5wCrFrdw/doO7n77DSxur8cy1WWVoijKJTfNnb5alsW6det48MEHeeMb3wiUEiAPPvgg73//+0+JX7JkySnX03/+539OOp3mH/7hH1RL7FlGfbMrijIpFouxfPnyk8oikQg1NTWnlCvKiYQQVFs1VFs1rEqWWii9e65ksDjE4exhjuV62Dq+m8PZTp4afp7d4wfpK2bQhca8SCtLoh3MjcxlSXwuzaE6NKEe5blSeK7H97/1BJ/84rvOGvfzn28DTfCud1c29OLIaJaPfe5HeJ7k6/e9i9bmqumo7iTfl2zf383PHt/Nw8/uY25zNf0jGa5f28HL13SwekkrQWsWjh6hKIpyhau0D5OpuOeee3j3u9/N1VdfzYYNG7jvvvvIZrOTo+a8613voqWlhU9/+tMEg8FTrpuTySSAup6ehVTCRFGUi0rKUlpfNVO/8gkhqA/WUR+sY2MNvKXtjdi+w5FsJ0eyXWwe3cve9CEOZDpJOwX+u/txAKJGiFWJxdQF6lgUa2VhrJWWUI36zMxSj/5yJze/ZhXVdbEzxhztHOI//v1J7vuH30KvYFjMrt5R/v5rDzCntYYP/N4rprVlx8BImh8/spOfPLqDWCTIWCrH625awa0bF7F4XoP6HCqKolzuZmBY4be97W0MDg7y0Y9+lL6+PlavXs39998/2RFsZ2en6rftCiXkxN2LoijKNEilUiQSCbYceB2x2Km/vmqiCts9UPHxhAhiu0fLaxLQEUJDoE8ug15aFxoCA8tcSMHeixAGYCKEjsBECAOBUZ6baFoVtkwhRKC0LiyEKM+xEMJC1xL4fg5NBBEigCaCaFoQrbwsRADQ0UUITQujixC6iKBpITShHjU5Fyklg8UR9qePsWVsP3vTRzmU7aYj3M62sWOTcYuj83Gkz4JYE43BalpC1TQEE1RZURJWiHD5sR7/HF9pmtCw1KNlF42Uknv/zzf5i797O5Ho6Tt7zedt3v/Bf+cDd72SlSvO3Ux5554ePvLZ/+H33nk9d9y6YtrquXVvN//1ixfo7B3lWP8ot2xczB03LmPN4laVJFEU5bQqvY1yXK+ivyOO5yGo7O+N7XpU2q+1EAKzwtG3fP/k15RKpWisr2V8fLyi/j4upYlr0Kve9yn0wJk7GPeKBXb9fx+ZFa9JufRUCxNFUWbEvLp/n5EvISl9JC5ID4kPeEjpAT4SD6SHL218mSvHuUhcpHSR0kHilecuUvo4MoWUNlI6+NIuLVNa9v0UUroU3E58WcSXBaQslpdL6wGjg7HCU0jsU+oqMIgGXkbaPoChRdG1CLoWxRClZcpzU0tgaHFMLY6hxdG1KJZehaVVYWixK/pmrdQKpYb6YA3X1a0GwPYdDqS72Tneyf50N/vSXfTkxxgspsg6Rb6X2QTA8vg8Ng8fY3mijc3DPayrmsuzg8fOcjZ4y7xVfGb9684ao0wfIQR/9aV3YZhnvlD/1n88yc03LKkoWfL0C4f52n88xsc++FrWrpxzwfXzfJ9Hnj/AA0/v5aFn9tHeXM1bblvN7ddeRWwKI/QopRvHXNHBcT0cz8NxfVzPx/W8cpmP43p4nk/edvB8ief7pckrLbu+j+f5pW2ej2FopHNFfF/iy/I0sez7J63HQgEGU1mklKUfl2WpTrK83+Q6IICC44IESXlb+SdnWd554hg18TB9o+lTbownVifKJ7a21MQ53D96ujfotO9bJGRNjqpUibkNVRzoGaoodk5dkkN9IxXFWoZBrnjq99iLNVbF6Boar+iYALFQgNFM5a+vtTbB4QrrDLCotY7dnf3nDgQWNNey59hARbH1ySg9w6lzxq1Z0Mym/d3njDN0Ddc793MgdYkIg+PZiupYGw8zlMpVFHshPLsw4+eYdjPQwkR56VIJE0VRZpVS6xKLCn+AuWikdPFkDt/Pl+Yyj+fn8GQB2xvF8zO4MoPnZ0vLfoac24vrp3D9FE55jtQpnpB8EeiYepJk4GoK3igBvZqAXktAryGo12HoSUJ6PWGjnoBeXW5xM7tZmslViXauSrRPlhU8myOZQbpyw+xL99OVGwFpMJDPEDODGEJDu4ITS7PZ2ZIlP/35Ng53DvGpj//aOY/zq6f38U//+TifvffXaGlMXlCdPN/n4ef287X/epLOvlE2LJ/DP374zaxfPveKTlBC6QY/k7fJFIpk8zbZgl1azxfJFmx8KRkcy5C3HfJFh4LtTi7bjks6b2M7LkXHw3bLc8fFdj3WLmxh84Ez3zzqmmDJnAYOdA+ia1pp0jUMTaDr2ill85pqONw7jBACXdMQAjQh0DQNTVCeCzRN0FwTp3s4hRClRJ2A8rDSAk2I4+UCDE0r/eIPMLGN0hwBAq00FxAwdELlvmomPhqTLQAmZieUR4IBamInj9L04o/UiS0IgpYxpVZviUiQxqrKfoxIRkM0Vp35UbgTGbpGwT53krA6Fsap4MZ/QsgysCps2QCQjARpqIpWHB8PB6hPVhYfm0JsVTRUUYIjEgxQGz/3qFwS8Co4XjhgEQ06lVQR0zAIVvg4YsFxK4q7UlQ6So6iVEIlTBRFUaaBEAaGiIN2Ya1qXC+H7Y9g+6PY3hiON4rtj+H6DsI5TNEbIuN0UvSG0ESYcaf3eB3QSViL8AkQMRuJGs1EzCYiRiMho5GY2YKhzc5fzYO6xZJEC0sSLdzadPqYoufi+me/IDXU88WXjf2H+nnimQPc+yd3nPO57x8/tJ0Hn9jLFz75NqoSkfM+p+f5PPz8fr7+/ac42jPC625Yxifveg1L5jWc9zEvNSkl49kCA2MZRlM5RtJ5RtM5RtI5xjMFBsczpLIFxrPF8rzAivlNvHCgm6BlEA1aREIBoiGLSNCivbGaVK5A0DIJWQbxSJCQZRIKmARNA9PQCVilGzXLNLBMnYBRmhu6hqGX5qahYeo6pqFjlsu1Sp8fUBRlWhWnkDA57SM5//yR6a7SzJLA2S4HVMJEmQKVMFEURbmMGHoYQw8TpvWcsZ5vU/AGybsD5NwB8t4AtpdmpHiIlH2EnuyTuDJHUK8j7Y4AgohRT8xsodpajGVUk7TmkrTmEjOb0cTs7tsjoBsEZvdLeMlIZwr81d/8hHs/+BoS8dBZYzdt7+Sx5w7ymT99A4HA+Y9Ks3lPF//4nUfIZm3mtVTz1++/gwVtded9vIvFcT36RtJ0D43TMzTOaCbPwe5hBscyDIxmGBzL4Ho+i+fUY7se1bEQ1fEI1bEQTTUxFrXVkoiGSISDxCOlaSJJYlTQwa6iKLNf4AI6xnYu4O/upaJamCjTSSVMFEVRZilds4hoLUTMltNul1Li+GnSTg/jTidpu5u000XKOcZQcT/dYy8cP5awaA5vBGFRE1hAbaCDmkAHUUONCqJML8/zue9rD/COt2xgycLGc8avWdbGqqtaz/vmvntwnH/+4dP8+LGdrF7Uwv/9g1ewrOMMzZQuoeHxLId6hjncO8LRvhE6+0exXY/N+3tIRkM018ZpqU0wt7GK1QuaqUtGqU9Gqa+KUh0Po6vWU4qiKCWqDxNlGqmEiaIoyhVKCIGlx6nR49QEl5y0TUpJ3hthzD5ano5Q9IoczDzOwfQjk3GNwRVIBPXBhdQHF1MfXEzSar0i+kpRLo3//MGzxKJBbn/F8oriNU2gnUenRQXb4d9++jz/+tNn2XDVHD579+u4ad2CS54A9Hyfo32j7D7Sz7GBMbYe6OZA9xCu53P9yg50XWNuQxVXL2ljTkMVzbUJQrPwF15FUZRLRbUwUaaTSpgoijIj8m4/lpun1CueKHVyV+ph7/g6x9c9WSyXauXhgSeGDy5PQi9tQ5u84ZFSXvKbn9lKCEHYqCFs1NAcXjtZfpOUZNx+houHGCoeJOeOcSj9OL357ZMx1YH56CJGU2gpzaGlNIWvImJUX4qXocwy9/9qJ5t2HuNzH3nTjJ7n2V2d/PdDW3hy22HufO1GfvPVV19Qk/QLMZLKse1gD9sP9rLrcD+7j/ZRk4iwZG49qxe08Ju3X82Cllrqq6Lq75miKMo0EH5pOtt2RamUSpgoijIj9oz8LVHHAiaGa5TloRpf3E6yVCYIUfAGkfjlYYIlsjx0cGm4YFkaEniyFy+BISLk3K7JI2nCQGAghIFAL60LHSF1pNAQwkATJhommrBKy8JEEwaasDC1JJ7MoYsAmgiU5xa6CKJrpWVDlHrY17UQhghhaGF0EcIor5eOa83aGx8hBDGzkZjZSHv0WgBuaLibnDvCQGEfA4V9pJ1h9qYeoye/g03l/RqDi0lYTbSEV9ASXkGNNUe1QlFOsnV3F796eh+f+OPXYc5Q8mIsnecf/utR/veJnVy/qoPv/tV7aKlPzMi5ziSVLbBpzzGe3dPJpj1d1CYiBCyDlfOb+Z3XbWTxnHpi4dnZ+bKiKMqsoB7JUaaRSpgoijIj1tR/jnj8wkaMmQopS4kVX7qlxIp0kdLDx8H3HVyZx8dBSgdPlua+dPClXYqRDp5v4/gpfFnEk0UcPz257PlFfFnE1KsYK+7DlXlcP4/n53Flnonu2CPmAtLOUUwtiqFFMLUophbB1CIYIoGuBbG0GJaewNLiWHocU4tjalGCepKAnsTQghftfatU2KimPfoy2qMvA+AVjR9gxD5Gb34Xvbnd5LwUe1O/Ym/qVwAE9TgtoWXMiVxNfXA+DaFF6EJ95bxUHe0Z4bNf+yWf//CbiEdn5vP98AsH+ML3HkNH8Onfv4Nbr150URKXUkr2HO3n0a2HeGrHEboHx7ltwxI6mmp4y02r6GiumbUJVEVRlFlJJUyUaaSuXhVFuSJMPL6jiYv/rL+UEl8WcWUe20vh+FlcP4PjZ3D8LI7M4vpZ8u4Ytj/OuD2I7afKsSnC5jz68lsmj2eIEAE9SV1oNUVvnJBRQ0ivIWTUEi7PQ0Y1YaMe/RK8Xii93zWBudQE5rI8+WoAMs4w3fkddOe2053bzsHMU/QXjzLu9GKKIC3h5cyJrKItvIqG4IJZPyqPUpnhsSx/+80H+bP33U5LQ3Lajz+eyfO57/yK+5/dwx3XLOWP33ojiejZR965UK7n88K+Lh54fh+PbztEXTLK9as6+JO338zS9gbVAauiKMolpPowUaaTSpgoiqJcICFE6bEdggT0qinv70sP20tR8MYoeqPl+RiOX2DcOUrOHWKkuJ+8O0zBG6U+uIL+wlYAQnoNYaOeqNFIwKgjbjYTK09RswlTm9kbxxNFzRoWmzeyOH4jAHl3nCPZzRzLbeNYbitHss9zJPs8AJYWpjW8go7IRupDC2kMzlcJlCtQOlfg3n/4EXe+8WUsW9g87cd/YsdhPvHvv2Rpaz1/f9fruWHV/Gk/xwTfl2zae4xfPLeXx7Ye4prlc1nR0cx7X/cyapPRGTuvoiiKMkWqhYkyjVTCRFEU5RLThE7QqCJoVAHzzhrrS4+8O0TW7Sfj9pFzBsi6AxS8cXpzz7PP6cGT9mR8XXAlEknCaitN5hziVhsJswVDm9l+FEJGgqWJm1mauBmAtDPIsdw2OrNb6Mxt5VDmGXJujmN9XyKghWkLr2BuZBVzIyupD7SrPlBmuULR4U//4Uf8+qvXcs2qs3+upypXtPnqj5/m3x7YxKuuXsyHfuMVJCIz86jP0b5RfvzULu5/Zg/rl7Sxcn4T7/+1l894KxZFURTl/AhfIvwzZ0XOtk1RXkwlTBRFUWYRTehEzAYiZgP1rDxle2m44GHSTi9pp4dxu5tR+xBDhb0cSj+IJ23qg2voy28nbrZQHZhHVWAe1dY8koE5JK25M9bXSMys46rELVyVuAWAcbuPI9ltRM0ajma3cSDzDAcyzwDQFlqHJkzmRpYxN7KcptB81QfKLGI7Lv/w3Ue5deNibt2weFqPvf1ILx/51/upioT4zO++htvWTe/xAYqOywOb9vPg5n109Y/z2muv4hsffht1qiWJoijKZU89kqNMJ3X1qSiKcgUpDRdcS9iopSG04qRtUvpk3H7Gip0MFw8yYh9ipHiYzuzTRPQ6Um43urCoDsyjJrCQ2sBCagOLqA52YM5AR7QJq5FVViOrqm5DSslQ8ShHc9s4mt1KX76bYbuHA5nSIzymFmRZ/HqiZjVzwktpDS8mqEemvU4nyjsO33h+E29evozGWGxGz3UlcRyXj3z1J6xZ1MqbX7Fq2o7rej7ffWwLf/f9R9m4eA4ff+dt1E9zAqNnaJyfPruH7z+6nY1XzeG3X72Rq+bWo6k+SRRFUWYP9UiOMo1UwkRRlBkxkD9I3owAojxChJj8DwAhEGjltXK5ELh+EU3o5Q5cJ+ZaeZjgibmOEBoaGkLoICUIgYauRqM4CyE0YmYTMbOJtujGyXJfuozbXQwV9zNU2M9QcT+H04+wZ/zH1AQWMFI8THVgHnXBJdQHl1AXXEJ1YB7aNLb4EEJQF2ynLtjO1dWvx5cefYVDHM3u5Gh2B525XRzJ7WTE7gWgymwCYdASWkBraCF1wXaaQ+2EpiGJkioW+PGBPfxo914OD4+yva+fL7/x9eqzVQHH9fjIP/+MjcvaeevN05cs6Roa4yP/fj/7e4a499dfwZuvWzFt/x5SSjbv7+LbD23hSN8I77x1Dd/96G8SC19+o1UpiqLMhHzROa9tlyvVwkSZTiphoijKjNibepigbyDLaX5ZTudLWRp+98RyKSd+CpB40sP1C/hMDBPsl4cHPnHu4+OVhxL2qbba6Mnvwi8fe4ImNDRhoAsDTRhoGOjCRAC6ZqILE11Y5bmJoZWWTS2KL11MLYChBTFFAEMLlOdBTC2AKYLomokpQlh6CEsLYWnhWfnYiCYMqgLtVAXaWRh/JVC6icy4/QzkdzNQKE0HUg+wZ/zHNIaWMVLcR11wKQ2hFTQGV1AfWk5An75WGJrQaQ4tpDm0kGtq34iUPn35wxzL76EztwfbK7I7/RxDxW66c4foLpQSKfWBNiy9noZgE3VWGzEzTtKsIm4miBoRQlpgsrWAJ33Sdp7BYpbOzCh7Roc4Mj7GjoEBDg6NEvYtXtbUxm+tWq2SJRUoOi6f/87DrJ7fMm3JEikl/7tpF//woyeojUf4j3veTkdjzbQc2/clD287wL/+8nkWNtfxpuuWc+2ydjRN/VtfClJKPM/H9X08T+L5Pp7nl+cS1/exbRfX95G+xJOyNPd9pKQ8l3i+xPdL3ysF2y19x/ilbxxfSpATI5uVln1Z2qZpgly+1P+TLFWIUsjEd1dpi5QTy0zWUb7odRyPL5dx0gq265329Z9OJGQxls5X9B4GLZN0rnDOuFg4wGgFx5RS4nr+WWNi4QCjqcrqV5UIMzCSrigWSol05zTv1YsloyEGRzMVHbOuKkrP0HjldUBQdNyKYpvrEnT2jlR8bITArvDYcxurOdA1WPGhaxIR+oZTFcUubKvj4U0Hzrjds8/9mbrcCL80nW27olRq9l3ZK4oyK1zf8LvE4/FLdn4pJT4evnTxpYcnHXzpUfQy+Lh40sGVNp7vHF+WDp60cXyHgp/B9Qvk3XHSsojjF3FlEccv4PpFokYNPYU9OH4e28/jydIvMLowqTJbKfoZgnqUgB4loEUI6FGCWpSoUQNCJ6THCelxwnqcoB4nbMSxRBhNuzxGihFCEDMbiZmNzI+XOm31pcto8QjDxf10556jP7+dvvwWtpb3aY++jLBeTUNoNQ2h1UTNpmmsj0ZTeD5N4flsqLkDgJQzQnd+P0PFfqLpHfTmjwAme9I72JPeQUhrZsgeptqspStfunCMG630FoZpCcxjV6oLiyRJI86u0WEWhVsQUmf32AA3NHfwloXLuX3eQgKG+qo8l2zB5k++9r/csmYBb7l+epIlY9k8n/zvB3hgxwE+8KrreNeN6zCNC///w/E8fr5pH9/8xXMsbKnlz37jFha31U9Dja8snu+TLzrkCw65okOuYJMv2OQKTqm86JDN2xQdl6LtULRdio5LYWJedCnaLo7rYbseruvheB624+G6/uTyvOZqdhzsxTtNJ4yaEOi6hq6V5h3NNRzqHkbTSi0X9RPnmkAXWmmuCRpr4nQPjqOJ0t8zIUrtGyeXRen4lOdtDUkOdY8gjjeCLLd8ZLJlpJg4VnklaBmTCYrJ1pOllVPKJo4bCQXOmAA5XV52XlM1+45VdqPcUB2je+DcyYB5zZUd09R1ckX7rDFzG6s52DVUUf2Wzmtg6/6eimIBLFMnmz/7+QGWdTSy81BfRcdctbB5SnWIhKyK6gClf/YdFdYDIBQwK269EQlZHO6ZQjIG6KrgswBcmR1Yq0dylGmkrgIVRbkiCSHQMU5p8RE1p+fX6RfzpINdTp7YXo68N07Ry1LwMxS9LMXyPOuOMVg8St5LlSY3hSNLF9wJsxHHzxM2qogYVUSMJBG9irjZQFCPEDVriBm1RM1arBnoU+RcNGFQE1xATXABixKvBiDrDNJf2EFfbhujxe10Z59gf+p/AYgYDTSE1tAcuprq4GKSVse0ttSIm9XEzdKjRdfXvR6AjJOir9hDf6GPvsIIA4UhfClw5TEybg4otSwxyq1M6oNxAprFhtq5bKiZR3MoyfqXz2FOLDlt9bzSDaey/MV//Jxfe/kKXrl20bQc84l9R/mPx17gYN8w3/j9t3L1/NYLPqbjevzo2V3828ObWb+wlft+//W01iYvvLKXOSklqWyBsXSesUy+NE/nGc8UGEnlSOcKpHNF0rkimfK8UHSIhQPkbYdwwCIcNAkFLcIBk1DQJBywiIUDSCBoGQQtk0Q0RMAyCJgGActACIFp6FiGjmloGJPL+gnLGlJyUlLE0DUMTUPTNNXaR6mI63oV3/+6no8/hRFSHNcFKvscup5XarlUIdv2TpskO52JllOVcjwXKSs7eNAyqKs6c39QqVSKxv/8s4rPfblQj90o00UlTBRFUaaBLkxCuklIj4M5tX1d3ybvpcg4w2S9UbLuGDl3lKw7SsodZNTuoa+wn6w7iqTUjjSgRWmPrMH28ySsehJmAwmzkYTVQMysI6JXXZTHSCJmHR3mzXTESq1QCt44A/mt9Oe30J/fwuH0L8g63QwObCWgV9EQWkNjaC0NobUkrHnTXseoGWeBGWdBdMkZYzzp4/oeplbqF0c5f4f6h/nMfz3M7962kQ2L2i74eHnb4e9+9jjffmoLb16/jM++49XEQhc2/LXjefz4+d08tPUArTUJvnrXm2m4Qka7cV2PwbEsfcMp+ofTpLIFjvSOMDSeZXg8y3imlIy9qr2B0UyeZDREVSxEIhYiEQlSX1VPLBIgFgkSCweIhQNEwwECpqEeQ1NmDWMKLc+m3krNmmL8lccJTPGi5nJw4rNzZ9quKBVSCRNFUZRLzNAsYlotMbP2rHG+9Mi6o6SdITLuMFl3lOFiJ+NOP125XYzbfTiyQECL4uNRZTVRZbVQZTVTa7UTt2qpCcwpJXVmSFBPMCd6A3OiNwBge1n685vpyy+mP7eZzsxDdGYeAiCgV9EYWktzaB01oWUkrAVoYuYfSdKFhq6rRMmFemrvUf7+x4/zqXfczoKms392K7H1WC/3fu/nzEkmuO83X8srly+8oON5vs8vt+7nq794ho0L2/jo226lLjH7EiWFokNn3yhH+0fpHhina2CM7oExqmJhjg2MUZeM0lATo7E6RnNdnLlNC6hNRqhJRkhEgirxoSjKS47q9FWZTiphoiiKMktoQidmnjmxIqUk544zYncx5vQyanczavdwKPMcffoeOnMvABDWq6gJtFFjtVNlNVMX7KAuOG9GEimWHqEtej1t0esBKHrj9OdfoD+/uZxAeZiB3KN4soCpxagLrqY+tI660DoS1gJ0bXZ/TUkpr7gbVikl//bYC+ztGeQr/+dNVEfDF3Q823X51lMvcN8vnuCmJR18/A23UBs9/9GOpJQ8vPMQX/nF01y7aA5f/r030Vg1s8NCT8e/c8F2ONwzwsGuIfYdG+RI7wjpbIGgZbJyQTPhgElrfZJrVrTTUpcgGr6wljeKoihXKuHB2RqQinP3Jawok2b3laiiKIoySQhBxEwSMZO0sfykbb70SDkDDBc7GbY7GSl2Mub0sGP8flxZ6tAuZtRSF+ygLbychFlPfXARCbN5Wm/4A3qCOdGbmBO9CYCCO8ZA/lkG8psZzG+mJ/cYPbnHAKgNLMDSYtSEVlMTXEN1cCWmduHDBl8MUkr+6ZHnGE3l+eBrXj4tnZWey+NP7qOhPs7CBY0zdo5s0eYLv3gSfMnH33orpn5hr2t7dx/3/s/PGUhl+NxbX8WrVyy+oM/bc4e6+O4TWzF1jb991x20zWAfJQd7hnh02yEe23aYha113PuOV1S8byZfZG/nAHuODNA/kmb34X4sU6ejpYYFrbXccvVC2puqr8zOGBVFuSzkC5V1OFtp3GVFdfqqTCOVMFEURXkJ0IRO0moiaTUxn42T5b70GLN7GCgeYqhwmMHiYTqzz9NVbo1iaRHqgwtpDC6hLjiX+uBSEmbLtCVRgkaSObHbmBO7DYCCO8xg4QUGcpsZKTzNYGEPg4XnJl4FSWsxNaHV1AZXkwwsIWy0XXYtOAazWT73wGM8f6CLKiPAk/uOcONV82f0nD39Y3z3J5twcg7v+52bWbn8wvsTebE9fQN8/PsPcuf167h9xYV17lpwXL7wyFNs7+qjrSrJN971Zupj5/+4zJ6eQe67/3FMXecPb7+WhY0X/ojQ6RzpG+HhLQd4eMtBHNdjb3mkkd6RFB9++82n/SxKKTnaP8r2A71sO9TDwEgGXQgWz61naXsDt65fSH1V7LL7HF9Mk0MKez626+GVO+YsDStcGjrY98vLE9t8H9f18H2Jf8JQwRNDCvvlYYd9WV73JY7nIf3ygPblGyo5OVSwPGU44Ykhgye7O5DHBwiW5bjyGgKB8+IheF/UT8KL79FMQyd/jlFoXnyA040mdCa6rlU0LC+AZegU7LPfGIeDFpncuesbDppkcsWKzgul0YEqfV1ByyBbqOw9M3QN26ns9cciQcYrHL4ZIDTF1whMDmNdKV0TFdcfmPwcV+onj+6sKM51ZuGwwuqRHGUaqYSJoijKS5gmdKoDbVQH2iB+42R5xhlioLCfgeJ+Bgr76cvvZOvofwAQ0OI0hJZQH1xKc+gqaoOLCOrJaalP0KihLXorbdFbASi4IwwXtjBceIHhwhbGinsYs3fRm/kRjp/C1JJUBZZTE9pI2GwjaS0hZDRdkpvPQ2Mj/M/eXTx3tAff9qlJhnnb6hUznizJ5m2+8J1HKQpJ65wali1tmdbj+1Lyb0+/wEO7D/L533gNbdWJCzrec51d/Nn//pJUocif334Tdyw7/1Yl3aPjfOGBp/Bdyf95xUbWtDdfUN1OZySd48EX9vPItkM8vfPo5A3PhsVtvHz5PK5fOY+Xrzh7B8Y/fHQ7i+bU8+5Xr6elNnHZJ0d8X5IvDyWcy9uT86Ltkc4VKNhOedhgh4LtUiiW5pGgRffAOLbr4jilYYNtx8NxXYqOR3tzNTsP9OKWkyOO6+F6Pt4JiYbVS1rYsqe7onrWV0UZHMlU/LpMQ684gQCl4YYrvcGNhgNTvoFe2tHA7kP9U6gPTCFfQkN1lP4K3585TVV09o6eNWbFwma2VzAk71XzG9l1sPLhdafy3nW01XLoWGXDGNdVV/75WLW4ha17K/vcAcxrreFw13DF8VAaJnkqCZBkPMRYqvIkzlQ/31c01emrMo2EnEoqUlEU5RxSqRSJRILx8XHi8ZnrXFS5uKSUpN1++vO7GCjspj+/m8HifhoCrYwU9xI322gIraA+uJz60HIS5lw0bfofQ3H9PKOFHYwWNzFa2M5ocTuOP0bInEfWOQpAxJiDqdcStzqIWwuImB1EzWbCRiOamL7fCbJukZ0jvTzZf4RfHTuKcHUcx+fYcIr1tS186PrrWVg3My0dJoxmctz79Z8y1J9ifkM1f/SbN9NUd2EJjRN1jozx8Z89yI3zO3jn+lUYF9BZ7mguz2cffowf7djNW1cs5wM3XUt1+PweORnN5vjqr57lhaM93HXLNVy/qH1akxCO5/HU7qM8ueso//3o1slfv69dMpf2xmpesWYBq+c3o2uXf+fB+YLDyHiWkVSuNJxwemJo4Rzj6fKQwtnjc8s06OwdOe39xOolrWzZ03Xa8wQDBqsWtXDw2BCmqWOZBpZRnpul4YRbG5MMjWYwdL00fLAxMS8PJ6xrJGIhcnkbXdfQhJgccljTtFPKdE0gZelxRE0IhFaal5ZLCQ8hBJpWmk/0NSMo7VP6yJTmJ5YjQCCQ0gchEOVhZSc+YhP7nVSGwJcvamFywnC0J348Jz+r5ZYwlfKn2IIAmNJQtOeK1TRR0Q25rgkc98XvxdnPW+nrEgI8r7JYn8qH4jV1jaLjVhQLoGuVt16ZrI9f+XsCpRZJU9lnKq2PAG69ZgmR4LlHAUqlUjQ21M6K67qJa9CNr/tLDDN4xjjXKfDM//7FrHhNyqWnWpgoiqIo5ySEIG42EjcbWRgv9dXgSZehwh4GCzsYyO+gK/sM+1M/BaDKTJKwOqgLraI+uJqa4DIM7cwXL5UytBB14fXUhdcDEx3ddjFS2M64vZvx4h586TNY2MxwofRYkSZqKfpjaBhEAysRQies12HodYT0JEG9Fk2zMLUwSAtdM/GlgY9P0fMp+kXG7CJZr8ix3Bg5R/LU4FEcN4DwDXaMDHJVpBVN85mTSPCxl72Cq5un77GlM9l5rJ8v/eRJUnYRaWn87luum7Zkiet5/OvzW3i28xh/essNLGmsO+9j+VLy/Z27+H+btzOczfHVt76R6zvmntex8rbDt55+gft37uO9117N/331DdOatOgaHueHT+3kvx/fxmgmj2VoLJvbyA0rOrht3SLa6pLTdq4L5fk+Q6NZBkbSDIxk6B9OMzCSRkrJnsP9DI9lGRnPkis4p/313DR06qqimKZOLBygJhGhvamaZDzE9WvnEwlZhIPW5DwcsoiETCzTIGiZBAOleSBgqGGIFeUK5tizb1hh4Zems21XlEqpFiaKokwr1cLkpUtKScbtYzC/k8HCswzmt5JyjgCgYVAVWEJT6CpqgiupCa4laNTMUD188m4fKfsgKfswo/ZhMk4XGecYGc/BlXlMESXtlZqA1wZX0ZXfhZSQ9UtJnaC+gP5iNzFjEcfyXeiiCV8KjmTztAab2T46xor4QnK2y4JoCzc1LmRD/Rxi1syPXGJ7Hv/x1Av8ctsB9h8bZF1bC3/266+Ytg5ON/f08PlHHuOORYv5jTUrMS4gIbF7YICPPvAQO/r7+cC11/CetWsJmlP/rcb1fX64bRf//tQW3rT6Kn5j/UosY3p+83E9n/s37+UHT+3g+QNdrJ3XTNF2ec2Gpdy+dtElHYq4aLt0D4zR2TfKyHiOfUcG6BlM0TM4Tt9Qimg4wNgJ/S7EwgHWL59DOlugJhGhOhGmJhmhvjpGNBwgGQuRiIVIxkKEAqZKciiKck6z6bpuoq4ve80nz9nC5OmffnRWvCbl0lMJE0VRptXEl9Xfbf4XwvEoWrlJsiYEAq3U9HniPyEI6hZFzyk1o0YrN6fW0NAmm1lPlqOhCQ2JxBA6utDLcw1d0yfLdKGdsk1Hw9SM45MozQ2hfhmdSQVvlKH8NgYLW0nZRxjOPwKUftqJmnOpCa6hLriOmuAywub0PlZxOo6fI+cOkHWGybiDFLxxXCkZc/pwfZtx18aXHohqMm6KoN5M1s0R1usxNAtTxGkI1lAfqKUtXEvIOHdz5un0yMHD/L9N29ndPcDIWI53b1zD771iY0XNqs+lL5Pmb558gmorxG+vW0tj7PyH4h3O5fi7p57kp/v2cl3LHP74+pczr6pqyseRUvLAngN89YnnuHHBPN7zsrXEgtOTlDo2NMb/PLOTHz67k6ZEnGzR5g0bl/HqtYupT17cJEmuYHOoa5jDPcOlefcww+NZ9ncOTj4as2ZxC32DKZrqEjTXJWiuj9Ncl6AmGaGhOkZddZTwNHwOFEVRTjQbEybXvPrcCZOnfqYSJkpl1CM5iqLMiNc0X080FsPHnxx9oDQaQWnUAZ9Sh1yu9PGkhy/9yVgfWVqX8nhZebsvJY7v4kkP23fwpI8rPTzplZb90rJbPubENk0IMm4O13dxpIvjlyZXukyMe1AfqKGvMDj5GkzNxNJMApo1OU+YUSSSoB4gpAcJ6QGCeoCgFigvW0SMEBE9RNgIEdCsl3RCJqhX0Rq9kdZoqUNZx88yUtjGUGEzw/kXOJb5KTlnH7uHX8DSqkkG11AVXEsysJaYtQRdm94WG6YWJmG1k7Dap/W4M0lKyePHjvL/Pf0sXtHn6MgYtcEwX/qtW7luQfsFHz9tF/n6lucYSOd4+/KVrGs+/45Ti67Lv257gV8dPkJfOs3fvurV3Nx+9g5Rz+SJw0f50mPPcF37HL769jdSEwmfd70mSCl5/mAX339mBw+8sJ9EJMgbNy7njnWLaa+vvij/r46l8+w50s+ew/3s6xxkf+cgg6Np8oVS/wnxaJCOlho2LJvLq65dypzGKtoaq2ipT1yU4akVRVFmPTWssDKNVMJEUZQZ0RSqIx6evVl7KSW272D7DkXfLs09m4JXJO8XKXilKe8VGS6OUfCLuL7LuJMm5+XJunlyXp6id3z4w/pADR4eMSNCzIwQNSKTy0kjRtQMkzBjJM04Af3K/KXY1CI0hK+hIXwNAJ60GS/sZLT4DGOFzYzkn2Eg9yAAtYElCHTiwTXEA6tJBNYSMBovZfUvqoxd5IcH9/DUsU5+vH8fyxL12J7Lu65ezW+vX0fIurDnyvOuw7/v2sK+4WFunjOPD65fdN4JA19KfnJwD5974nEyts09G67lbctXYulTv8Hf0tPLdzdvRyD4mze8iubEhf8dsV2Xn2/dz9d++Qz9Y2levWYJX/i9N3D1/LYL6sj2XIq2y96jA2w/2Mvh7mGe29VJ71AKAcTDAZrrkqxa1MyiOfV0tNTQ0VpLVTz0kk6yKoqiXCjhSYR25qyIqLDjYEUBlTBRFEU5LSEEAd0ioFvEiFzw8aSU5L0CGTdH2s2SdrKluZthsDDMAMN05XsYd9KMOWlsz0YXOjVWkpgZptpKUG0lqbKSVFtJqq0EVVaSiD67b650YVEdWkN1aA0AUnqk7f2MFTeTLW5ivPAc3alvMtFdZUBvpDa0gZDZQdRaSSSwEkObvYm5F7M9l2cGj/J4zxH+be8W2gLVdKdTtMXjvHn5Vbx5yTISwQvrPDfr2Hx73xb2DQ/xssY5fPrG2867nxIpJY8cO8xnnnmMw+Oj/OHql/Gby1efVx139vfzj088TcSyeP81G+moqT6vOp2oa2Sc/3p6Gz94dier25t5x8tX89p1S4mFZqavmVS2wNb9PWzZ38XIeI5fPL0Xx/UwdI2b1y3g2pXzWDqvgaXtDbQ3V6sWI4qiKDNBtTBRppFKmCiKcpJPf/rTfP/732fPnj2EQiGuvfZaPvvZz7J48eJLXbVZTQhB2Cg9plNPZZ2dOr7LuJNi1E4xao8xYo/Rmx9g1/h+Ruwx0m6KMSdFzIhSF6imtjzVBapoCNZRH6gjZkRnVUJFCJ14YAnxwBLgHQAU3F5SxRdIFbeQKe5kJPsjJM7kPjWhWzCEQci6ipC1nKB5FabeMGte91AxxfPDB9k+2stPu3YwnPNoD9eRcx1WtDXwf1Zs4DXzFhO8wE5OB/NZ/m3fJvqzGa5tbOfOl199QYmSh7sOcd8LT5BzHNY3tPHN1/wajZGp93uya2CALz79NE3RGH98w3Usqr3woZh3dvXzn09tZTSTp606wTff9+t01F94AubFMvkiW/Z18/yeYzy3p5O+oTSpbIGqWIib1izgD958HSsWNLF4bj3BC2wRpCiKolRGAOIsSZHzvTr40pe+xOc//3n6+vpYtWoVX/jCF9iwYcNpY7/+9a/zrW99ix07dgCwbt06PvWpT50xXrl8qYSJoigneeSRR7jrrrtYv349ruvykY98hNtuu41du3YRiVx4SwulcqZmTCZBzkRKSdrNMFQcYag4wog9yoHMQZ4cfob+wiAZN0OVlaTOqqUlVE9DsJ7GYCONwQYSZmJWJBWCRhNBo4n6yGsA8GWRnL2LTHEbWXsrQmYZz/+E8fxPAAhba7Hd/QTNRQTMRYTMlZhGA5YxD8uYgyYu3eNOru/Sne/hYKabXamDbB/rJe0UOJzJclWsg2E7wy3NK1kcb+QL1yxjbnzqHaW+2LaRHr59cBOGNLitbTHXN55fnyJQGsr2F8f286XtTxE1Aiytruf9q66hLTb14Yy39ffxvV07Gc8V+OB117Gw5sJHTZJS8nvf+D4gePs1q7hxybxpHXbY8312Hu7jmV1HeXrnUQxNY9PeLmoTEdYvbePtt65leUcTcxurZsX/W4qiKFckWeon76zbp+i73/0u99xzD1/5ylfYuHEj9913H7fffjt79+6lvr7+lPhf/epXvP3tb+faa68lGAzy2c9+lttuu42dO3fS0tIy5fMrl44aJUdRlLMaHBykvr6eRx55hBtuuOGc8bOpN/WXAl/6jNpjDBQH6S/001/op7fQR1+hn7SbZlViCboQNAabaQo10xRsptaqw9Rn16/hnp+h4Owmb+/E8frIFh+j6OzHlznCgZeRKz5djtSIBm/E9zOYRjOmPgddq8XQ6zD1egytGl1PYmhxNDH1x0ocr0jBT5F1U6TcETJOhr5CNznPY3d6N4aoYldqP62hxexJH6Ex0M5AYYxViZWsTs5nfc0CmsLJaXtfbN/jH3c+wlvaV9MeO/8WFkXP5fuHd/D1XU8T0UOsqW3m95ZtoDU6tUSJlJJnerv4p03PUx0K8fvrNtBRNb0tP8ZyBZLhC3ts6UQjqSxP7jzKE9sP8/SuozQmY/QMj7N+SRvXr5rPqgXNzG1QCRJFUa5Ms+m6bqKuL3/FxzGMs4yS4xZ4/KGPT+k1bdy4kfXr1/PFL34RAN/3aWtr4+677+bDH/7wOff3PI+qqiq++MUv8q53vauicyqXB9XCRFGUsxofHweguvr0NzXFYpFisTi5nkqlLkq9lMpoQqMmUE1NoJql8ZMfq/Kkx2BhgL5CL72FHraMbeJn+R8T0AV5L09LqJWmYBstoVaag61UB+rQxMx1kHkhdC1KJLCeSGB9ueTDSOnjeN0UnYMUQrdhO4ex3SNIWSRnbwb7OQx9Lnn32ORxwtZG0vZzAEQC15Kxd6OJMKYxh6ybQRMmAWMh484RfOmT8wWetDH0efTmd5AMLONYbhd1wVUcyu6lMbicA9n9tIaW0VvoYn6kmgXR+VwVX8ItDdexONpBS7h+xt5XS9P5kxWvOO/9B/Jpvn1wMy8M9rBtqI/fXLSWdy1aR11oasPu+lLy0NGDfHPbCyysruETN76ClvjUW6VU4kKTJVJKDvYO89i2w/xq60F2HOllbl0V0aDF216xmuuWz2Pp3IYZ7SxWURTlYsgX7GmJudwIXyL8s3T6Wt724mvWQCBAIHBqH1e2bbNp0ybuvffeyTJN07j11lt56qmnKqpTLpfDcZwzXk8rly+VMFEU5Yx83+eP/uiPuO6661i+fPlpYz796U/ziU984pTyoudiey4IUXqWtPzEqBClZQEn/SI7WszhSh9dCDShoSHQRGnShTa5rCEQQpBzbYqeiy40DE3DEBqGpqOpX3krpgudxlATjaEmVrN2styXPsP2ID35Lnryx3h25EmG7U7GnREagi20BOfSGppHc6iVxtAcLG36fs2fTkJoWEYbltFGjJtO2iali+MN4Hg9FN0eXG8Axx9CStD1BK4/jq7VoIkwvszj+WmyzsHSviLJuL0XgLwfQeKT0OcR0KJEjCRNoaVUWXNIWHOottpYVXULNVYTvxVoIGlenKFrL4SUkueHO/nOoed4fqCXoG7wroXr+fL1byZsTO1xppxj8/39u/jfQ3u4fe5CvnDba6kKhmao5ufP8322HOrh6V1H+dlze+geTrFmXjONVVHeeuPtXLOsnZrYhQ9rrEw/35cUbafcAl9Ozn1fltclvpQgS4k76Uscxyv1CXlCvASYWEceb7EvJa5bGtKeyaITl8tzJhdwXO+Uep6xPbcs1acSmiawpxJruxXFCk3g2JUd13W9ivrLNHSNYrGy8+u6RtF2zh0ImIZOrlBZbMDSyeUqu9k3DZ1svvLEgKFrFIqV1WOyPgGTTKYwhT0EjlvZezjBMnWyucrqtaCjjs//48/PGec6U6nzZcIvT2fbDrS1tZ1U/LGPfYyPf/zjp4QPDQ3heR4NDQ0nlTc0NLBnz56KqvShD32I5uZmbr311orilcuHSpgoinJGd911Fzt27ODxxx8/Y8y9997LPffcM7meSqVoa2vjtQ98GT0cmLz4BI5fkHLCxWVZSzjJnvH+iuolgPW1c3l26OhptxmaXk6gaOhCwxQ6uqYRNy36C2ksTcfUDCxNx9INTE0vLZfLYmaQ4WKWgG4Q0k0CuklIN8pzc7I8YgTIuAUiRoCIYRExAoQNa3I5YlgEdANNaEQM67JtnfFimtCoCzRQF2hgVXLdZLntF+krdNGTP8qYPcgLYw/RX+gkZlbRHJpHR3gxtcEmmoIdhI3Lu9muEAaW0YxlNBOpcMCU0k2Vhy+dUspPaCAFuja7Hl86k+Fihh93bWXrcDe/6NnDK5qW8LkNr2VDXfuUkzzdmRTf2r2ZQ+Mj3DZnEd969VsI6JfXJYftuDy7/xibDnTzP0/uYDST56blHbx8eQc3repg3YLWl/woNrKcLCgUnfLkUiwvF4vu8bKCQ9F2sB0Px/GwbRdnYtnxSn0tZQp4no/r+biuj+d5eJ7E9Txct1TueT6e6zOntZrd+3vxfXnC5OP7Eu+EZb/8K3EkHCCbK57j1RzXWJ+gb2C84vjqqggjo9mKYmurowyNZCo+9sqlLWzb2VVZ7LLWimNXLW9l647KYpcvbWHH7u5zBwKhoEm+goTFgo56DhwaqOiYVy1uYtfe3opily1tZufunopiVy5vZVuF78HSRU3s3ldZHQDmttVw9NhwxfEwtdcJpaSM653trv9Uc9qq6Tw2UlHsDdcumtKxZxMhJeIsvU5MbDt27NhJj+ScrnXJdPjMZz7Dd77zHX71q18RvMCR7pSL7/K6elEU5bLx/ve/nx//+Mc8+uijtLa2njHuTM0Xf3n73VN61jXjFHF8D0+WfsnzpcRH4ksfb2K9vCyRpJ0iw8Usbnkfx/fxpI/re7jSx5U+nu/jSg/XL62nnTyjdh7Hd7F9D9srz32XnGsz5uexfZewbnEgPXjOOl9d28am4c6zxiyKN7A/3Y8AYmbwpGlj7Tz+YPGNFb9Hl5qlBZgTns+c8PxyydvKrVH66MkfZszu5onBp+gtHMLSgjQFO5gXWURtYA4NwQVEzdndDFUIgcBAEyd8dV7ejUXOqeDZPDawm1/0buWZwS4SZoi3zN3Ah1e8mvrQ1Ee8mXD/0X3cOmcBV9e3XFYtagq2y5N7jrDlcA/fe2I7mYLNLSvn89aXr+QVqxawuLXusqrvdHA9n1Q6z9h4jvFUnnSmQDpTJJM9PjcMnc7uEXI5m3zeJleeQDI6nkfTBMGASTBgEAiYBAMmgYAxOQ8HLCQSy9QxTQPT0MvLOuGwhWUa1NfF0XWBoesYhoaha+iGfmqZrmEYOq/zVqFpojQJgaZppWVdoE8sl7e5vkTXyi0XtVIbRlFe17RSq0QhKLVAFALf85GUWzyWW0EiXtQKstQMEiHAcbzJz8XEx0Oc8D//5EdGCJAS70w3uaf5bHmeV/EQp76UeF5lwb7v453lkYQTSf8sdX6R07WeORPXreyYUsqKEwMTSbxKaALe9Nq15w6k9DlwKmyRM9V6TNZHE9z2itO31j09H8eZ6jm0ilsWtbZU8ZE/fs0541KpFI0NH5tSPS65CocVjsfjFV2r1tbWous6/f0n/7DX399PY2PjWff9m7/5Gz7zmc/wwAMPsHLlynOeS7n8qISJoignkVJy991384Mf/IBf/epXzJs376KcN2rOTFb/fPhSUvRcCp5zwuSespx3bTbUtpN1bbJukaxrkzthOesWSVilXxIkkHIKpE5o2lobmFo/EJejUmuUZuoCzeWSX0dKybgzSG/hIEOFA2we+V/6C/sRQmN+9GqSZpL64CLqgguJGvVX3A3q5S7vFnluZC+PDG5jKJ9nd6qXWxpX8sUN72RFcs60/Hv8zrKrp6Gm0yNvOzy++whbDnfzvSd3kLcdbl4+n9955QZuWbWAufUXPhLRxSalZHQsx+BIhuHRDMOjWUZGswyPZigUHLp7xxgbzzGWyuN5PvFYiBVLmhkezRKNBIhFg6UpEqSmuoZkPMSqZa2EQxbhsEU4ZBEJBQgFTSzLwDA09f+porxEOfalG1nufFXah0mlLMti3bp1PPjgg7zxjW8ESknJBx98kPe///1n3O9zn/scf/3Xf83Pf/5zrr768vleVKZGJUwURTnJXXfdxbe//W1++MMfEovF6OvrAyCRSBAKXX59D8wETQhChknImJ5HLYqeQ8opkC5PE8v1wfP/Bf9yJoQgadWTtOohfg1QusHLuEMMFQ4xUNjFzrGfMFjYh4/Poti1hPQI1YGF1AQXETdb0cRL+1GI6TZqj7NpZDdPD29jf/oYYw5cW7OMd7Zfz5rqBZjalXU5kLMdHtt9mC1HevjeU9spOC6vWDaf97/mWm5ZtYCmqsv7kTHP8xkcTtPTP05v/zhDoxm6ekYZGEozMJwmm7NZtayV8fEcNVVRaqoi1FRHWL6khap4mHg8RLI8BQJXxiNjiqIoFZuBYYXvuece3v3ud3P11VezYcMG7rvvPrLZLHfeeScA73rXu2hpaeHTn/40AJ/97Gf56Ec/yre//W3a29snr6ej0SjR6Oz/weyl5Mq6QlIU5YJ9+ctfBuCmm246qfxf/uVfeM973nPxK3QFCOgmdbpJ3RWaIKmEEIKYWUfMrGNebONkedYdZqSwn6Hibg6nH2LT0FcpeOO0R5YRNhpJBhaSDCwiaS3A0F4aCbvpUPSK7Mvs42DmGE8ObeZorosV8dXUB2t5XfONLE3Mx7rCkiR52+HRvYfZdKCLHzy3k4LrcuvyBfzRHddz68oF1CcurwtUKSWDwxmOdg3T2TtKV88oXb2jCAQHjw5SXxOjqSFBU32ClqYkizsaqK+NUV8bJ1ppxzuKoigvQcIvTWfbPlVve9vbGBwc5KMf/Sh9fX2sXr2a+++/f7Ij2M7OTjTteF91X/7yl7Ftm7e85S0nHedMHcsqly8h5Xmk2BRFUc4glUqRSCSmNLa9opyo4I0zVtjLuL2fUXsfY8X9hPUwjtdDzFpI3FpIzJxP3OogYs1DE7OvufB0SzvjHMke4FB2HyN2mieGn8UUJhuqr6Ux2MKq5DKaglfe40952+HRfYfZfLiH7z27naLr8YY1S1nW0sArVyykNh651FVESsnQaJZDnUN09Y6y50AfR7qHSWeK1NdEaWlI0tpURWtTFW1NSVoak6pViKIol43ZdF03UdebNvwZhnHmzlVdt8Cvnv3rWfGalEvvyvp5SVEURZn1gnqCxsgGGiMbJstcv0jWOULa3k/K3k9f7kGOjH0O2xsgZLQSthZQHVhFQK8iaLQTNOdhaDVXXIIAIOuO0l84zFCxi/2Z/XTnD2JpcfoL43REFrEwtowb625lbmQu+hX4aFOunCTZ0tnDf5WTJK9btYQ/ueMGbl22gNrYpUuSuJ7P0e4R9h0ZoLN7hO17exgZy1JbFaVjTi1L5zfwuleupL2lmnhMtZhSFEWZERV2+qoolVAJE0VRFOWyZ2gBEoHFJAKLTyp3/Qw55xBZ5xCe28do7kEK7mEKzhEMLU7EqCVgtGEZc7CMNoJGO6begGE0oYn4ZZtQ8XybjNtH2ukm4/TRVzjKqN1J3k1ztNBNSI/THFpKldXBisQ1tITmU21deS1IJmSKRR7Zf4Qtx3r47+d34Hger1m2mD99zY3cetUCaqLhi14nz/fp7Bll18E+9hzqI5stcrBziLnN1SyaV8/qZa285VVrqE6Gr9h/F0VRlMuR8H2Ef+bnbs62TVFeTCVMFEVRlFnL0KLEAyuJB04eqk9KD9vtxfYOY7vHsN1OssVnKNrPky88hucPIESEoFkaulTX6jD0OnStFkOrQdeTaFoSXatGEEHTY2giihDn97UppUTKIp5M43hpHH8c1x/D9kbJeSMUvSEK7jBjzhBZt5eiN07ONwgbtcTNuZj6HDqi15G02nhdYD5Ro/qKvwkfzeV5aN8hnjtyjJ/u2IeUkluWzOfPXnMztyyZT1Xk4rbQGBnPsuNALzsP9rHrYC818QhSSpZ2NHLrNYtZ1N5AKKgepVEURbnkJHC2nIhqYaJMgUqYKIoyI/571w4i0SgIwcRtnSgvawJ8WVrXEJTu+wSaOF5m6Bqu56NpAk0IdCHQhDa5LMpzQ9OQgKGVthmahq5ppfkJ6zoCXdMwdQ1D0zF1DVPT0a7wm86XKiF0AmYrAbP1tNultHG8Pjy3D9fvxfUG8fxBbOcQRfksvj+G74/ieeP4chxwALDMFbhuJ0IEECKAYSzB9o4g0BBaAsdLIfEAD6G1UnD24ssCprmK8eKzpWPoHeT9DIaWxNLr8LRqAnotMauDqtB1hI1GwkYTIaMBQzvzM9hXoq6xcR7cf4gH9x9gf+8wecfh5R1z+dQbXskNC+cRD16c98P3JUd6R9ixv4cX9nbT2TfKwrZa6qtirF3aym/ecTWxyEvr30ZRlCtDoeBUHCulJJ+3Z7A2M0NIiThLN51n26YoL6YSJoqizIhEIEgoEGQijS8lSGTpsVIp8fzSmo/E92XpF/jyNgmYmkbOcfCkxJcSX/r4J+zn+aXykGkyWsjj+j6e9EtzX56wXJr7UlJwXWzfw/F8HN/D8byz/shgaBpSQsDQCRoGAd0goOsEysuN0Sh51ykNQWyahAyDkGkSnlwvL1sGUdMiagUIm6ZK0lwGhLCwjDlgzDlnbOkzWUT6GTyZBVlAygJSFvFxkdIBJEgfH4lAB6EDFkJYaCKIEAE0EUXXomgidMW3DqmULyW7+gd44MBBHjxwiIDQ6Rwd4+YFHdy5fh0va28jZM58qw3H9dh9pJ99nQM8tfUI6VyReS01rFvcyp1v2EhbQ1L9m1H6f8F1fRzHxbZdHNsrzR2PYtHFdb3S5Pi4nofreLiuj+t6OLaL6/l4no/vS7zysuf5+N7xdd+XCKBYdPClRPrlv/vlv/nSL31nTCxrmqBYdMqjiJa/SyTI8nfFicu+V9pv4g//xPfNxPKJ5Y1NSbq6Ro5vm3wTTn4/JrS0VtN5dOjU9+wM76VlGeRzld+Its2p4eiRU49/JroucByvotj2eXUcPjhQUezceXUcqiC2Y349Bw/0nzNuXkc9B/f3nTOupbWaY53DFdURYMGiRvbu6a0otrEhQU/vaEWxkbBFOl2suB4AsXiQ8bFcxfGLljSxe2d3xfFLl7Wwc3tXRbFr1rXz7NMHKz42lDpInXVKF5Rn364oFVIJE0VRZsQr5y+Y9T2P+1Jiey5F16PouRRcl6LnUXTdyeRLxi6Sc1zyrkPecRgrFsiX13OOQ9SyODg2Qsa2ydg2ebd0EwBg6jqLq2sYtwvEAwESgSBxK0A8ECQRCBC3glSFgsQDQaoCIZLB0nZ143ZxlVpGBUEPolN7qasz66WLRZ7p6uKBAwf41ZEjtMeTDGfy3LKgg1sXzmd1cxPGCUMzzoSC7bDjUC+b9nSx98gAtuuxZG4965a08tH33k4iemV0yGrbLtlskUymQDZbJJspks0WsW2X8bEc+bxNLm+Tz9vk8w7RcIBjx4YpFhyKRZdiwaFQdCgWHNrn1bFta+dp70EaGhP0942ftS4By6BYrOyX7ZqaKMPDmdNu07RSC0NNL7U+jCfCpMZzpb+LglJCWpRaLp64LBAIjXJCZqJlI5PbAQRM/n0NRwL0945Nnvekv7snLpbLk1URRs5Q5/+/vTsPj7us1z9+z75kmaxN0jTd2Fq6IS3UsolQqYhF9CAICAURRMraA0IRKBWhuGFRNuEngudQAVHQowgHkYIcQKCILEKh+0KTJmnWSWb//v6YZugkk2SSzJq8X9c1VzLfeWbyTKbTzNzzeT5Pov+znS6bvN7kA5OKiiK1tnqTHm+xmBUIhJIaO67Koz17krvtyqpitexJfD/31VXjUXNTx6Djqqo8/T7W+yr2uJMa16O2K6DWluTuU2GhQ+1t3UmNjYQNdXYOLUCw2S3qGkI4FtobQCYrEjaSDsfCoTHSuyM8SNfXMIkJkkdgAgD9MJtMclptclrT8wl3IBxWh9+v9oBfbX6f2gN+tft9avP71R7waWdnh4KRsLZ3tKnV51OLv1sdgegnWzMqx6kj4FeFy61yl1vlzujXKneBPA6XxrkLVekqUKGdLXeRXRHD0PtNu/X3bVv15scfa+2WLSp1OHVQWYW+ddhh+szkyZpaVpax+Tz/5kf640vvaebUGh1+8EQtOfEwOfNgG99IxFB7e7eamzvU1tat1havWtu61NbapUjE0Pbte9TR4VNHe7fa27tVUuLWRx8m/uR+xoxavbf3E2ybzSKnyy6Xy6bp08aro71bDodNnhK3nA6r7A6bHA6bKioLdeBBNbLZLbLbrLLZrbLbLLLZrXI4rTKbzLJazbLaLNGv1uhXm9Uii9Uik0myWs0ym82yWD45mc2mT763RL83Iop+bzbLtHdZptliIixG0kKhsExK7t9LJNJT/zo4w4hWOA1FOBwZ0r/dSCQyYHFEovHJslosstqS3z3NMAy1t7eruvr7yU8oB7AkB6lEYAIAWWK3WFTudqvcPbQdPgzDUJvfp2Zft5q7u9Ts86qpu0vN3V3a3dWp9S1N2t3lVWO3V95gUB6HQ1WuQlUWulXlLlS1u0hVBYWqcRep2l2oce7CtIVCGJu2tbfq5Z3b9H87t6nF263/275Nkz0lOvmAaTp9xix9ekKdCrIU5n320AP02UMPyMrP7o/fH9Tu3e3avbtDTU0damyKfjVJ+uD9j7Vnj1ctLV6FwxFVVBapqfGTT+4dDqsOOWSSmps6VVTkVO2EMk0vdqm8rFCf+cw0FRQ6VFjoVEGBI3Zyux1yue1yueyyDeHNE5AvrNbk/13zDBhYMJiHH7xE1+gNfDmQJAITAMgzJpNJJU6XSpwu7Vcy8CfzPeFKQ1enGro7Ve/tUH1Xp/5Rv10NXZ0ym0xa17RDJpNJ1e4i1biLVFtQrMmeElU4C1RbUKzaAo8qXYX0XkFChmFoa0er/tmwUy/t3KZXd21XpatA21vbtKB2or4y7WCt+uwJqiv2ZHuqWePzBbWrvlX19W2qb2jTrvo2hUNhvffeTu1ubFdra5cO2L9KH30Y7flgs1lUUVGk2bMmqLq6RAcfXKuy8kKVlRWorKxAnmK3PCVulZS45XLl4ZsZAEgnAhOkEIEJAIxi+4YrB6my33HdoaB2dXXo4852fdzVrt3dHXq/Zbd2etu009smbzCgAodNEwo8mlBQotoCjyYUeFRbWKy6ghKNcxXJYkpv3wnkhkA4rPea67Wuaafe3L1Tb+1u0MfedtUWFGt2WY0unH2YPl1dpwPKKsZUyObzBbVzV4u272hRc3OHNmxo0Me7WvXxrla5XHZt3x5tIGoyRXt0zPvUZNXVlWnu3MkaN65Y1eM8Ki8vVGVlkYqLaQwMAMNGYIIUIjABAMhltWlqcZmmFieuWAlHImr0ebXD26qd3jZt97bqread+v3Wt7TD26omv1fjnEU6tKJWDqtFE9wlmlBQqrqCEtW6S1TuKOANYB6KGIa2du7RO3t2aUNbs/5v1xa911KvCa4yGZLmVtbqqkOP1iGVNZpSXDrqH2PDMNTS2qWt25tV39Cm9RsatH3HHm3b0axIKKLmvY0zJ4wvkdNuU+34Eh08vVYTaks1rrJYNTUejasslt3Oyy8ASBdT2JBpgL40Jpq+Ygj4iw0AGJTFbFa1u0jV7iLNq6zrc3koEtGu7jZ93NWubZ3N2tHVqud3fajt3hYV2C16p2WnxrtLNMFdosmFFapyFarWXaJad6lqXaUqto+OXUnyWTAS0qaOJm1sb9K/Wnbq/dZ6GRGzXtm9VWUOtxaNn65jxk/VZbOO0uyyGpU6h9Z7J9+0tHq1dfsebdzaqM3bmvTxrlZ99FG92juiO2RMO6BaXV0B1U0o0/Gfma6pkypUXeVR7fhSlZUSEAJA1lBhghQiMAEAjJjVbFZdQanqCko1v3JSn8u9Qb92drdqh7dFu33t2upt1Lo9m7Wzq0U7u1o1t7xWLcFO1ThLVOMqVY0rWpkyzulRlbNUpfYCmVnykxKhSFg7upq1pbNJGzobtKmjUR2BoF7avUEhI6JjKqcpYpg0r2KSZpfW6geHn6waV/GoDQC6uv3avH2PNm1t1KatTdqxc4/Wb2xQa1uX5hw8Qe9/VK9JE8o0ua5cX/vK4aqrLdPEunJNGF8ypMaSAIAMiRiSaYBQZIg7HWFsIzABAKRdgc2hA21VOrC4qs9lhmGoJeBVva9Vu7pbtKu7VR93t+jd9k36uLtZDb5W+cNBzSmdLCmscY4SVTo9qnSUqNpZqlJHgcrtHpXai2Qx8QZWkrpDfjX4W1TfvUc7ulq0s7tZO7ub1OL3699tOxQ2Iprl2U+SNLVwnA4r319L9j9SBxRVyTNKq33C4Yg+3t2mjVsbtWNXq/794S5t3Nqoto5ueTv9sljMmlRbpjnTajVreq2mTKrQlLoK1VR5ZLEQ1uET4VBY4SG84YqEwwoPcQlAMBCSktwW14gYCoeT31o2EokoMoTxwWA4qcA0YhhJ324wEErqNg3DUDg0lPuW/BwMI7r9cHJjjaTHSpLZbFLAH0p6vExSKJD87c+aO1kl5YVJjQ0Gk7/dSILfta8rkPT1cwYVJkghAhMAQFaZTCaVOQpV5ijUwZ4JCcd0hrrV4GvVbl+LGv1tavS16oP2bdrk3a732zeqOdAmXzigEnuRPlUyWd3hbnlsxSqxF8ljK1KZvVCF1gIVWQtVZCtUocUtt9WVV1UrhmHIF/GrI9ip9mCnWoIdagm0qyXQpu5wWJu9H6vJ3ya3xa03WzZJkkpshaqwj1O1q1QHFNVqYlWVal1lqnNXyGMfvUtqWtq7tGl7k7bu3KMPNjVo47YmFTpteuPtbZKkKRPKNH5ciY474iBN3RuMTKotG3Nb7Pa8CfR1BxTwhRQIhBTwhxQMRL8PBsLy+wIKBsIKBcMKBsMKhz753mSSOjt8ioQiCoc/OYVC4dixwmKXdu9qjb2R3fdrOBKRsc95m8OqjrZuGRFDhhE9RSKGZETfjPccr6zxaOeWpr3viaKXGzLizxuGphxUrQ/f3Rk71nOfo1/jzxcWu9TS1BF32b6/p33NmjtZ76zbkvTvecqB1dr8Yf2QHpvycUVq3t0x+EBJ+02r0cYPdiV92zM+NUnv/XNr0uOdbntSb5oP/tRE/fuf25K6TbvDmlSgcOCMWn343s6kbnOo4z1lbrXt6UpqrMttV/cQgoOZcyfr3SH8G6kaX6KGj1uTHp9JobA/21MYOiMiRQYIzozkQzjAZPT+KwAAI9De3i6Px6O2tjYVFxdnezoYQ7pCPu0JtKs12KaWQJtagx1qC7arNdCh7nD0+45QpzqCXtU4K7S9e7NcFqfcFrcKrG65zC557DY5LS45LU45zA45LU45LXbZTDbZzHbZzXbZzHY5TA6ZTJLFZJHFZJHZZI1+lUlGr0ZzhiGFFVLECCu89xQxDHVHfApGAgpGgjLJpLZgm3wRn/xhv/wRv1oCPnnDXfKGvDIk7ezao6ARfYMx2T1RewJdKrEXq9RWrAnuWllMNlXYPapwlKrcUaIqZ6lcFkcWHonM6fYHteXjPdqwrVEbtzdp045mNe/p1KbtTZKkw2ZMVCgU0X4TKzR9apXqqks1dWKFCtz5+XsxDEN+X1DeDp862rvV2eFTtzcgr9evLq9PXZ1+RcIR7WnskK87qO7ugHzdAfm6AiopL9CmD3bJ7w/J3x1UMBCSYRiy2iw6eM5EbdnQIJvdKpvdIrvdtverVU63XZGIIavVLKvNIqvNKpvNIqvVooIih/zdQVmsZlks5k++WsyyWC2yWM1yuezydQdltphkMptkMZtlNptktuzz1RT92vOS1Gw2SSaTzKbodcxmU+y4yWySYUiRcESmvZebTJJJ0a8y7T2/92soGH1j1HOZPvnySYWDSdEqhn2eun2KH/Y5YDJJwX6qARIVTZhMQ6w2ULQiKjLQG759WCxm+XzBpG/barWouyv5N8HhUDipD+Ttdqu6vMndbijJ23Q4beps707qNiXJ7rCpq9OX1FiL1SJfd3IhyECPeSKFxU61NHUmPd7psqmjPbl5S9Jf//jPpMeOVCjs13P//nFevK7reQ26cNIlspr7/38+FPHrr1vvzIv7hOyjwgRAWmyt36Mi7ycvEE37vlDd+2lhz/F9XrP2KdHtudykvS+Mtc+L3b3HzD0vrE3RF9RmU/SFeM/PMptNMpvNsphNo7YPAyS31Sm31akJGjfo2FAkpK5wt7r2BhJdoW55Q14FDK98YZ98YZ/8EZ9aA60KG9Hvg0ZAgUhQgYhflfZKfdy9TWEjFAtBwkZY5fYKNfob4n7WpIL9tL1ry95wxSyzyarxrslq8DXsDWFsqnKOV0fQuzegccpj9ajcXqsCazTMcVvcclsLVby3QsZpdoypf8v+QFBbd7Vo08fN2rSzWS1tXVr3/nZ93NimSdVlqt/dpikTKrTfhAodc+hUTagq1dS6clWWFubs78kwDLW3daut1av21i61tXSprcUrX3dAjQ3t6mjrVkd7t9rbulRWXqQN70crCBxOmwqLnZo0tVKdHT65CxxyFzhUUBj96ikpVPm4YrlcdjldNjnddrncDtkdVtkdVjkcNjmc0UDEbM6fCisAn/jPm7+S9NiRLslpb29XVc2Pk76NnGBEBq4iocIEQ0BgAiAt/ufv78npLvik/Hmfy6wWs/yBUPTYPpfvWypdUuhSU3t0i86I0VOeHR1jyJDNYlGXd1aUVQAAYa1JREFUPxgr2e4Z0/N9T/m2p9Cl3S0dCkf2lnYPoKqsUDub2uKOGUb00zuL2SSrxSyrxSyX06FwJCKb1SKbxSyr1SKbxSKb1SyHzSqTySS7zSK71SK7zRr76nbaZJLktNvksFvlsFnl3Ps1et4ip90ml8Mmu9WSs2/0RgOr2apic5GKbUXZngr20dnt19b6Fm2p36PG1k69vWGXtuxqlj8QVmNTh2xWiybVlGr+9IlafMxMTa0t15TaMtWOK5ElR978BwIhtTR3qqmxQ82NHWpp7tSepk4ZEUMbP6xXe2tXtHLAZNLEyRUKBkLylLpVXOJWSUmByiuLNWFShYo8LhUVR08FhQ5ZbbxkAxA1lNcHQ9rG3N73UCCU4GCuo4cJUoi/vgDS4pKvHj0qyhyNveFLOBxRKBxROBKRLxBSaO/5YCgc+xoMhxUMheUPhhUIhhQIhhUI7f0+FFa7N6xWr0/+QEj+YCj21RcIyRcIyu2ya0dTm7r9QQX3NpczZOjACZXataddLoddbodNbqddBU67HHarCvY573bY5Sl0ym61qMBpV5HLoQKnXQUOuwpc9px5Q4mxLRyOqL6lQ1t3t2jr7hZ93NimD3c0aWtDi0rdLn24vVEuh03HzpmqAqddi4+cqUk1pdqvply140pkzWIDVp8vqKbd7Wrc3a621i7t2Nas5sYONe1uVzAYVsAflNVmUVl5kcbXlsrltqu0vFB1kytUWl6gL58xX8UetyxWnosAkDYRQ9IAoQi75GAICEwAYAAmk0kWU3Tdvd0WPVaU4V6Z/mBI3f6guvxBdfsD6g6E1OULqNMXkNcXUJc/oM7ugPZ0dKmh1aIdzW3q7Par0xc97nbatKm+WRHDUIEjGqQUuR0qKnCq0GmXx+1Ukdspj9upiuICOR1WlbhdKimIHit0OmI9BIBkhMIRNbR2aHtTq7Y3tWlXS7s2fbxH2xpb1NDaqe69fQNqyop16JTxmlpTrs8esr/2G1+miZWlGpcjS2l+ee/ftH1rswKBkIo9Lnk7/aqsLFZlVbFqaks0Y3adKiqLVFFZLJc7Dz+FBYDRKGJIGmDZDYEJhoDABABynMMWXbZTUjiy7V4Nw1CXP6iObr/au3xq6/Kpo9uvti6f2rt8amzrVHNnlzY1NKuty6dWr08et1Pv7qiX02ZTSYFL4zwFcjmi35e4nfIUOFVZXCi3wxYXspS4nbJb+RMzWgXDYe1u61R9W4eaO7q0ubFFu1raFQpFtG7jTtW3dKimpFA7m9pVXVKkugqPJpaXaM7UGk2sKNHEcSWqqyiVcyil4lnwhZM/pdKyQjmdtmxPBQCQLJbkIIVy+5UKACBlTCZTdImO067q0uR7dxiGoe5AUC3ebrV4u9XW5Yt+9frU2tWtjQ3N2tnSFg1ZunwqLyrQ65u3y2w2q8Tt1JxJNdrj7VaJ26kSt0sTyj0ym03yuJzyuJwq3VvF0nPeSa+GrIkYhlq7utXY2aUWb5fq2zrU2OHV7vZO7W7zqr6tUw3tHaor8ejNLR9LkhbsN1Gd3X7VlhZrv3HlmnlstWrLijW+tFgTyj1y5PHjWTO+NNtTAAAMVSSigStMaPqK5OXvqxgAQEaYTCa5HdEeKbVlnqSv1x0IqrXLp7aubrV0RcOVti6fApGwdra2a319o9q6ffIUOPWvHfVq6/ap3efXoZPGa1PzHnlcThU7nTqoqkKdgYCKnU55nA5VFBXIbrWoyOFQsdOhYodDBXabip1OFTrsclDZIikadHkDQbX7fWrt9qnN55M3EFBjhzf6eHRHt+rcuHuPmr1d2tPVpT0d3QrtfSE5f+IE7dzTrsqiAo0rKtD4kmLNmlCtKk+hajxFGldcqGpPIZVEAIDcQoUJUohXOQCAtHDZbXLZbaopGVo1S4cvoA6/X22xN/lBNXm96vD51ebza1d7h3a0tand51e736/2bp86w0G1dvsUCIdls1i0YNIEbW5tUaHDrkK7XYV2h6aUlagzGFSBzSa3zaYCuz26U5E1enJZbXJarXLbrbKaLbJbPjk5LNboTklms6xmi6xmkywms6xmc9K9NmINhPd+jUQiCkbCChlGtHFwJKJQJKJAOCR/OKxAOKRAOKxgJCJvICBfMKTuUFCGpGZvl7qCQXUFg5IhNXR2qsPvV2cgoHAwEjvvtFrl9Ue393bbbJpRVSlvd1ClbqdKXS7VlXh0yIQalbldKitwq9ztUkVhgSoK3SpyjK2tiwEAowSBCVKIwAQAkDNMJpOKXQ4VuxyqLRn6Lkv+UEid/oDa94YHnYG9X/0BhY2Imru65A0G5Q0G1dzdpfZAQN2hoLqDIflD0UBiYolH/2qoVyAcViAcln9vcFFZUKDdXd4+P/Pw2gl6vX6nTPpkq0eTJJNMOriiUu80NkS3ue59RSM6bl8VLreaurrksFhjYc3syiptbWuVy2qVy2bTfiVlaun2yW23yW2za5zLrQq3WwX2aDhUZLOr0BGtvCly2OVxRit17BbLkH+fAADkGyMclmGE+7880v9lQG8EJgCAUcNhtcphtaq8ILVbGRmGoUA4rLBhKBSJbi8dikQUMiIKhsNxgYghI/rhlgwZhiHJJLMperKYTDKbzDKbJIvZLJMh2SwWWS1m2czmIVetAACAXgxj4J1wqDDBEBCYAAAwCJPJRG8UAADygWFIfes6e10OJIdXfwAAAACA0SESkUwD7IRjsEsOkkdgAgAAACAnGYahyEDLKxIIh0Lq2yWqf5Fw8m+g7U6bzGbzkOaTrHCS8xjKfIcy1kjwe/Z5/UlfP1cY4bAM0wA9TAbobwL0RmACAACQYYZhKBQMKxwMKxgIKRQMKRT45PtwKKKALxg9HgwrFAgpEjEU8AUUDoYVDoWj1w9HZDKZ5PP6FQ5Fz0fCEYVDYVltFnV1+KLnY8cjcrrt6mj1KhKOyIgYscuNiKHScR7t3tEswzCil0WiX41IRBUTylS/pXFvtfvey6INe2JvamsmV2jHRw3R6+9T9h47v3dszZRx2rmhfu9mFnvHGb3GSpo0vVab3tnec0H873Cf7z0VRdpT35rw95zI1JkT9dFbW4b0mI2bUKb6rU1Jj7e7bOruGPqbTU95oVoa25MeXzquWM27WpMeP+2wqfr3qxuSGls9uVIfb2xIauyk6bXa/O72QcdVTazQri2NSd2mJBWWFKiztW/D7YFMP3x/vf9acvdRksqqSxL++0nEZDYlHcUMNejJRSEjkO0pDB1LcpBCBCYAAGDMMAxDwUBI/u6AfF5/9GtXQP6ugPzdAQX9weh5X0Amk0kdLV4FAyHZ7Fa1NrYrGAjJXehUc32rgv6QisoK1LC1SUF/SMFASGXVJdrx0a69wUdYdftX6cM3N0dDj73hh9VmkbetOzanidNqte2DnXHznHnkQXr3/9bHHZs2d4o+eGNTn/s0ddZEbXpnW5/jU2bWafv6j2WxWmS2mGW2mGSxWDThgGp9vGl37JjZbI5+bzZpyowJ2vLvnTKZo8dNZlP0ZDLJVeRUW1OHTKZPjknRHj9ms0mKnZdMZnPP2eg4kynuvMNlV2FJwSeXS7GCANM+t1PocauitjR2n3o3RO45W1Dslt1pS/iYJ2qi7Kks0vip4xKO709xeZHMluQrC6x2q/xdQ3+z6SpyylnoSHp8YbFbNkfi+55IcVmhqidXJjW2rMqTdIWCp6JIVZMGv92yKo8iQ3jD6ip0yFlgT3q8JBUUu1RWXZL0+KKygqTvZ1tzx5DmgiyIGJKJwASpQWACIKG77rpLP/rRj1RfX685c+bo5z//uQ4//PCkr3/LBffLYXPFzpdVebR7x56krmt32tTV4Rt4jMOqrs6Bx/TwlBeq6ePWpMZK0RdabXs6+728emK5dm7cHT1jMiX8w+upLFLL7o7Ym4rom4+e76NvTIrLCtTR2hU7braYVT2xXLt3tsiy901Kz5uYytoyNde3yWIxyWyxyGKNvsGxWMyyWM2yWCwqLi2Qrzsgq80SO1lsFlmtFlntFtlsVtns0ZPVbomVFfeMtdmtstgssljM0evEbscqi80sm8269/bMslgt7OSCtOkJNXxev3xdfnV7/fJ5/erujJ6PngLydfkV9IfU1emTf+95d6FTu7Y2ytcVUNAXlLejW/7ugGr3q9J7r25QeU2Jtv77k3Bi2rypcSFE9aSKWBXBnKMP0kdvbZPNYdW0uVO0bf3HstmtmjKzTrs275bNbpPVblHQH5LNYZWryKmSykLZ7BNktVllc1hVUlGkugNros8luzX23DJbzbJao88ph9sukySrLfrctNossjltMpvM0fPWnuuaZbZ88ty2WKOXyaTosb3BiMVqTtuSAQDpk4tLctrb21U1fk3St5ETDEPSQD1MCEyQPAITAH08+uijWrZsme69917Nnz9fq1ev1qJFi7R+/XqNG5fcJ3Lfvf8CFRcXp3mmuaunpNyIRLeYjUQie7//5HxPCXxPSXwkYigSNhSJhGPfG0ZEkbChUCiscChaZh+JGNHS+1AkejuhyN7rR+TvDioUCisUiJbsB4MhBXxBdXUEFQxEy/p7Pvm2O21qbexQcJ+S/6KSAjXsaFYoFFEoEOp1W9ExU2fU6v03tsiQoYkHVKt+W3P0zaDVogMOmait6+vjQpue0MVqM8tqs6q8yqPO9u7YdXrCnNJxxerq9Mlmt6qw2KVQKCyr1SJ3kUuhUCga2FjNstqtcrpskmGSxRYNdyy26G2YLaZo8GO1yGozx95QWu1W2XqFSOjLMIzoUhB/QKFAOPb4x5aFxJaP7F1KEgwpHAjL7w/G/m2ZTCZ1tnoV8IUUDAQV9IcU8AcVDIRi3xeXFerjjbsV8Afl9wVVMb5Em9/dIX93QDKZ1NrYrunzpuqdVz6Mm5/JZJJhGDJbzHK67XK6HaqsLZXfF5TT7ZDTZVft/lUyW8wqq/LI7rRFx7kcKhlXrLnHzVBBsVM2q1WOAkf0Om677E6bHC67HC677A6r7E67bA4roQOAjLIkWcGU7LhUCISTr3bKFUbEkDFAhUl/S/WAREwG/2IA9DJ//nwddthhuvPOOyVF39zX1dXp0ksv1bXXXjvgddvb2+XxeNTW1jamA5OxwDD2Bjl730iHgmEF/KHY0oNwKKxgT9iyT4ATCe8NdvaO67nMarOovaVLoWBIVqtFHa1dCgXDchU51VzfFnc7JRVF2rW1KXobe+dQVVemLe9/HH2Tv7fHw+Rp4/X+G5sVDIZjn7K5i5zydQV08NzJ2rK+XnaHVQceMlHbP2qQzWZR3QFV2r1jjyxWiybsV6mGbc2yWCyxMMYwDJlM5r3LDkyqHF+i5l1tsSqisqoStTW1y2wxq7isUN727lilUUlFodqao9VL0VUKJhWVFqi1qSOuf0NxWYFadnf0Ct6iPSKKStxqberYp7eEoUgkovKaEtVvbYqGZ2FDVRPLtWNDQ6yvRWllsXZtaYz9ziKhSNzvqieEk6I9EVp2D95DwWqzqGZypVp2t8UqKvafWacdGxpkc9hkc0Qrmux7v7c7rLI5bKqsLVVXe7fsrmhYUVxaIMMw5NgbejhcdrkLHbI5bHK47XIVOOUssO8NQByy2a1UOAFABuTT67qeuR5r+rKspv6XqYWMoNYaT+TFfUL2UWECIE4gENC6deu0fPny2DGz2ayFCxfqlVde6TPe7/fL7/+kqV1bW5uk6B8tjCEmyWyXnHaLJIukoa03z4RwOKJwMBytdgiG9vacCCsYCCocDMvvCyq0t4on6ItW6phN0n6zx8caZkabY4ajjSr3hhUOl02uYls0wFBEDqdNskbXw1tcki1k2htuRBSMBBQyArHml5JkC1gUDPv39nmQZJJCCslkj8i8dwmXSYp9dRTZVGot2NtbIrpkS2aTCoqcKihzyLx3KVdBkVM1+5XGlmlY7VYZkYP2Lt2ILtuwWiwy76242Xc5lsncszykZwmJWRabtVfFUDaWZUXk83fLl3+bNgBAXup5PZdPn7GHDP+AWweHFMzgbJDvCEwAxGlqalI4HFZVVVXc8aqqKn3wwQd9xq9atUorV67sc7yuri5tcwQAAEDmNDc3y+PxZHsaA7Lb7aqurtZL9U8NOra6ulp2e+59uIPcQ2ACYESWL1+uZcuWxc63trZq0qRJ2rZtW87/YR2r2tvbVVdXp+3bt1OKmqN4jHIfj1Hu4zHKfTxGua+trU0TJ05UWVlZtqcyKKfTqc2bNysQGHx3KrvdLqfTmYFZId8RmACIU1FRIYvFooaGhrjjDQ0Nqq6u7jPe4XDI4ejbEMzj8fDiJ8cVFxfzGOU4HqPcx2OU+3iMch+PUe7LlybYTqeTIAQplR//8gFkjN1u19y5c/Xcc8/FjkUiET333HNasGBBFmcGAAAAAJlDhQmAPpYtW6YlS5Zo3rx5Ovzww7V69Wp5vV6dd9552Z4aAAAAAGQEgQmAPk4//XQ1NjbqxhtvVH19vQ455BA9/fTTfRrBJuJwOLRixYqEy3SQG3iMch+PUe7jMcp9PEa5j8co9/EYYawzGfm0RxQAAAAAAEAG0MMEAAAAAACgFwITAAAAAACAXghMAAAAAAAAeiEwAQAAAAAA6IXABAAAAAAAoBcCEwAAAAAAgF4ITAAAAAAAAHohMAEAAAAAAOiFwAQAAAAAAKAXAhMAAAAAAIBeCEwAAAAAAAB6ITABAAAAAADohcAEAAAAAACgFwITAACQUQ8++KBMJpO2bNmSstu86aabZDKZUnZ7yTKZTLrpppsy/nMBAED6EZgAADDGvffee/r617+u2tpaORwOjR8/XmeddZbee++9Ed3urbfeqieffDI1kwQAAMgwk2EYRrYnAQAAsuP3v/+9zjjjDJWVlen888/XlClTtGXLFv3yl79Uc3OzHnnkEX35y18e1m0XFhbq1FNP1YMPPhh3PBwOKxgMyuFwpKwqJBQKKRQKyel0puT2kmUymbRixQqqTAAAGIWs2Z4AAADIjo0bN+rss8/W1KlT9eKLL6qysjJ22eWXX66jjz5aZ599tt5++21NnTo1ZT/XYrHIYrGk7PYkyWq1ymodnS9rurq65Ha7sz2NpEUiEQUCgYyHVwAApBpLcgAAGKN+9KMfqaurS/fdd19cWCJJFRUV+sUvfiGv16sf/vCHseM9vUI++OADnXbaaSouLlZ5ebkuv/xy+Xy+2DiTySSv16uHHnpIJpNJJpNJ5557rqTEPUwmT56sL37xi1q7dq3mzZsnl8ulWbNmae3atZKilTCzZs2S0+nU3Llz9c9//jNuvr17mJx77rmxn9v7tG81iN/v14oVK7T//vvL4XCorq5O3/nOd+T3++Nu3+/368orr1RlZaWKiop08skna8eOHUn9nteuXSuTyaRHH31U1113naqrq1VQUKCTTz5Z27dvjxt77LHHaubMmVq3bp2OOeYYud1uXXfddUOa67PPPqujjjpKJSUlKiws1EEHHRS7jR4///nPNWPGDLndbpWWlmrevHlas2ZN3O9v8uTJfe5Lol4xJpNJl1xyiR5++GHNmDFDDodDTz/9tCRp586d+sY3vqGqqio5HA7NmDFDDzzwQFK/NwAAsm10fhQDAAAG9T//8z+aPHmyjj766ISXH3PMMZo8ebL+/Oc/97nstNNO0+TJk7Vq1Sq9+uqr+tnPfqaWlhb9+te/liT913/9l775zW/q8MMP14UXXihJ2m+//Qacz4YNG3TmmWfqW9/6lr7+9a/rxz/+sRYvXqx7771X1113nS6++GJJ0qpVq3Taaadp/fr1MpsTf/bzrW99SwsXLow79vTTT+vhhx/WuHHjJEUrIU4++WS99NJLuvDCCzV9+nS98847+ulPf6oPP/wwrv/KN7/5Tf33f/+3zjzzTB1xxBH629/+ppNOOmnA+9PbLbfcIpPJpGuuuUa7d+/W6tWrtXDhQr311ltyuVyxcc3NzTrxxBP1ta99TV//+tdVVVWV9Fzfe+89ffGLX9Ts2bP1ve99Tw6HQxs2bND//d//xW7//vvv12WXXaZTTz01FnS9/fbb+sc//qEzzzxzSPepx9/+9jc99thjuuSSS1RRUaHJkyeroaFBn/70p2OBSmVlpf7yl7/o/PPPV3t7u6644oph/SwAADLGAAAAY05ra6shyfjSl7404LiTTz7ZkGS0t7cbhmEYK1asMCQZJ598cty4iy++2JBk/Otf/4odKygoMJYsWdLnNn/1q18ZkozNmzfHjk2aNMmQZLz88suxY88884whyXC5XMbWrVtjx3/xi18Ykoznn38+dqxnXv356KOPDI/HY3zuc58zQqGQYRiG8V//9V+G2Ww2/v73v8eNvffeew1Jxv/93/8ZhmEYb731liHJuPjii+PGnXnmmYYkY8WKFf3+XMMwjOeff96QZNTW1sZ+j4ZhGI899pghybjjjjtixz7zmc8Ykox777037jaSnetPf/pTQ5LR2NjY73y+9KUvGTNmzBhwzkuWLDEmTZrU53ii37Mkw2w2G++9917c8fPPP9+oqakxmpqa4o5/7WtfMzwej9HV1TXgHAAAyDaW5AAAMAZ1dHRIkoqKigYc13N5e3t73PGlS5fGnb/00kslSU899dSw53TwwQdrwYIFsfPz58+XJB133HGaOHFin+ObNm1K6na9Xq++/OUvq7S0VL/5zW9i/VN++9vfavr06Zo2bZqamppip+OOO06S9Pzzz8fdp8suuyzudodaIXHOOefE/b5PPfVU1dTU9PmdORwOnXfeeXHHkp1rSUmJJOkPf/iDIpFIwnmUlJRox44dev3114c0/4F85jOf0cEHHxw7bxiGfve732nx4sUyDCNuzosWLVJbW5vefPPNlP18AADSgSU5AACMQT1v3HuCk/70F6wccMABcef3228/mc3muL4kQ7VvKCJJHo9HklRXV5fweEtLS1K3e8EFF2jjxo16+eWXVV5eHjv+0Ucf6f333+/Tv6XH7t27JUlbt26V2Wzus6TooIMOSurn9+j9OzOZTNp///37/M5qa2tlt9vjjiU719NPP13/7//9P33zm9/Utddeq+OPP15f+cpXdOqpp8aWL11zzTX661//qsMPP1z777+/TjjhBJ155pk68sgjh3R/9jVlypS4842NjWptbdV9992n++67b8A5AwCQqwhMAAAYgzwej2pqavT2228POO7tt99WbW2tiouLBxyXiu2B+9s5p7/jhmEMept33HGHfvOb3+i///u/dcghh8RdFolENGvWLN1+++0Jr9s7qMmUffuZ9Eh2ri6XSy+++KKef/55/fnPf9bTTz+tRx99VMcdd5z+93//VxaLRdOnT9f69ev1pz/9SU8//bR+97vf6e6779aNN96olStXSur/8QyHw0nNuae65etf/7qWLFmS8DqzZ89OeBwAgFxBYAIAwBj1xS9+Uffff79eeuklHXXUUX0u//vf/64tW7boW9/6Vp/LPvroo7iqgg0bNigSicTtrJKKEGUk/v73v+uqq67SFVdcobPOOqvP5fvtt5/+9a9/6fjjjx9wrpMmTVIkEtHGjRvjqkrWr18/pPl89NFHcecNw9CGDRuSCg6Snaskmc1mHX/88Tr++ON1++2369Zbb9V3v/tdPf/887FGuAUFBTr99NN1+umnKxAI6Ctf+YpuueUWLV++XE6nU6WlpWptbe1z21u3bk3qvvbsJhQOh/s03wUAIF/QwwQAgDHq6quvlsvl0re+9S01NzfHXbZnzx5ddNFFcrvduvrqq/tc96677oo7//Of/1ySdOKJJ8aOFRQUJHzTnQm7du3SaaedpqOOOko/+tGPEo457bTTtHPnTt1///19Luvu7pbX65X0yX362c9+Fjdm9erVQ5rTr3/967glUI8//rh27doV9zvrT7Jz3bNnT5/LeyprerYf7v1Y2+12HXzwwTIMQ8FgUFI0oGlra4urQNq1a5eeeOKJQecqRauC/uM//kO/+93v9O677/a5vLGxManbAQAgm6gwAQBgjDrggAP00EMP6ayzztKsWbN0/vnna8qUKdqyZYt++ctfqqmpSb/5zW8Sbge8efNmnXzyyfr85z+vV155Jbbl7pw5c2Jj5s6dq7/+9a+6/fbbNX78eE2ZMiXWsDXdLrvsMjU2Nuo73/mOHnnkkbjLZs+erdmzZ+vss8/WY489posuukjPP/+8jjzySIXDYX3wwQd67LHH9Mwzz2jevHk65JBDdMYZZ+juu+9WW1ubjjjiCD333HPasGHDkOZUVlamo446Suedd54aGhq0evVq7b///rrgggsGvW6yc/3e976nF198USeddJImTZqk3bt36+6779aECRNiVUQnnHCCqqurdeSRR6qqqkrvv/++7rzzTp100kmxXjVf+9rXdM011+jLX/6yLrvsMnV1demee+7RgQcemHSz1ttuu03PP/+85s+frwsuuEAHH3yw9uzZozfffFN//etfE4Y7AADkEgITAADGsK9+9auaNm2aVq1aFQtJysvL9dnPflbXXXedZs6cmfB6jz76qG688UZde+21slqtuuSSS/pUctx+++268MILdf3116u7u1tLlizJWGDS2NiocDisZcuW9blsxYoVmj17tsxms5588kn99Kc/1a9//Ws98cQTcrvdmjp1qi6//HIdeOCBses88MADqqys1MMPP6wnn3xSxx13nP785z8Pqc/Jddddp7ffflurVq1SR0eHjj/+eN19991yu92DXjfZuZ588snasmWLHnjgATU1NamiokKf+cxntHLlyliz3G9961t6+OGHdfvtt6uzs1MTJkzQZZddpuuvvz7288rLy/XEE09o2bJl+s53vqMpU6Zo1apV+uijj5IOTKqqqvTaa6/pe9/7nn7/+9/r7rvvVnl5uWbMmKEf/OAHSf/eAADIFpORTMc0AAAASTfddJNWrlypxsZGVVRUZHs6eWHt2rX67Gc/q9/+9rc69dRTsz0dAACQJHqYAAAAAAAA9EJgAgAAAAAA0AuBCQAAAAAAQC8EJsAY8uKLL2rx4sUaP368TCaTnnzyybjLDcPQjTfeqJqaGrlcLi1cuFAfffRRdiYLICfddNNNMgyD/iVDcOyxx8owDPqXAACQZwhMgDHE6/Vqzpw5uuuuuxJe/sMf/lA/+9nPdO+99+of//iHCgoKtGjRIvl8vgzPFAAAAACyi11ygDHKZDLpiSee0CmnnCIpWl0yfvx4/ed//qeuuuoqSVJbW5uqqqr04IMP6mtf+1oWZwsAAAAAmWXN9gQA5IbNmzervr5eCxcujB3zeDyaP3++XnnllX4DE7/fL7/fHzsfiUS0Z88elZeXy2QypX3eAAAASA/DMNTR0aHx48fLbM79xQk+n0+BQGDQcXa7XU6nMwMzQr4jMAEgSaqvr5ckVVVVxR2vqqqKXZbIqlWrtHLlyrTODQAAANmzfft2TZgwIdvTGJDP59PkSQVq2B0ZdGx1dbU2b95MaIJBEZgAGJHly5dr2bJlsfNtbW2aOHGi/vF6hQoLc/+TiN6awrZsT2FEmsIFGfxZRcO63u6QZ0jjW4Pufi/bM8Bl0ev2//toCzjiz/tcfcZ0Bex9jvkDlrjzIX/fP6URf/wYky/+vCUQX31l9vU6n+DDMUuCVkJWf68x/gRjfH1X3loSHPtkfP8vNC0DXGbtCvV7WX/MvuCQr9PnNroG/yQxr3R1Z3sGGKUM/m1hGEJGQC+0/kZFRcP7m59JgUBADbsjWv/GeBUV9f8atKMjooPmfaxAIEBggkERmACQFE3aJamhoUE1NTWx4w0NDTrkkEP6vZ7D4ZDD4ehzvLDQPOAfq1zlC+ffnPfVHbYMPihFvKHh/QlxDvF6jmD/IZY92DfQ6NESKJCtn4tbA05Ze92s1dL337HF2vcGLNb4+UfMCe6PuVdgYoo/bzbHByQW9QpMEqxmsyTIOHo/2gnHRPoetIYHCEzCAwQmoQECE+swAhNLCp5vRY7RFZoUOSQvb2yReoY5nO0pIB/t/W8/n5ZZFxWZVZyHr0GRm/iXBECSNGXKFFVXV+u5556LHWtvb9c//vEPLViwIIszQ7Iaw4UZ+1m7Q8UZ+TktA1SIjGaRvtlNUsJJflAWcqX+hW/IPfQALeLqP/ACAADINipMgDGks7NTGzZsiJ3fvHmz3nrrLZWVlWnixIm64oor9P3vf18HHHCApkyZohtuuEHjx4+P7aQDjFTDEJfj5Bp/ID1/NsNOQxZf/nx6l2sibvvoqjIpcFFlAgBADiAwAcaQN954Q5/97Gdj53t6jyxZskQPPvigvvOd78jr9erCCy9Ua2urjjrqKD399NOs78wDo7G6ZDDNY7T6ZKhCTlPCPib9jzcP2MdkwOu6rcPqZYIECE0AAMg6AhNgDDn22GNlGP2/cTKZTPre976n733vexmcVe5ozPOGr8gfYYchi39oFSVhZ+LGr5kUdpll6R5emNKfiMsuc/fIq0NGXZUJAADIOgITAMhz+VJdMtTlOCPpX9IS6P+6rQEqpqRoHxNrd/KVJ0nfLlUmqUOVCQAMWUskoFBkgF1yIqkN/jG60fQVAACMChH3KGwiW9B3u2sAAJAZBCYAkMfypboE6RFKcjedkHNoy39Czv5fHoRdg790GM6OOQAAALmGwAQAkHb5uDuO15+6agXDGU7ZbQ0m2a2Fc0kqtxemygQAAKQKgQkA5KnRXF0yWP8SdshJjZBreFsZU2UCAADGAgITABA75AxkNC3F6a/ha5sv9z7BjyS53AZ9UWUCAABSgY9/AABplenlOAPtkDOWhZwmWX2p2xUn1VsMp2p74VGNXXMAYFBNYZt84f7rAjrD7JKD5FFhAgB5KJPLcTJtJNsJI3UGavya9G1kcVnOqKwyAQAAGUVgAgBAnkhHQ9fh9jFBlrA0BwAy6qabbpLJZIo7TZs2bcDrtLa2aunSpaqpqZHD4dCBBx6op556KkMzRiqxJAcA8kw+VZfk0u44/fUvQf9CTrOsvvwtXY647TJ3scwHADAyM2bM0F//+tfYeau1/7fRgUBAn/vc5zRu3Dg9/vjjqq2t1datW1VSUpKBmSLVCEwAAP3KxYavw90hp8TuSxia5GLD17EqHX1MRmVoQi8TAMgoq9Wq6urqpMY+8MAD2rNnj15++WXZbNFNBSZPnpzG2SGdWJIDAMgZY7l/SdiZuoasGANYmgMAGfPRRx9p/Pjxmjp1qs466yxt27at37F//OMftWDBAi1dulRVVVWaOXOmbr31VoXD4QzOGKlChQmAMY8thcc2j7ObKpNRblRWmQAARqS9vT3uvMPhkMPh6DNu/vz5evDBB3XQQQdp165dWrlypY4++mi9++67Kioq6jN+06ZN+tvf/qazzjpLTz31lDZs2KCLL75YwWBQK1asSNv9QXoQmAAA0qbK2pZTfUyGosARkNfPTiuZxvbCAICRaAoXqDts6fdy795Kj7q6urjjK1as0E033dRn/Iknnhj7fvbs2Zo/f74mTZqkxx57TOeff36f8ZFIROPGjdN9990ni8WiuXPnaufOnfrRj35EYJKHCEwAADkhH5bjOOwh+QP86Uw3epkAANJt+/btKi7+pFdbouqSREpKSnTggQdqw4YNCS+vqamRzWaTxfJJaDN9+nTV19crEAjIbufDmHxCDxMAyCP5tEPOUJXavNmewrDYnKGs/vxQcq/vhiWfd8gZE+hjAgDDVlxcHHdKNjDp7OzUxo0bVVNTk/DyI488Uhs2bFAk8snf0A8//FA1NTWEJXmIwAQAkFAu7pCTL8yO7DZ2Cw9hB2Vrd3qbzYbcVOQAAPLXVVddpRdeeEFbtmzRyy+/rC9/+cuyWCw644wzJEnnnHOOli9fHhv/7W9/W3v27NHll1+uDz/8UH/+85916623aunSpdm6CxgBXsUAAICcQg8TAECu2LFjh8444ww1NzersrJSRx11lF599VVVVlZKkrZt2yaz+ZM6hLq6Oj3zzDO68sorNXv2bNXW1uryyy/XNddck627gBEgMAEAjCqldq9aAon7oZTYfWoNDKH8AqPGqO1fUuCSvN3ZngUAjFqPPPLIgJevXbu2z7EFCxbo1VdfTdOMkEkEJgCAvFJu86o5xQ1i+9taONFOOfnQ+NXiy8DP6Ka/CQAg9zSFi+QN9f93ujuc3d5jyC/0MAEAYISy3fg1GVZfenuVAAAAjDYEJgCQJ0bzDjnIjnQ3fAUAAMhnBCYAgJyR7NbC5Xm6BXE6WP3ZngGyju2FAQBICwITAADQh9WXmh4l1q7cX64EAACQCIEJgDGtMWzL9hRy1jhre7anMGyl9vRWoDjs+RUC5FP/ErYUBgAAuYLABAAwppTYE28h43Em3pq1wMEbeOQBluUAAJByub0vIgAg71VZ29QQ8qT8dtOxvfBYQsNXAMBotDvkkXOAbYV9ofyqEkV2UWECAMgpyTZ+HfR2BliW01+VyWhgycBds3Snpr8JAABALiMwAQCMWkPtZdLfspx0CDuGXuGRiTAklWj4CgAA8hmBCQCgX/nc+HUw6a4yMTvCcecNZ7ifkQCQfia3O9tTAIC8Q2ACAMhb5Uks30nFjjmJGr/23inH5hw91RQDbSmc7HIcqksAAEC+IzABECccDuuGG27QlClT5HK5tN9+++nmm2+WYdAgEpmTqj4msdsbQmiS68tyhiobWwoPNyxhS2EAAJBL2CUHQJwf/OAHuueee/TQQw9pxowZeuONN3TeeefJ4/Hosssuy/b0gD5GsltOid2n1oAzqbEFjoC8fvuAY2zOkIK+T/60mh1hRfyW2HnDGZbJZ0l01T7M/sHHWJMYk/B6adwhh8oSAEA2tQbdcgRt/V7uDwYzOBvkOwITAHFefvllfelLX9JJJ50kSZo8ebJ+85vf6LXXXsvyzFBp6VRjuDDb0xiWdG0tPBSldq9aAskFKx5nt9p8rkHHOewh+QOj609pKpbj5BpzF5UrAABg6FiSAyDOEUccoeeee04ffvihJOlf//qXXnrpJZ144okJx/v9frW3t8edMLqkqvFrlbVtSOOHsiwnmV4m/RlK89dEvUwGk2vNX3O1uoTlOACAfHDbbbfJZDLpiiuuGHDc6tWrddBBB8nlcqmurk5XXnmlfL482+4OVJgAiHfttdeqvb1d06ZNk8ViUTgc1i233KKzzjor4fhVq1Zp5cqVGZ4l8tVQK01KbV61JLncJpmlOZmoMum9LCdVktlSeLAxhCWjXIFL8mauBw8AjDWvv/66fvGLX2j27NkDjluzZo2uvfZaPfDAAzriiCP04Ycf6txzz5XJZNLtt9+eodkiFagwARDnscce08MPP6w1a9bozTff1EMPPaQf//jHeuihhxKOX758udra2mKn7du3Z3jGyDfprDQZrnRvMZwtQ234OtzlOLkclrAcBwCQCp2dnTrrrLN0//33q7S0dMCxL7/8so488kideeaZmjx5sk444QSdccYZLHHPQwQmAOJcffXVuvbaa/W1r31Ns2bN0tlnn60rr7xSq1atSjje4XCouLg47gQMJl2hSaq3GU60Y04yy3J6bzHce1lONgxWXTJQWAIAwFi3dOlSnXTSSVq4cOGgY4844gitW7cuFpBs2rRJTz31lL7whS+ke5pIMZbkAIjT1dUlszk+S7VYLIpEeDOF1ErX8pzh7pozlB1zesvF5q9DqS7JVljCUhwAQLb07rvncDjkcDgSjn3kkUf05ptv6vXXX0/qts8880w1NTXpqKOOkmEYCoVCuuiii3TdddeNeN7ILCpMAMRZvHixbrnlFv35z3/Wli1b9MQTT+j222/Xl7/85WxPDVmUqsavvQ210iRVhlJlkkgqmr+OVO8thQfqXzJQdUkyYUk6luNkIixhOQ4AjD17gm41Bwv6Pe0JuiVJdXV18ng8sVN/1dTbt2/X5ZdfrocfflhOZ3IfrKxdu1a33nqr7r77br355pv6/e9/rz//+c+6+eabU3Y/kRm59XEYgKz7+c9/rhtuuEEXX3yxdu/erfHjx+tb3/qWbrzxxmxPDchKlUmuNX9NRrLVJSMNSwAAyFfbt2+PW0reX3XJunXrtHv3bh166KGxY+FwWC+++KLuvPNO+f1+WSyWuOvccMMNOvvss/XNb35TkjRr1ix5vV5deOGF+u53v9unmhu5i8AEQJyioiKtXr1aq1evzvZUMEZka2nOUHbMSaTAEZDXbx/29Qdi9g8+Zl/J7KDTWzbDEqpLAADZlmzvveOPP17vvPNO3LHzzjtP06ZN0zXXXNMnLJH6X+IuSYaRvh3rkHoEJgCApIyztmt3KD1NfdO53XAqZLvKZDiBSI9Ey3FSFZYMZzkOfUsAAPmkqKhIM2fOjDtWUFCg8vLy2PFzzjlHtbW1sWU9ixcv1u23365PfepTmj9/vjZs2KAbbrhBixcvThiwIHcRmAAAkpZLoUkyhrM0ZyTNXzOld/+SuMsGWY6TzbAkU6guAQBk0rZt2+IqSq6//nqZTCZdf/312rlzpyorK2N9ApFfCEwAII9UWjrVGC7M6hxyJTRJxdKcoSzLSVRlks5lOckaSrPXbIclLMUBAIwGa9euHfC81WrVihUrtGLFisxNCmlBtxkAwJCla9ccaWg755TaktvtptzmVXk/YxPtmFNiH/4aGIc9cZgQ8aemBHe41SXZDksygbAEAACkEhUmAICck47lOZJioUnvipNElSbJLs1JdZXJh9ddGXd+xjU/7XfscLcSTnhbaQ5L0l1dQlgiydud7RkAQNa1BgtkC/T/dzkYtGVwNsh3BCYAkGdyYVmOlN6lOUMxnAawiZbpJLM8J9nmr8nasvQ/Bx3z3g+u7HNszhV9Q5ThVpcQlAAAACRGYAIAGLZ862eyr2RCk0RVJsPtZbL1/O8MaX4D+dfqviHK4efePuTbSUdYksldcAhLAABAOhGYAEAeypUqk3TLp9BkXx+eesOQ5pEKrz24rM+xI07/Sb/jUxWWZGubYMISAACQbgQmAIARSffSnKGGJpKGFJykKjR55+SVSf/MTHn50fglP8d86UeSCEsAAACSwS45AIARS+euOdLQds6RosFJzykZ/e2gs6/+ds55ddEqvbpo1ZDmly0v/uHqlIQl5u4AYQkAABj1qDABAOSF4e6cs29oMlDlSe9Kk2R2zsmXoGRfz//vNZKkz57wg4SXJxOWZAthCQBgMG0Bh6w2R7+XhwKmDM4G+Y4KEwDIU5WWzmxPIU66q0xSYbDKk96VJqX2xOOe+cxqPfOZ1ameXkb1BCc9rF2hnA1LzF0BwpLBsKUwAAApR2ACAEiZcdb22CkdqqxtQ16e05/+gpPBQpN8D0r29fz/XqPn//ca+pUAAAAkwJIcAEBa9IQm6WgIu29oMpxlOvtKZnedUrtX9879rxH9nFz27Evf1eeOuqXfy9MZlhCIAACAXEWFCYAxrdISzPYURr1MNIQdaeVJ70qT3lUmozks6fHsS9/Vsy99t8/xVIYlPUtr9j0BAADkKipMACCPVVo61RguzPY0BpXurYd7jKTypHelSbnNq1tm/z5lc8sXPdUmIw1KCEMAAEC+IzABAGREpkKTHsMJT/YNTcZiWNKjp9Jk0dwVSV+HgAQAAIw2BCYAgIxJZ1+TgfSEJ8kEJ2M5KOntmXUrBwxNCEkAALmmzeeS1TLAtsI+ulIgeQQmAJDn8mVZzr4yXW3SY7Cqk6umP5PJ6eSFZ9atlPRJtQkhCQAAGCuI1wAAWTHcZrCp2rp432axV01/hrBkEM+sW0lYkqu83dmeAQCMGbfddptMJpOuuOKKAcf99re/1bRp0+R0OjVr1iw99dRTmZkgUorABACQNUMJPxKNS1V4guT85f1V2Z4CAABZ8/rrr+sXv/iFZs+ePeC4l19+WWeccYbOP/98/fOf/9Qpp5yiU045Re+++26GZopUITABgFGg0tKZ7SmM2L7hx74hyFADlaGGJ2cf8Oqw5gsAAMaOzs5OnXXWWbr//vtVWlo64Ng77rhDn//853X11Vdr+vTpuvnmm3XooYfqzjvvzNBskSoEJgAwSoyG0KS3dC/bISwZOqpMAACjQXt7e9zJ7/cPOH7p0qU66aSTtHDhwkFv+5VXXukzbtGiRXrllVdGNGdkHoEJAIwiozE0GSmW7aQeoQkAIFd1Bezy+vs/dQXskqS6ujp5PJ7YadWq/v+2PfLII3rzzTcHHLOv+vp6VVVVxR2rqqpSfX398O8YsoJdcgAAY8a+ocmiKf/O4kwAAEA2bd++XcXFn+zY53Ak3op4+/btuvzyy/Xss8/K6XRmanrIEVSYAMAoQ5XJ4AhLRo4qkxzBDjkAMCzFxcVxp/4Ck3Xr1mn37t069NBDZbVaZbVa9cILL+hnP/uZrFarwuFwn+tUV1eroaEh7lhDQ4Oqq6vTcl+QPgQmADAKjaXQpNLSOabuby4hNAEAjHbHH3+83nnnHb311lux07x583TWWWfprbfeksVi6XOdBQsW6Lnnnos79uyzz2rBggWZmjZShCU5ADBKVVo61RguzPY00qZ3SLLv+YHuN9UlAAAgWUVFRZo5c2bcsYKCApWXl8eOn3POOaqtrY31OLn88sv1mc98Rj/5yU900kkn6ZFHHtEbb7yh++67L+Pzx8hQYQIAyDuDVZT0VJ30HkdYknp/eX9VdFlIohMAAGPAtm3btGvXrtj5I444QmvWrNF9992nOXPm6PHHH9eTTz7ZJ3hB7qPCBABGsdFWZTKcpTc91xlNv4dc85dtq3XixCv6XjBYaFLgSst8xgxCKQDIirVr1w54XpK++tWv6qtf/WpmJoS0ITABgFFuNIQmqehRQnVJDvJ2E5oAAFLKH7DIYu3/bW44EMrgbJDvWJIDoI+dO3fq61//usrLy+VyuTRr1iy98cYb2Z4WRiBfm6KmqqHroRO3pWA2GMhftq0e3hWpkgAAADmKwARAnJaWFh155JGy2Wz6y1/+on//+9/6yU9+otLS0mxPLW0qLcFsTwEJ5GvIM5YRmgAAgNGEJTkA4vzgBz9QXV2dfvWrX8WOTZkyJYszQqrk8tKcdIYjVJfkCZbnDA0hEwAAaUeFCYA4f/zjHzVv3jx99atf1bhx4/SpT31K999/f7/j/X6/2tvb407IXblUtdHfTjYju81g3ImwJPOGXWUiEQIAAICcQmACIM6mTZt0zz336IADDtAzzzyjb3/727rsssv00EMPJRy/atUqeTye2Kmuri7DM0a+2DcgSXVw0xOQIDeMODRha2IAAJADWJIDIE4kEtG8efN06623SpI+9alP6d1339W9996rJUuW9Bm/fPlyLVu2LHa+vb2d0CTH7RtWpHuJTiYqWvoLSupqd6X9ZyMDeocmLNsBAAwg5LcqYu7/bW7Ez1tgJI9/LQDi1NTU6OCDD447Nn36dP3ud79LON7hcMjhcGRiakiDdIQnmVz2Q1XJGLRvgDJWwxMqbwAAyAiW5ACIc+SRR2r9+vVxxz788ENNmjQpSzNCpoxkqUy6ltoM/DMJS3LZiJblJIvgAAAApBEVJgDiXHnllTriiCN066236rTTTtNrr72m++67T/fdd1+2p4YMSXY3nWw2kB0sLGE5zhjSE5qM1WoTAACQNlSYAIhz2GGH6YknntBvfvMbzZw5UzfffLNWr16ts846K9tTQwYNVC2S6UqSvj+fyhIkQLUJAABIMSpMAPTxxS9+UV/84hezPQ3kgGSrTTKFsAQDGgvVJgRDAABkDBUmAIAB7VtNQmUJ8gKhAgAASAECEwDAoFiGg+HISOPX/ni7CU4AYAyK+C2DnoBkEZgAAHIaYQkAAACygcAEAJCzhhOWsEMOAABIlXvuuUezZ89WcXGxiouLtWDBAv3lL3/pd/z999+vo48+WqWlpSotLdXChQv12muvZXDGSCUCEwBATqKyBAAAZNuECRN02223ad26dXrjjTd03HHH6Utf+pLee++9hOPXrl2rM844Q88//7xeeeUV1dXV6YQTTtDOnTszPHOkArvkAAByDmHJ6GF0dcnkdmdvAt7u0b1rDgAgrRYvXhx3/pZbbtE999yjV199VTNmzOgz/uGHH447///+3//T7373Oz333HM655xz0jpXpB4VJgCAnEJYMro83XSfjK6ubE8DAIA47e3tcSe/3z/odcLhsB555BF5vV4tWLAgqZ/T1dWlYDCosrKykU4ZWUBgAgDIGYQlSAt2ywGAMcPkswx6kqS6ujp5PJ7YadWqVf3e5jvvvKPCwkI5HA5ddNFFeuKJJ3TwwQcnNZ9rrrlG48eP18KFC1Ny/5BZLMkBAOSEkYYlZWa7Cmq2pmg2SLWsL80BAGAf27dvV3Fxcey8w+Hod+xBBx2kt956S21tbXr88ce1ZMkSvfDCC4OGJrfddpseeeQRrV27Vk6nM2VzR+YQmAAAsi4VYQkwIHqZAAD20bPrTTLsdrv2339/SdLcuXP1+uuv64477tAvfvGLfq/z4x//WLfddpv++te/avbs2SmZMzKPJTkAgKxrDNuGfV3CkvyR9V4mLM0BAKRAJBIZsOfJD3/4Q9188816+umnNW/evAzODKlGhQkAIG8RluSHp5vu0+crLsz2NAAAGLLly5frxBNP1MSJE9XR0aE1a9Zo7dq1euaZZyRJ55xzjmpra2M9UH7wgx/oxhtv1Jo1azR58mTV19dLkgoLC1VYWJi1+4HhITABAOQlwpL8lPVeJizNAQAMwe7du3XOOedo165d8ng8mj17tp555hl97nOfkyRt27ZNZvMnCzfuueceBQIBnXrqqXG3s2LFCt10002ZnDpSgMAEAJATGsO2EfUyoeErAABItV/+8pcDXr527dq481u2bEnfZJBxBCYAACCjqDIZJnqwAMCgLAGTzGZTv5ebAv1fBvRG01cAQM4YSfNX765JKZwJ0iknthcmfAAAAIMgMAEA5KU9kUC2p4BhyImwBAAAIAkEJgCAvEVokh9ycoecfFuSQ0UMAAAZR2ACAMgpI1mWg9yWM9Ul+RaWSPk5ZwAA8hyBCQAgr1Flkh8ISwAAQL4hMAEA5L2e0ITGr7kpZ8KSfEfYAwBARrGtMAAg5zSGbaq0BId0nZ7QpCAdE8LoQOAAAKOe2WeSRQNsHexjW2EkjwoTAACQNidOvCLbU4giLAEAAENEYAIAyEk0f0XKEJYAAIBhIDABAOQsQhMAAABkC4EJACCnDTU02b6zJk0zQd4pcI2+6pLRdn8AAMhhBCYAAGD0IVgAAAAjxC45AIB+NYYLVWnpzPY0hrVrDrIvKw1fCUoAYEwzByTzABvhGIHMzQX5j8AEANBHY7gw21Pog9AEgyIsAQAAKcSSHABAnN5hSS6FJzSBRb8ISwAAabBq1SoddthhKioq0rhx43TKKado/fr1SV//kUcekclk0imnnJK+SSJtCEwAADG5FI4ASSMsAQCkyQsvvKClS5fq1Vdf1bPPPqtgMKgTTjhBXq930Otu2bJFV111lY4++ugMzBTpQGACoF+33XabTCaTrrjiimxPBWnWGC4cMCzpuXzfU7YkU2XCTjljxGjcBScZY/E+A0CWPP300zr33HM1Y8YMzZkzRw8++KC2bdumdevWDXi9cDiss846SytXrtTUqVMzNFukGoEJgIRef/11/eIXv9Ds2bOzPRWk0UjCj2yGKCzNyX1pb/hKaAAAyIK2tjZJUllZ2YDjvve972ncuHE6//zzMzEtpAmBCYA+Ojs7ddZZZ+n+++9XaWlptqeDNElHwJHJAIXQZAwjLAEAjFB7e3vcye/3D3qdSCSiK664QkceeaRmzpzZ77iXXnpJv/zlL3X//fencsrIAgITAH0sXbpUJ510khYuXDjoWL/f3+cPDnJbJqtBshmasCxnFBqrS3AAAEmz+AY/SVJdXZ08Hk/stGrVqkFve+nSpXr33Xf1yCOP9Dumo6NDZ599tu6//35VVFSk6m4hS9hWGECcRx55RG+++aZef/31pMavWrVKK1euTPOskArZ6jvSGC5UpaUzjbfPdsO5JiXLcQhGAABptH37dhUXF8fOOxyOAcdfcskl+tOf/qQXX3xREyZM6Hfcxo0btWXLFi1evDh2LBKJSJKsVqvWr1+v/fbbb4SzR6YQmACI2b59uy6//HI9++yzcjqdSV1n+fLlWrZsWex8e3u76urq0jVFDFO2d7/p7+enKkjpLzTZvrNGdbW7UvIzkBzCEgBAPiguLo4LTPpjGIYuvfRSPfHEE1q7dq2mTJky4Php06bpnXfeiTt2/fXXq6OjQ3fccQevk/MMgQmAmHXr1mn37t069NBDY8fC4bBefPFF3XnnnfL7/bJYLHHXcTgcgybyuW6098LIdlgykMHmNpRApb/HsXHbRB06cduQ5oUsIShJXoFL8nZnexYAMOotXbpUa9as0R/+8AcVFRWpvr5ekuTxeORyRf9unXPOOaqtrdWqVavkdDr79DcpKSmRpAH7niA3EZgAiDn++OP7JOLnnXeepk2bpmuuuaZPWAKkW7qX8yC1RlRdQlgCAMhB99xzjyTp2GOPjTv+q1/9Sueee64kadu2bTKbaQ86GhGYAIgpKirqk3wXFBSovLycRDxP5XJ1SbJSEZq8SZVJ2hGWAABGI8MwBh2zdu3aAS9/8MEHUzMZZBwxGABgTHhz28RsTwGJEJYAAFLI6h/8BCSLChMAAxosMUfuGg3VJT32vS8s0ck9J05fPvSeGgQlAAAgxxGYAADyykiCoGc2H6xFU/6dwtngxOnLPzmTKARJFKIQlqQGjV8BAEgrluQAwCg0mqpLUu2ZzQdnewpjS4Gr7wkAACAPEJgAAIBhiasuQXYQQAEAkDYEJgAwylBdMjiqTEaOsAQAAIx29DABAIwau0PFA14+ztoe+/6/Pvq0zj7g1XRPCQAAAHmKwAQARpGxWF0yWEgy3LHoH9UlAIBcZfFLFmOAAYGMTQWjAEtyAAB5aXeoeMQByH999OkUzWbsICwBAABjBYEJACCrhhJ89IxNZaUIoQkAAAASYUkOAIwS+bYcp3fowXKZ3Ed1CQAAGEuoMAEAZFyuhSM/fn+Rfvz+omxPI6ctmrtCEbc929MAAADIGCpMAGAUyJfqklwKShpCnj7Hfvz+Il01/ZkszCZ3LZq7Iu58otDE3EUHvawqcEne7mzPAgCAUYfABACQdrkSlCQKSXr77ttfkSTdMvv36Z5OzusdlvSHEAUAkCusPkOWSP/b5JgCA22hA8QjMAEApE0+BSWS1BIsiH3/3be/MmZDk88ddYukka3bJUQBAAD5jh4mAJDncnU5TrbDkoaQJ3ZKxr5hSY+L1p2ti9adneqp5bSesESSIq7U9iyJuO19TgAA5LoXX3xRixcv1vjx42UymfTkk08Oeh2/36/vfve7mjRpkhwOhyZPnqwHHngg/ZNFSlFhAgBIuWyFJcmGI/tKFJRIUvM+xy9ad7bunftfw55XPtg3KNlXxGWXuTt9lSG9QxOqUAAAucbr9WrOnDn6xje+oa985StJXee0005TQ0ODfvnLX2r//ffXrl27FIlE0jxTpBqBCQDksVytLsmk4YQkPZIJS3qc/spFkqRHF9w77J+Xq/oLS3qkOzSJ+1luO6EJACCnnHjiiTrxxBOTHv/000/rhRde0KZNm1RWViZJmjx5cppmh3RiSQ4AIKUyVV0ylOU2iSQKS5qDBQnDkpbAJ8d6gpPR4LMn/ECfPeEHCrkH//wk1ctzAAAYrf74xz9q3rx5+uEPf6ja2lodeOCBuuqqq9TdzY5m+YYKEwBjWmPYlu0pDFsuVpekOywZSUCyr/7CkoRjA32PL3rhCknSM59ZnZL5ZMNnT/hB3Pme0MTaFer3OpmqNOlZpkOlCQAgXdrb2+POOxwOORyOlNz2pk2b9NJLL8npdOqJJ55QU1OTLr74YjU3N+tXv/pVSn4GMoMKEwBASqQzLBlpNUmPlmBB0mFJS6AgYVjSGnDGvu8JTvJJT1VJfwarNslkpQlNYYegwJXtGQBATrD4DFm7+z9ZfNFthevq6uTxeGKnVatWpWwOkUhEJpNJDz/8sA4//HB94Qtf0O23366HHnqIKpM8Q4UJAGDEsr0jzmD661Ui9R+W9LZvULKvTz+zXJL06qLUvdBKl2O+9CPJZZale+CmcyG3ddBKkx7prjih2gQAkA7bt29XcfEnr19SVV0iSTU1NaqtrZXH88mHPdOnT5dhGNqxY4cOOOCAlP0spBeBCQBgRHJ1Gc5AIYk0tCU4/YUlbb5PPtX/9DPL5fVH39y/c/LKZKeZVkec/hNJktUXH5CEUxCa9EhUcZKOEIVmsACAVCouLo4LTFLpyCOP1G9/+1t1dnaqsDC6hPrDDz+U2WzWhAkT0vIzkR4EJgCQh3Klf0muhSWDhSQ9RhqW7BuUJDLrjyvkD0T/xAZ90a+bv748qbkN1+Hn3h533tptDDg+laFJb71DlFQFKFSbAACyobOzUxs2bIid37x5s9566y2VlZVp4sSJWr58uXbu3Klf//rXkqQzzzxTN998s8477zytXLlSTU1Nuvrqq/WNb3xDLhdLKPMJgQkAYFhGU1iSKCiR+q8sSaSnukRSLCzZ15T/XqWI3xI7b/JZtGXpfyZ9+/uac8VPJUkW3z4HnSZZfYlDkpDT3KfKREpvaJJOVJsAADLpjTfe0Gc/+9nY+WXLlkmSlixZogcffFC7du3Stm3bYpcXFhbq2Wef1aWXXqp58+apvLxcp512mr7//e9nfO4YGQITAMCQ5VpYkqz+KkuGYrDqkqGYfNdPZPGbYuctPpM+vO7KuDEzrvlp3Hmr/5Pvw85eock+Qi7ToFUmUmZCk3TsrkO1CQAgU4499lgZRv9/Ux988ME+x6ZNm6Znn302jbNCJhCYAAByynDCkmSqS4ayDEcafnVJMkw+S7+XHXhrNCAx9wQjvUKRkCM+NEmFfA1NJKpNAABA+hCYAACGJN3bBw9VsktxUmEk1SX7LsdJl9AwluX0yNflORLVJgCAT1h9EVnDA/w9Cw78tw7YlznbEwAA5I98DUsyWV2yb/+SnoavybD4TP1eFh5gOgNdFnLF32bIOfCf/bBr8JcFIffwP2tJtKNOKvUEJwAAAKlAYAIAeSZbO+Tka1iSKqnsXdJj3/4lQxFyDHCZc+DbHAuhCcEJAABIBQITAEBWpTssyVZ1SX8G6l8yXEOpMpGyH5pkAqEJAAAYKQITAMCg0lVdkq2wJBWG0ug1Hf1LRlJlEh0z8tBkuNJdZQIAAJAKBCYAgKxI19bBPQYKS4ZaXZLMcpze1SVD6V+SLaM9NKHKBAAAjASBCYA4q1at0mGHHaaioiKNGzdOp5xyitavX5/taSGL0lFdMtywJBV9S/oLS4ZiqNsIJyPsTLy7TfLX7/+yRMtyYpeNIDTJ9WU5AICxx+KLyNI9wGmAHeOA3ghMAMR54YUXtHTpUr366qt69tlnFQwGdcIJJ8jr9WZ7alD2Gr6mUibCkuEsxRlt1SXJLMv5ZOzwXw7kcgNYAACAkcj+KzoAOeXpp5+OO//ggw9q3LhxWrdunY455pgszQrZkurqkmyHJblaXZKMsFOy+IZ//ZDLJGt3/1UsIadZ1n4+dQu7zLJ0p+cTuYjLLnN3IC23LUWX5Zi70nf7AABg9CIwATCgtrY2SVJZWVnCy/1+v/x+f+x8e3t7RuaF/JPNZTjSwGFJOqtL0tHwtT+9Q5WQ0ySrb2RLfZIRcltl7QoN+/rpDk0AAACGgyU5APoViUR0xRVX6Mgjj9TMmTMTjlm1apU8Hk/sVFdXl+FZAp9I5VKcRIazjXAuGaiXiTTw0pzBGsCOtJ8Jy3MAAECuITAB0K+lS5fq3Xff1SOPPNLvmOXLl6utrS122r59ewZniNEum0txkqkuyUeDhSYjuu0cbQI7ZnbLKRid/2YBAMiW3HxlAyDrLrnkEv3pT3/Siy++qAkTJvQ7zuFwyOFwZHBmyEfDWY6TzaU4wzXUZq9hhyGLP3GAEXFIZn/Ci0ZssH4mAAAAIDAB0IthGLr00kv1xBNPaO3atZoyZUq2p4S9RsMOOckaaliS6qU4iapLstXsdSAhh2QdJFQZah+TkTZ/HWk/EwAARsLaFZLVOsDfoRB/o5A8AhMAcZYuXao1a9boD3/4g4qKilRfXy9J8ng8crko9x5LUr1DTrqke1eckejd8NXky1wD2GwiNAEAAKMBPUwAxLnnnnvU1tamY489VjU1NbHTo48+mu2pAX0Mp7IkVVLR8LW/5TjZNpLmr7HbyNF+JqOatzvbMwCAUeuuu+7S5MmT5XQ6NX/+fL322msDjl+9erUOOugguVwu1dXV6corr5TP5xvwOsg9vJoBEMcw6GuA1BrudsIjle3qEgwd2wsD6WN0dWV7CkDeevTRR7Vs2TLde++9mj9/vlavXq1FixZp/fr1GjduXJ/xa9as0bXXXqsHHnhARxxxhD788EOde+65MplMuv3227NwDzBcVJgAQJ6otHRmewqjRr72LwmnsEdtOnfLAQBgNLn99tt1wQUX6LzzztPBBx+se++9V263Ww888EDC8S+//LKOPPJInXnmmZo8ebJOOOEEnXHGGYNWpSD3EJgAGNMqLcFsTwGjxFB3yEmXVIYq/f4MluUAAPJce3t73MnvT9xFPRAIaN26dVq4cGHsmNls1sKFC/XKK68kvM4RRxyhdevWxQKSTZs26amnntIXvvCF1N8RpBWvZAAAOSVV2wkDAICxx+wLymzpP9g3h6MfltXV1cUdX7FihW666aY+45uamhQOh1VVVRV3vKqqSh988EHCn3HmmWeqqalJRx11lAzDUCgU0kUXXaTrrrtuiPcG2UZgAgBIm3T2L8nU7jiJluMk0/A133fIGWh7YeQgGr4CwJBs375dxcWf7AjocDhSdttr167Vrbfeqrvvvlvz58/Xhg0bdPnll+vmm2/WDTfckLKfg/QjMAEA9JEvWwoPx1D7l4wGIadJVh8NnQEA6FFcXBwXmPSnoqJCFotFDQ0NcccbGhpUXV2d8Do33HCDzj77bH3zm9+UJM2aNUter1cXXnihvvvd78pspjNGvuCRAgAAQ5ZsH5Ohirhyo8EuAACSZLfbNXfuXD333HOxY5FIRM8995wWLFiQ8DpdXV19QhGLJVppyo6U+YUKEwAAhiDRcpzhNHy1+FOzS03IIVkT96nLCSG3VdauULanAQDAsC1btkxLlizRvHnzdPjhh2v16tXyer0677zzJEnnnHOOamtrtWrVKknS4sWLdfvtt+tTn/pUbEnODTfcoMWLF8eCE+QHAhMAQFqks3/JcOXrdsIAACB7Tj/9dDU2NurGG29UfX29DjnkED399NOxRrDbtm2Lqyi5/vrrZTKZdP3112vnzp2qrKzU4sWLdcstt2TrLmCYTAY1QQBSqL29XR6PR++9P05FRfmx6q8xbMv2FJLWGC7M2M8aaR+T4QQmye6QM1DDV6n/pq+pCEySrTAZrOlrogoTiy/+mDlB5YjF1/dY7wqThGP66WFi7e7/ZcBgTV8t3ck1hR1qhYm5OzCk8YPeXldqby8n0fQVgzC6urI9BeShUCSg51oeUltbW1L9PrKp5zXocXOukdXSfwPXUNivv/3rB3lxn5B9VJgAADDKhJ19QxMav45ihCUAEGPuCshs6X/Zqzk8BkJ0pEx+fPwLAADyEv1LAABAviIwAQCMKv0txxkI/UtyCzvlAACAXEBgAgBAivXuXwIAAID8Q2ACAMgJqWr4mk6JGr5m20i3FA65+l/nHXLyMgEAAIxdvBICgDxSaenM9hSAtEv1LjmjGg1fAQBIm9z7qAwAMqzSEsyrrYUxOvXeUnhkt5Wym8o4whIAwIh0dUvmAba9j4ywNBNjChUmAICExlnbM/rzSm3ejP68VAn6+Owhl5m7RnEAQ3UJAABpRWACAEiLKmtbtqeQM0w+msACAADkGwITAMgz9DHBaMVynCGgugQAgLQjMAEApM1Qq0zydVnOaGX1DbAGPA+M6uU4AAAg7QhMACAPZarKJNN9TJJRPoZDlXQ0c7V2G6m/UQAAgFGAwAQAkFa51MukxJ44cfA4Wd7QWyqqS6xdoaTHpno5zqiuLmE5DgAAGUFrfwAQWwvnklKbVy3BgmxPA4OwdOf3cp28RVgCAAMyurplmMP9Xx4ZxYE6Uo4KEwDIU2O5+etAy3JK7aNjyY7Zn9rbs/oyu/RmKNUlAAAAuYjABAAwoFT0McmlZTnJKnCk7hMow9n/J13ZNNz+JbleXTJql+NQXQIAWXPXXXdp8uTJcjqdmj9/vl577bUBx//2t7/VtGnT5HQ6NWvWLD311FMZmilSicAEAPLYaK0yYbec4UtHY9h0YzvhJBCWAEDWPProo1q2bJlWrFihN998U3PmzNGiRYu0e/fuhONffvllnXHGGTr//PP1z3/+U6eccopOOeUUvfvuuxmeOUaKwAQAMKhc3C0HkjXJZTtDXY4z0oav2VyOMyqrSwhLACCrbr/9dl1wwQU677zzdPDBB+vee++V2+3WAw88kHD8HXfcoc9//vO6+uqrNX36dN1888069NBDdeedd2Z45hgpAhMAQEbk47Kc3hx2+nLk+nKcUYewBADSor29Pe7k9yf+FCIQCGjdunVauHBh7JjZbNbChQv1yiuvJLzOK6+8EjdekhYtWtTveOQuAhMA2KvSEsz2FIYlU8tyMl1lMpqW5Zh8lmxPoY/h9i8Z9HaHWF3CcpwBEJYAwJAZ3i4Znd7+T94uSVJdXZ08Hk/stGrVqoS319TUpHA4rKqqqrjjVVVVqq+vT3id+vr6IY1H7mJbYQBAXiq3edXcz/bDpXavWgKJLyux+9QacKZzaoOy+E1Dv04O9CbJ9eqSUbUch7AEANJq+/btKi4ujp13OBxZnA1yFYEJAIwClZZONYYL0/5zxlnbtTtUPPjAMcTmDCnoG9mfU4tv6AFKsjLZv4TeJQCAfFFcXBwXmPSnoqJCFotFDQ0NcccbGhpUXV2d8DrV1dVDGo/cxZIcAAkNdes0IBlD7WMympblDIU5yWau+Y7lOP2gugQAcobdbtfcuXP13HPPxY5FIhE999xzWrBgQcLrLFiwIG68JD377LP9jkfuIjAB0MdQt05DbhitvUyQvGSX7aSjfwnVJSlCWAIAOWfZsmW6//779dBDD+n999/Xt7/9bXm9Xp133nmSpHPOOUfLly+Pjb/88sv19NNP6yc/+Yk++OAD3XTTTXrjjTd0ySWXZOsuYJgITAD0MdSt0wBgqFJVXTKqwhIAQE46/fTT9eMf/1g33nijDjnkEL311lt6+umnY41dt23bpl27dsXGH3HEEVqzZo3uu+8+zZkzR48//riefPJJzZw5M1t3AcNkMgwjPW3yAeSlQCAgt9utxx9/XKecckrs+JIlS9Ta2qo//OEPceP9fn/cNmxtbW2aOHGi/vF6hQoL8zOTbQrbsj2FYWsKJ250mp6fVTTs6+4OeZIe2xp093vZngEua+2nIawktQX6NnZr87n6HOsK2Psc8wf67ngT8sf3MIn448f03iXHEojvWWLu1cPEnCAD6F09Yk2wbMeS4FiiHiaWAfqa9NfDxDJAb5PhVJeYfSPflWrUhSVdVJcgfQz+fWEYQkZAL7T+Rq2trfJ4kv/bnQ3t7e3yeDw6xn6KrOr/tVxIQb0YeFJtbW1J9TDB2EbTVwBxBto67YMPPugzftWqVVq5cmWf4/MPa0rbHAEAAJA5zc3NOR+Y2O12VVdX68X6JwcdW11dLbu974ciQG8EJgBGZPny5Vq2bFnsfGtrqyZNmqRt27bl/B/Wsaq9vV11dXV9ttND7uAxyn08RrmPxyj38Rjlvp7K4bKysmxPZVBOp1ObN29WIDB49aHdbpfT6czArJDvCEwAxBnq1mkOhyPhvvUej4cXPzku2e30kD08RrmPxyj38RjlPh6j3Gc258cya6fTSRCClMqPf/kAMmY4W6cBAAAAwGhDhQmAPpYtW6YlS5Zo3rx5Ovzww7V69eq4rdMAAAAAYLQjMAHQx+mnn67GxkbdeOONqq+v1yGHHBK3ddpAHA6HVqxYkXCZDnIDj1Hu4zHKfTxGuY/HKPfxGOU+HiOMdWwrDAAAAAAA0As9TAAAAAAAAHohMAEAAAAAAOiFwAQAAAAAAKAXAhMAAAAAAIBeCEwApNRdd92lyZMny+l0av78+XrttdeyPSXsddNNN8lkMsWdpk2blu1pjWkvvviiFi9erPHjx8tkMunJJ5+Mu9wwDN14442qqamRy+XSwoUL9dFHH2VnsmPUYI/Rueee2+d59fnPfz47kx2DVq1apcMOO0xFRUUaN26cTjnlFK1fvz5ujM/n09KlS1VeXq7CwkL9x3/8hxoaGrI047Enmcfo2GOP7fM8uuiii7I047Hnnnvu0ezZs1VcXKzi4mItWLBAf/nLX2KX8xzCWEZgAiBlHn30US1btkwrVqzQm2++qTlz5mjRokXavXt3tqeGvWbMmKFdu3bFTi+99FK2pzSmeb1ezZkzR3fddVfCy3/4wx/qZz/7me6991794x//UEFBgRYtWiSfz5fhmY5dgz1GkvT5z38+7nn1m9/8JoMzHNteeOEFLV26VK+++qqeffZZBYNBnXDCCfJ6vbExV155pf7nf/5Hv/3tb/XCCy/o448/1le+8pUsznpsSeYxkqQLLrgg7nn0wx/+MEszHnsmTJig2267TevWrdMbb7yh4447Tl/60pf03nvvSeI5hLGNbYUBpMz8+fN12GGH6c4775QkRSIR1dXV6dJLL9W1116b5dnhpptu0pNPPqm33nor21NBAiaTSU888YROOeUUSdHqkvHjx+s///M/ddVVV0mS2traVFVVpQcffFBf+9rXsjjbsan3YyRFK0xaW1v7VJ4gOxobGzVu3Di98MILOuaYY9TW1qbKykqtWbNGp556qiTpgw8+0PTp0/XKK6/o05/+dJZnPPb0foykaIXJIYccotWrV2d3cogpKyvTj370I5166qk8hzCmUWECICUCgYDWrVunhQsXxo6ZzWYtXLhQr7zyShZnhn199NFHGj9+vKZOnaqzzjpL27Zty/aU0I/Nmzervr4+7jnl8Xg0f/58nlM5Zu3atRo3bpwOOuggffvb31Zzc3O2pzRmtbW1SYq+2ZOkdevWKRgMxj2Ppk2bpokTJ/I8ypLej1GPhx9+WBUVFZo5c6aWL1+urq6ubExvzAuHw3rkkUfk9Xq1YMECnkMY86zZngCA0aGpqUnhcFhVVVVxx6uqqvTBBx9kaVbY1/z58/Xggw/qoIMO0q5du7Ry5UodffTRevfdd1VUVJTt6aGX+vp6SUr4nOq5DNn3+c9/Xl/5ylc0ZcoUbdy4Udddd51OPPFEvfLKK7JYLNme3pgSiUR0xRVX6Mgjj9TMmTMlRZ9HdrtdJSUlcWN5HmVHosdIks4880xNmjRJ48eP19tvv61rrrlG69ev1+9///ssznZseeedd7RgwQL5fD4VFhbqiSee0MEHH6y33nqL5xDGNAITABgjTjzxxNj3s2fP1vz58zVp0iQ99thjOv/887M4MyB/7bs0atasWZo9e7b2228/rV27Vscff3wWZzb2LF26VO+++y69mXJYf4/RhRdeGPt+1qxZqqmp0fHHH6+NGzdqv/32y/Q0x6SDDjpIb731ltra2vT4449ryZIleuGFF7I9LSDrWJIDICUqKipksVj6dE1vaGhQdXV1lmaFgZSUlOjAAw/Uhg0bsj0VJNDzvOE5lV+mTp2qiooKnlcZdskll+hPf/qTnn/+eU2YMCF2vLq6WoFAQK2trXHjeR5lXn+PUSLz58+XJJ5HGWS327X//vtr7ty5WrVqlebMmaM77riD5xDGPAITAClht9s1d+5cPffcc7FjkUhEzz33nBYsWJDFmaE/nZ2d2rhxo2pqarI9FSQwZcoUVVdXxz2n2tvb9Y9//IPnVA7bsWOHmpubeV5liGEYuuSSS/TEE0/ob3/7m6ZMmRJ3+dy5c2Wz2eKeR+vXr9e2bdt4HmXIYI9RIj3NyXkeZU8kEpHf7+c5hDGPJTkAUmbZsmVasmSJ5s2bp8MPP1yrV6+W1+vVeeedl+2pQdJVV12lxYsXa9KkSfr444+1YsUKWSwWnXHGGdme2pjV2dkZ9wnq5s2b9dZbb6msrEwTJ07UFVdcoe9///s64IADNGXKFN1www0aP3583C4tSK+BHqOysjKtXLlS//Ef/6Hq6mpt3LhR3/nOd7T//vtr0aJFWZz12LF06VKtWbNGf/jDH1RUVBTrqeDxeORyueTxeHT++edr2bJlKisrU3FxsS699FItWLCA3T0yZLDHaOPGjVqzZo2+8IUvqLy8XG+//bauvPJKHXPMMZo9e3aWZz82LF++XCeeeKImTpyojo4OrVmzRmvXrtUzzzzDcwgwACCFfv7znxsTJ0407Ha7cfjhhxuvvvpqtqeEvU4//XSjpqbGsNvtRm1trXH66acbGzZsyPa0xrTnn3/ekNTntGTJEsMwDCMSiRg33HCDUVVVZTgcDuP444831q9fn91JjzEDPUZdXV3GCSecYFRWVho2m82YNGmSccEFFxj19fXZnvaYkeixkWT86le/io3p7u42Lr74YqO0tNRwu93Gl7/8ZWPXrl3Zm/QYM9hjtG3bNuOYY44xysrKDIfDYey///7G1VdfbbS1tWV34mPIN77xDWPSpEmG3W43KisrjeOPP9743//939jlPIcwlpkMwzAyGdAAAAAAAADkOnqYAAAAAAAA9EJgAgAAAAAA0AuBCQAAAAAAQC8EJgAAAAAAAL0QmAAAAAAAAPRCYAIAAAAAANALgQkAAAAAAEAvBCYAAAAAAAC9EJgAAAAAAAD0QmACAAAAAADQC4EJAAAAAABALwQmAAAAAAAAvRCYAAAAAAAA9EJgAgAAAAAA0AuBCQAAAAAAQC8EJgAAAAAAAL0QmAAAAAAAAPRCYAIAAAAAANALgQkAAAAAAEAvBCYAAAAAAAC9EJgAAAAAAAD0QmACAAAAAADQC4EJAAAAAABALwQmAAAAAAAAvRCYAAAAAAAA9EJgAgAAAAAA0AuBCQAAAAAAQC8EJgAAAAAAAL0QmAAAAAAAAPRCYAIAAAAAANALgQkAAAAAAEAvBCYAAAAAAAC9EJgAAAAAAAD0QmACAAAAAADQC4EJAAAAAABALwQmAAAAAAAAvRCYAAAAAAAA9EJgAgAAAAAA0AuBCQAAAAAAQC8EJgAAAAAAAL0QmAAAAAAAAPRCYAIAAAAAANALgQkAAAAAAEAvBCYAAAAAAAC9/H+HbefERDaNIQAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -592,11 +590,32 @@
     "\n",
     "How does it affect the solution before and after optimisation? "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/07-geometric-multigrid.ipynb b/docs/notebooks/07-geometric-multigrid.ipynb
index 3eefe36deb..52a31fce49 100644
--- a/docs/notebooks/07-geometric-multigrid.ipynb
+++ b/docs/notebooks/07-geometric-multigrid.ipynb
@@ -36,6 +36,7 @@
    "outputs": [],
    "source": [
     "from firedrake import *\n",
+    "from firedrake.__future__ import *\n",
     "\n",
     "coarse_mesh = RectangleMesh(15, 10, 1.5, 1)"
    ]
@@ -211,7 +212,7 @@
     "                       0)\n",
     "\n",
     "    value = as_vector([gbar*(1 - (12*t)**2), 0])\n",
-    "    bcs = [DirichletBC(W.sub(0), interpolate(value, V), (1, 2)),\n",
+    "    bcs = [DirichletBC(W.sub(0), value, (1, 2)),\n",
     "           DirichletBC(W.sub(0), zero(2), (3, 4))]\n",
     "    \n",
     "    a = (nu*inner(grad(u), grad(v)) - p*div(v) + q*div(u))*dx\n",
@@ -232,17 +233,13 @@
      "output_type": "stream",
      "text": [
       "    Residual norms for firedrake_0_ solve.\n",
-      "    0 KSP Residual norm 1.021257383821e+01 \n",
-      "    1 KSP Residual norm 6.404133272505e-12 \n"
+      "    0 KSP Residual norm 1.021257383821e+01\n",
+      "    1 KSP Residual norm 4.151410612505e-12\n"
      ]
     }
    ],
    "source": [
-    "solver = create_solver({\"ksp_type\": \"preonly\",\n",
-    "                        \"pc_type\": \"lu\",\n",
-    "                        \"pc_factor_shift_type\": \"inblocks\",\n",
-    "                        \"ksp_monitor\": None,\n",
-    "                        \"pmat_type\": \"aij\"})\n",
+    "solver = create_solver({\"ksp_monitor\": None})\n",
     "solver.solve()"
    ]
   },
@@ -266,18 +263,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f6ad0c79e204a479e535c7b10de85af",
+       "model_id": "6795ced4cb574153b229e1a73ebfcf04",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
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       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -337,31 +334,31 @@
      "output_type": "stream",
      "text": [
       "    Residual norms for firedrake_2_ solve.\n",
-      "    0 KSP Residual norm 1.793015736715e+02 \n",
-      "    1 KSP Residual norm 8.716276054615e+01 \n",
-      "    2 KSP Residual norm 1.669109319703e+01 \n",
-      "    3 KSP Residual norm 1.123132759507e+01 \n",
-      "    4 KSP Residual norm 5.140215836973e+00 \n",
-      "    5 KSP Residual norm 2.860823020181e+00 \n",
-      "    6 KSP Residual norm 1.241682759464e+00 \n",
-      "    7 KSP Residual norm 7.188188009066e-01 \n",
-      "    8 KSP Residual norm 4.044004541226e-01 \n",
-      "    9 KSP Residual norm 1.795717211991e-01 \n",
-      "   10 KSP Residual norm 1.184207774231e-01 \n",
-      "   11 KSP Residual norm 7.266399169233e-02 \n",
-      "   12 KSP Residual norm 4.748358849735e-02 \n",
-      "   13 KSP Residual norm 2.947011552603e-02 \n",
-      "   14 KSP Residual norm 1.649709615925e-02 \n",
-      "   15 KSP Residual norm 9.288959964288e-03 \n",
-      "   16 KSP Residual norm 5.115169026184e-03 \n",
-      "   17 KSP Residual norm 2.604131289585e-03 \n",
-      "   18 KSP Residual norm 1.155550083370e-03 \n",
-      "   19 KSP Residual norm 4.714818940666e-04 \n",
-      "   20 KSP Residual norm 2.159213406651e-04 \n",
-      "   21 KSP Residual norm 1.075446620939e-04 \n",
-      "   22 KSP Residual norm 5.902061753908e-05 \n",
-      "   23 KSP Residual norm 2.906398089999e-05 \n",
-      "   24 KSP Residual norm 1.199566391672e-05 \n"
+      "    0 KSP Residual norm 1.793015736715e+02\n",
+      "    1 KSP Residual norm 8.716276054615e+01\n",
+      "    2 KSP Residual norm 1.669109319702e+01\n",
+      "    3 KSP Residual norm 1.123132759507e+01\n",
+      "    4 KSP Residual norm 5.140215836971e+00\n",
+      "    5 KSP Residual norm 2.860823020180e+00\n",
+      "    6 KSP Residual norm 1.241682759464e+00\n",
+      "    7 KSP Residual norm 7.188188009074e-01\n",
+      "    8 KSP Residual norm 4.044004541225e-01\n",
+      "    9 KSP Residual norm 1.795717211984e-01\n",
+      "   10 KSP Residual norm 1.184207774229e-01\n",
+      "   11 KSP Residual norm 7.266399169257e-02\n",
+      "   12 KSP Residual norm 4.748358849764e-02\n",
+      "   13 KSP Residual norm 2.947011552624e-02\n",
+      "   14 KSP Residual norm 1.649709615901e-02\n",
+      "   15 KSP Residual norm 9.288959964180e-03\n",
+      "   16 KSP Residual norm 5.115169026355e-03\n",
+      "   17 KSP Residual norm 2.604131289702e-03\n",
+      "   18 KSP Residual norm 1.155550083346e-03\n",
+      "   19 KSP Residual norm 4.714818939982e-04\n",
+      "   20 KSP Residual norm 2.159213404854e-04\n",
+      "   21 KSP Residual norm 1.075446620076e-04\n",
+      "   22 KSP Residual norm 5.902061796985e-05\n",
+      "   23 KSP Residual norm 2.906398110291e-05\n",
+      "   24 KSP Residual norm 1.199566367313e-05\n"
      ]
     }
    ],
@@ -399,32 +396,22 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jack/Documents/firedrake/firedrake/src/firedrake/firedrake/dmhooks.py:270: RuntimeWarning: Creating new TransferManager to transfer data to coarse grids\n",
-      "  warnings.warn(\"Creating new TransferManager to transfer data to coarse grids\", RuntimeWarning)\n",
-      "/home/jack/Documents/firedrake/firedrake/src/firedrake/firedrake/dmhooks.py:271: RuntimeWarning: This might be slow (you probably want to save it on an appctx)\n",
-      "  warnings.warn(\"This might be slow (you probably want to save it on an appctx)\", RuntimeWarning)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "    Residual norms for firedrake_3_ solve.\n",
-      "    0 KSP Residual norm 1.021257383821e+01 \n",
-      "    1 KSP Residual norm 6.150697152777e+00 \n",
-      "    2 KSP Residual norm 6.499187546880e-01 \n",
-      "    3 KSP Residual norm 4.689516571282e-02 \n",
-      "    4 KSP Residual norm 7.942948895085e-03 \n",
-      "    5 KSP Residual norm 1.233190521507e-03 \n",
-      "    6 KSP Residual norm 1.185329361920e-04 \n",
-      "    7 KSP Residual norm 2.350517854641e-05 \n",
-      "    8 KSP Residual norm 7.877964594448e-06 \n",
-      "    9 KSP Residual norm 3.978917640367e-06 \n",
-      "   10 KSP Residual norm 1.018158170524e-06 \n"
+      "    0 KSP Residual norm 1.021257383821e+01\n",
+      "    1 KSP Residual norm 6.150697152777e+00\n",
+      "    2 KSP Residual norm 6.499187546880e-01\n",
+      "    3 KSP Residual norm 4.689516571282e-02\n",
+      "    4 KSP Residual norm 7.942948895084e-03\n",
+      "    5 KSP Residual norm 1.233190521507e-03\n",
+      "    6 KSP Residual norm 1.185329361925e-04\n",
+      "    7 KSP Residual norm 2.350517854704e-05\n",
+      "    8 KSP Residual norm 7.877964594351e-06\n",
+      "    9 KSP Residual norm 3.978917640385e-06\n",
+      "   10 KSP Residual norm 1.018158168602e-06\n"
      ]
     }
    ],
@@ -523,8 +510,8 @@
     "                       1, \n",
     "                       0)\n",
     "\n",
-    "    value = as_vector([gbar*(1 - (12*t)**2), 0])\n",
-    "    bcs = [DirichletBC(W.sub(0), interpolate(value, V), (1, 2)),\n",
+    "    value = assemble(interpolate(as_vector([gbar*(1 - (12*t)**2), 0]), V))\n",
+    "    bcs = [DirichletBC(W.sub(0), value, (1, 2)),\n",
     "           DirichletBC(W.sub(0), zero(2), (3, 4))]\n",
     "    \n",
     "    a = (nu*inner(grad(u), grad(v)) - p*div(v) + q*div(u))*dx\n",
@@ -546,31 +533,31 @@
      "output_type": "stream",
      "text": [
       "    Residual norms for firedrake_4_ solve.\n",
-      "    0 KSP Residual norm 1.793015736715e+02 \n",
-      "    1 KSP Residual norm 8.716276054615e+01 \n",
-      "    2 KSP Residual norm 1.669109319703e+01 \n",
-      "    3 KSP Residual norm 1.123132759507e+01 \n",
-      "    4 KSP Residual norm 5.140215836973e+00 \n",
-      "    5 KSP Residual norm 2.860823020181e+00 \n",
-      "    6 KSP Residual norm 1.241682759464e+00 \n",
-      "    7 KSP Residual norm 7.188188009066e-01 \n",
-      "    8 KSP Residual norm 4.044004541226e-01 \n",
-      "    9 KSP Residual norm 1.795717211991e-01 \n",
-      "   10 KSP Residual norm 1.184207774231e-01 \n",
-      "   11 KSP Residual norm 7.266399169233e-02 \n",
-      "   12 KSP Residual norm 4.748358849735e-02 \n",
-      "   13 KSP Residual norm 2.947011552603e-02 \n",
-      "   14 KSP Residual norm 1.649709615925e-02 \n",
-      "   15 KSP Residual norm 9.288959964288e-03 \n",
-      "   16 KSP Residual norm 5.115169026184e-03 \n",
-      "   17 KSP Residual norm 2.604131289585e-03 \n",
-      "   18 KSP Residual norm 1.155550083370e-03 \n",
-      "   19 KSP Residual norm 4.714818940666e-04 \n",
-      "   20 KSP Residual norm 2.159213406651e-04 \n",
-      "   21 KSP Residual norm 1.075446620939e-04 \n",
-      "   22 KSP Residual norm 5.902061753908e-05 \n",
-      "   23 KSP Residual norm 2.906398089999e-05 \n",
-      "   24 KSP Residual norm 1.199566391672e-05 \n"
+      "    0 KSP Residual norm 1.793015736715e+02\n",
+      "    1 KSP Residual norm 8.716276054615e+01\n",
+      "    2 KSP Residual norm 1.669109319702e+01\n",
+      "    3 KSP Residual norm 1.123132759507e+01\n",
+      "    4 KSP Residual norm 5.140215836971e+00\n",
+      "    5 KSP Residual norm 2.860823020180e+00\n",
+      "    6 KSP Residual norm 1.241682759464e+00\n",
+      "    7 KSP Residual norm 7.188188009074e-01\n",
+      "    8 KSP Residual norm 4.044004541225e-01\n",
+      "    9 KSP Residual norm 1.795717211984e-01\n",
+      "   10 KSP Residual norm 1.184207774229e-01\n",
+      "   11 KSP Residual norm 7.266399169257e-02\n",
+      "   12 KSP Residual norm 4.748358849764e-02\n",
+      "   13 KSP Residual norm 2.947011552624e-02\n",
+      "   14 KSP Residual norm 1.649709615901e-02\n",
+      "   15 KSP Residual norm 9.288959964180e-03\n",
+      "   16 KSP Residual norm 5.115169026355e-03\n",
+      "   17 KSP Residual norm 2.604131289702e-03\n",
+      "   18 KSP Residual norm 1.155550083346e-03\n",
+      "   19 KSP Residual norm 4.714818939982e-04\n",
+      "   20 KSP Residual norm 2.159213404854e-04\n",
+      "   21 KSP Residual norm 1.075446620076e-04\n",
+      "   22 KSP Residual norm 5.902061796985e-05\n",
+      "   23 KSP Residual norm 2.906398110291e-05\n",
+      "   24 KSP Residual norm 1.199566367313e-05\n"
      ]
     }
    ],
@@ -603,8 +590,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/08-composable-solvers.ipynb b/docs/notebooks/08-composable-solvers.ipynb
index 39e26a0fe7..78ba106b5f 100644
--- a/docs/notebooks/08-composable-solvers.ipynb
+++ b/docs/notebooks/08-composable-solvers.ipynb
@@ -128,17 +128,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "firedrake:WARNING No comm specified for VectorSpaceBasis, COMM_WORLD assumed\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "nullspace = MixedVectorSpaceBasis(W, [W.sub(0), VectorSpaceBasis(constant=True)])"
+    "nullspace = MixedVectorSpaceBasis(W, [W.sub(0), VectorSpaceBasis(constant=True, comm=mesh.comm)])"
    ]
   },
   {
@@ -251,18 +243,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0c53837a62cc4babbc3ba4d4ebb01a99",
+       "model_id": "449c533e3816477894a1baa628d51679",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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Gt8d25P4ESzTt7o4j1yOMIbhUO3qdHj29t1kNJhS8R64bdAaCDEHodYYj2xl1ut7/H7puMZhJCko5cl2n02PS9c6f0Ol6ExIGnQG9rjdpodfp0aHDoDOi16nw5dsOfcHXHRpHh77367pOjw7DF/cBukMx9SY9er9A63WGQ/f3Pl5Ve8/c9yYNxGD0TT/Zb0oTfNOpDtP3jMUfFFXB7nXS43HS7XbS4XbS3GOnw+6iy+6mq8tFbUsnB0ob+GBNPuF6I9NzMjjrlDEEWQL5mYkf4s033+T222/nmWeeYerUqTz66KPMmzePwsJC4uPjv7L9qlWruOSSS5g+fTpBQUE88MADzJ07l4KCAlJSUgD4xz/+weOPP85LL71EVlYWd911F/PmzWPfvn0EBQUBcNlll1FXV8eyZctwu91cc8013Hjjjbz++ut+ff5CHC+dqqoyiUt8o87OTiIiIujo6CA8PFzrcPqNoijkry9k8+IdbPhwO3XljYRHhxKVEEF8WizJQxNJHZZIem4qWaPTCI+WzLcQ/ub2enF4PNjdbhwez5GL3ePB5e39v1dV6XI5cXg8qKh0uVw4PB6cXg9Gg44Wux2nx0NEUBDV3R04vb0Jj2CTkYaeblxeLxkRkextrceleHF6vWRGRFDZ1Y7ZYGBIRAwN9i5MBj1mvZG86DiqbO2Y9AbMegMhZhM6HZj1BuKCQ+jxuDDpDZgOXbd5nEeuh5kteFWl97pOj8mgx6jTH7nfqO+9bjx0v9Fw6F99bxLk8H0Gnb43MaLvTX4Y9Qb06DDq5aywEOL7a++2s3lPGavWFJI3LIkrF52gdUjfaqAcpx6Oc8e+BELD+nemcXeXwoQRDd/pNZg6dSqTJ0/mySefBHqPi9PS0rjlllv43e9+962P93q9REVF8eSTT3LllVeiqirJycn86le/4te//jUAHR0dJCQk8OKLL3LxxRezf/9+RowYwdatW5k0aRIAS5Ys4fTTT6e6uprk5OTv+QoI4Tsyw0T86HS2drPqzQ2seXczB3eW4XF5iEmOZu6VJzFq+nBGzcghKTtBzi4KcZzcHi89bjc9Ljc9Thc2l5selwun10uXw0mPy40KNNts2N1u9DodzT099Ljd2F1ueryHEiJuDzGhVoqam7G7PYxJTmBPfT3BJhPZ0VG0OO0EGY0EGY2EB/eeqQoyGkkJD6fL7STIYCTCYkHVQ6jZTIwhmFCzmaFROiwGI1aTCaNBj8VgxGzoTXaYjYbe63o9FqOx9zaDEaMsmxNC/AhFhgYzb/oI9DodK3eVaB2O8BGXy8X27dv5/e9/f+Q2vV7PnDlz2Lhx43GN0dPTg9vtJjo6GugtFVBfX8+cOXOObBMREcHUqVPZuHEjF198MRs3biQyMvJIsgRgzpw56PV6Nm/ezLnnnttPz1CI/iMJE/Gj0dnSxcf/XsHSF1fRUtdGaEQIs86dwrSFE5l42miCrEFahziouN0emuo7aWvuYuT4DK3DEcfg9nixOV102Z10O1x02h102104PG7abQ66HE4UVaWpy0a3w4XJZKC6tYNup4uosGAO1DXh9noZkRJPVXsnVrOJvOQ4mrptWM1mMmIi6Xa6sJpNxIeFYDIYiAgOIsxiJtcYR7DJhNVswmLqTWYEm0yYDQasZhNBRiMmWUMvhBDasBpxympjn1DU3kt/jwm9s1i+7OsaOTQ3N+P1eklISDjq9oSEBA4cOHBc+7zjjjtITk4+kiCpr68/MkbfMQ/fV19f/5XlPkajkejo6CPbCBFoJGEiBj2Px8vS51fy3wf+h9PuImlIAqdffwqnXDyDuNQYrcMb0FRVpaWhk8rSJhrr2yktqqen20nBrt5aOnXVbViCTHyw8U6ZseMjqqpic7josDnosNnpsDnpsNmxO900ddnotDkwGg2UN7bS2eMkNiKEnWW1ONxuRmYkUtPaQWiQhaFJMdicLkKCLCRGhaJDR0iQmajQILITogkNshASZCLEYiHEbMJq6f2/2ShJDSGEGGxK29sIjZQTSQNNWlraUdfvuece7r333n7fz9///nfeeOMNVq1adaQ2iRCDlSRMxKBWfbCOJ3/5Is01rSQPTWTK/HEsuO5kQsKtWoc24HS02igtrKO2qoWiPdX02JxsX19MXFIEFaWNJKZEUV/bTniklc4OO3qDjqBgE/FJkdi6HISGy6mq46GqKl09Tlo6bLR12Wnv7KG5s4f2rh4UFcobWmnvthMRGsyuklq8ikJGUjQut4foMCuhVgvhIUHERYYQGmQmKTqc6LBgThqdTZjVQliwhXBrEEEmKWophBDi2A60NjMuOVHrMAYlLzq831h6+PuNCVBVVXVUDZNjzS4BiI2NxWAw0NDQcNTtDQ0NJCZ+88/9wQcf5O9//zvLly9nzJgxR24//LiGhgaSkpKOGnPcuHFHtmlsbDxqPI/HQ2tr67fuVwitSMJEDFrr/7eV959cgsflJW/KUK6853yZUXKc2lu6Kcqv5uDeaqrKmsjfXg4qtDR1ERUXRltTFxFRVnpsTlqbukjLiiNjaDwz544iJSOG5PQYUjNiiYoNlRbKh3gVhdZ2G83tNprbuunoclDX3EFHt4O6li5aOmzERoVQUNZAmNXC8Ix4PF4vsVEhRIQGExVuJTYihEl5aUSGBRMZGkxESBAmmeEhhBCiH1W1tVPd2clPZx67vawIXOHh4cdV9NVsNjNx4kRWrFjBOeecA/QWfV2xYgU333zz1z7uH//4B3/9619ZunTpUXVIoLc7Z2JiIitWrDiSIOns7GTz5s389Kc/BWDatGm0t7ezfft2Jk6cCMDnn3+OoihMnTr1ezxjIXxPEiZiUFr87+V8+vznNFW3cPWfL2b+1bPlbPrXUBSFioMNFGwro6K4kW2rDuBVFJrqOoiICaWj1UZopBVbl4Ps3CSS06PJykkifUgcGUMTSEqLwWj6cX9pVxSVto4eGpo7D126cLjcFJc309DShaIqNLfbyBuaiEdRiI0KJT05itioUHIyE4iNDiUmIoTIsCAsZmnhKIQQQjvryiqIDw1hWKycZPIFX84w+S5uv/12rrrqKiZNmsSUKVN49NFHsdlsXHPNNQBceeWVpKSkcP/99wPwwAMPcPfdd/P666+TmZl5pOZIaGgooaGh6HQ6br31Vv7yl78wbNiwI22Fk5OTjyRl8vLymD9/PjfccAPPPPMMbrebm2++mYsvvlg65IiAJQkTMei8/+SnvHDXGwybkM19H9zB8InZWocUUFRVpbK4kZ3riqirbOHz/+0gKy+JvVvKGDkpk/rqVvImZBIabmXirOEkpEYzdGQymcMSCbKatQ5fM13dDmobOmhs7qSqppXa+g46bQ5KKppJToyguc1GYlw46anRRIQFk5kaw4SR6cTHhBEdacVskrdbIYQQga+grpHs6Cg50eQjiqpDUfv3tf0+41100UU0NTVx9913U19fz7hx41iyZMmRoq2VlZVHzRJ++umncblcnH/++UeN8+U6Kb/97W+x2WzceOONtLe3M3PmTJYsWXJUnZPXXnuNm2++mVNPPRW9Xs+iRYt4/PHHv8ezFsI/dKqq9nOdZjHYDJT+9gAfPbuM/z25hIT0OG558loSM+O//UE/ArYuB7vWF7Ft9X52rC3C61VoaegifVgClSWNjJyURUh4MBNmDiM7N5mho1IIth573etg5nC4qapppb6hg7KKZlrbbBQW19PU3MWw4YmowNCseCLDg0lKiCAxPoKkhAiswT/eRJIQQojBw+52M+3h/+OJ889k1pBMrcM5LgPlOPVwnOvykwkN69/lyt1dCjNH1Qb8ayDEQCSnPMWgsW3ZHta8sxm3y8vPH7v6R58saWvuYuPSvRQXVLPs7S0MG5PG/h0VZOYkUV/dwkkLx5E7LoOx04aQMTzxR1VrxG53UVHZQmlZE01Nnew7UEt1TRuR0SGEhQUxMjeZiPBgRuWlcOkFU4mNCUOvlzNtQgghBred1XXo9TompMnyCF8JlCU5QojjIwkTMSg017Ty0j1vY+vs4Q+v3EzykB9npW1bl4P1n+5iw5K9lO6roam+g4ycRDxuLzHx4Vz0szlMOSWP4WPSfxR1R1RVpaGhg5KSRg4erKekpJEum5P29h4mTcrCGmIhOzOOWTNzSE2JwmyWt0QhhBA/XpvKq8iNjyPELDMnhRACJGEiBgFFUXjyly9ishg57xcLyJk8VOuQ/EpVVfZtLeWT1zbQ1tTFznVF5E7IpLmug1lnjGXEpCymzR1NQmq01qH6lKqqNDZ2UnigjpqaVnZsK8doMtBjdzF8eCJZWXFMmzaMjIxYgoKksKoQQgjR16bKKk4b/uM6jvI3L3q89O+sXm+/jiaE+DJJmIgBb807m2mtayNpSAJn3HCq1uH4ja3LzvK3t7D+0z0c3FuJ4lWJjAll2Jg0Tjt/Cnc+cw0xiRFah+kzLoeHoqI69u2tprCojqLCOnJHpBAVFcLI0anMvDWHlNRoWUojhBBCHIf2Hju7a+u445RZWocihBABQxImYkBz2l38719L0en1XP+3S34UFd1b6tt5/9+rKCmoZte6IkZNHYrZbOKc605i+oKxZAwfnMuRHA4X+/ZUU3Sgji0bi+nsdJCZHcfIMalcePEJZA9NwPQjWGYkhBBC+MKmymrSoiIZnZSgdSiDmuqDLjlqP48nhPiCJEzEgPbpCyvR6XVMnT+OuNQYrcPxqaaaNpb8dwNvPbWc1CEJVBTWMn5WDnPOn8LMM8djtgyuP2evV6Fofy0HCmpYt2o/ZrOJ2LgwxkzM4Dd3nUViUuSPIkEmhBBC+MPa0nKGxcZgNg6u4wkhhPgh5B1RDFguh4vty/bQWtfOubfM1zocn+lo7ebtp5ax9I1NhIYH43V7yRmXzs//egEjp2QPqqRBR3sPu7aWsX7lfpwuDwajgakzhvHrO88mMVkSJEKIgcmrKDi8bpxeLy7Fi9PrweX14lI8OL0eHF4PbkXBrfTe7/Z6j/zfqyg4vV48qhe3ouBVFNyqgkfxoqrgVDwoqoJHUVFUBa+q4lEU9DodTq8XVVVRUFFUFVWFSEswrY4eVBVUVACSrOHU2jqP9NnQ6XSkWMOp7+lGjw6DXk9skJU2hx2DTo9Rryc2KIROpxOzwYBRryfKHIzT68GsNxJuNqMCFoORYKMRk773X6vRRJDJhNVoIthkJMxsIcRsxvgj6tIWqFRVZU9NPVdNHq91KIOedMkRYmCRhIkYsNb9bysHthRz/i/PIDgkSOtw+p3b7eGj59ewY/V+tq8+wKipQwiLCuGPz17H0NFpWofXb+qqW1i38gAVpU2UFNZz0mkjOevCKeSNSsVglINoIYRvqaqKw+Ohx+umx+3C5nFjc7vo8bhwej10upz0eNw4vG56PG563G5ciodutwu7143D48HhdWP3eDDp9TTYu3F6v0iEODwePKpCckgodT1dx4whOiiYVqf9mPdFmYNp63OfDjDpDURZgrF5XBh0egw6HfpDyQy9TofVYMbp9aLX6dDrdOjo/TdFUai1dR4ZByDYYKLW1omq9qZQVFRMOgPF7S141d5kS2ZYJIVtzSiKikdVyI2MY3djPR5FwaMqjI1JYndjPQCjYuLJb2oEIMEaQmNXD0Dva9DdDcC4+CR21fVuPyUphYq2doZER+N0e4kMCiIjIgK9Tk9aZDgWg5Gk8FAigoKJDwslNsSK0SCfD/2pvLWdwsZmJqenah2KEEIEFEmYiAFJVVU+enoZXreXBTeconU4/a5gSzGP/+a/6I1GKorqmL5gLOf/bA55EzK1Dq1f1Ne0sWX9QT77cCdpmbEMy0vm0utOJHmQd/IRQvQfVVVxeD10Op10uXovHW7Hkf93uV3ogLqeLrrdLrpdTrrdLmKDreS31mNzu+h2u7C5XSRYQ6k9RjJjfFwyO5trj1w36fUEG03EBlvxqgrBBtOhWRQmwswWYixWEqxhBBmMWAwGggwmLEYjFr2RMLMZHb2zLswGQ++/+t5/jXo9Zr0Bk96A2WDEdOi62WBEp3JoFkfvTA6jTo8hwGZkqKqK89CsGLf38AwapTdx5PFgd3uwe3r/b3O56PG4URWVmekZdLtcGNETFRyM2WCgtLmNwuZmKlraKWttY1JyMturapmclsK2ihqmpKWwvaKW2UMz6ehxMCYliWCTicyYSDJio8iKjyI8ePCdRPG1jWUVnJiVQXrU4C0WHyi8qh6v2s9dctR+HU4I8SWSMBED0sEdZRRtL+X8W88gPCpU63D6jd3m4OUHPmbDp7tpb+pi2PgM/vjsdUyfP2bAL0fpaO9h9ZK95O+soLmxi1PPGMufHrmEmLhwrUMTQmjI5fHQ7nT0XhwO2p122p0OOg5d73A6sJiMHGxrpsPppMvVe1uXx4XL+0UzzRExcRS0NR65rtfpmJ6cTnlnG6EmM6FmCyEmMxHmIMbFJhNiMvdejGbCzWaCjL1LRayHbrMeXjpy6GI1mjDppbD0seh0OoKMRoL6+bDS6fHQYuuhxdZDW4+Dc0Z3YXO6yIyJQvWqVLZ0sKGoguq2DpwOD+PSk9hdVse0oenEhYeQFBHGuKxkclPjiY0I6dfYBps1xeUkR4QP+GONgUBBh9LPbYUVJGMihK9IwkQMSOs/2ELO5KFMP2eS1qH0m6JdFdx/0/PEJkXSXNfOlXcs5OzrZhNkNWsd2vemKAo7N5ew5L0dNNa3M2XmcK79xWkkpkRpHZoQwgcUVaXDYafVbqfFbqfF3kOX00ljj41Wu51Wh51go5H85kbaHHbaHQ5yY2PZ1lBzZAydDlTArDcQbrEQGRTE2PhEDDo9mRGRRJiDCLdYCLdYCDNbCDcHEWY2E262EGq2EGYyE2a2EGw0yZe/Ac5iNJIcEU5yxNcn1lVVpbGzm6qWDmpbOxidlkhtcwfNnTY+3rSf2JBgshNi8HoUhiXHMmt0FhOHpxIcNHA/W/uby+ulscvGhRNGax2KEEIEHEmYiAFHURRK9lTitDkYPiFb63B+MEVRWPzSWp65622yR6TS1W7j0cW/YdjYdK1D+94623tY+t52Nq8tJCE5krMvnsrICRny5UWIAUhRVdrsdpptNppsPbQ67NR3d9Hc00OzrQeX6qWsrY0Wew+tdjsJoSHUdHUeeXxeXBx13V1EBQUTHRxMXlw8ebFxRAUFERkUTII1hOvHTSTCEkRUUDCRliDCzBasJkl4iG+n0+lIiAgjISIMslM569B5FFVVqWvtIr+8jpLaFsrrWlm2rYj3Vu5maFIsU3LTOWF0JpNHpv/o66Hsqqpjf30Tk9JTtA7lR0GKvgoxsEjCRAw4+zcXs+XTXVz2h3MH/MG0rcvOk3e8wa61hYRGWBk3K4crfnMm5iCT1qF9LxUljbz/ynq6uxzkjUnj7kcuJTzSqnVYQohjUFWVDoeT+q4uGrptNHR109jdjUdV2N/URFN3N402G2lRkWyprj7yuBGJ8VR2dBBrtRJrtTI0JoaJycnEBFuJsVqJDwkhwmIhOthKdHAwERYLRoMsZRH+pdPpSI4JJzkmHCb23tbc3s3G/ApWbj/I0o372bS7nLT4CGaNH8Jp03OxBv84Z53sqalnZnYG4UFS+0UIIfqShIkYcDYv3kHykARmnDNZ61B+kKbaNu6+9ClMQUbcLg+/evwKps0bq3VY38u+XRW88e/VhEUEM2POSKaelIvhR37GTgitddod1HV1U9vRSVuPnbK2Nuo7u6nv7MJsNrKlshqHx0Ow0UiP14NBpyM2JISJqUkAjExIYHZICGmR4Vw3cSJxIVbiQkKIDg4myDQwk7rixy02MpSFM0eycOZIWjttLF1/gE/XFPDMf9fyv892c8mZE5kzM2/An4z5rpYUFHHisEytw/jR8E3RV6lhIoSvSMJEDDgNlc3EJkcxZGyG1qF8b+X7a/jDRU+QnBVPe3Mnjyz+NWlDE7UO6zvbs7WMN55dRbDVwkXXn8TI8QP3ZyLEQKKqKh12JzXtnVS3d1DT3onN5aKgvoGaji5qOzqJCbVS3toOwKikeGxuN4nhoSRHhDM0LppThmaTGBZKYlgocSEhxIRYA677ihC+Eh0ewiULJnLhvPGs3FjEZ2v38/6S3ezeV8MNl8wkIjxY6xD9or2nh3abnelZA3cZsBBC+JIkTMSA0tHcyao3NzL3qpMG7Bmg/dvL+PtPniPIasag1/HQh78mImZgdfo5WFDDR69voqO9h2tvm8fQEclahyTEoONVFOrau6hp76CspY2q1g6aum0cbGqhqq2DbqeL0SkJ7K1tIDbEyri0ZIJMJmYOySAlIpzUyHDiw3oTJBFBlgH7nimELxn0eubMyGXGxGyef2MDazcdpKa2ld/8bB7JCZFah+dzm8uqabPZGZ0y8E7aDFS9XXL69/24v8cTQnxBEiZiQNm7rpDIxAgmzx+YS1f2byvlP39+j/amLmacOY5bH7p8QNUraahp44NXNlCyv5ZrbptH3jg5IyXED9Hb4cNGWXMrFc3tlDe3UdHSBjpYV1yBx6swLj2JoqYW0qMiSIuOYFpWOhdMiCA1KpyUiAiSo8IIliUyQvwgwUFmfn71bIZnx/P6+1t57N8r+N0t84ka5O2Id1bUMikjBYtJvhIIIcSxyLujGFAqD9QQERPKiKnDtA7lOyveW8VrD33Cvi2lnHnNidx03wUYjAOjEKLT4ead51azZ2sZZ18+nRvvOD0gz1Y7HG52by9n6oyB9/vRV1VNK3UNHUyZkKV1KKIfuL1eqlo6qGhqo7C+mbKmVurau9hf10iPyw1ATnIcJoOe9JgoRqbEM390DhkxkaRGhRMdYg3IvzkhBpvTThyBwaDnzf9t4z+vreNXN81Frx+8f3srD5Ry2QnjtA7jR0VBj5f+Xf6oIDVMhPAVSZiIAWXLpztx9riITYnWOpTvpK6imf+7623yNxWz8NqT+Mmfzx8wyZLt64v47N3tDMlL4r5nr8FsDry3jbbWbhZ/sIMP3tlGR3sPL7zxU1LTY7QO6zsrq2hi5foiKqpbWLm+kOhIK+8+/1MpoDuAKIpKdUsHZY0t7K9toqS+BafHw5rCcjxehYSIUCwWI1lx0YxJT+TsiSPIjIsiMzaK6JBgSYoIEQBOmZFLWUUzuwqqWLF2P6edNELrkHyipq2DqtYOpmanaR3Kj4oUfRViYAm8bz5CfA2P24OqqAOuO05Xu42/XPss1SX1zLnoBK6/57wBkSzp6rDz0qNLaWns5KY/LCQhJUrrkL6irraNd1/fxPrVB2hp7mb4iGSsVjPNTV0DJmFSW9fGstX7Wbm2EEuwif1FdWSlx2Aw6BmSGU9nl52oyIExJby5vZutBZVYzEZOmTxc63B8zuZwUVzXzL7qRopqm+hxulm5rwS7y8PwpFhcXi9DE2MYlZbIgvG5DEmIISM2Uqa+CzEAXLZoKgVFdSxbd4BTZg7Ozm+bS6oYkRjPsPhYrUMRQoiAJUdtYsCo2F9D4bZSZl80XetQjpvXq/CPn76A2+Vh2NhMbvrLBZgtgV9rYPvaIpa+t42ps3M55azxAXfWu662jddfWEtxUT0lRQ2MHpdOQlIkF14+nakzhgX8gW1Xl4PV6wv55LO9FByoJSsrlrLKFkblpTBjylBOmjaMGVOHEhYapHWo38jp8rDnYC0bdpexJb+Crh4nDa1djMhOGHQJkw6bg/3VDeyvaqS6pYMtRVVUNreTER9Jl9NFTnIcozISuXPRqQxLiiU7IVoSI0IMYEEWExPGpLNheym791UzYfTgq9m1ubSK9JjIQb3kKBAp6FFkSY4QA4YczYkBo2RXOeZgE8PGZ2odynF7/eFPcTrcdLZ285f/3kxIWGC3KXS5PPz3qRWU7K/llj+fR1xihNYhHaW5qZPXn19HdWUzu7aXM2R4ApOmDuHiq2YwJsBbGiuKyo5d5axZX8Sny/Jxu72kp8dg0OsYlZfKxYumMHPqMEJDLFqH+o3qmjrZsKuUvQdrWbn1IKkJkRTXtAAwLieFqHArk0dmoKpqwCXajpfd5eZAZSP5FfUU1zWzo7iG2tZODEY98ZGhjMpI5OypI8hLSyAnOZbYiIHV5UoIcXxmTx/O+u2l7CioGnQJE1VV2V/dyLUnTdI6FCGECGiSMBEDRlNNKxExYWQNkIOW3esKWf7WRppr2vjb278gPjWw66401LTx0iNLyRyeyL3PXIVeHzizNHpsTt55bSO7d1Swd0cFoWFBnDBzOJdeM4vckSlah/eNWlu7WbIsnx27Kti2sxyAlJQoQkIsnHX6OGacMDSgl9yoqkphWSNrthazr7iOzQUVAIzNTcHp8tBjd3HOyaOZMjKDiSPSiAzwpGBfqqpS29LJ/ooGthZVs7esjiCLkZ0ltSTHhDNjVCbnzRjNqIxEhqfGEmEdWM9PCPH9pSZGYTDqOVjRqHUo/a68uY2SxlbGpSdrHcqPjlfV4VX794RCf48nhPiCJEzEgFGwvhC9QU9IuFXrUL5VV7uNf97yErYuB9fdfR5jZ+RoHdI32r25hGf/9jE33bmQ0ZOztQ7nCEVR+HzJXt59dSM11W04HG5OnDOCcy+awsixgZ04O1BYx7vvb6O8vJniskZUYPjwRMaOSuX0eWPIyozTOsSv5fUq7C2qZd22ElZsKCQpPpxdB2pQgeSkCBJjwzl5yjDuuPY0MlOiB9RMEq+iUFTVxK7iWgqrGtlQUE5zZw9jhyRhMRuZMTKTcUOTyUtPIDos8N9rhBC+ZQ0x09DSpXUY/W5rSTXTstPJiI3UOhQhhAhokjARA4bJYmLs7IFRqf6ZO98mLjmKrAgrZ99wstbhfKOlb2/hs3e386f/u5rYAFqCU1bcwBMPLMbW5aS8pJEZp+Rx2hljOWHW8ID9gu71KmzeUsrrb2yko6OH6tp2AGaflMOsGcOZOX14QHYZgt6ZFgVFdazbXsInKwtoabcxMieJhpYuLGYjp83IZdakIUwZk0F46MCZZeFVFIoqm9hX3sDaPSXsOlhLakIkrV09jB2azPVnTGXskGSGpsRiCKBZVUKIwBAVGUJ1UweKogTUzMsfanNxFTGh0q5cC14ftBX2Sg0TIXwmMI/chejD3u1g0+IdXPCrM7UO5VttX7mfsv211JY18fTKPwZsAVKv18tz//iUrg47f3vxeixBgVGM1ulw89pzqzmQX0PBriqG5CRy461zOevCKZhMgdldyOXysHr1AV56eR0ul4fWdhtms5GLL5zK3FNHkpUVuLNJSiubWbZmP2XVLazdVgxAQlw4keHBTByRxnUXTGfCyDRMA6CzE/Qmfirr29iyv5Jt+yrZVVJLS2cPU0akkxgTxq8vOZkJw1NIjg2c5KAQInBZLEZ0OvB4FMzmwPw8/65UVaW92865U0ZqHYoQQgQ8SZiIAaGutIHRs3LJGhXYyzBcDhdP/eEN6sqbufnvF5OUEZit+jxuLw/d8RYZwxO4/o7TA+as2f69Vbzy7Cp2bilFByw4ZwJX/GQ2MbFhWod2TE6nmxUr9vHCi2txOt14PAohIRZ+dtOpzDl1JOHhgTkTo6vbwYr1hSxevoegIDM7C6oIDjIRGxXKjElDOG1mLqNzUzAGaLKvr267k+37qtiwt4xNeytIiA2jurGdKSMyuO2ikxg3LIXEmHCtwxRCDEAeRcGtqBgHSNL4eJQ2tLKluIp7z5+jdSg/SoqqR1H7uUuOKjNMhPAVSZiIAaGmuJ69aw9wzZ8u1DqUb/TuM58TZLUw6eQRzL8sMNsfu1wenrjzXYaOTGHRdSdqHQ7Qm8B59d+rWL2sgNqqVk44cThnnj+ZydOHaR3aMblcHpYvy+fFF9fS3m4jIiqUyAgrV1wxg5NOyg3IZTeqqrIrv5qPl+9h1YZCcoYmcqC4gfSUKE6dmcu8E/OYNDYTc4DO4umrsq6NdbtKKattYfG6fRj0OqaPy+KiueOZOjKD7JQYmWoufvRe+2ALwRYTiXHhpCRGkpwQGbAz9QJVZ48Tj6oMqta7uyvqOCkvi9QYmWmnBVmSI8TAEnhH9UIcQ3tTJ6Nn5pKUnaB1KF+rpaGdrcvzKdtXw8/+eiGGADwb5XJ5ePZvHzF0VCpnXzlD63AAqK1q5bV/r2L54t2kZcVy+rkTueHWuVgDsL2uoqis/LyAF19YS21tO1nZcYSGBnH1NbOYMWN4QC6/6rY5Wb56H+8s3kFldSvDhybgcnuJiw7jjp/P4+TpwwmxBt5r3ZeiqOwvrePzrcWs3VFCh82BNcjM/Om5PHTb2YzPTSXIHBjLyoQIFLMmD6Wmvp3ahnYOFNdTWNxATGQIXV12sjPiyB2WyNCseOICdBZfIKhoaMUUgEnwH2L9gXJCLBZJKgshxHEYXJ8AYtAqL6imaEcZUQmBezbkjUeXsn97GWdefSKjThiqdThf4fV4eei3b5I7Lj1gkiWb1xTyj3vew9HjIndUCpfdMJspM4drHdYx7dpZwSsvr2PXzgpGjU5Dp9Nx+RUzmDUrJyATJZXVrXy6Yi/vLd6J3eFmSFYcEeHBzJo6lLtuO4OM1BitQ/xWXkVh1/5qtuRX8MnqAsLDggkONnPmrJGcOHEIGckDq0OPEP6WnhJNespXW9p32RyUVzRTWd3Ks6vX0NzcxbAhCWSmxzB+TDpJSZH+DzYAeRWFstpWTpwwROtQ+o2qqjhcHuaOzdI6lB8thf5vA6z062hCiC+ThIkYMKbMHxuwX44aqloo21dNclYcZ18/W+twvkJVVR676z1yxqRx7tWztA4Hr1fhv/9ZzSvPrGL0xAxMZiN3/OU8IqNDtQ7tK+rr2nnjv5v46MMdjB6dRnR0CHPnjWbe/NEBt6ZdVVV251exZEU+n64oACAzI4aw0CDOXziR6VOGYDYF9tu+oqjkF9WyYmMhq7YdpKXdxhknjeKac0/gxElDiY0KvN8RIQaasJAgRo9IZfSIVM6YOwaAxqZO8vfV8PxLa2lr7WbihEymTM5myJDAndnpa5X1bURGWsnJHDyvQXljG2v3l/HrswNjSa4QQgS6wD5yFuKQgg2FxKcHZgFVgPefXUnBllLmXDiV1AA8uPzvvz4nJj6Mc6/RPlnS3WXn/t+9Q31NG5YgE2MnZXHZjScFTOHZw9xuL598vJNnn/6cxKRIgoJMjJuQzv0PXESw1ax1eEdRFJWNW4r5ZFk+6zYdBGD4sESyM+M4f+EEhgXg72RfZVXNLF13gAMl9WzNr2DCyHRuvHAG08dnExVu1To8IQa9+LhwTjkpnFNOysPrVdi1q4LPV+zj3be3MmpUCiefMjLg3vt8rbCqkaS4cDKTorQOpd/kV9YzNiOJzLjB85wGGgU9Sj/XMOnv8YQQX5CEiRgQImLDyRiRqnUYx9TW1MmyNzcyaupQLv7lfK3D+Yp1S/ZwML+aO5+8QvMZOg217fz5V2/gdnlobenm7gcvYtKMwCvsemBfLQ8+8DHNTZ2YzUYyMmL56wMXkpgYqXVoR1EUlTXri/j4091s31OBoqiMG53G6JGpnHvmeGICcMbOl3XZHKzcWMSHy/dgMhtxujzMm5XHH382j7hoqakghFYMBj0TJ2YxcWIW9h4Xq1fv57GHPyV7SDxnnj0B6wCoe9QfSmpbaGjrIjcj8JPOx2vDgQoSwkM1Px4QQoiBQhImIuA5epzsWlXAhFNHaR3KMa18bytup4foxAhSsuO1DucoRXureevZ1dz/0g2a19koLarnpX99TvGBOobmJfPoi9eTMSSwXi+n082Lz63mnTc3k5UdT3iElVt/tYAJkwJrrbeqqmzcXMKb727hQFE9TpeHKZOymDg+k4ULxmINDtyzwKqqsntfNYs/z2fFhkImjU5nwsg05p80kuwAnkUmxI9VsNXM/AVjOe200axeuZ/HH1zC9FnDmTU7d9B/6V6+7SDo9SQMkgSuqqrUtXYyd3z/1wrbsWofE2aP6PdxByOvqsfbz22F+3s8IcQXJGEiAl5rfTuRceHEJAfe9FGXw82bj3+GJdjMeTedonU4R+lq7+GVx5byu0cuISQsSNNY9u+p4s+/epPW5i5OWTCan/x6AZHRIZrG1Ffh/loeuO9DomJC0Ol0zJiVw8WXTcNiCazOK3vzq3n+pTWUV7XQ1t7DtKlDmDIpm9PnjcESwJ0cbD1Olq3ez7tLdlJZ08rC08Zw368WMnV8FsYALJorhDiawajnlNNGcsL0obz6/Fq2by7hhp/NITRc288XX6lp7qDb4eLcWYF5sub7qG3tZGdJLXecN7vfx37/X8skYSKEGJQC9+haiENa69ppb+4mNgC7emxenk9MYgSWYBM54zK1DucIVVV58eElLLjoBJIztD1rv2dbGXff8hopmTEMH5nML+86i6AAmgGhKCpvvb6RtasOUFXZgtGo57F/XUXeiBStQztKTU0bz/xnJZVVLVRWtTJ5UhZTJ2dz5unjAjpRUlXbxuLle3jv012EWC1csHACp83KIy5mcJyxFeLHxhpi4cZb5rB1YzGPP/gJ1//sVOITA7eD3fe19UAVcVEh5A2igq+7SmsJCzYzPCWu38eOS/1qNyZxbAo6FPq7S87gnu0lhJYC9yhbiEPaGjoAiA7AlsIfvrCGsgN1/PqxK7QO5SiLX9+E3qBn+mkjNY1jz7YyHrr7fcIjg0lJi+E3fzkPUwB9uW9vs/HEQ5+yZ3c17W02rrz2RC687ISAmlVit7t45fUN5BfUsDe/mtzcJK6+YgYXLppCcAAlnr5MVVX27q/hk+V7+WRlPqNzU/jjLxYwY/KQgOssJIT4fiZPG0pEpJUnHvyEW393JjGxgysJumTbAUrrW5mam651KP1m28FqJg1Jw+CDIuv15c39PuZgJUtyhBhYAuebixBfo7utm/TcZKIDrOBmTVkTej0kZ8Ux84xxWodzRE15M/nbyrjt/gs0jePA3ireeG4NDbXtzD93Irf88UwMAfRleX9BDS//ZzXbtpQyamw6v/3jQiafMETrsI5QVZU1awtZtryA9ZuKiY0N5YwFY7j2qllEB2gxV1VV2bitlE+W72XdlmJOnpHD//3jcvKGJWkdmhDCB4bnJXPJlTN58ZmV/OKOMzCZAuc9/odo67ZT39bFubNGYQ0KzMT097GzpIaFU32zbCY+PfBmAQshRH+QhIkIeC317dSVNRESEVitRT9/dwt7NhSz4PIZWALkTL+iKPzngcVcfNMpWIK0myVRVdbEvb98nfZWG+dcegI33D4voJIlq1cU8MB9H2K2GJk2cxi/+PXpxMYFztnRxsZOXn51PZ98uhudTsfM6cO4/NLp5AxP1Dq0Y1IUlXWbD/K/T3eRf6CW+aeM4vWnryM5MfDqDglxmMfjpbPTQXd376Wnx0lXtxO73YXHo9DZacfp9GAxG2lp7cbrUQgNtdDa2o3BoMft8qLTQWSkle5uJzExodh7XISGBWHQ6bBazQRbzYSGBhEeHkx4hJXo6BDCI4IHVbHUEWPSKC1p4P03NnHhFTO0DqdfrNlTQlVTO9dnTtU6lH7T0mnD7fIycWj/LzdVVZXakoZ+H3ew8qLH289tgPt7PCHEFyRhIgKeCoybnRdQB5iqqlK2r5Yho1I59fzJWodzxJK3tpCaFUfO2DTNYmhp7OQPP32ZuIRw8sakcf1tgZMsURSV5/9vJW++soG8USnkjkjhxptPDZhlIqqqsnjxLl55dQNNTV1MmpzFybNzmTd3DHp94Pz+H6aqKhu3lvDGu1uoqGll/imj+P2tpxMboDNgBrOGpi42bC9h47ZSzlswjhMmZmsdkiZUVaW720ljUyeNjZ20tHbT0tKNy+2lsrKFtnYbUVGh7NxZjq3HxbAh8RQX937RS06OpLa2HYC0tGiaGrowm43k5CRRXd2CwWAgKzOGmpo2giwmHA43qJCQEE5VZQtp6TEcOFBLZKSVqvIWzGYjDrsLo1GP16UQGRlMe1sPw3OS6OzoYdzETFRFJXtoPIlJkaRlxpKUEhUw70ffxfyF4/nbne/S3NQVUMnn7+t/m/YRFxnKSWMCZ9bhD7WrtJbmThsj0vq/Jovb5Q7IZdNCCNEfJGEiAl51YR0dTV1ah3GUkvxqNi7dQ3xqNCMmBcYXk/bWbnZvLNF0KY7D7uLxv3wEgN6g5477z8cYIFO0XU4Pf//z/2ht7sJkMnDG2ROYd8ZYrcM6oqmpi+efW81nn+0lb0QKo0encfPP5xAZGVgzqw7bW1DNf15eS31TBydOH869vzub6KjA6nw0mKmqSkl5E9v3VLB01T7qmzqw2d0oikp8TNigTpg4nW5q69qprWuntc1GaWkT9Q0deL0Ke/OrexMZQEREMJ2ddqKiQhgzOg2ny0NychRpKVHkDk8kPDyYyMhgQoIthIYGYbWasIYEYQ02Y7EYf1CSXlVV7HY3PT1OOjt66Op00Nlpp6WpC6fdTVlJIx6Plx1bSlm/aj9p6bEcyK8hJy+JpJQoYmJDGTspk7wxaUREBv7fldFo4KQ5I1n52V4uuGy61uH8IFVN7bR19TAmO4mwYIvW4fSboupm5k/KwWzq/0N/t8NDT5ej38cdrBRVh6L2c9HXfh5PCPEFSZiIgNfR0kVYTGCdsd61rpARk7MZNXVIwMx8eenhpcw6fQxBVm2WB6mqyguPL2PzmkKyhidw72OXBkw3HFu3g0fu/5hd28tBhQceu4zR4wKnkN+mjcX8/YGPsAZbSEgI55KLT2DGzOFah3VM1bVt/N9zq6ipbycvJ4k//voM4uPCtQ7rR0FVVQqLG1i5vpDtuysoKm1gVF4yB8sa0et1zJ6ew7CseGZOGap1qP2ivaOHiopmKipaaGnpZv+BWqqqW0hIiGD33moAxo9Np9vmJDEhgqyMWKZMyiIuLpz4uDBiYkKJiQ7FoEHbat2hJTlWq5nYbymG2mNzUl3ZQunBBsqLG+lss7H8kz2899pG8kamkpQaxZCcRKbNziMlI3DrREydMYy/3PkO5186LWA+F7+Pjzbvo7yxjd9cMFvrUPrVuvwyTsjzzeeey+kmTGYWCiEGKUmYiIAXEm4lQePWuH0tf3sLFYV1XH/nOVqHAkBZYR2K18uMuaM0i+G9Vzfw8VtbGTs5i5t+ezpRAdI2trOjh0f+vpj1qw4wZkIGv/jN6aRnBsbvk9Pp4ZmnV/DpJ7uJiw9jyNAEbrttPhEBVq8HoKfHySv/3ci+wlqCg8zc/duFZAbY3+VgVV7ZzLJV+9lTUM3u/b2JgtEjUgiymEiKj+CseeOYNjGbiPBgjSP9frxehaqqVoqLG6hraGfv3mrKypqIjw9j/4E6goJMTJ82jPDwYObPG0NWZhw33XgKyUmRhA/Q5/xl1hALw/OSGZ6XfOQ2h93Fgfwadm8to7KkkQ2f7+d/r28kPjGCBYsmceLcUZgDrBhpULAZS7CFhvp2EpMGZv0ir6KQX1FPQmTooOqOY7M7CbGYmDA01Sfje1xeXA6XT8YejBQf1DBRpIaJED4jCRMR8Ip3lRObEq11GEfUVTQRGmElfVgiORMytA4HgFceX8YlPz1Fs7N6+3ZVsnVtEaqqsvCiKWQN6/810t9HR3sPd9zyCvV17UyYksWv/ngWcfGBMRuivr6dP93zPpYgE3q9jssvn8HceaMD7sysqqqsWnOAz1YUUF3bxi9+OofJE7O0DmvQa22zsWZDER8t2Y3JZGD/wXoAhg2JJzMthlNPzGXimIyAaoF9PFRVpaamjZLSBvbuqaawqJ4gi4ntO8oxGvXMOjGHyEgrixZNZmh2PKlp0cTHhQdkDR9fCgo2M25yFuMm9/6t1VQ2s3lVITs3FfPKUyt489nVLLzsBBYsmhxQrdozsuIoL2kasAmTdQVlbNhfwU2nn+CT1rtaya9oYHtRNQ/euNAn47tdbs1mt4of5qmnnuKf//wn9fX1jB07lieeeIIpU6Ycc9uCggLuvvtutm/fTkVFBY888gi33nrrUdtkZmZSUVHxlcf+7Gc/46mnngJg9uzZrF69+qj7f/KTn/DMM8/0z5MSop8FzqesEF9DVVXCogNnDff2lfsp2FLC3ItPQB8AB1T528qxhlgYNso3Z46+TVennQfvfo/aylYu+8lsZs4ZqUkcfXV22LnjllewhlhITIrkd/eeS2SA1NjYubOc11/dQFFRPdnZ8Tz19NVkZcVpHdZX1Dd08OLL61i7vojLLpnGn+86d9C0DQ1EHq/Cpi0lrNt4kM8+L2BkXgrFpY2EWM2cNjuP2TNymDwhE4t54CRJnE43hYV17N1bzf59NRworKOt1UZGZizWkCCGD09gRF4KP/nJyWRkxMrv19dISY/lvCtjOfuyaWxdU8iqT/awevEedqw9yPnXzWJUgCQx4xPCaW2zaR3G97Z810F0Ojj7hMD4HOsvB6uaGD80hfCQIJ+M73J4fDLuYKWoehS1n2eYfI/x3nzzTW6//XaeeeYZpk6dyqOPPsq8efMoLCwkPj7+K9v39PSQnZ3NBRdcwG233XbMMbdu3YrX6z1yPT8/n9NOO40LLji6vt4NN9zAn//85yPXrdbAm1krxGGSMBEBTVEUmqpaiAiQ5R0A5UV1BIdamDR7hNahAPDJG5u46KaTNdv/v+5fjF6vZ/Ks4Vx8/YmaxfFlPTYn/3pkCeVlTWRkxvLAE1cETLLkk8W7eP651bS12jjv/Mlce91JBAdIrZfDFEXlw4938vZ7W0lLiea5Z64lMVE6IPhKQ2MnH3+6iy3byyg82MCY0akoqkpYmIU//OoMZp0wFKt1YBSfdDrdFORXU1hYx6aNJRQW1pE3IoWWli5GjEzhmmtOZNiwRLKz4wZkNxitGQx6Tjg5j6mzc1nz6V7efWENT977AfMWTeasK6ZhMGj7mgZbzbS0dGsaw/dV2dTOx1v3s3DqCJKiA2MmYn9ZX1BOdqLv6t+43R6MPigmO1h50eGlf2fNfZ/xHn74YW644QauueYaAJ555hkWL17M888/z+9+97uvbD958mQmT+7tDHms+wHi4o4++fP3v/+dIUOGcNJJJx11u9VqJTEx8TvHLIQW5N1NBDR7txNFUQkJkJoOXo+Xz9/bhtPhZsyMYVqHw4HdlbhdHjKGarMEZsPK/RzYW0Vrczd/fvwyTAFwwOR2e/nnff9j/epCps0azq2/OzMgkiWKovL6a+t54bk1ZGbFcd55k7nkssArjtjY2MkLL69j/caD3PqLuZx8Ym7AxTgYqKrKrt2VvPe/7ZRWNFNT28aY0WkkJkRwwuRs/vjrM0kIkOVj30RRVEpLGti6pZTt28vweBTy91YxPCeZESNTOP+CKYwclUK0FITsVzqdjpNOH8PYaUN49fFlrPtsL0317Vz3m9M1KXJ7mKKqA/b94o3VO1FVmD8xV+tQ+pXHo5BfVs/Cab47yeNxeogIoJnA4tu5XC62b9/O73//+yO36fV65syZw8aNG/ttH6+++iq33377V94XXnvtNV599VUSExNZuHAhd911l8wyEQFL+283QnyDns4ehk/MCpjq6wf3VmHvdjJ0dCoRARDT5x/u5MIbZ2uy766OHh6/70O6O+3cctdZJKdr371BVVUeuf8jiovqGZ6XzM2/XkBUABzEud1e/nH/R2zaWExGZizXXHsiM2flaB3WV6zfcJAH/vkxEydk8sKz1xETYN2pBgO328vKVft5692tKKpCWXkzY0ankp0Zx1lnjGPi+MyAr9dhsznZvrWMosJali7ZS3u7jawhCYwclcLkyUP481/OHxTFWAeCyKgQfnbXWbz0yGesX5aP2WLi6tvmaRZPV5eDIOvAWTJ2WLfdyep9ZZw2fhgn5AyeYq8ARdWNqF6VcUOSv33j78nlcOG0S9HX4+XLJTmdnZ1H3W6xWLBYvjpDsbm5Ga/XS0LC0SfcEhISOHDgQL/E9MEHH9De3s7VV1991O2XXnopGRkZJCcns2fPHu644w4KCwt57733+mW/QvQ3SZiIgNbT5aBoexnWMN+su/2u9m4sJjI2lGlzR2sdCrWVLVSXNWtWu+SVp1eSmhmLwaDntIXjNImhr7df20jZwQZsXQ7+8uDFAVHg1eFwc++d7+B0ejCbDPz6N2cwYmSK1mEdxel08+yzq/h48S5++cu5LJg/ZsCeJQ5UdruLT5fu4Y03NxMXH05JaSMTxmcw45JhnHXGOOICvDVza0sX69cdZMO6QnpsLooP1jP7lBFcefUsJk3JJikpUusQf7T0ej1X3z4Ph93FphUF5IxJZdqp2tTgaGrqZIRGn0k/xPubCqhu7uDqUycFfMLyu9pVXEtYsIWkGN+9x3i9ChHf0j5b+EdaWtpR1++55x7uvfdeTWJ57rnnWLBgAcnJRyfrbrzxxiP/Hz16NElJSZx66qmUlJQwZMgQf4cpxLeShIkIaD2ddgBCAuRsZcHWUtqbu8keqf0B4eqPd3P2FdM12XdRQQ0fvrEZk8nAv97+WUAUv922qYT/PLkcs9nAXx+5jIzsrxYs8ze73cWTjy1lx/Zy4uLCeOTJK0hPD6xWvLW17dz7p/dITIjk6X9dRXYAvG6DSU+PkyVL9vDyKxvIzIqlubmLzIwYfn3bfE47dSTmAOpw0ld7m431a4v4/LN89u6uZMSYNDIyY5l5Ug5jxqYPuA49g5lOp+PaXy/gTz99iU/f2MK4aUMJ1qDuTdHBBuafPtbv+/0h3F4vmw5UEBkSxJmT87QOp99VNbRx2qRhPk2CO3tcOGxOn40/2Hj5fjVHvm1MgKqqKsLDv0iOHWt2CUBsbCwGg4GGhoajbm9oaOiX2iIVFRUsX778uGaNTJ06FYDi4mJJmIiAFLhHakIALqeL0TNyAqKGiaqquBxuRk7JJndipqaxOOwu1i/L58KfzPb7vhVF4cm/fcywEclMPyWPtEztu7vUVLXylz++zaix6Zw8bxRjNf75QO/Mkgf++iHr1hQybcYwbrltPvEBMOPly7ZtK+PJJz4jb2Qqv7jltIArPjuQORxuln2Wz/PPryY6OoTOTjtGvZ77/nQe004YFrBnse12FxvXFrF5/UFWrdxHVFQocxaM5pobZjNidGrAxi3AEmTizEtP4L9Pf86aT/cwb9Fkv+5fVVVaWrpJTIz0635/qKU7ili3v5yfnzGd4AHUgep4qKrK5zuKuWaBb38XVFUhTJZwBoTw8PCjEiZfx2w2M3HiRFasWME555wD9B7frVixgptvvvkHx/HCCy8QHx/PGWec8a3b7tq1C4CkpKQfvF8hfEESJiKg2dp72Lu+EGu49gmTuvJmdq4tJCU7nkiNu/ZsXrmfU84er0lxvw0rD1BZ2khoeDDnXjbN7/vvy+X08NKzK1G8KqnpMSw8b5LWIeFyebjnj+9QUdbEqNGp/PJXC4gNsOnKH7y/jVWr9nP66eO44MIpsgSnn3i9CiuWF/Dcf1bT3e3AZDYQFhbMQw9ewrhx6QH5OquqStH+Oj75cCc7t5fR3eXkxJNz+efjlzNqTLokSQaQE04dwfsvryd/a7nfEyZl5c3kjkjWtOjsd6WqKi+u2IbFZGDRdO2X2va3mqYOEqNCGTvUt8tAe7ocuHqkhsnxCpS2wrfffjtXXXUVkyZNYsqUKTz66KPYbLYjXXOuvPJKUlJSuP/++4HeIq779u078v+amhp27dpFaGgoQ4cO/SIWReGFF17gqquuwmg8+qtmSUkJr7/+OqeffjoxMTHs2bOH2267jRNPPJExY8Z836cvhE9JwkQEtJ5uBwBBIdq31CzZV0NYlJWccdoXhPv0zS385h8X+X2/LpeHfz+0BLPFyA23zycoAGYkvPh/K1m1rIAJU7O5+dcLtA4Hr1fhwQc+xnboy/Kd954bUMkSr1fhtVfX887bW/nDnWdxwglDv/1B4rhs31bGW29sYtu2MvJGpBAbG8r1N85m3LiMgEyU9NicrF5ewAfvbKWmqpWJU4dw/U9P5YTpwzAHyeHBQKTX6xk5MYt9O8rp6ughzI+zM/fmV5GT47vCor6wdl8ZYcFmLpgxhpgw7U/M9LddxbWU1rYyLM23M0E9Hi8mWaI34Fx00UU0NTVx9913U19fz7hx41iyZMmRQrCVlZVHLbmura1l/PjxR64/+OCDPPjgg5x00kmsWrXqyO3Lly+nsrKSa6+99iv7NJvNLF++/EhyJi0tjUWLFnHnnXf67okK8QPJEZEIaKqiMnpmTkCcsTqwvYyuth5yxmdqGkd9VSvxyZHEJPh/eceqT/dgMhkIiY9g1mm+a1F4vLZtLmHlsnxyRiTz89vnY7Zo/5b2xqsb+HxZATGxoTzyxJXEBlAxT5fLw5NPLGPTxmIeefQyhmjUjnqwaajv4O23N/P+u9vIyU0iJTWaSy45gekzhwdkoqSupo0P3tzMhrVFeD1ezr5wCifPHUV8QoTWoYl+kJIRw4blBdRWtJAzxn9JgB07K7jx+tl+298Ppaoqzy7ZzL6qBu67fL7W4fjE/rJ6RmcnYfTxMZTL7kYfAMdpA4VX1ePt5xkm33e8m2+++WuX4Hw5CQKQmZmJqqrfOubcuXO/dru0tDRWr179neMUQkvaf7sQ4ht0tnRTU1yvdRgAlORXA5CtcYeTNZ/uYZwGswJcLg8v/+tzmhs6+fMTl2te6NXW7eDd1zfS0tTF5dedSFqG9sVUP3x/Oy/+ezVjx6fz81vnkpwSpXVIRzgcbu69610cTjdPPHklCYny5fiH8noUPnh/K6++tB6n001aWjSnnDqCs8+ZiMkUeB+vBw/U8dbL62lu6kKn0/HT2+YxZfpQjEaD1qGJfhQaHozH46Wj3ea3fdbVtdPQ2ElKcuC8532bzUWV7K2o56wpI0iNGZzvh1v3VTFvqu9b2LudbkxBMsPkeKnoUPq56Kvaz+MJIb4QeEd0QnyJo8eJRYNK/32pqorBqGfUlCFk5Wk75bhwTxVn/t3/tUNWL91LfFIkMXFhTJ45zO/77+u5p1awfXMpp589gdPPnqB1OOzaUc77b20hITGCS6+cQfaQwJm9Ybe7uPMPbxEZGcIdf1hIVFSI1iENeCUHG3jj9Q2sXLGP0WPTSUmN5robZhMVHXiv7f69Vbz67zV0dvSQmBrNTbfNI2fEwFo6IY6f0WIkLNKKqvhvn8tWFHDm6eP8t8MfSFVVFm89gFGv57rT/FvrxV86uu2U1bYwaojvC2kqioopgDt+CSHEDyHvbiLgJQTAzIGW+g52rCkkKSOWkDDtWhzXVjQDYA31bxLJ61V4499rqKls4c6HLtZ8mcG+vVVUVjQTFR3CpdfM0jyexoZOXntxHdVVrfz8trlMnJytaTxf5nS6eeLRpbicXn5523zCA6RF90Dl9Sp88PZWXvjPasxmI7l5yVxz3YmMGZehdWhfcXB/LW+/sp4Nqw6w4NyJ/Ow3C0hJi9Y6LOFjPV0OTBaT3z4nPB4vB0sa+PVZ2ieuj9eWg1V8uGUf504bRWbC4Pyb2FtSx8jMBEZm/fAWsd/GYXMSHi1dco5XIC3JEUJ8O0mYiIDW3WbD7fBoHQYVhXUAZOZq2/Js04r9TJvj/9ohW9cdJDo+jNCIYKafnOv3/X+Zx+Pl6YeXUri/lp/eNo94jZeWuN1e/nrPe+wvqOGiS0/g7ADo0nOYx+PliUeXUnywgQcfvVSSJT9QQ307L/1nDcuW7GXM+HRGjErlimtmYQ6wM6v1NW38783NfPDmZs6/fDr/eftmEgNoeZjwrcb6DvQGnd/qXK1eW0hoaBARA+T9RVVVnv5kIzodXDF74CR5vqtdhTV4vAqhfpilaw4yYQnRvgi8EEL4QmAd5QnRh8PuwmLV/kO4sqSRuJRozQu+bllzgN8/cqnf9/u/1zeyd1s5N//hTM1rlyxbvJvOjh6mzRrOWX5um3ksrzy/Bp1Ox/DcZK647kTNZ7scpigKDz6wmJLiBh546FLCA6A190C2aX0Rjz+4hO5uByNGpXD9TaeQp3E9o74cdhf/fW4N61fuJzUzlmf++1MysuO1Dkv4WUVJI2aLyS9JMlVVef+jHdx+yzyf76u/bD1Yxc7SWhZMzGFIUozW4fhMa4eNGWOz/LKv9sZOEtK1nw08UCiqDkXt32OF/h5PCPEFSZiIgObscWIJgNa11cX1NNW2EZccqVkM3Z12FEUlws/1J6rKm3C7PAzLS+bUM8f6dd992XtcfPrhTupq27ntjwsxGLVN3hTsrWbpJ3vosTl58j/XYgmgtoovv7CWzg47f/7bBUQHYF2NgUJRVN57czP/98RyckemMHZCBr/41QKCAyCR+2UbVx3gwzc309rczS9+fyZjJvnni5IILG63h8rSJoaPTPFLcnvbjnISE8LJzvJt29r+oqoqj320nvS4SG6cP1XrcHzG5fbw2eZC7r7eP4ksk8WIWYq+CiEGKUmYiIBmDQsmNFL7L3vtzd3oDXpSNDxbu2dLKdM1WI6z9P0d5O+s5KxLphKscQHe99/YTGFBDWeeO4lxE7X9Qmi3u/jHff/D6XBx823zyMgMnLNrqz7fx9rVhfzm92eSlBSpdTgDlr3HxVOPLmXpx7vJHZnCGWePZ/6Z47QO6yhtrd08/fdP6OywM3nmMM66eCom04+n643X46WhqoWGqhYq9tfSUt9OR3MXjTWteNxeTCYDeoMes8WINTSImKRIskamkj0yjdThSQHRsr4/7dhUgk4Hw0f6vqivqqo89/Jafvmz03y+r/6yOr+U/Ip6Fk7JIzth8M4uOVDegKqqjB3mn1lw7c1d6PQyw+F4edHjpZ9rmPTzeEKIL0jCRAS05ppWImLDtA6Dwl0VqKqqacJkx/qDzDnHv+utvR4vpYX1DM1LYr6f991Xd5eDvbsqMej1nHW+9nVC/vvyOmJiw0hOjWLu6WO0DueIkuIG/nn/R/zmdwvJ1bij00DW0tTFvb97i9aWbjKyYrn59nnkBNjruWVtIY/f9yGjJmRy2z1nk6DhDDh/UBSFysJ6tn2ez/6tpVQV1VNT0oDJYsLt8uD19Na70ul1qIoKOsCrHrkNVFBBpwODQU9IWBDDJ2Qx74pZTF0wblC0V16/Yj9BVgsTpvu+k9ma9UXk5iSRl6Ntba/jpSgqTy7egFGv56b5J2gdjk/tKqwhMTKMhBj/HD95XJ6AbKUuhBD9Qd7dREBzOd2YNF7m4LS7aKnvIDw6hFANi9rZuhwM83PNhB2bStixqYSheUlka3xQ/MFbW9i+qYQFZ48nc4i2dRnKSht557+bMRoN/PvVGwOmbkl3t4N773yHy66cyexT/T8babCoq2njjlteITomjKjoUO594EJi47RP3B7mcnl45akVbFi5n4uvP5EzLpgSML+D/a29uYsty/ay9NX11JY10t1hx+M6lBjRAeiITYrEHGQiJjmCtKFJve/VUSGEhAX1JlOcHrrbbbTUd9BY2UxNaSMN5Y3YOuxsXbaXHZ/nk5ARx/X3XcC00wduEdCWxk7KSxoZMjyR2HjfFnx1u7385+W1/OkPZ/t0P/3pk20HCDabuHz2eFJjI7UOx6d2F9Yy0g/thA/zerzoDYPzPcgXpIaJEAOLJExEQDNbTIRoXHm/saaNhLRoYhK068bS1tyFrcuBwc9nQHdtLSNnVAqz52s7g8LhcFOwq5LE5EgWXTZN01hUVeWxf35CfEIEiy6eQkJipKbxHKaqKs/930qGDkvkoku0fY0GssryZu64+WXikyIJDbPwx/vOJziAuj801Xfw9zveIjzCyj2PXkr6ICzqauuy8/nbm1j8whpa6jqwdfWgKhASHkx4lJVh4zKYOn8sOeMySRkajyXou/98VFWldG8lK97cwMq3N1Ff1sj91zzNolvmc+UfzxuQCaiP392G0Whgph+Wbr730Q5OmDyE7MyBUbvE5fbw1CcbaO3q4aHrztQ6HJ9SFJWOLjsnTx7qt3067S6MAdYtLJAp6FH6eQlNf48nhPiCvLuJgNbZ0o1B4/X49VUtNFS1kjs+Q7MY8reXM2pSpl/36XJ5+PTdbdhtLu5++BK/7ruvzz7exfbNpcyYnUO6xrVC1q0ppLvLgcls4PSFgXM2evXK/az6fB//9/wNmhfDHaiqKpq57463sQSZSEqO5Fd3nhVQ08wP7K3ivltf56QFY7jy56cSFAAFsftT+YEa3n58Kds+L6CjuQujyUhYVAhjZuQw5+JpTD51JBH9tMRAp9MxZEwGQ8ZkcNWdi3j2D//ls1fW8u7jS0kdmsipF8/ol/34S2tzF/t2V6IDJpwwxKf7am7pZvHS3Tz2gLafC9/F2+v3UNvayfVzpxAfEap1OD5VUddKfnEdd1w7x2/7jIqPwCQJEyHEICXvbiKguQNgSU5jdSsA8anRmsVwYHclJ5zi3yUW+TvKSc2IISjYQoyPp3d/E0VRWb28gFHj0jj/8umaxQG9SaRnn1xOfW07/3j88oBJTDQ3dfLoPz7h9jvOID6h/39Wa1cdYF9+Ndf+5ORBW1C0sb6DJx74hObmLk48JY9f/O6MgCoIumX1Ae7/7Ztce9t8zrxocC3BKdpZzrN3vUXx3ipcDjeqqjB+dh5n33gKk04ehdHHv3OWYDO3PHIVJouRj/79Oave3jTgEiYfvrmFwoIa/nD/BT7/3fjXcys5+4zxRAVAQfbj0WV3sn5fObFhVq4+Vfv6V75WWNbAqKGJZKf6r6htdXE9+gB6vwx0XlWHt5+X0PT3eEKIL0jCRAQ0t8uj+VmL9uZujCYD8SnaJUwO5tdwxc3+7USwbvk+igpqueF2/7Ql/Do7t5ayd0clOSOSGTkmTdNYPvtkF3FxYQzPSWL8xExNY/myF/+zhtmnjmDW7Nx+H7upsYM3XttA0f464uLDOe/CKf2+D611dzl49tHP2Luzkrlnjg24ZMmaJXt5+M73+M0D5zPj1JFah9NvmmvbeOSXL1GwsRin00VUfARnXnMS5988l8hY/ydpr7/vIqoP1tPa0OH3ff8QBw/U8sGbmxg3JZvJM3xb7HX7rnIamzr5w6/O8Ol++tPzn21hw/4KfrNoNmHB2nZ684fNeysIDbZg8ENb6cOsYUE+T2wKIYRWAueIUBy3p556iszMTIKCgpg6dSpbtmz5xu0fffRRcnJyCA4OJi0tjdtuuw2Hw+GnaH+YiNgwgkODNI2hrqIZj9tLrEbtWb1ehbjECIKs/pt+r6oqVWXNAEzzwZfw72LZR7vJG5XKORdp+0Xd5fLwzn+3sHd3FZdcGThnnzdvLGbd6gNccc2sfj+z3FuvZQmlBxs4+bSRnHXexH4dPxB4PQrPP7mctSv2cfK8kfz8NwsCK1ny6R4eu/cD7nnyskGTLFFVlfeeXsbNJ9/HzlX7CI20cs1d5/Hijr9x/b3na5IsATCajMSnxmC3OTXZ//fh8Xh54v7FhIYFc/XPTvXp7JKeHhcP/WsZN994CsYA+hv5JrWtnby2aicpMeFcMGO01uH4RUFxLWNz/Fsgvrm2ze811gayw0Vf+/sihPANmWEywLz55pvcfvvtPPPMM0ydOpVHH32UefPmUVhYSHz8V4v/vf766/zud7/j+eefZ/r06RQVFXH11Vej0+l4+OGHNXgG301jVYvmyx5aDp1t1CphUl3W5Pc6LhUljezdXs7wkSkkp/tvWm9fHe09rPl8H2aLiRkn52kWB8DyJXvo6rRz1nkTGTo8UdNYDnM5PTz58FKu+8nJxPig/fbnn+XT1NRBQmIkV1wza1C0Xe3r7VfWs/i97cyYncvPf70AcwCtw9+86gD/+utH3PXYpYyb6tu6FP5i67Tz0M+fZ/uKAnR6HfMun8lP/nIRQSGBcea/rqxJ80Lj38V7r2+iq8vBGedN8nn3sDc+2MKJM4aTO2xgtBEGeO3zHbjcXn5x1kzMAVSPyFcaW7qob+pknJ8TJh6XF+OP4PUVQvw4DYxTBOKIhx9+mBtuuIFrrrmGESNG8Mwzz2C1Wnn++eePuf2GDRuYMWMGl156KZmZmcydO5dLLrnkW2elBAqv26t55fUjCZNEbbrklB6oY0iufw9Qd2wqASBP4yUwq5blk5QazVkXTCIoSLtaNoqismlDMZ2dduaePlazOPr65KOdZA+N5/SF4/t97O4uB58vK6D0YCMLzhxLmoaJM1/Zsq6IF/61gglTsrj+l6cRovFsti8r2lvNM3/7iF/86ZxBkyxpbejgVwv+zpbP9hCfFsMDH/2aXz5yZcAkS5qqWynfX8uYmTlah3JcSorq2bi6kLCIYM7zcfewrTvKWL2uiKsvGjgduPaU1fHaqp3MmzCcueOHax2OX+wpqkFVIS/bv0n90Egr+gCp6TUQqKoepZ8vqiqvvxC+In9dA4jL5WL79u3MmfNF5XO9Xs+cOXPYuHHjMR8zffp0tm/ffiRBUlpayieffMLpp5/+tftxOp10dnYeddGKx+3V/Ky20WhgyKhUwqKsmuy/pqKZIXnJft3nnu0VpA9NYOIM/7UlPJbVy/ZRVd7M5GnaxrFlYzEb1xQx88Qccvz8s/g6nZ12XntpPQvOHOeTWVivv7yOLRuLmTp9GIsuntrv42uttbmLR/7yIaPGprPwgskka1jUua/2lm6e/cdizrpiGtMHyTKc7vYe7jr/EWpLGhg9fTiPr/gjOeOztA7rKP/67augUzn359rWbToe9h4Xrz+3hgP51fzsV/Ox+DCh3NFp59V3NvP7204n6Hu0cNaCqqr8891VAFx28oRBVST5m5RWtXDatFyC/Fwsv7GqZdAWBBdCCEmYDCDNzc14vV4SEhKOuj0hIYH6+vpjPubSSy/lz3/+MzNnzsRkMjFkyBBmz57NH/7wh6/dz/33309ERMSRS1qadrMMPG6PpjNMXE4PZftraWvsxGDQ5mDgYH4N6T6eav1lXq/C7m1lVJY2MWKsdj/7lqYuaqpamDpzGCPHpmsWB8DmDQdJSY3itAVjNI3jyz54ewvDcxKYOr3/k0l1NW0sX5LPyNGpXHDJVM2Tlr7wn8c+IzE5iqxhCUyfre1yry9TFIWXHvuMhOQozrk8cGrl/BCKovDAjc9SU9zAxFNG8Je3b9W8NlVf7z29jF1rizj92pOJTQ6c5NmxqKrK/z32GWtXHuCmX80nd1SqT/f1zyeXMmFMOrnDAmMp4vFYtrOIveX1LJiUy5isgbOE6Idat6OEqAj/n9xJG5aIXqNjpIHIi84nFyGEb0jCZJBbtWoVf/vb3/jXv/7Fjh07eO+991i8eDH33Xff1z7m97//PR0dHUcuVVVVfoz4aCazCbOGSzE6WrsBiIzr//oQx8to1BMe5b/2jRUljQwZnsj0k3MJDdNuLf/61Qdoa7GRnBqNXq/dgUB9XTuLP9iB0+XRfKbLYTabk/0FNZxzgW/ayy7+3w5UVSUqOpSxEzL7fXytbVpbRHVFC63NnVzz81O1Ducon3+4k71bS/nZXQsHzVnx5a+tZ9/Gg6TnJvP7F34acMUhV727hVf+9gE5E7O49DcLtQ7nW33w5hZWfVbA6edOYOH5k326ryWfF9DZaefyC07w6X76k93p5sH31jA2K4lbFmrbit6fum0OHE4PY4b7fxbk/q0lmnc0HEgU1ReFX7V+VkIMXvLuNoDExsZiMBhoaGg46vaGhgYSE4995ueuu+7iiiuu4Prrrwdg9OjR2Gw2brzxRv74xz+iP0bbOYvFgsUSGGvK7TaH3wuefllXq41RU7JJG6rNmTWXy4Ot278dGw7srWbvjgrO0rgrza7tZeSOTmHGydp26Vn9+T5Gj01n0glDAmbK8Scf7qSjvYdJU7L7feyaqlbefn0TJpOBq284qd/H15rL5eGjtzZTWFDDX5+8IqDqlrQ0dvLm/63ilnvPJSR04BQe/SaKovC//1uG3qDjtievwuznpQLf5oNnl/P6Pz4mc0Qqd7/y84DqkHQs2zYWs2bFPmLiQrn2Z6f6NJlcVNLAv19Zy9P/uCzgX5cve+6zLTS2d3Pe9FEkx2hTe0wLe4vqqK5t83uHHDhcby4wPh+FEKK/DZxPQIHZbGbixImsWLHiyG2KorBixQqmTTt2Ibaenp6vJEUOLy1R1cBPR3s9Xk3PRna0dpO/pRR7jzZtJusqW0hK8+/08JrKVoKtZnJH+26a97dxONxsXn+QqopmRo7WblmQqqp88r+d7NlVycmnBUYtCa9XYdWKAi6+fIZPZiC89fpGhg5PZN4ZY8nIiuv38bX22f92sG1DMacvmsSkAJkxdNgHL60jZ0wqYwdJkVeAygO1NNW2kzUqlayR2i6t+zK3y8Pjt7/Cf+5+h8SseO5765dYwwIneXYspcUNPP3wUsqKG7jr/gsIj/BdUq2r28H9j3/Kb26eR0K8Nm2ev4/qpg5eWbGdpOhwrp7j29k3gWb3gWoyU6KJDPfvkhxVVTGajeg0nAk60PR3wdfDFyGEb8gMkwHm9ttv56qrrmLSpElMmTKFRx99FJvNxjXXXAPAlVdeSUpKCvfffz8ACxcu5OGHH2b8+PFMnTqV4uJi7rrrLhYuXKhZTY7vwutRNK2f0NlmAyAsUpuCr4217WRr0CHH3uNi+AjtipsW7K4ke2gCeaNSNW0rfbCwDovFyGkLRpOoUVvpvnZuK6Ozw860Wf3f9aGj3UZVRQvFRfX8+o+BvzThu3I63Lz27GpGjEnj3AArZFtb2czy97bz6Ns/1zqUflVb1ojX5SZ3UuAkgQ7uLuf+a5+lvrKZuZfM4OcPXhbwywka6zu487bX8SoKd95/vk9bCCuKyl8f+YQTpw5j2qT+n8XmS/98ZyXJ0eH84uyZBAXgz3TVnhKGp8SRHNP/SaiSymZOnOz/JLDX48XW0RNws8eEEKK/BN6nifhGF110EU1NTdx9993U19czbtw4lixZcqQQbGVl5VEzSu688050Oh133nknNTU1xMXFsXDhQv76179q9RS+k/S8ZG2X5LT3ANolTGoqmonx49k9l8tDRWkj1hALKRnatZHdubWMwn21zD+r/9vlfhfrVhdSVtLEAh+07f2+Pv5gByedMsIny4OWLt7D3l2VzDgxhyw/Fhr2lxWf7CY5PYaM7DjSswPr+X36xhamzx1FQgB16+kPh2cyBsKMxq4OG8/d+y5LXllHTGIEtz95NXMuCvwaF+1tNu68/XXiEiOYf+Y4Jp3g2y/Fr769CZfLw5UDqIUwwOq9JazJL2PK8DRmjwmcBN1hqqry0rJt/Ovm8/p9bKfLw5bdFZwyzf8tsZ12F6Nn5qAfQMu2tKagQ+nnIq39PZ4Q4guSMBmAbr75Zm6++eZj3rdq1aqjrhuNRu655x7uueceP0TW/8r2Vml65q+n20FIeBAR0aGa7L+hpp0R4zP8tr+q0iaG5CSSnhV3zPo2/tLc2MmosWmM07jg6PbNpQzPTWL6if4/CD2Wzk47NpuDeaeP7fexVVVl0/qDjB6bxtzTA6cbUH9RVZVdm0upKmvi5t+doXU4R7H3OPns3a088OpPtA6l3w0dk4HJYmLPugOaxeDocfLB/63g7SeWYuuyM3PhBH56/8XEJEZqFtPx6uq08+SDn1Je2sRVN85mwdkTfLq/NRuLWLv5IP+4Z9GAqltid7p5ZvFGjHo9d1x0ckAWTC6oqCc7KZpgH8zEKCxtYHhmHGM0qF/icXnZt6k4IF9zIYToD5IwEQFLVVUUr6LpWYvujh5snQ6CQ7Qpgutxe4hL8l/RuvKSRooKahmaq91yHKfDzZrP9xEeaSXZz/VbvqyxoYODhXVkD00gITEwCgeuW3UAW7eLNB/M/ikurKe6sgWAqdOH9fv4WivMr2Hz2oPkjEwma1jCtz/Aj7atLiRtSDyZA6ht6/FKSI9lyNh0Dmwp4eP/rOTM60/2277bmzp5+4mlbPhkJ/WVLSRlxnHXSz9l3CxtC0kfr65OO3+49TWK9tdyzU2ncPFVvm0zXVzWyMNPL+PBP11AVIT/OrP1hxc+20p1Uwc3nTGN7ETtZkd+k3fW7uXs6b6phbVzXzXNrd0ka1Bvxu1yYzANnORaIPCqOrxq/yaY+ns8IcQXJGEiApZyqEealgkTW6cDQLNigOVFDUT6cXZLc2MnkdEhpGfH+m2ffZUcbGD4iGRS02I0PWO1fUspYeHBnHTqCM1i6Gvn9jLmneGb2R+ff5ZPe2s3l1w1U9O6Mb6y/MOdqF4v88/17Rn672PTigJOOCVwfs/620/+fil3nvsQL//lXVxOF+f+bK7P/ra9Hi87Vu3jrSeWkr/xIKHhwUTEhvHbZ65l9rm+acPtC50ddn7/y1exWEyctWgyF1/lmyLPhzW3dvPXRz7hVz+by9CswFqu9m0qGtp4cdk2okODuWT2OK3DOaZuu5P9VY3ck32aT8bfs7+asbmpmvx+u52egK8BJIQQP4S8w4mApXgVQNuEidfjJTjEomn3BH8eAB3cV0t7q420TO26oxTuq2Hfnmqmz9J2GUzBnmo6O+zkalj89st6elxs31zKldf2f6tfVVWprW4lNy/Zp7NLFEX1aRvUr9+vwv49VeSMSuWEkwJvdkHhnmrOunKm1mH4TPrwZH777A384yf/5tW/vse6D7Zy9T2LGD0jt1/e37o7eti2ooClr6+j+mA9na02XA43U+eP4byfnsboacMGTKIEoK21m6ceWsLBA3UsXDSJn/1qvk/jdzjd/OEv73PqiXnMOmFgzS5TVZUXlm7F7fbymwtmYw0yax3SMa3cVczpk3J88nP0eBUKi+u56bIT+33s4+F2eTAa5evEd+GLrjbSJUcI35F3OBGwVFVl9MwcnxS3PF4drTbsPS5CwnzXvvHrKIpCdFyYX/dZU9kKQFqmdjNMGuo6CAo2MczP3YH62rmtjNS0aPJGadde+cv27qwgIsLqk+U41ZWtbFxbRHRMKLkjfbMGvrO9h2sveIpxk7K448/n+vXvuqy4gZCwIGLiw7FqtLzu63S19+D1eMnKGXzLcb5s1IxcHlryB5649SUKNhdz9wWPEpUQycRTR3LCGRPIGZdJ2HHMpuvpslNT0siB7aXsWLWP+vJmWhs66GjtxmI1MXRUOmdeO5v5V8wkItq/75/9oa6mjd/f+jr1tW1cdeNsLr1mlk+TJR6vwv2PfsqEMelctmiKz/bjK4u37OfDjQUsmjmaU8YFVpvww1RV5bUVO3n4p2f5ZPzi8kbaO+2MzNHmM9Pj8pIzwLopCSHEdyEJExGwdDode9cVYtCwrbCjxwVAkNX/Z626Ox14PF6/7U9VVSKirIyZmElsgv/XQR+2e0c5DrubrKHa1Zlobu6ipbmbnLxkgoICo1Vi8cF6ps7wzdnf7VtKiIoJ5ZR5o3w2A6RgTxWpGTE0NXb6PQlalF9LWVFDQM4uqa1oJikt+kfRkjMuNYY/v3M7+7YU8/5TSzmwtZTlr29g+X83YLaYCAoNIjTCSnBoEOYgEzq9HpfDTXtzF7aOHlQVdHodPV12nHY3JouRpMw4ho3NYMbCCUw7fSyRsdq9d/1QJUX13HPHW4SEWLj82hN9nixRVZVH/2853T1Orr985oCahQPQ3m3n4XfWEGQycs28yQEbf0F5AzHhVp+0EgYoLGlgythMMlO1qd3icroo31etyb4HKgUdSj/XHJEuOUL4jiRMRMBSld4lOToNu7U47E4Agqz+Pyvd3tLt1/ol3V0Odm4uJTktWrPuCF6PQmV5E9GxoURGaVd0sGhfLXq9jnET/deh6NtsWFPEooun+mTs+tp2zGaDT5NUO7aUsm9PNedd4pvn8E12bymlq9NO3rh0v+/723S29xARM7AKbP5QI6YMZcSUobgcbvZvLWHnygJK9lTSXNeGrdNOZ0s3iqqiKCpulweXw4M11ELykATiU6NJzIwjd9IQho/PGBCdbo7H1o3FPPPYZzQ1dHDRrxew8LxJPt/nS29upL2zh/vuOBujhicmvq8Xlm4lyGTkytMmkhIbGIW5j2Xp1gMsmjXaZ+Nv2lGGxWzULGHkcXkJibBqsu+BSvVBW2FVEiZC+IwkTETAOlL0VYOaB4c57W5Amxkmne09fu2Q01jXDkC8H/fZV0NDOxERVsZNytQsBugtPOt2e0nL0G5p0pc5nW6CrWZGjPbN8qDVy/fR0tzFmPG+SyiUHWwgIyuWsRP8n4RqaexgWF4S2cMDqzsO9K7/t/wIZpcciznIxNhZuYz9mq41qqoG7KyB/vTx+9t58qFPSU6J5s6/nM+sU/J8vs93P97B0s8LePKBS7Bq8Pn2Q20+UMkry7YzIiOBS08Zr3U4X6vD5uCz7UXcfK5vahQpikp5dQvnzOv/VvPHy+lw4XZ6NNu/EEL4miRMRMBSDyVMdBomTDxuL5ZgE+Yg//+pdHfYsfhxOUhrcxfRcWFkZGvXIaG2qpXmpi6CgrWtM1F8sJ7omBCGDA2MbhGlxY2Ulzb5pL1xS3M3icmRZA+LJz7BN8kyr1fBaDSgeBWG+HmdvaIoGE0GYkKDAjIxYRyEHYn6y2BPlrjdXp5+ZCmF+2qJiLTym7vO8kvNpKWfF7Bu00Ee+vMFxET5bxZjf7E73fz1teXodTp+d/EpmAO44Oinm/czZ8JwLCbfxFhe1Ux1TRtjc7WrteVxeTCZB94MJS0pqg+W5EhbYSF8Ro7URODSQebIVPSa1jBx9q6VN/v/i1ZXRw+h4f4rNtva1E1rUxdBwdp9qWxtsTFmfAZZQ7RNVFSVt9De3kNqgMwwqSpv5oQZvun0UVHWSMGeKnQ6nc++oDbWdbBzSyntbTbi/Fwfp73VRl1V25EZa4EmJDwYh8OtdRjCz1pbunnsgcV8/P523G4Pj/37Wr8kS1avL+Rfz6/i5utPIXmALmd65qONxEWEcsVpExmVFbjFkhVF5a2Vu1k4zXctw3fvq8YabGaohic6VEUlc2RgFEcXQghfkISJCFg6nY7ygmrN6mlA73R5g1GvSQxOp5vwSP+tC7Z1O4mODSUqVrvOElXlTezZWUFUtHY1Hbxeha4uB2PHZ2jaoenLSoobfNbdpbK8mbDwYHLyfNc+uaGundT0aCZOGeL3WQMdbTbqa9oIi/B/p6vjEZsYSXeHXeswhB8V7Kni51f/m88+3sVZiybx6LPXkJgU6fP9rt9czLsf7eDvd5/HEA1bx/8Q+WX1vLZ8Bw2tXdxwuv/rIX0X24qqsAaZyE33XTKjoqqF007MxajhcVJPt4O6sibN9j8QHW4r3N8XIYRvyF+XCFzqoSU5Gs4ydLs8mMzaTPftau/B7MclOU31HbQ2d2tabNVudxMZZSXGz+2Uv6ypsZP2NhvGAEmWADQ1dJLuo1bPtm4nMXGhxMX7buZHe5sNS5CZ4BD/10pwOjykZ8f5vUX38YpLikD1KjTXt2sdivAxRVF569UN3HvHm+h0Oq656WR+dvt8v7S63ri1hPsf/ZTrr5hF3nBtW7Z/Xy63hyc/WIeiqtx1xRysQYFde2XNrlIWnTTGZ+OrqsrKDYUkaNwZyuv2EBwaWO3ahRCiP0nCRASsQ/kSTdexDx2VxohJWX7dp6qqvPz4MravL/ZrfYPOjh4AwiO1OxNfUdZEe1sP8T6o1XG8mho6SUiKIFvjZUFf1tTYSVy8b77wlxTVU17S5NPXvLG+g5KiekJCg3y2j6+jKgoRUSEBWb8EQK/Xk5AazYFdlVqHInyordXGQ3/5H8/9azkAv77rLC65epZfippv2FLMa+9s5i9/OIcxA3jpxL8/3szu4lquWzCFqXmB08HsWBpau/hwfQFzJvimFTxAVU0rBr2BcRr/TJ12N16PomkMA83hGib9fRFC+EbgVsoSP3qHizVqWXmgYGspYX5cFgO9s1r++8xKAL8Wmw0KNjMsL0nTpQttLd3odBAZqd0sl6bGThrqOvxy1vd4VVe1+mwGiNVqZtTYNN/O6lFVho9IJjbB/7M8dHod5cUNDM0L3LPqw0alsn9HBTPn++5stNDO9s0l/ONPH9DWamP+WeO46oaT/TaLbt2mg/z9sSXc94ezGTc6zS/79IV95fW8tHQrMeEhXDl3otbhfKsP1uVz6sRhhFl9lyTemV9FV5ednCHa1nHxuDwBs3xVCCF8QRImImAZTUY8bq9mS2IAFK+C3s9rgw+3MgYIDfdfsqaytImD++s0mQVwWERkCNExoRg07BzSY3MyLDeJRA1nuXyZy+EhItJKdIxvulnsz6+hqqKZCB8mBu12N20t3Rj0/v+5BlstdHXY6e50+H3fx2vqqSNYffvr9HQ7sGr49yf6V0+Pk/88sZzFH2xnaE4SZ50/mYuvmum3mlgrVu/ng0928tc7z2XsAJ5Z4nR7eG7xFsKtQdx5xRyfJiH6g8vtYXN+ObdeNNun+9ldUM3ovBTMGh4jARiMepKyA69leyBT0KHQz11y+nk8IcQXJGEixNdQFAVFUf3+5d3pcJOaFUtzQyfBVv/NcujpcQJoNrNCVVX251eR4Ifih9+kqbGTgwfqAuaLa1e3ndrqVsIjfJPQsIaaSUmN9unPvcfmpKmhE4MGHa8io0LIGZmMx+v1+76PV3R8OAkp0az9ZDfzLgzsQpbi+OzeXs4zj31GU0MHSSnR/PxXCxgx2n9Ji8Wf7eGZ51dx/z2LGJWX4rf9+sIz/9vAql0lLDpxNDNG+3eJ7Pfx+Y5iuu0uRmf7buaHqqo0tXRx0rThPtvH8eps7aZVajB9J9JWWIiBRRImQnwNxdu7GEjv57PiXq9CdVkzBoMeo8l/+05KiSIo2EywRm2FnQ4PmUPiiY3TtoCdwaBn9Nh0wv3Y0vmb9NhcjBid6rNaBz3dLrq7HT49SxkVE8Kocel+rclzWHiUFaPJiNvp8fu+v4v5F03hjX+t4OSzJ2K2yEfzQGXrdvD6C2t55/WNqCpces0sLrpyBsHB/itQ+sa7W9i8vZSH/noRw4cM7DP/u4treGXpNhKjw/nF+bO0Due4LN18gEWzx/i0/lp1bRu7C6q54XLtXxOPy4PRLEtyhBCDlxyVCfE1FEXBYNL7fbqrx33oTLgOzGb/JS8K82torO/wa2eeL3M4XBQX1hMWpm2ioq62nb27Kwm2BkYHBofDRV1Nu8/Gb2vtxu3ybTLB0eMif2cF007K8el+jkWn02HrclBb2YLX68VgCMwD+7wJmYSEBvHZ25s58/IZWocjviNVVdm4pogn/vEJLU1dzJidw1kXTmG8H4uGq6rKsy+uYfnqfTx434VkpMX4bd++YHO4uPs/SxiTncxPzp5OaHDg1JX6OvvK6tm6r5J7rp3n0/0UFNaSnBhJ7jBt65dAb901c4B3LAo0MsNEiIFFuuSIgKWiMmxCFqpGZV91Oh1et4JX8W/1d4+nN2GiQ4fej2fk3W4vRqPB7zNqDnM6emu3aH12PSw8iLxRKQT58YzwN/F6FYbn+q5g6bDcJIb6cHyA8EgrOh047C6f7ufr5I5Npa2lm5qKFk32f7wu++Vc1izeTX11YMcpjlZT2cofb32dP/32TcLDgznvkqn89t5z/Zos8Xi83P/wJ1TVtPLUPy8b8MkSgEfeXEVNcwcjMhOYOiJd63COy7srdzNn0nAiQ32b+N+8vYyk+HDMJu3Pe7ocbk27GQohhK9p/04rxNcwmYwc3FGGUYO6B1pSVZUR4zOoKmvEDx0njxiSk6hpnQe328uQ4QnEJWhbbLWutp39+TWYA2SKsaKolJc2+mz8irIm2lttqKrqs4PemNgwRoxNx+vVpvXkiLHplBU1cGBPFenZgdMuuq+UrDimnjKC/z65gl/89Xy/FQcV34+9x8V/X1zL0g934XZ5GJabzC9+dzrD85L9GofN5uCe+z/Ebnfzt3vOIyJAlhP+EKt2FlNS3UxmYjQ3L5qpdTjHpbWzh7qWLm46d5pP96OqKjvzqzj39PE+3c/xsoYFExwgNb8GCplhIsTAIgkTIQKMqsK+nRUYTQa/Fsncv6caLU8Seb0KJUUNpGfGaRcEYLVaSEiMCJgvq0ajniwf1iEYOSaNlqYuXC4vFh/N7omIslKwq5LIKP+26D5saF4yDruL8mLfJZ76yznXnsgDv3yVd59dyYU/PVXrcMQxeL0KyxfvZsmHO9m3p4rQ8GBuun0+py4Y7ff3jcbmLn53zzuMGZnKT6+bjcWizZLK/tTc3s19L35Gt93FS3+8hCA/Lk39IT5YvZfWDhujh/g2YVZR1Up0uJWJYwJj1k1TdYskTIQQg1pgfCMQ4lgOfXtXtVmRg6qqZOUlk6jh1GbVj09eVVV0/pzS0oeiqKSkRRMapu2BV3eXg4b6joCZYmw0Gjiwr8Zn4zc1dLJvbzU93b5ru5uYEkXW0Hg62np8to9vkjksgc72HtYvLziy5C1QGQx6br5vEXs2l/L5/3ZoHY74ElVV2bL+IPf/8R0evu9DDuyt5tLrTuSFd25m7plj/Z4sKSqu56ZbX2b8mHRu+cmpgyJZoigqD/53FR3dDn56znRyMwZG0VqXx8O7K3dz4anjff7ZsXNvJTV17eQM1b5+CYDH5cUYAEuDBpLDM0z6+/J9PPXUU2RmZhIUFMTUqVPZsmXL125bUFDAokWLyMzMRKfT8eijj35lm3vvvRedTnfUJTc396htHA4HP//5z4mJiSE0NJRFixbR0NDwveIXwh8kYSIC1uFjDn8mDb7MYNBTtr+W+sofRz2B3NGpDB/h36nkfdVUtWLXqM7FYYe70SiKRpm6PoKCTGQP8d0ykuxhCSSlRNHWZvPZPuITI3A43Hi9CoqfawJBb6ereedMoKmhg4KdFX7f/3cVHhXCTXefw+qPdrJ5xT6twxHA3p0VPHzfh9x16+usXbGfU08fw9Ov3cRVPzmZ8Ej/z5xava6QW3/7BtddMZNbfnJqwMyI+6H+u2wHm/aWc+b0EVwxf5LW4Ry3z7ccJCzYwtypvi9sXVXdytSJmZhMgbFs1BxsIiRi4C8D+zF68803uf3227nnnnvYsWMHY8eOZd68eTQ2Hns2Zk9PD9nZ2fz9738nMfHrE3YjR46krq7uyGXdunVH3X/bbbfx0Ucf8fbbb7N69Wpqa2s577zz+vW5CdGfJCUsApZOp2P0rNxv39BH9IcOQP1dd0GnA71Bh16vQ1EUvxVhLcyv0XSGyZFEhVfbREV4eBChYUG43YExEyEkNIjtW8vwehWffSlqrO/4f/buOr7q+nvg+Oveu+7u7o0lo7u7O5RQMQgDVEzQr4GBLfFVCYsSpZHuhm0wYox1d/d24/fHxJ/yRQXc/Xzu4PN8PPb4+sX5OWcw7u7nfM77HEqLq/Hx087TXLlcjoWlCQmXs8nLKcNVhK6t6C7+XL2YyYn9V4lo7yN4/Lvl5mPPtPmD+PajPahUKroMCBM7pQfS1YuZ7N1xkb3b4zAyNiAi2otJM7vTtqM430NqtYaftl7g+3WnWPzyCDq2gu/lO3U9o4AvNx/H2FCfJ0d1QSHSAPJ7selAHJEBrpiZaHeTj1qt4cCRa0wc3V6rce5GaX4F1o5WYqfRqmgANS37fute3jl9/PHHzJo1i5kzZwKwcuVKdu3axerVq3nppZf+5/Pbt29P+/bN33u3+/c36enp/WVBpaKiglWrVrFu3Tr69OkDwJo1awgODubMmTN06tTpHr4SiUS7pIKJRGfJZDKunU76/UZajPg3ixZC0tNToFZpUMs0CBlarpChUoozlBOav+6wSA8sRZpzcZNKraG6qp5GLa/avVOmpoZERHlQVVmHlbVpi1/fzd0WlUpNYX5Fi1/7jzp09Ucul5FyPV+Ugkmbtp5UlNWSmVpETXU9pq3gzL1viCuPvDiEr9/dQUVpDYMnSW8khaDRaLh4Po39Oy9ycHc89o4WePs5MHVWT7r2ChbtZ1JtXSPvfbSb5NRCvvxoKl6edqLkoQ01dY18sek49lamPDOxJ062FmKndMeuJOdSUVPP+L6RWo+VllFERWUdUToyvwTA0s4ccxG6rFozbQ59rays/NOvGxoaYmj4v4W8xsZGYmJiePnll3//NblcTr9+/Th9+vS/yiUpKQkXFxeMjIzo3LkzS5YswcOj+Xs2JiaGpqYm+vXr9/vnBwUF4eHhwenTp6WCiUQntZ7yveSBJJOJezRCrpAL3vFws7NFgwaVgF0OweFuBLQR70iOvr6CyxczKcgrFy0HAGcXa4LauFJf1yBqHjfp6SvITCumrFQ7R2acXK1w87ClvKxaK9e/ycXDhutXsrmRkKvVOH9FLpfTf0QUl86lcnjXJVFyuBdegc48u2QCZw9eY/UHu2jSkULe/UilVHFk72UWz1/PS7O/Iy2pAA8fex6d158vv3+c7n1CRCuWZGaV8Oa722hobGLlZw/fV8USjUbD+98f5Ny1TLqEedO3XYDYKd2VH/fEYm1mjK+b9v9M4uIzCQ9xI8BfN+aXAOSmFuhMR6YE3N3dsbS0/P1jyZIlt/284uJiVCoVjo5/7ix1dHQkPz//nuN37NiRtWvXsmfPHlasWEFaWhrdu3enqqoKgPz8fAwMDLCysmrRuBKJNkkdJhKdJpPLxZv6CoR18kVf4GFmenpyvAOdKMqroKlJhVAng9OTCikurNTqetm/Y/DbhpbGBnFvCJuaVFy/mkNNtW4UTACi2nlRVlqNtxZmmbh72pGdWULCFe0NlgWIiPbC1NyIC6eSeOzp/lqN9VcGjonm2N7LbFp9jIFjogX/u32v7F2seOmzqaz+YDfvPf0Dj70yHGcP8YZR328qK2rZuy2WYweucuNaHiHh7vgHOzNxRne69ApEoRB3VsTJ00m8++FORgyN4tEZPdC7T+aV3LTvzHUOXbiBj6stz07sKXY6d6WgpIojMUn858khgsS7EJeBqamhTn0PKBuVOjNPpbXQZodJVlYWFhb/36F1u+4SbRo8ePDv/xweHk7Hjh3x9PRk06ZNPProo4LmIpG0lNbxblHywBK7w+TGpUxBV/sC6BvokZaY3/y1Czg/xcBIH7Vag0qpRk+ENz9GxgaYmhnSUN8keOw/sne0ICTUldpacYfP/lFDg5Lc7DLaauHYuqu7DW3C3bVeJLO1tyAo1I362kaKCyuxcxC+5d7C0oROvYKIOZXEvi0xDJ3QUfAc7pWRiSFPLR7FoW2xLF2wnl4johg6tbNgM47uR4lXc9j183kunEqmpKiK8Ggv2nfxY8zUzkR18BF9U5ZSqeKr1Uc5fS6FF54bTC8RZ3ppS0p2MW+v2Y+rnSVLnhqKUSvb9LP5wEU6h3rRO9pP67GUKjW1tY307Oav9Vh3w8HdFhMLaeirrrCwsPhTweSv2NnZoVAo/mc7TUFBwd8OdL1bVlZWBAQEkJycDICTkxONjY2Ul5f/qcukpeNKJC1Jeqcl0Wmufk6iDiLVN9BDKXCrqYFR8xtGjQZqa4TrcvAJcCQkwp16kQoWhoZ6NNQ3id7ZYWSkz7UrOZQWa/eIyt0ICnGlqrJOK9fW01OgVKo4c+IG5VrclAPgG+DElYuZnD1+Q6tx/s6wiR3JySjhzJHr1NXqThfRnZDJZPQdFc2Ln0zh2oV0XpqyklSRjji1VpUVtWzbcJZnp3/NC4+vYe+2ODx97Bk2rj1zXxrK258/RNuOvqIXS/ILKnhv6W7Ox6bxzuIx92WxpKaugZeX7aShUclDg9vh49q6jhlV1zWw+cAlgr0d0RPgwUrijTziL2cRGaY780sAUq9kiTr/rDXShbXCBgYGREdHc/Dgwf/PS63m4MGDdO7cucW+1urqalJSUnB2dgYgOjoafX39P8VNTEwkMzOzReNKJC1J6jCR6LSi7JJ7G/3dQvT09aitEvamysDg//9a1lRp5yb5dkqLqrl2KYuGukbMzIUfiCmTyQgOdcPQSNwnjNY2pigUcirKa0XN448srU04fTxRa9fv2NUfhULOjWs5dOiqvfkBPfqFEHMmmVNHrjN0rDgrQy2tTZkwswdff/Qr2348zaRZvUTJ499wdLPhxU+ncHLvZT56cSNtuwUw4uGu2LtYiZ2aTmpsVBJ3JoXdv8Rw/XI2VRV1qFRqug8IISzKk75DIzAz150n5MdPJvLBx7/Sp1cIKz6dhpHIr4naoNFoWLL2ACaG+ozuFcawbm3ETumu7Tt1HaVSyZg+EYLEi4nLwMrSBG9Pe0Hi3Sm5QiYdyWml5s+fz/Tp02nXrh0dOnTg008/paam5vetOdOmTcPV1fX3OSiNjY1cu3bt93/Oycnh4sWLmJmZ4efX3GX1/PPPM3z4cDw9PcnNzWXx4sUoFAomT54MgKWlJY8++ijz58/HxsYGCwsL5s2bR+fOnaWBrxKdJRVMJDpNrpCjEXhLzR/p6StQNgk7U0NPX0F4B28KssuoqawXLK6JWfM515qaBsSajlBeVktxUeU/f6IW2TuYN2+NKRQ3jz/y9rXnzAntdWV4eNnz3ddHuXwxU6sFE99AZ+rrmqighrKSKqxtzbUW6+8Mn9KJK3Hp/LD8EB17BuEd0PragGUyGd0GhdOhdzB7N53jvWd/wCfElfGP98LBxVrs9ETX1KQk7kwqF8+lsnPzBVQqNcomFUbG+oyd1oXOPYMIDncTvZPkj+rqG1n+30Pk5Vfw9Jz+9O/T+ooId+rnQ5fYdyaxeW7J5F469edwJ5RKFWu3nWVQlxBsrVp+e9ntJKUU0L+3eFua/kpVaQ2KVjIPSldoc4bJ3Zg4cSJFRUUsWrSI/Px8IiMj2bNnz++DYDMzM/907DM3N5eoqKjf///SpUtZunQpPXv25MiRIwBkZ2czefJkSkpKsLe3p1u3bpw5cwZ7+/8v9H3yySfI5XLGjh1LQ0MDAwcOZPny5ff4lUsk2ie9wkl0mkwuE3WGiYef42/HcpToCfSGQCaTkZKQR01VPZUCdjnYOZjj5mVHXY14szssrU3IyiimsUH5+xBYoTk4WgJQWKDdNbt3w9PbgbMnk6ipacDUtOUHuIVFeRAU4sK1+OwWv/YfyWQyBgyPYvWXBzj462XGPdRFq/H+ioGBHiOndKamqp7P/rONj9Y+JvisopZiYKjP8Ie70nd0NL9uOMNbT31LSLQXvYZHERTp0epuRP+Nqopa4s6mcHTvVXIyiklPLmwueqvURLTzZuj49nTo5o+RsYHYqf6P64l5fPz5HhRyBa++NAw3VxuxU9KaKyl5HDqXhJmxAe/PG45xK5tbAnD4fBKFpdWM6hMmSLy6+kZOn03m2TkDBIl3N5SNSvQMWufrpwTmzp3L3Llzb/vvbhZBbvLy8kLzD4sYNmzY8I8xjYyMWLZsGcuWLbvjPCUSMUkFE4lOs7KzQCXg4NNblRZWkpVcQGODcAUTACMTA8ELJgqFnOz0YqqrhetquVVAkDMatYayshocnSxFycHUzIjo9t5oRPy+u5WxiQFdewWRmpRPWKRni1/f2saMpkY16cl5VJTVYGmtvSemfQaHceF0MmePJzJ2amfRbugjO/pyYv8Vrsdns3HVMaY80VuUPFqKiZkRYx/rxYhp3ThzqHkFMUCfkW3p3D8UK1szkTNsecomFTeu5RB7OpnEy9lcOJmEmYUxVZX1WFiZENq2+bhNlz7BWv2e/jdUKjU/rj/ND+tP88iM7owb3U6QeRhiKa2s5aXPd1BUVs17Tw/H07n1FYY0Gg2Hzt6gbwd/2vg6CxLzWkIuTo6WtI3UrfklAP5tvTEyEXYTS2un0cjQtHCHSUtfTyKR/D+pYCLRaXXV9chFfEJ6cwBrY30TJmbCzfUwMTWkBKgsE65gYm5pAkCViLM7VCoNV+OzKCupFq1gAlBdVUdaciEqlRqFjqxvNDLS59rlbK0UTAB6D2zD2pWHuXA2lb6DtPfU1N7REktLY84cTyTuXCptO/pqLdY/eWz+IOZNXsGuTecIifQgUsRcWoq+gR7dB4XTfVA4GUkFnDl4lefGfoF/hDvtewTRrmcg1nbmlBZWYm1v3qo6UOpqG0i+lsvVi5lcjcvg0vk0DI30qaqow9rWDANDfTr1CiKqoy/tuwdgruObO9Izivn0s70o1Wo++mASYW3cxE5Jq5QqNSs2HaeotJopQ6Lp3U63tr3cqXNXMjh8Pok3ZguzShjg7LlUNCoNLs66ddxOrVZz6WgCo57Svc4XXaZGhpoWPpLTwteTSCT/TyqYSHSaXCEXtcPk5gBSoVfd+oe6otFoqBVwi4e5ZfPNRWWFeAUTW/vmp+AlxVWi5QAQEuaGnp6CgvxyXHSkNT6ynTfXr+Ro7foR0V4oFAoSLmdptWACMH5aVy5eSGfzdydFLZgYmRjy6keTeO3Jb/ng5Z/49Icn7qv5H57+jnj6OzJuVk8SL2URfzaVhVP/i0eAI6f3X/3930d3D8Q32AV3Pwed6W6oq20gI6WQlIQ8Uq7ncul8GnlZpcgVcpRNKswsjGlqVNG2sx/e/o5Ed/UnOMytVRytUipV7NgZx8qvDjNoYDhPzOqFyQPwhP6L9cfYfvQqw3q0Yc6E7mKnc882743DwcaMfh21N+/pVjFx6URHeQkW704pG5tnvOkbSLcTEonk/iW9wkl0mlwhRy1iwcTIxABDY30a6oSd61FWXE12WjF+bYQbPGpjZ4ZcIaOyXLjNPLdycbXBzs6cojxx54dYWZty7XI2WeklOlMwCQpxYcUne5k9f6BWul78g1xwcLRk745LPDq3H8ZanPMQGOpGYIgL9fVNJF7NJlDEJ+tefk48tmAwG785ypvP/MgHax7DVMBuMiEoFApC2noR0taLSU/14dS+K5zed4XSwkrSrudxev9VGuqbMLcywdnTFncfBwLC3bF3ssTZwxZHV2uMtTA7p6aqnqKCCorzK8jNKKG8rIarcRlUlNWQnlTw29Dt5rXuhkYGyOVy2ncLwMPHnogOPgSGubW6P6ukpDw+/HgP1dX1vPfOeKJ08CZYG/afvs6GvbE42JgxZ0J39HSkc+9uJaTmc+FKJrMndxesuFhSUo1Go6FjBx9B4t2NhrpGXH0c0ZMKJndFV4a+SiSSOyO9wkl0mkJPIWrBxNBIn4baRhobhN2Uc7OVXMjjMZZWpqhVGspKqgWLeStbe3OKC6soyBe3YOLl4wBARloRHbvqRtu4m4ctIWFupCTlExDk0uLXl8tldOsTRMzZVM6dTKJnP+1u6Bj7cBdenv0dG9ecYNHSSVqN9U96DQ4nPSmfa3GZLHl+A2988TB69/GazC4DQtkc+x8S47OIP5tC8pVsUhJyKS2sQq3WcCM+m+N74mmsV2Jjb05pURWu3nYolWqcXK0xNjPE0sYUKxsz9PQVmP62ktfQWJ+mxuaBzTXVDejpKSgvrUFPX0FeZgkKhYzUxAIMjfVJupaLpY0pRXkVmJgZUlvdgL6BgialCoVCjr6BHq6etkR28MHTz5GANq54+jm22j+XurpG1n57gvj4LMJC3XjskZ5aLUrqkmsp+bz11V7ah3gwe2I3bH47/tka/bjzPBpgQJdgwWLGxKWTkVmik0e2mhqU5CTno9BrnQUwiUQiuRNSwUSi07zauKH+h4nc2mT021PV+hrhjsYAmP1WMKmpFK7bw9qu+TiMmAUTJ2crzMyNqK4Sb/AsgK+/IyamBqTcyBc1jz+SyWRYWZsScyZVKwUTgD4Dw1i/5gR7tsVpvWAS1cGHwaOiObw3nquXMmkTIe4ww2lz+/Hx679wPT6Lz/+zlWfeGK0z82u0wdjUkMjOfkR29gOaB1kWZJeRlVZE6rVcKstruHg6GbVaQ2lRFeXF1dRUN1CQXYZCT45KqW4ucDSq0DfUo6lJhYGhXvOGKyN9Guubmrvz6pW/dek1YWpuRE1VPVa2ZjQ2KFHI5Ti6WuEd4ISdoyVuXna4ejV3uDg4W/5pnWVrdvJUEtu2xlBSWsP85wbRpo2r2CkJprCsioWfbqOxScWQbiGE+LS+Fd43ZeaWUlJWw8jeYViaCzcfJzWtiKgIDyx0cCaPsrEJrxA3QYfi3w+koa8SSesivcJJdFpeWiEy8eolGP92rrxOwFkiAE7u1ngFOAo6O8XGzgz/EBfUavE6emzszGhqUpGUkCdaDgAOTpYo5HKSEnWnYALQuXsAp47d0Nr1PX3s6d4nmNKSagryynF0ttJaLJlMxuAx0Rzdf4Wdm88TEu4u6gBSuVzOs2+M5oNXfuLw7kuYWZjwqJaOP+kimUyGk7sNTu42tO8R+Kd/19iopKSgkvLiKspLqqmtbqCyvAaVUk1leS0GhnpUlNViam5EVUUtVjZm1FTXY21njlKpwtrWDIWeAksbU8zMjbC0NcPG3hwTU8NWNXT2buVkl7F85UFiY9N55JEejB4VrTMzYoRQ39jEq5/uwMXekr4dAxnSPUTslP6VH3de4OL1HF58tL9gMTUaDfsOXGXY4HDBYt6N+rom0q9lt9rOL4lEIrkTUsFEotMUIg99FavDRE9PQfqNAmwdLQSLaW5hTGZqEeWlNYLFvJVMJsPV3YaczBLUag1yuTg3UzKZjB59Q0hJyqemul5n5iREtvNmyaItPDanD9Y22lkT27GbP0v/s51dW2J4ZHZfrcS4KbCNK70GhrL7lxiiO/rSb1ikVuP9Ez19Bc+/M47l7+7gl+9OABoeeXbgA38zYGCgh7O7Dc7uujHPR9fV1jawfv0Zftp0lqHDIlmzZhZOjuJt/RKDRqPh3a/2cTk5j07hXsyd3HqHvAIUlVTx6/Fr9Gjni7ebrWBxU9OKKCuroV20t2Ax70ZTQ/NDHWmGyd2RZphIJK3Lg/HoTNJqNc8wUYkW38rWDP8I998HDwrF0toUAGWjcHFlMhn2jhaUFFWJWqRqE+GOX5AzRQXiD369fjWX69dyRc3jj0xNDekzKIwLZ1K0FqNnvza07+LHqaM3BOlweviJ3kR39uW7/x6hSsAjaH/FwECP2a8MZ/ikTvzy3UmWvraZeoGHPktaJ7Vaw9498cx+ci2xMWn8562xzJs34IErlgCs2XKGvKIKPF1seHveMPQUrbvo+MP283i72DB1aHtB48bFpRMZ7k5IsHaOYf5bykYVoV0DpYKJRCK5r0kFE4lOkyuaz8qLRaEnJ+lSFpVlwnZdWPxWMBH6eExYtBdtItwpFXGtr7WNKVcvZZGVXixaDtC8WtjZ1Zq0lEJR87hVaLg7B/Zc1tr1DY308Q9yJjOtiMN7r2gtzk02duZ06RVMeWk1P313Uuvx7oSBgR5PvDiEIePbk3g5m4WPrBL174RE98XFprPkne0s+/IAw4ZH8elnD9Ohg3grs8W092QCX28+RVZeGR8tGIWZFlYmX0nOpbFJmGHs5ZW1bD90GX09BWGBwhYuzpxNwdTEQGePcjXWN3LlZOID34V3t27OMGnpD4lEoh1SwUSi01z9nRBx5uvv2x9qKoUdQmptZ46zuw1KpUrQoolGreFybAYFueWCxbyV528batJTi0TLAZoLJgV55Zw/lSxqHrfq2jMQZZOKkiLt3cCPGNeO4FBXNnx7QpBuoyFjomnfxY+Na44Tc1o3fr/19BXMe30kvYdEYGCox7NTVpKsQ91GEt2QnFTAB+/vZOGLG7G0Mua7H55g3PgO6D+gN5CXrmfzy/6L6CnkLHluJG5O1i0eI6+4km9+Pi3YfKGdh69gZmLAwyM7CDpzp66ukerqBtq30711wjc1/nYkR99AX+RMWhfNb0dyWvJDKphIJNojFUwkOq0sv1zUIzkm5s2zK2qrhS6YmJKXVUpDXRMVAna3OLpaAZCfUy5YzFt5+zkQ3tZTqwWBO2FqZoRvgBPXLmfR2CjsWum/Y2pmhLWNKYf2aa/LxMbOnIAQV0pLajh55LrW4twkl8t57JkBRLTzZsXSX6msEG6d9t+RyWRMm9uPwePao1ZreOu5H9m3NUbstCQ6ICuzmHfe2saTj6/CyFCfNd/OYu68AVhZmYqdmmiy8sp48aNtXL6Ry+I5Q4gKbvk1uGq1mo++PcRzD/dGIcAWpcrqetb+fAZTI0O6RQvbMXTxYiY3EvN1u2BS14iJhTEykeaNSSQSiRCkgolEpyn0FKIeyblZMKmpEna2gqWNGXK5DLVaQ25GqWBxXd1t8fC2F7RIcysXNxtuJOQSdy5NtBxu6t47GDNzY65fzRE7lT8ZOCyS61dz0Gix/WrCtC6YmBqwdsVhQf4OOrvZ0H9YJNkZxfzw3yNa/druVp9hkSz+YirIZOzfGstHr/0s+OYsiW7IySnl/SU7eGTG1xib6LN85UyefnYgrq4P9kDc0opaPvv+CJXV9TwypjP9OgX+439zL7YciqdTmCeeLsL8fm/+NZba+iYmDW8n+Masc+dTcXO1xsXFStC4d6OpQUltZR0GRlKHyd3QABpNC3+I/UVJJPcxqWAi0WnNBRPxOkzMLI1p094HA0NhB5opFHIsbcxQ6MnJThNuhoazmzWZaUWk3hBvna5cLsM3wImMtCLROzv8g5wpKa4i9lyqqHncqm0HHxKv5XItPktrMewdLOjVvw0KhYzDe7XXzfJH/YZFMGpKZ7ZtPMvW9WcEiXmn/INd+XLjbCxtTDlzJIGFj64i4VKm2GlJBJKZWcx772xn9qw1lJfX8tEnU5m/YAgBgc5ipya6uvomXnh/C2di03hoWHseHdtZK3GyC8o5HpPC6H4RWrn+rapq6tl7LIEuUd4M6Sn8SuSMtCL69W0jeNy7odFoCGznK80wkUgk9zWpYCLRaQo9cYe+mlqYcPV8KjkizNNw8bBB1aQiO0244aeuHs3rEnMyhetquZ3ojj5Y2ZiSllQgah5hER6EhLmRJ+JMl9tRKOSMGNuOE0e1e1xm8ozuFBdV8c2yg9QJsClGJpMx46k+RHfyZfumc1y9qFsFCXNLE179aDJPLBxKRkohqz/dx6qP9wiyTUgijsTruby56Gc+XLKT4qIq3nxnHO++N4HwCA+xU9MJSqWKz787TEJKPtGh7jw+satW5nyo1Gp+3Hme52f0FeQoDsDPey6SnV9OeJArBvrCPjTJzi7l0qVMAgOdBI17t2oqakm8kIK+odRhcjfUyLTyIZFItEMqmEh0momFMRqBN8X8kbll89DX6grh151aWJkAkJdVIlhMU3MjrGxMyc8pEyzm7Ti72VBSVEWiyEM2DQz1MLcw5vC+K5To2JaU/kMi2L01Vqt5WVqbMPXRHri62bBlnTAdH0bGBsx5aSiV5bV8u+IQhfnirpe+lUwmo9/wKJZvnou+vpxt607zyuNriNWx4cCSe6dWazh7Opnnn/mB5Z/vp7FRxeNP9WXpp1OJjPIUdPCnLtNoNLz31X52HLxCvy5BvDt/BPpa2uby487z+Hs64OZopZXr36qqpp4L8RmYmRgwdmCkIDH/KP5SJo6OlkREegoe+240NTR3gUodJhKJ5H4mFUwkOk/MDhNDYwP09BVUlQs/08PO0QKAojxhbxiDw90pL62mvFS8OSZBbVyRySA9VfyVvp26BaCnJ9e5YznWtmb0GxzOIS2uGAYYMb49ZSVVrF9znLwcYTqPXN1tWbR0EldjM1j09I/UCDx0+U64etjx9soZPPvGaDKSC1j7xX7enr+efIF+jyQtr66uke1bY3jqsVW8/+4O7BwseGb+YN55bwJh4e5ip6dzvtt6jt1HruJoZ87T03piqoX1wQBJGYVcSy5gdN9wrVz/djbvjiPuahaThkVr7ev6O8eOXcfDwwYjHZ8NIlPICe0aKBUR75K0VlgiaV2kgolEp+npK1CKOMNEJpNh62SJQktPzf6Og6s1Tm7Wgqx1/SMbOzM0GshKE2+tr4ubNc6u1sSeFb9I0bl7AIZG+hw7mCB2Kv9j+Nh2bPjuJHW12jsuY2Cgx5znh+Af6MyKpXsEG8Ya0c6bOS8NxcTMkHcW/kRTk+5sKrpJLpfTZ1gkX29/juAId07sv8KSFzay+pO9VOnIph/JP8vKLGHF5/t59YUNrPn6CO06+LBy1aO89OoIfPwcxE5PJ63bfp6v15+gU4QXn7w6FjtrM63EqW9s4uO1h5k/vbdgN+XVNfVs3BWDoYEeowYIMy/lj+rrm7h4MZMOHYXdynMvqkqrSb6YLnYaEolEolVSwUSi0xR6eihFvlEyMNInJ60ItcBHg+wcLcjPLqNY4CMJnj72AGSIMLflJplMho+/I7nZZaIfhbGxNcPb14GYcyk61+ng5eNAu06+HNwbr9U40Z19sXO04NzJJE4dSdRqrD8aMrYdke29SbiUxdcf7xP87+CdsrYz46mXhvHFxtno6+txcGccs0Z+yk+rj1GvxWKW5N41Nig5evAaLzzzI598sIuLMen07R/Kus3zmPVkHxwcLMROUWftPXaNZd8fw8hQn1mTuuKhxY01y9cdY+LgtjjYmmstxq027IjBy8WGySPaYW0p/JrouLh0fH0c6NBB9wsmSqUKI1PhO3BaO7VGppUPiUSiHcJOsZJI7pK5tanou9Isrc3IUhVQU1mP+W9zRYTg4GINQGN9ExqNRrCnaz6BTgSHuVEg8tGCsLae5GSVcv1KDl17BYmaS//B4ahUas6cSKLvoDBRc7nV6IkdWfzCBvoNjtBq+/aT8wdRVFDJ5+/tJDTKA0uB/i48/GRvlCo1G1cdR6En5/H5A3W2/ds/xJUP1z5GzMkkVn2yl7PHEtm89jiTHu/FgJHRmP62plwiDo1GQ2pyAXt3xXNo3xXMLY0JCnFl2MgoQsLcdPb7SpccOHmdd5btoW0bN2aM60ywr/aGkp6MTcVQX49eHfy1FuNWlVV1/LQrhsZGFW/OHyZY3D86eyaFqup63Nx0f1V1fZVuPURoLW6uAm7pa0okEu2QOkwkOk2tVqNsEu9IDoCFTfMTpqoyYWd6OLo2F0waGpRUCBjb08eB6/FZJMRnCxbzdsIiPUhLLtSJ2SEduvqTeDWHQwKt170bQW1cCYvy5OCvl7Qax8bOjOHj21Nf18T6Vce0GuuPZDIZM2b3YfTUThzcdYmVH/4q2LGgeyGTyWjXLYBlP81h1ENdsHWw4MC2OB7u/wHrvzpMQa64A5UfRIUFFWzdfJ4npn/NooWbuBSXzkMzu/PpiuksfH0EbcLdpWLJHTgTm8p/Pt8NGg0ThkQTHaq9TUEl5TV8t+UsM8ZoZ0XxX9lx4DJqtYZh/cJwtBO+y0ij0ZCTXUrnzn6Cx74XTY1N6Bvo9pwViUQi+bekDhOJTtMz0EMl8pEcC+vmgklFWQ0u3vaCxbW2MyM4ygOVUi3o2lILKxPsHCxIu1EgaGfLrXz8nTC3MOLihXRR4v+RrZ05bTv4UF1VR1lpDdY2wrdp/52JD3flled+pNeAMEy12B7de2AoFy+ksWXDWQJCXOgzWJghjHJ5c2dJTXUDV+IyWfb+bma/OBi5QOtF74VcLqdbvzZ07RvCuWOJ7NhwlvVfH+GHFYfoPSScPkOjiOjojUIhbZfQhuKiKo4fSeDUsUQuxmTQvrMvbdt5029QGL7+jlKB5C6dv5TBKx9sJzzQleH9wujeQXs39Gq1htWbT/P0tF6YGhtoLc6tSstrWLPpFIYG+kwe0V6wuH+UmlpEbEw6Eyd3EiX+3TI2N8Y3XFqxfbe0MaRVGvoqkWiPVDCR6DQ9PQVNjeJ2mDh52BAc7UVtpbCrheVyOdUVdWSlFlFaWIXjb0d0hNC+ewBZaUXk55bj7Cpc3D+Sy2X06h9KekohRYWV2Is8U6D3gFA+/M82Du+7zJhJuvVm1j/ImQ5d/Pl1ayzjpmrviaxMJuOxef1JSshl++bzBIe54+wmzPeHXC7nuUUjWPnhHrZtOIuRkT4z5/YVZSDz3ZDJZHTsGUTHnkGkXM/l8K54tq8/TcypZIxNDOg3oi19h0cK+vf7fpWTVcLp4ze4cT2PIwevYWpmxJCRUUye1pWIKC8UerpbYNNl8QnZvPTeFhqblAzoHszAHiFajbdux3lcHCxp4++s1Ti3+uGXs9Q3KOnfPRgXR0tBY9905nQSxsYGRETo9jrhmwozS8hJKRA7DYlEItEq6d2DRKfpQoeJgaE+CTHplBZWCh7b2b35DHNeVomgca1tTbkSm0HK9TxB497K19+RK3EZxOnAtpxuvYMxNjHg0N7LOnkkZOrM7qxbe5xiLX+fWlga89T8QSReyeGDxb/Q2Cjc30+5XM5TLw7m0Wf789O3J3lzwQatbghqab5BLjy2YBA/HnyJGU/3x9DIgLNHrzN94FL+8+yP7Nx4lrLiarHTbDWamlTEXUjj6y/38+ik5Xz87k42/nAKE1ND3vpwIpt2Pses2X1p295HKpbco0vXslnw9s8E+znx3KN9GNFfu11lV5PyuJyYy+Rh7bQa51b5hRWciUkjLMiFaePEK4jfSMhj0OBw9PV1uxB8U1NDE/qG0rPXuyWtFZZIWhfpVU6i08ytTZGL/ATZ0q55On95ifA3Ms4etgDkZQpbMPEPdgEg+Vou3fpq92ni34nu3LwlIOZMCgOGR4qWB4CxsQGjJnRg66Zz3EjIJTDEVdR8buXiZsPQ0dFs2XiOWfP6aTVWeLQXj83rx65fYvjqk73MXThUq/H+SCaTMWF6NwwN9DhxKIGFT6zljU8mY2Mn3BaNf8vc0piBo9sxYFQ0aTfyObjjIjkZxXz59nb2b43FwFCP7gNDadc1AJffXgMkzUc10lMLuRSTTkZaEYf2Xqa+ronojr507h5Ap24BBIe6oVBIxZGWcPl6Dgve3kx9g5Iu7XwZO6StVuNVVtfz6ZrDLHl+BHK5sDd/a386Q1ZuGWFBrjg7iNNdUlZWw6mTN3h+oTjDZu+FmbUp7vrCdgJJJBKJ0KSCiUTn1deIO4XdytYMgAoRCibeQc4ER3pQWVYraFy/EBfsHCwoKxF3pa+jsxU9+oWQmV6MSqkW/Slxlx6BrF97gl1bYnWuYAIwaVpXHh2/nC49AmgTod1z5aOndCY+NoP9u+IJifCgj8Dbg0ZO7oS9kxUfvPYzH7+xjUee7odPgPY2dmiDTCbDJ9AZn0Bn1Go11y5mEn8ujb1bYzi+7yrLl+yiXVd/nN1tiO7sR2i0F2YWxmKnLRilUkVyYj7X4rO5HJfO5bhMPHzsSUspoGNXfx5/egDtOvri5GIldqr3nUsJ2Tz/1s8E+TrRqa03U0Zqd6aHRqPh/ZX7eGJyV+xszLQa61ZpmcUkpxagpydnxkRhh8z+UVxMOlZWJnTopPvrhG/KTysUfTB/a6TWyJC1cEeItFZYItEeqWAi0Wl6+nrU1zSImoOVnTmm5kbU1wqfh52jBQkXM1Gr1cLGdbBAo9Fw8tB1nl0s3uBXaC6aHDtwjYQr2YRGijtcLiDYhS49AsnJKqG6qh4zHVsTa2pmxGPz+rHtp/MEtXHTaoFJLpfxwpujeWvhJj7+zzacXa0JDnPTWrzb6dI7iI/XPMqiZ9bx0pPf8vSrw0XtiPo35HI5oW29CG3rxeQnepGRXMC54zfIySzh180XSEnI481n1/1eQAmJdCcozB1HV+v7YoCpUqkmN6uEpIQ8crJKiD2bilwu40p8NqZmhnTqFsCEaV2JjPbEN9BZ6iLRokvXslnwVnNnSacobx4a3VHrMTfsuECInxPtwoSf3fH1j8dJTClg8ugOonWXABw7koCzizXW1ro1VPzvyGQyjLU4aFwikUh0gVQwkeg0PQM9mgSckXA7Ng4W1FTVky/wsRgAN6/mrTzZacWCbqyRyWQEhrlx6lACOZkluHnaCRL3djp2D+Cn709x/lSS6AUTmUxG5x6BfPT2dvbuvMhYHdxk0HdwGHt3xLFl41mtDoAFMDM3Yt7Cobw893tWLzvA84tH4ehspdWYt/IJcOLLHx9n1ecHeOv5jUx5rAcPPdFL54fB/h2ZTIaXvxNe/s0dM0+9OJSE+Ewunk2ltLiKX3++wKVzqWSkFGLrYIGHrz2+gc74Bjnj7m2Pm5cdRgJuF7kbKqWK/LxycrPLyEgppLK8lrhzaeRklVJbWw8a6N43BHcvO9pEuDPvpaF4+jgIfkTjQXX+YjovL9lKkJ8Tndt5M1WAYkn89RwuxGfwwctjtB7rVlev53D8bDL6egrGDI4UPP5NjQ1KLpxPY+q0rqLlcC+qymqwEnkge2uk0TR/tPQ1JRKJdkgFE4lO0zdQoBS5YGJubYpcIaesSPjjKfbOlhgY6tFQ30RZcTU29sLNaQgOc+PMkevcuJIjasEkJMydwBBXjh+8xoyn+oj+NL13/1C++fIAsedSGTWhg8496ZbJZDzz0jC++HA3XXsFaX3LkZunLc8vHskrc79n8fz1fLByOhaWJlqNeSsrGzOefW04VtYmnDpynUsx6bz07lgcnKwEzUNbjEwMiOrkR1Sn5lWu814fSXpSAdcvZ5GXWcKl82ns3HiWxkYlapUGD18H6usaierk21x88XPE3NIYO0cLbOzNsbYxw8TMsMXXMqvVaqoqmldvlxZXUV5aQ0FeOQ31Tdy4lktleS2pSQWoVGoC27iQlV5Cuy5++AU50X9YBL5Bzvj4OWJsopvFnvvdmdhUXnt/Gw2NKnp08mfC8GitxyyrqGHLnjhemzdY8NdSjUbD8rVHCfF3JjrCAycRu0tiY9Pw9rGncxd/0XK4F00NTRgY6oudRqvTXDBp6bXCLXo5iUTyB1LBRKLTjEwNRd9IolDIsbI1o6xI+C05crmckChPLp5JITutSNCCSWhbT/T0FVy9mEmfoRGCxb2VQk+Ot78De7bFkZFahJevg2i5ABgY6jF6YkfW/vcwp48l0q13sKj53I6bpy1t2/vw8ds7eH/Zw1p/Oh/RzpvnXh/J1g1neOuFjbzzxUOCv4lW6Cl49JkBhEd788WSnbw+bx0PP9mr1R7R+TsGBnoEtHEloM3/z9FpamwiJ7OUzNQicjNLyEoror6+ifjzaeRmlhB/IR1PPwcykgubr2Goh6m5Ef4hLlRV1OHl70hleR229ubU1zehpydH30APjVqDmYURleV1mJobUVJUhUajobqqnrraRkzNjEhPLsDF3YbYcymYmhtRVdG8gt0vyJn83HKiOnhjaKRPWFtPBo5si5unLW6eNtg5WIpeAJU0O3jiOm99souQAGf69QhmzOAorcdUqtS89dmvTB/fCWtL4Y+hnDqfwuWEHAwM9HjnpVGCx/+jk8dvUFFei6eXeA8n7oWzjwMO0mBqiURyn5MKJhKdpm+gT1NDk9hpYO1gQVpCLkqlCj2BW/1vDp3NTCkkvIOPYHF9g5zRqDVcjcsQLOZf6do7mLMnkog7lyp6wQRg8IgoflxzjJ83nNHJggnAuKmduZGQy69bYxk6RvtPivsNjaCspJpVX+znq0/38dSCQaIci2nfzZ9P1j7GR29s5Yt3d3LhVDKznh2AqY7Nm2lp+gb6ePk54uXn+D//rqGhidKiKkqLqigpqqK8tJqKslqqymt/P+qnUWsoLqhAqVSRk1GC6rfXuoYGJV5+DmSnF+MV4Eh+TjkmpoYo9BSYmBri7GaFg5MF9s6WdOgRgJW1Cda25tjYmWFrZy7NN2gFfj10maUr9qPRaBjeP5zBfUIFibt+2zk6tfUmIljY2Uc3GRjo4e5iTfdO/tjZCjto9o/Uag1ZGSV06RbQ6gqINy6kYGr+4AyibinaWAMsrRWWSLRHKphIdJqegR6NDeIeyQFw9bYnLSGHypJqbByFbdv1+O0GKDOlUNC4Bob6+Ldx5drFTCrLa7CwEm8QXWQ7b5qalOzZFsdoHZgbYm1rxoSpXdi1JYb42AzC2wo/qPCfKPTkTH+yN/NnrSEi2gs3T+0/BRw/rStqtZrVXx6kuqqeF94cLcqRJVt7c9758iH2b49j2fu/kpFSyNRZPWnXtXW1u7cUQ0N9nN1scHazETsViQ7RaDT8+PM5vvr+GKHBrowfEU3vLoGCxD50KpHk9GLeeE64leS3ah/pxbdfPoJSKe6Wl4SrOVy9lMUjs3qJmse9aGpoQt9QupWQSCT3N906fC+R3ELfUI8mHSiYmJgboVZpKCkQ/liOd4AT/qGuNNQL32nTuVcgDi5WXL2YKXjsPzIw1KNrryDSUwpJF7hw9Ff6D42gsryO9WuOi53KX/LwsmPm7D6sXnaQJoFWP06Y3o3h49tTUlTJZ+9sF3zD000ymYwBI9uyctNTOLta8dqc71n5wW7KS2tEyUci0SVqtYbV607y3aZTGBnqM2NCZ8GKJakZRWzeFctLsweI3lGhp5BjJPIMjlPHE7GyMqGNwFvGWoKrnzNWAj9Euh9otPQhkUi0QyqYSHSavZstXUdo/zjBP7H97Q1BSX654LE9fO1JupLDmUMJgs9z8QlwpjC3nMsx4h/L6TUwjDYRHpw9fkPsVABwcbNh8Mi2NDQqSbiSLXY6f2nIqLao1GrWrDgkSDyZTMbsFwbj5mnH3u0XWbv8ICqVOEUTAGc3G154eyzz3xzN5dgMHhv1OQd2xImak0QipqYmFW9/sotvN54m0NeJT96aQIe23oLErqyqY8mXe3hl7iCMjaThvhqNhqQb+QweHqlzA8T/iVqtJvFCCg01DWKnIpFIJFrVul6dJQ8cc2tTQrsK89Tr79w8hlOcXyF4bCd3GwyN9aksq6GsuFrQ2G2iPFDoybl0Pk3QuLcTEe1FXk4pu7bEoFbrxrOUMVM6cu1SFt9/fVTsVP6STCZjwWsjyM4o4YxAxSa5XM68l4YydVYPNq49ydI3tqASse29udskiiUrp9NzYBvWfHGAZx/+ioRLWaLlJJGIoaa2gbc/2cW52HTs7cxZ8FR/2gS6CBJbqVLzzme/Mmtqd9yctbu9q7VITy0i7kJ6q+wuaWpoQk9fgZ50JOeu3Zxh0tIfEolEO6SCiURyBxxcrDCzMBalw0Qul+P52xyTtBt5gsY2Mjagz5AI1Co1VRW1gsa+lUIhp/fAMApyy7l6UfyOFwB3Tzv6DAojO6OEq/G6e/NtYWXC5Bnd+PidHeRmlwoSUy6X8/DjvRn7UGfSkgpZ8urPNIo8wNnCyoR5r47gzc8fwsLKhEVP/8A7L2wkN6tE1LwkEiEUFVcx96X1HD6RSHCAE8vfn4KXh3BbWZatOUz7KC86RHoJFlPXxV5Iw8bWjKhoYTp8WlJDbSPKJhV6+lLBRCKR3N+kgolEcgdsHS2prqilRIQOE4C2nf3w8LEn/Ua+4LEdnC1Ju5HPxXOpgse+Vf9hEQS2ceHgr/Fip/K7h2f1pKS4itXLDoq+AvvvBIe58cjsPix59WfqBZqHI5PJmPXMAHoPCuX4wWu8//ovVFfVCRL77/gFOfP2sod5bvEoCvPKefahr/jy3Z2UirA6XCIRQnJaIS++uZnKqnoiQ91ZtGAYjvYWgsXfvu8S1TUNjB2i/XXFrcneXZcIj/TAoBV2aTT+9nNE6BXy9wVpiIlE0qpIBROJ5A7YuTS3DxfnlYsS38bBgszkQpKv5Aoeu20nXwDizohfMPH2c0StUnNk7xXq6xrFTgcAZ1drBo9sy+W4TC6cSRE7nb81cHgknt72LP/wV8GKOzKZjIkzuvPc6yNIvJbD/EdXUyhCp9bt8urSJ5iP1z7GI88O4GpcBk+OX87XH++htLhK7PQkkhZzLjaVuQvXkZpeTNcOvix9cxzmZsKt2Y6Nz+TSlSyef0r8Ia+6JDurlLSUQrr1FP/Y8b1orG8iuKMf5tbibdBrtbRxHEc6kiORaI1UMJFI7oCZpTGGxgZUlAo7Q+Qm3+DmM+YpCcIXTAJD3Yjs6ENhXrlOdFAMHBGFSqXm1NHrYqfyuykzuxES7sZXn+1HpdTdYaIymYynXx5GWUk1G9aeEDT2oJFteebl4ZSX1fD+az+TeDVH0Ph/RaGnYNDoaD794XGmze5DzKkUnhz3Jcvf36UThR2J5F5pNBo2b4/hjfd2YGNlysMTOvHsE/0wEPAIRVZuGcvWHGbOI70xNGh9XRTadOb4DSLbetKhk5/YqdyTxvpGEs4mi76WWSKRSLRNKphIJHdAJpPh5utAblqRKEUD70AnHFysMDU3oq5W2In0evoKTEwNuXAyiZwM8Wc99B4UhqmZEds3nRM7ld/Z2JnToYs/GalF7NkRJ3Y6f8vAUI/nXhvBnq2xghed2nf1570V0yjIreCFx9dw4uBVQeP/HUNDfYZN6MDn6x7nsecGcT0+m6fGLeP9VzaTkijs7CCJ5N9SKlWs+v44X/z3IA0NTUyb2JnHHu6OXC7cU+iKyjq+/v44rzwzBBsrqQvhVocOXMXUzAhjk9a5Lej3IzlG0pGcu6XRaOdDIpFoh1QwkUjukKWtGfW1jVSVCz/81NjUEENjA65fzCQ9Ufg5Ju27BQBw4WSS4LFvZWZuTIeu/iTEZ5MiwkyXvzJ2Sifad/Zlx+YLVFfVi53O37KxM+ONjyfx+bs7uSrwphgfPyc+/24WPfq14a0XN7Hq8/069YTSwECfASOj+PT7Wbzwzlgqy2p5/pFVPP/IKk4dvo6ySXdylUhup7yilgWvbmL95nOEh7jy8TsTGdQ3VNAcGpuULHp/G0P6heLrZS9o7NYgL6eM6qo6evQOFjuVe9ZQ30RAOx9MzIzFTkUikUi0SiqYSCR3yMHVBoDCHGG2jNwq4Le1gzeuZAseO7qrH0FhbqQnFwge+3aGjm2Hf5Azx/brUIeCkT79h0WSmlTAD9/o7prhm7x9HVn41hi+/+oImenFgsa2sTPnmdeGM3RsO65czODVed9TXlYjaA7/RC6X06lnEO+smMZHax7D3duOlR/s4uHBH7H52xPk55SJnaJE8j+SUgr44LM9XLychburDS/PH0pEqLugOajVGlasOUqfbkF0ivYRNHZrcexwAsVFVXTo0jqP4wA01DZw44L4s81aI2mtsETSukgFE4nkDjl52ODsaUdJnjibcvxDXQFIuiL87AcHJyuamlQc3HGR2hphjwTdTmAbVxR6crasP6MTW1du6tkvhLYdfEhJzCc9uVDsdP5RVAcfBo6I5LVnfqSoQNgNMfr6ejz9ynCGjGnHtUtZLHn5J67E6ca66Fv5BDjxzOsjWfHTHKY+3ouL59KYMfQTPl68hQM68ndCItl36Cpznv+RU2eTGTkkkuUfTcXF2UrwPP773TH09RWMHBwpeOzW4uihBNp18MWshYfvFuaWo1YLM0ersb558Lq+dCRHIpHc56SCiURyh+ydrcnLKCY/U5w5HkERHgSEu5GXIWw3wE2degbS1KQi9nSyKPFvNXJiRxrqm9i7TXdmhshkMp6cP5CrFzP5/P1dqNW6f6i498Awxj/chU/e2U55qfBdHv2HRfLZt7NoalTywuNr2PTtCVQq3Ryca2pmxLAJHXh72cMs3zQbR1cr1ny+n1dnf8eShZs4cfAaDQ3CrGyWSG5SKlV8sfIgO3+9REODkumTu/Ds7P6YmBgKnsuOPZcoK6/hyek9BY/dWuTllKFRqeg7qOWPSX2x6BfBNhHV1zQgl8tQ6CsEiXdfubnVpqU/7sGyZcvw8vLCyMiIjh07cu7cX8+Hu3r1KmPHjsXLywuZTMann376P5+zZMkS2rdvj7m5OQ4ODowaNYrExMQ/fU6vXr2QyWR/+njyySfvKX+JRAhSwaQVupsXN4Dy8nLmzJmDs7MzhoaGBAQEsHv3boGyvX84etgCUJAtTsHEO9CJ1IQ8rsVlUl0pfFdFp15B+Ie4cD1e+CNBt9O9Xwgduwewd3usTt1ge/nYM+6hzmSlFXF472Wx07kjw8e1JzzKk5fnfk9lhfAzenz8nXh32TQGjIhi77Y4Xnh8Dfm5un3kxSfAiamP9+a7PQuYOa8/puZG/LTmOJN6v/9b8eQqNTo+y0bS+hUVV/LCqz/x87YYUlILef/Nscx8qJugw11vOnwikUPHr7Ng9gBR4rcWRw5cJTO9mHYdfVv0usomFQo9hWAFk6b6JtRqDYaGUodJa7Vx40bmz5/P4sWLiY2NJSIigoEDB1JYePsO2draWnx8fHjvvfdwcnK67eccPXqUOXPmcObMGfbv309TUxMDBgygpubPD2RmzZpFXl7e7x8ffPBBi399EklLkQomrczdvrg1NjbSv39/0tPT2bx5M4mJiXz99de4uroKnHnr5+jWPMMkP1OcGSYGhvr4BDkDkBgv7KBOAL9gF8pLa9i7NQaVDgzp1NfXIzjMjYzUYk4dThA7nT+Z/GgP7J0sWf7hr5QWi7OK+m5NmtmdfkPDeXnOd1SJUJAzMjbguddHMnNuX7LSivjoja3s2RqjE6us/45CISe8nRdPvzaCj7+dxX++eAgbe3PWfnGACb3f45M3t/LzdydJTy7Q+a9F0rpciE1j1pxvaWpS4uVpy38/n0an9i17E36n4uIz2borlrdeGSWtD/4HSYl5dOrmj6lpy3YAZSYXEBLt2aLX/FsyGUEd/TA0bp1bfsSkK1tyPv74Y2bNmsXMmTMJCQlh5cqVmJiYsHr16tt+fvv27fnwww+ZNGkShoa3//7ds2cPM2bMoE2bNkRERLB27VoyMzOJiYn50+eZmJjg5OT0+4eFhcXdfwESiUCkgkkrc7cvbqtXr6a0tJStW7fStWtXvLy86NmzJxEREQJnLqxTu+J4susbfPbsdy12TRtHC9p08MHASLw3gxEdfQmMcCdThPkYMpmMLn2Cqaqo43KMbsyaGDq2HcYmBpw4nKBTN6PGxgbMero/1VX1fPffw2Knc8fGTOlM+y7+fPD6L6J0mgB06xPCyo1zMDE15JO3tvP5kp0U5pWLksvdUijkhEV78cTzg/l6y9Os/GkOvkHOxJ5JYcX7u5k6YCkfvvoze7fEkptVqlPfs5LWQ6VS8/26Uyx+ZyvlFbU4O1mx4pOHcfttMLnQEpPy+eKrg7y2YBhmLVwEEJM2Ohcz04o4cSiBbr1afjtO4uVsXH7rhBVCZUkV188mS0dy7oVGSx93obGxkZiYGPr16/f7r8nlcvr168fp06fv/Wu7RUVF89w/G5s/vz79+OOP2NnZERoayssvv0xtrTjvOSSSOyEVTFqRe3lx2759O507d2bOnDk4OjoSGhrKu+++i0r11x0CDQ0NVFZW/umjtWmsbyI9IYec1JYrLMjlcipKqjl/6JpoNzo+wc4kXsoi9sQNUeJ36ROCX7Azly+kiRL/VhaWJgwf154jey5zOVY3ijg3RXXwYfIj3dmzLZajOrTN5+/IZDKmP9WHkHA3Fj71rWiba2ztzXnj48ksfHssJw9d4/EJy9izNUanjl79E5lMhru3PSMmduSd5dN4a9nDvLRkHB6+DhzafYknx33Jy09+y9vPb2THpnNcvZhJfV2j2GlLdFxRURULXtrA6m+P4+vlwPPPDOSVF4ZiLNJT/szsUj5Zvp//vDwSeztzUXLQhhs38lm9quW3nR05cBVDI306dQto8WsnXcnGP9Stxa/7Vxrrm+c1GUhDX3XKre/fGxpuP5S8uLgYlUqFo6Pjn37d0dGR/Pz8FslFrVbz7LPP0rVrV0JD/39mz5QpU/jhhx84fPgwL7/8Mt9//z0PPfRQi8SUSLRB6ptsRf7uxe369eu3/W9SU1M5dOgQU6dOZffu3SQnJzN79myamppYvHjxbf+bJUuW8Oabb7Z4/kKysDEDoLK0ZY9DOHvakZ1SSGVZDZa/xRBScFRzu23CxUzUajVyubA1z9C2npQUVLL75/NMebI3CoX4Ndfh49vz8w+n2LDmOOHRXmKn8yfjH+7CgV2X+PKD3YRFeWJjJ/z3zN2SyWRMfrQnCj0FX763iyfmD8Te0VKUPPoMDqdtRx9WfbGfrz/dz65fYnj65aH4B7e+I4UGBnqEt/MmvJ03Ex/pTlOTktTEfBLis8nPLmPr+rOUFlXh4GyJb6ATIZEeOLlY4+lrj52DhWBzCSS668SpJL5adYSKijrc3Wx4enZ//HwdRMunoLCSRe9s5eUFQ0XrbtGWDetP88QTfVr0mhqNhrjzaQwbHa2VAldVeR0OLlYtft2/oqevIKSTP3oGUsHkbmljDfDN67m7/3mN+OLFi3njjTdaNNadmjNnDleuXOHEiRN/+vXHH3/8938OCwvD2dmZvn37kpKSgq+vOMcKJZK/IxVM7nNqtRoHBwe++uorFAoF0dHR5OTk8OGHH/5lweTll19m/vz5v///ysrK/3kB1nXm1qbA/z8BaSnOnnYA5GeUiFIwcXCxwtbRguqKOjKSCvEOvP3QLW1RKOR069+GHRvOcjUug/B23oLGvx0HZytGTuxIwuUsblzLISBEd26mTc2MWLBoJKu+OMBn7+7gjY8mtZob3wnTu7Hr5ws8//ga3v5sKu5e9qLkYWVjxoLFo+k1IJnlH+7m/dd+IbK9N9Oe7IOFlYkoObUEfX09AkPdCPztifCs+QOprqonPbmA1Bv5FOVVcHj3ZWQySLmRj5uHLUGhrphbmeDuZY+dgzn2zpbY2lugL3JLvEajoaqyHkNDPQylp80trr6+iR82nObH9c2dpBPHdWD6Q11F6yoBKCmp4j/vb+e5Of0J9HP85/+gFUm6kY+LizWOTi1bKE5KzONqfBYTHu7SotcFaKhvQtmkFPTnS1lhBdfOJGEgDX3VKVlZWX+aB/JXs0bs7OxQKBQUFBT86dcLCgr+cqDr3Zg7dy47d+7k2LFjuLn9fedTx44dAUhOTpYKJhKdJBVMWpF7eXFzdnZGX18fheL/31AHBweTn59PY2MjBgb/+4bL0NDwL19gWwtLWzPkchnFOc1zAlrqTYRXsAs+Ia7kZxUTGCXgcLXfyGQyOvUOZvfGs1yLTRe8YALQfUAo1+OziDudrBMFE4Ch49qxdcMZ1q86xuKPJoudzp9EdfChTaQHWzecZfeWGIaOaSd2Snds6Nh2WFgZs3j+el54czTBYeIVTqM7+7Fi/Wx++fEU61YdIyejhE49Ahk6rj1698kZejNzI0KjPAm95bWlsqKW7PQS8nPLyMsqJS0pn73bYinMr6C2uh6Npvn7rLKyDitrEyysTLCwNMHO0QKFnhxjEwNMTAzRN9TDyEgfPX0FCoX89983Gc0DA1UqNUqVCmWjCqVSRV1dE/W1jTTUN1FdVY9KpaKooJLK8lrUGijMq8DWwZzy0hrMLY2ZPKM7Xn7idTzcjxJv5PHOezspLa3Gw92Gh6d0pV+fEFFzKq+o5aXFP/P4zJ5EhLauhyl3Yt260zzxZO8Wv+6FUym4edrSrpNfi187PTGPwEiPFr/u32n47QihdCTnHmnpZLeFhcUdDVA1MDAgOjqagwcPMmrUKKD5IevBgweZO3fuPcfXaDTMmzePLVu2cOTIEby9//l94sWLF4HmexaJRBdJBZNW5F5e3Lp27cq6dev+dHzjxo0bODs737ZYcr+wsDVDrVLTqFJTX9OAsZlRi1zX1tGS1Gs55KQWtcj17oVXgBMatYYr59MYOrmT4PFD23pSVlLN7s3nmfpkH524WXXztKPXwDBOHblOyo18fAOELyT9nUfm9CU/p4zN350krK0nHiJ1a9yL7n3bYGFpwrsvb2bOi0Po1CNQtFwMDPWY9EgP+g6NYO2XB1n+wa/EX8ig77BwOvcKajXdO3fLwtKEkAgTQiJuf3NaV9tAWUkNFWXNH9VVDVRV1lJTXU9JcRUN9UrqahtobFBiZWNKQW45arUatbr5HbuTszXFRZUoFHIMjPTQ09fD3tGCpkYVxiYGWFgYY2RigKW1BX5BLlhamWBhbYqZmeF9+3suNqVSxbr1p4mJyyAru5TQNq688uIwnJ2tRM2rsrKON9/dxrQpXWkfrRsF85aUmJiHubkRTk5WLXpdlUrN9s3n6dgtQCsdYQkXM/ER+OdefU0D+oZ6yPXEP5oruTfz589n+vTptGvXjg4dOvDpp59SU1PDzJkzAZg2bRqurq4sWbIEaJ6leO3atd//OScnh4sXL2JmZoafX3MhcM6cOaxbt45t27Zhbm7++zwUS0tLjI2NSUlJYd26dQwZMgRbW1vi4+N57rnn6NGjB+Hh4SL8Lkgk/0wqmLQyd/vi9tRTT/Hll1/yzDPPMG/ePJKSknj33Xd5+umnxfwytM7IxBBDEwMaahupKK1usYKJi3fzjW5uenGLXO9etGnnBcDl86kt2j1zp+RyOb0Gh7N57QlizyTTobt4N9B/NHVWT2LPpPD9ysO88bFudZkYGukzc3Yf5k3/mndf3sznax9rVW3MEe28eeeLqSx6dj3ZmSWMndpZ1Btle0dLXnhrDCMndeTHr4/y5vwN9BjQhuHjOxD+29+PB4mxiSHGJoa4uN9fcyQeVOnpxXywdBfXE/MwNTXkicd6MX5se9FnRlVV17Nw0WYmjGlP9y7+ouaiLet+OMXsOf3++RPv0sULaZQWV9N3UFiLXxsgMT6LXsMitXLtv9JY30hTgxIjk9bdkSwGbc4wuRsTJ06kqKiIRYsWkZ+fT2RkJHv27Pl9VmJmZuafZuXl5uYSFRX1+/9funQpS5cupWfPnhw5cgSAFStWANCrV68/xVqzZg0zZszAwMCAAwcO/H7/4u7uztixY3nttdfuOn+JRChSwaSVudsXN3d3d/bu3ctzzz1HeHg4rq6uPPPMMyxcuFCsL0EwgW29qa9toLK0BicPuxa5pqObDWaWJtRV17fI9e6Fp78j7XoEUphbRm5mCa6eLfO13Y3eQ8K5EpNO3OkUnSmYuHna0b6bP/t3XCTxag6BbXRnlgmAl58jT84fxOE9l/nqs33MfXGo2CndFQ9vBz5d8yjLl+7h07d3MGfhEAwMxP0REtDGlTc/nULMmRR2/nSOF2atoVvfEMY81Jk2ArenSyT/lkqlZuOms3z7/QmCg1zw9rLnpYVD8fcTv2OuuqaBN9/dzugRbendI0jsdLTi6tVsbGzNWnx2CcDR/dfo2NWfUC29Lhka6WNlK+xcNQsbMwLa+aBnIH6XqeTezZ079y+71G8WQW7y8vL6xy2R//Tv3d3dOXq05TdQSSTaJPXRtUJz584lIyODhoYGzp49+/uwJGh+cVu7du2fPr9z586cOXOG+vp6UlJSeOWVV/400+R+VVfTwI3YdCqLq1rsmvoGephbmXD1fGqLXfNuyeVyDAz1yEwu5Mo5cfLwCXSmvr6J3ZvPUyNi8ehWD83qRWAbV7ZtOCN2Krc1ZEw0tvbm7Nh0nkN74sVO565Z2Zjx/BujUKnVvPb0D5QU6cbK8ehOvixaOok3PpmMnr6C+TNXsfCJb7l4Lk20FeCS1kOj0XDlchZL3tpGQUGFKDkkJxewaPEvrF57DJVKTViYGyuWTdeJYklVdT0LX9vEgH5tGNCnjdjpaM3+fVeYMrXlB7LW1TVy9MBVvP0dkMtbvjOvvLSauprbr47VpuykfJJi0tDTl5693jWNlj4kEolWSAUTyX3r5tOW8uKWvalz83WgoqSaqvLaFr3u3Qjv2DxF/NJZcQomMpmMfsOjaKhv4sT+q6LkcDtOrtYEhrpycHc8cSIVk/6OTCbjmVeH4xvoxO5fYkhPKRQ7pbtmYKDHgkUj6T0ojGdnfMPVi5lipwQ0/9527hXES++O5c3PpmBta8rCJ9byzLSvOX00EZVKLXaKEh1TU9PA9q0xLH1/F6dO3GDGoz1xFHiFdkNDEz/+eIonn1rL6dPJ9OgWyJefT+PRmT1F7+ACqKqq5813tzF8SOR9XSyJjUnD0FAPe3vzFr/2+VNJ2Nmb03eQduYzJF7KIjBc+OG7TQ1NGBjpS3OMJBLJfU8qmEjuW5Z2zW98KlqwwwTA1ad5C0RumniDXyM6+RIY7k51RZ1oT9B7D40gtK0XMaeSRYn/V6Y+1hNjEwM2f38StVr3bpJNTA1Z+NZYkq/n8Z8XNlBdVSd2SndNJpMxeFQ0C98ey7pVR/ll3Wmd6eSQyWR06hHIwnfG8sHXM3B0tuI/C9bz6KjP2bMlhloRnsRKdIdGo+FKfBZL393B0iU7MTLS5+nnBvL4U31xdrESNJeYmDQee/Qbdu+6iIGBHtOmdeWlhcMICtSNTREVlXW8+MomBvULZVB/7cze0AUajYZffr7A5CmdtXL9PVvjMDbWx9NHO8O+Ey9lEvgXA6G1ydbVhjZdAgSPe3+QaelDIpFog1Qwkdy33AOcCenoR1OjskWv69vGjTYdfMjLFG/wq6e/I4W5ZZw/ep0skboUbO3NMTU35NieeHIyxPu9uJWVjRnTn+pD7OkUjuy9InY6t+XpY8/zb4xCqVSz/MNfW233Q2iUJwsWj+LM0UTef/Vnqip1p/gjk8mIaOfNqx9MYMXG2US09+bn708xdeBHrPniAJkiFjwlwissqGTddydZ+u5Ojh+5zthJHVn89lgGDArHUOABzCUl1bz99jb+u/IQubnl2NiYsXzZdGZM764TXSUApWU1vPn2VsaPbUe/+7izBODE8UT8/RyxsjJt8WsXFVQSdy6V/kMjWvzaN6VczcGvjZvWrv+XcS9mkH41W/C49wXpSI5E0qpIBRPJfUuhp+Da2eQW7wRxcLXm6tkU0hNyW/S6d0MmkxH525aCOBE7PAaNjgZg75YY0XK4ncGjo7GxNWf15wdoqG8SO53b6tYnhP7DIjm4O541yw6Knc49s7EzZ8nyaQSEujDvof/qzBGdP/LydeC5RSP5aPWjTJnVg8zUImaN+ZIXZ63h4O54nf0ekfw71VX17N4exysL1rPyi/24e9jyzAuDeerp/nj/1ikoJKVSxY7tscyY/l8OH7qGRqPhmWcG8NnnD+PlJfzw7r9SUFjJ8ws3MGZUNH16hYidjlaplGp++ek84yZ2/OdPvgcHd1/C28+BHv1DtXJ9tVqNhY0phkbCb11ramhEvxVte5NIJJJ7pRuPMiQSLbD67SxyeWHLzjBx92/eSJSVnN+i171bUV38uHohjaxU8eZgtO8eSIcegcSdTkE5R4Wevm4MEzYyNmDm3L7s2RrLlvVnmDSzu9gp3dbUx3qQlpRPfEw6+3ZcZMDwSLFTuicKhZwxU7oQEOzKxrXH8QtyZsqjPXXm++EmCysTxk/vhvphNXFnUzl9JJGPF29l2ZJdjJzUgQ7dAwgKc5PO5Ldi1dX1nDmZxNGD17CyNsEvwJkXXxuBlXXLdw/cjbi4dL78fD/p6cX4+zvi6WXHE0/2wcZG2M0m/yQru4QPlv7Kk7N606G9j9jpaN2+vfH0GxiKqWnLr8bVaDTs33kRD297rG208/2XmVyIsRZyvxONdU3YubX8zJcHgjY6QqQOE4lEa6QOE8l9y8reAplMRn1ty84ssLIzx9renNoqcbfDRHUNoDC3nINbY1v82NGd0tNX4BPoTNK1HM4cvS5KDn+lz9BwmppUrPvqKEUibb74J3K5nBfeHINapeKzt7dz6UKa2Cn9K6FRnix8ayz5ueW8PPs7MkQs5v0duVxOdGc/5r48lB/3LuDhJ3tx8Xwaz07/hsXPruO7FYfITJWO7LQWZaU17Ps1ntde2MA7r/9CaXE1c54byIKXhzNybDtRiyW5uWUsXvQz33x9lPT0Yry97Xlqdl9efmWEzhVLkpILeOX1n3nskZ4PRLGkrq6RPbsu0X+AduazXL+cjbm5sVYL4QlxGQRHemrt+n+nrrpeKi5LJJIHgtRhIrlvWdtbABryW3i+hkwmw8nTjstnklE2iddVYedogVegE+mJ+VyLzSCik68oeQwe146N3xzl183n6dZPd866y+VyZi8cwjMPf8Xm707x1AuDxU7ptoxNDHjzkyk8M+MbNqw5jrWtGR7e2hkOKARTcyNe/M8YTh+9zqtzf2DkpA6MntIZPT3d6ja5ycrGlNFTOzN6amcyUgo5e/wGu3+J4ZcfTuPqYUvnXkF06xeMp4+DdHOgIzQaDempRcRfzOTogavI5DL6DGjD3PmDcHK2Ejs9oPk40I8/nuLo0QQK8isxMzNk/oJBDBocgUKhe8+qLl7K4OvVR3n9lREE+Iu/ylgIu3dcZMToaK3NjdmzPY683DLad/XXyvUBslIKGfFwy69C/idKpQrvMHecvIQ/2nZf0MiaP1r6mhKJRCukgonkvmXtaIlGA2WFFWg0mha92fEMcCLhQho5aYV4Boi30SC6WwC56cVci00XrWDi6GLN0AkdOLL7EjkZxbh66s5Z/MA2royb3pWf1p6kc89AIjvo5lNTW3sL3vn8IZ57ZBWvzv2eT9c+hq29hdhp/SudewYRHO7OD/89zPxHVvHs6yPw0fEbMU9fBzx9HRg/vSvJ1/M4fuAah3+N5+zxG1RV1tG9bwjRXfxoE+mOvr7041NIlRW1XIxJJ+ZsKinJhTg4WtClRyCL3h0n+nGbP2psVHJg3xW++uowVZV1tAl1o2NHP2bM7I6lpYnY6d3WseOJrF5zjLf+MwZ3N1ux0xFEaUk1J08ksvTTh7Ry/draBo4fvMrgUdFaLRYnX8nG0c1Ga9f/K411jSSeT8XQWJzjQBKJRCIk6R2f5L5laWuGTCajqUFJbWUdpi34ZtXjtyJJ5o18UQsmHXoFs+3bExz/NZ7Js/uKlkenXkHs3HiWnRvO8sTCoaLlcTvjp3Vlz5ZYvliyixUbn8TAQDeH1Hn6OrD4o0kseWUzn72zgxf+MwZzC2Ox0/pXrKxNmfvSME4duc7bL26i98Awxk/vipGxgdip/S2ZTIZ/sAv+wS7MnNuXjNQizh5LJCO1iJef/BYjYwP6DAnD29+JqI4+uHo8GDeZQqqtaSDhag4XL6RxMSYdV3db7B3M6T0glDkL3HRmm8xNarWGw4eusmbVMUpKqjExNaBjJ18ef6IPXjrcMbZ9RxzHT1znow8nY2urW0eEtOm7NceYNrMHcrl2nsqfOHANfT2FVo/jVJRWY+NgLkrnW0NdIwAGOv5arqs0muaPlr6mRCLRDt16xyGRtCCFnoJ2/UOpr66ntLCyRQsm3iGuBEZ5kZsu7pyD4CgP9A30SLueR1FeOfYitaO37eKHq6ct544n8vCcfpiY6c5TJ0trUx6fP4BdP11g89qTTHm8l9gp/aWIdt7Me2koby/cxOJn1/Husod1vrhwJ7r0CiKinRffLT/EUxNXMOelobTr4id2WndEJpPh5euAl29z6/lTLwwm9kwK8RfS2bj6OPt3XKSsuJrwdl5Ed/YloI0rLu420vGdu1RcVEnC5RyuxGeSlV5McVEVUe28CQlzY+zkTjrVRfJHGo2GUyeTWLvqKGbmRuTlleMf4MSTs/sSKdJsiTuhVmv45psjJN7I5803RmNmZiR2SoJJSy0EDURGae/PZ8/WWJzdbPDU4jamhNgM/EKFXycMUF/TQEjnAJxF2DYlkUgkQpMKJpL7WkluOalXsigrrMC9BY8DePg5khiXjoOrdYtd817oG+jRtqs/J/ddIfbEDQaO7yBKHnK5nPEze/DZf7ZycGccwyd1EiWPv9JvWCT7tsax/ptj9BgYipsOHRu6Vdc+Icx7eRi//HiaDxb9wkvvjNO5p+n3wtTMiKdeHEK/4bms/HAP2zee5YkFg1pdd4a5hTE9B4TSc0AoGo2G3KxS4i+kEx+TzsY1J0hLKiCivTdm5kYEhbkREu6OT6ATJiJtstBFlRV1JCfmkZ5axJVLmaTcyCc41A1LaxPahLszcVpXnRuIeiuNRsO5syn88tN5Ll3MQKlU4+vnyKuvj6RX7xCtdS60hMZGJZ9/theZQs57Syagr2PbrLRJo9HwzYpDPDmvv9ZiZKYWIlfIGDyirdZiAFyNTaeLltYV/5P6mgaunb6BvQjHge4L0pYciaRVaf3vwiWSv2HjaEnqlSxK81t2S4q1gwXmViakX89t0evei26Dw7lyIZVT+6+KVjAB6DEojK+W7mbrD6cYOqEDcrnuDDaUy+U8/doIXnh0FRtXHee5N0bqVH63GjKmHXW1jXz1yV6WvPwTr7w3/r6ZmeEf7MKH38zgwM5LLHtvFz6BTkx+pAem5q3vCbdMJsPVwxZXD1sGj4kGoDC/gqSEXK7EZHD6yHV+/SWGvOwy2nbywdzCGJ8AJ/yCnXH3ssPeyfK+7kRpbFCSk11KekohxYWVXI7LJC25EDdPGyytTQkMcWHclE74BThjYNg6vr81Gg1nzyRzcN8VDh9KAKBzV386dfZj4OBwnR1ufFNZWQ2LF/1MVJQnM2b2uK+//27nwtkUfP0ccddiofbXrbEkX8/jrc+mai0GQHVFHX5tXLUa46801jcfyTEykQrB90Qa+iqRtCqt4x2KRHKPbJws0TPQo6KkqkWvK5PJiOoRRGlBBQ31jRgaiXdsIrKzH5VldVw8lUR9bSNGJuLkYmJqyKAx7Yg9nUzMySTadw8UJY+/4uFjz5iHu7Dm8wMEhbsxdFx7sVP6W2Mf6kJ9XSMxZ1L4cNEvLHxrLAodvxm7U3K5nAEjoujWN4SNa47z/qs/076bP4NHR4u2daqlODhZ4uBkSdfewQColCoy04pJTynkxtUcYs+mcu7EDa7EZeLqYYuZhRFunrYEhLhiaW2Ks7s1Ti7WWFqbtIqb2fr6JgpyyykqrCQns4TcrFLKy2tJvJaLkZE+ZuZGePk6EBjiwuQZ3fDydcBYpNeof0OlUnPq5A1+WHuclORCnF2ssLMzY9LULgwZGtkqCj7paUV89NFuhg2PYuDAcNHyaGpSkXg9j9AwYY+TNDWp+H71cd5eOklrMRoamjh34ga9B4VhrMViQmNDEwXZpeiL1H1YV12Pnr4Cw1b4d1kikUjulu7/hJdI/gV7NxuUjUqKc8pa/NpWduYc2x5LdnIhviKdIwawsjUjuK0n12LSiTuVRGcRV/uOnNqZrd+fZtOq4zpXMAEY+3BXju29wvfLD9Guqz+OOrKC9K9MeawnSqWKdd8cA2S8+J/R6N0nnSbQXGSbObcfBbllfL/yCHu2xDJhRje69w/R6Q6gu6HQU+Dt74i3vyO9B4X9/uuV5bVkpReTlVZEdmYJOVkl7NseR15OGcomFTKZjMgO3jQ2KLG1N8fd2x4DQz2srE2xsjXF3MIYc0tjzMyNMDYxbLFVtSqVmvq6Rqqr6qmpqqeyopbyslrq6xspyCmntKSamqoGcrJKMDDUIy+nDEdnK9pEuGNopI+PvyNunna4e9piYaWbW2HuRmOjkqOHr/HjdyfJzirFz88Re3tzxo7vwOChkRga6eYQ6VudP5fCZ5/t4/kXhog6W0Wj0fDF5/to3174jWXbfr5Av0FhWGhxmPbJgwnkZpby8rvjtRYD4EZ8FgFh7lqN8Xca6xpRNqnQuw+Oi4pBpmn+aOlrSiQS7ZBe6ST3NSu75tWspQUteyQHwDvYBYC0hBxRCyYAfUZEUZhTxql9V0QtmDi6WNNzUBiHd10iMT6LwHDx3tDdjp6+gvn/Gc3bz2/kkze28u6KaTp9Yy6TyZj2ZB8ALp5P571Xfuald8e1+i6MWzm6WPP8f0aTlpzPt8sOs3HNcWbO60e7Ln6tosviXlhYmdAm0oM2kR5/+nW1Wk1tTQOFeRWUFlf9/r+11fXcuFpCXW0jxcWVVFXU4eFlx6WYdAACQ10pyC3H2sYMlVqNvr4CT18HCnLKkcll2DtaUFxYiZ6egsYGJUqlCidXa9KSC2hoUOLqbsO1+Cwa6pt+L8L4BTlTX9eIhZUJHt72GJkYEOrugZ2jBXb2Ftg7WtwXQ4lvp6Kilt3b49i6+Tz2DhZkZ5Xi6mrN2Akd6NknpNXMFdJoNGzaeJbTp5J47/2JuIk8c2Lnzos4OlrQvYewBfWS4ipOn7jB+59M0WqcnZvP4xvgjF+QdrfnJcRmENrBW6sx/o6ySYV/W28s7cxFy0EikUiE0jp+4ksk98j2tw6CkvzyFr+2d0jz2eGs5IIWv/bdiuzix5eLt3D2cAIqpUrUoxtjZ3ajMK+cXzdf0LmCCYBvoDMDRkTx7bKD7Nh4jpGTdWtA7a1kMhnTn+qLnuII3604jFzxC8//Z3SruWG7G95+TrzxyWQS4rNY9/Uxvlt+iIee7EWHbgH3beHkVnK5HDNzY8zMm+ed/JPGRiW1NQ3U1zbQ0KCkvr4RZaOapkYlGjQom9SoVGoUChlqNchloNBXoKenwMBAD31DPQwM9DAy0cfExBAjY4MW61ZpjTLSi9m6+TwH917BxNSQ0pIabO3Mee2N0XTvGdSqfm/q65tY9c0RcrLLePvd8aJvwjl9Komrl7NY+PJwwWN/s+IQ0x7prtVic1pSPiqlmtFTtP8z5fLZFAaL+LOruryGpNg0Og+LFi2HVk0a+iqRtCr33ztuieQP7FysCe3sj7EWNlR4BTpjamHEjYsZLX7tu+XqZY9XgBPpN/K5fD6VyM7+ouXiF+yCiakhe3+JYeyMbrj72IuWy1+ZMLMbKYl57Nx0jradfHH31r0cbzX18V4YGumzd2ssr8/7kTc+maTVM/JiCg53560vppIQn8XerXF8u+wQE2d2o2vfEJ0fqik0A4Pmggc6una3NVCp1Jw9lcy2n8/T1KTi8qUsAPoOCqVn72AiojxbXcGuIL+CxYt+oVNnX956Z5zohZ6U5AK2bYnhP2+PE/z38vLFTCytTIho66XVOLs2XyArrZiufYO1GkelVGFsaoiZFo8W/ZOGuuahr9IME4lE8iBoPY9KJJJ7YONoyZXTScSfSGzxaxsaG2DrZEXKlWw0GvFL+wPGtcfR1YqTey6LnQoTZ/VEo9Hw89rjYqdyWwo9BTPn9acov5L3X9lMU5NS7JTuyLhpXRk1pROXzqfxwWu/UFleK3ZKWhUc7s6zi0aw4M1RXInL5Ilxy9ix6Rx1tQ1ipya5D5QUV/Hj2uM8Pu0r3ln0M7Hn08jNLmXEmGhWr3uSZxYMJrKtV6srlly4kMbSD3czaXInZszsIXqxpKiwki+/2M/CV4YLPhxXpVTzzYpDjJ3YUatxaqrrSb6eR7/hEVo/ppZyLRcrkY/CaDQagjv6Yy4Vau/NzS05Lf0hkUi0QiqYSO5r1g4WyOUyairrqK9p+ZssnzZuVJXXUpxb3uLXvltRXf0pyC7j5N7LqFRqUXMJjfZiwOi2HNt7hXwtDNxtCa6etjz54mDqahrZuOqY2OncsaHj2vPK++O4fiWb+Y+sojCvXOyUtM430Jk5Lw3l7S8fIjO1iFdmf8+aLw9QXFgpdmqSVkalVHPmZBKvvbCRRQs3sfbro2SmF9Ohsx9PPd2fb354knnzB2l17ay2qNUafvz+JMs+38dTc/rSq7d2Ox3uRHV1Pe8t2cH8BYOxFuHmeu+ui3TtEYi9g4VW4xzceYnrl7IYNl7729cun0shrIPwQ3P/qKKoioSzSejAsyKJRCLROulIjuS+ptBT4B/lhbJJRUl+Oa6+ji16/dCOvmQk5JByJQt7V+sWvfbd8vR3xM3HnuzUIq5eSCO8o6+o+fQeFsm+LbFs/OoIz7w5WtRc/srAUW2JO5PMDysOExzuQXQXP7FTuiM9+odibmHCfxZs4PN3djBzXj98A7U7ZFAXOLvZMOeloVRV1rFnSwzL39+NXCFnxIQOhEW3vmMTEuGkpxayb3c858+kkJ5aBICDowXdewUxbFRbIqO9kctb7/dPRXktS9/fhbWNKV+umIGpFo6h3q2mJhVL3trGzEd6iFKAKiqsZM+Oi3y0YrpW46jVanZsPEtURx88BDjemZteRJ9R4s4O+f1Izn069FnrpBkmEkmrIhVMJA+ElPhMivPKWrxg4ubjQNq1HJIvZ9FpYHiLXvtuyWQyBk/qxLa1xzm++5LoBZPIjj6ERHmSeDmbguxSHEXeznA7MpmMea+O4Hp8Nt+vOIR3gCM2rWTqf1RHH5aumskbz65nwSOrefWDCbTvKt7sGiGZWxgzfno3VCo1547f4KfvTvDNJ3vpPTiMfsMjMbds/etsJf9eSVElhw9cY9+uS1RU1FJaUgNASJgbXboH0H9wODa2ZiJn+e9du5LNkne2M3J0O8aOb68ThUO1Ws1HH+yi/8AwQkVaf/vVF/t5bE5f9LW8VezS+TSaGlWMntpZq3Gged5OemI+1iL/nKqtqkOukKMv8BGr+4ZUMJFIWhXpSI7kvmfn0tz5UayFoyE31wknX85q8Wvfi+jugRTmlnP813hUSpWouchkMmY83ZfU63ls+OqIqLn8HTMLY15dOomM5ALeW/iT6L9vd8M30JmP1jyKo4sVm9YcZ+emc2KnJCiFQk7nXkG89flDvPz+eCrKavnkza28++ImLpxMQtmK/iwlLaO8rIZdW2OZ/+RaFsz+jq8+3096SiHW1qYMGhbJJyun8+nK6Ux8qEurL5aoVGrWfX+SpR/s5qVXRzBuQgedKJYAfLXiEOER7vTqEyJK/DMnbmBtbUp4lKfWY21bfxa1WiNIh2JaQi4+Wl5ZfCca6xpRq9QY3qeDxyUSieSPpNKw5L53s2BSlFPa4tc2szShy+Bwmhp048bM098Rr0An0hPzuXQmhbbdAkTNJ6y9D+Htvdm3JZbxj/bExVM35wIEtHFlxrz+rHh/FxtXH2fK473ETumOOThZ8vHqR1mycBNfvruT3KxSHn2mv6irpcXg7GbDjHn9UClVxJ5J4dDuSyx/fxedewXRe3A4vkHOOnMzKWlZJUVVnD2VxME9l7kan4VcLkOpVGNgqEenbv70GxROx67+GBrpi51qiykuqmTt6mNUVdbz6ZcPYyHixpRbbVx/GoVCzpBhUaLEr6tt5PtVx3j3kylaj5WXXUpNZR0jJ3cU5DU3/kwyoSJ3jwKYWpoQ0jkAI1NxV1W3WlKHiUTSqkgFE8l9z8XbHjMLY4q1UDAB0DfU59Sv8ZQWVGDjaKmVGHdjyKROrFt2kCM74kQvmMhkMqbN68e3n+9n85pjPP2Gbs4yARgxuSP5uWWs++oI3gGOdO4l/sDEO2VqbsSbXzzEN5/sJf58Gq/O/o5XPpiIhdWDdzRFoaegfbcA2ncLoLqyjhMHr7F941mux2fTvV8begwMxcPHXiqetGIajYb0lEIuxaazd+clUm7kY2NnRmlJDTKZjF792hAW5UnXnkFYWOpOIaGlnDyeyJef7mXyw10ZPrKtTn0v/7rrIlWVdTz2RG/Rcvjph1OMm9IJSwFe/7avP8ONqzks+mSy1mMBXDqdzHPvTxQk1t/JTsrj2ukb0pEciUTyQJBe6ST3PSs7C6rLayjS0raWgAhPjm6N4calTDoNCNNKjLvRoU8Iy9/cyok9l5n9xmitrzj8J6HtvDExNWTPTxcYNqmTTrQT345MJmPa7D7EnEziw1d/5rMfnsBdgAF+LUWhkPPE84PZty2OL97ZwRvPrmPOS0Px1dHfbyGYWRgzaHQ0g0ZHU1pcxYmD1/jy3R1UlNXSd2gEkR188G/jglwunU7VdbU1DcRdSOPCmRTOnrxBcWEVhkZ6NNQrUSjkRLT1JLKdD116BApyoyyG2toGVnx5gOqqepYsnYyXjr0+HTuSwLkzKby2eLRoRZzrV3O4fi2Hh2f11Hqsmup6ju+/KtjcJJVShbmViegrhQEaan8b+mokDX29J9pYAyytFZZItEYqmEjue/buzcNGi7O102HiH+mJX5g7uamFWrn+3XJ0tSa0vTfXL2Zy4Wgi3QaJX8SZ8ewAzh+7wU+rjrHwQ/Gfjv0VYxNDFn0yheVLdvLOCxv4aO0sTM1aV8vxgJFRePrYs3Lprzw3/WvmvTqc/iPEaY3XJTZ25oyY2JEREztSXlpN7JkU1n1zlMyUQrr0CaZNpCeRHX0w0YHtIpLmDStJ13OJPZvKuZPJVFXXkZPV/BpuaWWCiYkBHbr6071PMNEdfO/7P7drV7J5/53tdOkWwLxnBmKgY0/2z51OJvZCGi+/NhKFnjgFyKYmFZt/PM28F4cIUrDZuyWW6qp6Rgow7BUg6XI2pua68fNI30APVz8n9O+jY24SiUTyV3TrJ65EogUOrja06eyPtt4++Ye7k3o1G0tbM8Y82VdLUe7O4EmdSE3IZe9P53SiYOId6MzYmd35ec0xBo1rR4QOnMH+K25edox+uAuL5v7A+y/9xOLPpqJQtK4OhMAwNxZ/MoX3XtrElh9Oce1SJk++MOS+muHwb1jZmNFnSAR9hkRQX9dI3NlUYk4m8esvF2iob6JtJ1/advbFN8il1f3Zt1YN9U0kXssl4Uo2F06ncO1yFuYWxpQWVwPg6mGDp489XXoE0rGrP4HBrqLdmAupsUHJmq+PcPJYIgteGUZEpPaHmN6tS7EZbN5wljeXjBe1kLN+7XGCw9xw/m1umTaplCq2rjtNWLSXIKuEAS6eSiKii25sQivMKiEnOR/j+7xQqS0yTfNHS19TIpFoh1Qwkdz3bJytSLyQirJJRX1NA0Yt/APeyMQQzyAXEi9moNFodOI8ecc+wXz2iorY44mUFlZi42AhdkoMn9KJbT+c4psPf+WzTbN1+hhE+24BPPJsf84eTWTN5/t57LmBYqd016xsTHlnxXQ2fHOUH1Ye4frlbF75YCLuXnZip6ZTjIwN6NwriM69gtBoNORmlhJzOpm9W2OJOfUTXn4OhLf3pk2EB75BzuhpeUXpg0Cj0VCQV0FyYh5X4jKIj82gsqKOwoJKAPT05CiVavT1FQwcFkHbTr5ERnth3cq32tyta1ey+XnjWUzNjFix5jFMdfDm9Ep8Fvt+vcSit8dibCLe8YzUpHwuxabzwZfTBIl38uA17B0sGDutqyDxAJLiMxn6kDDdLP/E2ccBYzMj9I2k2wiJRHL/k17pJPc9uVyOnYs1+RnFFOWU4h7Q8jMdgtv5oDqTRG5aIa4+ji1+/btlam5MlwGhxBy/wcl9lxn+kHBv6v6Kg4sVo6d35fKFNI7sjqfPsEixU/pb46Z3Iyu1iM1rjuPmacugMe3ETumuKRRypj7Rm6Awd3754RRzJ61gzsvD6D8iUicKe7pGJpPh6mmL62/bnNRqNak38rkSm8lP355A2aSitqaB4HB32kR54hvkjK29+PMEdJlarSY3u4y0pAKSE/MozKvg7Ikb1FQ3YG1rRllJcweJs5sN9o4WRLbzpl1nX4JD3XBysRI3eZHU1jbw/erjnDh6nXkLBtGhk/bX1d6LhKs5rFpxiDffG4+ZiEdFmppUfLZkF/NfHyFIR5hGo+GntSdQq9REtPfWejyA+rpGKkqrMbc0FSTeP7l2+gbV5bUYmejGEaFWR9qSI5G0KlLBRPJACO0SgJ2rjdYKJv4R7uz+7jgJF9J1omACMHBCB47tvMiOb08ybGoXnbhBnjCrJ/t+ucDqj/bQpU8IRiI+kfwnMpmMea+PID+7jEO7LuHkak2kDh8l+jvRXfzwDnBk6Wu/8PHiLSQn5PDw7L6Y69AqUl0kl8vxC3LBL8iFUVM6NXdG5JaTEJ9FQnwWG745SmV5LR6+Dnj7O+If7IKnnwOOLlY63UGlDSqlivy8cnKzy0hPLiAztYjioioux6bT1KhCoSdHpdKgr6+gqUmFvr6CgGBnPLztCQ5zIzjMDRsdGGYptgtnU/h86a/0GRjKyrWzdLKrBOD6tRy2/XyBRe+Ow0KAgad/Z/2a43TtHYynQEdjrsRmUFfTwJQnegn2c/V6bDptuwcJEutONNQ2IpPJMJCOeUokkgeAVDCRPBBkchlXTt0gP71IK9cPadd8I50Qk0q/CR21EuNuhXf0xc7RkqyUQhJiMwiJ9hI7JUzNjJj+zAA+W7SFLd+dZPKT4q2evBP6+nq89slk5j/8FW/NX89Ha2fh5a8bBbG7ZWNnztvLH2bPLzGseH83pw5f5/n/jG61RSAxyGQynFytcXL9bUbCnL40NSnJySglJTGPtKQCtm88S0FuOYGhbigUcty97fD0scfR1QYXd2sMDFvnDYZaraairJai/HIK8ispKawkJ6uUnKwSGhuUXL2UhVql/lPXiKWVCU2NKgwN9WjbyQ8HZ0v8Ap3xD3bGw8v+gZhBcqfKSqvZ8P0pzp1O5vlXhxOug7NKbrp+LYcVn+1n8bvjsLYWt+MhKSGXwrxynntthGAxN35zlMaGJroPCBUs5oWj12mnI6vumxqV2LnZYGJurBMPYiQSiUTbpIKJ5IHg6NE8t6Ewq0Qr13fzc8DCxpRCLW3iuRdyuZz+49pzZEccZw5e04mCCUD/0dHEn01l41eH6T0sAic3G7FT+lsWlia8vXwaz037ijWf72fuq8Oxd7IUO617IpfLGTKuPUHh7ny0aAvLluwiqqMPjzwzQKe7fXSZvr4eXn4OePk5ADCZ5nWmleW1ZGcUk5VWTOLVHPZtv0hFWQ2V5bWYWxoTGOqGoZE+9o6WOLlaYWltio29ORZWxigUws1JUSlVXLucTU1VPXW1DZSV1lBRVgNAWnIBxQWVGBrqc/1qDmqVGlNzY2qq6/90DVMzQ9QqNQaGevgGOmFhaYyXrwM+/k64e9vh6Gwl3Vj9BbVaw56dcaz96ihDR7Vl5dpZOj2c+erlLLb/fIHF747DRuSZMg31TXy+ZBcL3xoj2HDm1MQ8Lpy4wZMLh6KvL9xb6GsX0pi2YIhg8f5OfU09eamFWDxgM4VakgwtDH1t2ctJJJI/kAomkgeCs7c97oHO1Nc1auX6crmc4HY+nD9whdrqekx0ZBXtgIkdWPfFfrZ/e5yJT/bGVAeOYCgUcoZN6cThnRf56r1dLPryYbFT+kdObja8texhXpi5itee+palax/DXOQ29H/DJ8CJT757jB9XHuGnNcfJSi9m8qyehLcT5jz+g8DCyoQQKw9CIjz+9OsajYbK8loK8yooyCunOL+CG1dzyEgtoqy4Cj09BcVFVSgUcoxNDTG3NP69o8XUzBBrWzNkMhmGRvqYmBoik8vQN9BDX0+BTC5DLv/z22a1So1SqUatUdNQp6SxUYlGo6Gqspa62kbUKg1rlx8EwMTUkNqaBgCMTQyoq21+vbS1N0etUiOTgaunDWjAxc0GDx977BwscHazxtXDFpvfcpPcmcSEXL78aA9e3vYs/fJhPHR8IHN8XAarVh7mjSXjsbYRf5bG5h9OMXBkJG6/zRwSwsZVxwiOcBd0plVpYSWeAU46s0q6rrqBgGgfTMzFfz8hkUgkQtCNV1+JRMsc3GzJSszD3Ep7b/KC2npxdt9lEmPTieqhG2eNHZytad8rmHOHEzi8PY5hD3UROyUAQqI86TeqLXmZJVw4doN2PQLETukf+QW78vqnU3hj3g+s/GA3814bgZFx6+3KMDDQZ+bT/encK4hvvzzAi4+uZuj49jzyTH9MpTfCWiOTybC0NsXS2hT/EJe//DyVSkVtTSNVFXXUVNVRXdVAbU0DyiYlVZX1lJfWUFlRS2V5HcomFXr6cmprGtGoNc2PGjVgaKSPUqlCT0+BuaUxSqUKAwM9zC2NMTYxxMbOHFNzI5zdrDEy0sfRtfl/raxNcXSxxshYH2tbM2ztzbGxM8faxhSFnrQl6N8qL6th/XcnOXUskZlP9KZ3/zY6X2i6cDaFA3su89YHE0SfWQIQezaVq5eyePuzqYLFzM0s4fjey0x5sregHXlxJxLx8NOdo6D11fXciEnFzb/l58E9MDSy5o+WvqZEItEKqWAieSA4eTYPg8vP0M4ME4CQ9r7IFXKS4jN1pmACMHhyJyrKarhw7DpDp3bWmTfmjzw/mMeHfMSyt7excvuzOt2GflPbTn689P4E3p6/nvKSGt74YqqgbdnaEBTuzptfPsyGr49y4WQSj4/+gqdeGkrXviE6873yIFIoFJhbGAsymHfttme1HkMCSqWKX7fH8d2q4/QfHNY81FXE7TJ36uTR62z56TxvLBF3G85NleW1/LjqKK+8M+5/Oqq0aefGs4S282LEZGFX+ybEpDNsejdBY/6d+trmLjQjHR1ILJFIJC1NmrgmeSDYOFthaGKAnr4e9bX1//wf3IOASE+MTQ24eOy6Vq5/r9r3DqassJKzB65xLSZd7HR+Z21rxoznBpGfVcrW706Knc4d69InhKcXjeTSuRS++vBXVEqV2Cn9awYGekyb05dn3xiFo6s1by/YwJfv7CA/u0zs1CSSVk+j0XD6eCKPT11JUmI+n6yYxuNz+7WKYsm+XZc4cyKJtz6YqBPFEo1Gw6fv7GDUxI6CrvQuyi9n+7oz+Ie4YmElXIeNWq0mMS4DT38nwWL+k9rqetwCnHH21Z2ul1ZHo6UPiUSiFVLBRPJAUCjk2LlYU5hVQpGWbgKNTAzwDHDm2vlUlE26cxOtUMgZ/NsTsZ0/6FZhYvCE9vQZEcX3X+wnI7lA7HTu2KAx7Zjz8jB2rD/D0ld/RqVSi51Si/AJcGLpmkdZ8NYYju69zONjPmfT6mM0NjSJnZpE0irdSMjltQXr2fj9KWbPH8T8l4fh5iHczI1/Y8vGs5w+nsi85wdjrCNDofdsi8XCypjufUMEjbv3lxiMTQ0YK3CnR/LlbPxC3XSq26+uqo7sG3nUVNSKnYpEIpEIQiqYSB4YN4/l5GlptTBAaCc/NGoNKZeztBbjXgyc0IGIzn5cPpdGaVGl2On8Ti6XM+7RHgB8vmgLanXrKTwMHt+BR54dyIkDV1jz6d5Wlfvfkcvl9B8Rxartz9J3WCSnDicwa9TnnDhwFY1GeoQlkdyJnKxSln+8h8UvbqJbr2A+Wj6ddq1khbdGo+GbLw9QVFDJa2+P05lho+kphRzcHc8Tzw0UNG5JYSUbvz5Gv2FR2NgJ19UCcPVcCh37tRE05j9RNqkIiPbByVO3hxTrNKnDRCJpVaSCieSB4ezdXDAp0OIck4hugahUKi6fTtJajHthbW+OnZMlJfkV7P7xtNjp/Il3gBMTZvVErpCza8NZsdO5KxMe7cEjzwxk89oTfLq4dRV8/omltSnPLBrJ7JeGYetgwbJ3d/LqU9+SlJArdmoSic4qLqrks/d38cxjq3BwtGT1ptkMHhGFQq91vN1SKlV89t4u5AoZs+b105m86+sbWfrGFp5aMAhjE2FnZ/z87QnQaBgzQ/g5Iif3XCask5/gcf9OZUkVN2JSqatuEDuVVkum0c6HRCLRDt34SSiRCMAj0Bk9AwW5adormARFe6PRQPypG1qLca9GzugOwKl9V2io160jFpOe6E1FWQ2rP9pDQU7rmpsxelpXps3tx7G9l/lq6a/3zfGcmwLauPLR2seY88owstOLeXrySlZ9spei/HKxU5NIdEZZSTUrPt3L7OlfY25uxKqNcxg3tTPGrWiTVk1NA4ue30BAsDOPPNVXp46BfPPpPoaPb49voLCbWUoKK9m/JZYJj/XE3slS0NjlJdXoGygwFWDw892o/231uJGZNPRVIpE8GKSCieSBYe9qi7JRRW5qodZimJgZ4R/uQXZKIUodGwbqH+ZG31FtSb+ey7FdF8VO508MDPV57u2xNNQ1sW75wVZ39GPKE72Z+cxAtn5/ig9e2qRTM2xagkwmo1u/Nnyz7RkeeaY/h3+N55Fhn/LNR3uoKKsROz2JRDSlxdX899N9vP/GVlRKNSu+fZxHZvfFUsDBoC2hqKCCNxduZMjItgwZFS12On9ycPclyktrGDA8SvDYG785Rn1tI4PGtRM8dsyRBNr21J2Nezc1NSpxD3LB2kHYAtJ9RTqSI5G0KlLBRPLAcPZxACA3VbvDRaN7h5CXXqRzc0wAOg8IRaPRsOWbIzpXlAiO9GDa0/3Yt/kCu1vZ0RyAkVM78+TCoVw4kcTnb229LwelGhjqM35md1b8NIcRkzuRlJDDzCEf8/3yg9RUaWf7lESiiwoLKvj+66M8NnE5DQ1KnntlGHOfHyzo5paWkpyYx+vzNzD98d506x0sdjp/kp5SyNnjN3ju9ZGCd7wU5ZWTmpDD6OldBe8uATh36Bodegs73PZOVJZUkXU9F/V91k0pkUgkf0U3JnlJJAJw8rTDxdeRsoIK1Go1crl26oUhHXwAuHTiBoFRXlqJca869Q/F2dMOAyN9Lp1KIrJrgNgp/cmYmd05vOMi33ywm6gufri0sqFyox7qgpmFER+//guFueUs+uwhTEzvv7Zlc0sTZi0YRFF+BRu+PsrGb44RcyqZDj0CGDmlM6Zm4q8flUi0ISujmF/Wn+XIviv0HxrByh+fwEGEm+mWcub4DTZ9f5LX3xuPq7uN2On8SW1NAx+8/jPzF40SZQXz+pWHSbqSw8sfTRY8trJJhVwuwyNAd9YJ32RgqE9ge1+s7C3ETqX10kZHiG49A5NI7itSh4nkgWFkYkhTQxPV5bUUa3FORpv2vhgY6ZN+XfeGYyoUcsY81pPEi5lsWnlI7HT+h4GhPs+/P4GAMDeWvrgJlY4da7oT/Ua05eUPJ5GRXMh7z2+gvKRa7JS0xt7Jknmvj+Cb7c/i5efAjysP8+Ijq/hhxSGqpJWTkvtIwuUs3nxxE88+uhpnV2tWb57L7AWDWm2xRKPRsOn7U2zZeJbFH0zUuWKJRqPhu5WHGD6+PX5Bws4tAcjJKObGlWyGTe6ErYPwhYGr51MxtzLRqTkyN+WlFZJ4PkW6P79PLFu2DC8vL4yMjOjYsSPnzp37y8+9evUqY8eOxcvLC5lMxqeffnpP16yvr2fOnDnY2tpiZmbG2LFjKSjQbve3RPJvSAUTyQPF1ccRgJwU7b0wG5kaEtrRj1O742jUseGqAP3GtMPC2pS4EzdIvpItdjr/wz/Ujagu/iRczGTjf4+Inc496T4glIXvTeDyhXTmP/QVuZklYqekVU5u1jz7xmhW7XiODj0C2fjNUaYNXMqGb45ShcywfAAA4j9JREFUXKA7a6wlkruhUqo5duAqzz6yiu+/OkpIuBvfbX2GCQ93wdrGVOz07lljg5LlH+2hMK+Cdz6dopPzVn7+/hR11Y0MEmmeyg9fHiQ3o4Txv629F9rZ/Vfo2C9UlNj/pP637TjGUifhPdOVLTkbN25k/vz5LF68mNjYWCIiIhg4cCCFhbef9VdbW4uPjw/vvfceTk637366k2s+99xz7Nixg59++omjR4+Sm5vLmDFj7v4LkEgEIhVMJA+UoPY+2LlYk6vFgglAaGc/GuqaSLiQqtU498LIxJCxs3oR1sGHLauOip3ObY2f1ZOw9t5cPJNCwsVMsdO5J5GdfPlg7WMo9OS8M389N3SwONXSnFytmT63H2t/XcCwiR3Yvu4M0wcv5aPXfyY1MU/s9CSSO1JZXsvGb08wd9pXbP7hNCPGt+fNjycz/qEumLbyzSDFhZUsnP0d7h62zHlhEHp6CrFT+h/xMemcPJTAnJeGiNJhkZqYz9Ff4xkzoxtWtmaCx9doNFy7kEZYJ1/BY9+JeV/MZMX5JXQZIfwgXEnL+vjjj5k1axYzZ84kJCSElStXYmJiwurVq2/7+e3bt+fDDz9k0qRJGBre/rXwn65ZUVHBqlWr+Pjjj+nTpw/R0dGsWbOGU6dOcebMGa19rRLJvyEVTCQPFEtbc4pzSslOztdqnKgeQVjbW5AYl6HVOPdq0KROJF3J5vC2WLK1uDXoXikUcha8N57UhFw+WLCh1Q4U9W/jypvLp9FQ38QLM77hzOEEsVMShK2DBY8+N4ivtj7N9Ln9KC+tYfb4Zbw0azXnjyeiVkvDAiW6RaPRcP1KNkvf3MrLc78nKSGP2S8M5rM1j9JncDj6+rpXWLhbl+My+PSdHcyY3YcREzro5HGP/JwyvnhnJy+9Ow4DQ31Rclj78R78gl0YNb2rKPEzEvNwcLMW7ev/J3auNvhGeEozTP4NjUw7H0BlZeWfPhoaGm6bQmNjIzExMfTr1+/3X5PL5fTr14/Tp0/f05d1J9eMiYmhqanpT58TFBSEh4fHPceVSLRNKphIHiiufr8dyUnWboeJf4QHTU1KTuyI1Wqce2VhbcqQKV3QaDTsXqebP6Ac3Wx45q0xFOSU8eOXB3Ruq8+dcnG34ZMfniCqky9fvrWdX7492Wq/lrtlZmHMhEd6sOjTKby4ZBw1VfV8t+wgjw77hJ+/PSHNOZGIrqa6nl0/X+D913/hjQXrsbM3Z/HSibz23njCojx1sqhwtzQaDTs2n2fVFweY99JQIqK9xE7ptupqG1jy8maefnUYji5WouQQfy6V88cS6TM8EjNzY1FyuHQqiW5DIkWJLRGIFtcKu7u7Y2lp+fvHkiVLbptCcXExKpUKR0fHP/26o6Mj+fn39lDxTq6Zn5+PgYEBVlZWLRZXItE2aUuO5IHiHuCMe6AzhTnanSmhUCiI6h7EiZ1xlBdXYWWne6smRz/Wg/izyWz/9jgjpnfDyd1W7JT+R/fB4SRfy2HTf4/g5m3HkEmdxE7pnphbmfDKx5P5/I2tfPXBbkoKK5nxTH/0DR6Ml2B9fT36DI2k95AIrl3KZOeGs2xee4If/3uYLr2DGTyuPSGRHvfFzalE92k0Gq5czOTMsUR2/XyBwFBXho1tz4LFI9HXv7/+TtbVNvDp2zuwsTPj/eXTMDTSza4FtVrNh6/9wuBRbQkTqaCjVqvZsPIwnfqEMGRSR1FyADi6PZY31z4hWnxJ65aVlYWFxf93//zV0RmJRHLn7q93BhLJP3DwsCM/vQiVUk1jQ5NWW17b9QkhP6OYy6eT6D68rdbi3Cs7RytC2nqRfDmbjcsP8sySCWKndFtT5/bjwrEbfPXuTgLD3fENcRU7pXtiYKDHgnfG4uXvyJbvTnHjSjavfjIFq1Y8PPJuyWQy2kR60ibSk7LiavZvj2X3T+fJzSqluqqegaPa0mdoBNY6WGCUtH75uWUc2h3P4b1XqK2pp9/QSJb/+AQuOlgsbgnpKYWs+HA3PQeGMXhUW50uSP6w4jA2tmYMGiPOkFeAY7/GE3c6mYVLJ2IgUjG7MKcUIxNDzHVwEK+k5dzrkNZ/uiaAhYXFnwomf8XOzg6FQvE/22kKCgr+cqBrS1zTycmJxsZGysvL/9Rl8m/iSiTaJh3JkTxQFP/H3l2HR3llDxz/xt3diBGCJBAI7lbcneLSAkVa3LW4FSlWoFhxKO6uwSFISEICIULcXWbm9wdbfssuXQpkLNzP88yzm2HmnjPpZN73PXPvuVqaOHnaIZVIefNSvsty/BuV50Xga26dDpRrnC/RdWhjLGxMeBORRHxUirLT+SBdPR2mruqFl48T80btJCsjV9kpfTYNDQ26DKjHqFntCQ+OZd7oXbwIilF2WkphYW1Mt4H1+f3EaPoOb4JHGXu2rj7P7B//YObIHVw/95T8/AJlpymoufTUbI4fuMu8Sfv4vusaXr9MYNi4Fmw/NpoBw5uU2GLJ2WMPWTj1AIN/bEarjv4qXSy5eCKQN1EpDJ3QSmk5FOQXcuXkYyrW8KB+y4pKy+PGqUDqta6ktPjC10NXVxd/f38uXLjw7j6pVMqFCxeoVauW3Mb09/dHR0fnvceEhIQQGRn52XEFQd5EwUT46vjULoOplTFRofLdtcPK3hwPH2fuXQpCIlHNJpfW9uY0aFOZxwEv2LX6rLLT+VuOrtZ06F+P2Mhklk3cp/ZNQ2s0KMvK3cOQyWBs7984d1g1e90ogqamJn41PJm0qBu7Lkzgm/ZVSE/JZum0g/RvsYxfZv7Jw1thKvs3JKiezIxczh59yKr5x+jVajnXLgRRu2FZdp8Zx+T5XalSwxMtrZJ5+pOTnc+qBcd4cOclyzYNxKuco7JT+p+ePohg/7brDJ/SBm0lNtY9vP0Gty4+p/fwJmhqKu+9EXD6CdWbFO92wmmJGWydd6hYxxS+kBx7mHyKMWPGsHHjRrZt28bz588ZNmwY2dnZDBgwAIC+ffsyefLkd48vKCjg0aNHPHr0iIKCAmJiYnj06BFhYWH/eEwzMzMGDRrEmDFjuHTpEvfv32fAgAHUqlWLmjXVc9m1UPKJJTnCV8fMyoSM5CyiQ+XfXKp+uyrcu/ScsMDXeFdxl3u8z9F1aCNO7rzJ+YN36TasCU7uNspO6YPqNPOh5w9NuHslmD3rLvLt8KYff5IKc/GwYfbaPiybepA9v10mJDCS7ye1VtmdERTBxMyQ1t1q0LpbDaIjkrhy6jGXTgbyKjSOxNh06jStQP0WvlSo7FpiL3iFz5OWksXNy8GEBsVw/sRjypRzpEnrSmz/viGWX8kSr9CgGJb/fJS2XarRqpNqzyoBiI5IYvuai0xb0h0TU+U0WAVITcrk0vFH1Gnmg281D6XlkZKQjq2zBVb2ZsU67sun0ZhaKH57ZEH1de/encTERGbMmEFcXBx+fn6cPn36XdPWyMjI9wqIb968oXLlyu9+Xrp0KUuXLqVBgwZcvnz5H40J8Msvv6CpqUnnzp3Jz8+nefPmrF27VjEvWhA+g4bsa9muQfhsGRkZmJmZkZ6e/o/WRaq6ywdus3Dgepr0rM34Dd/JNVbQ3XDGtllKzzGt6DuxrVxjfYndq89y6cgDPCs4MXFlH2Wn87ckRRKmf7eFhzdeMHNdP2o2Ka/slL6YTCbjyB832bj4FO5l7JmyomeJXSbwOWQyGS9DYrl65ilXzzzB0saE6IgkajYsS/1mvvhWdfuqi0xfK5lMRlREEreuhPDkfgT3b4dTwa8U9ZqUp1bDstjYFe9FpyqTSKTs33ad54+jGDCiKW6l7T7+JCVLS8lm4uDfGTW9HRUquyo1lxVTD3DlxGPWHv0Rh1LK++w9vu0ahQVFdPyuUbGOe+z3S7iUtsevfrliHVeVqMt56l95ekyfj5a+frGOLcnL4+XPU1T+dyAI6kjMMBG+Oq7lnTC3NSUiKFrusbyruGNqacSdc09UumDSqldt9m+4SFRYPF2HNMZDRRuramlrMfmXb5nU7zf2b7yMg6sVrmpwcfC/aGho0KFPHUqXd2Lz0lOM67WBYVPaUq+Fr7JTUwkaGhp4lnXEs6wj/Ud9w8vQOG5eCOLmhSAe3AwjIz2HKjVLU6tROarULo31V3Sh/LXJyy3g8YPX3L0Wyr2AMArziyhTwYl631Rg7OyOX1UD5b/EvUll/bLT2DmYM3VRN7UoHublFrBsxp/0HtZY6cWSF0+jOXvwPl0G11dqsQTg+slHjP2ld7GP+/x2OPXaKq+ZriAIgroTBRPhq+PkaUdGcha5mXlIpVK5rlfW0tKk2bd1eHjlOYkxKdg4Wcot1pcwszSmy/eNObb9Gif+uMHI+aq5Yw683aJ3/NIejOm2htlDt7Fi/3BMLdT/QsnH342Za/qwfMpB5o/ZTffnb+gxtBH6BrrKTk1laGho4OntgKe3A31+aEJcTCq3Lwfz+N4rVs05TFGRlApVXClXqRRVapXGp4qrWlxACh8mkUh5GRLH00eR3L4awtOHkVSq5oaTixU/TGiFr78r+vpf59+HTCbjzJEHHNhxkx/Gt6JKTU9lp/SPSIokLJiwj8o1PKn3TQWl5iKVSln381H8anvSY2hjpeaSkpCBlpYWNo4WxT52enIm5jZixoFK+cyeIx8dUxAEuRAFE+Gro6ung6OHLakJGcRFJOLoId8ZCmWruHHg17PcOvOYtgMbyjXWl2g/sD5n993m5K4A6repTKXaXspO6W+5edkzcfm3LJu4j/XzjjJ6fld0lLQNZHEytzRm9rq+HN0ZwK51F7lx/hkTl3SntIrO+FE2eycL2veqRftetcjNySfwzktCn8Zw5fRjDmy5RtmKLugb6FKpujt+NTwpXd4RHR31f5+UVBKJlFcv4nly7xUxUSlcOv0UmVRGkzYVqdO4HKOmtsXRRTWLzoqUnJDBb7+cRUtHg+W/D8LUTD22oJXJZKxbeILS5R3p1Ke2stPhwpGHPH8UybhF3TA01lNqLtdPPKJqo+JfMlOQX6iyX9R81UTBRBDUijhzFL5KZSq7cXHfLSJDYuVeMKnSsBw6etrcPvtEpQsmRsb6dB3amF+nHWDL4uMs//NHpe4W8DE1GpWj14gmrJ97DG1tLUYv6KryTQ7/CQ0NDdr3ro2PvxuLxu9l9awj1G5ani4D66GlrbxdJFSdgaEeNRuWo2bDcvQd0ZS4mFSCHr5dvnFsz20eBIQR+jQGb19natQvi6uXHWV9nTFWYrPJr112Vh5hwbE8ffCaoEeRyGQyngdGUaFyKarX82b+2j6ULusgGvz+i0wm4+LJx+zaeIX+w5sofYbGp9qy8izZ2fn8MKWNslMhKyOXYztu0qB1JRq3r/zxJ8hZ2NMoeo9pWezjvn4eg6mlaPgqCILwJUTBRPgqOXq+LZJEBEVTs6WfXGMZGOnzTY9aXDl8j6z0HIxV+NvAFj1q8uBqCKFPIrl0+D5NOlVTdkr/U7s+dYgMT+TqyUBcf79G50H1lZ1SsfEs58iq/cPZve4iW1ec5fblYMbO74KTm7WyU1ML9k4W2DtZ0LiNHzKZjDeRyTx9EMHT+695eCecjctOoaGhQa1GZTEyMcDb15kyFZxw87ITy3jkID+/kIjQeF48f0NCbDq3r4YQ+TIRv+ruGJkYUK2OFxWquOLhZScKgx8Q9yaVVXOP4eBiybLfB6ldv5b9W66RnprD2DmdVKIQv33FWcKDYvhpXmelF9oTYlKIjUjCVg4zQV6HxFLaT7l9YoT/piF7eyvuMQVBkA9RMBG+Sq7lnLCyNyctIUMh8bwru3Fy2zXunHtK4y7VFRLzc2hpa9G8Rw1unn3ClsUnqNOykkr30NDQ0OCH6e3Izcxl08LjmFoY8k2nqspOq9joG+gyYEwLKtf2Yvf6i4zs+it9R31Du161VOKiQ11oaGjg5GqNk6s1zTu+fX+kpWQT/DiS8OBYnj14zdaVZzEy0ScpIQNXT1t8q7pj72SBexl73ErbYW4lvqX9J2QyGckJmUSExRMbncLzwChehsSio6tNTGQKpcs5ULGqO72HNqKsrzPWdqZKv2BVZZIiCUf23uHy6af0HFSPWg3LKjulT3Zi/x1uXwlm7rp+aOsovxgW+iSKiNA42vWpg0c5R2Wnw7Vjj2jQVj6zXILvhNNhWFO5jC0IgvC1EAUT4avkVt6Z5Lg0Hl19rpB4NZpVREdPm2d3wlS6YAJQrVF5KtctQ0JMKse3XaPL0CbKTul/0tLWYuTPnYl+lci2ZaexsjWlSt0yyk6rWPnV9MTLx4nNS06xYcEJwp69ofv3DXDxsFV2amrL3NLo3RIeeNsAMi46lfDgWMKCYnj9MoEb54NIik/HwFAXXX0dSnnY4uFtj4OzJU6uVji6WmPnYK4SF4GKlpdXQFx0Km8ik0mMzyD8eSyRrxIBCH4chbWdKb7+btjYm1GzgTfuZexxcrUShb5P8CIohlXzjuNV3pH5a3qr5fKxC8ce8vjuK2av7qMSxXeJRMqvMw+RnJDBjLV9lZ0OANeOP2Dmlu/lMvabVwk4iuOEIAjCFxEFE+Gr5Ohph56BLlEhsRQWFMm9YaiZlTF+db25sO8W383sjL6RchvM/S8aGhoMmdGB4S2WsGPZaeq1qYyds2o3jTMw0mP2xoHMHb6Dn4dvZ+H27/GuVErZaRUrI2N9Rs3uSL2Wvvw2/wQ/dFxNz6GN6DKoProloOGtsmlqauJYygrHUlbUa+bz7v70tGyiXibyOiyB6IhEIsLiCbj0nLTkLAryi9DU0sTeyRwHZytsHcywc7LAxt4Ma3szbOzNsLQ2Rt9Adf/eP0Qmk5GVmUdyfAYpyVnEv0kl4U0aeXkFvHj2hrjoVIxM9Il6lYidoznevs5YWJnQvEMVXDyscfW0w0QNL+5VRVZGLtvWXCA2OpWh41tSwU89P8uunnnCga3XWbBxIEYm+spOB4BzB+8RF53KiFkdVKIA9To0FhMLIyzksIuNpEiCjZOlKFIKgiB8IXGWLXyVtLQ08WtQjoyULKJC3+DhI/8T0hrNK3L3wjPuXnhKvXb+co/3JVzLONC6dx2Obr3GjmUnGfdLb2Wn9FEW1iaMXdydcT3WsHzSfqb92hsXT/k29FWGyjVL88ueYfyx5gK3Lz3n8vFHjJrdEZ+q7spOrUQyMzfCrIoRPlXc3rs/L7eA+DdpxEYlE/8mjTeRySS8SeNlaBxvIpPJTM9FU0sTqUSKkYk+FlbGWNubYmxiiLmlIWZWxhgb62NsaoCJmSGGRnoYGuthaKSLnoEu+ga66Olro639eYdpmUxGYUER+XmF5OUWkJtTQG52AXl5BWSk5pCVkUtRkYTEuHTSUrPR0dEmPDiW1KRMLG1MeR4YCUB5v1IUFUmxdTDDxcOG+s19cXC2wN7JHHtnS7HrUDGSSqWcPxbIyYN3qde0AkPGtVTb2UsBF4O4evYJc9f3U5l+KwlvUtkw/xj+9cpQr2VFZacDwO1zT/mmew25jB0ZEouBsWoUqoT/IHbJEQS1Is50hK+WhZ0Zt08H8vJJtEIKJnVa+XFq+3We3Q5X+YIJQO8xLYl9ncS1E4E06VydymqwzMXR1YqfNw9i4ehdTO63iWV7hqn87JjPoW+oy+DxLQl7FsPq2YcZ3+c3un3XgI7962IudkRQCH0DXVw9bXH1/PB097zcApISMkhNzCQ1KYuU5EzSkrNIT8kmJTGTyPBEMtLeFi5kvN0qFsDKxoTkxEwAHF0siYtJRUdXG/cy9sRGp6CppYlbaTui/rX8xc3LjpchcUglUjzLOvA8MIrCgiLKVnThyf0IAHz93XhyPwINDQ0q1/Qg+nUyJqYGeJR1ID+3AFNzI+ydzHH3eturxdLGGCsbUyxtTERBREFCn8WwdtEJ7BzMmbq4Ozb2ZspO6bPduvycLSvPMm/DAKzkMHPic8hkMv5YdR6ZTMZ3k9qoRN8cqVTKhQN3WHF8nFzGjwiKoUxl0fBVEAThS4kzIeGr5eH7tkjy8kkk9Kwt93jmNqaYmBtyesc1+k1up/Lf/JiYG1KnVSXuXnrO2ukHWHN6Arp6qv+R4VneidELujKl/0aWjNvDlFW9sbRVjZP24la6ghPLdw/jzMF7bF95lpN7b9Nn1De06l4DbbHTiFLpG+ji7GqNs+s/29VIIpGSl1NATnYe+XlF5OUWUFBQRGF+EYUFRUilMgoLJUglUjQ1NSgqkgKgraOFTCpDU0sDXT0dOvUFHV1tdHW10dPXQc9AB109HYyN9dEz0FGJC0Xh/yUnZLB19XlSk7MYMPIbKlVT75liAReDuHA8kHkb+qtU0efysUec//MeI3/uhJ2ThbLTAeDZnZeUqVQKAzkt0Q26E0abQY3kMrbwZcQuOYKgXlT/6kcQ5KR0JVd86niTlZ6jsJgNOlTl0dVgbp99QkMV37IX4Juu1Tmz5xYAh3+/Qrdhqt0A9i/lq7gxc31/fpm0n0l9fmPRziFYWJsoOy250NLSpFW36tRuWp6ty89w5eRjTu+/y5DJbahUw1PZ6Qn/kJaWJkYm+irT60GQr7ycAg5sv86dq6E07+hPi07+aGmpd6+J6+efsX31uX8VS8yVnc47qcmZrJ97hDIVXWjWRXWOu+f23qJxZ/nlE/UiDmcve7mNL3whUeAQBLWh3kdnQfgC7hWceXYzlIDjD5DJFHPkqt3aj4p1y3DrVKBC4n0pTU1NRs7vRsijSP5Yfoo3EYnKTukfq1zbixFzOhIblczS8XtJT8lWdkpyZW5pzE9zOzNkUhsMDPWY1H8Ta+ce5c3rJGWnJgjCv0iKJFw4/ohhXX8lP7eQ+ev70bprNbUvllw6EcjVk4+Z/9sAlSqWAKybfQRbRwt+nNdFZX7Pudn5BD98TaU68lnqWlhQhJ2zpcq8XkEQBHUmPkmFr5aBsT5Ope3ITM0mMTpFITFNLY0xNNbn+vEHZKRkKSTml3Iv50in7xpSmF/E7tXnFFZcKg7VG5Zj6q99eBX8hkl91pOWrB6/8y9RxteZpTuHMGl5T+5cCWZI2xVsWHCcNDV5vwlCSSSTybhxIYhhXdcQ/CSahRsHMmh0c5XYqeVLnT54j32brjJkcmus7VRnGQ7A1ROBXDv1mDrNfXH3Vp3ZFjdPPaJZ9xpy28Hm1dMojMzU/71VYsnkdBMEQS5EwUT4qtVoUQm38k6EPX6tsJiNOldHJoNbZx4rLOaX6vVTc2o0rcD5/Xc4f+COstP5JDUbl2fUz52JfpnIsgl7SE3KVHZKcqehoUGDlhX57fho+v3YjMiweAY1X8rudRfJyylQdnqC8NWQyWTcvxnG+IGbuXQqkEkLuzJ8chvsHM2VnVqx+HPbDQJvv2Th7wNVpsHrX1ISM9i+4gz1W1Wi63cNlJ3Oe07vvEntFvLbqefVs2jKVhVLMgVBEIqDKJgIXzULOzNePY3ixYMIhcWs3swXGycLzvxxQ2Exv5S+oR7t+tcH4Lc5R0iJT1dyRp+mZtMKTF/bj/CgGCb2Wk9SnHrl/7l09XToMqg+E5f2oGW36uxef4l5P+3kyB83KSgoUnZ6glCiBd59yaLJ+9mz6Qr9RzZl2tKeeHg7KDutYiGTydi++hw3LwQxYno7zCxUY+vgv8hkMlZNPUhCTCrfjmiKlgo1wY55GY+ZlTGO7h/eYas4PLkRgpef2CFHVf3V9LW4b4IgyIcomAhfNS8/NwAigqIUFlPfUA+/emUJuhPOm1fq0xOkSn1vWveuQykvO9ZM269WS3MAqjcqx9jFPUiISWX5hL3EK2gZliowtTBi8PhWbD49FlsHczYuOsH3rZZxev8dCkXhRBCKjUwm4/G9l4wfuImtq87RvIM/izcPxKeKm7JTKzYSiZRfZx8hIzWHuRv6qWSj4jP775CdmUv/cS1x9bJTdjrvOb0rgJrN5De7BCDudRL2bjZyjSEIgvC1EAUT4atW2s8NaycLngW8UGgBoEn3GpSp7EbAyUcKi1kc+k1oTXxkMjdPP+HKkQfKTueT+dfz5uffBxH1Mp6x3dfy+kW8slNSKBsHc0bO7shvJ8ZQr0VFVs8+wuAWyzi9/66YcSIIX0Amk3HnWghj+v7G/i3X6TG4Acu3f0/lmp4laivn/LwCFozZTX5+IUMnt0HfQFfZKf2XNxFJbJh7FE1NTdr3q6vsdN5TWFBEwOlA6rapLLcYmanZlPJ2KFHvuxJH9DARBLUiCibCV83I1AADI33SkzIV1vgVoEKN0mSkZnF08yWkUqnC4n4pE3NDRi3qjp6BDmf331a7pTkAvtU9mb6uP4UFhayefoCQwEhlp6RwjqWsGDSuJRtPjKZSLU8uHnvIwG+WcGjrddHjRBA+gaRIwqUTgYzssZbTf96j/6hvmPNrH/xre5W4C9bMtBymDt6Cl48TY+d3QVtHdZa5/KWoUMKWpSexdjBj7JLuKrdLzK0zj6nT2g99Q/kVmkLuv8LGyVJu4wuCIHxtVOtIIghK4FO7DO4+LoQ/VtyFs6amJt/0qEVCVAqB10MUFrc4VG9SgWbda/LwaggrJuxRu6U5AGV8XViy+wcy0nKY2Hs9964EKzslpXB0tWbMvC6MXdCVWk3Ks+WXM0wZtJk/Vp8Tu+oIwv+Qk53P4T9uMmPkH9y4EMTI6e2Z8UsvKlXzKHGFEoDYqGQm9N9I/ZYV6f5dQ5V9jX+sOsv100/oNeIbbB0tlJ3Ofzm54zoN21eVa4zXz6MpV100fFVlooeJIKgXUTBRQ2vWrMHNzQ19fX1q1KjBnTv/bNeSPXv2oKGhQYcOHeSboJrx8C3Fq6dRPLsVqtC4TbvXpExlN87tvqXQuMWh34Q22Dpb8uh6KBcO3lV2Op+lVGk75m35DntnS7YtP825P9XzdRQHOycLhs9oz9bzE6hY3YMj228wpOVy1sw5QrQa9dkRBHmLf5PKnk1XGNz2F6JeJTJsQiumLe+Jt4+zslOTm+ePIpk0YDP9f2pOu161lJ3O3woMeMGD66E07eRPw7byW/LyuWLC47G0NcO9vJNc4zy8HIRXZXe5xhAEQfiaiIKJmtm7dy9jxoxh5syZPHjwgEqVKtG8eXMSEhL+5/MiIiIYN24c9erVU1Cm6sO7qgcAIXdfKjSurbMVppZGXD92n8y0bIXG/lJGJvqMW9EbG0dzfp2yn+hw9ewFYuNgzpI9P2BhbcLyCfvYufqcWs6YKS6WNib0H92cbZcmM2Bsc+5eDua7FstYNnEfD24ots+PIKgKmUzG0wcRzB2zi7F9N6Ktpcm6gyMZOb09zu4lu7Hm9TNPWDxhL1N++ZYaDcsqO52/lZaUyeLRu8nJyGPYjA7KTueDTu64TuX68v0dSiRS8nIKMDI1kGsc4QuJHiaCoFZEwUTNLF++nO+++44BAwZQvnx51q9fj6GhIb///vvfPkcikdCrVy9mz56Nh4eHArNVDx6+pahUvxxovF2PrkgtetfF1MKIG8cfKjRucfCt4Um91n7k5xawbPROCvILlZ3SZzExM2Ta2n40bOvHjdNPWDFp31e/c4yhsR4tutVg89lxTF3VC4CpAzczpNVyzhy4S3ZWnpIzFAT5y87M5dieWwzttIptq89Tu3F5tpwaQ5cB9VRuG93iJpVK2bH6HLvXX2LBlkF4+6ruDBqpVMr2X86QlZHDpJW9MDRWvV17crPzeXQthPrtqsg1TkRQNOVrlpZrDKEYiIKJIKgVUTBRIwUFBdy/f5+mTZu+u09TU5OmTZsSEBDwt8+bM2cOtra2DBo0SBFpqh0dXW0KCwp5fC2YiOcxCo1dvbkvMpmUQ+vPq+W3973GtKRmMx9Sk7PYtui4stP5bLp62oxf1pM6zX04e/AeU/ptJF308EBLW4u6zX0Zu6gbvx4eRfkqrpzYfYve9eazasafhAcp9u9FEORNJpMR8jSKFbMOMajN22U34+Z2YcmWwTRu44eOjrayU5S73Ox81v58lFeh8Sz9Ywj2Kt5AdO+6i5zafYuh0ztQWkWXRl0+dBf/RuXQ1deRa5xnt17gVk6+S34EQRC+NqJgokaSkpKQSCTY2dm9d7+dnR1xcXEffM7169fZvHkzGzdu/Mdx8vPzycjIeO9W0pWr/vYbmee3Xyg0ro6ONt98W4fXwW94ditMobGLg46uNt9N60BGShZ/brzM7fNPlZ3SZ9PU1KTXqGaMXdKdvJx8xnVbw+vQD/9dfY08yzny07wuzN8ymP6jm/P8wWumDvqdkZ1Wc3zXLbIycpSdoiB8toy0HA7vuMkPnX5l78areJSxZ9Ox0fwwuS1eFb6eC9DYqBTG9lqPsZkBU1d8i4GRnrJT+p8Cb4Vx4o+b1GtVkRY9aig7nQ+SyWSc2HaN1v3qyz1W7KtEylcXM0xUnWj6KgjqRRRMSrDMzEz69OnDxo0bsba2/sfPW7BgAWZmZu9uLi4ucsxSNVSsWxZHDzuCFdzHBN4uy6lYx5tze24qPHZxcHS3YdSC7ji6WbNj6UkS36QqO6Uv0rRjVYbNaE92Rh6jO6/mzqXnyk5JpRibGtC+bx3WHvuJ6b/2wd3bnr0bLtG34UIWjd3D/euhSCSKXdomCJ+jsKCIgItBzPlxJ4sm7ON1eAI/ze7A9BXf0u7bWhh/ZX0g7l0LYc2cw/QY0oj+PzVXuS15/1NibBoLRu7Ays6MUfO7quzOPY8DXlC+mgd2LlZyjSOTyXh+Oww7139+vicIgiB8XMmfW1qCWFtbo6WlRXz8+w024+Pjsbe3/6/Hh4eHExERQdu2bd/dJ5VKAdDW1iYkJARPz//eem7y5MmMGTPm3c8ZGRklvmhSxt+dNy/jQQnnW/alrDEy1efCvlv0m9IBSzszxSfxhRp28OfpnXBO7LjB/GFbWbx/JDq66vvxUt7fnZWHf2T9nMPMHbaVrkMa0+vHb9DUVO0LCEXS0NCggr8bFfzdyM7I5frZp5w/dJ91Px8lL6eAhm38aNTOD4+yDip7ISN8faRSKUEPI7l0IpDE2HQkEilN21WmVpNy6BvoKjs9pZBKpezfdJVzh+4zdWUv3Mv89/mEqinIL2LzgmPkZRfw08JuKl3cOvLbRTp811jucWJfJWDnai0+b9WBPHqOiBkmgiA36ntF8xXS1dXF39+fCxcuvNsaWCqVcuHCBUaMGPFfjy9btixPnjx5775p06aRmZnJypUr/7YIoqenh56eak/DLW6Wdua4VXBGS1uT1Pg0LOzMFRq/7eBGPLzynCuH7tJxaNOPP0EFfT+jI88fRJCZls0fy08yYFI7Zaf0RWwczZmwoherpx1g1+pzpCZlMmBCK0zMDJWdmsoxMjWgeZdqNO9SjTevk7h8PJCLRx/y5O5LcnMKaNCqIg1aVyrxO4oIqkkmkxH6NIarp58QE5FEcmIGDVtVoueQhlirYYG6OGWkZrN04j5sHMxZsfcHlS48/Lv1s//k6vFHTFnbD/eyDspO52+9eZWIhoYGvrW95B7ryY0Q/OS8C48gCMLXSBRM1MyYMWPo168fVatWpXr16qxYsYLs7GwGDBgAQN++fXFycmLBggXo6+vj4+Pz3vPNzc0B/ut+AUr7uXJ+5w2e3QqjbvuqCo1duX45XMo4sm/VadoMbKiWszN09XWYtn4gYzv9wr5fz+Ne1omGHfyVndYX0dPXYeySHpSv4sbvi0/w8HooU9f0VdnGgqrA0dWab4c3oecPjQkLesOV44GcOXCXhzfDyMnKo25zX+o296FUabuPDyYIn0kqlRIcGMWN80FEvUokPiaV+i18GTSuBS6icAdA8KNIlk3ezzcd/ekyuL7azKA7f/Aup/fcouPABtRtUVHZ6fxPhzdepGaLSgqZ9fHkRihdf2op9zhCMRAzTARBrajfVdlXrnv37iQmJjJjxgzi4uLw8/Pj9OnT7xrBRkZGqs1Jj6rxre3N+Z03eHozROEFEw0NDVr0rsPqcTu5duQ+jbuqZvO6j3Fws+bHxT2Z1f83Dm64SCkvOzwqqHdxQUNDg1bf1sKjvCPzh+9gzcw/adKxKq171RJTn/8HDQ0NvCo44VXBiYHjWxD6OJprZ55w5uBdHgWEkZqURa2m5andtAJlKjqLzy3hi+XnFRB4+xW3LgYRE5lMZnoudb6pwKAxzXEVBbp3pFIpf/5+jWM7Axi/pDs+Vd2VndI/9uR2OCsm7aVJp6oMnNRG2en8TxmpWTy8Gsx3szorJJ5UKqWUt6NCYgmCIHxNNGTquJepoFAZGRmYmZmRnp6OqampstORm5jwOH754XdMLI2YuftHhcfPy8nn537rkQHz9o1S64vxQxsvsXn+EaztzVl5YhxmlsbKTqlYpKdks3bWn1w9HkjdlhX5cX4XjMUSnU8ik8kID3rDzfPPuHUhCGNTA6JeJlKtgTe1mpSnYg1PjEz0lZ2moCYSY9O4ey2Uu1dCSErIwMBQl1qNy1GjUTkcS8m3yaY6SknMZOvy06QlZzFmQVfMrdTnszkuKokZAzZRWFDEyiOjMbUwUnZK/9PuX06hratN1+HfyD1W3OskNk3fy7Ttw+UeSxWpy3nqX3mWHTUfLb3iPc5J8vMIXjVF5X8HgqCOxAwTQfgXRw87YsLjSQtIJzs9ByMFXwjrG+rh5VeKPb+c4snNUCrW8VZo/OLUflADntwKI+DMEzbM/JMxy3uhraOl7LS+mJmlERNX9KJMRRcuHnrAqPYrGb+8J+WquCk7NbWhoaFB6QpOlK7gRN8fmxEXncrdK8HcufScQ9tuMHfUTspXLkWd5r6Ur+yKZ3lHld+tQ1CcvNwCggOjuHs1hAc3XqClrYmLhw31W/riX7cMpuaigPl37l4JZtWMQ3ToV4ef5nVWq1ld2Zl5zBr8O5IiKTM3DlL5Ykl+bgHn9t5i1ZmJCon3LCCUSvVE/xK1IZbkCIJaEQUTQfgXDQ0NKtb15trhezy79YLqzSspPIe2gxpx5LeLXDl8T60LJpqamoxb2YeV43dz+ch99A11Gbmwu1rPmvmLpqYmnQc3xKeqB4tH72Jc97UMntyGdn3roKWt/kUhRbN3tqBtr1q07VWLvJwCHt99yf1roTy585IN845hbGZA47Z+uHjaUbGGBy4eNiXifST8M4UFRYQ+jSEsKIaAC0GEPI7Gr6Ynpcs78uOcTnj5OImC2kfk5RawefFJkhMymbm2L6UrOCk7pU8iKZLw29zDRIXFM3PTINy8VbfJ61/O77tFs561FDYD8d75J/QYp9pLlARBENSVKJgIwr/x/6YiN48/IPDqc6UUTKzszWnSvRbHf79M20GNcCurvuuRDY31GTC5HY9uhHJq501K+7jQqk8dZadVbLz9SrH62E9sXXKSbctOcfVEIOOX9cTRzVrZqaktfUNdqjcoS/UGb78pTXiTxqOAMF6/iGf32gusmX2YKnW9MDYxoEJVN3yquuPqZScumEuQvNwCQh5H8fR+BElx6Vw+8Rh7F0tqNPSmQ586+FZzF0u2PkFwYCQrphzEp5o7E5Z0R99QvbZOlslkrJ11iPMH7zFyXleqNyqv7JQ+qqhQwqENF1j052iFxJPJZES/iBP9S9SIhuztrbjHFARBPkTBRBD+TYUapSnIKyTw6nOl5dD+u8ac2HKFA6vPMG7NAKXlURzsS1kxY/N3/LH0JGum7cPC1oRazVV7V4NPYWiszw+zO1GxZmlWTzvAkrG7adKxCq2+raVW091Vla2jOc06v23APHhiK2Iikgh5HMXDGy84+PtVLh19yOuwBMr6lcK3mjteFZwo4+us8tP1hbdkMhlx0amEBcXw9F4EzwMjMTTWJzsjlwr+blSrX5Z+PzXDvIT0QFKkgoIidq0+z8WjDxg9vwuV65RRdkqf5cQfNzn5xw3a9a9H8+7q0Qz9xvGH1GxWESt7c4XEiwyJxadOGTHzThAEQU5EwUQQ/o2jpx0V65cFGWSkZGGqhBN1Fy972g1uxK0zgcRHJWPnot6NCytU86BZj5o8uhHKouHbWHxwFGUquSo7rWJVt2VFyvu7sXnBMdZMP8iN04/5aWE37JzV+7+dKtHQ0MDZ3QZndxuatK8CQEJsGsEPI3n+6DURoXHsWnOBokIJpTxtcfO2x+tfvVI8yztiIprzKpVMJiM+JpXwoDeEBb1526z1aggA9Vr4YmlryoDRzfGu6IKhkZ6Ss1VvIY+j2PfbJXT1dFhz5CdM1LSvy6Uj91kz/QBt+tbh++kd1KIgIJFI2f3LSWZsH6awmA8vPsW9govC4gnFQPQwEQS1IgomgvBvNDQ0sCtlzbk/rvPkejB12il2e+G/NO1eiyMbL7J/9RlGLP5WKTkUp0YdqxIflcyDqyHM6LuB5YdH4+huo+y0ipWlrSnjln9L5bpl+HPTFUa2+4U+P7Wgde/aYraJnNg6mGPrYE79Vm9nLRUWFPEqJI5XIbEEPXjNpeOP2LbiLLp62hibGuDu7UAFfzdsncxx87LHyc26RDQjVjXZmXlEhsUT9TKR8OA3vAyOIyI0FksbUyysjSldwYlaTcvTa0RT7J0t1OJCWB3k5xWy69dzXDz6iB9mtKdW0wrKTumzPboRyobZhylb2ZVBk9upzbK7gFOPKF2pFI5uiju+RYXG0l30LxEEQZAbUTARhP9QpVEFzv1xnUdXgpRWMPHyc6XaNz5EBr8hKTYVawcLpeRRnLqPbEZCTCpPboUxrfc6lh36EQtbM2WnVaw0NDRo2rkaleuWYc2Mg6yd+SdB9yPoOfIbSpW2U3Z6JZ6OrjZlfJ0p4+tM8y7VACjILyQyLIGXwbG8ConlxbNoDm29RmpSFhVreJCamImLpy1lfJ2xtDXFydUaR1dLzCyNxYX8/yCRSEmMTSPmdRLJ8ZmEBcUQ/TIRmUzG47uvcHCxxK2MPW5ednTsVwePsg7YOYniiLw8vPGC1TMPUa+FL2uP/aTWM6pCH0cy+7vNeFd2ZfLqvugbqEffFYlEys4lJ5i6+TuFxSzILyT0YQS2YjajehEzTARBrYiCiSD8B7+G5XEr78TDS8+Umse3Y9owuvkCDqw6w9AFPZSaS3HQ0NBg+LyupCVlkpmWw/S+G1i0byRGpgbKTq3YWdmZMX39AK6dDGTvmvMMb7WUrkMb0/2HJujpq8fJf0mhq6fzbhvjf5eWkkX0yyQiw+OJfplIwps0zhy4R3xMKmYWRuTnFWDvbIm9iyWupW0xszTGxtEcO0dzrOzMMLUwLNEzhwoLikhJyiQxNp3E2DQSY9NIiksn5nUycVEpmFkZERESh6OrNb7V3DE1N6RZ56q4eNjg4mGDrp6Osl/CVyEtOYt9v13i1oUgRv3cGb9apZWd0heJDItn3axDGJkYMHpRD8zUqH/N1cP38PR1wbm0vcJiPrsVRoWaXgqLJwiC8DUSBRNB+A+WduZoamoSERJNXEQi9gqcWvvvylXzwL9xBc78cZ0uI5tj7aj+s0y0tLWY8GtfpvdeT/jTaGYP3Mjs7UMwMCx5PQs0NDSo39qPynXLsGXRcc4fuMvNM4/5bkp7/P+1C4ygPOaWxphbGuNT1e29+4sKJcTHpBIXlUJsVArx0SkkvEnjwc0wEt+kYedsyfOHr9HW0cKvVmmyM3OxsDbBytYUUwtDzCyNMbUwxNzKGCMTfUzMDDE21cfQWF+psyuKiiTkZOaRmZ5LZnoOWem5pKdkk56aTX5+EbGRyaQmZWJsasCjW+GkJWfhWMoKGTJsHS2wdTDH2cOaspVKYe9iib2zBRbWJmLGiJJIJFLO7L/DHyvP0rxrddYeG602MzH+TnxUClN6r0NDQ4O524dg52yp7JT+MUmRhKO/X2bc6n4KjXv//BOqNvVVaEzhy2n861bcYwqCIB+iYCIIH1CzdWV09XV4ciNEaQUTgD6T2/Fzv3XsW3WaHxb2VFoexUnfQI9ZW75nco9fkUokzB/yOzM2f4eObsn8ODIxM2TU/G48vx/B6mn7mdb/N1r3rk23oU2wdVL/IlhJo62jhZObNU5/sz10UaGElMRMkuPTSU/NJjE2ndTETLIycgkPekNGag7pqdkYmRoQ9jQaqVSGTzV3gu5HYGCkh7OHDbnZ+egb6GJgqIulrSkFBUXo6mpj62hOZnou2jpaGJvqU5BXhIamBhoaGmjraFFUKEEqlWFsok9aShaSIin6hrqkJWdRWFBEQX4RUqmMrPQc8nILsXU0J/hRJNlZebiWtiP0STQ6utqYmhtiamGIqfnbAo+zhw0OpayoUMUVa3szugyqj5WdKWYWRqIgooKCH0Wyds5hPMo6suiPobh42io7pS+WFJfGhp8PkZOZx4Kdw3DzdlB2Sp/k4oE7lPJywMlDsUsvn1wPoc+UDgqNKRQDsSRHENRKybxCEYQvVKl+OXYtPIKlvTnf9KqrtDzK+nvg6ePCqa1X6TKiWYlZp2xkasCc7UOZ0Hklz+68ZPGIbUxc0w9tnZL7kVTO343Vx8ZwbMcNzu2/w/dNF9J1WGM6f9dI7b8Z/ppo62hh62iOraP5Rx8rlUrJzS4gKz2XnKw8srPyycvNJyczn7zcAvJyC5AUScjLKaAgvwg9Ax3ycguRFEkoyCsiKyMXqVSGTCZDT1+bwgIJGpqa6OhqISmSoqWtib6BLtb2Zujq6aCnr42egS56ejroG+pibGJA1+8aYGisj6GRHibmhujpi6Uy6iolMZPDW69x6ehDBk9qTf1WlUpEQSs1MZPJ364j8U0qc7cPwdtPvXZRK8gvJOBUIN/P6aLQuAnRyZT2c0VPHD8EQRDkquRenQjCF6hQqwwGxvo8vPyMgvxCpa7H7zulAzlZeRxYfZYfFpWMWSYA5tYmzN8zgkndVpEUm8rSH3cwflVftLRL7q4lWtpadBhQn4ZtK7N1yUmObLnGtROB9Bz5DfVb+5WIix/h/2lqamJkoo+Rib6yUxHUWH5eAUe23eDg5qu06FadDafGYmhcMt5TaUmZrJm2n4Q3qczYMBCf6p7KTumTndx6FRtHC+xdPzwrTV7unn1MKW9HhcYUioeG7O2tuMcUBEE+Sm7HOkH4Ajq62jTrUw9tHS2e3QxVai6lK5bCys6c45svERkSq9Rcipu1gznzdw8nJT6DK0ce8MvYXUgkUmWnJXfm1ib8tKg783YMwdjUgIUjdzB32BaeP4hQdmqCIKgImUzG5WMPGdJyGckJGaw4MIIB41qWnGJJchaTe67l9vlnTF/XXy17O2Vn5nL/UhA9RrdUeOywwNdUayb6lwhfZs2aNbi5uaGvr0+NGjW4c+fO/3z8/v37KVu2LPr6+vj6+nLy5Mn3/l1DQ+ODtyVLlrx7jJub23/9+8KFC+Xy+gShOIiCiSD8DU/fUmSmZHPn9CNlp0LfKe3RM9TjxJbLyk6l2Nk6W7HowCgc3Wx4HRrL0lHbkRRJlJ2WQnj5uLBk3wimru1HYYGEMZ1XMXfYFqLCE5SdmiAIShR4K4yl4/dyaOt1xi3uwbDp7XEoVTKWZMLbZThLRu0gIzWbqesGULVReWWn9Fn2rzqDdxU3LGxNFRo3LyefFw9e4ajgnilCMZHJ6faJ9u7dy5gxY5g5cyYPHjygUqVKNG/enISED5+D3Lx5k549ezJo0CAePnxIhw4d6NChA0+fPn33mNjY2Pduv//+OxoaGnTu3Pm9sebMmfPe40aOHPnpL0AQFEQUTAThb1RrXgl3HxfiI5ORyZQ719HJ044Wfetx5LeLPLsVptRc5MG+lDVzd/9ARnIWlw/fZ9lPf1BU+HUUTTQ0NKjbshLT1w9g2KyOPLv3ilmDN7Fqyj6S4tKUnZ4gCAr08vkbpg/azIopB6jWwJtf9g/Hp5q7stMqVkmxqUzsvprHt8MYtagbNZv5KDulz5IQk8rNk4/o/MM3Co/94OIz/BqqZ5FJUB3Lly/nu+++Y8CAAZQvX57169djaGjI77///sHHr1y5khYtWjB+/HjKlSvHzz//TJUqVfj111/fPcbe3v6925EjR2jUqBEeHh7vjWViYvLe44yMjOT6WgXhS4iCiSD8DUt7c3T1dbhx9B5RocpfCtP9xxYYGuuzY+ERpRdw5MGhlDWL//yJUl72RITEMve7TRTkFSo7LYXR0dWmXb96bLo4mUbtq3D56EOGt1rGpgVHSUvKVHZ6giDIUWRYPPNH7mDGoM3Ualqe306Po2HbymhqlqzTtPioZGb034iOjjYzNg6iRhP1LJYAbJ9/hI5Dm2CghCVSt04+pFbrKgqPKxQjJc8uKSgo4P79+zRt2vTdfZqamjRt2pSAgIAPPicgIOC9xwM0b978bx8fHx/PiRMnGDRo0H/928KFC7GysqJy5cosWbKEoqKiT38RgqAgJetILHzVpFIpsa8SirUHRq02b09I7p4JLLYxP5e5jSn9p3ck9GEElw/+7zWm6srO2ZJ5u36gML+Q2+eeMm/IZnKz85WdlkIZmRjQ+6cW/H5lKp0GNeDYtuv0rz+PHb+cJi05S9npCYJQjKJfJbJk7G7WzTlM6QpObDo/kVY9a5XIbdajwuIZ12U1sa+TGDy9A9XUdBkOQMjDCF49i6bZt3UUHlsikRJy7yVlq6lfg1xB/jIyMt675ed/+BwqKSkJiUSCnd37y7rs7OyIi4v74HPi4uI+6fHbtm3DxMSETp06vXf/qFGj2LNnD5cuXWLIkCHMnz+fCRMm/NOXKAgKJwomQokgk8no7jyMft4/EfcyvtjGrdPWH1sXK64dvltsY36JFn3qYWJhxOZZB8nLKZmFBGtHC5b8+RNl/d1IiEphUrdVpKd8fYUCcytjug9vypar02jRvQZP74TTv95cNs47SnJCurLTEwThC7x+Ec+mhccZ2+1XHN2smfprX7oNbYy+YcncIjY0MJLxXVfj7GnL/J3DqFy3jLJT+mwymYyNMw7w/dyuaGkp/jQ66NYLarX2U0psoXj8tUtOcd8AXFxcMDMze3dbsGCB0l7n77//Tq9evdDXf38W1pgxY2jYsCEVK1Zk6NChLFu2jNWrV/9tcUcQlE182golgoaGBjbObxvivX4eU2zjung7oquvw/PbYSTHphXbuJ9LV1+H737uir2rNftXn1F2OnJjbm3CzzuGYWxuSOijSGb0WUdcVLKy01IKS1tThs7syMSVfWj1bS1O/HGD+cO3sXbGQeKjU5SdniAInyA8KIZ5I3Yw4du1mFkasfnCJHqN/AZjUwNlpyY3gTdfMLHHr2hqavL99A6U81fvniyXDt7B1NKISnW9lRL/+pF7lK+pvgUnAbk2fY2KiiI9Pf3dbfLkyR9MwdraGi0tLeLj3/+SMT4+Hnt7+w8+x97e/h8//tq1a4SEhDB48OD//bsAatSoQVFRERERER99rCAogyiYCCVG5SY+VKjjTVxEYrGNqaGhQZOedfCt660Su+UA1GlTBW1dbfatOEVsMb5WVWNsZsjcnT9Qt40feTkFjG2/nJfPopWdltJY2pry/bT2bL02Dd/qnlw8fJ+BDeezduafhAcVX5FQEITiJZPJeBQQxtT+G5k3cgdeFZ3ZcmkyXb9vVKILJQAXD91jWu91VKlXlqUHR+FezlHZKX2RnKw8zu0OYPCszh9/sBxIpVISo5NFw1fhb5mamr5309PT++DjdHV18ff358KFC+/uk0qlXLhwgVq1an3wObVq1Xrv8QDnzp374OM3b96Mv78/lSpV+mjOjx49QlNTE1tb248+VhCUQRRMhBLD1NKYZzdCCLkXXqzjVmtWkSfXQ7i078NNrRRNQ0ODYQt6YGFrxv7Vp5WdjlzpGegyaU1/KlTzJCU+g9kDN3L/cpCy01Iqc2sT+o9vzbYbM/h+WntunnnMiNbLmNJnPXevPEcqLb4ePoIgfL6iQgmXjj5kdJfVbF16ijrNfVh/cizdvm+EoRIahSqSTCbjyO9XWDFuN25lHRgxrwuObtbKTuuL7VxynPLVPXF0V86FXci9l+gZ6KKrp6OU+ELxkOeSnE8xZswYNm7cyLZt23j+/DnDhg0jOzubAQMGANC3b9/3Zqj8+OOPnD59mmXLlhEcHMysWbO4d+8eI0aMeG/cjIwM9u/f/8HZJQEBAaxYsYLAwEBevnzJzp07GT16NL1798bCwuLTX4QgKEDJ6yomfLXcKrhgZmNKXjE3CS3t54a9mw1Jb1JJiU/H0s6sWMf/HK5lHanfsRoHVp2mWlNfarX0U3ZKcqOlrcXIRd2xcbLgypF7zOi7geHzu9Gqt+Kb7akSIxN92vevR6tva3H52ENun3/KjH6/4eJpS/uBDWjS0R99ww9/syQIgvxkpuVwau9tAs4/BRl0HtyQ2s18vpqeE5IiCWunH+TkHzdo8W0tvpveoUQUiF4FRRP3OokJ6wYqLYeA4w+o16Ga0uILJUv37t1JTExkxowZxMXF4efnx+nTp981do2MjHxvp67atWuza9cupk2bxpQpU/Dy8uLw4cP4+Ly/29WePXuQyWT07Nnzv2Lq6emxZ88eZs2aRX5+Pu7u7owePZoxY8bI98UKwhfQkJXE/UmFYpWRkYGZmRnp6emYmpoqO52/lRCVRG/PUejoanMk9Xe0dYqvHrhj3p/8Mf8wI1b0o+13TYpt3C+Rm5XH97Vm4uRpy6ydI9A3KvkXxxcO3GHFuF0YGOvRqlcd+kxo89VchHyMTCYjMCCMw79fISM1m8jQOJp1q0GbPnVwdLNRdnqCUOK9ev6Gi0cfcGLXLarW96bjwPqUq+yq7LQUKjsjl1WT9vLgWgh1W1Vi+NyuaOtoKTutLyaVShnXZgk9Rrei+je+SsthSPWprL42G32Dktkc+HOpy3nqX3n6DpqPlm7xFhElBXk82TxF5X8HgqCOxAwTocSwcbbCyMyQ7PQcokPjcKvgXGxj127jzx/zD3Nl/y2VKZgYGOszanlvpndbyfYFh/l+bndlpyR3TbpUx9bZkm0Lj7J31RleBcUwYW1/jExKdh+Af0JDQwO/2l741fYiNiKJ4ztvcHbvbUICI9Ez0KF1rzrUaFKhRFy8CIKqKMgv4taFZxzZco3oV4l0/q4hG06Nw8bRXNmpKVzs6yRmDdxI1It4hszuRLv+9dDQ0FB2WsXiwr5bOHvZK61YAvAs4AVeld1EsUQQBEHBxFezQomhoaFBnQ5VKVXOiVdPIot1bI+KpajXsRpxkYkkxqjOziTVvvGlbjt/Dq+/wItHEcpORyF8a5Zm7Mq+lCpjz7M74czut57o8OLbSrokcHCz5rup7dlxexate9UiJyOPuUO3MGvwJrYuPsGbEtwsWBAUIfplAhvnH6NP7TkEnH1K61612HFjGt2GNPoqiyWPboQyuecapFIpUzcMoP2A+iWmWJIcm8aOhUfpO6mdUvO4eewBjXvUVmoOQvFQlR4mgiD8M6JgIpQoBsYGRD6P4eXj18U6roaGBm4VXEiMSuGyijR//cuwRT0pW9WD5SO2UlhQpOx0FMLB1Zrlx8dRv70/TwLC+KnVEu6ce6LstFSOvoEujTtWY8WR0aw+PhbPCk4c23GdQQ3ns3zcbs4duENuMff8EYSSKjc7n7P77zCu2xpWTztIYX4hC/4YysQVvWjcwf+rbMQpk8k4vTuAqb3WIZPKmLi6L3VafnxXDHUhk8nYs/IU3X5sgbWD8hpSFhUWcfdsIH71yyktB0EQhK+VWJIjlChlqrjjXdWDrPScYh+7cfdaXD14mxcPI4p97C9hZW9Oi771WD58C/tWnKTXBMV+C/bXriz/3hhMEYxMDBixsDvm1iYc/u0i66YdIPjha3qNbSX6mnxAaR9nSvs403NkM26cCuTBtRCWj9vN2hkHadOnDlXql6VizdLidycI/0YqlfL4VjgXDt0nISYVgFbf1qR2c9+vfmlEXm4Bv07ex5WjD6jbuhLDZnfG3NpE2WkVq2tH7hPxLJqh87opNY8HF59Rv1P1Yu3NJiiR7F+34h5TEAS5EJ+8Qoni7lOKkHsviY9MYtSvA4t1SrCjhx1GZoZcOXibHuPb4uFbqtjG/lLf9KxN0O0wTm27Rs2WfngqKDdJkYSVo/9AR1+HEYt7KnwKtqamJn0ntKGsvxuLhm1h9y+niAmP54f53TGzMlZoLupC30CXJp2q0aRTNfqObcXFQ/d4/uA1BzZcwsrOjFa9alOtUTlK+ziXmCn1gvApZDIZr4JjuXTkAZFh8USGJdC0oz/fjvwGh1JWyk5PJbyJSGT70hNcOfKQdgPqM3hae3R0S9YpZWpiBruXn2D61mFoaSm399PZP67ReWQLpeYgFCNRMBEEtVKyjm7CV8+1gjPaOlrk5xSQ/CYVayfLYh3/m151Cbn3kpvHH6hUwURDQ4PeE9tx/ch9lgzdzKqL0xQyPfz53Zec33sLmUyGkakBA6d3lHvMD6nexIfVZyaycNgWXj2PYfg3C5i0bgA+NUorJR91YedsSc+RzZDJZIQGRnLp8H2e3X3JjuWncHSzplm3GlRvVB63sg6ieCKUeFHhCVw9/oiI0Fge3wqnXqtKdBvaiPL+7uL9/29unArkl7G7kEqkTF7Tj/rtqig7pWInk8n4dfxOWvSpi6OHrVJzyUjJpCCvkLLVPJWahyAIwtdKzL0WShQdXW0qNShPbmYO4YERxT5+/U7VsbQx5dj6cxQVqla/EGtHC4Yt7omJuRE7FhxRSEyfWl6MWtYLfSM97l14yp5fTikk7oc4utuy+M+fKF/Nk+TYNOYN3sS+1WeRSKRKy0ldaGho4O3nytBZnZiz9XsW7PqBSrW8eHI7jB9aLmFwo/nsWnWGkMDId0uwBEHdyWQyIkJi2bnyLAtG7uCnjquIjUqmWdca7Lw1kxE/d6ZCVQ9RLPmXgvxC1s88yJHNlzG3NmHZ4dElslgCcPXIPTJSsmk7qJGyU+HKwTuUreYp3ocliGj6KgjqRcwwEUocO1drAELvv6RGq+I9mTM2N8KnrjeX9gZw+9Qj6rSrWqzjf6nGXWty+3QgB1adoWpTHyrVLSv3mC361EUmk7Fq7E5ePYtBz0CHjkObyj3uh+gb6vHTsl5UrOXFyR3X2TL/CPcvBzFudT9sHJXXsE+daGlpvtueWFIk4fHtcK6fDCTkUSQ7lp/Gyt6Mui0rUa1ROXxrlEZXTxxGBPUhkUgJeRRJwLmnxL5OJjAgjBpNy9O4gz9jl/b4Khu3/hMxrxJY+uMOgh+8pmEHf2Zu+b7EbueeEJ3MppkHWHJsnMJ7c33IjaP3GLNusLLTEARB+GqJM12hxPGpU5bXz6KIfZkgl/FbDWxE7MsEzu24pnIFEw0NDUYu601kSCx7lp3Avbwzppby7+XRsm89crPz2b/6DId/u4Smpibtv28s97h/p3GX6pSp7MriH7YSfD+C2f3W0/3H5tRrUzK/DZUXLW0tKtcpQ+U6ZZBKpYQ8jOTm2SfEvEpgWt8N6BvqUqWeNzWaVsC/flms7MyUnbIg/JfsjFweXA/lzqXnJMenE/kinhpNKtC6Vy0mruxV4npvFCeZTMaFg3dZO3U/ZfxK8ePiHjTvWavEznaQSKSsGvMHfSe3x76UjbLT4eWTSDQ1NbF1Fr1zShTRw0QQ1Io4SxBKHHcfF54FhPLmZTwymazYT+x86nqTkZJF6P2XJEanYONcvH1SvpSJhTE/LPqWSe2X8svIrcz4Y7hCTm47DWuKto426ybvYf3UfWhqa9J2YEO5x/07zp52LDs2lkO/XWTr/KPM/24zrfuHMmBye4xMS+Y3o/KkqalJOX83yvm7ARDzKpE7F57x/GEEq6fsp6hQgntZR+q19qNsFVcq+Lujqy++rRcUTyqV8io4lvtXQrh/LYTMtBy0tbWo1qgc7frWwbOCk0rMHFB1manZbFl4jLP7buHiac+wOV1w9XZQdlpytX/VaazszWnavZayUwHg1NbLtOjXQNlpCIIgfNVEwUQocVwrOOPu44KRmSEJUcnYlbIu1vE1NTVp830Tbh69z8U9N+g+rm2xjl8cKtb1pue4Ntw4/oCTW6/QekBDhcRtN7ghkiIJx7dcYfuCoxTkFdL5h28UEvtDdHS16TaiGWWruLPsx+0E349gaKO5/LSsN/4Nyyktr5LAyd2GjoMb0hHIycrj0Y0X3L/ynIuH77F92Un09HVo3Kkqjq7WVKrthUd5J7FlsSA3CW9SeXTjBQ9vvODxrXC0dTQpW9mNJh398a/vLWY/faKHV0NYNuYPkuPS6TOuFV2GNinxBdDg+y8588d1Vl2YqhIzaPJy8gm9/4rv5vdUdipCMdOQydCQFe+UkOIeTxCE/ycKJkKJo62jjZGZIU9vhBB6L7zYCyYATXrUZsv0fcS+TKDzjy3R1lG9P6We41rz7NYL1k/ag5efG2UquykkbsehTdA30mP12J1smnkQDQ0NOg1TTk+Tv1Ss7cXa81PYMOsA5/bcYs6ADbTsXYc+49uI2SbFwNBYn9rNfand3BeZTEbs6yQeXAvlZVAMe9deYPOCY/hU98DY1BCfGh74VPfEs4Ij2tqq93cjqIeEmFSCA1/z4EoIgbfC0NAAK3sLKtfxol2/unj5OqOtrdytYNVRbnY+m+cdIfBGKNo6Wszb9QNV6su/F5ayZaZms2To70xYPwgTcyNlpwPA1YN38K1bVvTVEQRBUDJxtiqUSGWrlybk3ktePY2kXqcaxT6+ua0Z9TpVJ/T+S+6eeUwtFeyNoa2jzfgNgxnRYA67lx5nzK/9MbGQfz8TgJZ96qKrp83hDRfYMvcQqQnpDJzRSanf2hmZGTDmlz7UaeXHyR3XObLpMjdOPmLkop5Ub+qjtLxKGg0NDRzdbHB0e7v+f8S8LrwMekPo40juXXrO3jXn2bniDBoa4O3nSnl/d3xqeOLl4yyKV8IHSSRSIkPjCHoQwavgN9z9Vy+S+q39sHE0Z8TPXahQ1Q19Qz1lp6rWAm+Esn3pSYLuvqRRx6oMm9MZEwvVKB7Ik0wm45cft9GsVx3KqdDWvbdOPmDQvB7FPu6exUdpNagRplYmxT628A+JHiaCoFY0ZDIxh0v43zIyMjAzMyM9PR1TU1Nlp/OP3Dx2j5+7LadsdS9+uTJbLjFC7oUzqt4sKtYvx5IzU+QSozg8uRnCxLZL8W9cgdl7Ryl07X7A6UAWDN5IYX4R3X5sTt/J7VViWUZmWg6b5vzJ2d0BeFUqhaO7DUPmdMHCRj3e3+pMKpUS+SKeZ3df8uzeK4LuvcLQRJ+I4FhcSttStUE5nNxtKFPJBdcyDqIh51coJSGD0MBIgh9F8vpFLIEBYSCDsn6uVG1YFo/yTpT1KyUKJMUkMz2bLfOPcWrnTXxqeNJhcEPqtKyk7LQUZv+qM0SGvGH06n4q09sm+F44f8w7xNxD44p97CltFjLv2ESVWHZUXNTlPPWvPCv3moeWrn6xji0pyOPhzqkq/zsQBHUkzkSFEsm7qieSIimh919SWFAkl4uuMv4elPH3IOReOC+fROLhW6rYYxQH39reDJjRiT2/nOLg6jN0/bGlwmLXalGJuXtHsWvpcfatPE1UaCwTNwxGz0BXYTl8iIm5IaOX96Zx52qsGLOTK4GRPLoWQv/J7WjWs5bKnDSXRJqamrh5O+Dm7UDr3nUASI5Pf3uB/PA16SlZbFpwlNysfHR0talQzR1HV2s8KzhT2tcZ1zL26Okr9/0jFA+ZTEZyfDrhz2KICI0j5OFrQp9EYe9iRUZqNt6VSuFfvyy9f2yOW1lHlSi2liQymYwbJwPZuvAYWRm5NOlcje9ndcL0K5hV8pfH14I5ue0KK89NUanP/asH79BuWLNiHzc3Ow9nL4cSVSwRBEGQNzHDRPgodanc/6eZnZfy8vFrJm8fSflaZeQS49qhO/wydBN1OlRj7Ibv5BKjOMhkMlb+uJ3TO64xbfsw6rb1V2j8V0HRTO++iqTYNJr2qMWQn7upzFTvvJwCdi4/yYtHrwm8EYp3FTdGLOhO6YqqWQD7GkilUt68SuLFkyhCH0cS/iyG8KAYPMo7EnT3FU7uNriXc8TV2wFXL3tcy9jj4GotLqhVWG52PtHhCbwMfkNESCyZqdncvRJMRko2Tm42VKnvja2jOV4VXfAs74SxmaGyUy7R4qNT2Lv6LKf+uIGtsyU/LulBlfpfVyPsxOgUJnVczsSNgynj56bsdN5JiUtjUptFrL8zr9iLOA8vPSP4Thg9J7Yv1nGVTV3OU9/NMPlWTjNMdokZJoIgD2KGiVBiGZkaEh+RyLOAELkVTGq2roKBsT6X9tyk/6zOWDmo1hbDf9HQ0GDogh6EPoxg7y8ncXCzwVOBM2Lcyzuz/NRENs08wPk9AQTfe8nPe0bh8K8+F8qkb6jLoGkdiAh+w68T9/DsTjjrZxzArawjfSe0wdRSMX1fhP+nqamJs6ctzp62NOrwtrgnk8mIi0rm1fNYXgW/IS4qmcuH7xMTkYRUIqVSbS8yUrJx8bTFubQtTu42OLnb4uRmg7GZ6I2iCDKZjJT4dKJfJRL9MpGkuDTCnkQT+SKehDep+NXxoiCvEDdvB8pXdadVr9q4eTtgaFy8Fw7C3yssKOLQbxfZ9ctpzKyM6Ty0Cb3Htvzqljfl5eQzp99auo9uqVLFEoCL+wJoN6SpXGa8RDyNxLeOd7GPKwiCUJKJGSbCR6lL5f4/ndtxldNbL+FSxpGf1slv9sfRDec4/tsFarWpwoDZ3eQWpzjERyUxtvkCNDU1WXlxGha2it1qMzsjh3kDN/Dg8nOqNq5ArwltKVfVQ6E5/C8ymYxLf95l05xDpCZkYGxuyMCpHWjWoyZaYscNlVSQX0TMywRiIhJ5HRJLZFgC6cmZBD+KJD+3AICqDcuRmZaDQykr7EtZ4eBqjY2jOXZOFlg7WKCrJ747+KdysvJIfJNKfHQqcVHJpKdk8yr4DbGvk4mNTMajvCNxkclvZwF5O+DgZk2p0vaU8rLD2t5MLAVQovuXn/Pb7D8pyC/EwtqU4fO74enjrOy0FE4mk7F4yCYc3G3pO1m1Zlrk5eQzrOY01tyYg6FJ8Rd6p3dYwrTdPyp9WWxxU5fz1L/yrNJTPjNMHuwWM0wEQR5EwUT4KHU5EP2n6NA3DKwwBhMLI/bHbZTb+uTs9Bx6e/2IppYmO0JXyOUkpzgF3Q5jYtvF1OtQjR9X9lP4iVNRYRG7lp1k19Lj6OhpM2ZVPxp1Lv6djL5EdkYuu345xfN7L3l+7xUuXvZ8N6sT1RpXUHZqwj8kk8lIScgg5lUiSbFpRIbFExeZTEJMKrGvk0hLzgKgQjV33kQkY+1ojrW9GaVK26FvqIelrSmWdqZYWptgbm2CqYUR2jolt2hWkF9IenIWqUlZpCZmkJaUSVJcOklx6QCEPHpN4ps0sjJyKVvZldTETOxdLHH2fFsIcXC1wtHVGodSVmI5jYp58yqRPzde4sS2a5hZGfP9zE407OivUj07FOnA6tPcuxjE3H2j0NZRrWLp2Z3XiAmLZ8DMLsU+dlFhEcu+38DELcOLfWxlU5fzVFEwEQT1pFpHCkEoRk5eDrj7lkLfSI9XTyPxrOgmlzhGZoZ0GtWSR5efcer3y3RWYFPVz1G+RmkmbR7Cz73XkJ9TwJRtwxTa+0FbR5s+E9tibm3M+qn7OLLxIq9DYuk7qZ3KnMAbmRrw3cxORIXFsWnOIe6ce8qelac5tOECg6Z3xNPHRdkpCh+hoaGBlZ0ZVnYfnkWVn1dAYkwaSfHpJMSkkhSbRlJcGvHRqUSFx5OSkIGphRGvQ+PePce/vjdxUSmYWhhhamGEtYM5unraGJnqY25tgp6eDoYm+hgY6WFkYoCevg76hrroGeiib6iDrp6OXN7jEomUgrxC8nILKMgrJDc7n9ycfApyC8nKyCU7M4/szFyKCiQkx6eTlZ5LUZGE+OgU0lOySE/JxrO8E09uh6Otq4WltSluZR3Q0dXCys4MB1drKtb0xMbRAhsHc6zsTFXuQlP4b5lp2exZdZajv1/B1MKIDoMb0mtMy6+6oHXtyH2uH3vInD0jVe49LJFI2b/8BAuOTZDL+GGPIrBW0WXDXx2xrbAgqBXVOloIQjHS0NDAtbwzl/fe5PHlILkVTABaD27M3qXHeRMeT9shTdBV8V086rT1p//0TuxbcZKdC4/QZ0oHhU6V19DQoN3gxriVc2bugHUE33tF9Is4Rq/qh5EKzdBxKW3P7O3DeHg1mN0rTvHwSjAjry6k87AmtO5XH3tXa2WnKHwmPX3dd31S/o5EIiEzLZe0pEzSkrPIzsglNTGT9NQsMlNzyM3JJ/FNKlkZuZiYGfDqeSw5WXnkZOVR3t+dp3dfvhvL3MaEtMRMtHW0KOVlR1JsOlrammjraOHl68Lr0Dg0NTXQ0Hxb6ElJyEAmk2FsZkhmajZSiQyJRIKrtwMvHkdRWFBEUaEEJ3cbwp5GA2BgrEduVj4AWtqaVKxZmphXiRiZ6GNkYoBLaVuKCiWYWRphamVExZqemFkaY2phhLmVMeY2JhibGohlM2qusKCIE9uvs2fVGYxMDfCr583g6R1wLeOg7NSUKuTBKzZM3cuiI2NVsjfVrRMPKFu9NNaO8ilqBN16ge9X1thXEAShOIiCiVCi+TWswI3Dd4kIipZrHEt7c1oNakTYwwjO77xBq0GN5BqvOHQf25qs9Bx2LT6GgZE+XX9S/MyYinXKsOrcVOb0XUtCdAo/fjOfaVuH4VbWUeG5/C+V65elYp0yXDp4h8MbL3Fsy1UOb7xEy9516f5jc6zszZWdoiAHWlpabwsJVp92cSWTyd7N9sjLKSAvt4DCvEIKCoooyC9CUiQhP68QSaGEoiIJmlqaFOQVIpXKkEllaGlrIimS/isHTdB4+7+aWproGejSolsNtHW10NHVRldfFx1dLfT1ddHV10bfUA99Qz3Rl+UrJJVKuXr0AZcP3+f2uae4lXXgu5kdv7rdbz7kzasE5vZdx+TN3+PkaafsdP6LTCZj18IjTPx9qNxiPLzwhBb9G8ptfOGf05C9vRX3mIIgyIc4oxJKtCpNfZHJZFw9cItRvw6Sa+POzj+2ZJDveOJfJ9K0d1109XTkFqs4aGhoMGBWF968TODsruuY2ZjQrFddhedh72rN8lMT+XXCLs7vCWBK518YNr8H9dorduvjj9HS0qRpt5rUa1uFk9uvsWflGQLOPObcvlu06lOXrsObYW5jouw0BRWgoaGBgaEeBoZ6mIkZ8IKcyWQy7l8JZsu8I7wMiqG0rws/Lf2Wpt1qiK22gdTEdOb1W8fgOV2oUKO0stP5oDtnArH3sKVUWSe5jF9YUEhBXoHK91j7aoglOYKgVsSRVCjR7N1ssXG2JDs9h9D7Lz/+hC9gV8qaFgMaYGhqwIXdN+Qaq7hoaWkyafMQ7EpZs2LEFq4fva+UPPQN9Ri7uj8jl/bC3tWaeYM2sHbSbgryC5WSz/+iZ6BLxyFN2HJnDp2HNUFLS5M/119gcreVbJ7zJ6mJGcpOURCEr4BUKuXh1WBGt13O7AEbKCqUMGh6B5Ye+onmPWuJYgmQk5nHjO6rad63Hg06VVd2Oh8kk8n4Y94hek2U3449wXfCqFBLbCcsCILwOcTRVCjxGnavg7GFEffPP5Z7rO7j2vLmZQLbZx8kLydf7vGKg66+DlO2DMWzkivHfrvAvfNPlJKHhoYGrfs3YPiib3F0t+XcnpvM6buWN68SlJLPxxga69Phu8ZsufMzPUe3xMTckANrzzOg+nR2LD5G4ptUZacoCEIJJJPJeHA1mPGdVvLbnENEvoij2/BvWHZkNF2GNilxW8Z+roK8QtaM30nl+mVpN7ixstP5W3dOP8LGxQoP31Jyi/HwwlMqNSwvt/GFT/fXspziugmCID+iYCKUeF6V3clKzebemUC5x7JxtqLt900oKizi3I5rco9XXAxNDJh3cAxpyZnM6fUrj648V1ounr4urL44lVZ963PvwjNGNJrLlUN3lZbPx5iYG9J3YlumbxlKr7Gt0NHV5t7FIAbWmMEvo3cQ9SLu44MIgiB8hEwm49bZJywZuZ2pPdfwKiiGhu392Rowiz7jWn/Vu9/8p6LCIhYM2oCeoS4DZnZWdjp/SyqVsmPuIXpP7iDXOK+eRFK2hpdcYwiCIJRUomAilHh+jSpQqUF5EqKSyUzNknu87uPboa2tzdbZB8hMzZZ7vOJiamXMwiPjcPK0Y/u8QwReC1ZaLkYmBnw3pyvj1g5AR1+HLT8fYunwLWRn5iotp48xMTek9/g2bL37M3Va+2FibsTZ3QEsGb6FOQM28OxOODKZ+BpIEIRPIymScP3EI374ZiGzB/xG+LNoeoxqztZbs+k+shmmlkbKTlGlSCRSts45hI6uNsOX9FLpXZ+uH76Lo4etXGeXZKRkIZFI0VPx3fu+KjKZfG6CIMiFKJgIJZ6x+duTyaToZB4oYLmJha0pbYY0IScjl+MbL8g9XnGysDVj3p9jyEjJYkbXFQReVd5ME4Cm3Wqx4vQkLB3MOL83gGndVvHsdphSc/oYI1NDuo1szta7P/Pjsl5kZ+YRcCqQce2WsXDIZq4cvkdRoUTZaQqCoOJyMvM4svkyA+vM4ZexO8nNzqP/pLYsPzKGfhPbiELJB0ilUlaM2kp0eDzjNwxS6T4uRYVF7Fl8jL4z5DsD5uHFJ9RsXUWuMQRBEEoy1T2SCEIxqte5Jr71yvLkmmIKAJ1GtsCrihs75x8m7nWiQmIWF0t7cxYdn0CZKu6sGLWV+xeeKjUfBzcblhwdT/+pHcjPK2BcmyVsnLGf/NwCpeb1Mbr6OrToVYffrs9k2u/fU6tlJa4efcDCob8zoMZ0Dm+8RFpSprLTFARBxSTGpLJ57mEG1pnNntVn0dTSpP+ktqw7P5nuI5thZCp2OvkQqVTKxun7ycvOZ8qWIejoqvZOdWe2XqGMvzvOXg5yjXP7xEMqNawg1xjCpynu/iWij4kgyJcomAhfhcqNfXhyLZjL+wKQSKRyj2dgrE/rwU0ozC/kwC8n5R6vuFnZmzNl61D0DPWY1WMVdxTQ/+V/0dLSpMfoVoxfMwAPH2fO7r7JpI7LCJbzzkfFQUtLkzqt/JixZQgrTk6gYadqaGtrsWH6fvpUmcrSEVsJfvBKLNcRhK+YTCbj6a0w5n2/iUndVnFg3QXsS1kzcmEPNl2dRtv+9TEw0ld2mu+o2ueVRCLll5FbiQ6NZfz6wejqqXaxJCczl5O/X6L3lI5yjSORSJEUSXAqbS/XOIIgCCWZKJgIXwUXb0dqtauKU2l7nt1QTG+Opr3qUr9zdY6tP8uT68rrB/K5LGzNWHx8ArXbVmFO71+5sDdA2SnhXt6ZlWcn02t8a8IeRzGm5SL+WHxM5Web/MW7ihsT1w5g6ZGxfDumFSbmhty7/JwxbZYy4psFnNx+jRwV7tMiCELxys3O4+T2a8zqu47xHX/h5slAqjf1YfmRMaw4PpbaLSqipa2l7DTfSU3IYOH3mwh5EKHsVN6RFEn4ZeRWtLW1mP7HcHT1VbtYArBv2XFqtKqMtZOlXOOE3AnDws5MrjGEzyCT000QBLkQBRPhq+FSxoHnt18QcOy+QuJpaWnSflgzAHYtOKSQmS3FzdTSmFEr+lG+hhfLhm3m5NbLyk4JbR1tOnzflF8vTaN8jdKc3xPA0HqzeHA5SNmp/WNWDub0mdCGbffmMmrxt/jWLM3Lp9FsnHWQQTVnsfynHaJJrCCUYOFPo9g6/wi9/KayeuIeioqk9PypBdvu/syQ2Z0pV9Vd2Sm+RyqVcnH/beYO2EDbQQ0p668a+RXkFzC3/zrycvL5YUkvlZ9ZAhD3OpHHV4Pp8lMrucd6dOkZddpXlXsc4dNoSOVzEwRBPjRk4oxc+IiMjAzMzMxIT0/H1NRU2el8tud3XrBp0i40tTRYfHa6wjrnb5yyi8OrTzNkSW/aDW2mkJjFrSCvkE0z9nN0w3m6jGrBwNld0NRUfr1VKpVyats1Ns85iL6hHv6NKzBoRifMbdTvfRr1Io7rJx5y+LeLZKS83V2pQg1Pqjf1oXGXGlg7mCs3QUEQvkh2Ri43Tz3i2JarvAiMpFw1D7S1tWjVty51Wvmho6ut7BQ/KOJ5DOsm78Xb351e41urzG4rOZl5zOmzBi8/V/pP66hSM3H+l5+/XUXN1lX4pldducf6qd50ll2apTa/m8+lLuepf+VZreNctHWKd4ldUWEedw9NU/nfgSCoI9U8OguCHHhX9ST2VTxJ0SmEPYzAq4piviHrNLIlJzddZM/io9TtUB1Le3OFxC1Ouvo6DFnQA0lhEWf/uE52Rg7DFiv/2zxNTU1aD2hAzRaVOLD2LIfWnSfg5CO++7krTbvXUukdEv6Ti5c9PX9qSedhTbl95gl3Lz7jwr5bPLsdztb5R2ncuRp+9ctSu5Ufhsaq08tAEIS/J5FIeXwzlLO7A7h5KpDSvi7ERybTaUhjmvWshau3o7JT/FvZGbnsWHiUnKw8hs7vjnt5J2Wn9E5aUgbz+q+ndMVSDJjRSSUK+P/Ew0vPAGjSs7bcY71+Hk2FumVLfLFELcljCY34+lsQ5EbMMBE+Sl0q9//E9tn7eXT5GX6NKtB3RleFxT299TJbZuzFr0EFJu8YobC4xU0mk3F43Tk2TN6DT+0yTPtjOOZWJspO653Aa8Gsm7qXvKx8TCyMGLagB+Wreyo7rc+WHJfGxQN3uHP+KU9vvd1OWc9Ah1Z96uFb2wv/RuWVXrQSBOF9MpmMV0ExXDx4h8uH7mHvas3zuy/xb1SeZj1rUb2pj0r/3UokUi7/eYd9K8/Q/vvGNO9VR6WKz7ERiUzruoI2AxvRYWgThc0W/VIF+QWMqD2D8ZuG4lXZTe7x/ph3EA9fV2q3K/lLctTlPPXdDJMOcpphcljMMBEEeRAFE+Gj1OVA9E8EBYTyU/0Z2Lvbsi1kpcJOtCQSKT81mMmL+6+Ye2Q8VZtVUkhcebl84DYHVp+mILeQ6TuH4yLnbRE/RVFhEcc2X2bHoqPo6GpTo3kl+k5uh7WDhbJT+2wymYzwJ1FcPHiH68cekpebT2ZqDkamBrToXYeKtbzwq19WpS/CBKGki3mVwNXD97l8+B5mlsY8uRWGWzlHmvWoRf12VbBSg9mFTwJesHH6ftwrODNgekfMrVWnIA4Qcv8lG6fvp1nvujT7to6y0/kkuxcdoSCvkH4zuygk3uIBa/lx7WD0DFRjCZU8qct56l95Vm8vn4LJnSOiYCII8iAKJsJHqcuB6J+QyWTM6LiE5DcpDF85gAq1vBUWO/zxa5YOXk9mSjYbHizEyNRQYbHl4dntF/zcaw2F+YVM+2M4lRuUV3ZK70lLzODopkvs+eUkOvo69J7QlrYDG6JvqKfs1L6IRCLhaUAYV47c5/rxh9i5WBH2OBIjUwMada5G5fplqdKgPPqGJf8kWRCULeZVAjeOP+LasQfoGugSdCccG0cLmveqTa3mFfGo4KzsFP+R6LA4Ns36E2MzA9p915gyfq7KTum/XD96nzXjdzJ23UCqNvZRdjqfJCYslqntlrIm4GeMzOR/7I98HsPO+QeZvGOU3GOpAnU5TxUFE0FQT6JgInyUuhyI/qnNU3ezd/ER2v/QnOErByg09rbZB9i14BDdxrZh0LyeCo0tD/GRSWyZdYCrh+4ycHZXOo9srnLToyOC37Bx+j5S4tPJSMmmz6R2fNOjVolY111UKCHwRgjXjz3k5qlHuJZ15MnNF+gZ6FC5fjlqt6pEtSY+KvctsSCoK5lMRkTwG26efMTNU4EAvHwWg4WNCc2/rYN/o3KUr+ahNj01kuPSOPb7Fa4cukufiW1p2KmayuUuk8nY98tJnt8Np9+0TrirSRHqLzKZjEmtF9F+2DfUbuuvkJj7lx2jVDknarSqopB4yqYu56nvCibtfpZPweTodJX/HQiCOhIFE+Gj1OVA9E+9Dopi9cjfSU/KZN29hWjrKK73cWFBEUsGrefK/gDmHZtI1W8qKiy2vORl57N8+O/cPP6AFv3qM2h2VwxUsCnpo6vBbJ59kFdB0VSoUZoOQ5pQs0UllSvwfC5JkYSnt8O4eTKQgNOB6BnoEh2egIaGBmX93WjQ3h+fmqXxqOBcYl6zIChCYUERz+6Ec/vsE26fe0JuVj5pSZlYOZjT4tvaVKxdhgo1PFWqz8fHpCdn8ue6C5zbdZM2AxvQ6YemKjn7Li8nn9VjthMTlsCMnSOwtDNTdkqf7NTWy9w+8ZCZ+35SyGevTCZjVO1pLLs866tZpqku56miYCII6kkUTISPUpcD0acYXmMKLx68ZObBsdRpV02hscMDIxhVdwbuvqWYd2wiZirUNPVzyWQyTvx+mbXj/sDF25Gp23+gVBnV6WvyF6lUyu3Tj9k85yDRYfFUaViOHqNbUbGO4pZmKYJMJuPl02gCzjzm9tknhD2JwtLWlJSEDCztzGjYsSplq7jhV9cbEwsjZacrCConOS6N+5efc+f8U14+iyY2IgkAl9J2NO1ek4q1vSjj56pyszE+Jjszl0PrznN002XaDWpIm0ENVXYGWkJUMj/3/hW/BuXpM6UDuvrqd/Ef/zqJqe0Xs+jkZKwcFdNHK/jOCwKOP2DAnO4KiacK1OU89a88a7SVT8Hk9jFRMBEEeRAFE+Gj1OVA9ClOb73E8fXnsCllxcx9YxUe/+j6s2ycvJsaLf2YunNUifnGP/Dqc1aP2YG0SEKfqR1p1LWmslP6IEmRhAv7bhFwOpCAk4+oVNebPpPa4VPTS9mpyUXSm1TuXQri7oVnPLwWQmlfZ54EhKGhoYGXXymqNaqAb20vyvm7q+VFiSB8qYK8Qp7fe8m9y895cDkIXT0dgh9EoKWtiU+N0tRuVQn/huVx8rBVdqqfJSs9h8MbLhDyIAIzKxN6jW+Ng5uNstP6W4+uPmfNuD9oPbAh7Yc0VctjpFQqZXbXFdTrXJ2m39ZVWNxVIzbTqEdtfOuWU1hMZVOX81RRMBEE9SQKJsJHqcuB6FNkpmbRw2UYMpmMHWGrsVLwDioSiZSJzefx4sErRv06kCYKPJmSt6TYVBYP/o3H14LpPqY1PSe0Vcmp3vB2R51zewLYvfwk1g7m6Bno8u3Y1vjWLqPs1OSmsKCI4PuvuH8piAdXgslMyyYuMhkAXX0dGneujq2z5btv0HV0FbdkTRAURVIkIexJFIE3Qnl0LYRnd8LxqujCszsvsXIwp1bzilSqU4bKDcpiZGKg7HQ/W1piBmd3B7B/1Rn86pel59hWKt2IViqVsn/FKY7+doGJm76nYt2yyk7psx1ec4aQ+6+YsHmIwgo+BXkFLP9+AxO3jVDLItPnUpfz1HcFkzZyKpgcFwUTQZAHUTARPkpdDkSfauOkPzi/8xodR7aix4T2Co+fEJnEjM5LeROewOobc3Atp7onsZ9KIpGyb/kJDqw6hZW9BZN+H4KHbyllp/W3CguKuHTwNnt/OUXMywQad6tJ0241qdygXIk/6UxLzuTxjVACr4fy6HoIRiYGvHgcCYCxmSGlK7pQobonFap7UtbfDQMj1etPIwgfU1QoIexJJGFPorh7/hlPb4eRk5mHqaURhflF+NbyomZzX8pX86RUGXu1/7uPfZ3EkQ0XOL3zBv6NytNrfBuVLpQAZKRksXLUNmTIGL6kl8K/yChOr4Oimd19BcsvzsDcRnHnTZf23ODNy3h6TemksJiqQF3OU//Ks2Zr+RRMbp0QBRNBkAdRMBE+Sl0ORJ/q8bXnjGs8G+9qpVl5fY5S1qLfPHqP2d1+oV6nGozb+D36Jexi9PH1YBZ/9xtOHnZUb+lHxx++Uek1/xKJlBvHHrB35SnCn0RRumIpeo5tRc0WfmrV0PFLJESn8PRWGI8DXpAcl8a9i0Hv/s23the5mXmUq+pOWX93ylV1x76UtdpfXAolT1Z6DsH3XxF07xURwTE8uBxMfm4Bnj7ORIXFU66qO5Vql8GvnjdelVzR1lH/XbMAXgS+5uCac9w4/pAWvevSqn993Ms7KTutjwq69YKFg3+jYZca9J3aQaHN2Itbfm4BP9afxeD5PRTe2H3xgLX0n9MNWxdrhcZVNnU5TxUFE0FQT6JgInyUuhyIPpVMJuPn7r9w/dAd5h6dSPWWlZWSxx/zD7FjzgGafFuX8ZuHlriLz4zkTNZP3MXFfbfwa1ie0b/2x66U6q6dh7fvjQeXgzi/N4BLB+7g4GZNhyFNafZt7a9uhkVKfDrP7obz7FY4SbFpBJx5jFQiRVNTA21dbfQN9fCu7IpvrdK4lnXEq2IpLBT4jaogFBVKiHgeQ8jD14Q8jCArPZeA04Hv/r1SXW90dLSoUMMTnxqlKVPZtUTtHiKRSLl95jGH1p2nqEiCWzknOg//BmdPO2Wn9lGSIgm7Fh/jzI5rjF4zAP/GPspO6Yttm32A3Ox8hi7updC40aFv+G3iTuYcGq/QuKpAXc5T3xVMWs2RT8Hk5IxP/h2sWbOGJUuWEBcXR6VKlVi9ejXVq1f/28fv37+f6dOnExERgZeXF4sWLaJVq1bv/r1///5s27btvec0b96c06dPv/s5JSWFkSNHcuzYMTQ1NencuTMrV67E2Nj4E16xICiOKJgIH6UuB6LPcWz9Wdb8tJXm/RowesMQpeRQWFDE+GZzycvKo+3Qb2g9uIlS8pAnmUzGpX23uLD3Js9vh/HdvB606FdfLYpDL59G8ee680QExxAfmUzLPvVoO6ghNk6Wyk5NKfJy8gl9FEn4k0ie3Aon5GEEKfHplK/uQdCdlwBYOZhTu0VFzKyM8ajgjKePCzZOFmrx31tQbQX5hUSGxhH1Io6guy95Efial0ExOLhaExkaB4B3ZTecPW0pV9WD8tU8KOXtUCJniGWmZnNm5w2eBLzg2a0wWvarR7vBjbBR0G4sX+pNeDw7Fx8jLTGDsesGqeWWwf/p0t4ADv16mqXnp6Krp6vQ2Ftm7MW3blmqNquk0LiqQF3OU1WtYLJ371769u3L+vXrqVGjBitWrGD//v2EhIRga/vfDa5v3rxJ/fr1WbBgAW3atGHXrl0sWrSIBw8e4OPzttjZv39/4uPj2bJly7vn6enpYWHx/59LLVu2JDY2lg0bNlBYWMiAAQOoVq0au3bt+sLfgiDIhyiYCB+lLgeiz5GblcvwGlOICn7D5mfLKVVWOVOXE9+k8GPdGaQnZrDo9FR8Stg2t39JjElh1aitPLwcRJ32/gyY2RV7V/WYOpwSl8bJ7dc4sfUKlnZmOHva0XZQIyrULP1VFwJkMhlJsWm8fBbN83uveBEYSdjjSKwdzXn5LObd45xL22JhY4ZbOUfcyzni6u2Iq7cDRqbq21BTkB+ZTEZqQgYRwW949TyG18GxhD+NJjI0lqJCCV6VSvEiMBILW1O8KpXCr643LqXtKOPniqllyf2WUiaTEfIggpsnHnF000UsbM3oMqIZjbtUx8BYPWa/yWQyTm27yrY5B+k5oR3tvm+s0ks1/6nXz6NZN+4Pflo7CHtXxc6izM3OY2zDWay+Nb9EFgc/Rl3OU//Ks1ZL+RRMAk59WsGkRo0aVKtWjV9//RV423TZxcWFkSNHMmnSpP96fPfu3cnOzub48ePv7qtZsyZ+fn6sX78eeFswSUtL4/Dhwx+M+fz5c8qXL8/du3epWrUqAKdPn6ZVq1ZER0fj6Oj4KS9bEBRCfReJCkIxMDA2oGZrf/Ky8rm46zr953RXSh42jpZM2TGSeb1WsWXmXib+/gO2pdSjkPApbJwsmXNgNJcP3GbN2B3cPhXI4Lndadm/gcqf5Fnam9N7Qlu6/diC22cec/i3C4xru4Rq3/hSq0UlGnaqjqGJelywFCcNDQ1sHC2wcbSgxje+wNsLooToFF4FxRD+NIrI0DhCHkbwJOAFTwJe4Ohuw5tXicDb2Sh+db0xMTfEpbQdzqXtcS5th4WNyVddiPpa/FVwiw6LJyE6mdDASCJDYnkdGoezpy3P771699iyVdyo1sQHT19nSvu64OnjgpW92VfxPslKzyHgVCBHfrtI+JMoGnSsyqSN31GtqY/Kf3b+u4SoZFaM2oqJpRFLTk2ilHfJuDjKzshhbs+VDF7wrcKLJQAXdl2n5aAmavVeEOQjIyPjvZ/19PTQ0/vvnQoLCgq4f/8+kydPfnefpqYmTZs2JSAg4INjBwQEMGbMmPfua968+X8VRy5fvoytrS0WFhY0btyYuXPnYmVl9W4Mc3Pzd8USgKZNm6Kpqcnt27fp2LHjJ71eQVAEUTARvnodR7Xk0OpT7F92jPYjWmBhq5xpwT51vOk3qwu/DN3EzC7LWHZhBoZqvJ3l39HQ0KBR15pUrFeW9RN38eevpzmz/SqjVvbDy89N2el9lK6eDvXa+VOvnT8vn0Zx/dgDNs06wMYZ+2k9oCENO1WjdEXV3RFIETQ0NLBzscLOxYqazf+/6WFOVh6vg98Q8yqRsMBIXofG8joklvioZC7sv/3ucT41SxP+NBondxscPWwpU6kUppZGOLjaYO9qjaWdaYn4RvprIZPJSE3MIDYiibjXScS8TCDmVSJpSZmEPowgL6cAAE9fF8KfRGFsbohrGQcq1S1D487VcSvniFtZR4zNDJX8ShRLKpUSdCec0ztucP3YfUp5O1KhRmnGrxuAq5oVGqRSKae3XWXfylM071WXrj+1VOvGrv9OKpXy28SdNOlVlxpK6IUmlUo5tekCC09PVXhs4TPJ/nUr7jEBFxeX9+6eOXMms2bN+q+HJyUlIZFIsLN7v9eRnZ0dwcHBHwwRFxf3wcfHxcW9+7lFixZ06tQJd3d3wsPDmTJlCi1btiQgIAAtLS3i4uL+a7mPtrY2lpaW740jCKqkZBytBOEL2Dhb0bhnHaJDYzm1+SLfTlZedbtF/0ZEPIsm7FEEC/r+yqz9Y9DSLhm7N/wnK3tzpm77gYATD1g7fieLBm2gSuMK9JvWCSM1uTDy8HHBw8eFbqNacPXIPR7fCGVE47l4+rrQql99GnSs9tVd5P0vhsb6lKvqQbmqHjTtWuPd/RmpWcSEJxAVFk90eDy52flkpGTxOjSWsCdRJMWmvuuPAlCpjhcp8RnYuVhh62KJq7cjxmYGb2e6OFlgZW+Ojq44vCmKRCIlLTGDxDeppCZk8CYikfioFOKjkkEGgddDyM8rBMDOxert/YCNkwXlq3vi5GGLS2k7SnnZ4+Jlj4Wt6Vcxa+TvxEYkcGHfbc7vvYWRqQHaOloMmdeNBh2rYagmy27+XdSLOFb9uJWCvEJm7R6JWznV3t74U+2Yc5CM5Cx+XDNIKfFvn3xIlaYVMbEouUvRhH8uKirqvSU5H5pdIk89evR49/99fX2pWLEinp6eXL58mSZNSl6PPuHrIM4oBQHoOrYt31UcR+TzGNoPb46RqfIucgcv+Jafu6/g1okH/D59L4Pn9yzRFw+1WlfBr0F5Dq05y85FR7l26C5DFn1L/Y7V1GYWgb6RHs2+rUOzb+vQY3Qrzu66wZ1zT1g/dS+1WvjRtEdN/Bur19R5RTK1MMa0qjHlqnq8d79EIiXpTSqxr5N48yqR2NdJxEYkIpPJeBX0hqiweABK+7oQ9iTq3fPKVXUnNiIJK3tzrBzM8KzgjJa2JhY2ppjbmGJpZ4qZlQnm1sboG+qV6L+vL1FUKCE9OZO0pCzSkjJJScggNzufN+HxJMenkxyXhpmVMXcvPENSJAXezg56eisMAG0dLao1qUD56p44uFnj4GqNc2l77FwscXC1Qd9QsU0xVVlaUibXjtx/N9MqJjyeRp2q0bRnLcqowcy7DynIK2Tv8hNc+fMOrQY2oP2Qb0rcZ+DF3TeIfhHL+M1DlXa8OrLmND+uGayU2MLn0ZC9vRX3mACmpqb/qIeJtbU1WlpaxMfHv3d/fHw89vb2H3yOvb39Jz0ewMPDA2tra8LCwmjSpAn29vYkJCS895iioiJSUlL+5ziCoEyiYCIIgFsFF1oMaERUyBuOrDnNt5M7KS0XbW0tJm8fztLvNnBy00VMLIzoMaG90vJRBANjfb6d2I467f3ZNudPlg/bzOG15xi26Fu8/+MiWtU5l7Zj4IxOSCRSAq8Hc35PAAfWnOOXH7fToGM1GnaujndlN3GR/g9oaWm+W9rjV/e/GyHnZOWREJ1C4ptUEqLe/m9iTApSKWSkZBMdHk/40ygyU7Pf64XhW7M0T/51Ua+rr4NfPW+SY9MxtTTCxMIIexdLtLS1MDIzwNjMEGNTQwxN9TE0McDIWB9DE330DPUwMNJV+aJeUaGE3Kw8crLzycnMIy87j8z0XLIzcslOz0EikZIQnUJmWg4ZqdkYmxsSfO8V6SlZZKXnvve7AvCp4cnTgBcAmFkZY+tsiX+j8u/62Di629BvUlvsXKywtDMrcRfIxSkrPYebJx5x+dBdigqKeHYrjMoNytGke03qtK6Mrr76bn187/wTjm44j5aONvMPjcXWxUrZKRW7pzdD2DJjL0vPT1fa8tmn14OxdLDAwUP1t5AW/o1U9vZW3GN+Al1dXfz9/blw4QIdOnR4O4RUyoULFxgxYsQHn1OrVi0uXLjATz/99O6+c+fOUatWrb+NEx0dTXJyMg4ODu/GSEtL4/79+/j7+wNw8eJFpFIpNWrU+NtxBEGZxC45auhT9kzfuHEj27dv5+nTpwD4+/szf/78/7nH+n9Sl+7jXyrs4SuGVZ2EqbUJ20NXYmRmpNR80hLSGd1oNonRyfy0djBNe9VTaj6KIpPJuHHsPhun7sXSzhx7N2sGzOyi1ifceTn53DodyKUDd0iMSaGwoIj6HarSoGM1SpVxUHZ6JZZMJiMnM4/k+DRSEzJJTcwgNSGDwvwiol/Gk56cRXpyFlZ2ZgTeCCU7IxeA0hVdCHv8/zNWzKyMSU/Oem9scxsT0hIz0TPQxcBIDy8/V+KjU9DT10FXX4dSXvYkx2ego6uFjq425tYm5OXko6mlhZa2JlrammhqaqKpqQEaGmhqaqBvoEtebgEymQyZDIxN9clIyUYikSKVSDEyNSQlIZ2iAglFhUXoGeiSlpRJQV4hBflFFOQXkp9bQF5OAXn/+l9rOzPi/rUEBqBMpVKEBka++9mnhidPb4cDb3vP+DcsS1xkMmZWxphZGeNaxgFNrb9m55hgbWeGuY0JFram6Oqp7wW9smSkZBFwMpBrR+9TkF/Ik5sv8KjgTJMeNWnYoRqW9uq9tW7c60R+m7KH8MdRDFv8LTVb+ik7JbmIDHnD9PaLmfLHSLyreiotj/m9VtFjYns8KroqLQdVoC7nqX/lWfub2XLZJefmuZmfvK1wv3792LBhA9WrV2fFihXs27eP4OBg7Ozs6Nu3L05OTixYsAB4u61wgwYNWLhwIa1bt2bPnj3Mnz//3bbCWVlZzJ49m86dO2Nvb094eDgTJkwgMzOTJ0+evFse1LJlS+Lj41m/fv27bYWrVq0qthUWVJaYYaJm9u7dy5gxY97bM7158+Z/u2f65cuX6dmzJ7Vr10ZfX59FixbRrFkznj17hpOTcrbQVVWlK7vTfnhzAo4/4M9Vp+gzvYtS8zG3NWP+sUn88sNGln23AV19Xep3LvnVdw0NDeq2q0r1ZpU4vukifyw4zI2j9+k1sR2tBzXG2Fz9eoLoG+rRsFN1GnaqTkZqFrdOBXL50F2ObLyIvasNdVr7UbdtFVy8RPGkOGloaGBkaoCRqQGl/sHvtqhQQlZ6DhkpWWRn5JKZnkN2ei55OflkpGSTk5lHTlYeOZm5FBVKyEzLIS8nn7ycAmRSGdkZuaQkZFCQV4iWlhaP/zUTA8CjgtN72yxr62pRVCB5L76Llx1RL/5/uvO/FzMAyvm78/z+/8+UsXW2JOFfRRodfR309HUwMjXA1MIIG0cL9A11sXWxokJBEYbGehga62Npb06zHrXezp4xNcDE3BBjM0NMzI0wMjMQM0LkICE6hZsnHnLjxCPysvMIexyFvZs1LXrVYfjinmrXwPVDcjJz2bv8JCe3XKbLqBZM3DQEPYOSueQq6U0KSwatZ8iS3kotljy/9YL83PyvvliiluTY9PVTdO/encTERGbMmEFcXBx+fn6cPn36XWPXyMjI92ZR1q5dm127djFt2jSmTJmCl5cXhw8fxsfHB+Dtce/xY7Zt20ZaWhqOjo40a9aMn3/++b1eKjt37mTEiBE0adIETU1NOnfuzKpVq77s9QuCHIkZJmrmU/dM/08SiQQLCwt+/fVX+vbt+49iqkvlvji8ehrJEL8JuJR1ZNnlWZhbK//1RjyLYtw3c3Et50TXsW2o2aqKslNSqLTEDA6sPs3hNWfRN9Kn5/g2tBncuEScjKcnZ3Ln3BOuH33Ag8tBVKrrjXcVd2q3royHj7NYtqPGZDIZkiIphQVFFOYXUVhQiFQiQyKRICmSUlQkQSZ9O5MEmQzpv02n1tB4W+zR1NJEU0vj7UwUrbc3HV1tdHS00NbVRkv77c/ifaJapFIpoQ9fc+t0IHfOPSUhKpnsjFzsSlnRok9dqjaqgGdFlxLx300ikXL2j+vcOvEQfWM9Bs3uqtazAT8mMzWb8d/8TPvhzWk5oJFSc1k+ZANthzTDq4q7UvNQBepynvpuhklTOc0wOf9pM0wEQfhnRMFEjRQUFGBoaMiBAwferTcE6NevH2lpaRw5cuSjY2RmZmJra8v+/ftp06bNP4qrLgei4rJp8k72LT1Gp1GtGLrsnxWV5C30wSsmtZxHQW4hM/ePoVrzSspOSeFiwuLYNvdPUuMzePMynp7j29K8b/0SsxtKVkYODy4FEXAqkDtnH1Oprje2LlZUb+aLb60yJeZ1CkJJlJqYwcMrz7l77ilvXiUS+jACABcve5p9W5vKDcqVqCKoTCbj3rknbJqxDwNjA76b240KNb2UnZZc5Wbl8etPW3H0sKPXFOXtpgfw+NpzDq06ycz9Y5Wah6pQl/PUv/Ks03Q22trFXDApyuOGKJgIglyIM3A18jl7pv+niRMn4ujoSNOmTf/2Mfn5+eTn57/7OSMj4/MSVlNthzbj8K9nCH3wkpiwWJxKK3+ZRJkq7sw9MpHFA9aybtwO0IBqzb6uoolTaXumbP2BF48i2DHvML+O2cHds4+p0dKPb3rVVfuCgrGpIfXbV6V++6oUFRYRdCec22ces2bCbkwsjLBztqRqUx+qNvbBwlacDAmCMuXlFBB8/yX3LwXx4GIQRmYGPLn5Ak1NDcpW9WDw7C7UaO6Ls2fJa8b59NYLts4+SGp8OoN+7katVn4lphD0d/JzC5jVZTmefq58O7mDUnORyWSc3XaZvjO7KjUPQRCEr4V6X2EIn2ThwoXs2bOHy5cvo6//95XtBQsWMHv2bAVmplrsXG3oPqEd22ftZ9OkXcw8oBrf4JSv6cX434cxrd0iZndZzox9o6newk/ZaSmcl58bc/b/xPM7YexceIRVP25j77ITdB/bmqY966j1zhJ/0dbRpmIdbyrW8ea7OV158yqBexefce3IfdZO3E2ZKm6Ur14a/4blKVvVHW0d8VEuCPJUkF9I+ONIAq+H8uhqMM/uhGPlYEb862SMzQxpUK0qLfvWw79ReUwtjZWdrlyE3H/FjvmH0NTSpHG3mjTrXfer+OwpyCtgfu/VlK3hSf9Z3ZReHLpx+C7IwN2nlFLzEL6ATPb2VtxjCoIgF2JJjhr5kiU5S5cuZe7cuZw/f56qVav+zzgfmmHi4uLyVU3zy87IYUyDmegZ6NH/5+5UaeKr7JTeCb4bzvY5B3h06RkTtgyjYde/387taxB0O4xzO69zausVLO3N6fpTK5r3qau0bR7lLT+vgOB7r3hwOYgHl4IwtTJGV08bv/rl8KtfFhcve5Xf6lYQVF1edj7P770k/Ekk9y4G8fzuS+xdrYkMiUVTU4PSlUpRv0NVylfzpExlV7S0tZSdstyE3H/FsU0XuXfuMd1Gt6b1wIYloofUP5GfW8DC/r9iZmXKqF8HKv2ztbCgiAW9VzF0WV9sXayVmosqUbslOU1myWdJzoVZKv87EAR1JAomaqZGjRpUr16d1atXA2+by5UqVYoRI0b8bdPXxYsXM2/ePM6cOUPNmjU/Oaa6HIiK2+ktl1g2eD2efq6sujlPpbbQDA+MYHKbRRiZGtBldBtaD26s7JSU7sWjCPYuP0FGUhYvn0bSelAj2g1pipW9ubJTk6uM1CyeBrzg0dVgXjx6TeKbFHxre1OxThkq1fHGwd1G6d+ICoIqk8lkJMakEnQ3nOjQOO5dfMaLwEikEinlq3sSdCccJw9bKtXzpnLDclSq642JuXK3nVeEZ7de8Ofaszy7+YIuo1rQamBDDI2L9yJPleVl5zGzy3LK+HswYE43pRdLAPYtO0peVr5YjvMf1OU89a886zaWT8Hk+kVRMBEEeRAFEzXzqXumL1q0iBkzZrBr1y7q1KnzbhxjY2OMjf/ZtGF1ORAVN6lUyuL+awi8FET7Ec3pMbGDslN6T2RIDIsHrOPFg1f0nt6Z3lM6igtjICo0lj/XnOH8rht4+3vg4G5Lx+HN8PBxUXZqCpGenMmTgBc8uRlKWmIGz++9olxVD3xqeeFbuwylyogZKMLXLSM1m/DHkQTff0XIgwjSkzMJvvd2u2ZzaxPSkjJxcLPBp2Zp/BuXp3yN0tg4Wig5a8WQyWTcv/D0/9q77/go6vyP46/dTXY3m957I5WQ0CH0riiK4lmwe57lPLuenlhO9OfZy6mHZzsVr3hg1xPEQlGa9BIgCZCQQkivu5vtO78/AlEiGsEkm4XP8+E8kp2dnfnsjmS+897vfIf/PvMZteUNXDbvHKZeOAa9Qdf9i08iphYzr/zp3/gH+nHDM1f0i2NrY3UzT/32JR7+6O5Tbn90x1vaqZ2BydReCkxWSmAiRG+QwMQLLViwgKeffrrznukvvvgi+fn5AEyZMoWUlBQWLlwIQEpKCuXl5T9ax/z583nooYd+0fa84UBUf7CRVYvXMf2yCYTF9FzD9kBBBX8YOY+ErFge/uhu4tNiemzdPaH+YCMPnPsU/sEGErPiuOXFq0+Ja8p/iabaVpb8YwX/e30FbU0mpl00lonnjWT0GUPRaE6dwMBstFC4qZQ9G0so3FxCVUkdyVmxDJ+Sw4BBCWQMTcEQeOp8ayxOLU01rewvqKCs8BD7tpezb0c5oZFBFG050LlM3tgMzG0WBo4eQM6oNAaPzyTiFAlIjrDbHKx6fwMf/O0LopMiGHf2MKbNHduvelb2lebaVu6b/QT5s4Zx1fwL+0VYAvDstS8z+szhTDw/39Ol9Dve0E4FCUyE8FYSmIhuecOB6NZx91H43T5ufP5qzrt1Vo+ue/HTn/DGff9lyJRBPPXlA/2m8XSEqcXM/819nh3f7OG0yyfyh2evxD/Y4Omy+g2bxc63H27k/b8to3xPFTHJEcy+bhrTLx1PSET//P+5N7lcbiqKD1G6+yC71u1j7/YyAEZOzyU2JZLMockkZcVK8Ca8it3moHJvDaW7D3Jg90GMLWa2LN9Dc33HXd78A/0wGy1Ax61+o5PCyR6RStbwVLJGpJwSl9gcS0t9G0veWsXGZTtQ+6i58NYzyT/z1AqVf6iqpIanf/cKky8aw3k3neHpcjpt+Wonn778JQ998Md+1wbpD7yhnQrf1zlxyvxeCUxWr3q4338GQngjCUxEt7zhQPTZq1+y4r9rCAwL4OEP/9Sj67ZZ7Nww4h5CIoOYftkEzr7+tB5df09w2J28ds+/Wf7OWsJjQ3j4g7uIOwlvZ/lrKIrCrnV7+fbDjSx5cxUajZoJc0Zy5m8nkzc+65RuhFpMVkr3HGT/jgr2bS+nYm81hkA/kjJjSR+cRNrgRFKy407qgS2Fd3t/wZe88fCHnY9zx6Sz67v96Px8SclJYMTUgcSnRZM5LIW41MhT/rK0vVsPsOTNVWxYtp2hk3OY84cZZI9M83RZHlW8aT8Pz/0r1z52KdMuHt/9C/qItd3Go5c8z43PX01sapSny+mXvKGdChKYCOGtJDAR3fKGA1Fbo5G5cdfhdLhYuPdF4tNje3T9hRv2cfuEP6P10/LKtqf63aU50BEILHrqU/758HsMnjyQi+8+l2HTcj1dVr/UWN3MF/9aTdGmEjZ+sZP4tGjO+O1kpl88jrDoYE+X1y/YLHbKig5RsrOCmvIG9hdU4na6iUoIY+ysIYw9c6inSxSi05aVe3ji+n8wYFACqTnxZI8cQGpOPAnp0RL0HWZtt/HtR5v45oONlOys4MzfTmLW1VOIjA/zdGket/qjjfz9jreZ98+bGTJpoKfLOcorf/wn4fGhXHjnbE+X0m95QzsVfhCYTOqlwORbCUyE6A0SmIhuecuB6JU/LmT1Bxs443fTuOLBnh9BfuH8xWxbvgsFhb+uerjfNsI3LtvGY5cvwNZu48bnr+Ls62ac0r0nfo7L5Wbr8l0s++e3tNS1UbiphFGn5THj0vHknzn0lLx+/+coikJdZSM2q4OkzJ4NJYX4NdxuNyqVSv7WHUNpQQXffLCJJW+uJGVQAufeMIP8M4bI3zc6/qYtfvpTtiwv4Obnf0vywARPl3SUXWuK+Oy1r7j7rZtO2cukfglvaadKYCKEd5LARHTLWw5EW77awbyZf2HE6UN4bOl9Pd7l2m5zcOu4Byjfc5BrHr2EC+48u0fX35PKCw+y4NaF7Nt+gJGnD+HOV67DEOjn6bL6teb6VlYu/o4v/7MGnV5LVWkdk84bxbS5Yxg0JkNOxIQQXsPYZGLNZ1tZ+sZKasobOO/G0xl/znCSs+M9XVq/YTFbeeHGf1BX2cSDi24jJKp/9S5sN1q4bfwD/PndO0mS/fazvKWdeqTOSRMf7JXA5NvV/9fvPwMhvJEEJqJb3nIgcrvd/HHKfHatKeKJLx5gxGlDenwbZXsqefyyv1G2q4Knvn6QIZNzenwbPcXc2s4z173Kuk83M+bs4Vz54AWkDU72dFn9nqIolO6qZPmidax8bwMhEYGYjRam/GY0Uy7IJzU3UcITIUS/47A72bZyD1/+ezWbvipg2tyxDJs8kDFnDZPeJF1Ul9bx8Ny/MmbWMC67/zf4avvfINfPXvsyg8Znc8bVUz1dSr/nLe1UCUyE8E4SmIhuecuBCOD95/7Hyv+uIXt0Ore8dF2vbGPZWyt584FFJGTE8MCiOwiLCemV7fQERVFY+uYKXrrtbdQaNTc/fxUzfztFTvh/IZfTRcG6vaxYvJ61/9uCf5ABX50Pk84bxcQ5o0gdlCCfZT/z9I1voffXERIRSEhEIFHxYQSG+RMU5k9QWAD+wQbp2i5OGm63mz0bSlj57npWf7yZIROzyRyeytSLxpxyt0b+pb5bupX3n1vCtEvGM+uaaZ4u55hWf7iBL95axSOf/kmOMb+At7RTOwOTCb0UmKyRwESI3iCBieiWtxyIAEwtJi5N+gM2i5239/+NmOSeH1FeURReuOkNlrz6FflnDeehD/7Y72/BWrhhH3+/859UldSSNz6LO165jpDI/r0v+xu7zcGObwpZ8d53bPh8O26Xm6jkSMbMHMy42cPJHJ56yt95w9McdifnxN981LzcsRnsWr+v87GP1qcjPAn1JyDYQECIgeTsOFwuN/6BfvgHdUwhkYH4Bejx89fhF6DHEKgnINhwUp68uFxu7DYHFqMVU2s7ppZ2LO022lsttDWbaWsyYWw2owC1FQ0Ym9tR3ArTLspn1pUTPV3+KUdRFIo2l/LtR5tY/dEm0oYkkZgRy7SLxkgPuJ/hdDj55/99wKp313Pvv25m4Oh0T5d0TAf3VfPgnKd4duVDhPazy4T6K29pp3YGJuP/3DuBydpH+v1nIIQ3ksBEdMtbDkRH/POhd/luyRZGnj6E3z16aa9sw9pu4/HLX2TPd/uYctE4bnr+t72ynZ5kbDax4LaFrHp3PSNPH8w5fzid/DOHebosr2S3Oti5upBvPtrEd0u3Y2w2kzkilYwhyYyZNZQhEwei1UsX+L5mtzpY9u81tDaaaGkw0tpgJCDEQMnOStqaTRibzPjqfWlrNB31uryxGRT8IFQB8A82YG5t73ycPiSJkl0H0flp0fn5ojNoyRqWQmNNK1qdD1qdL756XwJDDGh8NPj4dkxBYQGggNpHjUatRu2jJjDEgMvVMUipWq0CVOgMWlAUFEVBUcDHR4NKBS63gtvlxu1yo9X5Yrc5cDldOB0dk8ZHjc3iwGF3Yrc60Op8MLdZsFns2KwObBY7/kF+NBxqxmK2YTXbMAT6UVvZSLvRQrvRitutkDsmndZ6I/7BfgQEG4hNiUSr9yUo1L+jh05oAOGxIYRGBhIYFiC9dPqYy+Vmz4Z9rP10C2s+3cKAQYnEpEQy6TejyMlPl7C2GzVl9Txx1QLSh6Zw5fwLO/5d9kPWdht//f1rnHXddAZP6r+X/PY33tJOlcBECO8kgYnolrcciI6oLa/nyvSbSc5J4IV1j+Ln37MHpSNqyuq4afR9BIb5c/E9c7zmOuPVH27guRtep73Nwvm3z+Kye8/DP9jg6bK8lsvpomDtXtYv3cb6pduoq2hkyMRsDEF+jDotj1GnD5bbdvYjdpsDu9WBqaWjJ4WxtR2n3YGp1YK5rSNAaG+z4HK6aDdZaTdZsZptBIUHUFPeiLXd1hFGWOwkZMRQUlCJw+bAbusILFQq+OFRNXNoMnu3lx9Vw6DRaezeWHLUvMBQA8bm7wOa5Ow4yosOAaBWq1Br1OSNy6RsTxUa3+8DmYjYYJxOBV+tD1qdD9FJ4Tjtru+DHT8toVFB+Gp98fPXoffXYQjQExwegCFQjyHQDx/f/nnHr1OdxWxl+zeFrF+yjQ3LdpA1IpW4AVFMOGckA/PTJbT6hVYsWstnr3/NpN/kc+6NM/ttDxxFUXjyygWkDU3hwj/KLYSPh7e0U4/UOXlc7wQm36yTwESI3iCBieiWtxyIfuj1ef/m3ac+4Q9//S2/ue2sXttOwZoi/nzOk1jNNp5Ydj9Dpw7qtW31pPqDjfzt1rc4sKsSt8vFrQuukd4mPUBRFMr2VLF5eQHrl2yjaGMJ2aPSaDdZGTEtl5EzcskZk45Or/V0qaIXKIqCSqXC5XLjtDtxOly4nC7UGjVulxuX043b7QaVCo1ahVtRQOl4nY/WB7Va1XlrXJUadH66znni1FBb0cDmrwrY8MUOtn9TSN74LDKHpzB21jAyhqXI/wvHoa3RyMt//BclO8u5560/kDYkxdMl/az3nvuMhoON3PDslbKfj5O3tFMlMBHCO0lgIrrlLQeiHzq49xD3zHyE5JxEHvrw7l69Q8CyN1fy6ctf0FTbwhOf30/KoMRe21ZPUhSFLxZ+w6v3/Ju0wclExIdxw9OX97tbK3ozY5OJHWuK+O7zHWxZvgun3Ynd5iB3bCbDpuQwfGoOKYMSpDu9EKcou9XBnk372bRsJ5u+LqCpppXskQMYPXMwo08fTExKpKdL9ErfLd3Kize/ydnXTef8289C59e/Q+rNX+7grT8v4pkV83utV+zJzFvaqZ2BydgHeicwWf+Xfv8ZCOGNJDAR3fKWA1FXz/zuJb5YuIpb/34ts2+Y2avbeuvBxbzz6IcMm5bL3W/dSGRCeK9uryfVVTbwj3v/yzfvf0dcWjQX3nE2Z/xuipzE9zBFUSjbfZCtK3ezdeUedq3bS2JWLHUVjeSNz2L41BwGjcskKStWPnshTlIul5vSgkr2fLeXDct2smv9XgZPyCI2NYqRp+UxZEI2en+dp8v0Wm2NRl6b9w6HSmq5+uELyZs40NMldWv/9jIeveR5nvryz0Qmek/boT/xlnaqBCZCeCcJTES3vOVA1FV1aS1/vf4VaivqeW3Hs+j8eq8RqigKb9z3Dh/8dQlx6TE8t+ohgiO857MCWPvJJj5/axWblm0na1Qat7x4NRnDUj1d1knLbnNQtKmEHauL2Lm6CJfTzZ4N+wkKCyB3XAYjpueRPiSZtMGJ/f4uTEKIY3O73ZTtPsieDfvZunIPO9cUo1LB9LljScyKY/jUHGJTe/5ubqcaRVFY/eEG/n7nP5k6dxxXzb8AvRf01Kgtr+fVu//FJfPmkDF8gKfL8Vre0k49UueU/N4JTFZtkMBEiN4ggYnolrcciI7l5TsWsvQfX3P901cy+4bTe3VbLqeLhy94jsZDTWh8NTy25F4CQvx7dZs9zWKy8p/HPmTTFzuoKDrE6VdO4uqHL5LLdPqAtd1G8ZYD7Fq3l4K1xdQfaqZqXw06g5YR03JJyo5j4KgBZI9KIzg80NPlCiGOwWF3sm97GbvX76OuspGV721AUdzkjs1kyOSBDJmQTWquXIbXkw7urWbB7QtpqW/jjpevJWtkmqdL+kVa6lu5e8YjXP/k5Yw6Y6iny/Fq3tJOlcBECO8kgYnolrcciI6ltbGN28bdT1uDkYX7/kZQWO+eaNosNub/5lm2fLmDyReN5Y5Xr8c/yPvuQFNRVMUrd/2LLV8XMGh8FvlnDuO8m2eilcFK+4zT4aS0oJLd3+2jpryetf/bSkNVMwDjZw9H66cla0QqmcNTSctL7NUeVEKIY2uqaaFwUwmFG0so3lLK/h3l6A06Bo3JYPj0XDKGpjAgL1HuaNMLrGYr/33yUzYs3cr0yybwm1vOROPjHXd8Mre1c8/pf+HCP85m8oVjPV2O1/OWdmpnYDL6/t4JTDY+2u8/AyG8kQQmolveciD6KR8v+Jxv3l1Hztgsrnvy8l7fnsVs5W83vcHKRWtJH57K40vv87qeJtDRxXnzFzt45U//5uDeaoZOHcRpl09k6sXjpfHvIfVVTRRtKqW8qIod3xayd1sZdosD/2A/ohLDSR+STNbIAaTlJZKSk4DeICGKED3F1NJOya4KijeVUrzlAHu3HSA2NRJTSzvZI9MYODqNnPx04gZEyV1OepGiKKxcvI5v3vsOvUHHtY9f4lXjhllMVl68+Q2yR6dz7o29O77aqcJb2qmdgcmoXgpMNklgIkRvkMBEdMtbDkQ/xeV0cdPoeZTvruTlrU+RMiip17dpMVm5f/YTmFvb0Rt0PPLJnwjy0ssonA4nn7+5khWL1rJn/T5SchO55pG5jDpjqJwUeJjL6aK8+BD7t5Wxb3s5+7aXowIKN5WgVqtIyIhhxIw8wqKCSc1NIHVQAmExIbLfhOhGc10rJQWVlBZUUlF8iN3r91F9oI60wUnEpkaRNSKVrBEDSB+ShCHQz9PlnjJ2r9/Lv/7yAW0NRn7/9OUMmZTj6ZKOS7vJwp/PeYoxZ4/gwjvP9nQ5Jw1vaadKYCKEd5LARHTLWw5EP6dww16eu+4VAkMDeGblQ31y/bjFbOWxS17guyVbyZ81nNtfvY6IuLBe325vaTda+OD5pWxbuYvd6/aSPTqN3z50EUOnDpIT8H7E6XBycH8tJTsrKN1ZgdloYf2SbbQ2GAEYeVoedpuT5Ow4UgbGkzIogcSMGILCAjxcuRB9z251cHB/DQd2H+yY9hxEo1Gz8csCYlMjSctLYlB+GomZcWQMS5bxgzzk4N5q3nxwMUUb93PNo5cw5aKxXtfT0dzWzsMXPMu4c0Yx5+YzPF3OScVb2qlH6pw68r5eCUxWbn6s338GQngjCUxEt7zlQNSdBbe+wd7Npcy6dhpn/G56n2zTbrWz4La3WLV4HUFhgTy2ZB5JAxP6ZNu9pbmulff/uoRPX/6ShMxY9AYdl93/G4ZPz5WBDPuxptpWDuyq5NCBOvZuK6O86BAVxdUkZsayb1sZwRGBJGXGkjsuk6AwfxLSY4hPjyY6KcLrTkyE6MpqtlG5r5rKvTU0VDdTtLmU8qJDVJfWkZOfjtPpIjWnoxdWWl4iKQPj8Q/2vvGnTjZ1lY3859EP2bhsO+fedDpzbpzpFXe/6aqtycRz173K4EnZ/Oa2s3ptO5+8tIycsZmn3B13vKWdKoGJEN5JAhPRLW85EHXHYrby+yF3YTFZ+fvmJ/vsmmeX08Xzf3idbSt2oVKpuOftm8gdn90n2+5NjdXNvP/XJSx5fTlRSRHo/XVc/KdzGXfOCAlOvITb7aauopHKfTVU7K2mcm815tZ2dq4pprXRBEDe+CxaG9qITY0iPi2K1JyOy3piUyOJSgiTWx6LfsNuc1Bb0UhdRQOV+2o4uL+WqpIa3C43O1YX4xegIyEjloEjBxARF0pydhzJOfFEJYTJ36x+pv5gI4uf/pSq/bWk5iUy965zCI7wzt49DVVN3H/245x78xnMuqb3vqwp213Bq3f9i798Ng+NxjsGv+0p3tJO7QxMRtzbO4HJlsf7/WcghDeSwER0y1sORL/ErrVFPP3bBcSlx/DY0vv77FISRVFY/NTHvHHfIqKTI7nm0UuYesn4Ptl2b2uubeWLt1ex+OlPaTdaGX/uKEbNHML0S8fLXXW8mLHZROW+WuoqGqjYW01VSR2HDtQRGGJg68o9AKjVKoZOzsHtchGVFEF0UjjRieFEJUYQnRhOeGywBCqix7jdblrqjdQdbKKmrJ6a8gZqyusB2LpyDw1VTaBSkTcuE63Oh/j0GOLToknIiCYhPYaIuFC5fLCfqy2v573nPmPL1wWMPH0Ic++aTUS8917KWl54kL/d9Aaz/3B6r94Nx9pu4+7pDzPvX7cQnx7ba9vpr7ylnSqBiRDeSQIT0S1vORD9Um8+8F8Kvt3DlLnjOfemvr2O+IuFK3n3mf9RUVjFlQ9dyGX3/+ak+WbT2Gxm6Rsr+PilZTRVt5CYFcfkC8cw+/oZhEQFe7o80YOMzSaqyxqoKaunpcHIgd0Hqa1spLaiAXOrhZb6NqAjUAmLCWHwhExcDjcR8aFExIUSGR9GWHQwYbEhhEUHo9X5evgdCU9TFAVTi5nGmlYaqpqpr2qivqqJhkPNGJvNVBRXU3ewEYfNyfCpObTUG4lJiSAmOYKkzDgi4kOJS40kKjFcQjovVF54kOXvrOF/r37N6VdO5oI7ziLSi4MSgJ3f7uGFm/7BrX+7hiFTBvXadhRF4e+3v0V2fgbTL53Ya9vpz7ylndoZmAy/Fx9NDwcmLisrt0pgIkRvkMBEdMtbDkS/lNPp5O5pD1O+5yDPrHiIAYOT+3T7O7/dw2OXvkhgWAAxKZHM+9ctJ9W18narneXvrGXL8gJWf7ABX60Pc245g8nnjyFjeKqnyxO9zO1209rQ0QugrrKRuoNNtLdZqNhbTUNVMw2HmvEP8qOssKrzNUFhAQyekEW70UJoVDAhkUGExQQRGhlMcEQgIZGBBIcHEhwRiK9WToa9id3qoLXBSEtDG21NZhqrm2mqbaO5rpWm2lY0GjXFWw7QWNOC3eogdVACppb2jmAtoSNci02NIjwmmKjEcKISwwkINkhPkZOAoijsWlvMpy9/ydYVuzj3xpnMvn4GodHeH7B//uYK3n36Ux764I8k5yT27rbeWE7B6kLufuumU/bfhbe0UyUwEcI7SWAiuuUtB6LjUVtRz/9d8AztbRYWbHwC/6C+DSxqyup56PxnKNleRv5Zw/jdXy7p8+CmtymKwtavC/j4719woKCS+oONZI1M45w/nM6E80ahN+g8XaLwEJfLRVujicaaVhqrW2iqacFstFJTVk9zXSvNdW047A6q9tfSbrR2vm7QmAzK9hwkKDyA4PBAAkP9SR4Yh8vpJiDEQGCIP4Gh/gSFB2AI0BMQbMA/2IB/kB9ave8pezLRExx2J+Y2C+1t7ZjbLBibzUdNLrebmgP1tDaaaGsy0dZowmF30HCoBQBfrQ85+WlYzDZCo4IJiw4mNCqImORIAkP9CY8NITw2hJDIIBlk+CTnsDtZ/78tvPvs/2ipa+Pie85h2sXjT4rbM7ucLl69+1+Yms1c99TlhPZy78qC1YW8etfbPLV8PoYA7//8TpS3tFOP1Dlt2LxeCUxWbHui338GQngjCUxEt7zlQHS8tq0o4O35i4lMjOC+/9zW5ydT1nYb/37kAxY/9QlavS93vHo90y+beFKe1FWV1LDk9eUUbdzP7nV7CQgxcNZ1M5hy0VgG5CV5ujzRj9ksdlobjLQ2GmlrNtNS10Zbk4nWRhPGJhM+Wg0VxdWHT9zbMTabCY8LobK4+qj1JGbE0NZkwhDohyFQjyHQj5jkCOw2J3p/LX7+evT+OkKjOv7G6fy06Py06A1a/AL0+Phq8NX5otX7oj3801frg4/Wp/M5T53oK4qC0+HCaXfidLiw2xw4bA7sVicOuwOHzYnNYsdqsWO3OLBZbKBAa6MRa7sdi8mK1WzDL0BPVWlHSNVutNButBI/IIrtq4uwtds7tzd4YhYVRYcICPHvDKmSsmJxu90EhQUSHB7QEWpFBBIaGURIRCCGIL+T8m+b+OXqDzax9I3lfP7mSrJGpjH14nFMmDPqpLmEqrmulTfvXwQo3LLgml6/1PBQSQ2v3/Nvrnn8MhIyTr1xS37IW9qpnYHJ0Hn4aHr2SyOny8aK7RKYCNEbJDAR3fKWA9GJ+Phvn/PynQv5/TNX9uqt/n6Koih8/o8VrFi0hh2r9jBl7jhueekagkID+ryWvmCz2Fn94QaW/mMFao2agjVFZAxPZfbvT2Ps7BEEhZ2c71v0LZfLTbvRgrm1ozeEqbWddqMVc6sFi+n7MMDldtNa34a13Y7VZMVithEUHsDBfTVY2+3YLB1TxtBkCtbtO2obWSNSKd5y4Kh5Oj8tiqKg8VGj0WjQ+KhR+2hIHRjPwZJa1GoVKrUKtVpFUlYcVftrf1S7j68Gh92JW1FAUXC7FZKz4ygtqMTlcuN2uXE53SRkRFNSUInT7sLldAGgUqk41iE9LS+RQwfq0OoPh0B+WtIGJ9JwqBm9QYdfgA69v57opHCcdldnoGQI0BMYasAvQI9/kAH/YD8Cgg3o/XUSfohfxO12s33lbv736tdsXV7AGVdPZdrF48gamebp0nrUnvV7eeLKBVx012zOun5Gr//7aG1o4+7pD3Pdk5cz6oxhvbotb+At7VQJTITwThKYiG55y4HoRCiKwlt/XsR/H/uQB969k8kX9N4o9j+ndGc5j176Alq9L8YmE39aeBODJ+V4pJa+Ul5UxVf//Iav31mDj4+G5tpW8mcN47TLJzHi9MEyEKjoNxRFweV0Ybc6O3pvWB3YrA6cDucPenY4cThcuOwuXC4XLqf78ORCrVFhtzlxu9wo7o4QxFfrg93q+NG2VCpQqVWoVN+HK1q9L26XgsanI4TR+Kjx1fqgUqvx1WrQ+Gi+7+mi9UGn1+J7uCeMr65jvgQcoi/VVjTw1b++5cu3vyFtaDKDxmVx2uWTvPbWwD/F7Xbz7tOfsnLROu587XqyRqX3+jat7TbuOe3/mHn1NGZd23u3KfYm3tJO7QxMhtzTO4HJjif7/WcghDeSwER0y1sORCfK5XTx3PWvsH3FLu7/7+3kjM3ySB0Ws5W3H3qPD577jPiMGMbOHslv/28uOr+T+9a8DoeTrV8X8PV/VrN/axmHSmvxDzYwde44xp87iiGTB6Lx0Xi6TCGEED+j3Whh3Seb+eo/q6naX0PehGzOvHoqeROzT8rArqGqiZdufwudn46bX7yagBD/Xt+m3ebghT+8Rmh0CNc+flmvb89beEs7VQITIbyTBCaiW95yIPo1rO02/jTjYQyBBm584WqSsuM9VsuGJVv57xMfs3tdMdn56Vz/1OXkTRjosXr6krHFzLpPNrNy8TocNge71hYTHBHIzN9OYdjUXAZPyj5prncXQghv53Q42b5qD8v/s4Z1/9tM/pnDGDYtl0nn559Ud3/r6tsPvuMf977DxX86lzOvmdYngZDT4eTZa1/B7XJxzz9vQa2WwZGP8JZ2amdgktdLgUmBBCZC9AYJTES3vOVA9Gu1NRm5e/rDmFrMPLfyYaJTojxYi4m/376Qqv3VFG3Yz7k3zuTqv1x8UjdAu2qqaWbtJ5v59v0NKCgUrC4iMCyAM6+ewsAxGQyfnid32hFCiD7mcrooWFvEqsXrWfvJJoZMymHA4CSmzh1P7ADPHTf7Qkt9KwtueZP6g03c/daNfTbYqtPh5LFLn0frp+WuN26ULw668JZ2qgQmQngnCUxEt7zlQNQTmmtb+NvNb7B/2wGeXfUwkQnhHq1nw9JtvHjTP4hJieTg3mp+/8wVTJk77pT7Zqmxupn1n21h7cebaGsysX9bGVq9LzOvmsKAwUmMPmMoEfFhni5TCCFOSnarnZ2ri1j94QbWfbqZtCEpZI4YwOQLxzAgL+mkvOTmhxRFYdXidbw+7z9c/Kc5nPX7GX12Vyy71c6z174MCvzxjT+g1Z/cl+meCG9ppx6pc3run3olMFm+66l+/xkI4Y0kMBHd8pYDUU9prm3hod88jdZPy58W3uzx0MRisvKfRz/gvWc/I31YKn4BOm56/mpST9Hb8RqbzWz6YjvrP9vCgV2VVBYdAmDi+aOJT4tl5OmDyRmTIeOeCCHEr9DWaGTL1wWs/XQzm7/YQUJmLMOn5zHp/HzShiSf9CHJEdWltSy47S38AvT89uGLSMiM67NttxstvPCH12htMPLQh3dLr8qf4C3tVAlMhPBOEpiIbnnLgagntdS3Mm/mXwgI8eeuN28kxoOX5xxxoKCCtx96l7UfbyI6JZLRZw7jigcvIDQq2NOleYzT4WTX2mI2LN3GrrVF7D18m9dBYzMJjgxi+PRchk/LIy49+pRp3AshxIlQFIXSneVs+nInW77ayZ71e8kcMYDxc0aRP2sYiX0YFPQHdqud9577jG/eXc95t5zJzKun9Gnvzua6Fh44+wliB0Tzp4U3Sc+Sn+Et7dTOwGTQ3b0TmOx+ut9/BkJ4IwlMRLe85UDU00wtZp68agH7tpTwxBd/JmVQoqdLQlEUvnl3PV/98xs2LttOUEQgF/3xbM69+Uz55gmoLa9n05c72LFqD1uXF2BqaSdlUALmVgtDp+QwcuYQBo5OJzo50tOlCiGExzXXtrJtxS62LC9g7+YSmmpbGTEjj3GzRzJsWu5JdxvgX0JRFNZ/upnX7/0PeRMG8rtHLyYksm+/mKgoquKRi55lyORB3PDcVTJmSTe8pZ3aGZjk3NU7gcmeZ/r9ZyCEN5LARHTLWw5EvcFutfPKH99m9Qff8eB7d5E3sX/crcZmsfPhC0vY8c0etny5k7DYUK5+ZC7TL5uIr1YaVgAul5t9W0rZ8c0etq3cze51xSRkxlG6s5zo5EgmXZBPQnosuROyiE+PkR4oQoiTnqnFTMGaInZ8s4ftK3djbrMQHhfK8Gm5jJo5hMwRA07pyxn3bz/AK3/8Jw6bk5teuJrMEQP6vIYtX+3g7fmLGTQum+ueuvyUG7PsRHhLO1UCEyG8kwQmolveciDqLW63m9f/9G/2bS1l9g2nM/micZ4uqVNLfRv/ffwjtq3YxYGCCmJSIvnt/81l8kVj5RupLuxWO0WbSyn4tpCdqwtx2jsu5wEYOnUQfgF6csZkMHBMBpnDB6Dzk+7PQgjv1lLfxp7v9lKwuoidqwsp3VFO7oRsUnMTGTplEHkTBxIY6u/pMj3uUGktbz+4mKKN+7nmsUuYeP6YPg/RFUVh6etf89Ktb3L9M1cw5+ZZfbp9b+Yt7dTOwGTgH3snMCl89rg/g5deeomnn36ampoahgwZwt/+9jdGjx79k8u/9957/PnPf6asrIyMjAyefPJJZs3q+H/V4XDwwAMPsHTpUkpLSwkODmbGjBk88cQTxMV9f0lfSkoK5eXlR6338ccfZ968ecf5roXoGxKYiG55y4Got33+j+X87eZ/cO2Tl3PerbP6VY+E2vJ63nnsI5a/swb/ID+0el8ue+B8pl06Aa3O19Pl9UtOh5P928vZva6YuvJ6vvlgA821rcQNiKa2ooHUvESyR6UzaFwmAwYnk5gV12d3RRBCiOPldrupLD5E4Xf7Kd9TycYvdlC1r4bkQQkMm5ZLTn4GeROyCY0+dce96qruYAPvPPoRO7/Zw9k3zODs35/ukWOmxWzl+d+/yq41Rdz69+vInzW8z2vwZt7STu1vgcnixYu58soreeWVV8jPz+f555/nvffeo7i4mKioH4/dt27dOiZNmsTjjz/O2WefzTvvvMOTTz7J1q1byc3NpbW1lQsuuIDrrruOIUOG0NzczG233YbL5WLz5s2d60lJSeGaa67huuuu65wXGBiIv7+Et6J/ksBEdMtbDkR9Yc/6Yh46/xkGjc3kroU34x/o5+mSjlJ9oJZFT3zCl2+vImt0OtUltZx325mcff1pBITIgejnKIpCbVk9e7cfYPeaYgo37qdsVyUKYLfY0fvrmHR+Pv7BBtKHppA2NIWkrLhTuvu6EMJzGqubKd1Zzp71+yjatB+3y03hxhKyRgwgZ1wmOWMyyBmTKT1IjqGusoFFT37CgYIKRp4+mPNunYXBQ8fz8j2VPP27v6PV+3Lb368jOcfz46V5G29pp3YGJlm9FJgUH19gkp+fz6hRo1iwYAHQEbomJiZyyy23HLO3x9y5czGbzXz22Wed88aMGcPQoUN55ZVXjrmNTZs2MXr0aMrLy0lK6ri7Y0pKCrfffju33377cb5LITxDAhPRLW85EPWVhkNNLPzzIoo27OPP797ZLxs3dRUNfPjiUpa8+jVJA+OpLD7EmddMY84tZxKb6vk7/ngLu81O+e4qireUsG/LAYwtZr77bCsup4vErDhqy+tJyU1kQF4SA8dkEJ8WQ8qgRDlBEUL0GEVRaDzUzP7tZezfUc7B4ip2ri6iqbqFCXNGoffXkTU6nexRaaTmJsrlmD/j4N5DLH76U5qqW8jOT+e8W8702JcJiqKw5LWv+fCFz0jIjOPut24iMDTAI7V4O29pp/ZFYFJZWXnUZ6DT6dDpfrwtu92OwWDg/fffZ86cOZ3zr7rqKlpaWvjkk09+9JqkpCTuvPPOo4KO+fPn8/HHH7Njx45j1vX1119z+umn09LS0llXSkoKVqsVh8NBUlISl156KXfccQc+PvK3S/RP8n+mEMcpIi6MO179PQsfXMzLd7zN9MsnctoVkz1d1lGikiK44ZkrufTe8/jqn9+w6KlP+d8rX/H1f1aTOy6LObecydCpg/rVZUX9kVanJWN4KhnDU+Fwz1G71U75nioqiqoo3Lif0h3lfPPed2xfVUhNWT0Agydmo/XTkjwwnuScBFIHJZCQGeuxbzCFEN7BZrFTUXSIg/uq2bu5lNKCCqxmG8WbS0nIjCVtSDKDxmUx65rppA1Nxj/I4OmSvcLudcW89+z/sJptDJ+Rxw3PXunRz665rpU37v0PX/3zG2549krm3NK/LvMVvUulKKh6+PvqI+tLTDz6S7z58+fz0EMP/Wj5hoYGXC4X0dHRR82Pjo6mqKjomNuoqak55vI1NTXHXN5qtXLPPfdwySWXHBXi3HrrrQwfPpywsDDWrVvHvffeS3V1Nc8991y371MIT5DARIgToPHRcM1jl7J95S6euOJFynZVcOn95/e7xmtQeCDn33E2s/9wOms+3sS7T3/Kuk8309Zk4sWb/sFZ189gxuUT+/yWid5Mq/8+RJl+6QSgoxtrbXk9B3YdpGx3JcYmM1uWF7BtxW5cThcpgxIo232QiPgw0ocmE50UQUJmbOcUERcqd0IQ4hRis9ip2l9DRWEV5YVV1FY0ULy5lEP7a9D6aZlw7ii0Bi0TzhvFgLwkBuQl4Reg93TZXsVhd7L6/e/47LWvcdgdnHXtDKZd5vlxvdZ9som/Xv8KyYMS+evqR8gZk+nResTJ5Vg9TDzB4XBw0UUXoSgKL7/88lHP3XnnnZ2/Dx48GK1Wy+9//3sef/xxj9UrxM+RwESIX2Ho1Fxe2/EsC259g98PvYt5/76V3HHZni7rR7R6LdMuHs/UuePYva6YZW+upHjjfpa89jVv3PsO4+eMZtZ10xkyZZAMbHoC1Go1sanRxKZGM272iM75Druz46So+BCVRYeoKDqExWhh2dvfYGu3A5A7Pot9Ww8QlxZNXFo0mSMGEBQe0PE4NYrw+FA0GhknRQhv43a7qT/YxKGSWg7uq6alro3izSVUFldTW97A4EnZmFraSR4YT1J2HBPPG0VqbiJRSRESoP4KdZUNLH19OXvW78VH68Ml985hxGmDPf6ZNtW2sOCWNyjbVcnoM4dz4wu/xT9YLt88JSlKx9TT6wSCgoJ+0WVJERERaDQaamtrj5pfW1tLTEzMMV8TExPzi5Y/EpaUl5ezYsWKbuvJz8/H6XRSVlZGVlZWt7UL0dckMBHiVwoKD+S+/9zO2k828siFzzH5wrFc/dil+Bn6X0quUqnIHZ9N7vhsrn3iMtZ8tIGPXvycb95bz4HdFbS3WjjtiklMu3QCKYP639gs3sZX60NKTgIpOQlHzXe73TQeauHgvmrqDzaSMSyFqpJayvccpPpAHaU7KzqXjU6OwMfXh5iUSGJSo4hOCic6OZLopAiiksIJiwmRrtxCeIjNYqO2opGaA3VUH6jH2Gxi37YyDpXUUnOgDkOQAbVaRUJGDGlDkhk2NZdzbjiNxKw4opIiJKDuIXa7k01Lt/H5Gytwu90k5yRw69+vJSEj1tOl4Xa7+fwfy1n25gpqKxq45W/XMPH8MZ4uS3iSWwFVDwcm7uNbn1arZcSIESxfvrxzDBO3283y5cu5+eabj/masWPHsnz58qPGMPnqq68YO3Zs5+MjYcm+fftYuXIl4eHh3dayfft21Gr1Me/MI0R/IIO+im55y2Ba/UFbo5FFT37M2o83ctvL1zN8ep6nS+qWoijs+W4vX7y1im/f/46k7HgKN+xjwJBkZl0zjdGzhstAsX3I5XLTcLCJQ6W1VJfWUlPeQM2BOmrK6rGYbFQUVXUuGxgWgK3dRkR8GFGJ4UQmhnf8nhBGRPyRKZTA0AAJVYQ4ToqiYGoxU3+wibrKRuorG2k3WSjdUUFteQO1FfXEZ8Sya00xkQlhxKRGkZOfjl+gnrgBHT3GYgdE9btLNU8mpQXlfPXPb1nx3zVkjUpn8gVjmHh+Plq91tOlAVC8uYRFj3/Imo82Mu3SCfzh+asJiZB2VE/zlnbqkTpnpN3eK4O+fl3y/HHfVviqq67i1VdfZfTo0Tz//PO8++67FBUVER0dzZVXXkl8fDyPP/440HFb4cmTJ/PEE09w1llnsWjRIh577LHO2wo7HA4uuOACtm7dymeffXbUeCdhYWFotVrWr1/Phg0bmDp1KoGBgaxfv5477riDM888k7fffrtHPxMheooEJqJb3nIg6k+2r9zFxws+xxDkx3VPXE5odIinS/pFbBY7m7/czldvf8vGz7cRGhtKXXk9uROyyT9rGBN/M4b49GN31RR9w2q2UlfZSG15A43VLVQfqKO+spH6g420Gy2U7T6I0+HqXD4oPACr2UZ4bChhsSFkDk8FICwmhNCYECLjQwmJDCIkKpig8ACPd1sXoi/YbQ6aa1pprmul8VAzDYeaaaxuRnEr7N16gIaqJhqqmkgbnEThhv2Ex3WEkpkjU9HqtcQkRxCdHElMSgSRiREeHxfjVFJbXs/qDzfy5durCAoPJG9iNqddMYm4tP5zbGqqaebdpz/lw+c/Izo5kttfu4ERMwZ7uqyTlre0UzsDkwG39U5gUvrCcX8GCxYs4Omnn6ampoahQ4fy4osvkp+fD8CUKVNISUlh4cKFncu/9957PPDAA5SVlZGRkcFTTz3FrFmzACgrKyM1NfWY21m5ciVTpkxh69at3HjjjRQVFWGz2UhNTeWKK67gzjvvlPFLRL8lgYnolrcciPobh93Bxy9+zrYVBYyeNZyzf3+aV93usa3ZxPpPNvPNe+uxmKzsWtMxavrkC8eSkBnL2NkjyBgxQE6w+xm3201bg5H6qmbqDzbSVN1CY/X3J4RanS97Nuyjtd4IQM7YDPas3weAWqMmJDKI+PRotHotIZFBBEcEEp0aid5PS3BEEEHhAYREBhEYFkBAiEH2v+gXFEXBYrLS2mCktb6NlgYj7W0W6isbaa5rpbmujZa6VtoaTdRXNWFsMgGQOz6T+somwuNCCY8NITU3EZ1BR0R8GJHxYYTHhxIZH4bGR8YR8qS6igY2fr6Nr/71LVX7qjn796cx8vQh5IzL7Fd/gywmC5++/CX/eeR9nHYnv/3LxZxz4xno++EluicTb2mn9sfARAjRPQlMRLe85UDUXzXXtvD2g4tpqW/j7BtOZ+TpQzxd0nEzNpvYsGQbaz7aQHVpHaU7y1GrVcSlxzBoXBb5Zw1n+PQ8/IOl67m3cDqcNNe20VLfRlNNCy11Hd+2N9e20dpopLm2ldb6Nlob2ojPiKVg9dG3GUwflkJpQQUBof4EhQUQGBZAZHwYvjpfAkP9O6eAUH8CQvwJCDbgH2wgIMSAIciAn79OLhMSP3Ik+DC1mDG1tGNqacdittJS14ax2Yyx2YSx2YxW58uBXZW0NRppazDS2mjCP9hAS10rarWK4MggBo3NwNRqITQqiNCoYEKigomIDyUkKoiw6BDCYkIIDPPvVyfcooOiKFQUVrH2k02UbC9j+8rdTPjNaMafO4rhM/L63ZcPdpuDr95exdvzF9Nc28o5N87k/DvO7le9Xk5m3tJO/T4wuRUfdQ8HJm4bX5e+2O8/AyG8kQQmolveciDq78p2V/Dm/f8lIMSf39x+FulDj91tsb+zWexsW15AwepCVvx3LQ1VTQwck0HxphIGjslg0vljyBqVRtaotH7XqBUnxu12Y2610NZopLXBSFujEbPRQnNtG21NJoxNJozNJkyt7bQ1GDG1tGNsMWO3OnDanT9aX0CoP+1tFgxBfvgH+mEI8mNAXhLGJhOGQD/8AvX4BegJjQ5Go1HjF6BH76/DL0CPn78eQ5AfWr0veoMOvUGHzqBFZ9DKia+HuJwurGYb1nYbFpMVW7sdi9mKxdTx2GKyoigKzXWttLdZaDdaaG+z4h/sR3lhFebWdkyt7ZjbLCSkx1C0qaRz3RofDXkTsmisbiYwNKAziItPj0GlUhEcHkBQeCDBEYEERwYSEhFEQKiEIN7IbnOwa3Uh3y3Zyt4tpdQcqGP8nNFM/M1ocidk98vjid3mYPm/v+Xfj7xPXUUDky8cy9k3nM7QqbmeLu2U4i3tVAlMhPBOEpiIbnnLgchbFH63l4XzFxOXFsP5t59FQmacp0s6YYqicKCggoI1Raz5cAO71hSRmpfEvq0HMAT6Me7ckQwYksyQyYMYMCQZH+nWfkpxOl20t1k6Tohb2g+fGJsxt3acNJvbLJ0n0FqdD9UH6mk3WWlvs2AxWYlMCKN4S2nnLZiPyMlPZ8+G/T/a3oDBSdQfbESn16L10x7+6UtCRiytDUa0el98dT5odb746n3R6nwJjgjEYXfiq/XBV+uDz+FJb+joAePjq0Hjq8HHV4OPT8fvWp0vKrUKtUaNxkeNWq1GrVEffqzp+F2t6lhGrUKl/sFj1eET+R90rlGpVNDx3+G7TSpw+KeiKCh0PHa7FdwuN4pbweVyo7i//93ldON2uXG5XB2/O124XW4cdidOhwuno+Ony+FCrVbRbrLisDlx2Bw4HC4cNgdanS+tDUbsNgd2qwOH1YHd5iA4IpCq/TVY2+3YLXZsh6fknHh2rSnG8YNQzC9Aj8VkBUCr9+0IuQL0ZI0cQMOh5o6A7HBIFpUYhqKAf7AB/yAD/sF+BIb4dzwOMRAY4o9eeiKdtBRFobL4ELvXFbP2o03s/HYP484dSXxGLGPOGk7a0JR+G3xZTBaW/2c17zz6IfUHGxl15lBOu2IKky8a229rPpl5Szu1MzBJvaV3ApMDf+v3n4EQ3kgCE9EtbzkQeZvd64r5z6MfEJUUwW9uO4uk7HhPl/SrWUxWClYXsnV5AYXf7aNwwz4Ut0JoTAhWs5XccdkMm5FH5ogBZI1Kk+u6xS/icrmxtduwmGxYzVas7Xas7TZs7bbDPzseu5wu2tus2K0dJ/R2iwOb1Y7eoKOxpvmoEMBhc2K3OgiLCaaqpBan3YnT7sLh6AgRopMiqCg6dMx6Bo3NYPfhcV+O67lxmexet/eYz+WOy2TXCTyXNyGLgjXFx3wuPDaExuqWjuBH69MR+mg1DByVTtmeg/jqfPDV+uKj9cFX50NsahTGJtPhYMkXrd4XnZ+W0KhgnA5nRwh1eNLqtQSEGPDx9UHv39HTR+/f0dvHP9APvb9Oxv0QP1JbXs/2lbvZsWo3B3ZX0lTdwthzRpA3PpsRpw8hJLJ/tzEaq5tZ/p9vWfTEx5iazQyakM3pV07mtCsn98seMKcKb2mnSmAihHeSwER0y1sORN5q75YS3nvmU/wC/Ljz9Rs8XU6Pams0UrC6iAO7yln3yWZKtpcxcGwmu9cWo/HRMOmCMQSFB5A9OoPs/DTi02Pl22TRbyhKR4+Ojh4aLlyHe2m4XW7cbjdul4LL6cLlcuN2unG53KAoHc+5FRR3x+8dPxVUKlVHrxDl6G0AqDUqFDeoVIBKhUp1pOeJCo1Gfbi3yg96qhzuxaLxUaPx8UHto0aj0aDRqDt+99Gg8VXjq/VFo5FvvEXfc7vdVBRWUbyphO0rd7Hz20ISs+LQG3QMmTqIYdPzSB4Y7xV/84s27uPbD77j4xeWovHVkDQwntk3zGT65RPx1codkjzNW9qpnYFJ8s29E5iUL+j3n4EQ3kgCE9EtbzkQeTtzqxn/YH9Pl9GrzG3tFG8qYdeaInav24vidrNtxS4AcidkU7arkswRAxh+Wh6xqdFkDE8lJjXKKxrUQghxKjM2m9i/7QC71hZT+N0+GqqaaG0wMnRKDlmj0xkyOYeU3CSvCfAsJgsrF61j69c7+ebddUTEhxGREMYFd8xmwm/ypQdVP+It7dTOwCTpxt4JTCr+3u8/AyG8kfQfFKKfONnDEgD/IAPDp+cxfHoe0PENZGXRIQo37OPg3kM4bA4KVhdibDaxb+sBAIZNy8XpdJE2JIXs0ekkZMSQPChRLucRQggPMbWYKNlezt6tpTQeaua7z7ZSV17PpAvGEBDqz/RLJ5AzLouYlEivCrwVRWHP+r0se2M5e7eUUrqznIzhqUw8P59zbz6TwZNyvOr9CCGE+PUkMBFCeIxarSY5J4HknITOeXabg/LdB9m/7QD7th2goaqp46483xaSPiyV/dsOoFKpGDdnFLgVknISSB+WQnxaDAlZcej8tB58R0IIcfJwu91Ul9ZyoKCSA7sqKNleRsmOclwOFwGh/mQMTyVnbCaTLxxL2pBktHrv/PtbWVzFinfWsPqD76gpq8PWbmfUGcPIP2s4s66bQUxKlKdLFCeTjtG9e36dQoheIYGJEKJf0ep8yRieSsbwVM48PK+j0V5H2a4KSneWc6CgEmu7jS1f7WTtJ5vImzSQXWuKUKlUjDpjKG6Xm4TMWJJzEohJjSI+PYbIxAiv6QouhBB9yeVyU3OgjoqiKiqLqqgpr6d4YwkVRVVEJoYTHB5Ial4So2cN55J5c0jJTfL6cLq88CBbvtjBFwtXYm23cWh/DSqVilnXTWf4jMGMPWekjE8ihBBCAhMhRP+nVquJT48hPj2G8XNGd8632xxU7a2mqqSGYdNyqSiqQq1Sse7TzWz+cgcDBidTurMcAF+tD4nZ8UQmhBGTGs2AvCRCooOJSYkiJiUCvwA/T709IYTodYqi0FTTQnVJLQf3VVO1r5qmmlb2binh0P4aUgcnoVKpSMqOJ3NkGmNmDSdlUCKRieEnxWUobrebwu/2se6TTRSsKaRw/V4Gjc+mdGc5SQPjueHZq5h04VgiE8I9Xao42bkVoId7hLilh4kQvUUCEyGE19LqfEnNSyI1L4kJPwhS3G43jYeaOVRSS2XxIQ7tr6b+YBPlew6yfeVubEu3MWBwEqU7KwDIGZtJ1b5qopIiiU6JIG5ANOGxoUQmRRCdHElEXCjBkUGo1dJDRQjRf7UbLdSWN1Bf2cChklqqD9RSW1ZPbXk9B/fV4LA5yB2fhc6gIz49hryJWcy4YiKJmXGEx4WeFMHIDzVWN1Pw7R7W/28zZbsrKd3REaDnjs8mJjWKoVMHccuCaxgwOPmke+9CCCF6hgQmQoiTjlqtJjIhnMiEcIZMzjnquSPfstYcqKOmrJ6asjrsFjs6g466inp2rNrDmg83di6fOyGbXWuK8NX6EJMaRXBkEBHxYYTHhhKfEYt/sIHw2BDCY0MJiw2RnipCiF5ht9ppqGqi4VAz9ZWNNFQ1YjHZOFBQTl1FI8GRgWz9uoDAUH9S85IIDAsgNjWK4TMGE5ceTdyAaKKSIvDxPXmbfu1GC9tX7GLbigIKN+yjeOP+zr/hAEOn5pKdn8GE8/PJHD5AQhLhGTKGiRBe5eQ9agohxDGoVCrCY0MJjw1l0LisHz3vdrtpqWujvrKRusoG2hqNZI9Op6GqCavJyoHdlRRv3I/D7iRvYjYFq4s6XxscEYjd6iA0JoQBeUmggpDIYEKjg4lOjsQQ6EdIZBDBh6fAUH9psAtxClMUBXNrOy11rTTXtdJc00pTTQttTSbqyutprG4mIMTA1uW7MDaZ0PhoiE6OIDQ6hMjEcFLzkhgxY/Dh3nARRCdF4h9s8PTb8pjd64qZf95TAGSOTEPjoyEkKpjfP3Ml+WcNJyEzTv7mCiGEOC4SmAghxA+o1WrCYkIIiwkha1TaMZdRFIW2RiON1c00VbfQVN1MY3UzLXVtNNW20FzTgsPuZPfaYkwtZgDyJg2k4NvCznVkjhzA/m1lBEcEEhQRSPLABNxuN0FhgQSFBxAeG4pfgJ7AsAACQwMICAsgMNSfwFB/r70ThRAnO5fTRVuTCWOjCWOLiZa6NtoajLQ2GnHanRwqqaWlvhVjk4mmmhZa6to6LpOZkE11aS2h0R1/e+IzY4hMDCd7dDqRSRFceOdsIhLCCY4MksGrf0buhGxSBiWSOyGbMbNHkDcxB0Og9PoT/YxCL/Qw6dnVCSG+J4GJEEIcJ5VKRXBEEMERQQzIS/7ZZR12Jy11rbTUt9Fa33b4dyN2q420ISm0NRppbTDSVNPCwb3VtDUacbvchEQG0VLf1rmeqKQI6ioaANDqfRk0Loummhb8gw0EhPiTNDAeW7sd/2A//IMNBEcGodNrMQT5dUyBBvyD/TAE+uEX6IevVv78C9GVoihYTFba2yxYjBZMLe2YWs2YWtpx2Ow01bRiajZhamlHrVFTtb8GY5MJY7OJqMTwzh5nKpWKEacNprainuDwQIIigkjKjiMyMZyMYSmERAUTEhVESFQIodHBBIUHyBhJPcDPX8/rBc95ugwhhBAnEWkxC+HF3G43Hz6/hLyJA0kbloqPj8bTJYkufLU+neOp/BKKotDeZqG1sQ1TczvGZhPGJtPheUZMzWZMzWbUPmpUahXmlnaq9lejKArbV+7GYXMAHHWHoCPShqZQsr2ss660YSm01LXh56/HL1CPX4CehMxY2tss6P316P11+AXoCQj1x8fXB71Bh95fh86gQx+gQ2/QodNr0Rm06Ay6jp9+WjnxE33C7XZjs9ixtduwmr+fbBY7VrMVi6ljspptqDVqGg81YTFaaTdasJpttBsttLdZOn4aLcSnx7Dzmz24D99tYtC4LPZvLyMgxIB/sIHknARs7XYCQjp6eoXHh5KYGdvRCywsgMDwAIJCAwgKD8Q/xF96ggghjk3GMBHCq0hgIoQXK99dyat3/ROVSkVkQhhpw1IZdcZQMkemkzYk+aQe3O9kpVKp8A82nPA4BHabA3Nre8eJYJuFdmPH7+Y2Cw6bg7ZGI+1GKxajBR+tD/WVDR2PTVZa69vQ+Wkp3lyC1WzDYrLidrkJiwujqbql22376nxw2Jz4an3Q+nWEJ0kD42mubUWr9+2c4jNiaa1vQ6vX4qvzQav3xVfnS0CIAcXdsR5fnS8+Wh+0h3/31frgo/XBx1fT8VPr07Gcry8aX03H/MPPdTzuWFbj0zFfrVHL2AW/kqIoOB0unA4nbqcbp8OJy+nGaXfisDtxHp4cDidulxu71YHD5sRhc+C0O7HbnLidLmxWOw6b8/DzDnx8NbQ1mbBZ7DisDuxWBzaLHY2PhtaGNmwWO3aLHZvFjq/Wh6baVmztNuzWjnBQb/DFarZ11hk7IJp2owW/AD16Q0fol5KbiLHJhF9ARzAYFhuKf1BHjytDUEevq8BQf/yeuBxDUEcvLUOQH1qdr6c+biHEycrtBty9sE4hRG+QsykhvJjL5WbcuaNorG6meON+Gg41s/WrndgsdnLGZaHV+zLqjKEkZSeQOSqNsOgQT5cseplW54s2KpjQqOBfvS5FUXDYnVjNVmztdmzt9o4gxWzFYXN0PG63dX7Lb7c4sFkPn9xaHdgPn+CGRAZ1nAzbOk6GLUYLh0pqO06abd+fPIdEBlFzoA6H3dlZgyHQj3aj5bjqzhmbyZ71e380X+OjISI+lLZGExofNRqfjkBFrVGj8VETGhOCqcmMWqP+flKrUGvUhEQFYW5tR6XumKdSqw7/VBMUHkB7m6UzkFGr1aDqCL8Cw/yPeq4ztFGBf7CB9rZjvzf/YAPm1nYURQGlY190TGAI8qO9tR23+/C8wz/dbjeGIAPmFjNut4Lb5UZxK7hcbhS3G0OgH21NJtwuNy6nG7fr+8kQqKe1yYTL6cLtdONyug5PbvQBOlrrOy4V+6Ho5Ehqy+uPWf+R59Qa9eHAS4OvzpfQ6BDsVjtanW9H4KXXEp0cQXurBa2fL1q9Fv9gP0KigwkINqBSq9AdDt90Bi36AP3hXk069AYtOj8dOoMvfgF+nb2fpGeHEEIIIXqKBCZCeLH0oak8/NGfcLvdlO+upHDDPnZ+u4c96/dyoKC8s/v53k0l5E0cSG15PUOn5RKXFkPakBTSh6cQHhsm37yLY1KpVB0BjM4Xwvpuu0eCGqfdid1q7+zF0NGTwXX4pwOX03W4x4ML1+GeDy6HC5VaxdnXTcflOvyc043rcG8IVOCwOTuCgSPBgdOFy+1G46PBbrF3zD8cNhwJFHz9fLGabB3z3B3PHfldq/fFYXV2hhoodIYLR8Ig5XB36SOhB4Bao/7JMEjjq8FqtnX82zwcvnRMoFJ1hD8+h4MblaojvEGlOtxrQo9a3XHJlkbT8VOtUaP31+NyuND4fB8IaQ7/1Bm0gAqNjxofX5/Dy2g6Hmt98PHR/KAnz+FePFoNvr6+HT+P9ADS+uB7uJePVq+V8EIIIbqSS3KE8CoSmAhxElCr1aTmJZOal8ysa2cA0NrQRtHG/ZTtqiAiLgyX001dRQMH91bz5cJVBIYFYGwyERgWwIjTBhMcEUTmqDRiU6NJzI4jJPLX91AQ4kT8MKiRO1wIIYQQQghPkcBEiJNUcEQQ+bOGkz9reOe8tiYj5XsqKdleTv3BRvasK6ZsdyXrP93ccRnP4UsZMkemUVtWR3xGLEkD44kdEENKbiJRiRHEpEYREOLvwXcmhBBCCOGlpIeJEF5FAhMhTiFBYYHkTcghb0JO5zy3201rg5GKPQepLa8nZ2xW56UQjdXNnWNBpA9LYf+2MrJGpVNzoJbolChSBiUQEhlMUk4CIZFBhMeHETsgGkOgn1zmI4QQQgghhPBqEpgIcYpTq9WE/sQgoW63m5a6Vg6V1NJY1UjV/lpsVjv7tpRSX9nId59toa3RRN6kgRR8W0hAiD+mFjN6fx2jzhiKqaWdsNgQEjLiCAjxJywuhIj4cEKjggmJDsbPX++BdyyEEEII4SFuBejhHiFu6WEiRG+RwEQI8ZPUajVhMaGExYT+5DIWk4WGQ800HWqmqaaZxkPNNFQ1AWBqKWf/1gMUbthPTUkNMQOiObS/BoDcCdmU7ignODKI3AnZWExWgsICiEuPQavXEhQeSFhsCIGhAQSGBRASHYxOr+2T9y2EEEIIIYQQEpgIIX4VvwA/EjP9SMyM+9nlXC4XbY1GjE1mWupaaW9rp6mmldb6NhTFTXVpHS31bZhb26ncewhjowm1j5rasnpUKhWKoqDV+xIcGUxAqIHA0AD8gw0ERwThH2wgIMSfkKggDIF+GIIMBEUEdvweqCcg1B+/ALlMSAghhBCepShuFMXd/YLHuU4hRO+QwEQI0Sc0Gg2hUSGERoWQlB3/i1/ncrkwtbRjM1sxNpsxtZixmm2YW9sxt5qxmu2YW820NrTRcLARs9FCe1s7PlofGquaaDdaiUqOoHR7GQADx2RSXVqLX4Ce+IxYHDYHeoMOfYCe4MggtDpfdAYdgyfnkJqb1EufhhBCCCFOSYrS85fQyKCvQvQaCUyEEP2aRqMhODwQwgOJSor81etzu904bA4sJitWsw2n3YnVbMNqtuKwO7Fb7FjNNtQadQ9UL4QQQgghhPBWEpgIIU4parUanZ8OnZ8Ofn3+IoQQQgjxyym9MOir9DARotfIV6hCCCGEEEIIIYQQXUhg4oVeeuklUlJS0Ov15Ofns3Hjxp9d/r333iM7Oxu9Xk9eXh5Lly7to0qFEEIIIYQQndzu3pmEEL1CAhMvs3jxYu68807mz5/P1q1bGTJkCDNnzqSuru6Yy69bt45LLrmEa665hm3btjFnzhzmzJnDrl27+rhyIYQQQgghhBDCe6gURS568yb5+fmMGjWKBQsWAB0DWCYmJnLLLbcwb968Hy0/d+5czGYzn332Wee8MWPGMHToUF555ZVftM22tjaCg4NpbW0lKCioZ96IEEIIIYQQv5K3tFOP1Dk94FJ8VNoeXbdTsbPc9E6//wyE8EbSw8SL2O12tmzZwowZMzrnqdVqZsyYwfr164/5mvXr1x+1PMDMmTN/cnkAm81GW1vbUZMQQgghhBBCCHEqkcDEizQ0NOByuYiOjj5qfnR0NDU1Ncd8TU1NzXEtD/D4448THBzcOSUmJv764oUQQgghhDjFKW53r0xCiN4hgYn4kXvvvZfW1tbOqbKy0tMlCSGEEEII4f0UpXcmIUSv8PF0AeKXi4iIQKPRUFtbe9T82tpaYmJijvmamJiY41oeQKfTodPpfn3BQgghhBBCCCGEl5IeJl5Eq9UyYsQIli9f3jnP7XazfPlyxo4de8zXjB079qjlAb766qufXF4IIYQQQgjRS9xK70xCiF4hPUy8zJ133slVV13FyJEjGT16NM8//zxms5mrr74agCuvvJL4+Hgef/xxAG677TYmT57Ms88+y1lnncWiRYvYvHkzr732miffhhBCCCGEEEII0a9JYOJl5s6dS319PQ8++CA1NTUMHTqUZcuWdQ7sWlFRgVr9fcehcePG8c477/DAAw9w3333kZGRwccff0xubq6n3oIQQgghhBCnJkUBeniQVhnDRIheo1IU+Rcmfp633N9eCCGEEEKcWrylnXqkzmnaC/FR+fboup2KgxX29/r9ZyCEN5IeJkIIIYQQQgjRBxS3gqLq2e+r5ftvIXqPDPoqhBBCCCGEEEII0YX0MBFCCCGEEEKIvqC46fkxTHp4fUKIThKYCCGEEEIIIUQfkEtyhPAuckmOEEIIIYQQQgghRBfSw0QIIYQQQggh+oJckiOEV5HARHTrSDe/trY2D1cihBBCCCHE9460T73lshQnDujhUp04enaFQohOEpiIbhmNRgASExM9XIkQQgghhBA/ZjQaCQ4O9nQZP0mr1RITE8OamqW9sv6YmBi0Wm2vrFuIU5lK8ZY4VniM2+3m0KFDBAYGolKpftW62traSExMpLKykqCgoB6qUHgD2fenJtnvpy7Z96cu2fenLk/se0VRMBqNxMXFoVb37+EZrVYrdru9V9at1WrR6/W9sm4hTmXSw0R0S61Wk5CQ0KPrDAoKkkbUKUr2/alJ9vupS/b9qUv2/amrr/d9f+5Z8kN6vV5CDSG8TP+OYYUQQgghhBBCCCE8QAITIYQQQgghhBBCiC4kMBF9SqfTMX/+fHQ6nadLEX1M9v2pSfb7qUv2/alL9v2pS/a9EOJkI4O+CiGEEEIIIYQQQnQhPUyEEEIIIYQQQgghupDARAghhBBCCCGEEKILCUyEEEIIIYQQQgghupDARAghhBBCCCGEEKILCUxEj3vppZdISUlBr9eTn5/Pxo0bf3b59957j+zsbPR6PXl5eSxdurSPKhU97Xj2/euvv87EiRMJDQ0lNDSUGTNmdPv/iuifjvff/BGLFi1CpVIxZ86c3i1Q9Jrj3fctLS3cdNNNxMbGotPpyMzMlL/5Xup49/3zzz9PVlYWfn5+JCYmcscdd2C1WvuoWtETvv32W2bPnk1cXBwqlYqPP/6429esWrWK4cOHo9PpSE9PZ+HChb1epxBC9CQJTESPWrx4MXfeeSfz589n69atDBkyhJkzZ1JXV3fM5detW8cll1zCNddcw7Zt25gzZw5z5sxh165dfVy5+LWOd9+vWrWKSy65hJUrV7J+/XoSExM5/fTTqaqq6uPKxa9xvPv9iLKyMu666y4mTpzYR5WKnna8+95ut3PaaadRVlbG+++/T3FxMa+//jrx8fF9XLn4tY5337/zzjvMmzeP+fPnU1hYyBtvvMHixYu57777+rhy8WuYzWaGDBnCSy+99IuWP3DgAGeddRZTp05l+/bt3H777Vx77bV88cUXvVypEEL0HLmtsOhR+fn5jBo1igULFgDgdrtJTEzklltuYd68eT9afu7cuZjNZj777LPOeWPGjGHo0KG88sorfVa3+PWOd9935XK5CA0NZcGCBVx55ZW9Xa7oISey310uF5MmTeJ3v/sdq1evpqWl5Rd9Uyn6l+Pd96+88gpPP/00RUVF+Pr69nW5ogcd776/+eabKSwsZPny5Z3z/vjHP7JhwwbWrFnTZ3WLnqNSqfjoo49+tofgPffcw5IlS476Euziiy+mpaWFZcuW9UGVQgjx60kPE9Fj7HY7W7ZsYcaMGZ3z1Go1M2bMYP369cd8zfr1649aHmDmzJk/ubzon05k33fV3t6Ow+EgLCyst8oUPexE9/v//d//ERUVxTXXXNMXZYpecCL7/tNPP2Xs2LHcdNNNREdHk5uby2OPPYbL5eqrskUPOJF9P27cOLZs2dJ52U5paSlLly5l1qxZfVKz8Axp4wkhTgY+ni5AnDwaGhpwuVxER0cfNT86OpqioqJjvqampuaYy9fU1PRanaLnnci+7+qee+4hLi7uR40r0X+dyH5fs2YNb7zxBtu3b++DCkVvOZF9X1payooVK7jssstYunQp+/fv58Ybb8ThcDB//vy+KFv0gBPZ95deeikNDQ1MmDABRVFwOp3ccMMNcknOSe6n2nhtbW1YLBb8/Pw8VJkQQvxy0sNECOFxTzzxBIsWLeKjjz5Cr9d7uhzRS4xGI1dccQWvv/46ERERni5H9DG3201UVBSvvfYaI0aMYO7cudx///1y+eUpYNWqVTz22GP8/e9/Z+vWrXz44YcsWbKERx55xNOlCSGEED9LepiIHhMREYFGo6G2tvao+bW1tcTExBzzNTExMce1vOifTmTfH/HMM8/wxBNP8PXXXzN48ODeLFP0sOPd7yUlJZSVlTF79uzOeW63GwAfHx+Ki4tJS0vr3aJFjziRf/OxsbH4+vqi0Wg65w0cOJCamhrsdjtarbZXaxY940T2/Z///GeuuOIKrr32WgDy8vIwm81cf/313H///ajV8v3dyein2nhBQUHSu0QI4TXkCCV6jFarZcSIEUcN6uZ2u1m+fDljx4495mvGjh171PIAX3311U8uL/qnE9n3AE899RSPPPIIy5YtY+TIkX1RquhBx7vfs7OzKSgoYPv27Z3TOeec03kHhcTExL4sX/wKJ/Jvfvz48ezfv78zJAPYu3cvsbGxEpZ4kRPZ9+3t7T8KRY4EZ3LvgZOXtPGEECcFRYgetGjRIkWn0ykLFy5U9uzZo1x//fVKSEiIUlNToyiKolxxxRXKvHnzOpdfu3at4uPjozzzzDNKYWGhMn/+fMXX11cpKCjw1FsQJ+h49/0TTzyhaLVa5f3331eqq6s7J6PR6Km3IE7A8e73rq666irl3HPP7aNqRU863n1fUVGhBAYGKjfffLNSXFysfPbZZ0pUVJTyl7/8xVNvQZyg49338+fPVwIDA5X//ve/SmlpqfLll18qaWlpykUXXeSptyBOgNFoVLZt26Zs27ZNAZTnnntO2bZtm1JeXq4oiqLMmzdPueKKKzqXLy0tVQwGg3L33XcrhYWFyksvvaRoNBpl2bJlnnoLQghx3OSSHNGj5s6dS319PQ8++CA1NTUMHTqUZcuWdQ76VVFRcdS3TOPGjeOdd97hgQce4L777iMjI4OPP/6Y3NxcT70FcYKOd9+//PLL2O12LrjggqPWM3/+fB566KG+LF38Cse738XJ43j3fWJiIl988QV33HEHgwcPJj4+nttuu4177rnHU29BnKDj3fcPPPAAKpWKBx54gKqqKiIjI5k9ezaPPvqop96COAGbN29m6tSpnY/vvPNOAK666ioWLlxIdXU1FRUVnc+npqayZMkS7rjjDl544QUSEhL4xz/+wcyZM/u8diGEOFEqRZG+kEIIIYQQQgghhBA/JF/7CSGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEFxKYCCGEEEIIIYQQQnQhgYkQQgghhBBCCCFEF/8P0CWHwcpC11oAAAAASUVORK5CYII=",
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/mZlDYmHFQiOnbluYWcDWwhbmZhannmdpxkbFBx9n/9lYWMPX1v/UbTMzc1iZDeZPsOvspJ+tx5yfrw++jp3GWrARdTN2YnnafSdP8Nnzhl7LT9f5dYv/PcYH9Af3aTDoMXgCzfZz8PHB17N/ezZyPxg0IKPRH/3L/lGY4I+GOljIR6xYILFfp0KfVgW5RoUejQrSvn709Ksh69dAJlOjqaMXJVWtWL+3AE7mlpgYHYwlM5JgayPmn4z8E2vXruXnEu+//z4yMjLw+uuv83OE0tJSeHl5/b/n7969GytXrsTEiRNha2uLF198EXPmzEFhYSH8/f35c1566SW8+eab+Pzzz/l5yOOPP87XWVRUxF/DXH755fychWW9azQaXHvttbjxxhuxZs0ao78HhJwLs4HBHE1CfherYeLs7Iyenh5e/2Q0jcYWHCjF4Q3HcPCXHDTXtPFpMa7eznxqjV+EDwIifRAUE4DQxED+GCHEuFiQQqnVol+j4ZdDS79WC7Vu8LpuYAAytYpfZwELmVrNr7OghqWFGTr6+6HSauFsa4sGeQ9ULPCh08LOyhKtfXKodToEO7sgv7OFBzrY4yHOzqiTdfMAR7izO1r7ZbCyMIe1uSVi3TxRr+iGlbkFrM0tYG9txQfN2XVPO3v0adX8MauTtxVa1anbjtY2PLjBb7MggQULJpifetzSfPC25cnH2Sgvv2SPscuT11kAgwdGzAeDH+w+dsrPLgkh5O/qlvfj8Ilq7N5bithIX1y1bDzEzlSOU4f281iRNxwcRzbTWC7TY2xc6196D1iQZNy4cTwbfei4mNUsZNkfDz300J++XqfTwdXVlb/+qquu4sFmPz8/3Hvvvbjvvvv4c9j+sA6dbGD30ksvRXFxMeLi4nDkyBGkpaXx52zevBkLFixAQ0MDfz0hYkMZJuS8w+qN7F57EHt/OMyn1bDpNO5+bphz1VQkTIxCQmY0fMO8aXSRkHOk0erQp9GgT61Bn0oNBbtUq3ngQaZU8ftZZF6qUPDAh7mZGaR9ffw1/ey5upMBEY0W7g4SlEml6NdokeTnjRMtLbCzskKYmys6VP2wtbTki5Pd4EgVu+7v5ASZRgVbC0s429hgwBxwsLaGu4Udv4xwZVMWLCGxsoKlBctusOSBEBbgsLa0GLxtbg4bS3ZpAWsLSx6IIISQ842Lgx3mTozjn9O78iqF3h1iIGq1Gjk5OXw6/xA2PWbWrFnIyso6p3X09fXxDBFWGmCoVACb2sPWMYQFiFhghq2TBUzYpYuLy6lgCcOez7Z9+PBhLF26dER/TkJGAgVMyHmjt0OG3z7cgS2f7eadaxyc7TF5aTomLE5F6uxE2EoGT8DIyNBotGhv6UWXVIb4McFC7w75nUCHQsVSslWQK9Xo7VdC3s+yMzToVih5sIN1emqXKfjjVlYWaOjsgVylhqujHUqa23kGSJy/F+q7eyGxtkKsnyfa5QpIrK0R7O7Cn8vu93K0h5WFBZztbOFoY40YS08eCGGP2VgNBjPYbRbIYPexQAh7PiGEEAFILKGi2cYGoR8YXEZ6nUNZLOfSyEEqlfIMEZb9cTp2u6Sk5Jy2+eCDD/KMkKEACQuWDK1j+DqHHmOXw6f7WFpa8qDL0HMIERsKmJDzoj7Jlk924ZsXf+btd33DvbFg1QzMuDQTngGsGBv5u1j6ZUdrL+qq2tHW0o2qshb0yVUozBuspdPc0AUbWyusz3qMMnYM+G+gUKrRo1CiR9GPHoWKX/arNDzQ0atQ8o5KNW2d6O1TwcPZHrnVTVBqNIgP9kFjZw8cbG0Q4evOgyf2tjbwcXXg9R3sba3h6mCLMG83/hx7WyvY29jwKSgSm8HrLEODEELI6FLV3QUHFxpIMjVsSs3pWAHWp556asS388ILL+Dbb7/ldU2GapMQMlpRwISMag3lzXj7X59B2tjJa5Kkz0vB/Ounw95JIvSumZyeTgWqSpvRVN+BshMN6FOokHOgAp6+zqitaoOPvytamrrh5CJBb08/zC3MYGtnBS9fFyhkSjg40VDVuQZAZH0qdPQo0CXrR3dvH6S9feiWsY4IQE1rJ59j7uxgh7zKJt75JNjXDWqNFm6OEjhIbOBkbwtPF3s42FrD180Jbo52mJoYBkeJDRztbOAksYWtFRW1JIQQcnYlnVKk+PkIvRujko73YRrZ79+h9dXX159Rw+Rs2SWMh4cHLCws0NrKumv9D7vt4/PH/+4vv/wyD5hs374dSUlJp+4feh1bh6+v7xnrTElJOfWctra2M9an1Wp555w/2y4hQqGACRm1Dvx8BD+9vRlatQ6x6RG46snllFFyjro75CgraEB5fgPqq9tRkFPDOyB2tMvg6umIrnYZnF0lPGjS2S5DYKgngiO8MGlOAvyD3eEX5I6AYA+4ejhQC+WTWGCjs1sBKVu65OiRKdEs7UGPXInmDhkPkHi42qOwupUHNqKCvaDV6fh9LDji6iTh2SFpsYFwcbTj88yd7W1hRRkehBBCRlB9Vzcaentxy6Szt5cl4sWCJedS9NXa2pq3Bd6xYwcuvPDCU0Vf2e3bb7/9d1/HuuA899xz2LJlyxl1SBjWFYcFPdg6hgIkbIoQq01yyy238NsTJkzg7YZZ/RS2fWbnzp1826zWCSFiRAETMipt+HA7Nn2yE+0NHbjm6Usx75ppNJr+O9iXVG15KwqPVqO2og1Hd5fwk/v25h44uzvwzBIHFwnPEgmL8YVfkBtCo30RFM6CJN7wDXSHpdX5fdKu1w+gq6cPrdLek4sMSrUGFTVStHbIeEtHFiiJjfDhLWE9XB0Q5OfKL6NDvOHh5gB3Z3u4ONrCxppaOBJCCBHO/upaeDnYI9KDBplMLcPkr2Atha+++moe+EhPT+dthRUKBW/zy7DON6xd8OrVq/lt1kb4iSee4O1/Q0JCTtUccXBw4As7zr7rrrvw7LPPIjIy8lRbYVbnZCgoExsbi3nz5uGGG27g7YxZ0VgWoGEFYalDDhErCpiQUeentzfh08e/ReTYMDyz/kFEpYYJvUuim/JRV9GG3P1laK7rwM6fjyE01hf52dWITwtBS0MnYseGwMFJgtTJUfAOcENEvB9CIn1gK7HG+UomV6KptQdt0l7UN3aiqaWH1weprJXCz8cZ0i4FfDydEBTgBmdHO4QEuGNsfBC83B3h5iKBtRV93BJCCBG/wuY23pmMBpoMQz9gxpeRXudftWLFCrS3t/MgCAt+sKwQ1uJ3qGhrXV3dGVnC7733Hu+us3z58t+tk/LAAw/woMuNN97IM0kmTZrE13l6nZOvv/6aB0lmzpzJ179s2TK8+eab/+CnJ8SwzAbY2RMho6C/PfPrB9vw89ub4R3kiTvevg4+IWdW4j5fseyQvANlOLqnGMf2lUGn06OjVYagSG/UVbYhPi0U9k52GDspEmExfohI8Ied5OzzXkczpVLDgyEtrT2orpWis0uB0ooWtEtliIzy4a1xI0K94OJkB19vZ/h4OfNLid35G0gihBAyerAW7xNe/S/eWr4Ik8NDYApM5Th1aD/3F/jBwXFkpyvLZXpMSmgS/XtAiCmiIU8yahzddgJ7vz8MjVqH29645rwPlrB2vllb8lFR2IBt67IRmRSI4mO1CIn2RUtDB6YuTkFMSjCSJ4QjOMrnvKo10t+vRm1dB6qq29He3ouikiY0NHbBxc0ejo62iI/xg7OTHRJi/XHZxRnwcHeEuTmNtBFCCBndchua+ffd2ECaHjHap+QQQs4NBUzIqMC64Hz+5DooevvwyJe3wy/c57zNJDmwKQ8HN+ejqqgR7S09CI72gVajg7uXE1bcOgvpM2IRlRR0XtQdYQl0ra09qKxsQ3l5C7+UKVTo7u5DWlooJPY2CAvxxORJ0Qjwd4W1NX0kEkIIOX8dqqlHjJcn7K0pc5IQQhg6OyCjomgpax1sZWOJi+6cj+hxETifsKBA0ZEqbPz6IO9ew2qTxIwNgbS5B5MXJiMuLRQT5iTyWiSj/X1oa+tFaUkzGhs7cexoDQ8K9fWrERXlg9BQT0yYEIngYA/Y2lJhVUIIIWS4Q3X1mB11fh1HGZsO5nwZ2XUSQgyFAibE5LFpOJ3NXfAN98bCG2bifKGQ9WP7umwc2HQC5fl10OsG4OLuwKfezF6ejsfevxbuPs4YrdRKLcrKmlGU34DSsmaUlTYjJs4frq72iE8MwKS7ouEf4EZTaQghhJBz0N3Xj+NNzXhwxmShd4UQQkSDAibEpKn61fj53S0wMzfHqudXnhcV3TtauvHTh7tRWdiAvP1lSMiIgLW1FS68fiomzk/m9UhGI6VSjaITDSgraUZ2VgV6e5UICfNEfFIALrl0PMIivGF1HkwzIoQQQgzhUF0DAl1dkOg72CWFGMaAAbrksHUSQgyDAibEpG36dBfMzM2QMS8FngHuGM3aG7uw+ZuD+O6d7QgI90ZtaRPGTI7GrOXpmLRoDKxtRtefM+vkU1bchJLCRuzfXcyDQh6ejkhKDcb9jy+Bj6/LeREgI4QQQoxhX1UNIj3cYW05uo4nCCHkn6BPRGKy1Eo1cradQGdzN5beMQ+jVU+nHOve2YYt3x6Cg5MddBodolOCcNtzFyM+PWxUBQ16uvuQd6QaB3YVQ6XWwsLSAhmZkbjvsQvg40cBEkKIadLp9VDqNFDpdFDrdVDptFDz61p+XanTQqPXQ6MffFyj0526zl7LXqcdYPfp+W3NgB5avQ4DA4BKr4We3x7gl7qBAWj1epibmfHXsfpOerDHBvjzXWzs0Kns49cHeLN0wFfihCZF76k+G+yz1l/ihJY+OcxhBgtzc3jYStCl7IeFmTks+W179KpUsLaw4Lddre34z2Jtbgkna2u+ZhsLS9hZWsLKfPBSYmkFWysrfmlnZQlHaxteXJS9ngiL/Z6caGzB1ePGCL0rox51ySHEtFDAhJis/T8fQUl2BZb/ayHs7G0x2mg0Wvz6yV4c21OMnD0lSMgIh6OrPR794HpEJAZitGhu6MD+XSWorWpHZWkLps6Ox5JL0hGbEAALSzqIJoQY/kRRqdWiT6dBn0YNhVYDhUaNPq2aBwB61Sr0aTU84MEu+zQaHuiQa9To12n4a9lj/VotrMzN0dov568bCoSwx7UDevjZO6C5T3bWfXCztUOnqv+sj7FARNewx9ipkZW5BVxt7KDQqnkQw8LMDOYngxksWCKxsOYBE3adLew/dumv1/PgyNB6GDsLK34fey9YoIP9v5WZBSq6O3gAhgVbQhxdUNolhV4/wH+eGBdPHG9r4cEZdjvZ3ZffZhLcvVDQ3save0vs0Sbr49f5eyCX8+spXr7Iax58frqvP2q7uhHu5gaVRgcXW1sEOzvznyfQxYkHXnydHOBsawcvRwd42EtgaUHfDyOpprMbpW1SjAsKEHpXCCFEVChgQkwSO6j79b1tPNti/g0zMNoUZlfgzfu/gbmlJWrLmnltkuW3zkLs2BCMBi2NXcg+UI6tv+QiMMQDkbF+uOz6KfAb5Z18CCEjHOhgAQ2VCjL14NKjUZ66LtOoeUCABSlYcEOuVvFLDzsJCjpbeFCE3WaX3hIHNJ0lmDHG0w+50qZTt1lAxM7Siq9DN6DngYbBLAorni3hbiOBt8QRthaWsLGwgC173NISNuYsm8Ka7w97PsvK4Jfmg5csyMGusyCItQXLyBi8za6bDeBkFsdgJoclC46ILCOD/VuoTmbFsOyYwQwalhmjhUqrRb9GywNK7LpCzYJRGgzoBzApKBhytRqWMIernR3/OaukXSiVSlHb0Y3qzi6k+fkhp74J4wL9cbS2EemB/sipbcK0iBD09CmR5O8LOysrhLi7INjDFaFernCyG32DKIaWVV2LKaHBCHIdvcXixUI3YM6XkV3niK6OEHIaCpgQk1R+rBplOVVYftdCOLk6YLToVyjxxYu/4eCm4+hulyFyTDDPKJk4L8nkp6Ow6TZ7NuejILcW0jYZZi5Mxr9fWwl3Tyehd40QIiC1VotulXJwUbLLfn695+RtdmljZYnyLil6eHBk8D6ZVs2ntQyJc/dEYddgVgPDsikm+gWhprcLDlbWcGDTP6ys4WxtixQPP36dL5bWfAqJreXgVBHJyfskQ1NHTi7sOgtokP+PfT/ZWlrCdoQPK1mApUPRx5euPiUuTJRBoVIjxN0VA7oB1HX04GBZLRq6eqBSapES5Ivj1c2YEBEETyd7+Do7IiXUDzEBXvBwth/RfRtt9lbUwM/ZyeSPNUyBHmbQj3BbYTbtjhBiGBQwISbpwPpsRI+LwMQL0zBalOXVYvXNn8DD1wXS5m5c9eBiXHD9NNhKrGGq9Ho9cg9XYvOPx9DW0o30SVG47s7Z8PF3FXrXCCEGwKZu9Cj70dnfjw6+9EGmUqGtT8Hv61T281oWBdI2Xg+DBURiPDxwtLXx1DrY+Ro79GcZFk42Nnx6RrKXD592EuLswgMe7H62sKwOJ2tbnr3hZG3DgyKOVtb8fhbkoJM/08ayc9hJPFv+KLulrVeO+o4eNHX2IDHQB03SHkh7FfjtUDE87O0Q5u0OnVaPSD8PTE4MRWpUAOxsTfe7daSxwGObTIFLxiYKvSuEECI6FDAhJnkSXnmiDiqFElFjwzAafp4Nn+/D+4+vQ1hcAGTdCry+4X5EJgfBVPV292HLjzk4vK8U3n4uuODSDMSPDaaTF0JMNAjS1d8PqUKBdkUfD3q0yGWQ9vVBquiDekCH6q4uHhxhQRFvB3s0ygZrZDCxnp5olsvgamsHNzs7xHp6IdbDE662tnCxteM1LlalpMLZxpY/x8WGBUBseIYHfWaQP8N+R7ydHfmCsAAsSftfIKW5U4aCmmZUNnWgprkT246W4cddxxHh64H0mCCMTwzBuPig874eSl59M4pb2pEW5C/0rpwXqOgrIaaFAibE5BQfrkD2pjxc/shSkz+YVsj68faD3yJvXykcnCVImRyNK+9fBGtbK5ii2so2/PTlAchlSsQmBeKJ1y6Dk4tE6N0ihJwFO6HsUarQIpOhVa5Aq0yONrmcF/Asbm9Hu1yONoUCga4uyG5oOPW6OB8v1PX0wEMi4UuEuztS/fzgbieBu0QCL3t7ONvYwM1OwgMk7LqlBU1lIcbFjg/83J34gtTB+6TdcmQV1GJXTjm2ZBXj0PEaBHo5Y/KYcMyeGAOJ3fmZdcK640wKC4aTLdV+IYSQ4ShgQkzO4Q3H4BfujcwLx8GUtTd14YnL3oGVrSU0ai3uffNKTJibDFNUlFeLbz/cA0dnO2TOikfG1BhYnOcjdoQIrbdfiWaZHE09vejq6+dZIC29crT0ymBtbYnsugbewYVNkenTaXmXFQ97e6QG+PLXx3t7Y5q9Pe9Scn1qKjztJfC0t+dBENYalhBT4+HigMWT4vnS2avAlgMl2LS3EO9/sw8/bz2OlYtSMWtSrMkPxvxVmwvLMCVydBSVP3+LvlINE0IMhQImxOS01knh4eeK8ORgmKqa4kY8suIt+IV6oVvai9c23IfACB+YmhNHqvHtB7thJ7HBilVTET/GdP9NCDG57JB+FRq7e9HQ3cMvWfeRwpZWNPbIeJDE3UHCW4UyCb5eUGg08HFy4PUgIjzdMCMiDD6ODnxhgRB3e4nouq8QYihuTvZYOT8Vl8wdg11ZZdi6rxg/bT6O40WNuGHlJDg72eF80N3Xh25FPyaGmu40YEIIMSQKmBCT0iPtxe61WZhz9VSTHQEqzqnGCzd9zIu5Wpib4ZVf7oOzu2l1+ikvbMSvaw7xzjfX3T0XEXF+Qu8SIaOOTq9Hc7cMjd09qO7oQn1nD9rlCpS3d6C+qwdylRqJ/t7Ib2qFh70EKYF+PPNjUngw/J2dEODiBC/HwQCJs62NyX5mEmJILEg4KzMGmalh+OTbg9h3qByNTZ24/9a58PN2wWh3uLoBXYp+JPqb3qCNaXfJGdnP45FeHyHkfyhgQkxK/v5SuPg4Y9w805y6Uny0Ch89/SNvGZy5KAV3vXKFSdUraW3swvovD6KyuAnX3j0XsSk0IkXIPzHY4UOBamknaqXdqJF2obajC+zYd39FLbQ6PW+VWtbegSBXZwS6OWNCaBAuHuuMAFcn+Ds7w8/VEXY0RYaQf4R1zbntmmmICvPCmp+O4I0Pd+ChO+bBdZS3I86tbUJasD9v3U0IIeT/o09HYlLqShp5NkZcRiRMTUV+Pb5+ZSOKsquw6NopuPmZi2FhaRqFEFVKDb7/eA+fgnPBFRNx44MLRDlarVRqcDynBhmZpvf7MVx9YyeaW3uQPjZU6F0hI0Cj0/G2p7XtXShtkaK6vZNnjxQ3t6FPreHPifbzhJWFOYLcXRHv74V5idEIdnfhgRE3e4ko/+YIGW1mT4njNbjW/nwUH329H/fePAfm5qP3b29XSRUuH58i9G6cV/Qwhw4jO/1Rz5uxE0IMgQImxKRkb8qFqk8ND383mJLmWin++/g6FByqwOLrpuKmp5ebTLAk50AZtv6Qg/BYXzzzwbW8WKTYdHXKsWH9Maz//iifJvTpt7cgIMgdpqa6th27DpShtqEDuw6Uws1Fgh8+uYUK6JoQvX4ADR09qG7rQHFTOypbOqDSarG3tIZni3g7O8DGxhKhnm5ICvLBBalxCPF0RYiHK9zs7SgoQogIzMiMQXWtFHmF9dixrxizp8ZhNGrs6uFT/TLCAoXelfMKFX0lxLSI78yHkN+h1WgxoB8wue44sm4Fnr3uAzRUtmDWivFY9eRFJhEskfX04/PXt6CjrRc3P7IY3v6uEJvmpi78sOYQDuwpQYdUjqg4P0gk1pC2y0wmYNLU3IVte4qxa18pbOysUFzWjNAgdx4kCQ/xQq+sH64uppESzlp2Himsg421JWaMi8Jop1CqUdEsRVFDG8qa2tGn0mBXUSX61VpE+XpArdMhwscdCYE+mD8mBuHe7gj2cKHUd0JMwOXLMlBY1oxt+0swY9Lo7Px2uLKetwmP9PIQelcIIUS06KiNmIza4kaUHq3CtBUTYSp0Oj1euuVT3jY4MjkENz97MaxtxF9rIGdfGbb8eBQZ02IwY8kY0Y16s0DJmk/3oaKsBZVlrUhMCYK3rwsuuWIin44j9gNbmUyJPQdKsXFrPgpLmhAa6oHqug4kxPojMz0CUydEIjMjAo4OthAzlVqLE+VNOHi8GtkFtZD1qdDaKUNcmPeoC5j0KJQobmhFcX0bzyDJLqtHnbQbwV4ukKnUfDpNQrAPHls2E5G+HgjzdqPACCEmzNbGCmOTgnAwpwrHixowNnH01ew6XFWPIHeXUT3lSKxTctgysuukDBNCDIWO5ojJqMyrgbWdFSLHhMBUrHl1E6//0dspx7Pf3A57R3G3KVSrtfjmnR28qOsdT18ETx9niIm0vRdrPtmPhjop8nJqEB7ljbSMcFx6dSaSRN7SmE3VOJZXg70HyrBpWwE0Gh2CWCaJuRkSYgNw6bJ0TMqIhIO9DcSsub0XB/OqkF/ehF1HyhHg7YKKxg7+WEq0P1ydJBgXH8yLmYot0Hau+tUalNS1oaC2hWeQHKtoRFNnLywszeHl4sADIxdkxCE20BvRfh7wcDatLleEkHMzbWIUDuRU4Vhh/agLmLDP6OKGNlw3NU3oXSGEEFGjgAkxGe2NnXB2d0SoiRy0HN9fiu3fZUHa2IXn190JrwA30XfA+fy1LQiJ8sFT718Nc3PxZGn0KVT4/ussHD9Wi/xjtXBwtMX4SVG47NrJiIn3h5h1dsqxeVsBjuXV4mhuDb/P398V9vY2WLIgBZnjI0Q95YYdVJdWt2HvkQoUVTTjcGEtvz85xp9nmPT1q3Hh9ESkxwcjNS4QLiIPCp7t52vq6EVxbSuOlDUgv7oZtjaWyK1sgp+7EzITQnBRZiIPkkQFeMBZYlo/HyHk7wvwceWB0vLaNow2rCNXZVsnUoL8hN6V845uwIwvI71OQohhUMCEmIzCA6UwtzCHvZMEplC35D93fA6FTInrn7gIyZnRELPjhyvxwfO/4ebHFiNxXBjEQq/XY+fmfPzwVRYaG7p4F5wps+KwdEU64pPFHTgrKW3GDz8dRU2NFBXVbTxZNirKB8kJAVgwNwmhIZ4Q81Sy/LIm7D9aiR0HS+Hr5YS8kkb+M/j5OsPHwwnT0yPx4HWzEeLvZlKZJDq9HmX17ciraEJpfRsOFtZA2tuH5HBfXnslMz4EKRF+iA3yhpuj+D9rCCGGJbG3RmuHDKPNkcoGTAgL4nWVCCGE/D4KmBCTYWVjheRpplGp/v3H1sHTzxWhzhJccMN0iNmWddm8C86//3sNPEQ0Bae6ohVvvbgBCpkKNZVtyJwRi9kLkzF+cpRoT9BZoOFwdhXWfJuFnp4+NDR18/unTY3G5MwoTJoYJcouQ0OZFqzA4f6cSmzcVYiObgXio335iQILJMzOjMHktHCkJwXDycHOtAIkde0oqmnFvhOVyCtv4tOIOmV9SI7ww6qFGUgO90OEvwcsRJRVRQgRB5YB2NDewwP4Ysq8/KcOV9TD3YHalQtBZ4C2wjqqYUKIwYjzyJ2QYfrlShzacAwX37sIYpezqxjVxU1oqm7He7seFW0BUp1Oh49f2sS74Tz/2SrY2IqjGC2r+fL1x3tQUtCIwrx6hEf74Ma75mDJJemwsrIQbe2XPXtK8PkX+/n1zm4FD4xcekkG5syMR2ioeLNJquqk2La3GNUNHdh3tILf5+3pBBcnOz7F5vqLJ2JsfCCsTKCz01Dgp66lC9nFdThaVIe8yiZ09PYhPS4IPu6OuG/ldIyN8oefh3iCg4QQ8WJtwFlMQavVw9panN/nf+dzslvej6Xp8ULvCiGEiB4FTIhJaK5qReLkGIQmiHsahlqpxjuPfIvmGiluf+FS+AaLs1WfVqPDKw9+h+Aob6x6cIFoRs2K8+vx5Qe7kZtdBTbmNf/Csbjypmlw93CEGKlUGuzYUYRPP9vHr7MDalab5NabZ2LWzHg4OYkzE0MmV2LHgVJs2H4CtrbWyC2sh52tFTxcHZCZFo7Zk2KQGOMPS5EG+4aT96uQU1SPg/nVOJRfC28PRzS0dSM9Lhh3r5iKlEh/+Lg7Cb2bhBATpNXrodEPwNJEgsbnoqq1E9kV9Xhq+Syhd+W8pB8w58vIrpMyTAgxFAqYEJPQWNGC/H0luPbfl0DMfnh/J2wlNkibHod5l4uz/THLgHjrsR8QEe+PZddPgVgCOF99uBt7thWiqb4T46dEYdHycRg3MRJifQ+3byvAZ5/tQ3e3As6uDnBxluDKKzMxdWqMKKfdsBHFvIIG/Lb9BHYfLEV0hA9KKloR5O+KmZNiMHdKLNKSQ2At0iye4eqau7A/rwrVTR3YsL+IdxuamBKKFXPGICM+GGH+7pRqTs57X6/Php2NFXw8neDv4wI/bxfRZuqJVW+fCtoBNh1n9HyeHK9txtTYUAS4U6adEGhKDiGmRXxH9YScRXd7LxInxcA3zBti1dHajSPbC1Bd1Ihbn7sEFiIcjWIn+h88/ysiEgJwwVWZEAMWIPn6w93YvuE4AkM9sGBpKm64aw4kImyvy1oD79pZiM8+3Yempm6EhnnCwcEW11w7GZmZUaKcfiVXqLB9TxG+33AMdQ2diIrwhlqjg6ebIx68bS6mT4yCvUR87/XZ3vviqmbsPFKBfccq0aNQQmJrjXkTY/DK3RdgTEwAbK3FMa2MELGYPC4CjS3daGrtRklFC0orWuHuYg+ZrB9hwZ6IifRBRKgXPEWaxScGta2dsBJhEPyfOFBSA3sbGwoqE0LIORhd3wBk1KopbEDZsWq4eot3NOTb17egOKcai66ZgoTxERAbnVaHVx5Yi5iUINEESw7vLcVLT/4IZZ8aMQn+uPyGaUifFAUxysutxZdf7OeXCYmB/EDziiszMXlytCgDJSw4smlHPn7ckIt+pQbhoZ5wdrLD5IwIPH73QgQHuMMUCrbmFTcgu6AWG/cUwsnRDnZ21lg0OR5TUsMR7GdaHXoIMbYgfze+DCdTKFFTK+WfEx/s2QupVIbIcG+EBLljTFIQfH2pc8rQZ1B1UyemjA3HaMGyDZVqLeYkhwq9K+ctvQHaALN1EkIMgwImxGSkz0sW7clRa30Hqosa4BfqiQtWTYMYD5DeePxHRCcFYuk1k0XRTeabj/bgy/d3IzE1mI/ePfjsRXBxc4DYtDR349tvDuHXX44hMTEQbm72mDM3EXPnJYpuTjv7dz5eUI/NOwqwaUchvy8k2B2ODrZYvjgVE9PDYW1lKfpMkoKyJuzIKsXuo+W8W8/CqQm4dul4TEmL4HVWCCH/jKO9LRLjAviycE4Sv6+tvRcFRY345PN96OqUI3VsCNLHhSE8XLyZnYbGCki7uEgQHTJ63oOati7sK67GfReIY0ouIYSInbiPnAk5qfBgKbyCxFlAlfnpg10ozK7CrEsyECDCg8tv3t0Jdy9HLL1W+GCJXNaP1Q99j5bGLt6ZJzktFJffOFU0hWeHaDQ6bPwtFx+8txM+vi6wtbVCytggrH5xBewk1hBbkCEruwIbtxVg/6Fyfl9UpA/CQjyxfPFYPnIsdtX1UmzZX4KSyhYcKajF2Pgg3HhJJiaOCYOrk0To3SNk1PPydMKMqWyJ5UHtvLxa7NxRhB/WHUFCgj+mz4gX3WefoZXWt8HX0wkhvq4YLQrqWpAc7IsQz9HzM5kaPcz5MtLrJIQYBgVMiElw9nBCcFwAxKirvRfb1mYhISMCl/5rHsRm/+YTKC9owGNvXyl4hk5rUzeevvdbaFjr3Q45nnh5BdIyxVfYtaSoCS+/+Buk7b28gGtwsAeee/ES+Pi4iC5QsvdAGX7bdBw5J2r57ZTEQCTGB2DpojFwF2HGzvBpAbuyyvDL9hM8y0il1mLu5Fg8eutcXmOFECIMNs0wNTWUL/19auzZU4w3Xt2EsHAvLLpgLCQmUPdoJFQ2daC1S4aYYPEHnc/VwZJaeDs5CH48QAghpoICJkT0lH0q5O0uxNiZCRCjXT8egUalhZuPM/zDvCAmZfkN+O6DPVj9+Q2C19moKmvB5+/uREVJMyJi/fD6Z6sQHC6u94u1Bv7s4z34fu1hhIZ5wclZgrvunY+xaaGim3qTdbgSa3/IRklZCw80pKeFInVMCBbPT4bETryjwHzaUFEDNuwswI6DpUhLDMLY+EDMmxqPMBFnkRFyvmJZJfPmJ2P27ETs2VWMN1/ejImTozB5WsyoP+nefrQcMDeH9ygJ4LLP3+bOXswZM/K1wo7tLsLYaXEjvt7RSDdgzpeRXichxDAoYEJEr7OlGy6eTnD3E1/6qFqpwdo3t8LGzhoX3TwDYiLr7sOXb2zBQ6+thL2jraD7UnyiHk/fuxadUhlmzE/ETffNh4ubPcSktLgJLz7zC1zd7flJQObkaFx6+QTY2Iir80p+QQM++Xwvauo70NXdhwkZ4UhPC8OCuUmwEXEnB0WfCtv2FOOHzbmoa+zE4tlJeObexcgYEwpLERbNJYScycLSHDNmx2P8xAh89ck+5ByuxA23zoKDk7DfL4bSKO2BXKnG0sniHKz5O5o6e5Fb2YQHLxr5Wms/vbuNAiaEkFFJvEfXhJzU2dyNbqkcHiLs6nF4ewHcfZxhY2eF6JQQiGkU6bNXN2P+ivHwCxZ21P7E0Wo8ccfX8A9xR1S8H/71+BLYiigDgk1j+W5NFvbtLkF9XQcsLc3xxrtXIzbOH2LS2NiF9z/ahbr6DtTVd2JcWigyxoVh0YIUUQdK6pu6sGH7Cfy4KY+3L7548VjMnhwLT/fRMWJLyPmGtXy/8Y5ZOJJVgTdf3ohVt86El494O9j9XUdK6uHpao/YUVTwNa+qCY521ojy9xzxdXsG/P9uTOTs9DDjy0ivkxBiGOI9yibkpK7WHn7pJsKWwr98uhfVJc24740rISYb1hyCuYU5Js6OFzxY8soTP8HJxQ7+ge64/9mLeK0KsejuUuCtVzbhxPEGfv2q66bgksvHiyqrpL9fjS/XHERBYSPPLomJ8cU1V2bikmXpvMWuGLGAXX5xIzZuz8fGXQVIjPHHo3fOR+a4cNF1FiKE/D3jJkTA2UWCt17eiLseWgR3j9EVBN18tARVLZ3IiAnCaHG0vAFp4YGwMECR9ZYa6Yivc7SiKTmEmBbxnLkQ8jvkXXIExfjBTWQFNxur29nUZt5KeNLCFIhFY40UBUercffqiwXdj5L8enz78V5e6HXe0lTc8egiWIjoZLm4sBFffLQHR7OrkJAchAceXYxx48MhpqDD3n2l2La9EAcOVcDDwwEL5yfhuqsnw02kxVx5bZWjVTxQsj+7AtMzo/Hfl65AbKSv0LtGCDGAqFg/rLxqEj57fxfufHAhrKzE8xn/T3TJ+9HSJePTcSS24gxM/x25lY1YnGGYaTNeQeLLAiaEkJFAARMieh0t3Wiuboe9s7hai+78IRsnDlZg/hWZvIaJGOj1enz04gZcevMM3rJXKPXV7XjqX2vQ3anAhZeNxw33zBVVsGTPjkJer8TaxhITJkXizvsWwMNTPKOjbW29+OKrA9i46TivpzJpYiSuuGwioqN8IEZsWtP+w+X4eVMeCkqaMG9GAta8dz38fMRXd4iQIVqtDr29Ssjlg0tfnwoyuYpndWm1evT29kOl0vIpbx2dcui0ejg42KCzU86LaGvUOrCapy4uEsjlKri7O/COMg6OtrAwM4NEYs0Lpjo42MLJyY4XkXZzs4eTs92oKpYalxSIqspW/PTtIVxyZSZGg70nKlHf3o1VIRkYLTp6Ffx3NjXC3yDB8qbK1hFf72ilgzlfRnqdhBDDoIAJEb0BACnTYkV1gMkODqqLmhCeEICZy8dBLDZ/l42AUE9EJwcKtg8dbb145JYv4OnthNikQKy6WzzBEnZi/8l/d2HtlwcRm+CPmDh/3Hj7TNFME2G/Vxs25OHLrw6ivV2GtHGhmD4tBnPnJMHcXDy//2dklBypxLc/ZKO2sZMHSh6+awE8RJoBM5q1tstwMKeSZ/hcND8F41PDcD5iv5MseNHW3ssDjyzQ0dEhh1qjQ10dK5SsgKurA3Jza6DoUyMy3AsVFYMnen5+Lmhq6ubXAwPd0N4q423Fo6N90dDQAQsLC4SGuPN6QrY2VlAqNfwLytvbidc/CgxyR0lJEw+g1Nd08Ncq+9W8LpJOrYeLix26u/oQFe2L3p4+pKSGYEA/gLAIL/j4uiAwxAO+/q6i+Tz6K+YtHoPnH/sB0naZqILPf9fPh4rg6eKAqUniyTocifol0l4F4gJHviaLRq0R5bRpQggZCRQwIaLXUNqMnnYZxKSyoAFZW07AK8ANcWniODHp7pTjeFaloFNx2MnBm8/+yq+zGioPrl4OS5GkaKtVWrzw9M+8Uw9LG194wVjMXZgMsWABkk8+3oOtW/N5wdnExEDcftssfvIlRvmFDfjoi31oae/BlIlReOqhC+DmKq7OR6M9MFBZ046cE7XYsruI/zso+jU8KOjl7jiqAyas/XdTczdfOrsUqKpqR0trD3Q6Pa/zwwMZAJyd7XiWiKurPZISA3n7bT8/VwT6uyImyodnfbAghr2dDc8CkUisILG35W25bWws/1GQnv379PdreNYKC47IepV8XzraZVD1a1Bd2cYzXI5lV+HA7mIEBnmgpKAR0bG+PGji7uGA5LQQHnR2dhH/3xUL8kydFY9dW/Nx8eUTYcpYZkmXrA9JYb5wtLPBaFHWIMW8tGhYW438ob9GqUWfTDni6x2t9ANmfBnpdRJCDIMCJkT0ejpkcHQX14h13v5SxI0LQ0JGuGgyXz5/dQsmL0iCrUSY6UHsBOHTN7fh8N5ShEZ546k3LhNNNxyFXInXVv+GvJwaPiL84huXIzFFPIX8DmVV4IUXf4XEzoaPVq+8dDwyJ0VBjBqauvDfj3ejsaUbsdG+ePS+hfDydBJ6t84L7G+stKIVuw6UIud4LcqqWpEQ64fy6jaegTRtYjQiQ70wKT0Co0F3Tx9qa6Wore3gWSLFJU2ob+iAt7czjuc38OeMSQ6CXKGCj7czQoM9kJ4WCk9PJ3h5OvIpMu5uDnz6jLGZnZySwxaPPymG2qdQoaGuA1XlraipaENvlwLbN57Aj19nITY+AL4BrgiP9sGEabHwDxZvnYiMzEg8+9j3WH7ZBNF8L/4dvx4uQk1bF+6/eORb7wppf0E1xsca5ntPrdLAkTILCSGjFAVMiOjZO0ngLXBr3OG2r8tGbWkzVj12IcSgurQZep0OmXMSBNuHH786iN++O4LkcaG4+YEFcBVJ21g2uvvaCxtwYHcJksYG4877FyAoRBy/T6w+wvvv7cCmjcfh6eWI8Ahv3H33PDiLrF4Pw0bKv/wmC0WlTbCztcYTDyxGiMj+Lkermjoptu0uxonCBhwvHgwUJMb582khvl7OWDI3BRNSw+DsZAdTxDJD6us7+dSY5tZu5Oc3oLq6HV5ejiguaYatrRUmTojkGSHz5iYhNMQTN984A36+Lvy+0dCmlxVPZcvp2Xos4+T4kWrUVbbh4M5i/Lwmi7fvnb8sDVPmJMBaZMVIWYDcxs4GrS3d8PE1zfpFOr0eBbUt8HZxGFXdcRT9KtjbWGFsRIBB1q9V66BWqg2y7tFIb4AaJmydhBDDoIAJEb2KvBp4+LtBLJpr2+HgLEFQpA+ixwZDDL58cxtW3jJDsFG9orw6HNlXxkfAF69IR2jkyM+R/jt6uvvw4B1foqW5G2PTQ3Hvo0vg6SWObIiWlm78+8mfeHFelh1wxRWZmDM3UXQjs+zfdPfeEmzdUcizS+68ZRbGpYYKvVujHptqsvdgGX7dfJxPISsub+H3s5obIYHumDklBqlJwaJqgX2uv0+sBkhlVSvyTzSgtKyFB35yjtXwWh+Tp0TzaWjLlo1DRJgXAgLdeAaTGGv4GDr4kDIulC9MY50Uh3eXIvdQBb58ZwfWfrAHiy8fj/nLxomqVXtwqCdqKttNNmCyv7AaB4trcfOC8QZpvSuUgtpW5JQ14OUbFxtk/ayGiVDZreSfeeedd/Cf//wHLS0tSE5OxltvvYX09PSzPrewsBBPPPEEcnJyUFtbi9deew133XXXGc8JCQnhjw1366238m0x06ZNw549e854/KabbsL7778/oj8bISNFPN+yhPzBAbajm3jmcOfsKkZhdiXmXDoe5iI4oCo4WsNHKCMTDDNy9Gdkvf14+Ykf0VTXictvmoZJs+IhBr09/TxYwt4bVlDxoaeWwkUkNTZYwck1Xx1EWVkLwsK88M571yA01BNiw+pCfPbFfuw7UIbLV07A048vHTVtQ8VIq9PjUHYl9meVY+vOQsTH+qOiqg32EmvMnhaLaZnRGDc2BDbWViZVb6S0tJlnjRQXNaKktBldnQoEh3jweiFRUd6Ii/XHTTdNR3CwB/1+/Q7/IA9cdJUHLrh8Ao7sLcXujSewZ8MJHNtXjuXXT0aCSIKYXt5OPNhnqrbnlfPORxeMF8f32Egpr2/HmAh/ONnbGmT9aqXWIOsdrfQD5nwZ6XX+VWvXrsU999zDAxUZGRl4/fXXMXfuXJSWlsLLy+v/Pb+vrw9hYWG4+OKLcffdd591nUeOHIFOpzt1u6CgALNnz+avOd0NN9yAp59++tRtiUR8mbWEDKGACRE11ia3vb4DziKZ3sHUlDXDzsEGadPiIAYbvz2EFTdPF2z7767ewANH4yZH4dJVUyAGrCbAu69tRk11Oz8xe/GtK0UTLNm4IY8Xd2UnjRctH4frrp8KO5HUehnCCof+8lsu1v14BIH+bvj4/evg40MdEAylta0Xv23KQ3ZONUrLW5GUGAA9CxQ72uCRexdi8vgISCQ2JhMgKSxo4EGSQ1mV/JIVMe7okCEu3h/XXjsFkZE+CAvzNMluMEJj9VjGT49FxrQY7N2Ujx8+3Yu3n1qPucvGYcmVE3gnHyGxNsqs3owpqmvvxm9HirE4Iw6+buLIRBwpBwprEOZjuPo3Go0WlgYoJjta6WDGl5Fe51/16quv8sDFtddey2+zwMmGDRvwySef4KGHHvp/zx83bhxfmLM9znh6njn488ILLyA8PBxTp049434WIPHx8fnL+0yIEOjTjYhav1zFT97sRVLTQafVYeePR6FSapCUGSn07qDkeB00ai2CI4SZAnNwVzFK8uvRKZXj6Tcvh5UIDpg0Gh3+88zPOLCnFBMmR+GuhxaJIljCfo/XfH0An368FyGhnrjoonFYebn4iiOyVqyffrEfB7LKcdedczB9Sozo9nG0ZM7lHa/Djz/noKpWisamLt7JhRUvHT8uDI/etwjeIpk+9me/11WVrTiSXYWcnGpotXoU5NcjKtqPB0iWX5yO+AR/uFFByBHF/ianLkhC8oRwfPXmNuzfmo/2lm5cf/8CQYrcDmGBPlP9vPh2Ty4GBoB5qTEYTfjfZHULFk8w3CCPVqWFs4gygcmfU6vVfGrNww8/fOo+Nvg1a9YsZGVljdg2vvrqK57FMvxz4euvv+aPsaDJ4sWL8fjjj1OWCREt4c9uCPkDfb19iEoNFU319fL8eh7EiUgMgLMI9mnnL7m45EZhKvnLevrw5jO/QN7bjzseXwK/IHdRnIS+tvpXVJS18AKKt983H64iOIhjQZyXVv/Ku+GwjJdrr5uCSZOjITYHDpbjxf/8htSxIfj0g+t5lxEy8r8Lu3YX47sfjkA/oEd1jZRnlISFeGLJwhSkjgkRfb0OhUKFnCPVKCttwpbN+ejuViA03JsHRsaNC8fTzy4fFcVYTQELBt/6+BJ8/tpWHNhWAGsbK1xz91zB9kcmU8JWYjpTxobI+1XYU1SN2WMiMT569BR7Zcoa2jCgG0BK+P+KCo80VvBV1U9FX8UwJae3t/eM+21sbPgynFQq5VNnvL3PHHBjt0tKSkZkn9avX4/u7m5cc801Z9x/2WWXITg4GH5+fjhx4gQefPBBPg3oxx9/HJHtEjLSKGBCRK1PpkRZTjUkjoaZd/tX5WdVwMXDARPmJAq9K2iq60BDtVSw2iVfvrcLASEefDRz9uIUiMG6r7NQXd4KhUyJZ1++VBQFXpVKDZ567HveEcfaygL33b+Qj7yLbRrFBx/sxm8b8vCvf83B/HlJJjtKLFb9/Wps2nIC3649zH8vK6vaMHZMMDJXRvJACWuFK2adHTIc2F+Og/tL0adQo6K8BdNmxOGqayYjLT0Mvr4uQu/ieYuNCl9zz1zeWefQjkJEJwVgwkxhanC0t/ciTqDvpH/ip0OFaJD24JqZaaIPWP5VeRVNcLSzga+7k0E7XTn/SftsYhyBgYFn3H7yySfx1FNPCbIvH3/8MebPn88DI6e78cYbT11PTEyEr68vZs6cicrKSj59hxCxoYAJEbW+3n5+aS+S0crCI1XolsoRFi/8AeGe347jgisnCrLtssJG/PLtYV6g8d11t4qi+O3RQ5X46O3tsLa2wHOvXY7gsP9fsEyIk+S339iCYzk18PR0xGtvX4mgIHG14m1q6sZT//4RPt4ueO/dq3kRWjKy7Zg3bz6BL748iJBQD0ilMoQEu+O+u+dh9sx4WIuow8lw3V0KHNhXhp1bC5B/vA5xSYE8Q2rS1GgkJQeZXIee0YwFOK+7bz7+fcvn2PRtNlImRMBOgLo3ZeWtmLcgGaZEo9PhUEktXOxtsWhcLEab+tYuzE6LNGgQXNWnhlKhMtj6RxtWEnXka5gMqq+vh5PT/4JjZ8suYTw82ICXBVpbW8+4n90eidoirFPO9u3bzylrhBWcZSoqKihgQkRJvEdqhLA0T5UaiZnRoqhhwqZ7qJUaxKeHISY1RNB9YSOJLP36kpumCVKI9+3nf0NknB8mzohFYIjw3V0a6zvx7KPrkJAchOlzE5As8L/PUGbJi8/9gv17SzEhMxJ33D0PXiLIeDnd0aPVePutrYiND8Cdd8wWXfFZU8b+/bdtLcAnn+yBm5s9env7YWlujmf+fREmjI8U7Sg2C/Jl7SvD4QPl2L2rCK6uDpg1PxHX3jANcYkBot1vAt6ifNFl4/HNezuxd9MJXgjW2N+RrOCrj49pZRttOVaG/cU1uG3hRNiZUAeqc/032XmsAtfON+zvwsCAHo40hVMUWLDk9IDJ77G2tkZqaip27NiBCy+88NTxHbt9++23/+P9+PTTT3mnnYULF/7pc/Py8vglyzQhRIwoYEJETdHdh/wDpZA4CR8waa6RIndfKfzDvOAicNeew7uKMeOCMYIU9zu4qwR1VW1wcLLD0ssnQGhqlRaff7ALet0AAoLcsfiiNKF3CWq1Fk8++j1qq9uRkBiAf907Hx4iS1de/9NR7N5djAULUnDxJek0BWcEU9N3bC/Exx/tgVyuhJW1BRwd7fDKyyuRkhIkyveZnVSVFTdj4y+5yM2phlymwpTpMfjPm1cgISmIgiQmZPzMOPz0xQEUHKkxesCE1eOJifMTtOjs3/nd/2zHUdhYWWDZROGn2o60xvYe+Lg6IDnC3+DTp9V9VMPE1NoKs2KsV199NdLS0pCens7bCisUilNdc6666ir4+/tj9erVp4q4FhUVnbre2NjIgx0ODg6IiIj4377o9TxgwtZtaXnmqSabdrNmzRosWLAA7u7uvIYJa1E8ZcoUJCUl/cN3gRDDoIAJEbU+uZJf2toL31KzsqgRjq4SRKcIXxBu09ps3P/SCkECAR++shnWNpa44Z55sBVBRsJn/92F3dsKMTYjjBd5FcMJ88sv/gbFyZPlx55aKqpgCdu/r786gO/XHcEjjy3B+PH/O8gh/0zO0Wp89+0hnrnDWul6eDhg1Y3TkJISLMpACWu/vWd7IdZ/f4RnaaVmhGPVLTMxfmIkrG3p8MAUsemR8amhKDpWwwtzOxoxOzO/oB7R0YYrLGoI+4qq4WhnjYszk+DuKPzAjCHql1Q1dSIy0LCZoFqtDlY0Rc/krFixAu3t7XjiiSfQ0tKClJQUbN68+VQh2Lq6ujOmXDc1NWHMmDGnbr/88st8YS2Dd+/efep+NhWHvfa66647a2YLe3woOMNqrixbtgyPPfaYwX9eQv4uOiIiojagH0DipGhRjFiV5FRD1tWH6DHCTvdoqe+El58L3L2NP71j96YTvG6JvZczJs82XIvCc3X0cCV2bStAdJwfbrtnHg/kCO3brw5i57ZCuHs44LW3roKHiIp5soDX229t4916Xnv9coQL1I56tGlt6cG6dYfx0w9HER3jC/8AN6xcOR4TJ0WJMlDS3NiF9WsP4+C+Mt4q/YJL0jF9TgK8vJ2F3jUyAvyD3XFweyGaajsQnWS8IMCx3FrcuEqYrm1/N7vkg82HUVTfimeumIfRqLi6BYlhvrA08DGUul8DcxEcp5kK3YA5X0Z6nX8Hm37ze1NwTg+CMCEhIfzv5s/MmTPnd5/HAiR79uz5W/tKiFCEP7sg5A/0dsjRWNECMagsaOCXYQJ3OGFz01MEyApgJ9tfvLsT0tZePP3WFYIXemUZHD+syUJHuwxXXD8FgcHCF1P95accfPbhHiSPCcJtd82Bn78rxIJ363n8ByhVGrz19lXw9qGT439Kp9Vj/U9H8NXnB3inocBAN8yYGYcLLkyFlZX4vl7LS5rx3RcHIG2X8UDOLXfPRfrECFhaWgi9a2QEsemSbMS/p1thtG02N3ejta0X/n7i+cz7M4fL6pBf24Il6XEIcB+dn4dHiuoxN8PwLew1Kg2sbCnD5FwNwAz6ES76ytZJCDEM8R3REXIaZZ8KNgJU+h+ORcotLM2RkB6O0FhhU45LT9Rj0QvGrx2yZ0s+vHxd4O7piHGTIiG0j9/ZgZzDVVhwwVi+CC3vWA1++i6bByIuuyoTYeHeoirk+dgj38HFxR4PPrIYrq72Qu+Syassb8W3aw5i144iJCYH8ayS62+YBlc38b23xfn1+OrDvejt6YNPgBtuvnsuz8oio5OljSUcXSQY0Btvm9t2FGLRAnG0lz/X7/QNR0p4IebrZxu31oux9Mj7Ud3UgYRwwxfS1OsHYCXijl+EEPJP0KcbET1vEWQOdLT04NjeUvgGe8DeUbgWx021Un4pcbAxet2Lbz/ci8a6Djz2yqWCTzMoyq9HXa2Un5xedu1kwfenrbUXX3+2Hw31nbjt7jlIHRcGsWCZD2+9vgVqlQ7/unsenETSottUsb+F9euO4NOP9vCWwDGxfrj2+ilISgmG2JQXN2HdlwdwcHcJ5i9Nxa33z4d/oJvQu0UMjBXgZPUkjPU9wbJZyitbcd8S4QPX5yq7vB6/ZBdh6YQEhHiPzr+J/MpmxId4Iz70n7eI/TOspbCTG3XJMcUpOYSQP0cBEyJq8i4FNEqt0LuB2tJmfhkSI2zLs0M7ijFhlvFrhxzZXw43L0c4ONth4vQYCIkdnL/36haUFjfxKQVeAk8t0Wh0eO7JH1Fc2IgVl43HBSLo0nP6e8WCJRXlrXj59csoWPIPtbZ04/OP9mLb5nwkjQlCXEIArrx2Mg+ciElLYxd+XnuY1ylZfsVEfLTudviIaHoYMay2lh6YW5gZrc7Vnn2lcHCwhbOJfL6w7JL3NmaBxdmvnGY6QZ6/Kq+0EVqdHg5GyNK1trWCjb3wReAJIcQQxHWUR8gwyn41bCTCfwnXVbbB099N8IKv2XtL8PBrlxl9uz+vyUL+0Rrc/sgiwWuXbNtwnE8tmDA5CkuM3DbzbL78ZC/PcImK8cOV108RPNvl9LZ+L7+4AZUVrXjxFRYsGX0dIIzp0IEyvPnyZt4qOC7BH6tunoFYgesZne3z8puP9+LArmIEhHjg/W9uQXCYl9C7RYystrIN1jZWRgmSseDDT78ewz13zIWpOFJej9yqJsxPjUa4rztGq84eBTKTQ42yre62XngHCZ8NbCr0A2Z8Gel1EkIMgwImRNRUrIaJCFrXNlS0oL2pC55+LoLtg7y3n88TdjZy/Yn6mnZo1FpExvph5qJkCKm/T41Nv+Siuakbdz+6mNeVEVJhfgO2bDzB27O+/dF1sBFRW8UvPt2H3p5+PP38xXATYV0NU8H+5n5cexj/fWs7YuL9kTw2GHfeOx92Igjkni5rdwl+WXsYnVI57nx4EZLSjHOiRMRFo9GirqodUfH+RgluHz1WAx9vJ4SFGrZt7UgGeN749QCCPF1w47wMjFZqjRZbD5fiiVXGCWRZ2VjyLBNCCBmNKGBCRE3iaAcHF+FP9rqlct4yz1/A0doT2VWYKMB0nC0/HUNBbh2WrMyAncAFeH/69jBKCxuxaGkaUlJDBS+k+tIzP0OlVOP2u+ciOEQ8o2u7dxZh355S3P/wIvj6ChfkM3UsQPfO61uw5bfjPFiy8IIxmLdIXIUtuzrleO+FjTw4xooxL7k0g7f+Pl+wtsit9R18qS1uQkdLN3qkMrQ1dkKr0fH3gn12s5bjEgdbuPu6IDQ+AGHxgQiI8hVFy/qRdOxQJZ9qEhXvZ5Tgw8df7MO/bp0NU7GnoAoFtS1YnB6LMO/Rm11SUtPK/32SI42TBdctlcHMnDIczpUO5nwZ6XUSQgyDAiZE1KSNnXD2cBR6N1CaV8sPPoQMmBw7UI5ZF441+slIVWkLImJ9Mc/I2x5OLlMiP68OFubmWLJc+Doh33yxH+4ejvALcMWcBUkQCzYF5z+rf8X9Dy3mBUnJ38PaVT/10Hfo7JAjONQDt98zF9Eiez+z95XizWd+QcLYENz95AXwFjADzljTzOpKW3B0ZwGKj1ShvqwFjZWtvMApy4LTaQfrXbETtwH9AHiXTd3AqftY4032PxZQYIESe0dbRI0NxdwrJyNjfsqoaK98YEcxbCU2GDvR8J3M9h4oQ0y0L2Kjha3t9Veyxd7ecJB3xrl53niMZqx+iY+LI7zdjXP8pFVrRdlKnRBCRgJ9uhFRU6s0/GBYSKp+Ne+S4+RmDwcBi9opZEpEGrlmAhutZAsLmIQJfFC8/rts5ByqxPwLxiAkXNi6DNVVbfj+m8P8BOvDr24UTd0SVl/jqce+x+VXTcK0mcbPRhotmhu78OAdX8LN3RGubg546sVL4OEpfOB2iFqtxZfv7MDBXcW4dNUULLw4XTS/g4YYuc7elo8tXx1AU3Ub5D39/OSMGfyRzeDh68KnA7j7OSMwwnfws9rVngdEeDBFpYW8W8E/x9vqpGisakNrTRsUPf04si0fx3YWwDvYE6ueuRgTFphuEdCOtl7UVLYhPMoHHl5OBi92/dEX+/DvRy6Aqdh4tAR21la4YtoYBHiM7uDi8dImxBuhnfDpgyus0DA5N1TDhBDTQgETImqscJ29wJX32xq74B3oBndv4bqxdEllPGBiYeQR0Lwj1YhO8Me0ecJmUCiVGhTm1cHHzwXLLp8g/Bz4/2yEl7czll2aDm8fcRx48/T4/+5CRKQPVqwU9j0yZXU1Ujx4+xfw8nWBg6MNHn1mOexE1P2hvaUHLzz4HZycJXjy9csQNAqLuipk/di57hA2fLoXHc09UMj6MKAH/y5wcpUgMiUYGfOSEZ0SAv8IL9jYWv+tv5eq/DrsWHsQu9YdQkt1G1Zf+x6W3TEPVz16kUkGoH774SgP4k4ywtTNH389hvHjwhEW4mkyNT3e2XgQnbI+vHL9IoxmLJOmR9aP6eMijDqwZCmybmFipoc5X0Z6nYQQw6BPNyJqvR1yWAg8H7+Fz4/vRMyYYMH2oSCnBglpIUYfxd70w1H0K9R44tWVENLW3/KQc7gKmdOiESRwrZD9e0v59CArawssWCye0eg9u4p57ZL/fnKD4MVwTVV9rRTPPLgONrZW8PVzwb2PLRFVmnlJfj2euWsNps5PwlW3zYStCApij6Sakkase3MLju4s5HVILK0s4ehqj6TMaMy6dALGzYyH8whNMWABkfCkYL5c/dgyfPDIN9j65T788OYWBET4YOalmTAlnVIZio7X8VlIY8eHG3Rb0g45Nmw5jjdeFPZ74a9Yd+AEmjp7sWpOOrycHTCa1TZ3oqCiGQ9eN8to23T1coYVBUwIIaMUfboRUdOIYEpOW0Mnv/QKcBNsH0qO12H8DONOsSg4VoOAYHfY2tnA3cDp3X82WrZneyESUgKx/IqJEBILIn3w9na0NHXjpTevEE1gQtrei9df2oh7HlwIL++R/7fat7sERQUNuO6m6aO2oGhbSw/eenEjpFIZpsyIxZ0PLRRVQdDsPSVY/cBaXHf3PCxaMbqm4JTl1uCDx79DRX491EoNBgb0GDMtFhfcOANp0xNgaeDfOdaJ7Y7XruadPn79cCd2rztkcgGTX9Zm84LYj6y+2OC/G+9+vAsXLBwDVxEUZD8Xsn4VDhTVwMNRgmtmCl//ytBKq1uREOGDsAB3o3YSZMWVybnRDZjxZaTXSQgxDAqYEFFjhfyEHrVgHXLYAbuXv3ABk/KCRlx5u3E7EezfXoSywibccI9x2hL+ntwjVcg/VofoOD/EJwUKui9bN+bB09MRUdG+GJNq3IyfP/LZR3t5zZLJ02JGfN3tbT349uuDKCtuhqeXEy66JB2jDcsY+uD1rcjPrcOcRcmiC5bs3ZyPVx/7Efe/uByZM+MxWkibuvDavz5HYVYFVCo1H6VedO1ULL99Dlw8jB+kXfXMCjSUt6CztQempLykCevXHkJKehjGZRq22GtOXg3a2nvxyL0LYSo+2ZqNg8W1uH/ZNDjaCdvpzRgO59fCwc6GF0g3FomjrcEDm4QQIhTxHBGSc/bOO+8gJCQEtra2yMjIQHZ29h8+//XXX0d0dDTs7OwQGBiIu+++G0qlEqaAdcixc7AVdB+aa6W8PSUrLCgEnU4PTx9n2EqMl37P5vfXV0v59QkGOAn/K7b9ehyxCQG4cEW64Nkl33+Tjfzj9Vh5lXhGnw9nVWD/nhJcee3kER9ZHqzXshlV5a2YPjseSy5KxWij0+rxydvbsW9HEabPjcdt988XV7Bk0wm88dR6PPn25aMmWMJ+r358bxtun/4McncXwcFFgmsfvwifHXseq55aLkiwhGFTgLwC3NGvUMFUaLU6vLV6Axwc7XDNrTMNml3S16fGK+9uw+03zoCliP5G/gibhvP17lz4uzvh4sxEnA8KK5qQHO1v9OCnsWusjYairyO9EEIMgzJMTMzatWtxzz334P333+fBEhYMmTt3LkpLS+Hl9f+L/61ZswYPPfQQPvnkE0ycOBFlZWW45ppr+EHVq6++CrFrq+8QfNpDx8nRRqECJg3V7Uav41Jb2Yb8nBpExfvDL8h4ab3D9XT3Ye/OIl78N3N6LIS0ffMJyHr7edAgIsoHYqBWafH2q1tw/U3TeYvjkbZzawHa23t4YVsWkBkNbVeHW/flAWz4MQeZ02Jw233zYS2iefiHd5fg3ed+xeNvXIaUDMPWpTAWRW8/XrntE+TsKOStfudeMQk3PbsCtvbiGPlvrm4XvND4X/HjmkOQyZRYeFGawbuHfbs+G1MyoxATaRpthJmvdx6DWqPDnUsmwVpE9YgMpa1Dhpb2XqQYOWCiVet4wJEQQkYj0xgiIKewIMcNN9yAa6+9FnFxcTxwIpFIeEDkbA4ePIjMzExcdtllPCtlzpw5WLly5Z9mpYiFTqMTvPL6qYCJjzBdcqpKmhEeY9wDVNZKmIkVeArM7m0F8A1ww5KL02BrayVoHZVDByvQ29uPOQuSIRYbf81FWIQXFiweY5BpKju3FaKqvA3zFyUjUMDAmaFk7y/Dp+/uwNj0UKz612zYC5zNdrqy/Aa8//yvuPPfF46aYAmb6nLv/BeQvfUEvALd8eKv9+Ffr10lmmBJe0MnaoqbkDQpGqagsqwFWXtK4ehsh4sM3D3syLFq7NlfhmtWmE4HrhPVzTy7ZO7YKMwZE4XzwYmyRgwMALFhxg3qsywxc5HU9DIFAwPm0I/wwtZJCDEM+usyIWq1Gjk5OZg163+Vz83NzfntrKyss76GZZWw1wwFSKqqqrBx40YsWLDgd7ejUqnQ29t7xiIUNhVG6FFttv3whAA4ukoE2X5jrRThsX5G3eaJnFoERXgjNdN4bQnPZs+2ItTXSDFugrD7kZ1Vgay9ZZg0JRrRRv63+D0sePP15wcwf1GKQbKw1nyxn//cGRMjsezSDIw2rKvIa8/+goTkICy+eBz8BCzqPFx3hxwfvLQBS66cgImjZBqOvLsPjy9/DU2VrUicGIU3dzyK6DGhEJN3H/gKMBvA0tuErdt0Lvr71Fjz8V6UFDTg1nvn8c5OhtLT24+vvj+Mh+9eANu/0cJZqGlf//lhN79++fSxo6pI8h+pqu/A7AkxsDVysXyWDTxaC4ITQggFTEyIVCqFTqeDt7f3Gfez2y0tLWd9DcssefrppzFp0iRYWVkhPDwc06ZNwyOPPPK721m9ejWcnZ1PLazuiVC0Gq2gGSZsykN1cRO62nphYWEhWMHXIAOnWg+vmXL8aDXqqtoRlyzcv31HuwyN9R3ImBSJ+OQgCOnwwXL4B7hi9vwkiMX6ddmIivZGxsSRDyY1N3Zh++YCxCcG4OKVGYIHLQ3hoze2wsfPFaGR3pg4TdjpXqfT6/X4/I2t8PZzxYVXiKdWzj/9mV688QM0VrQidUYcnl13l+C1qYZjNVXy9pVhwXXT4eEnnuDZ7wUD/vvGVuzbVYKb752HmIQAg27rP29vwdikIMREimMq4rnYlluG/JoWzE+LQVKo6Uwh+qf2H6uEq7PxB3cCI31gLtAxkinSwcwgCyHEMChgMsrt3r0bzz//PN59910cO3YMP/74IzZs2IBnnnnmd1/z8MMPo6en59RSX18PoVhZW8FawKkYPZ1yfuniOfL1Ic6VpaU5nFztjVq/JDzKBxOnx/BCgkI5sKcEXR0KPvJvbi7cgUBLczc2rD8GlVoreKbLEIVCheLCRlx4sWHay274+Rg/UXJ1c0DyWPF0Axoph/aVoaG2A53SXlx720yIyc5fcpF/pAq3Pr541IyKb//6AIqyyhEU44eHP71FdMUhd/+QjS+fX4/o1FBcdv9iiN36tdnYvbUQC5aOxeLl4wy6rc07C3k22xUXj4ep6Fdp8PKPe5Ec6os7Fgvbit6Y5AollCotkqKMnwVZfKRS8I6GpkQ/YIjCr0L/VISMXvTpZkI8PDx4lkNra+sZ97PbPj5nH/l5/PHHceWVV2LVqlX8dmJiIhQKBW688UY8+uijfErPcDY2NnwRg36F0ugFT08n61QgIT0MgRE+gnVmUciN27GhJL8B+cdqsUTgrjR5OdWISfRH5nRhu/Ts2VmExOQgpI0PF03K8cZfcnlB3LT0sBFfd2N9J9atOcR/1mtumIrRhv1N/frdYZQWNuK5t68UVd2SjrZerP3vbtzx1FLYO5hO4dE/yy75+b/bYG5hhrvfvpoXcBaT9R9sx5qXfkNIXACe+PI2UXVIOpujWRXYu6MI7p4OuO7WmQYNJpdVtuLDL/fhvZcuF/37crqPt2ajrVuOiyYmwM9dmNpjQsgva0ZDU5fRO+T8r96cOL4fCSFkpJnONyCBtbU1UlNTsWPHjjMORtntCRPOXoitr6/v/wVFhqaWsBFksdNpdYKORrIMk4LsKvT3CdNmsrmuA76Bxk0Pb6zrhJ3EGjGJhkvz/jNKpQaHD5SjvlaK+EThpgWxv5GNP+fiRF4db6srBmzK1O4dhbj0ikyDZCB8tyaLdwGauzAZwaGeGG22/nwMRw9WYMGyNKSJJGNoyPrP9yM6KQDJo6TIK1NX0oT2pm6EJgQgNF7YqXWn06i1ePOeL/HRE9/DJ9QLz3z3L0gcxRM8O5uqila89+oWVFe04vHVF8PJ2XBBNZlcidVvbsL9t8+Ft5cwbZ7/job2Hny5Iwe+bk64ZpZhs2/E5nhJA0L83eDiJDH69ySbOs26XpFzM9IFX4cWQohhUIaJiWEtha+++mqkpaUhPT2dtxVmGSOsaw5z1VVXwd/fn9chYRYvXsw764wZM4a3Ia6oqOBZJ+x+oWpy/BU6rV7Q+gm9XQp+6egiTMHXtqZuhAnQIYcVFIyKE664aeHxOoRFeCM2IUDQttLlpc2wsbHE7PmJ8BGorfRwuUer0dvTjwmTR77rQ0+3AvW1Hagoa8F9j4p/asJfpVJq8PUHexCXFIilIitk21QnxfYfc/D6utswmjRVt0Gn1iAmTTxBoPLjNVh93QdoqZNizspM3Pby5aKfTtDW0oPH7l4DnV6Px1YvN2gLYdYV7LnXNmJKRiQmpI18Fpsh/ef7XfBzc8KdF0yCrQj/TXefqESUvyf83Ec+CFVZJ8WUcRGCDGwpevpElz1GCCEjRXzfJuQPrVixAu3t7XjiiSd4odeUlBRs3rz5VCHYurq6MzJKHnvsMT4KzS4bGxvh6enJgyXPPfccTEFQrJ+wU3K6+wQNmLAOOe5GHN1j0xVqq9ogsbeBf7BwbWRzj1SjtKgJ85aMfLvcv2L/nlJUV7ZjvgHa9v5dv60/hqkz4gwyPWjLhhPIz6tD5pRohBqx0LCx7Nh4HH5B7ggO80RQmLh+vk3fZmPinAR4i6hbz0gYymQUQ0ajrEeBj5/6AZu/3A93H2fc8/Y1mLVC/DUuursUeOyeNfD0cca8RSlIG2/Yk+Kv1h3i3wVXmVALYWZPfiX2FlQjPSoQ05LEE6Abwv4GPt92FO/eftGIr5vV2Mo+XosZE4zfElvVr0bipGiYm9C0LaHpYcaXkV4nIcQwKGBigm6//Xa+/F6R19NZWlriySef5Ispqs6vF3Tkr0+uhL2TLZzdHATZfmtjN+LGBBtte/VV7QiP9kFQqOdZ69sYi7StFwnJgUgRuOBozuEqRMX4YuIU4x+Eng0rvqhQKDF3QbJBDuYPHShHYnIg5iwQTzegkfz58g5Xob66Hbc/tBBiwqb8bf3hCF786iaMNhFJwbCyscKJ/SWC7YOyT4X1/92BdW9tgULWj0mLx+KW1ZfC3UccWWN/RNbbj7df3oSaqnZcfeM0zL9grEG3tzerDPsOl+OlJ5eZVN0SVuj1/Q1ZsDQ3x4MrpouyYHJhbQvCfN1gZ4BMjNKqVkSFeCJJgPolWrUORYcqRPmeE0LISKCACRH1CY5epxd01ELe0wdFrxJ29jaCtVX29DVe0bqayjaUFTYhIsZP0GkTe3cWwclFAj8j1285XVtrD5+Sw6YGefuIo3Dg/t0lUMjVCDRA9k9FaQsa6jr49YyJkRhtSgsacXhfOaLj/XgrYTE5uqcUgeFeCDGhtq3nyjvIA+HJQSjJrsRvH+3ColXTjbbt7vZeHiQ5uDEXLaweVIgnHv/8FqRMFraQ9F8Jljxy19coK27CtTfPwKVXG7bNdEV1G159bxte/vfFcHU2Xme2kfDp1iO8fsnNCycgzEe47Mg/8v2+fFww0TC1sHKLGiDtlMNPgHozGrUGFlamE1wTA92AGV9Gep2EEMOggAkRLTaPmhEyYMKCJYxQxQBrylrhYsTsFpbZ4eJmj6AwDwilsryV108JCHQXdMQqJ7sKjk52mDozDmKRm1ONuQsNk/2xc2sBujvlWHn1JEHrxhjK9l9yMaDTYd5Sw47Q/x2HdhRi/Azx/J6NtJteuAyPLX0FXzz7A9QqNZbeOsdgf9usnsKx3UX47q0tKMgqh4OTHZw9HPHA+9dh2lLDtOE2BFan6OF/fQUbGyssWTaOB0sMue/sZJvVLbn31jmICBXXdLU/U9vahc+2HYWbgx1WTkuBGMn7VSiub8OTYbMNsv4TxQ1IjgkQ5Pdbo9KKvgYQIYT8E/QJR0SLZZcIHTBhB98su0TI7gnGPAAqL2pCd6cCgSHCdUcpLWpE0YkGTJws7DSYwhMN/KQlRsDit6fr61PzKUJXXTfVINlcTQ2diIn1M2h2CQuCGrIN6u9vV4/iE/WITgjA+Kniyy4oPdGAJVdNwmgVFOWHBz64AS/d9CG+eu5H7F9/BNc8uQyJmTEj8vnGMgGP7ijEljX70VDegt5OBdRKDTLmJeGiW2YjcUKkyQRKmK5OOd55ZTPKS5qxeFkabr13nkH3X6nS4JFnf8LMKbGYPN60ssvYZ9enW45Ao9Hh/ounQWJrDTHalVeBBWnRBvl31Or0KK1owc2XT4FQHafY9G9y7gzR1Ya65BBiOPQJR0R9IMQKiRmiuOW56ulU8I4x9o6Ga9/4Ryd5bp6ORm8pzASGCJdh0trcA1s7K0QauTvQ2brRBAS68U49YpCfWwtnZ4lBpuM01HUia18Z3NwdEBNvmDnwvd19uO7id5CSFooHn15q1L9r1obV3tGWF1BmBY3FhBWWZoHZ0OjRNx3ndAmZMXhl8yN4667PUXi4Ak9c/DpcvV2QOjMe4xeORXRKCBzPIZuuT9aPxso2lORU8UySlhopOlt7eAt4G4kVIhKCsOi6aZh35SQ4uxn383MkNDd24eG71qClqYvXLLns2skGDZawk+3Vr2/C2KQgXL4sHaZmQ3YxfskqxLJJiZiRIq424acfy3y9Ixev3rLEIOuvqGlDd28/4qOF+c5kNUyiTaybEiGE/BUUMCGixQ4S8/eXwkLAtsLKPjW/tJUYf9RK3quEVqsz6kGds6sESakh8PA2/jzoIceP1UDZr0FohHB1JqRSGTqkckTH+sHWVhytEivKW5CRaZjR35zsSri6O2DG3ASDZYAUnqhHQLA72tt6jR4ELStoQnVZqyizS5pqpfANdDsvWnJ6Brjj6e/vQVF2BX56ZwtKjlRh+5qD2P7NQf7z2zrYwsFZAjsHW1jbWsHM3JxninRLZbxtKWu0Y2ZuxoMmqn4NrGwseV2SyORgZC4eiwkLkuHiIdxn1z9VWdaCJx/8Dvb2NrjiuikGD5awz/zX/7sd8j4VVl0xyaSycJhueT9e/X4vbK0sce3ccaLd/8KaVrg7SQzSSpgprWxFenIIQgKEqd3CptnVFDUIsm2T7pIzwjVHqEsOIYZDARMiWgP6wSk57KBZKMp+Fb+0lRh/VLq7Q27U+iVymRK5h6t4oVWhuiPotHrU1bTDzcMBLq7CFR0sK2rigYOUVON1KPozB/eWYdmlGQZZd0tTN6ytLQwapDqWXcWnWl200jA/wx85nl3FC2jGpgRBbFjmjbO7aRXY/Kfi0iP4woIhxUcqkburEJUn6iBt7oKitx+9HXLoWdFv/QBP91crtZA42MAv3BteAW7wCfFETFo4osYEm0Snm3NxJKsC77+xFe2tPVhx33wsvijN4Nv8fG0Wunv78MyDF8BSwIGJv4tNxWHBkqtmp8LfQxyFuc9my5ESLJucaLD1HzpWDRtrS8ECRizDxN5ZIsi2TdWAAdoKs3USQgyDAiZE/EVfBah5MISNYgqVYcJOpIzZIaetuZtfehlxm8O1tnbzaScpacK2E2aFZ9mc+MBg4aYmnU6l0sBOYo24RMNMD9qzvQgdUhmSxhguoFBd3orgUA8kjzV+EKqjrQeRsb4IixJXdxyGBQRYYc/zEcsiSZ4cw5ffy4AQa9bASPrtpxy8/com+Pm74bFnl2PyjFiDb/OH345hy85CvP3iSkgE+H77pw6X1OHLbTmIC/bGZTPGQKx6FEpszSnD7UsnGew4qaahAxfOHflW8+dKpVTzwq+EEDJaUcCEiNbAyYAJS8EWilajg42dFaxtjf+nIu/ph40Rp4N0SmW8ZkpwmHAdEprqOyFtl8HWzkbw6S9u7vYIjxBHt4iqijbUVLUbpL0xm3rk4+eCsEgveHkbJlim0+n5CDYr5Bxu5Hn2rBaQpZUF3B1sRRmYsByFHYlGymgPlrCg7HuvbUFpUROcXSS4//ElRqmZxAIl+w+V45WnL4a7q/GyGEdKv0qD577eDnMzMzx06QxYi7jg6KbDxZg1Ngo2VobZx5p6KRoau3iHHKFo1axLjullKAmJTccZ8Sk51FaYEIOhIzUiXmZASHwAzAWtYaIanCtvbfwTLVlPH2+JaSyd7XJ08mCFcCeVnR0KJI0JRmi4sIGK+poOdHf3IUAkGSb1NVKMzzRMp4/a6jZeX4St21AnqG3NPcjNrkJ3lwKeRq6Pw7o+Ndd3ncpYExt7JzsolYOZbOT80dkhxxsvbuDZJRqNFm98eJ1RgiV7DpTi3U924/ZVM+BnotOZ3v81C57ODrhydioSQsVbLJl95ny36zgWTzBcy/DjRQ2Q2FkjQsCBDja4xY7VCCFktKKACREtdvJWU9ggWD2NoXR5C0tzQfaBTcNwcjHevGCFXMVrh7h6CNdZor6mHSdya+HqJlxNB5YNIZMpkTwmWNAOTaerrGg1WHeXuhopHJ3seIFbQ2lt7kZAkBtS08ONnjXQ06VAS2MXHJ2N3+nqXHj4uPBsMnL+YAHK2675EFt/y8OSZWl4/YNr4eNr+ODFgcMV+OHXY3jhiYsQLmDr+H+ioLoFX28/htZOGW5YYPx6SH/F0bJ6SGytEBNkuGBGbX0HZk+JgaWAx0l9ciWaq9sF274ptxUe6YUQYhj010XEi7VE4IETCBowsbK2FKzdKJvjbyztLT3olMoFLbba36+Bi6sE7kZup3w61sWFZUKwaRxi0d7aiyADtXpmgTJ3Twd4ehku84O9nza21rCzN36tBJVSi6AwT6O36D5XrE7RgE4PactgDSEyevGMg68O4qkH1/LA4bU3T8et98wzSqvrrCOVvH3wqisnIzZK2Jbtf5dao8Xb6/fzgsCPXzkLEltx117Zm1eFZVOTDLZ+VuNn18FSeAvcGUqn0cLOQVzt2gkhZCRRwISIPV4i6Dz2iIRAxKWFGnWb7CDoize3IedAhVHrG/T29PFLJxfhRuJrq9vR3dUHLwPU6vgrwQlvX2eECTwtaHgQx9PL0WCtTGsq2w36nre19PDt2DvYQohuW86u9qKsX8KYm5vDO8ANJXl1Qu8KMaCuTgVeefZnfPzudn77vseXYOU1k41S1PxgdgW+/v4wnn3kQiSZ8NSJD387jOMVTbh+fjoyYsXTwexsWAbMLwcKMWusYVrBM/WNnbAwt0CKwP+mbNoy63BH/noNk5FeCCGGId5KWeS8N1SsUcjKA4VHquBoxGkxQ1kt37y/i183ZrFZWztr3klEyKkLXR1ynlHk4mIvaHCitbnHKKO+56qhvtNgGSCsQ0ZCcqBhs3oGBhAV5wcPb+NnebCi0TUVrYiIFe+oemRCAIqP1WLSPMONRhPh5ByuxEv/Xs+DJvOWpODqG6YbLYuOFXd94Y3NeOaRC5CSGAhTVVTTgs+3HIG7kz2umpMKsVu/vwAzUyPhKDFckDi3oB4yWT+iw4Wt48KLvoooI5MQQkYaBUyIaFlaWfIuNUJNiWFYVw9zI88NHmplzDg4GS9YU1fVjvLiZkGyAIY4u9jDzd2B140RSp9ChcgYX/gImOVyOrVSyztosPfFEIoLGlFfK+XbMORUKxYMszA3/r+rncQGsp5+yHuVEKuMmXHYc88aXgtAIuDfHxlZfX0qfPTWdmxYn4OIaF8sWT4Ol149yWg1sXbsKcb6jbl47rGlSDbhzBKVRouPN2TDSWKLx66cZdAgxEhNHTpcUIO7Vkwz6HaOFzYgMdYf1gIeIzHs+9o3THwt28VMDzO+jPQ6CSGGQQETQv4gw4XNOTf2ybtKqUFAqAekrb38ZM+YB/eMUJkVbCpScUE9vI1Q/PDPMkzKS5pFc+Iqk/ejqaETTs6GCWhIHKzhH+Bm0H93FoRiU50sBOh4xWryRMf7QavTQazcvJzg7e+GfRuPY+4l4i5kSc7N8ZwavP/GVrS39sDX3w233TsfcYnGC1ps2HoC73+yG6ufXIaEWH+Ysvd/PojdeZVYNiURmYnGnSL7d+w8VgF5vxqJYT4G/b5s75Bh6oQoCK23U45OqsH0l1BbYUJMCwVMCPkdet3AqRoDxu7S0lAt5aOQllbG27avvyuflmMnUFthVpwzJNwLHp7CFrBj73tichCcjNjS+Y/0KdT8RMtQtQ765GrI5UqDjlK6utsjISXIqDV5hji5Sni2mkalhZjNW5GOb9/dgekXpMLahr6aTZVCrsSaT/fh+zVZvA7XZddOxoqrMmFnZ7wCpd/+kI3DOVV45bkViAo37ZH/4xWN+HLLUfi4OeHO5ZNhCrYcLsGyaUkGrb/W0NTFM0xuuEL494RNybG0pik5hJDRi47KCPmDDBMLK3Ojp7uyaUicGWBtbbzgRWlBIy/OaczOPKdTKtWoKG2Bo6OwgYrmpm7kH6+DnUQcHRjY+9LcaLjRu65OOa+bY0jKPjUKcmsxYWo0jI2dtChkSjTVdUCn08HCQpwH9rFjQ/h0uK3rDmPRFZlC7w75GyP+WXvL8NZLG9HRLkPmtGgsuSQdY4xYNJztwwef7cX2PUV4+ZlLEBzoDlOmUKrxxEebkRTmh5sumAgHO/HUlfo9RdUtOFJUhyevm2vQ7RSWNsHPxwUxkcLWL2HY94e1yDsWiQ1lmBBiWqhLDhGtAQwgcmwovxQCO9HSafTQ6Y1b/V2rHQyYmMEM5kYckddodLC0tDB6Rs3pU5EYoUfXHZ1sEZvgz7NtxIBlHEXFGK5gKavXEmHA9TNOLhJezFfZr4YQYpIDeA2VxtoOiNnl/5qDvRuOo6VB3PtJztRY14lH71qDfz+wlmemXbQyAw88tdSowRL2vbH61Y28c8o7/7nc5IMlzGtrd6NR2oO4EG9kxAXBFPyw6zhmpUXBxcGwgf/DOdXw9XKCtZXw455qpUbQboaEEGJown/SEvI7rKwsUX6smp/En0/YKGHcmGDUV7fBCB0nTwmP9hG0zgML2IRHecPT21nwDBNWCNVaJCnGrI5OTVWbYVs5dyr4752hDnrdPRwRlxzEgz9CYNuuLmtFyYl6BIWJp130cP6hnsiYEYdv3t6BO59bbrTioOTv6e9T45vP9mHLL3l8lD0yxg93PrQAUbF+Rt0PhUKJJ1f/wosrP//kRXAWyXTCf2J3bgUqG6QI8XHD7csmwRR09vahuUOGm5dOMOh22Gc165CzdMEYiIHE0Q52Iqn5ZSoow4QQ00IBE0JEhs17L8qt5S2VjVkks/hEA88CEAo7ma4sa0VQiKdwO8Hb7NrA28dZNCerrO5HqAHrEMQnBfIpBGq1DjYGyu5xdpWgMK8OLq7GbdE9JCLWj2e31FQYLvA0Ui68bgpe/NdX+OGDXbjklplC7w75nc+q7RuOY/MvuSg6UQ8HJzvcfM88zJyfaPTPjTapDA89+T2S4gNwy/XTYGMjzJTKkSTtluOZz7bywqmfP7oStkacmvpPrN+Tj84eBRLDDRswq63vhJuTBKlJ4si6aW/ooIAJIWRUE8cZASFnc/LsnQUQhMBGcUJj/eAjYGoz2wdjbsvMmCktZ8mk8A90g4OjsAdecpkSrS09okkxZhlWJUWNBls/615TlN/AW9oaio+/K0IjvNDT1QchhER6o7e7Dwe2F56a8iZW7IT79meW4cThKuz8+ZjQu0OGfUZmHyjH6ke/x6vP/IKS/AZcdv0UfPr97ZizKNnowZKyihbcfNcXGJMUhDtumjkqgiXse+Dlb3ajR67ELRdOREywaRStVWu1fDrOJTPHGPy7Ize/Do3N3YiOEL5+CaNV63hhbfLXM0xGevk73nnnHYSEhMDW1hYZGRnIzs7+3ecWFhZi2bJl/Pns9/z111//f8956qmn+GOnLzExMWc8R6lU4rbbboO7uzscHBz4OltbW//W/hNiDBQwIaI1dMxhzKDB6djBb3VxE1rqzo96AjGJAYiKM24q+XCN9Z3oF6jOxZChbjTswF0MbG2tEBZuuGkkYZHevENSV5fCYNvw8nGGUqnhI/OsmLKxsbo8cy8cy1u8FubWQuycXO1x8xMXYs+vuTi8o0jo3SEA8nNreZDk8bvWYN+OYsxckIT3vr4ZV980ndfoMbY9+0tx1wPf4vorJ/FgiVgy4v6pb7Ydw6H8GiyaGIcr56XBVOzMLoejnQ3mZBi+sHV9QycyUkNgZSWOaaPWdlawdzb9aWDno7Vr1+Kee+7Bk08+iWPHjiE5ORlz585FW9vZszH7+voQFhaGF154AT4+vx+wi4+PR3Nz86ll//79Zzx+991349dff8W6deuwZ88eNDU14aKLLhrxn4+QkUIhYSJaLCqdOPnMqLQxmZ88ADV23QUWKDK3MOMn7uzk0lhFWFmXHCEzTE4FKk62cxaKk5Mtz3JhNVXEgHVOyTlSzX8PDXVSxLojdUrlCIswzGgu+x12cpagOL8BzY1d8Bcgayt1YiSfFrR/WyGSx4VB7ALCPHHVPfPw+SubeXefiXMShd6l8xL7ndnyax62/JLLC0Enp4bg0msnY2yGML9DLJC7bv1RfLnmIJ58eAkyTOB3+VyV1Lbi7e/3wc7GCjdfOBEWAhUg/zu+256LlCh/OEhsDP7vv313EVYsHQex6Gzpgau3i9C7YVLYUY6etUIc4XX+Va+++ipuuOEGXHvttfz2+++/jw0bNuCTTz7BQw899P+eP27cOL4wZ3t8iKWl5e8GVHp6evDxxx9jzZo1mDFjBr/v008/RWxsLA4dOoTx48f/jZ+EEMOigAkRdcCkKKv81Im0ENsfCloYewoGCxrozQZgzE2zII1OK0xRzqGfOzEliNe7EJJOP8Cn5agN3Gr3XNnb2yB5TBBkvf1wcbUf8fUHBLrzYAwLmhhSemYk/3uqLGkRJGASPzaYTwmqq2qHQq7kgSixC4/zx3UPLMCHz/+Knk4F5l9KB5LGwLIa845UY9tvedix8QQ8vZ34lLLLb5iKzGmxgn0n9fWr8cIrG1FR1Ya3X7kcIcEeGC0U/Wq89d0+eLrY418rpsLH3QmmoqCiCT0KJS6emWLwbVXXtqOnt59PwxILZw9HOAqQZWXKDFn0tbe394z7bWxs+DKcWq1GTk4OHn744TMGN2bNmoWsrKx/tC/l5eXw8/Pj03wmTJiA1atXIyho8HeWbVOj0fDtDGFTdtjjbLsUMCFiZDrhe3JeYtkWQk6NYFkmxs54GMpsYe2UdUbMcohNCkBUvHBTclh6cX5eHVqbuyEkXz9XxMT7Q9mvghiw4r911VJ0dRpmyoyPvwsCgtzR3SWHIfkFuaGkoAFlxU0QAjsQnL1kDI5nV2HXhuOC7MPfERLti7tWX8Kn5nzy0gbejYUYhk6rw+4t+Xjynm/w0K1foLq8FUFhnrj+jtl4+8sbMXlGnGDBkrr6Dvz7+Z+hUmvw/htXjqpgCQtQvfjlDmQX1WFiYihmpkXBlHy9+RhcHewQHmD4f5PcE3VIigtAVKQ46pcwTVWtosnIJEBgYCCcnZ1PLSxYcTZSqZRnL3p7n5lZym63tLT87e2zOiifffYZNm/ejPfeew/V1dWYPHkyZDIZf5yt29raGi4uLiO6XUIMiTJMiKiZsZRcoaq+AkgcH87bGxu9K0q0D9qbe/hBiLFmBteUt0Ha1mvQ9rJ/xPpkhxa1StgTQvaelxQ2QiEXR8CEGZMWgq5OOUINUMskMNgDDXUdvJWyIbGpDPaOtjh6sByr7pwNIcy9KBV7t+Tju0/28uvG/tv+uzz9XPDQG5fjk5c24oU7v8KqRxbDN0i4YtSjTW9PH7b8fAx7txeirKgZcUmBiIz1xYprJmPitGhYWAhbK+JAVjme/89vWLJwDK6/ZgosR0m9kiFbD5Vg59EyhPm7464VU2FKWjtk2J1TjqdvXmCU7R3NreVZh2L6HdCqtaKpp2IqDJlhUl9fDyen/2VonS27xJDmz59/6npSUhIPoAQHB+O7777D9ddfb9R9IWSkmMbRIjlvCZ1hUna8zqitfRkra0tUl7YM/uxGrJ9ibWvF32s2LYdlNRgbqw9g72ADlVIDIbH0+7gEf/T1CVt89nQqlRZNDV0Ya4Bp66wzEWstbOggmbunE2ISAqDsU/PAnIeX8VPuWR2V8dNikHOwHFt/ysHCSzJgKmwlNrjlyQt555yX7/0G05aMwcLLJxitxtFoVFrYiA0/HMHRgxW8tXZSagjGTYzARZdPwJj0MME7ZbGOTh98sgdZ2ZW4/+75mCZgTS9DqWyQ4tlPt8Hfwxmrb1kIWxPr9PP99jxMSAjB9NQIg29Lq9Pz76WpkyIhJl6B7pA4UdFXsWDBktMDJr/Hw8ODB4OHd6dht/+ooOtfxTJJoqKiUFFRwW+zdbPpQN3d3WdkmYz0dgkZSXSkRUTNP8JH0EKkLHihNXKqKQtcMCyxpk9hvCyHsChvxCUH8m4mQrCxseTBEqEzO1hXmqKCRl4EVSxi4vx5DRND1Y5hJ2aH9peh24CdcpjwKB8U5NXh8L4yCGXRigw01nbg0O4S9PeJJ4voXLAT+JkXpuKB1y5D0dEaPHTZ+6gSaIqTKWeT/PztYdx19Ye4/8ZPseXnXASHeWLR8nG4/aGFePbNKzA2I1zwYElLaw9eeHkjjhyrxnNPXjQqgyWKfhUefuc3qNRaXDE/DWH+pjXNSN6vwvfbjyM21Jt/jhpaaVkzTuTXIyVRPPVLmKqCekHrn5kiMbQVZtNiUlNTsWPHjv/tl17Pb7O6IyNFLpejsrISvr6+/DbbppWV1RnbLS0tRV1d3Yhul5CRRBkmRNTaGzr+XunvEWJpZYk+mXFPqqyt//dnqZAZ5iT5bDrb5Sg6Xg9Vv5p3iTE2doISmxAAm5MBI6G4utnzbjQ93X0QC1YIN2tfqcHWn5EZyX/msqJGpGcarn7AlFlxyDlUgYO7S7BwmTAtQ51d7XHJtVPw4Sub8PPXWbj0hmkwNd4Bbnjg9ctwYEs+XnlgLcZOisKSKzP51B3y/7ECzrmHKrHxxxyU5DdA1tPPCx1PnhOHxDHBmLkwGQ6O4hkh33egFC+9ugkzpsXhvdev4kHc0YZN/Vz92XZIbKywdFoiFk2Kh6nZerAEWq0WF81INsr2cnJr4eIsQWiwJ8SEFYynKTmmibUUvvrqq5GWlob09HS8/vrrUCgUp7rmXHXVVfD39z9VB4VlhhQVFZ263tjYiLy8PDg4OCAiYjDL6r777sPixYv5NBzWLpi1LGaZLCtXruSPs7oqbGoO27abmxvPhrnjjjt4sIQKvhKxooAJETVWAHXAyF1qTsempmg1WqNvMyk9FK0NXVD0Ko22XYnD4DxXhUIFoaojdHf1Qdp+ZoV3Y/P0chzsGtMm7H6cLjTck2eAGEpQiCe++HAPL7pryIBJeLQvlP0a9ECBrg4ZXN0dIYTFl41HQW4Nvnp3JzKmxiA0yvTSgFmAcdK8JKRPj8WW77Lxwl1fISzOHxffOA1efq4432k0LEhShbzsKvz2/VH+N82yBW3trLDsqomYMDWGF7oWOpPkdP1KNd797040t/TgzttmY/YM0wsinKsfdh7H1kOlg3VLVk4T1b/DuWBZeZ/9fBjzJsbB3WXku5edTXllK2ZPF65L0++RdSpgYSL1oM6HGiZ/xYoVK9De3o4nnniCF1xNSUnhxVqHCsGyrI/Tp32yAMiYMWNO3X755Zf5MnXqVOzevZvf19DQwIMjHR0d8PT0xKRJk3i7YHZ9yGuvvcbXu2zZMqhUKsydOxfvvvvuP3wHCDEc+oQjosam4whZwyQowvvktBwtzzYxBnbgWFncDIVMiV4jZjl4eDkiIMQD/Qq1oJkU9bVSXvh1qAissXl5O/PLtlbDttn9K4JDvXD4QDkPZrGCfyMtcUwQYuL8UHSiAYb+3Z6zeAw+eXs7dmzKx/IrJkIILIvrgssm8L+xN57+Ga98tsrotYpGirWNFRZfmYmZS1Ox6dtDeOaWzxGXGoJpi8cgJiXI5E5E/wlZTx9yD1diz5ZCNNZKUVPRNhj01umRnBaKhRePQ/qkSF4vSWxKSpvx6pubYWFugUcfWoQAfzeMVgWVzdiZXQ4HO2u8eMdi2JlY3RJm15FytHXKceGMRKMF07IOV+Cu2+ZAbFjRV0tr0/z8JMDtt9/Ol7MZCoIMCQkJ4dlhf+Tbb7/9022ydsPvvPMOXwgxBRQwIaLm4uHERwaF0tnWi/qKVn4Cb6yACWMrsTZ6wIRNyWiokUIuN15Wy3BRMb4Y0A+gq0sBb5/BwIWx2TvYInVcKAYE/L0bzk5ijcxpMagqb0FiSvCIr9/VzQEatR41Fc3o6VLwaSuGMmN+Io5mVeDwvlIsu3yCYCf0KRnh2L+tACUnGrD247247KbpMGUSB1ssWzUNS66ahEM7B1sQMzMuGIsJsxPg4u6A0YZljLBpZMeyKlCa34CjB8rh4GQHWa8STi4SJIwdnG4zcUasQX+n/wn2/fb1N1n46pssXHfNZCxfmmaUehhC6eztw0Nv/or2LjleuHMxgn1NLzDEThh3Hi7DzPRIxIcP1mUwtKLiJvh4O2NsirjqlzCRY0N5UWpy7gYGzPgy0uskhBgGBUyIqPXLlTAXcIR0qACrWqnhJyTGIrG3QQcrUNhlvICJo7OEX8oErN2h0w2g8EQ9ujrkggVMGLmsH9UVbfxkhgWSxIAXo81vMEjAhJk+Nx6fvb8LRw9XYeY8w42aeno7w9nZDof2lSI3u4oX2BTKqnvm4Y6V72HDd9mISwniQRRTxzLiJs9L4ktteSsO7SjE3cveQmRyIMZNiUHa1Gi4ejjyYLCrp6NJZaCwIr0VRU0ozKtDYW4tjh+p5jWPWE0SV3cHnm3DuiCNyQjHuMlRcBR5546aWilef2MLtHo9XnnpUiTGB2A0Y5k+7323D+2dcly2IBXT08TV7eVcZRfU8gyTp241Tith5nB2FQZ0A/DzFdd0O1Yk9PieYlx4i/gyX8RMDzO+jPQ6CSGGQQETIvoaJkJmmAwVIDV2q9vIBH8+itVnxC4ejs52p7pICMXdc3AUvEMqg5DiEgP4KG9rSzf8RJIan5IWipKCRoOtPzk1hBdmK86vN2jAhLn4qkzkHa3B918cEDRgwkZFH33lUjx28+d46eF1eP2rm0ZV/Y/gSG++LL9hKkqP1+PE4So8ePl/ERTljaxthaceT50cjfBYPwRGeIkmu4EFR2or2/j0xMqSJh4caa7v5N8JLLOEZZJo1DqMnRCBUPYzZEYiNjHAJKZWsfoXv/6Wi/c/2IV5c5Nw0w3TIDkPRujf+mYvftlTiEVT4nHbJZNhqr7fkgsvNwfMyjBcvafhcnJrkDomBGKcjjMUqCWEkNGKPuGIqLGDY72AARM2NcbGzop3jjGmLqkcDdVSRMQbr/Com4cDr3bf2228zjzDseCEh4cj2puFrR/i4mrPsznqazpEEzBhNUbee20Lbr1nrkGyXiJj/Hj9li2/Hsf1t8+CnQHrPEQnBCA6zo+3sC4tbEC0gCPrIRE+WHXvfKz9aA/+/a+v8dKnq/i0rNGEBcLixobw5dJbZuDg1gJkbS3gWSbVJc08eMKCwo4uEvgGuyMwzAtRSYHw9HGGb5A7vP1dYWeA2jls2mF7aw+kLT1oqu3gba1Z5gibFlZT3nqy6PZgW3cbW2teJHDcpCgEhXkiOT0M0YkBJvdvVV7ejP+8uplPfXzhuYsxRoQnwYawLasE3245xgMNLFhiKZLMvb+quKoFRwvqcOvKyUYLLnZ0yPkASkZ6GMSGHRv5h3nDkgImJln0lRBybugTjogaGy3UC5xhoupT8xomxjSUSm7M6THOLvbQ6wb4dBihuHs6QtomQ2uLsAGTkDAvfllb3c5b7opBQJA7z3ypLG9BVIzfiK+fdV6YNCMGOYerkH2gHFNnGbZDx7IrJ+LhW7/A2k/344mXL4WQps1PQk15C4py67D6vm/x1FtX8pP10WrinAR8f+xplJ5gWSeVqChoQGVxEzrbZLzIdtmJBuzbfAJqpRZuno7obJfBP9QDWq0ePix44mADZzd7uLg58PfJ/mRLXhZc1qgHCzYr5Cp+QtndqeDPaa7rgIWFGapKW/nzyoua+DpYcJR16OqTq2BlbQGNVscDgmzE2j/YHSnpYQiO8EZUvD+/NNV/l/5+NT77fD9OnKhHYkIAVl031aBBSTEpqmzBMx9swbi4INy6YhLcTk7/NEVf/3YErOTlnImxRs0uqa3rEOWULY1Ki8aKFlhYmmYAjBBCzgUFTIiohcQHQP8nFbkNyfbkqKpSYbypMQxLN2cUvcbL9nD1GJwOI2TAxMfXBQ6OtpDLhCs8y4RHekNib43KshaIBas1wTJfcg5VGSRgwsyYm4hvPt2PzT/nGjxgMiY9DPMvTMWuLSdQeLwO8cnCFjO86vZZePXxH1Fyoh5vPr0e/3pqqWjq1xgCyxhJmRDBF4aNYLNW5vXV7agqakJvtwJ5WRU8gMICJt1SOQ+CsOewkyOdVj8Y4FDrYGVjCY1GxwMlvMOVrRWv+8Sz85Tak1l6Gtg72vKsElaAlj3Pwtwc3v4uvK2zh7cz79LlHzKY4eLl63xGO0tTduBgOX5en4OOTgXuuXse4uP9cb5o65Lhwdd/hlqjw4JJcYgLM70W3kPqmjrR0aXABdMT4XwySGgMVdXtGJMcBCcR1uTRqjUIiQswalH80YCKvhJiWugTjohac3UbzISLl8Du5LxyNp/emHwCXRES5W3U2ilsSk5knB8v4iYUtg/sxKu8uBlC8vJx5idz5aXiCZgwEyZH4eDeMoOtPzjME5NnxKKzQ47W5m54+7oYNAA0/6JU7NlWgN++P4K4pEBBC5Cyk/O7nlqKlx5Zh10bj8PBSYLrDTT9SYzYe+8T6MaXcVOiz3hMrdaio7UX3VIZujvkPBuEBVRY0IR18mKBkp6uPh4QYa19WeaJQq7kxWVZvQ5WkJVlC7KMEhYQdXZ34JkrrLi1KRWd/asaG7rw7vs7cOxYDa67bgqWXpgqmhoxxqBUa/Do67/Cz9MZMzOisWByHEzZ178dRV5JIx64frbRtskCmVu3F2LR/CSIkbJfg5qiBpPN/CKEkHNBARMiahYCF30VKsOEHVTXlLXC3dvJqNOA6qraeQq9UNjJk3+gGxrrOvjINpsmItR+TJkZx6e/sBM/sdRJYIVfVz/xE1bdNoO3AjaEjEmRePnpX7Dhpxxcd+tMGFJ0vD+mzU3Axh9zkJoRjlmLUiAkdtB/33PL8e7zv+LHL/az0xVcd9fc8/5kwNraEr6Bbnwhf44V6/7mm0NY991hLFyUgk8/vYG3hD2fsBP95z/YivyKZoxPCsHtK023yCvT3iHDpn1FmJIWjtAAd6Nml3R1KZCWGgox0qgGB3WohslfQzVMCDEt58fQGTHxGiaDRf+EwFLHWTvOocKDxuLsas8vtWqdUYMEnt5O6GiXCRqkik8ORESMLy8GKSQ2/aWksAklRU2C7sfp7O1tMGNeIo4eqjTYNthUnHETI3BwT5lRMpyuvGk6UieE44v/7obMiFPQ/ig4cOsji7H40vH48YsDePmx76E0ctFnYppYkHfL5hO49ebPcCynGk8/swx33DHnvAuWMJ/+dAjN7T0I9nPDs3csgqWFaQcdv/rlCEL93HD5wnFG3W5ubg1SkgIRF2uYaZj/FDtGSciMpoAJIWRUo4AJEX9bYa1wJ+9srn758Xr0dhk368LpZMDE2NNjElNDeMCiU8C2vq5u9ig8Xo/6GimExAqs+vq7orqyDWKSkBSI7ZvzDVroODLGF3XV7di1pQCG5ubhiInTYtHdKce6Lw5ADFjQ5KYHFmDBxeNQmt+AB6/7WNC/CSJ+ucdqsPq5X/DO29uxaPEYvP7GlUhPF65ltpC2HCjGh98fRH1zF16590I4GKBlckFFE9Qa4xRj7+7twy8782FlaYHEaOMGLg4droS9xFq0U7nUSjUKDpSe91l4f7eGyUgvhBDDoIAJETX/SB8IWPP1VPcHRa9xi5Cyuf8s/Z3N/zdm0GRAP4D8Y7VobeqGUIJPdqipqWqH0AETVsfjyMEKiEnm1Gie8cQygQxlyfI0xCb449vP9xsl22jBRak8q2Xtp/uQkyWO95udANzx+AWYviCZ1+i467L3USGibCMiDhXlrXjpxd/w4ANr4exihy++ugnLL06H1Xl6Anm8pAE/bsvjbYNX330BAnxcR3wbzdJefPRDltHqC/22qwAOEmtceUG6UWvusM5KcrkK49LE1054iPrklBwrayuhd8WksOCGfoQXCpgQYjgUMCGi1tXSLeiUHInjYO2KPrmxAyb2aK7v5J0leoyY3cI6VjAtjcIFTEIjvJA0NtigAYFzweqWhEf5oCi/nhe9FAu2XywLZ+fWfINmfUTF+aOzQ4EDu0tgjIKrq/41B8lpoXjv5U3o7TFeO+0/wk6OWPec+cvH8ekWz9z9NbauzxF6t4gI1NdJ8dwzP+PmGz+GrY0VPv38Btx+xxy4uAxmB56PWEbJA6/8jPyyJjx52wKMiR35NrhsAOH/2DsL6Kiu9uvvQtzd3V0IhEBwAsHd3d2luBV3l0Jxd3cnEBIg7u7urtBvnUPp1/bfvgWamXsnOb+17prI5J47k5k79+zzPHtvP/kMc0e2p8bcgqa4tBInrvpAVkoSrVyEWzEUGJiM6KhMfgsmFdWQUZDGDxz5jTEYDIYwYIIJg/ceJly25HwRTMpKhOutoKgiRw1PySQtPSlfaOPq6qvCwFhdqCLNX9HRU0F0RDoC3iWAa1q3t4acvDQiw9LAJzx7ONFjIsaKgmLQqJY0WvnEwedCeQ9q66mgUw8npCbl4szPLwT62L6VDj2csGrvcKKg4PENf2xfflXoyVkMfpCWlo/NG29j3JgjkJYRx4FDYzFrjid0dRu2IW5+UTl2n35BBYZx/VrAw+3PSUt1xfVnwXCzN6TeKMLgyn1/lFfWYEjPpkJPzHr3Ph56usrQ0RFcWtl/paaqFuXFFTRKnPH1kE838hFXpxvXD4rBqMcwwYQhAoIJdxUmcorSsG1mQkvyhQm5MCOiCfFQSU0QnoeGtp4y9a6Ij+YuTpcIRaSyIykhh/PKDuLlkZdbAv938eATTVxNEBWejvDgFIGNoa6hgHadbNG48Q94/lBw1Sx/xKOHI/oMa4GbF31x47wP+IS5tS72XZxGo3F9XkRg0fijiAhK5vqwGEIiOTkXm9bfwrSJx1FYWI7tO4dj3vxusLDURkOnorIGCzdfh49/Akb0aIbx/VsIZJzUrEJ4+cWhr4cjhEFJWSUevopAS2djdGsr/Ehk8hno0dEWfIYI25ZNTZmHCYPBqNcwwYTBa4hgwGWFiayCDMLexyONAz8NHQMVfKz5iNQE4Zmf6hp8jktMSxZeVcvf4dLcBEoqskiIyeL0OOwdDaiXSQaHni7/JKj16t8Ur18Ktl1m6JjWyM0pwS/7n9J+emG0wIyZ2gEubqa4dekdwgL5JUjIK8pg2fahmLyoO5LisnFs1yMc3fFAKGlCDG6IikzHmpVXsXXjHfpeWLN+ADZsGgQHRwOuD40XEJ+tPaeeIyIuEy52+pg02F0gPh8fP33C2TvvsWBMR6G04hCuPghEamYhHKx0ISEu3EWT1NR8BAUlw9JSC3ymrKgcUR/iIC7JKky+hU/4QSAbg8EQDEwwYfAa0hv7q5CTYv6IvOJn09fSIuHHnSooydDbjJQ8oY0pKy9FhYrMtAJwCWnPIB4mpIqCS0hlkbyCNJ4/CqWVJnyiUzdH3LvhL9DjUlSWwfDxbaCrp4Lr54RT8SElLYHpi7ujuLAcJw8+Q3Ymt/HSf4VMBj16OuPAlRkQF2+Em+feYumk4/DnmTkw4/shrZC+b2OxYPYZHNjzGNXVHzFpakds2zUcTs6GQjX+5Ht1wabDj3H7aSg8Wlphw7xeNElGEBCxxNxQA3qaSkKrLvkQnETNXvt7OkHYBAclQ1NTEY5OhuAzpCWHwCpMGAxGfYYJJgzew2WFiaS0BL0QKCkUvqeHmqYCvc3JEO6E0dpBn0a8FuZz52NiZatL7CKQGM99pK9bKwuIiTXiXVuOsqocPLo64JkAI4YJvQY2Q0FeCc4f90JGWr7QvHRWbhuCMP8krJx1FmVCNl3+GnQN1LDu0BjMWd0XSbFZOLH3MdbNO49MIT1HjLqHVFHduuGHqROOYvOG21DTUMDseV2xftMg2Dvoc314vOPUjXe49yIMmmrymDWqLWQFEB9MiEnKRnhsFvp2dICwuHIvAAFhKRjSw0Vgj+t/8epVJAwMVCDFc2+QHxo3gp27JRMRvxEWK8xgiBZMMGHwGiJWkJJfriAXAapaitRLRdho6CpDS09ZKLGuf0RFTY4aiKUkcBfrq6OnDG1dZfj7ci9StGhtAUkpcbx6GgG+0bN/U1w49QYV5YJrl5GQEMP0Bd1gbqmNg9seCM2MlSTmkEoTGTlJrF90GTU1/Ekq+mO6DzGEPXJrLqwd9fH6cSg2LryIYzsfooQnST+MfyclOQ8H9zzGsoUXcPzICzR1NcGho+OxeFkvmJh9jjln/Jlzt97jyPnXcHM0ws5l/aGmLCeQcSqra7DjxHPMG91eaJPy0rJKXLzrB0kJMfTpLBy/lD9SWVlDE3Jcmws3led7KMkvRWxgIteHwWAwGAKFCSYMXtNYTAy1HE+UiPt7WkIOjTMUdoVJZmoBcoXckmBook5vkzjwbfkCuTA2MddEemoB560wKqpyMDbVgN+7ON5VOhiZaKCpmymePgwW6DguLUzp6/Hdmxh4v4iCsOjWvymcmhkjIigFR3Y8Evp78GtRVpPD1MU9sPfiNIiLi+HpnQBM7L0Ll4+9QqUAxSzG91NdVYuXT8OxcPZZ7NxyF4F+iejYyQ7nrszExCkdoKHxucKP8X95+Coc+0+/onHKE4e4w0CAiTUHzr3C4K5NoKEqD2Fx4bYfjHRUMLRXUygrCj8mOiAgEaYmGnB15b9gQha0pGSFX4Ej6nz69QeBbAwGQzAI18WKwfhG5JVlOc9KU1SWQ8rHLJQVV0L+N18RYaCho0xvqytr6Kq+sFbXTCy1YG2vhyyOWwvsmxgiLSUfkaFpcG9nxemxdOrqQCt9fF7HoGMXe/CJvoObY9XCC/Do6ijQ8u0p87ogJ6sYezbdgZ2zARSF9F4YOaU9aj9+wsWjXtQEetI8T96Wf5vb6GLriQnwexODozsfwvdVFK6c8MKQSe3QubcL9QhicAc5j8bHZuHh3WA8exRKPaqsbHTRo7czNXfm6+uKTzx5E4n1+x+gia0exgxoAWtTwZmSvvGPh6S4GNq5mkNYFJdU4PJdP+pbs2ZeD3CBr08cSkoroafH/6jqyhJ+LSKICl+igOt6nwwGQzCwChMGryEryrU13LXkEBRUPq8wlRQI19NDU/ezYFJVVYsiIY5taKKByOAURASngkvsnQyQEJvNC+8QV3dzRIWl4ZmQ4nW/1e/F3tkQT+8HCbxVq+fAZqisqMH5o68gLGhyzrQO6DvcDU/vBuHQ1vtCawv63uNt2soC+y9PR58RLaGqoYAnNwMwstMWnD/8HFnp3BoqN0Sys4pw48p7TB59BCsXXUJQQCJGjG2NXQdHY9GKXrB10GdiyVfg4x+Pn/bcozOzQd1c4GInuKSgvMIynLruizH9BBNR/E/cfhJCTX97eNhDU034VUbk3JaWmo8WLcwgCtRU10Bcgt8+KwwGg/FfYRUmDF4jJiGGjxy35CiQKheAihY6xp/bVYRV5m/tbEBNb4UZW0rSeYjZYUJ0llArW/6KibkW5BWkEPiB+/5oVTV5NHE1QWlJBQryy6D8m4jGFwaPdMfSuWfRrrM9ZAVYHt3e0w6BHxJw/YIvLGx00KGrg9C8QkhlSVlpFUIDkrF/8z1M+7Er/TlfIcfWysMW7h1t8O5VFG5f8MX5Iy9w5uAztO/mgA7dneHY3BiNG7N0CUFAIoC9XkTA+1UUAv2S0KyFKZo0NYZHF3uYmmsygeQbeR+UhKVbbsHBUhc9PezR2lVwE3oiWBy78hazRrWDrLQEhEV+YRmOX/KGpIQ4hvZqBi6Ij8+Bv18iBg91gyggLS8NUwcWsf2tCMKklZm+MhiCgwkmDF4jJtYYNdXcVphoGajA2sUI5cUVQp9wkTjjlPgc5GeXQPO3Fh1h0Ky1BTV9zUwvpOarXNCo0Q9o18kOiXHZyMkuhjrHngLtO9th60838fxRCPoN4dfFrLmVNlxbmuP+DX8MGC64FVkyyZwwsxNiItJx68p7WNvrQ1tPWWjvh7kre+HQ1ge4ecGXth+NndGRE0Pmb33Omre1oltcZDqe3w3GrfNv4ecdC2kZCXj0aoKOPZ2E+v6ur6Sl5OGtVzSiIzPw4mk4ZOWk0K23M4aOcoejsxFt6WJ8O8ERqVi86Tqqa2rRubU1PNvYCHS8c7ffQ0dDEbbm2hAmZ675orKqFp1aW0NHUxFc4PM2BtLSEnB05Hec8Beyk/OQFpfF9WEwGAyGQGFXDwxew4cKEwlJcUT4JSI/u1joY2vrf+5hzkjJE+q4yqqyCPVPQlxkBriErASHBiQhgAdpOa3aW9MJLmnL4WNLyPCxrXHuhBdyBfw6VVCUxtR5XRAVmoYtq66hulp4708imkz9sSvGz+mEyyffYM38CwJNCKprTK10MGF+F5x9uhhjZnWCpJQEfF9GYrTnNvw05yzuXPRFQW4p14cpMtTUfETAhwQc2fcY44ccwI4Nd3DxjDdkZCWxdutgXLozFxOndUSTZiZMLPlOgsJTMX/dVVibaWHu+A7o1UmwVWVhMRkIiUrH0B5NIUwys4vg45cAeysdjBrAnSAeHZGBLl0dIC7ObyH4CzVVNRCXZGuv3wqLFWYwRAt2lmPw3vS1EccryIpqn935C/OEP5HRNlCltxnJwhVMzK116G1seDpadRTsauK/pbMQ/Hzi0LmnE7iErPr1GeSKG5feIToiHZY2uuATOnoq6N7XBdcvvsPEmR4CHcvBxQgTZnrg7jU/HN75EDMWdYcwKzYGjW5FIz9fP4vAosknsHrnUKj89j4VBYjZqGffpujcxwUJ0Zl4ejsQaUm52LfuFh7f8IeEpBhae9qhqbsFdH47BzA+t2okxmcjyC8RSQk5VLwknjouzU1p/LdbKwtY2+mhcWMmjtQFIZFpmL/uCq26aNnUFP27NRHoeMWlldh1/Dk2LuhFKwyFyYnLPkhJL4C9lS60NbipLikoKIP3m2gsWMSN2ez3IKcsC31x4VYCMRgMhrBhggmD91SWcevCrqQqR2+LOBBMjK20Ye1kgOKCcqGOa2ajQ31MCvK4jfTV1FZCGw8bJCfmUi8XrleJW7axxPkTr3H3uj/vBBPCkFHuGD/wAFq2sYCto2D7yvsOa4Fg/yQ8vhsMG0cDdBByelDvoW5Q11LCluVXsWP1TYyb5QETC8EldggsPttSm27E4Do8MBnB7xLw8IYfvB6F4cDGu2jqbk4rzVxamMHOxQhyCtJoKJDI0tioTIQHpyIkIBEhAckwMFFHQlwWmrubY9Kszmja3BRaOkpcH2q9IygiFQvWXoWVqRbcmhhjWG/BenqQqr3Nhx5h8lB3qKl8/swVFgnJuYiNz4KYWCOMGSxck9k/EuCXCCUlGbi68T9O+AuZCdmcG/OLIiQC+Ic6rghhscIMhuBgggmD14iJi6GyrIrTY1BSk6dxoJXlwj8ONU0FRAQm08mUUMfVUKAXsG+eRWLOKu6MX7+IJq+ehCMiNBV2Ttyay1lY61DRhHgllJZUQo5nMbHEs4FUfty8/B5WtnoCFZjICvDCNX2xdtEl7PjpJvW6IXHUwqRleyvsOD4eK2efw+IpJzFrWU9OK6L+a7uRXRMjug2d3A5JsVl45xWNtOQ83L/yAXERGVgz59zvAoqNkz6s7PVpmlZ9MDCtrf2E9JQ8xERk0PeXv288fY2FBqdCVk6SVo8MGuUOJxdDmFpqsyoSQbfhrP1cWeLmbIwRfZsLfMwLtz/AxkwLTe2F791x5KwXouKyMLSvK2fVJYRXLyKgraMM5d+M5kUBcu6RFqDROIPBYPABJpgweO9hUiNEj4S/Q0VDAWUllcgUclsMQc/ocypPakKuUBNryDiW9nrwfhZBJ2x6hmrgiuatLXD5tDfee8dwLpiQ56VFG0tsX3cLD+8Eoj8Pkww6drXHw9sBuH7RV6AGsAQiGM1c1B1LZpzGsf1PsGBVHypwCRNSVbLv7CQc3fMEaxdcxLAJbTBicjvem8H+2+vMyFyLboSpP3ZHRHAyAn3jkZ9bgvtXPyDoXTyS4rJpbLGBqToVEUyttKFvrA49IzVICTFd5Fv4WPsRmRmFSE8toMdfXFiOgHcJSEvJR3l5JfAr0LqjDfSN1GDrqI+Zi7vTqHNht2g0VN4HJmLJxhuwMtNCi6bGGC4EsSQ4Mg0fgpOwZUk/CJuwyDR4+cZCXKwx+nXlru2zuqoWH94nYPgod4gSJQVlUOLYkF0UITZodW2FxkNrNQaj3sAEEwavEZdojFqOBRPqo9K4EQpyhN+eoq6tSP0MSKwwMYNUUReeTwOpFvB5EYno0DROBRMbe33a/uL1NBxjpnbgfDW9fSc7/LLvCfzfxVNPE76tdJPnZ/biHti79R7c21kJPOVIz1AVC1b1xtIZp7Fq3nlsOTQaCooyECZKKnKYs7wnlJRl4P0iknpcLN7QHxpa9aNVQ0pGAs5uZnQjzFzRG4kxWYgMSaH+RkHvE6hhLDHg/fTxVxiYaqCyohrObqafxRczTeqbQirWyDlEWUUOMnKSdR7LTCrhSoo+R28TYacwvwxZGYX0/BUdnk7FkfiYLHz8+AmWtjpIScxD05ZmMLPSQqcejlTwMTHTpObKDOHj4x+P5Ztvoqr6I9q4mWNQTxeBj1lQVIbrDwKwfGZXoZ9LySLEgRMvYWOuDRdHA2hxWF3i758AYxN1tGhpDlGCmL4SY3zG9wgmdR0rXKe7YzAYf4AJJgxeIyUryXkiCbmIIz4mBTnCT8khExobZ0ME+sQhNSFHqIKJXRNDiIk3RlhgMjp0dwRXkLYSY3MNPLgZgKT4HBiZaoBLiIDVd3BznPj5Od6+iqLpOXyDiBgkGWTHutvYvH+kwFfnHZsaY+6K3rhxwQdrF17E+r0jhH4RTSpKxs/uDAcXY+zdeAcrZp7DyCntRLZF538hISEGC1tdun2hproGacn5SI7PQXpyHo0Fr6ysQfD7BPp98IdEGJppICk2+/M+JMVoq6G5jQ4VOYzMNVFcWAFVdXn6d8TPQVxCDL9++hVyClL0d+T+eTkl9JxMWtJIQhFpA0uMzYKOvgr838XR+5D9EcystGk0ubOrMSSlxGHfxBCevZvQ16eeoQrUNBQ5F0AZn3n6OhJrd96FjYU2PNpYo19XZ4GPWfvxE9buvo/RA92grCj8NhTv93EIiUij76f1i/uAS954RaOosByGRtwtTnwP2iYa0GDG1AwGo57DBBMGrxGXEKcrGFyjrKGAhIh0akIoJuRS/y+ms8lx2XBwNRHauGS1l0yWwgKSwDXu7a3h+zoGAe/iORdMCF17OePs8Ve4esGHl4IJgbTjkDSf+zf80b2f4FeKPbo7oiCvFEf3PsbhXY8wdX4XTtpimrUyx84TE7B99Q3s3XAHH7xjMXFOZzqRr+/nSlJJQra/UlVVg/ycEroRwaMwvxRFBeUoKSz/vdWPvNdzs4roOS4tKY+2zpBzXVVVLYzMNJCamAsjC01kphXS2F7yvyW32npK0NBSoNVwrm0saJWPsqo8VNTkoKomz/wNRID7z0Kw7eBj+lro2ckBXTvYCWXc8zffUUNZR2vheh99gQgl+jrKaO1mDrXfPme5gKQ/pSTloWUrC5ETEKM/EJG04RhR1xWCiAFmscIMhuBgggmD9x4mpLeXa3SN1ZEQkYbivFKoaAq3bNfgtwkQEUyECakQMLfVpckdxYVlUFDizojOqakxampqaZVJXx74hiirymHQ8Ja4e92PJsU4NBG+UeHXVOaMntIe8yYeh6OLEV3VFzQDR7nTtoxj+57SCgRiCstFyxKpkli/bwQe3wrA/s33qVfG8IltqWFqQ0RSUhzaeip0YzC+QASSs1ff4fDpV7Cz1sXAXi5o39JSKGM/845CbGIuVs8VXiT5X2nmZIST+8ZRkZBLIsLSEBaUgnET20HUIAta4pJsKsFgMOo3/Gq+ZzD+AvkgruGBYCIjL0W9AfKyhN+WY2yhBXM7XeoDIGxatLOEho4SbcvhEtI+QPw4EuOy6cYHOnV3pG0K5497ga8YGKlh7LQOOLb/KWqEFP04aHQr9BzYDHk5xdi9/pbQE56+QFZqO/dugkOXpkJbVwnLp5/GoS33qK8Gg9HQIVUNx869walL3pCSFMeYQS2EJpbEJ+Xgyl1/LJ7WmfOKCrHGjejj5xJvrygaJ2wr5JSxukDXTBtKQl5Eqg/8KqCNwWAIBiaYMHiNup4q3HsJvp3g31D97YIgL7NQ6GOTBIyY0DT4PIsQup+LiYU2stMLEeLHfVtOO0972DoawNcrGnxAR08FXXs3QVV1LY085ivd+jTBx0+fcPzgM6GMRyZA0xZ2pUbBD28F4sSBp9TkkytIVcXCdf0xb01fhPgnYUKfPXhyO4DTY2IwuISIp+t23sXJi29haaqFnWsHwbWJsVDGLi6pwMZ9D7B0RhdISzFzX/KZHhOdia49nXhnIP5vEDE86kMcqsqquD4UBoPBECiidXZmNDhIQo2du3BWvf4XX9pwcjOLhD62lr4KJKXFUVxQRpNyhImtswFt7SApHFxD2koy0vJpGwxZHeUD/YY1R3hQCk4feQm+QgSM+ct7ITUpDz5CEpuIWTGJgx0+sQ0unniDbauvU08MrvhcbeKMjYdGo62nLY7vfYI5Iw8jIiiFs2NiMLigrLyKiiXv/BOhriaP+VM7wdZSRyhjE5PX9bvvY+Lw1tDTFmx6l6iQGJ+DgA+JIlldQtpxiDG8GGvJ+W4Pk7reGAyGYGCCCYPxFZC2FDkFaU4qTMjk0/A3H5OE6Ayhji0lLYEO3Rzx6SOJCy0Hl5DVt/ae9shKL0RYIPcVLwR9QzV06GJPxYiwYP5OvhWUZDB0TCvsWH8b6an5QnvdjpzUHv1HtEBCTDY2LruKao4NnMnzMHNZL6zZM4J+vXLWGaxfeBHpKXmcHheDIQxyckswY/F5PH8dBWsLLRzYPAxGBsJLZdl//DmaORvB1clIaGPyHf8PCVBRlYOzi3AqfOqSqvJq1NZ8hJg4E0wYDEb9hgkmDMZXtuSUFpUjj4MKE0KTFmYwMFFHYnSm0MfW0FZEQnQmAt/Fg2s69XCEpa0Ont4PBl8YObEt8nJLqE8I1xHY/wtrez2Mm9aBChckNlZYlR0TZ3dG+y528Hoajs0rrqG05HPkLJeQuNt1+0di7qo+yM4oxJwRh7Fvwx3kcxAdzmAIg9iEbPy45gqKSyrhZKePlfN7QFNdQWjj33oUhNKyKvTvJvi4YlHi4d0gODgZUJ8uUaP6t88RYUfI1wuYiQmDIVIwwYTB+ArUdD6XD+dmCL/ChKCioYDk2GzEhqYLfewmbqb0NsCHe8HE2EyTVru8eBiKyopq8AFtXWXqZRISkIwPPnHgM549nWBorI4DW+8LTdwhosngMa0xd0UvRIWnYd74Y8jmoFLr746rZQdr7DgxAePmdKbx2VMGHsCRHQ+Qn1vC9eExGHXGO/94zFh0DvGJuXB3NcW2NQMgLye8mG3/4GQEhaZgwVTuTV75RGpKPhListGqLfdtx98rmFg3N6Ot04xvRBDtOKwlh8EQGEwwYTC+AjlFaUhKS6AoX7geIl8wtf7cYx4XIXzBxNJOD07NTehKPB8qKDx7OVPDTu+XkeALw8a2go2DHg7vfoyPtfw1EyWTlVlLeqAgrxQXTrwW6thdejfB7CU9UVhQhs3LryIqLA18oLFYY3Tp64JdZyZh1LQO8POOw5QB+3Bg811eCDsMxvdCztdXbvlh9abbUFGSxchBbpgz2QMSQmyhSEkvoK0408e1h6SE6FVRCBLiKeXUxBCubmYQRaorqxHhG8t5LDODwWAIGiaYMBhfOdHUM9VAekIOJ6KBsaUW9VGRlZdCRblwHemJqZuMrCQ+vIlBWhL3Xg/tu9hDVk4Kty69A19QUZOHa0tzJMXn4MHtAPAZUvo9d3kvPLjhL3TRqZm7OTYdHIWs9CIsnHQcr5+GgS9ISoqjxyBX7Dk3CRPmdkFkcCqmDtiPzUuvIC5KuN5BDMZ/hUxij572wt6fn6KqqgajBrfAhJGt0aiR8Fahi4orcOS0F5bO7kYFG8afefYkjH6WScuIZlrQ7y05Uqwl51shl5GC2BgMhmBgggmD8ZUoqsqhsrwaJYXCNz+VlpWkFS6RgclIjBK+j0mzVhb0logmXCMnLw1Xd3NEBKcijgNPl3+i/zA3NGthittXPqC0pBJ8RkVNDqt3DMGeDXcQJuSkGBMzLew5NRFtPGyx9sdLOLrnMa9WKCUkxGmizq7TE7FwfX8UF5RjwbijdPN+HklNDhkMPlNYVI75yy7h/JV3cLDRxY71g9Glo51Qj6G6phYrN99ENw87mBqpC3VsUSAjrYD6ObVpbw1RpaqyBhZNTSAjJ831oTAYDIZAYYIJg/GVaOiq0NvsNOGkjPwVi99iB6NDU4U+tou7Gazs9ZAYmwU+0L1/U5hbaePVYx5VKEiJo1MPJ8THZOHML/yNGf6CsakmFq3th9OHXyA5MVfoFTmzl/ek/8fQwCQsm3maturwCZLy49bWCusPjsL24xOgb6yGQ1vuYmTX7bhy8jUy0wq4PkQG4/8QE5eFLbsfIDAkBfq6Klgyrzsc7fSFegwk9v3g8Zfo0MoKbi4mQh1bVHj1PAK5OSVwbSma7TiEqvIqRH/g3ttMFGGxwgyGaMEEEwbjK9EyUIG2oRryMrhJyjG306W3MaHC937Q0FJCTc1HPL0diPIy4bYE/R2WtrpoLNYI18/78CJ15QttPWzQxNUEcVGZSIzNBt9xdjWBZy8nLJ99FjlZwk2IERcXw6ylPdGtX1OEB6Vg45LLCA3gR1z0XzGx0MLsFb1x8PJ0DJ/UDoHvEjCm+07sWHUdT3jynmAwHj0Lw/QFZ+HtG4ve3ZxwYPtw6GgrCf04fj71CuLijdG7q5PQxxYVXj6LQFNXU8jVsfludnohPn36JDQPE4I4a8lhMBj1HCaYMBhfibq2MjKScpGZzI2Ph5WjASwc9OgxcIFbW0sqmvi/jQUf6D24OS0JfngzgFdeN1PmeSIsMBl7Nt+lK618p72nPQaObImd62+hMF/4VR6kKmf3yYmoqa6lviaXTr6mpr58hPgNEJ8TEkl84NI0aOoq4fiex1g27RQ2LrqE10/DqV8EgyFMSEvb3kNPced+EKqqajF6aEvMmdYJMjKSQj+W2w+CUFBYhimj2wp9bFFqx/n140d07FL3bVJ7V14TWhJRZVkV9cRpLN5YKOPVK76k2tT19h3s378fRkZGkJKSQvPmzfHu3T/7w4WFhaF///70/uR1tmvXrv9zn40bN6JZs2aQl5eHhoYG+vTpg6ioqD/dp127dvTv/7hNmTLlu46fwRAGTDARQb7l5EYoLCzE9OnToa2tDUlJSVhYWODevXtCO976gqaBKr3NSuVGMCHGr/ERGQgPSEZpsfCrKtzaWcHcRoeaYfKB1h42aN7aAg9v+fNqgm1koo4BI1ogJSEHzx+GQBToOaAZHJwNsWTGaRQXCd+jx8RcCxv2j0LnXs5UACPCSWY6v1teSNXJ8EntcerBfIyd2YkaMl8+7oUh7Tf/Jp6EoYznXjYM0ScntxgLl13G1Zt+iIvPxuY1/TF2RCuhmrt+4fnrKDzzisT8aZ05GV9UePEkjLZBNm1uWqf7Jf5KJPVLWIJJTWUNXRQghtkM0eTixYuYN28eVq1aBX9/fzg6OsLT0xPZ2X9fIVteXg4TExNs2rQJWlpaf3ufly9f0jmHj48PHj9+jJqaGnTu3BllZX9ekJk4cSIyMjJ+37Zs2SKQx8hg1AVMMKnnJ7fq6mp06tQJiYmJuHLlClV5jxw5Al3dz+0djK9HU++zh0lmMjceJhKS4jCx0qZfRwUL16iTYGatQysQHt7ww0cemHSSlg5rez0kxefC+3kE+MTQ8W2grqWIA1vvIz+Xmyjqb2XI2Nbw6O6AJdNPoYQDQU5KWgJzV/TG2Bkdqdi0ffUNPLjhx4so6/9F48aN4NDUCLOW98KOkxPx094RUFGXx4m9TzCo/SbsXHMDV0+9of4/fH8sDNHig38CJk4/iZqaWhgZquLnPaPg1qxuJ+FfS0BwMm7c9cfapX1YfPC/EBOVAbdW5pCVrdsKoOTYLNi4GEJo/PADrJqbUUN6hmim5OzYsYMKF2PHjoWNjQ0OHToEGRkZHDt27G/vTypHtm7diiFDhtAF2L/jwYMHGDNmDGxtbekc5cSJE0hOToafn9+f7kfGIaLLl01BQeHbHwCDISSYYCJifOvJjfw8Pz8fN27cgLu7O61Madu2LT2J1We87wZgivtq7J5zqs72qaKpAFtXE0hIcXcx6NjcFJaO+kjmwB+DrFq17GCNkqIKhPjxw2uCmIaSSMbXzyN4NRmVlpbAxFmdaFrOqZ+fQ1ToN6wFmrU0x5YV1zipNCG06mCDQxen0yjrnWtvYc/GO8jOKIQoQMQTexcjTF7QFUeuz8Khy9NhaqUNf584HNx8D8M7b8PWZVfx8Lo/0lPyefWaZYgOpKLu9DlvrFp/gybiaGsp4eDOkdD7zZhc2ETFZGLv4adYPr8H5OpYBOASQVQuJifk4PWzCLRqV/fpOFEhqdD5rRJWGBTnlSDSN5a15HwPvwpo+wbIgioRMTw8PP5kdk6+f/v2bZ091KKiz75/Kip/Pj+dPXsWampqsLOzw5IlS2j1CoPBV5hgIkJ8z8nt1q1baNGiBS2P09TUpCemDRs24OPHf64QqKqqQnFx8Z82UaO6sgaJEWlIi687YYE810V5pXj/LJyziY6JtTaiglLg/zqak/FbdrCBmbU2Qj4kgA8oKMrQdpIXD0IQ4s8PEeePhqpDx7XGg5v+eMmjNJ9/E8VGT+0AGwc9LJp6krPkGlV1eazeMRSL1vXHm2fhmDRoP6024VPr1dc8l/rG6ug1uDnWHxiFtftHYvHGATAw1cCze0GYMmAflkw5iXULLuL2pXfU96ay4rOJIoPxT+TklGD+4gs4dtILpkYaWDDbE0sXdqciLRckp+Zj54HH+GlJb6iryaO+EB2diWNHXwqkHYckqrm1sqjzfceEpsLc7nOanrCuswgSzPSVV/z1+p1c0/8dubm5dC5A5gZ/hHyfmZlZJ8dCDIjnzJlDF2zJ/OMLw4YNw5kzZ/D8+XMqlpw+fRojRoyokzEZDEHA6iZFiP91couMjPzbv4mPj8ezZ88wfPhw6lsSGxuLadOm0Z5C0tbzdxDDpjVr1kCUUVCRo7fF+XXbDkFSclLjslFcUAbF38YQJtbOn8ttIwKT6QcREXGEiV0TQ+RlFePe1fcYNqU9XVHnmp4Dm+HqGW9cOO4FBxcj8AlipvrkbhD2bbkHe2dDqKgJ/zXzPRP9oePb0l74fZvuYvI8T6hrKnJyHB26OqBJcxMc3fsYR3Y9xt1rfpi1pDvMrUWvpVBCQgwOTY3pNnhca9pGER+ViYjgVGSmFuDGeV/k55RAQ1sRppZasHEygJaOMgxN1aGmoSA0XwIGf3ntHYPDR1+gqKgC+noqmDWtE8xMNTg7nqzsYqxcfwNL5nfnrLpFUFw4/xaTJ3eo032ShZaA9wno0ddFIAJXSWEFNHSEl4okJt4YNm7mEJNggsm3IogY4C/709f/c4w4udZfvXo1uIAs1oaGhuL169d/+vmkSZN+/9re3p56LHbs2BFxcXEwNeWmrZDB+F8wwaSeQybVxKX68OHDaNy4MVxcXJCWlkZ7EP9JMCFqL/FJ+QJRqP96AuY78sqyf1oBqUvBhJCZlMeJYEIuhlQ1FVBaVIGkmGxqBCtMiEDSqpMtbl/wRVhAEp38cY2GthJNzIkISUF0eBosbHR5laoyf2VvHN37BLs33Mbq7UNEZuI7aHQr3L36AQsmHce63cOhb6TOyXEoqchh/qq+aNc5Fge23sPm5dfg1MwYo6Z0gIKSDEQV4sFjaadHN8LEeZ60hYt4ncRHZyInowjP74UQmwDERWdCz0AVVna6kFeSof8LNQ15qGsrQlVdgUa4cgmZCJYUV0JSUoyunjPqlsrKGpy58BZnz3+uJB08wBWjR7hzVlVCyMsrwU+bb2Hu9E6wNPvzIo6oExOdCR0dZWhqKda5d0lYcAoGjWyJuoYkxtXW1Ar186UguwjhPjHUX43BH1JSUv7kB/JPXiOkHYbMC7Kysv70c/L9Pxm6fgszZszAnTt38OrVK+jp/e/KJxJgQSCLukwwYfARJpiIEN9zciOqrbi4OP27L1hbW9NyO9LiIyHxfy+4yMn1n06wooKiqhx16c9N++wTUFcXEUbWOjCx0UVmSi4sf6v2ECbkcbi1t8a9i74I908UumBCaN3ZDpHBKQh4G8sLwYTQfUBT3Ljgg/NHX2HV9qHgW2uOrZMBblzwxb3rfujerylEBeIRo6AkjVXzzmPhmr6wtudOOHVpYYaD56fh2llvnDv6CmlJeXBrY4nuA5rRlc76gJy8FOycDen2R4ifTGpiHk0OykjJR0JMJh7e9Ed2ZhHKSyup2R95nRUXV0BJWYYKSaRdTU1TAY3FGlGfHxIxKy4pBikpcfp8EfHzy/NGzo5kH6TlqfbjR9RWf6RRtRUVNagsr6aTMSLmkArHnKxiFBeWgyRmZ2cUQVVDnppByytKY+iY1jAy467ioT4SFZ2B9ZvuID+/FAb6Khg5zB0eHWw4PSbim7J41VVMGtsWjnaitZjyNZw79xaTp7Sv8/1+8I6DnqEqmrqZ1fm+E6MyYOlkAGFS9VsLIWvJ+U4E1NlNxJKvMVAl1/9kEfXp06c0+vfLIiv5nogd3wu55p45cyauX7+OFy9ewNj4368TAwMDf5+zMBh8hAkmIsT3nNxI3+C5c+f+1L4RHR1NT0p/J5bUFxRU5fDp4ydUf/yEyrIqSMtJ1cl+VTUVER9OvFFywBVGFlr49dOvCH2fgO5D3YQ+PmnLKcgrxb0r7zF8SgdeTFb1DNXQztMe3i8i6Wq8qYXwhaT/xbjpHZGZVoArp97AvokhDDiq1vgeWne0pZPvDUuuYPqP3ahIwRUSkmIYMq4NOnZ3xIl9T3Fgy30Ef0hCxx4OaNHOSmSqd74V8vzbOJLt7yenFeVVKMgrQ1HB5620pAolxeUoK61EXm4Jqipr6X2qq2qhpCKLrPRC+plAIkEJWtrKyM0ppiIKMbUWExeDuqYCaqo/UrFFQUEaUjISUFRWgJmVDhSJIKMsCzk5yXr7nHMNEazOnX8Lv4AkpKTmw85WF0t/7AFtbeG1XPwdRJRbs+EmRg1zRzMXfgjmdUlUVAbk5aWgpVW3zzMRI29deY/mrSwEUhFG2nRJ1LkwIddWRIRtJMZ9ay7j+yDV5KNHj0bTpk3h6uqKXbt20fhfEixBGDVqFE3VJK36BLLQGh4e/vvXpGKdiB1ycnIwMzP7vQ2HzDtu3rwJeXn53/1QFBUVIS0tTdtuyO+7desGVVVVBAcHY+7cuWjTpg0cHBw4ey4YjP8FE0zq+clt6tSp2LdvH2bPnk0V35iYGGr6OmvWLNRnpGQkISkjgaryahTll9aZYKJj/Hmim56YC66wbfrZpyPkfXydVs98LUR4a9fVAVdOvIa/TyxcW3M3gf4jwye2pWkkpw89p4ahfIK0KYyd1gEzRx+hwsOeExNEqozZsakx1u8djpVzziM1OQ/9h7fgdKJMPFUWru2H3kOa4+yRl1gz7wLadLZFz4GuNOK3oSEtI0k3Hf365SPRUElMzMWWbXcRGZVBo2cnT2iHgf2bce4ZVVJaiUUrr2BQv2Zo3dIc9ZFzZ7wxbfr/N9avKwI/JNCI+Y5d7CEIooJT0K6HE4RJdWU1aqpq6fUWgz8eJt/C4MGDkZOTg5UrV1Jhw8nJicYCf/FKJHHAf/TKS09Ph7Oz8+/fb9u2jW4kfZNUkxAOHjxIb9u1a/ensY4fP07jhsli7ZMnT36fv5CW//79+2P58uXf/dgZDEHDBBMR41tPbuRE9PDhQ6reEuWWiClEPFm0aBHqO5ZNjFFZXoXi/DJoGXz2HvmvaOqpQE5RBhWlleAKQ3NNNG1jiez0AqQn50H3N18VYdK+mwNC/RIR8DaON4IJqTJp1socj28HIiosDZa2/PEyIRiZaWLKvC54/iAEh3c/wowfu0OUMDDWwK7j43Fg2wPsWncb0xd1o0amXGJhq4s1u4bBzycOdy6/w8KJx9Gqow36jWhB26AYDFGCVCFcvOSLk6dfw9pKB8ZG6li8qDvMzbivmCstq8KaDbfQt1cTtG9jhfpIWFgqVFTl6ty7hPDycTiau5vDTkDnJSLKK6nKCd1c36KpCcQkuK8yZXw/pEL9n6rUv4ggXzAyMvrXlMh/+z2Zl7x8WfcJVAyGIGF1dCIIObElJSXRqDBfX9/fzZK+nNxOnDjxp/uTWGEfHx9UVlbSUrilS5f+ydOkvlJRVoVo/0QU55bU2T7FJcSo6WLY+3hwBRHESGtCcmw2Qt9xcxwmltrUiJC05ZCyf74wYmI7KpTcvOADPtKtnwuNzL196T2ePQiGqEEMWBes7oOPnz5h+awzyMvhR+S4i5spVm4bgtU7h9IWsXljj2LR5JMIfJfAWQQ4Q3Qgr5HQkBRsXHsTWVlFnBxDbGwWVq66hmMnXlHhxN5eDwf3j+aFWEIrS5ZfQmcPW3TuYIv6yuNHoRg2vO4NWSsqqvHySRiMzTWot1pdU5hfSq93hE1qTCZi/BJoCx/jG/lVQBuDwRAITDBh1Fu+rLYU5tbtpE7PVANFeaUoKSwHVzg0/+wiHuTLjWBC2jE8ejpTM8jXj8PAF7R0lWFpp4un94IRwJGY9G/P2+xlPWls7L1rfkiMy4aoQapKSPJP+y72mDPmF4QFJoMvzy3xMVm8oT/W7B4GZVVZLJp8ArNHHcHbl1F0Espg/JGysircuuGHbZvvwvt1NMaMbwtNIUdoV1XV4OxZb0yZegJv38aiTStL7NszCuPHtuW8gotQUlJJPUt6dnOq12KJv18CTXlSV5ev832/946Bmro8OnYRjD9DVFAKLB2Eb75bU1VDDV+ZjxGDwajvMMGEUW9RVPt84VNUhxUmBF2TzykQ6QncGb86upnSCyQSL8zVCnr77o6wa2IEP+9Y8InhE9pSo8orp99QY0u+ISMriUVr+yM2MgM/LbyA0pIKiBrkArlrHxcsWtcf546+xLVzb3lTyUGTpNpYYtH6/thyZAw0tZXw0/zzGN9nDx5c90M5ByuxDJ5VkwSnYNuG29i28Q5NDZo11xOTpnaEto5wDVX9/BIwYfwvuHc3kIojo0a5Y/GiHrCy5EdSRFFxBX5cegldPOzQpZNgvDf48pq4dvUDhg5rIZD9P7gRAGlpcRiaCMbsOyooGZb/YAgtSFR1VWDb0kLo49YPfhDQxmAwBAETTBj1Fn0Lbdg0N0NNdW2d7tfUVg+2ribISM7l1MeEeJi8fxmJFI6qFEhriay8JF49CEZaEnfPxd+1jYye2gH+b+Pw4mEo+Ai5cCatLbW1n3Bg632RrX4g8bfzV/WBz8sobF52FSXF/BF/iHBCzGqXbRmEgxenwbGZMa6e9sZwz+04vvcJkjkUPBnCJzurGOdOvcG2DXfg9SIS/Yc0x6p1/dG5iwMkhWzAnJdXinXrbuLnQ8+Qnl4IFRU5HNg/GmNGt+ZFVQkhv6AMa9bdwMD+TeFRjytLCK+9omBupgklJdk63zeJ4SbVjp26O0JQxIWlwcxWT2D7/8dxA5OQGJYq9HHrBawlh8EQKZhgwqi3NBZrjHDf2DqvBNHQVUaYbxwSI9LB5WTQ6beUggAOKzy69HWhtw+v+4FPdO3rAhVVeRzb84S2DfGRVh1s0KmHE20fOr7/KUQVFTV5bDwwChZ2Opg54mfetOj8ESNTDcxd2Rvbj43HsIltkByfg4n99uHHicfp88/X1wjjv1FaUol7twKwdP55HNr7GPoGqpi9sCumzuoE498qBYUdFXz7lj/GjP4Zz5+F08qG2bM7Y/eekTAyEr559z+RlV2MBYsuoF8fF3RoZ4P6zMfaT7h2+T0GDP7/XnB1ydN7QTA200CbTnYC2T+polRQkaWmr8Kmpqoa4iKU9sZgMBjfCz+WMhgMAaD0Wy9yYXbdepjom39OJEqJ/ZwtzxXOLc0Q9iEBKfHc+WA0a20J1zaWNC2ndvpHarjJB6SkJTB2Rkc8uOGP6+d9MGRsa/CR4RPaICEmE8F+iXh0OxCdewo3FrKuIHGn/Ya1hIW1Li6e8IKZlTaGjW/Lm9fDFxSUZDBwdCt8GvkJAb7xePsiCjtW3cD+jXfRe4grXFtbwMpej/XkizClpZXweRODl0/DoaQsAzMLbfy4vBeUlOu+euBbCAhIxL49j2lksLm5JgyN1DB5SgdaXcInUlLzsGXbfUyZ2B6uzUxQ33n0MBgennY0vrmuIYLY4zuBMDBWh7KKYF5/xPxdWgDH/jVUV9RATa/uPV8aBIKoCGEVJgyGwGAVJox6i5K6Ap34kGjhOt2vmjyU1eVRXsJtOoyzuwWy0wvx9IZ/nbcdfS1kQkwSc2LC0+DzMhJ8okN3B9TUfMS5wy+Rw1HyxdckHi1c0w+fPn7E7nW3EPQhAaIMadEh/iyZ6YVYMu0UkjgU8/7teXdpYYYZS7rj7MP5GDmlHQLfJ2DO6F+was45nDr4jFahMESDgvwyPLofjOULL2D9imvIzy3F9LmemL+kJ3r3b8qpWJKeXoBVK6/ilyMvqVhibKyOqdM6YsnSXrwTS2Jis7B0xVVMGNe2QYglJL3mwd0gdOosGH+WyJBUyMtLC1QIjwhIgrWTIbigorSSicsMBqNBwCpMGPUWZXUFKrln1rG/BrlA0DJUQ4hPLGpruKuqUNNUgJGlFhKjMhHun0SNYLmg64CmuPjLS9y/8h6tPGx5NSmetqgbZo88jCunvDF1YVfwEWJQu2bnMMwe8wsuHPeCsqocXZEUVWTlpfDjT/3w9mUkls04Qys3+g5rATExflWbfEFJRRZ9h7egW1JcNny9ommC0bUzb6FroEqTd1p5WMPQRINNDngCWblPjM9BcGAyjWv9odEP6NDZFjPmdYGWtnCNW/9XOxBJv3n5MgJZmcWQk5PEvPld0KWrI63I4huBQUk4cuwlViztBQtz7qOMhcG924Ho1ddFYL4xD24FICO9AM3cP7fPCgLiYdZrZN1HIX9Ne5mxvT60jITf2lYv+PWHz1td75PBYAgEJpgw6i3KmoogwR0F2UX0ArsuJzuGFlqI+JCAtIRsGFpwl2jg0soC6Ym5CPdP5Eww0dRRRvdBrnhxL4iav+oa8qcX39JWFwNGu+PyiTdo0dYSTq78XDVVVVfA+j0jMHfcUSybcRq7TkygPxNlWrS1grWDPs78/Bzzxh3FnBW9YMLziZihqQbdBo52pylGXk/C8fx+MBVRiKFt6442cGlpBlsnfYiLs49PYVJcVI5Av0T4+cYjLjYbGpoKaNnGEis3DOC83eaPVFfX4smjUBw+/Jy+Zmzt9NC8uRnGjG0NRUUZ8JFXXlE4dvwV1v7UD/p6qmgI5OeV4s3rKGzbNUIg+y8vr4LX0zCaJiZIsTg2NBWaeioQNtUV1Yh6Hw9JaW7agRgMBkOYsCs+Rr1FUVWOiiQ1VbUoL66AbB1erBr8JpIkR2dyKpi4trPGzZOv4XU/GEOndeTsONzaWeHORV/cueCLyYu6g08MHOWOB9f9sXfjXRy8OAUSEvw0qSMT9VXbh2Dj0ivYvf42Fv7UD/IK0hBlyER2xuIe8H4RiXU/XkJ7T3sqRhCPGT5Dzhvm1jp0I144SfE58H0VRW+XTDlJj79DN3sYm2vBubkJrURh1C0k/jkiLA2BHxKoUKKrrwp1DXm072yH6fP1eJMm84VPn37F82dhOH70FU3BkZGVQHM3U0ya3AFGPK4Yu3U7AF6vI7F961CoqvKrRUiQnDr+CqPGtkGjRoJZlX/9JBziYo0F2o5TlF8KFQ15Tirfqiqq6a0Ez8/lfIUs5pGtrvfJYDAEA7+uOBiMOk7JadrJDpWllcjPLq5TwcTYRheWzkZIT+TW58Da2QDiEmJIiMxATkYh1DkqR2/S0gy6hqp45xWFkdM9ICPHn1UnRWVZTJrXGXcvf8CVE28wbFI78BUSgztzcXesW3SJemls2D+S9+LC19CynRUcmxrh1IFnmDr4IKYv7o6mLc0gCpDJCEnZIRuBtHb5+8Qh+EMiLh7zwuPbgSjILYVDUyO4tDCFha0udPRVWPvON5KbU4yIkDSEBicjJTEXuTklcG5qDBt7PfQf6sarKpI/QqoXvd/E4MTRl5CTl0JGRiHMLbQwZVpHOHHkLfG1As8vv7xAVHQm1qzuCzk5KTQUEoi30q+Ak7Pg/j/EcFxbT4W28gmKCP8kmNkJP06YUFlWBZsWFtDmIG2KwWAwhA0TTBj1mrz0QsSHptC2HP06bAcwMNNEVEAijRjmEiKWNHE3x5tHofB/HQ3Pga6c+YUMHNsGu3+6gad3AtBziBv4hEcPJzy6EYDzv7xCG0876PGobeivuHewwcwlPXDt7FtsWXkNi9cP4N1q+vcgKyeFqT92g0fPdBza+gC3Lvpi8vwuIledQap+2na2oxuZLKen5FPxhCQdXTz+GgkxWXBsZkwnzyRxx8ZBHyaWWpDhKMmCjxQXVSA2KoP6kIQGJSMuOhPWdnpQVJaBrYM+Bo9y550h6l8h//t3vnE0kjYoMAm1tZ9gaqaJZSt6o117G4FVLtRV29Ce3Q/xQ+NG2LRxEMR5lmYl6P/bLwefYcrMTgIbIzk+G40a/4CuvZpAkIT5J6KlgOKKv0YwCX8bDXUO2oHqBSwlh8EQKUT/KpzB+B+oaCpSwSQ/s25TUpQ1FCCvJIPEyHRwTauuDgj9EA/vx2GcCSaENl3scXjbPdw44009TYiIwhfIscxa3gsLxx/FxaNemLu6N6+O769069cUFeXVOLzzITYuuYylmwbWG88M0uay9ZcxeHInCPs33aViwtBxbahZrKhBqkiI4EO2rv1c6M+yM4sQE5GOUL8kvH0RifvX/JCRWoAmbiZUbDGx0IKZtTb0jdSgrqVYrytRqqtqkZaaj8S4bORmFyMkIBkJsdnQM1ShlV+WNjoYMMyNRv9KSIqJzITb1ycWTx+F4vmzCPqzFu7mcGthBs+uDrw1N/5CQUEZTe1xdjbEmLFt6vXr7+/44BtHhS19AQq192/4Uw+ktbuHQ5CUFlXAzFYXXFBd+bklR0qGCcHfBTN9ZTBECtG4QmEwvhMVLUWISYihKK+kTvdLLjKd21ghP6sIVZXVkJTirm3CqYUZigsqEOgdg8ryakjJcHMsZAW9S7+m8H8bC783MWjW2hJ8wsBEHf1GtsTxPU9g5aCH7gOagc/0H9ESlRXV8POJw9aV12hcL2kzqw8QsapzL2e06miDi8e9sHnZVTRrZY6ufV04S52qKzS0FOnm3t6afv+x9iOSE3KpaBAdlgZ/33i8ex2N0IBkKrTIKUhBz1AVFja6VETQ1leGlo4yrbYQhclsZWUNstILkZNdjLTkPFpxU1hYjqjwdEhJidNKG9LORMSRoWNa0a9JMpSo8fHjJ3i/icaZE17UdFZbRwlqanIYMrwlunV3EgnBJzEhB9u330OPns7w9HTg7DhI3HtUZAbs7PWEPu7pY15Yt22IwMaoqqqh7+/2XewhLUAxobqqBlmp+bTKlKtIYXKulhTB9zKDwWB8K/z/hGcw/gOkXLS2uha5aQV1vm8lNXm8uuWP1NhsmHLUR0yPQ1UO1k0MEe6XiADvGLTgMNq39/AWuHH6LS4d9eKdYELoP9Idrx6G4vSBZ2jqbg5NnkSQ/hPDJrSl8Y3nfnlFZDr8+FNfiNWTSpMvItvYGR7ISi/A6UMvqDnvoDGt0LqTDa8rgL4FInIZm2vSjUyivlBcWE69OlIScpCanIe0lDw8IjGkaQU0rpyIJU6uxrRKQ1VdHvrG6nRSTrw8lFRlabWKvKI0FSTIxKyuomqJMECEOhKLW1ZSSdNpCgvKUVlZjay0QpouUlZSRY+XHA85XvI+snXUh6SUOEzMNWnLm76hKhSU+JkK863tKy+fh+PsqTdITcmHmZkm1NXl0X+gK7p2d6KPWRR4/y4Ou3c/woKF3Tj1ViEVOnv3PEKzZsJPLLt59QM8uthDQYBm2m+eRiA9OR9LNgyEIIkOToGFvT64gqTkkPMUWZBifDs//Pp5q+t9MhgMwcDOdIx6jZLa52hWUglS1xhb69DbhIg0TgUTQodezshOK4D3o1BOBRMSMdy2iz2e3w1CVHAKLB24u6D7O8iK2Lyf+mLdgovYufoGNhwcxeuJOZk0j5rSgX4d+D4Rm5ZexeINA0S+CuPvXjcLfuqLhNhMnNz/nFadjJ3pQY1hRaHK4nsgYoKtkwHd/sinT59oQkx2RhHyc0t+vy0vrUR0WB5t1crNLUZJUQUMjNQQ5JdI/87STpdWeiiryOHjp0/Ul4IkLxGR44dGP0BdU4G2xZCWESLCECFOS1cZCbFZqKqqha6+CsKDU1BVWfO7CGNmpU3FE3KsBsbqtHrNTt8AapoKUFNXoPusD6bEf0dRUTnu3QrAjSvvoa6hQMUSXV1l9B/kirYdbETGV4gIFJcu+uKtdww2bR4MPY49J+7cCYSmpgJatxGuoJ6XW4K3r6OxeecwgY5z58p7mFpo0/eOICGGr3auxuAKIpaYNzGGopo8Z8fAYDAYwkI0PvEZjO9E9bcKgrzMwjrfN0nKIaTEZoFrnFqaYd+q6/B9HkFbALhs3eg/thWyMwpx/8oH3gkmBFNLbdoOcnL/U9y++A69h/LLoPavEMFg9NSOEGv8AqcOPkejxteouCAqE7ZvwdhMC6t3DkVEcArOHXlFU3VGTGkH11YW9VY4+StEwJOTJ5Ujn/1OvqYCgggsleVVVPgglSC11Z9QU12LX/Erams+0aqRxo1/wKdPAPEibSzemAon5DUkLilGb6VkxCEjI0kFkLqqVhFFkhJzqUjy9GEorYDKzyuDqpo8lq/ui9ZtrUTquSHtUkd/eYG01AKs2zCQ8yQcItqEhaRg0ZKeQh+bGL2OGtdaoGJzQkwmPtZ+Qt9hgv9MCfGNQ1cOP7tKC8sQ45+AFj0+ezcxvhFm+spgiBT174qbwfgDajrKsGthDmkBJFQYWWpDVkEK0YFJ4BpdI3UYWWghMToTIe/j4dTCnLNjMbPWoRONh9f80H9MK+ibqINvDBrbCnFRGbhz6R2auJnSdge+M3xSO1r+//CGP1bMPIvVO4cItEeeS6wd9LF273AqnDy8EYCT+59h8NhWcO9ow3tTTWFDxA4qnvE0dlcUIIKSr3csbl59T30uQoJS6M87drFD2/bWcHQ2FDnBLiuzCKtWXoNbC1OsXT+Ac6EnLjYLN6/74ad1A4T+XIYEJkNRSQaOTYwEOs7dKx+QkpAL946f/YsEBVkUIdc0cgJsLfo3qio+m74yDxMGg9EQEJ2lEgbjO1NyQt/GIPh1VJ3vW1JaAqpaSogLTaVlz1zTeUAzaOoq4c2DEK4PBYMntqXPydUTXuAjpAJn7MxOyMksxualV1BTUwtRYMAod/QZ5oag9wnYsvwa9cGozxDhZM7KXpi/pg81SZ08YD9uX3qHivIqrg+NUQ8gbRpnT3hh0qjDWL/yKvzfJyA9NR+9+rng2LkpmD2/K5yaGImcWPLhQwK2bb2HIUPdaBIO12IJMQTet/cxFi3tKXRzXFLxQapL+g9uLtBxykoraTKOR09HgbepxYWnUw81LiGf79bNzSHPhNr/lpJT1xuDwRAITDBh1GtI/G+jRj+grLgClWV1P8kysdVDSWE5ctPrvuXnW3F2N0dWagHePAyhK6ZcYudihM59m1CD1UwBGO7WBbqGqpjyY1dUlFXj4lFiqioakHSfpZsHIDI0FfPGHaXtT/Ud0kY1fXF3rNs3AsnxOVg67TSO73tCPTkYjG+dQPu8icHyhRexctElnDjyEsmJuXBtYYapszrhlzNTMHNeF4HGzgqKT59+xdnTb7B/zyNMnd4R7X5LauKS0tJKbNp4G/Pmd4UyB5Prh3cD4d7GkvrQCJKnd4IQGZSCHgMFn74W8i4O9q7CN839I0U5JYjwjQEP1ooYDAZD4LCWHEa9hlQSmDsbUYMy4mOia6pZp/u3a26KpIg0xIWmQF1XGVxiSNIpTNSRGp+DsA8JcGhuyunxtO/hhEfX/XHx8AvMXtMXfMSzTxME+MTizMHnsHYwgEtLM4gCbTrZQV5BBj/Nv4A9629Tg1QiKtR3tPVUqHBSUlyBB9f9cGDzPTRq3Ai9BrnC3kX02iYYwiMxPhuP7gXjvU8cEuNz6M80iPloOyv06NMETi7GVFwXVYoKy7Ft810oq8hi38ExkBVAG+q3QtqbNq69ibHj2nAiQJHKlge3A7H94GiBjkOMmm9f9IVzcxNqjixo0hNz0KEPt94hv7fk1FPTZ4HDPEwYDJGCCSaMBkFccDJyMwrqXDDRM9FAQngaYkNS4ObpAC4hk8WuQ9xw84QXvO4FcS6YODU3gY2zIaJCUpGVmg9NjtMZ/uk5m7msFyKDU3H64DMYW2hCRURc/8nF+bajY7F6znnMH3cMy7YMQjN37rxrhAmJ1B04uhWtpHrnFY3Lp17jl50P0b6rPTx6OkFeUfTjbBn/nbycYjx/Eo5Hd4No6g0xcCXY2OuhZWsLdOrqABVVOYg64aGp2Lj+Fnr3bYr+A5vxQjgkIsL2LXfRydMedhzF3x7e+xgTpnekiVGChLRI1lR/RN/hLSBoyDkvMSoTyhx/TpWXVFCxmphGM74DJpgwGCIFa8lhNAjjV0KuAFpDvsQJE8GED7i0tkR2eiG87gdTYzguIRftY2Z1RHxkBi4cfgG+Qozzlm0bgqTYLGxadJnz5+1bIFUl24+Ph6aOEi4d96Imtg0J4s3Qop0V1u4ZgSWbB6KooBw719zAhh8v4cObGBqdy2hYFBaU4e4Nf8ybcgLzp53C4T2PkRiXTdtBuvRwws5Do7Hr0GgMHtFS5MUSMnk+d/oNtm25h8XLemHAIFdeiCWEwwefwcFRH+062HAyvs/raPo/d3A2FPhYN8/70nYoYVQoJkSkw0TAkcVfQ3VFNT59/ATJemo8zmAwGH+EScOMBiOY5KTl1/m+5RRl0LKrA2qq+DExI205RpZadAUqyCcOTVpZcHo89s1M4NDMmLbmDBzfFjqG/PQFsLDVxZiZnXBw811cPOaFYZPaQVTQ0FLEjmPjsXHRJezbcAfpKfkYP7sTp9HSXLXrjJnpQQUvf584PLsXhAOb71JBpX1XB5haafNmMsmoW/JySuDrHYOnD0IQFpxCW2tqaz9Rg1G3Vubw6OKA5u7mNGWqvpCbU4wTx16hpLgSu/aNhAKHiSl/5eL5t1TM7NbDmZPxK8qrcfroK2zYOUzgY2Wk5lOPtN5DmwvlnBvsE0tbgblGVlEGNi0sICXLbVS1yMIqTBgMkYIJJox6j46xOq0iyBWAYEIQlxSH9/1g5GcV0VQeruk2xA3n9j/Fi9sBnAsmZII6aqYHTu55jCvHX2HWan56mRB6DW2OzPQCnDv8grbmtGjHvWHi1yIrL4U1e0fQtpTg9wlYNu0Ulm4ZDAWlhteaQiYtzVpZ0K20uAKvn4bj1kVf2nbV2sMWbTztYGCizsQTEYYkdJCqkSD/RDy8E4S46EyoqMnRlhvyf23nYQt7Z0O4t7WCgiJ/hIS64o1XFPbteoihI93Rs3cTXr2W798NpB5DEya35+wYLp/xxoBhbjRKWNDcOu+D6LA0rNw5FMIg6G0s5m4eDK5JjclA+Nto1pLDYDAaBOxMx6j3KKkpoLSwDDkCSmuxcDTEyxt+iA5Khltne3CNawcbHFhzA68fhGDa6r4Cjzj8N+yaGkNGVhIPLn9AjyFuvCgn/kdxZ1oH+L2JwdZlV7H7zGToC8HAr64gK7qTF3TFo5sB2Lv+NlbPOUcNUkllRUOFCKVd+rrQLT+3hIon+zbcpq07Hbs7wsnVBOa2OmjUiHWn8p3ysioEfEjAB584+L6JRm52CSSlxFBVWUtf+45NDOHU1AQt21gKZaLMBeXlVTi47wlKSyqxcdtQGPHs/PTqRQTe+cRh+aq+nIk4kWFpiAxPw8iJbQU+FokS9nocJjTfJFI9J68kw3mkMKGq/DfTVylm+vpdCCIGmMUKMxgCgwkmjHqPuv5ns9HcVMFUmJg7GcLMXh/p8dngA5q6yrBrZozIwGR8eBmFVl24F3HGzOmM96+icfnoKyzayv3q2D8hLSOJlTuH4cDGO1i/8AK2n5gIWTnRKjnu3NsZhibqOLTtPuaOPoKZy3qiUy9uSuP5BDHz7TW4Od0K80tp2865X14iOS4bLTtYw9bJkBoVE3GPAV4krMREpsPfNx7v3sSipLQCaSmfz+FEEJGRkYCruzlad7CGi6tpvf+/EWPXzetvoWUrC8yc7UnbjfjEu7ex8P+QgCXLe6OxWCPOXjNXzr7FzB+7CUWweXjdn4pXvYVg9kqICUml1YR8QFxCDLpmWhCvR21uDAaD8U/w6xOXwRAAGroqsG1hDkFdPpk76CM+LBWKqnLoN6Uj+ABJy4mPSMfDy+94IZgYW2qj/9jWuHr8FboMaApHHvRg/xN6RmroO7IlVs44g82LL2PV7uF0BVuUsLTXw6qdw7Bp8SVcP+ON8KBkTFnYrV55OPwXlFTk0KGbI90qK6oR4BtPK4vuX/uAqsoaNHEzRZMWpjC10hG5/72oQp73qPB0RISm4sPbOISHpNA0pPzcUvp7XQMVKgSSChLiR2JprcvZxFyYVFfV4viRF3jzKgrzl/aAo5PgTUy/lSD/JFy54Is1GwdyKuScP+EFa3s9aP/mWyboao8b597C3sVIKFHChEDvGDi25EcSWnZKHtJiMyFdz4VKQfHDr5+3ut4ng8EQDEwwYdR7VLSVEPUhHrU1H1FZVgWpOv6Al5KRhKGVDqICk2hvPR/6yZt3sMbupR/h7xWF/OxiqGgocH1I6DnMDTfPeOOXrfex+9I0XrdBEP+LcXM6wfdlFI7veYwJcz0haiipyGL9wdG48MtLnDn0ApEhqdTXRN9IjetD4xWkZY0Yw5KNvH/Tk/Ph9zYWD2/4w8/7MozMNKhxsa2jAW1vEhNwRGlDgDzPWRlFiI3KQGhAEoL9k1BcVIHsrGL6ezGxRtS0lcTBevZwpAKWk4sRlEU81eZ7qkquXvSlVW4Hj0+ALA8np6HBKXh0Pwgr1/WHtAx37RnxMZnU02bLvlFCGe/N03Coayig/yh3CIuY4GR0HyGcapZ/Q9tEA9JyUhCXYtMIBoNR/2FnOka9h0zMSVJOZlIuTcrRt6h7Twfrpib46BOD9IRs6Jpogmtk5aXRsrMd/Lyi8eZRCHqOEN5F3T+hoaOEvqPdEfIhAS/uBaNDDyfwmQGjWyElPgdXjntBz1AVXfo1hahBqiOGT24PK3t9XDvjjRlDDmL6kh7o1MuJF8Ie3yDPia6hKt0Inz59Qnx0JkL9k3H55GsquhIvDWsHfdg6G1IBRVWdez8BPkOew/TUAiTEZFGBJDujCL6vo1FWWkUFkIK80t9TjtQ1FeDU1BhNW5jC2k4PWjpKaIgQr5LTx7zw+mUkZs7vAlc3wcfVfg8RYWk4evAZ1mwaCDkOW0VIK87ujXcxb0UvoVSEEcHv8onXNFbXsZkxhAGphCvKL4W8oiz4ADF8LS0sh5QMP1qERA6WksNgiBRMMGE0COxaWkBNV0Vggom5oz7unfJCxIdEXggmBM9Brnh1JxC3T75Bj+EteTFBHjSxLR5d+4Bj2x+gZQcbSHG4IvlvkOdr5opeyEwtwLO7QdDSVYYTj1uJ/hcuLc1o8s+25dewY9V1xEakYeS0jrTlgfG/xVYzKx269Rnm9rkyIr0QEcEpdCPVO8WF5TAw1YCxuSbMrXVgaKYBTR0lXldQCapFITOjkIojibFZSI7PQW5OCUL8E1FT/ZG2z3z8+CutGiETXHJrYa1N2xlIGwXZiM9MQ+eDbxz2bLuPDp52OEQ8lHhYVUIgxqo3r37Ayg0DoCAEw9P/xfnjXnBvbw1DIbXGhPonoaKsCsMmtxPa52qkfyKatLYCXyCmr+SxS7A2TwaD0QBgggmjQfBDox8Q6h2NzMQcgezfpunniXSEXzw8BjUHH3Bobgo1TUWkxGUjwj8JNi5GXB8SLS0fPbszdq+8juun3mDoFO6iJ78GcXExLN85FPNGHsbaeeepCayROT8EsW+FTEbXHRiJB9f8cHDzPXg/j8SCn/qKrAjEBWSCQIQzslGmd0RNTS3SkvIRF5VBqyhIhDERVSzt9Ohqt76xGvXe0NRVgY6+MiQkxUW2UoSkC+VkFiIrsxh52cXUhDUtJY/6bIQFpdAV9z9WjRBzViKWSEqKoYmbGTS0FWFmqQ1zIpQYqTcID5KvpSC/FBdOe1Pz1AXLesKBh14lfxRLDu5+jFUbBkBZmduKh5iIdGRnFGLu8l5CG/PiLy9RXVWD1p3thDbmh5eRaMqTqPua6lqo6alARl6aFwsxDAaDIWiYYMJoEGgaqP1uVCYI9Mw0oKAii2wBJfF8D2SFu9OAZnhxOwA+T8N5IZgQOvV1QbBvPC4efo72PRyhpfc5xYivkNXTdQdGYe6ow9TPZMaynlDXUoQoQl4T3QY0g5WDPravvI79G+/CubkJxs3uzOtqH76LasTnhGyEofgcZ0oqT1KTcpGSkIuosDQ8uhWIooIy+nN5RWkqqBATXnVNRWjpKkFRWRYq6vJQUJJG48aNhVoZEh6SirKSSlSUV6Egv4weJyEhNgu5WcWQlBSnca1EECHtfiRO9Y/IyknS3xHDT1NLLSgoSsPIVAMm5lpUMNLUVmITq3/g06df8eBOAE4cfonufZrQqhI+mzOHhaTg1tUPVCxR4dhThhgF79l4F4vW9hOaOXN8VAY+vI7GlEXd6XtfWIR/SMCo+d3AByrLKpERnw2FBuYpVJeQs2Gdm77W7e4YDMYfYIIJo0GgbawOfUtt2gcsqIko8TF5/yQU5aWVkOFJFG3nwa44t/cxbp30wuAp7SHLgxYMcmHbY5gbnt8JxOFNd7Fy30jwHSLqrN0/EgvHHsXyqSex7cQEyHNchv5fMLHQws5TE3D20AtcPu6FlMRcDJ3YFg5NhdOP3xBQUJKBjZIBbBwN/vRz0tZDRBPi5ZGVUYjczCJEh6UhKT4HBbklEBNrTFtZyPuEJFAQceVLRQsRJkgFBxEfyKSaROmS6jkS8Sku1ph+3ajRny+biZBBDFQ//foJVRW1qK6upcdQUlyOivJqfPr4K04ceErvS/ZHPFoIxMCT/J5AfFrIfojmoWuoQnvldfRUYGCiDjUNBWjrKUPXQJVOoJkw8vVERaRj3/YHMDJWx7Z9I2HAc0Pm4IAkHD30HKs3DoSyCvdeGlfOeMOztxP1mBIWF4++grWjvlA9rYhxu6GFFm+ipCtKq2DhYkIrTBgMBqMhwI+zL4MhYDT0VJESlQF5JcFd5Fk1MYLvoxBE+SfCuQ0/eo01tJXRrJ013j2PwPNbAegxoiX4gI2zITz6NEFGch4+vIpG0zYW4Dtm1rpYsWsYVs88g0Nb7mHm8l40YUVUkZAQx9hZnWg6zMl9T/Dj+GPoPrAZxs3uRKsIGIKBCAqkmoRs5jY6/3i/jx+JwWw1SooqUFZSgdKSKipm1NbUoqS4EoX5ZSguKkdxYQU1oxUTb0Tv/+snklf52QCQiCq1tR+pCEOEF/K1hIQY/VpaRpK2acnKS1HBQ0pKHJq6n2+VlGWhqaMMKWlxKtAQwYTcl0ySG4uxlKD/SmFBGc6fegPvV1EYO7k92ney5b3QRLxVnjwIwdotgzj3LCH4+8bTNrB1u4cLbcz05Dx4PQzBsCnthVqRF/A6CgZm/GkFrSytRLRfPPTM694PrsHw6w+ft7reJ4PBEAhMMGE0CLQMP5vBZSYJxsOEYNPMFI0aN6LRf3wRTAhdh7rREvsPryLRfXgL3lyYj1vQFZO6bcf+dTdx6NYcXpehf4H4MCzePAjr5p1HYV4ZVu8dLtSybEFA2nPW7BuJC0de4sObGEzquxdTF3eHe0cb3rxWGiKkLYeY8grDmPfEzTkCH4MBKljdvxWAU0e90Kmr/WdTVw7TZb6WNy8jcf3ye1pZwmUazhdIhdbZoy+xdP2A/1NRJUjuXPSFXVMj9Boq3GjfCL9E9BjdCnyhsvxzFZoUTw2JGQwGo65hjmuMBoGKthIkZSQgJi6GyvI/99/XFRZOhpCWlUDgq0iB7P97adbeGgXZxfB9Eo5wv0TwBbJyPWZuF2Sm5OPGqTcQFUi6z6yVvRH0Lg6Ht96nHhCiDqk6GDW9I+as7kOrDNbNv4B962/ThCAGg/HfIC1Qb72iMGn4IcREZWLnwVGYNMNDJMSSR3eD4PM6Bmu3DOaFWEKey13rb6PP4OZCjfQmZse3zvnA3EaXttsJ02w5KiAJhuZa4Auk7VjPQhvapvypehHZWOG63hgMhkBgggmjQUD8ANR0lKnpa46AJoGkRNfQQhvh7+NpiTyfHnvX31bE7pzhlzDRdVAzdOjljNN7HyMpNguiAulfn76kB26f98G2ZVfx8eMn1AeIt8m24+Mxf20/vHwYgkn99uDSsVc0EYLBYHw70RHpWD7/PC6e9sa0eV0wb0kP6BkIz3Pjv3D9oi8VemYu6Eo9bfjAg5v+1Bi5dUcboY778JofXRDpL+RKj9iQVJjZ6fGq2q+ipAKp0RkoKyrn+lAYDAZDKDDBhNHg2nIyBBQtTLBzM6MeAnEhKeATnoNc4djCDCHvEpCfUwy+QMxyB4xvQ7/es/I6XU0TFboOdMW4OZ54/SQUx3c9FKlj/9d0pV7OOHprDjr2cIL38whM7LMHr5+E0dVdBoPx75DI5QM7HmDVj5fQqp01th8YjaYiEuFN3ue/7HuCnKxiLF83gDdmo4lx2Xh6LxiT53oKdVwSoX3xyCt49HCmXj7CJOxdHJp72IJPkAUhYvqqZchvk2JewypMGAyRggkmjAaVlEPIEqCPiWMrS2rWGPI2BnxCWV0ealqKyMsswr2zb8EnjC20MGhiW+r/cveCL0SJQePbYNxsT1w58Rq7VomW4PNvEFPS2St7Y9riHlDVUMD+DXewbOpJxESkc31oDAZvyc0pxu7NdzF7wlFoaCri2KVp6NrLGY3FGomMz8ruTXfRqPEPmDjTgzfHXVlZjW2rr2Pq/C7UsFiYXD35mqhI6DdG+D4ibx6EwN7NDHyiOK+Emr6StBzG90EihQWxMRgMwcCPT0IGQwgYWGpDTKIx0hMEJ5hYuRiT6yoEe0eDb/Qe05reej8KRVUlv1oshkxuT41pj21/gKw00fLN6DvKHaNmeODVwxAc3na/3rTnfMHCVhfbT0zA9KU9kJqYi1lDD+Hozoe0p5/BYHymIK8UB3c9xLTRRyAvL4WjF6djwPAWkBahJK2ysiqsXHABFtbaGDe1I6/aQH7Z9Qg9BzaDqaW20KtLHl/3x6AJbaGupSjUsQvzSiEu0RiyQjB+/hYqf4sel5Jjpq8MBqNhwAQTRoNBXVcVtdUfkR6fLbAxZOSkYO5ggNS4bLpSxyfM7fXQsU8TJEam49XdQPAJCUlxzF3XH1UVNTh34KnItX4Mm9weY2d74sZpb2xZfIlXHjZ1AZk4tfKwxS83Z9PY4ef3gzGuxy78sv0BFboYjIZKfm4pft71CJtX38DH2k84eHISxk3rCEUhGoPWBTlZRViz6CK69W6Cbn1cwCee3guiMdqdezoLfeyLv7xCZXk1ugxoKvSx/V5EoElb/iTufaGmuhb6VjpQ1hCugFSvYC05DIZIwQQTRoNB20SD3qbHC9Zc1KW9DfVJ4ZuPCaFFZzsqRlz/5QXvRAlrJwOMmuWBR1c+4J6IteYQeg9vgSmLuuPD6xjsWXujXhqlEmFr4NjWOHh5OnoNdUNMRBrGdtuB0weeoqxEMOlTDAYfyc4qwukjLzFh8AFUVdVi7tIemLGgq1CTW+qK2KgMrJh3AaMntUer9tbgE8S3xNcrGnNX9BZ6xUtORiHiI9LQd7S70KtLCO+ehcO1vXDNbb+2JSclMh2f6lk1JYPBYPwT/HDyYjCEADEo0zHVREFWEfWaIOaWgsDG1YTeBr2OhqWzEfiEWyc7aBuqQUJKHEHeMXBytwCf6De2NZ7fDsQvW+7BuaUZdETMVK7PiJaQU5DCjhXXkJ1eiJW7R0BGtv6VLcsrymDi/C7IySzChSMv6Sqsn3csXNtYoPewFpCV4z5+lMEQBClJubh23hcvHoWiU3dHHDo7GRocTKbrCh+vaFw6/QYrNg2Err4K+ER5WRW2rLiKeSv7cBLBfP7Qc8SEpmHJ9qFCH5tUKTZq9AMMLPgTJ/xH4dyymSmU1BW4PhTRRRAVIfxaA2Mw6hWswoTRYJCSkURNVQ1KC8uRK0CfDNtmplSQIK0vfINEDPeb0BZRgcm4dOgZ+HghtmDzIFjY62Hbj5fwkWdtTV+DR68mWLJ1CJJis7FpwQXah15fIauuM1f0wi+35sDITANnDz3Hj+OO4szBZyhhkZOMekRESArW/HgJc8Yfg7auMo5dmYFp87uIrFhCKgwvnfam0cGrtgzmnVhCju/UoWfUt8TMSri+JYS0pFxEh6aix1A3anotbMLex0NeSYZXPjJfyEjIRtT7ODY/ryfs378fRkZGkJKSQvPmzfHu3bt/vG9YWBj69+9P709em7t27fqufVZWVmL69OlQVVWFnJwc3WdWlmCrvxmM/wITTBgNCl0TTXqbFie4E7OUrCTsmpvB+14Aqnlmrkrw6NcUCsqyCHgdjdjQVPANczs9OLc0R0RgMi7+/AKiSOvOdli0aRBCPiRi3ojDSE/OQ31GS08Zc1b3xdHbc+HaxhIXf3mJUZ7bcOGXl8jN4k+MNYPxLRBPkldPwjBn3FGcPvwSNg56OHVjNgaNbAllFVmIKtVVtTiw/QGyM4qwftcwXvqtXD3tjYrSanThyE/lzL6nSE/Kw8DfYu+Fje/jUDT3sAMfqfwtHUeaVRKKfErOxYsXMW/ePKxatQr+/v5wdHSEp6cnsrP/3uuvvLwcJiYm2LRpE7S0tL57n3PnzsXt27dx+fJlvHz5Eunp6ejXr9+3PwAGQ0gwwYTRoLBqZgI1HWWkC1AwIdi1MKMGphEf4sHHSpv+E9vB3tUE14++BB8ZOLEt7JsZI9AnjgonooiTmym2nJhAYznXzztPVyvrO1q6yhg9wwMn7s9Hj8GuuHXOB6O7bsP2FVcRH5XB9eExGF9FcWE5Lp58jRmjDuPKmbfoNbAZ1uwYioEjWkJWxJNBcrOLsWjaKegbqGL6wi4QE2sMvhHsl4g3zyIwfXE3Tios4qMy8fJ+MI0RVlKV46S6JvxDAuzdTMFHZu4di4PvN6JlL+Eb4TLqlh07dmDixIkYO3YsbGxscOjQIcjIyODYsWN/e/9mzZph69atGDJkCCQlJb9rn0VFRTh69Ci9X4cOHeDi4oLjx4/D29sbPj4+An28DMb3wgQTRoNCUVUeuWn5SI3NFOg4zm2soKyugKiAJPCRLkPcEBOaiuc3/ZEqwNSg/9I6NH/TQMRHpGPL/AsiayhqbquLNQdG0RjnhWN+gc/zCDQESAn7+LldcPjGLCqgkISLaQP3Y/HEY3jvFUU9hBgMPkEmqZGhqdi25gaWzDiNmIgMTFvYFbuPj0eHrg4QF+efsPCthAQkYdf62xgzrQN6DXLlZbtHZloB9q6/g8UbBtAWTS44seMBzKx10Ge0OyfjJ0VlQENPmbPH/2+o6arA1NGQeZj8F379QTAbEXyLi/+0VVV9rgj6K9XV1fDz84OHh8fvPyPefuT7t2/fftfD+pp9kt/X1NT86T5WVlYwMDD47nEZDEHDBBNGg0LX7LeWnFjBVpiYOxqgpqYWr2/7g4+Qlpxuw1rSScK9c/z8gNLUU8Hstf2QlVaAs/ue8C7V52vR0VfBzjOT4exmin1rb+HayTci+1i+FTkFaQwa1wYrdw3DjxsHUOHr1P6nGN9jJ66efM18ThicU1ZaibtXP2DzimtYPf881NTlsWrbYCzfNBD2zoa8FBW+FXK+uX3lPY7ufYKZi7vD0YVfZuRfqCivwsYlVzBrWQ9o6ihxcgzB7+Lx/lUUOvR0gpy8NCfHQAzZW3Vz4mRshujHCuvr60NRUfH3bePGjX97CLm5ufj48SM0NT9fF3+BfJ+Z+X2Lil+zT3IrISEBJSWlOhuXwRA0LCWH0aDQt9CGvqU2stME6ynRuHFjOLe2wus7ASjMLYGSGv+iJvtOaINg31jcOumFXqNbQUtfFXyjdVcHxIan4dLPL6BnrIZuQ9wgihDzvqU7hmLP6hs4vOUe8rKLMWZ2J4hLNIxTsLi4GDp0d0L7bo4ID0rGnQu+uHLiNc7+/Bwt21uj64BmsHEyqBeTU4ZoCAihgcnweRVFxRJLO1306N8M81f1pq/V+gQRIXatuw0VNTlsPjAKklL8rFogVWdbl19D1z5NYM+RoEOO4cKh53DrYINuQ5qDK17e8seaE5M5G58h2qSkpEBB4f9X//xT6wyDwfh66teVAYPxL2gYqCEzMYea+VVX1Qi05LVpBxtkJuUi5G0MWvdsAr6hpqkEmyZGiA1JxcUDTzF74yDwkeEzPPDhVTQOb7gDSwd9mNroQhSRkBDD/PX9YWSuieunvKmnybKdw6AkwuaR3woRRGydDOlWkFuKx7f8ce/ye6Sn5KO0pBKefZqgQ3dHKPNQYGSIPpnpBXh2LxjPH4aivKwSHt2dcODsZOjwUCyuCxLjsnFw6z209bSnQgSfBckzB59DRVUOXfpxY/JKeHU/GAFvY7Fo22B6vuaC7LR86jNGRHZG/eV7TVr/bZ8EIpb8UTD5J9TU1Oji3l/Tacj3/2ToWhf7JLekdaewsPBPVSb/ZVwGQ9CwlhxGg4J4Y+iaauLTx09IjxdsW45LexvEBCXB50EQ+MrAKR2grC6P9MRcZKXkg48QUWvZnuEwt9PF+llnUVpcAVGFTFgGjG2NWat7Iy4yA+vnnkNMeBoaIspqcrRd59jduRg1vSNMLLRwYu8TrJl9Bqtmnsbrx6Goqqrm+jAZIk5RQRnuXHmP9YsvYdLA/UiKz8bUBV1w6vZcjJ3esd6KJY9uB2DTsiuYMLszuvV14bVY8uxuEBVNp/zYjbNjIAsoL+8Fw6G5Cdp0deDsON7cD0Lr7o6cjc9oOJC2GGK4+vTp0z9VWZHvW7RoIbB9kt+Li4v/6T5RUVFITk7+7nEZDEHDBBNGg8OupQUUVOWQEi3Y1A5VLSWY2Onhw/NwfPzIT5NLNS0ltO3hjOC3MTi39xH4io6hGvqMaY2M5DxsX3RJ5E1Dm7e1wu7zU0GsTOaPOIzHN/jpdSMMiCGcU3NTLN48COee/ohOvZugKL8M25ZfxZgu27Fz1TUE+MTy9j3E4B8lxRV4dCsAezbcxvBuO+D1NBwt21nh/MMFWLJhIJo0N6XieX2kvKwKezbehv+7eGz/ZRzMrXXAZ0L9E3H55GtMX9oDYhwa69449QY+zyIwYnpHek7iircPQuDasW7jhAtzinFi/fU63SeDvx4m3wKJ/z1y5AhOnjyJiIgITJ06FWVlZTThhjBq1CgsWbLk9/uTypDAwEC6ka/T0tLo17GxsV+9T+KrMn78eHq/58+fUxNY8jsilri5iWbbNaP+w1pyGA0yKac4rxSp0YI3l2rTqwk+PI9AbFASLJsYg48MnNIe985648nV9xg0tSN0jdXBR9w722HotI54/zISFw4+w7Dp/99hXRTRN1HHmgMjsX3ZVVw4/AJRQcmYtLg7b5MRhIG8ogy6D2pOt9TEXBrt+fxeEBKiM5GTUQR3D1u06WIPW2fDejvhZXwfhfml8H4RiejwNDy5GwwLax107O6IU5PaQaWBtHiRx75j7S30HNAM3frxu6qEQN7jp/Y/w/KtgyGvwI3BKqEgtwTP7wTSzxj7ZiacHUd+dhFNx1HVUqzT/caHpkJBWfjxyAz+M3jwYOTk5GDlypXUcNXJyQkPHjz43bSVVH38UUBMT0+Hs7Pz799v27aNbm3btsWLFy++ap+EnTt30v3279+fpvh4enriwIEDQn3sDMa38MOvDSWugfHdkFgyogiT7PSv6YvkOy+u+GLTuEPoOLQlFv48UaBjhb+Pw/we2zB0XjeMWtQTfOX83kc0YtjUVheLdo8EX/lY+xErJh5HwJsYrDo4Gm4dbSDqkFPwzTPeOLLlPowttLB019B62ybwvc9PfFQGXj0MxauHIVBRl6cTLbd2VmjT2R72TY0atMjUkF8XKYm58HkZhRC/RPj5xsHWyQCtO9qgRTsrqGvW7aSTz5DqK1KlERGcgrEzPGD0WxocnyFR44smHMOsFb2oAMolu5Zdwcu7wThwaza0Dbg799456YWa6lr0ndi+Tvd7+9hz6JtpwamNNeoronKd+uU4TVZsQGMpqTrd98fKSsSvXcr754DBEEVYhQmjwWFoowslDQUkhqcKfCxSVaKgIot3j0N4LZh0G94Sl39+hpTYLAyc3AEmPDVWbSzWGEt2DsPi0Ydx+cgLaBuqwlAEJgf/C7IK3GekO8xsdHF0230sGP4zpi7tidZd7Lk+NN48P6ZWOnQbM6sT4qMz4f00nG7+3rEoLipHEzcztGhvjSYtzaDWgCbKDY3KimoE+yfhvVc0PryNRU1VLSxsddG6ky3mr+nboAyU/2hke2j7A2hqK2HZ5kEiIR6S/+P2ldcwYmoHzsWSmNBUPLrqhwET2nAqlhBe3wvE/J0j6ny/Eb5xaN2TOzNdBoPBEHWYYMJocBDTV9KSU1FSSb0wBNmvTNoGOg9zR8DLCOSk5UNdVwV8RFFFDgMmdcDtU164e+YNZm7gZ2IOgaQHLNw2BPMG7ceaKSex6/J0KCiL/kTJzsUIq/aPxI6lV7Fh3nkMjkjHkCntISUtwfWh8Us8sdSm28hpHZGZVgDfF5EI/pCAPT/dQG3tJ9g2MYS1owGatDCDXRNDkZhAMv65ciI+KpNGAPu+ikJoQDIcmxlBV18V037sBnsXQ0hJSTTYCpuHN/1x5bQ3pi3shiZuphAFSJXgxh8vwbm5KRW6uIR8/h9cewtOLU0xZEoHTo8lP7uYpouo6yjX+b6L8kqgpM4qDnjFd3qO/Os+GQyGQGCCCaPBQSZQOiYaKMguphHDOiaCrVCwamKEK/sewedhMHqOawe+0ntcGzy65It7596iTQ9nOLY0B18xMtfCoh3DqAHsofW3MHfDQIhzFANZlyipyGHNwVG4dfYtzh18hjdPwrBo62BafcL4v2jpKqP38BZ0qyivQtC7eESHpuHlg2BcOe4FKwd9Kjg5uhpTY1kzGx2Ii4v+66Q+CyQJMVkI+ZCAtJR8PH8Qil8//YqOPRzg3sEas5b1hI4+P0VnYZKXXYzDOx+hsfgP2HFsPBQUZURG5Dm46S59H/Yb2ZLrw8HTmwGICEzGgs2DICMnyemxvL4biKbtrQhRDeQAAMQXSURBVAWS/sPXhZoGDRNMGAyRgl05MhokFs5GeHbJB8lRGQIXTJq0s4a4pBh8H4XwWjCRlZOiMcP7ll/B8S13sOPabE7TAv6N5u2tMXxGRxxadxtiYo0xd+NA3pscfg3kMfQe0ZJWnGxeeBF7V99ESw8bDBjXmrYkMf4eaRlJuLWzptuoGR60+iQ84HP7xu0LvvB/G0vFFEt7PTRvYwVDc01Y2etBjkOzyYZOWWklYiMzEOqfhPDAZDqhjghKga2zAVxbW2LDgZEws9JmBr+/QZ6fZ/eCce7IS4yZ3pHzCo1v5fjuRygrq8K0pT24PhQaT3/7tDfadndEh97/38SSK2JDUzBiXtc6329SRBoUVJjhK4PBYPwXmGDCaJDomH4WSYiPiVtXJ4GOJS0rhU5DWuDljQ8oLSqHHI9XA7sMcYP/qyhEhyTj+Q0/dOzXDHym10h3JMfl4NW9IBge80L/8W1QXzC11sGey9Nx/uAznNj1iLaezN8wALpGalwfmshUn5CtQw8nOtFMT86jEaahfkkIeBeHI9vvU3GqRXsryMpLUyGF+GEYmWuyNh4BUFVVg8ToLMREpCM7o4i22CTH58DJ1Zg+/83czWk7lYm5JhMG/8GrZM+629DWV8H2Y+NFzq/l8nEvFBWUY/5P/XghxJ/a9Qhx4WmYs74/50J7dlo+MhJzoSGASpCkqAyYOXHrE8P4v/zw6+etrvfJYDAEAxNMGA0SQ2tdqGopoTC7WCjjWTob4d5JL7x7HIoOA1zBV8hExXNIc3g/CsHxLXfh3tWR1x4a5EJ32opeqCipwC+b7kBBWQad+jVFfYE892PndYFzS3OcP/QMMwfuw6hZndBreAteTDpEBfI60TVUo5tn36a/p3REBicjLjIDYf5JOLH7EWTlpZCbXQxDUw3YNzWmggtJLiKpI0qqbJX2ayDiVF52CRJjs5CRmk8rRkjKEWmZS0vOh5m1NhyaGmPElPa0wkdNU4HzCSvfPT9uXnyHFw9CMXR8a5oAJGrcvfwOvi8jse7gaIiJcy+GRYekIDE6kwruJtY6XB8OvG4Hom1PwVS5RL6LQ5+pHgLZN4PBYDQUmGDCaJAY2eghL7MQga8ihDJe884OtC0n7F0srwUTQrP2NnBuZYHstAIaczhgSkfwGSLyzFzbH6kJOTi5/QFUNRTQpJUF6hNObqYwt9PF0a338fPGu4gNS8fgSW2hb6LB9aGJLGSF/ksLzxcDyMzUAiqgxIanISk+G2+ehCM3qwjSMhKQkBKHgYkGTCy1oK2nAl1DVegYqtF0Ej5MAoVNZWU1fb5I5U5OVjHiIjKQnJBDfxcZnEKFEHsXI6hrKcKtrSUVnshzxoS+rycmPA171t+BuY0ONuwfIZLtY09vByD4fQLW7B3JC/Gd+OTsW3Wd+sCsPDAKfMDrjj9WHZ8kkH2nJ2RTzzYGg8FgfD9MMGE02JYcSWkJpERloKa6VuCGoYqqcnBqZYmnl3wwcVV/SMlyazD3vyCrvZNX9sH0LltxevsDtO7hDE09fpvGSctKYs2RcVg3/TTWTj+FTacmwdLRAPUJ4jEza01ftO5qj8Mb7mJa370YOqU9BoxvA4l6YHjLNWQir2OgSrfWne1+/3lRYRlS4nOQFJuN1MQcWjnx9nkECvNKUV1Vi0aNG0FLVwnaeqrQ0FaEpq4yFQnUtBTprYqaHKSk+ft+/6cqkdKSSuRlFSM/rxRZ6QXITi+kIklMWDoVSkg1TkpCDjR1lGg7k7KqPDz7NIG+iRoMTTUhL4KTe75A/DVO7n+KjNQCTFnYFbZOonkue/UwBFdOvMbGI+Po64UPPL76gb5+Z6zuwwsBKik6A/LKslAWQIoNqU4ihq9MpGQwGIz/BrvKZjRIiImgU1trFOeXIiU6HSZ2gr8gbe7pgPdPw/D+aSha93IBnzG00Eb3Ee64dcILp7ffw4KdI8B3lNXkMX/LYCwYsh87Fl/G8n0joP+bV019wtnNDDsvTMWZ/U/h+zwCL+4EUiHFrqkx14dWL1FUkoViE1nYNTH6088rK6qRlV6IjJQ8eksqLYioEB+dSb8uKaqgYsqnj5/oZFFZVQ5qWgqQk5eBkooMFVHl5KTopE1eUQYyspI0qUNGVoKKuWQ1XlJKDGJiYt8tehAxuKqyhh5rRXk1KsqqqehRXFBOJ+W1tR+Rk1mEwoIymh5EqmsKckugoq6AiKBkuh8bJwMa10zEIH0TdbTxtIe2HvGHUYKWngpLHapDSJXTk9tBuHf1PVp72GLygq4iW7309lk4Xj0KwbpDo3njt5KdXoCfN9yGS2sLtO7qAD7g+zgUnQY3F8i+iam9tBw/hCrGX2ApOQyGSMGudBgNFmVNRfg+CEJ8SKpQBBP3bk64f+o1wnzjeC+YEIhjf0ZSLrzuBqFjf1fapsN3dAxVsfboeGyaew5LRv+C7Rem8r465nuQkpHAhIVdERuWhr1rbmDhyMMYNLEt+o5pRaOJGYKHCBrE64RsfwcRKYgfSkFOCQpyS5GfV0KrUoryy5CfU0LNiosLPwsX5DqXtAgQVNXlkZdTQr8mEbok7YdUwJGWFuIJQkQY4qlCqjsIxKQ2PiqTCjOmVtrUM4QIJSRSOcQvkd6HtMaQr0n1mLObCVKT8mgFiImVNqoqqqGgJEsFEGPzz14tKupyUFVXgIq6PBNEhER0WBoObL5LW7yWbRlMq5NEFZ8XETQRZ/3PY+nriA8QAfHMnif0duLiHrzwzSEC2dMr77DrzgKB7D8xPA0WzszwlcFgMP4r7EqI0WAxsf8sksSHJANDWwp8PCV1BcgryeDBaS+MXtKL9ys/5Fjduzni/fMIHFhxBfsf/AgJSf6fMkxtdGnE8NIxR7B1wQUs3TMCKhr8uGiva8xsdbHj/FQ8vPoBp3Y/wr2Lvhg5qxO6DW5Oo5YZ3AoqeoZqdPtab4XK8mqUl1WiqrKWCi7V1bWoqaqlAsinT7+ipuYjFUYaNfqBVn0QSAXCr59+RaPGP9B0n36jQAUW0qYlKSUOSWlx+nNSzUK+5sNEkfH/IULZib1PUJBXirEzO8GxmWhXipHKkqd3grD+5zG8En1e3A7Ek2sfMHNtP9o2xwfC3sXDwtGAtpQKgvB3segxvr1A9s34b7CUHAZDtOD/7IfBEBBmjoawc7ekUb/Com2fpgh8FQnfRyFox/PIXkKnga54eMGHfn3j2EsMmspvA9gv2DQxwqpDY7Bz8WUsHnkYm89Opi079bW9rNsgV7T0sMGJHQ/x8l4wHlx+j8lLesCxuSnXh8f4hv8jad3hi9cDQ7AQcezKqdd49yoann1d0KWfC30NiDKvn4Th1N7Hv4klSuALBXklOLTuJiwc9NF5AH8+dx9f9EGH/oI7npSYTOiZawls/4z/CBM4GAyRQbQ/nRmM/4CxrR7CvKPx9o4/LdMVBi27O8GhlQV87gdBFCBmcTM3DEJUYDLO7LiP9MTPbQCiAIninfFTX+oxsW3hRdoKUZ8hrThz1vXH5MU9IC0jicVjfsGBdbeQnpTL9aExGIw/GHE+vROIqQP3oaqiBhsOjUb3gc1EXix5fjcIr+4FY8PhsbwSSwgH19yEho4yZq8fwJvnuaKsCpEBSXB0F0yrK6lKI+2ofHm8DAaDIcqwMymjwUJaYnTNNFFSUIac1HyhjKmgIgcZOSm8vuNPDWdFAWNrHfSb2I62Bpzf+1ho4lJd4NrOGsv2jURCZDoWjzxEPSTqOxb2eth2djIW7xiKdy8jMbnnLvy88Q4KReT1xmDUR8h5883TcEwduB+RIanYdGQcxs/15EVSy3/lwdUPuPTLK0xe0h1qmvxpwyG8uhsEr/vBcPe0h7Elf6otvO8HovPg5gJLsEkITYGsoui/tuq96WtdbwwGQyAwwYTRoGnexRFGNrqIDU4S2pjt+7uCaA4+D4MhKgyf44nmHrZ4cvkdnlx5B1HCrYMNZq3tj9T4HGz/8QJNAanvEJ+Ktl0dcPjOXIye3RnJsVkY77kN5w8+o60ADAZDeEKJn3csFo47iuf3g7B400BMX9KDxjHXB66dfIMg33hsOjaONwavX8jPKcapXQ/RppsjBk5sCz7x4Kw3WnYRXFJPQlgqrJqylkwGg8GoC5hgwkBDT8ohKzEx/p/TJISBa2d7qOsq4+GZNxAVpGQk0WtMG/r14Z9uIj+rCKKEm4ctVhwYjbjwNCwafgi5maJ1/N8LMfscML4NFm0bgq6DXHH+0HOsn3MWN894U0NRBoMhOILex2Pzksu48MtLjJnpgeXbhsLEUhv1RQgifiXeT8MxY0UvKCrzIzr4j8e3Z9lVZKcVYNgMDzTmkQl2WnwWjRXXMf77hK26IORNFMydWEIO301f63pjMBiCgQkmjAaNuZMRvU0MTxGq+ODU2grh7+KQ/ls0qCjQpI0luo9wh4G5JvYvvyxSrTkE1/bWmL9lCL2A3vHjRWQJqQ2LDygoy2LCwm44+mA+NLSVcGTzXUzqth0PLr+jve4MBqNuIOfF4A/xWDjuF5zY8xiefVyw5eg42DX5/FlTHyCJTvvW3ERxQTnW/Tyal0bFDy+/Q1lJBcYs6ApDc03wiQfn3sKts+CqSwiZSbnQMlIX6BgMBoPRUGCCCaNBY+ZkBDVdZYS9jRGqANBxcHNYOBvh7b1AiBKjf+yOrOQ8eD8Iwcub/hA1XFpbYu2x8UiJz8L8wQeQFJOFhoS6thJmrumLw3fnoXUXB+xdcxMTuhDh5D2rOGEw/gPk8+OdVxTmjTqMy8dfY8iEtthxahKc3UzrVZRzVWU1Ns47j6qqGkxZ0oPGZ/ON9MRc/LzuFvUH6T26FfgEEajfPghCqx7OAhuD+LIZWGrXq9ddvYN5mDAYIgUTTBgNGlkFaUjLSqEot0Roxq8E2+ZmKC4oxa2jz/Hp0yeICvJKMpi1eTAkpcXx6LKvyLXmEOxdTbHi4BjUVNdg74oriApKRkNDx0AV4xd0xZG7c+HYwhTPbgdgXKetuH7iNfM4YTC+MfWGJMTMHHIAD659wJhZnfDTvpFwaWle7yasJYXlWDbhOMztdDF/wwCIifOnzeULtTUfcXzbPahpK2L+1sG8S4kh3mXu3Z0gJSM4oSnKLwHquioC2z+DwWA0NPj1ScJgcIBdSwsY2+kjLlh4E2ey8tVpSAtkp+Qj6HUURAnXjrboPNgNAa+isOvHCyLXmkOwsNfH1vPTUFxYjkUjDuHDy0g0RHQM1TBv/QDM3zgQLTra4PjOh1g6/ijO7H3MUnUYjP9BeVkVbpzxxsqZZ2j6zcwVvbFy53A4NjOpd0IJgcSz/zjmCNp0dcDgie14+xjP7HmE1w9CMHxGJxolzDfunX6Ndr2bCnSMpIhUWLsyw1c+wzxMGAzRggkmIsj+/fthZGQEKSkpNG/eHO/efV1qyYULF+hFTp8+fQR+jKKEib0BNX4N84kW6rgeg91oW87j8z4QNUb/2AMaeioIfB2Np1ffQxQxMNPE+uMToaWngpM7HuDxNdF8HHWBpq4ypq/sjRNPfoSDqwlunnqDyV13YP9PN5EqQj47DIagyUovoCauE3ruREpCDqb+2A3LdwyFpZ0e6isRgclYPPYoxszxRK/hLcBXgt7GwP91NDz6uaBdT8G1vHwvaXFZUNFQhLGNrkDHCXgRDnNnY4GOwWAwGA0JJpiIGBcvXsS8efOwatUq+Pv7w9HREZ6ensjOzv6ff5eYmIgFCxagdevWQjtWUcGyqQm9jXofL9RxNfRUoaAii9e3/VBSWAZRgpj8Ldg1Auo6Sti39DJS47JE1tNj64VpUFaTx44fL+Hs3sciWTFTV6ioy2PMXE+cfL4EY+d74v2LSEzssh3bF12C/xvh+vwwGHyBvO5D/ROxbt45zB91BGKNG+Hg1Zm0qkTPuH4ba75+GIItP17E0p3D0LydFfhKYW4Jtsw9j/LiSkxdyc9FIVJd4tzGSuCGvKStkrQbM3gM8zBhMEQKJpiIGDt27MDEiRMxduxY2NjY4NChQ5CRkcGxY8f+8W8+fvyI4cOHY82aNTAx+SwOMP5cYeLYxhr44XM/ujDpMqIVTTB5cycAooZ9c1O07u6EqopqbJ97FtVVNRBF5BVlsPzAaLTr6YQ3D0Kwa/GlBp8cIyMniS6DmuPoowVYtmc4/dmycUcxudsOPLzyHmWllVwfIoMhcEjKyu0LPpjSbw9O7n2Clh1scPz+PAwY25p3Mbp1DfHWOr33MY0i33h8PCzt9Xh9rKd2PkRpcTkW7x4OGTn+pfZUlFUh0CsKbXo1Eeg4ieGpsHEzE+gYjDqACSYMhkjBBBMRorq6Gn5+fvDw8PiTFwb5/u3bt//4dz/99BM0NDQwfvx4IR2paCEuIUYNQIO9IpEYkSbUsV097fHrr59w/dATkVy9Hz6vK9w626EgrxQnN9+BqCIhKYaF24fC3dMOj65+wNLRR1DEPDzQWKwxWnnaY/7mQdh3YxZsmhji7nkfjGi9AXtWXkNcuHDfLwyGoCHn4ajQFOxafR3je3xuu1mwbgC2Hp+ADj2cIC4uhvoOmdwfWHsLCdFZ2HZmMrR4biB68eAz3D/vgykr+sCMp61RL66/h0t7a0hIiQt0nDCfGBhZC7blh8FgMBoaTDARIXJzc2m1iKam5p9+Tr7PzMz82795/fo1jh49iiNHjnz1OFVVVSguLv7TVt+xdv28IhPhGyPUccnFd6dh7kiKTEeYTyxEUWyauLwPivNLce3IC/g+CYWoQsTH4bM602SFyvIqLBi0H0nRf/++aoiYWutgzvoB2HB8Am3bifBPwrLxxzCz317cOedDV3cZDFGFGEDfOO2Naf324eKRVzCx0MIvt+di2pKeMLdtOBPQjJR8zB9+CHKK0li2axikZSXBZ4J8YnH3jDdad3NAlyHNwVcR7u5JL3Qf3UbgY2Uk5MDmt+sZBn9hpq8MhmjBBJN6TElJCUaOHEnFEjU1ta/+u40bN0JRUfH3TV9fH/Udh1ZW0DHRRKSQfUy+tOU4uFvi8QVviCI6xuqYtXEwdIzUcHrbPeSkF0CU8ejbFFNX9kZZcSXm9t+Ld88juD4kXiGnII3eo9xx4PYcrNg3EsaWWrj483OMarcJm+dfgN/raCrsMhh8h7TevX0Wjp9mn8XmHy8hKS4bc9b0wYpdw9BrWAv6Wm9IfPCKwv6fbmDI5PbU4JVvkbx/JSejEBtnnoaqpiJmbRjI2+Se4LcxsGlmAk19VYELMxG+sdA0/PrrPQaDwWD8O/W/trQeQUSPxo0bIyvrzwab5HstLa3/c/+4uDhq9tqzZ88/9foSxMTEEBUVBVPT/xs9t2TJEmos+wVSYVLfRRMLF2Okx2dRHxNho2WgBlkFKTy95IPRS/tARVMRoka7Pi4IfReHu6ffYMPUE9hyeSatPhFVbFyMsfvGbBz66QbWTT2BgZM7YPjsTrQKhfEZMjmxdTGiW1lxBV4/CsWT6344uPYWNR1s18MJ7Xs5wcRKm7cTGUbDg3wGhgck4/ndIORkFFGTTI9ezmjR0RpS0hJoqM/J5V9e4fF1PyzbPRzGFv/3eoJvVFfV4ujG26gsq8acTYN4LW7dPPwMfSZ2EPg4GQnZVCxh51sRQBCeI6zChMEQGKI7o2mASEhIwMXFBU+fPv09Gphc6JDvZ8yY8X/ub2VlhZCQkD/9bPny5bTyZPfu3f8ogkhKStKtIaGiqQQjWz00FmuEgqxCKGsqCXX8nhPaI+BlBF5ef4++U/6/R40oMWllX0T4J9LEnzM77mHs4l4QZUgC0I+7hmPv8is4t/cxCnJLMPbHbtQklvFnSCKD54BmdEtPysWLO0F4disAIe/jUVFejbbdHNC2u2O9TxRh8BOy8h4dmoZXD0KQlpiLvJxitOvmiKGT20FNBAXquqS4oAzbFl2iiWG7Lk7jtfDwRw6tuYZXdwKx9MBoGFtpg6+kJ+RQAcO+pbnAxwp5EwUnAafwMBgMRkOECSYiBqn8GD16NJo2bQpXV1fs2rULZWVlNDWHMGrUKOjq6tK2GikpKdjZ2f3p75WUPgsBf/05AzBzMsSTs2+ol0ir3k2FOrZzG2voW+jg0p4H6DGunUhWZxAzu+WHxmF+v524tO8JjK10aeWJKCMpJY75W4fApokRjm25i4DX0Vi2fxRvjQX5gI6hGoZN74ih0zogNjwdL+8E0WSdAO9YlJdWUhPZVp52MDD7sxcTg1GXkMWEyKAUvHkSTo1bs9IK0KaLPcYv6AJ9JtxRIgOTsX3JZXTq64IBE9qITAXdk6vv8eCCD/qOa4tWXRzAZ24ceQa3Lo5CqfoIeRONgXO6CnwcRh3AKkwYDJFC9GZlDZzBgwcjJycHK1eupEavTk5OePDgwe9GsMnJySJz0cM37FtaUsEk1DtK6IIJuZjqMsIdexechddNP3QYyE/zun9D20gNs7cMxeoxh3H152cwMNeEia1oiwvkf9NtWAuY2Ohgw/TT2L/qGjr2bYruw1uw0uf/AXluiFkm2cYt7ILo4FR4PQzBw6vvEfg2FgW5pWjhYYOWHrawcNBj5y3Gf6aqshpBvgnweRaOtOQ8lBRVwL2TLcbP84QhE+j+JCZdO+aF22ffYuHWwbBragxRIcQ3DrsWX0THfk0xbnEP8JniglIEvIrExNX9hfZ/NbDUEcpYDAaD0ZD44VdRzDJlCBXiYULMX4uKiqCgoID6SlpcJnZOOwZ5FVmsOj9b6OOTZJa1ow/RRYL1l2aJ9GT8+pHnOLrhJtS0lLD77gIoqsihPlCUX4YDq0kpeBBadXXA7A0DIMdadL4J8pETF54O7ydh8HkaTlsAUuJz0KytJVp0tIFDc1PIyktxfZgMEYEYf773isb7l1HIzS6GtIwEWnSwRvP21tAxEKzJpiiSn1OCEzseoDCvFPM2DoSSquicmzNTcrFy7C/UrHf3zblQUJYFnzm/8z7EJMQwcHongY+VmZSLX1ZcxPJT09EQEZXr1C/HaTVrAxpL1u3n3MeqSkTuWcr754DBEEVYhQmD8RskJSctLguFb4tQVlQOWSFPhKVkJGHuZIALO+8jxDuaJueIKr3Ht0WITyzePgzBz6uuYd6O4RATbwxRR1FFFot2DYeFgz6eXffHrN67sXDHUFg3MeL60EQGIgSa2erSbdTszshMLcD7l5E0jej6yTdYN+ssbJwN4O5pDxtnQ5ja6PA+rYMhPCorqmmrzftXUfB/E0N9p/RN1NGmqz1cWllAQYkJmP8EeZ/tWXkdfUa7Y876/iJV1VVWUonVE47hY+0nrDoynvdiSVVFNR5f9MGeh4uEMl7Y22g4tmb+JSIDa8lhMEQKJpgwGH+YyDm0soTXjQ8I84mBq6ej0I+h5/j21FH/5Y0PIi2YkAvxBbtHYvfC83hx0w9SMhKYuWmwSFfN/PGx9Z/QDnZNTbBl7jksGHwAE5b0QK9R7mgsJvqikLDR0lNGz+Et6EbSdYLfx8PPKxoh7+Lx8/rbkFOURoeeTtA31YRDcxM6Oa4PryPG10GqCYhha2x4Gt4+DUdUcCqc3ExhZqOD2T/1g7mdLhPUvkJkOrrlHvKyS7DqwCgqVooSH2s/4vC6G0iJzcKqX8bDyJK/Jq9feHLJB52HthBaBeKHJyEYsoDfLUoMBoMhqjDBhMH4Ay6dHOB9xx9BryI4EUxUtZTQcXAL3Dn2goonRlai248sIyeFsUt6IfBNNO6f9YaZnT66jXRHfcHSyQB7b8/Bia33cHL7fby6G4SF24dCx0iN60MTWYiw5trWim6E7PRC6neSFJOF8weeYv+aG2jSyhxy8tKwbWpEvRcMzTXZhLmeTe6jglMQ6peI3MwivLgbDC19FTRvZ4k+I91h38yYtWx9A5FBydi19Crsmhnjx62D6XtM1Fr4Dqy+jidXP2Dm+oFwbW8DvlNb8xHXf36KzdfmCu05So3JZP4lIsQPv37e6nqfDAZDMDDBhMH4A7bNzVBdWUMFE67oPbED7h5/iSt7H2LB/s/pR6KKloEqVh6diDPb7mH/8ktQ1pBHC09+pxp8qyg0bU0/OLiZ0fjhrfPPo2PfJtQkVpTK3fmKho4SOvf/bMA8YVE3GglLJtMBb2Jw9dgrPL8VgKTYbFg5GdCJNDGYtbDX4325PuP/T/RISxapHgn9kIiIoGT6niorroCtixGatbHC6DmdoVRPPJCESXV1Lc7tfYJnt/wxd8MAOLtbQBS5e8Yb9868Qa8xreE5WDTM0N/cCYBbZwe6ACIMkqMyYOduwSrvGAwGQ0AwwYTB+AM6pOy/jRXtBS3OL4UCBxfq+uZa6DWhPXweBiErJQ+a+qJtXGjbzASdh7jRSpPN009iy9VZsHA0RH2CGMDauBjh6Mbb2L/iKt48CMacTYOgqSfa/zs+QSYDesbqdOvYuwn9WXZGISIDkhERmITE6Eyc2/+Uru4amGrAyFKLCiik/YD4oMgzc17OxRES7UsMf0ncNDVrfRVFf9e6iz1UNBQwdq4nLB30ISMryfXhijREVLx0+DkkJMWx/+YcyIuor8vzm37Yv+IKeoxyx6QVfURCEPj48RPO77yHlaemCm3MgGehMLbVF9p4jDqAeZgwGCIFE0wYjD9ALsg0DdTw+MxrhLyOhHsv4cYLf8FjcAvcPPIMl/c+xIwtwyDqtO/blIo//q+isHLUz9hxYy50jNVRnyATvgU7hsG5lQWu/fISM3vtxMg5XdB9REtWbSIgNLSV6Namm8PvfhcJUZlIiMpAuH8Snt8JxMldjyAhKUbTeIwttWnlgoauEozMtaBrpFYvzIj5aNCZHJtF04/iItMRH5mJxOgMqKgrQFlNjopYJFJ6+AwP6mEjChNhUaCqsgbn9j3Gs1uBmLayN1p42EJUIQL7z2tuwMrZEOOX9BKZtru39wNh5mgAHSPhfb6lRGdgMPMvYTAYDIHBBBMG4y80aW9LBZPAl+GcCSbmToZo1skOyZHpyM0ogJq2MkSdwTM7IzutgKbnLB9xENuvz4ayhiLqE2Ti59G/GRVN9q+8igOrriHcLxFDZ3aCgZkm14dX7xGXEKMtOWTzHNCM/qy6qgbJsdmIj8ygQkpMWCqun/BCQW4pNZEtyCmBvqkG/RsieukaqkHHUIVGYbOJ/P9eSSdVImlJucjLKqFtNanxObSSJPh9ArT1VWBkoQUjc030He0OEyttaOoycURQkDa1vauu02qdA7fniHRFVXRwMtZMPApLZ0Ms2TsKUtISIvOeOLv1LpYdnSi0Mcn5LTogERqsmlG0YBUmDIZIwQQTBuMvOLWzgZGNLgKeh3F6HMPm9cBcz424suchpmwcAlGHTJSmrx+IwtwSlBSWY8Won7H50kzIKkijvqGqqYgVh8bC614QLu5/gundtmHglA4YPK0jJKVE4+K/vkDaEr7EGP+RwvxSpMbnIjkui070icHswysfaNuIorIsqiqroaWnQg1HDc00qICirqMETR0l+v9VUJap15VDpFonP7cEORlFVBghGzFhTUvKQ2ZKPhRVZZEYlQkdQzXqH0PifInfDEkxIht53hmCpzCvlLbf+DwNx6y1/eHUwgyiDKlMOrj6OmTlpTF38xD6vhMVXt34AFN7feiZaQltzDCfWNi6mQttPAaDwWiIMMGEwfgLKppKdCKUGJWKzMQcaAmxtPaPWDczgUsHWzw88xoDZnpCTUf0q0xI7O6P+0ZhxYhDiAtNxZpxR7Dm1GRIy9Q/zwIiELXp7kSrTY5vvoMnV97D+2EwJi7tDZffUmAY3EGMRMlm19ToTz8nHihENCGiQEZKPrJS86mY4u8di5z0QmjqqSAiIIm28pDJaVlJBZTV5KGqoUBFFDLBI7dKqnI0zYWs9MspSFEzUy6rK2prP6K8pBIlRRUoKSpHaVEFivLLUFRQhqqqWmQk56Egt4S2LgX6xNGJuI6BKn7Fr9DQUaatT3omarByNKAiEmmlIY+bVYxwV83w8PI7nNn9CJ4DXXHg9lyRqcT4J7JS8rF0xEH6mlp3ajJ9r4lS9PGtYy+wYO9ooY7r9yQETT3shTom479Dzpp1feZkZ2IGQ3AwwYTB+BvcujtDQkocIW+iOBNMCCOX9MLa0Qdxac8DTNs0FPUBKWlJrD4+CUuG7MOnjx+xYfIxmqRD2inqI2TCPGvDIET4JWLv8stYPuYw9TUZNKUjNHRFXwSrbxAhhHibkO3vIIJKfk4J8rKKqNhAKjBIW09pcQU1NC0uKKc/J5VTsaGp+PTpVxrpSlqzpGUloWeijoqyKjq5lZaRoG1AJNFEQkKMpgIRQYMcAxFZqitr8UOjH+gEkvyMjE32JycvRStkPtZ+ojGxRNwgFSHVVbX096VF5aisqKH7iwxMRllpJQzNNBEdkkrfZ6QahIg65JYIPOSYtA1UYdvEEGpaihgwvg1UNRVopQ0TRPgH+Z8e+OkGTKx0sPnMFNpSJurkZhbi57XXqai38exUGFlqQ5R4duUdDMy1oWsi3NbLkNdRGLm0j1DHZNQBrCWHwRAp6ucMhcH4jzi2sca5TTehoqWETsNbcXYcVi4mMLXTx/0TrzBgRud606dMJpM/nZqCH/vvRti7eGyZcRKL9o+GmHj9PSVZuxhh7+15uH36DR5ffodJHpswcGoH9J/YXuRXhhsSRLggQgTZ/o1Pnz6hoqyaVnOUl1airLQKlRVVKC8ht9V0IyvTleXVVOyQlBanQgf5GRFLiAhDBBDiCyIpJYaa6o/4oVEjiEs0pmJJY7FG9LVDRA7SAkPuIyktAUlJcSqkyMlLY+DEtrS6hSTPkLQUSSnWKiOqEKHuxgkvGqc9YXF3tOnmWC8ELSI4Lhl2EDnpBbSyxNJJtFLUiI/I2/tBmPTTAKGOm52aBzMnQ/qeZzAYDIbgqL+zEwbjP2DbwgLSclIIeBFGL4a47McftbQPnWxd2fsI0zbXjyoTgpKaPDZcmIHFg/ZQY9tts09j4Z5RtG2nvkIeW5+xbdCupzNObL2Hm8e94HU3iJrCkvad+jD5Yfx/SGsfacshG4PxvRA/nZsn3+Dq0VfoMsgVP9+fT0Ww+gDxtNq//DKy0wuw8udxsHM1hahx78QrqOsoQ8vw76vSBMX7R8EwsNQR6piMuuGHXz9vdb1PBoMhGOqvYx2D8R8gZeudR7amq8lh3tGcHouZgwFUNZVw5+hzJEdloD6hpq2EDeenIz+rGC9v+mPn/HO0N7++Q8SiOZsHY/3pydQzYtPM01g39Tgi/BO5PjQGg8ETSGXRi9sBmNx1O/Kyi7HrygyMXdC1/ogleaVYMvQAfJ+EYcXBMSLp7UQ8jPyeh2PI3K5CHzs2KAnNOjP/EsZ/Y//+/TAyMoKUlBSaN2+Od+/e/c/7X758GVZWVvT+9vb2uHfv3p9+TxZ+/m7bunXr7/ch4/3195s2bRLYY2Qw/itMMGEw/gFTewOU5Jfh3YNArg8Fo5b2hqSMJO4ef4H6Bmkz2nxlFnSM1JEUnYFts07RloSGgLmdPrZemoFlB0bTdot5/fdQ4SQlLpvrQ2MwGBwS5BOLbQsv4vqJ11iwZQimruhNfWbqC6QNZ+us0yguKMOyg2PRtL0NRJHLex7CsokRlDUUhDpuZXkVYvwToCNkzxRGHXuY1PX2jVy8eBHz5s3DqlWr4O/vD0dHR3h6eiI7+++vQby9vTF06FCMHz8eAQEB6NOnD91CQ0N/v09GRsaftmPHjlFBpH///n/a108//fSn+82cOfPbHwCDISSYYMJg/APNPB1hbKePrOQ8utLHJbqmmugyqjVuHn5GYwTrG1oGalh3fhqK80rx4oYfts85Qw0uGwLkQqJVV0caQzx1dV+EfUjA6v/X3l2AR3U1fQD/AyHu7iQkISEkEAjB3d0dihQoUNyKa4FSrGiBAgVKoWhxdw1ugRhxIR7iLt8zJw2FfPRtabNZyfye3m5W2L3Zu9l7z9w5M6N2YuPcw6IQImOs/AjyeYsFI3dh/dyj8GjuiB+OjBdFgxUJTcGc1X8TXj4IwKTv+6FBOxfIo9jId7h37jl6f922zF/76bXXcGshn0EmJjvWrVuH0aNHY8SIEXB2dsa2bdugrq4ughyfsmHDBnTo0AEzZ85E9erV8e2336JOnTrYvHnz+8eYmpp+tJw8eRItW7ZE1apVP3ouLS2tjx6noaEh8d+XsX+LAyaM/QUq+Eqdcu6eeoxwf+lPhek/uYNIxd638qTUAziSYGZtiFW/T4G1gylC/KKwbPRO5GTlojxNA+s2rCl2XpuDlt3r4MapZxjfaS12fndKzPNnjCmusIAYrJi4DwtH7kLDNs746cIMUeuI6uAokpjwBCwcvgOVKyth4Y6RqN9aPoMl5JcVJ9FzbGtR76ys3T/3DA071ynz12WlSMrZJTk5OXjy5AnatGnz/jb6vqHrnp6en/w3dPuHjyeUkfJXj4+JicHZs2dFRkpJNAXHwMAAtWvXFtN18vLyPv+XYKyMKNaemJVr1JEiKji2VGtgNOxSdEDy6OILSJuukTaGL+gJ/2chuHHsf88xlVcmlvpYfuBr5Gbn4sHlV1g+ZpdowVqeaGipYciUDvj55jz0Gtkcp/fewfBmy7Hvhwtizj9jTHFEBMdh9fTfsHXpCdjXsMDOK7PQaWBDhWyzHh4Qgxl9NiEqNB6jFvSAh5xOwyF+z0IQ/DoC7QY1LvPXpmMcv8dBcPKQvwK5TPJSUlI+WrKzP30MFR8fj/z8fJiYfDyti65HR0d/8t/Q7Z/z+L1794pMkl69en10+6RJk3Dw4EFcv34dY8aMwYoVK/DNN9985m/KWNnhgAlTCJRx0d9yHIY5TkF0UEypPW/jru4wtjLA7ROPIAs6fNEUWnoa2LX4mJjDrIgMzfWw+vcpcHK3QWx4ouiik5xY/gIFugaa6D++DXbfmo8O/evj1cNADG+6DDuWn0JCbLK0V48x9h+EvonBzpVnML3fZpjbGGLe5qHoN7aVaAetiPxfhGFm302wtDPGiv3jULtJNcjz8caOhUfx1bK+qFSp7A+jve+/QcPOblJ5bVa6XXJKeyFWVlbQ0dF5v3z33XdS+z1pas/gwYNFgdgPUd2UFi1aoGbNmhg7dizWrl2LTZs2/WVwhzFp429bpjB1IIwsiwrihfpEltrzWjmai2k5Pg8CkBAl/XoStC6jv+0r2hce2XQRitxF5tt946Cpqw7/52FY+MVWRIcnoDzSN9bG2EU9MWvDF+g0qCHO/noXK8bvxY8LjyEmIlHaq8cY+wyB3pFYPmEfvhn0I3T0NbDr6mwMnthWdMtSVC/uvcGsAZtFuv9XC3qgurt812S5fuwhtPU1UKuJo1Re/87Jx3BuIL8BJybZoq/h4eFITk5+v8yZM+eTq2BoaIhKlSqJaTMfoutUU+RT6PZ/+vjbt2/Dz88Po0aN+tu3g7rz0JSckBDuFMhkEwdMmMKo3doFNRo7IjokrlQDMa0HNoZrE0eZ6JZDGnepAyVlJRxefx5Rpfi7yhpNHXUs2/81mnRxQ1ZGDqZ3X4eg1xEoryhw8tX87thzez5c69nh2okn+LLFCvy46HcxCGOMyW5GwnPPAMwbvgPLJ+6DQ01L7L4+B32/aqnQgRJy7fhjzB+yFXWaOmHNsUmwrW4OeZaRloXLv3li1OKPO36U5dTjuIgELvjK/pK2tvZHi4qKyicfp6ysDHd3d1y9evWjzxddb9iw4Sf/Dd3+4ePJ5cuXP/n4Xbt2ieenzjt/5/nz5yKgamxs/A9+Q8bKHgdMmMLQ1tfE67t+8HscWKrP69GuJrzu+OH64U8XtSprFMQZ990A6Bnr4MimC1BkKmrKmL1lOGp42CExJgVLvtyBJze8UZ5R9s3wmZ2x9+5CEUC5d/ElJnRei7lfbMOjmz7igIcxJn3U6ev6qWeY2mcT9qw5j8btXbDt3HT0+6qlKOCt6EGikz/fxPoZv8HGyQwTlvcRU4/k3f7VZ+Bczw7mttIZ2FHtEtovKqtUlsrrM9mfkvM5aGrMjh07RK0RHx8fjBs3Dunp6aJrDhk6dOhHGSqTJ0/GhQsXxBQaX19fLF68GI8fP8aECRM+el6qnXLkyJFPZpdQgdj169fjxYsXCAoKwv79+zF16lQMGTIEenp6/+btZEziFK+qGCu3bGpYQcdIG1mlXCTU3s0GpjZGiH/7DokxydA30YG0VXEyR7OeHji68QI82riiYUc3KKpKSpUw8fv+MLLQw82Tj7Fw6HaMX9EPnYaUfbE9WaKhpYruw5uKaTo3Tj/DgyuvsHDYT7CyM0b3L5ujdU93qKp/+swSY0xyUpMycP7QA3heeSXS5HuPaoFG7VzKTc2J/Lx8/LjgGM79ehcdBjXE6AU9FCJAFOwdgejQeHyz9UuprYPnmado2sNDaq/PFEv//v0RFxeHhQsXisKtbm5uIiBSXNg1LCzso05djRo1woEDBzB//nzMnTsXDg4OOHHiBFxcPu52RQVdKWg6cODA//ealPFC91OwhWqW2NraioAJBW8Yk1UVChWxPykrVRQppsJRNBeS0vtkVWx4PIbYTRLdBU6++xlKlUsvHrhv+e/4dcUJTFg/DF1Ht4YsyEzLwlcNF8HCzhiL90+AqobiD46vHn2I9TMOQE1TBZ0GN8YX33QpN4OQv0Nf5S88A3Di55tIeZeOMP9otOtXH12+aAxzGyNprx5jCi/Y5y2unXqKswfuo24zR/T8shmq166C8iQ9JRMbZx/C09t+aNKpFsYv6wulypUg7yhzb0aX1RgwtRPqtXWV2jqMqTcPm24vgaqaYhYHVvTj1OL1dB25ApWUSzeImJ+TBa9dc2X+PWBMHnGGCVMYVPRVQ0cd6ckZiPCPhk0Ny1J77kZd3EXA5OaR+zITMFHTVMWkdUOwoN8G/PLdCXy1rD8UXes+9WBsqY+9K0/h0MaLCPaOxDc/DheteMs7mqrl1shBLFEh8Tiz/y4uHXoAvxdhUFGrjM6DG6N+6xoKMXhhTFbkZOfh/tXXOLn7tmgR3Ht0C2w/PwNG5roob6hd8OIvdyD8TQzGLOmFbsObiu8lRXD18H1YOphKLVhCXnu+gUNtGw6WMMZYGeNTs0xh0IFZ4x51YV3dAsFeYaX63FVrWqNpTw9Eh8UhLlJ2OpN4tHVFk27uOLHtKt48Lx/VxV0b2GP6hqGwrmaK1w8DsWTYNkQEll4raUVgZmOI0fO6Y9+Dxeg8uCEyUrKwbOxuLB61E3tWncVbBS4WzFhZiAiKxY4Vp/FFo6XwvPRK/J3tuzsf/ca0LJfBkud3/TFn4BaRBTFv+wh0H9FMYYIl1CFv38pTGDq7m1TX497pp2g1oJFU14EpVg0Txtg/wwETplDUNNUQ5hOJoJehpfq8dOBHNVLiwhNxQ0aKvxYb9/1AONWtinUT9iA3Jw/lgVkVQ6w7MwPNurvDyzMAUzqtxsPLXtJeLZlDZyJb9fTA+pNTsenMdNjVsMDpfXcwssUKrJvxGy4ffYjMUq75w5iior+VS0ceYka/Ldg0/xhys3Px3a9jMWv9YLTq4V4uC3HSVMALv3li3uCtKCwoxKxNQ9G44993xZCn3+/ghvPoN7kDDM2kV5AyLzcPjy69gFuz6lJbB8YYK694Sg5TKNXq2MKxblWkJWeU+nO36t8Qt449wJtnspXJYWCqiw5Dm2Ld+N04vP4cBn9TtmfBiruyfFgYrCzQNJwJK/uLrjEnfrqGrfOPwvdZKAZP78R1TT7B3sVSLAMntsPd8y9EjQEKmvy48Jioc1KnmRNqNrDn946xEt9vL+8H4urxJ4iNfCdu6zSoARq1dy33UyOyMnOwec5h3Dz1FE0618K4Jb3F97EiuX3yCUJeR2Ds8n5SXY+n116jWa96pVqbjUkRZYOUdkYIZ5gwJjH8zcsUiq2LtWi7FxMWj0mbvyzVlGDzqiaiRsrNYw8wYGZXVHW1hqxoO7ARvB8E4Pze22jQ0Q12ZbRu1A1hw9RfUVm1MiasGljmKdgUpBn6TRc4udvg+3G78dsP5xEZGIOvV/SHjoFmma6LvKBBXuteHmIZOr0Trh1/DJ+noTi6/ToMTHTQaXAjeLSsLoIripJSz9jnZhUE+0bh+smnCAuIQVhALNr0dMegiW1hZm0g7dWTCTSt75c1Z3Hz5DN0G9EMo+Z3FwXXFcm7uBT8tu4sFuwZh0qVpFv76dKvt9F7YgeprgMrRRwwYUyuKNbejZV7VWpYiqKW2Rk5SHj7DoYW+qX6/G0HNxEBmXtnnspUwIQGtkNmdcOdk0+weuwubLw2v0zSw30eBeHKoftigKGhrYYvF/SENNRr7YJNF2dh5bjdCPaJxPi232H21hFwqW8vlfWRFyaW+iLjhLaf/4swXD/xBK8fBWHfuvMwtzEUXXbqtXSGjZMZB0+YwgsPjMWtM88R4h8lskqadqqFfmNbwtndlj//H6AMtR+mH0BBfgHmbBmGZt3qQNHQd+LmmfvR4YsmMK9qLNV1SUlMRU5WLpw87KS6HowxVl5x7jVTKHSGq1ZzZ2SmZiDwRelPnaGUWH0jbZzedlnMKZYlhuZ6GLdqILR0NbDvu5Nl8pouDR0wae1g0dL48dVXOPjDeUiLua0xVv0+Bc4edqJI3/JRO3F40yXk5xdNGWJ/jQaDjm5VMHZxLyzd8xW+O/A1ajV0gNeDAHzdcTVGtVyBAxsvio47xVOwGFOEQXGIXxT2b7iE7ybuw5SeGxEVnoB2fetj//1FmPBtb9SoW5WDJX/Iyc7FtkXHcHLXDTH1Zu2JqQoZLCG3Tj5GSmI6uo5sKe1Vwc1jD0WwhD+HioOLvjImXzjDhCkckyqG4tL/SRDqdyrdgzlNXQ24NHHE9UOeeHD+ORp3qwtZ0qpvAzy48AJHN15E3TYuqNXESeKvSWfgaOCxcfp+BL+OFC1se45tA2lQVVfBlLWDUbOhA87tu4PdK07iyQ1vzNg0DEbm0ivYJ0+ohklxe2KacvXyQSDunHsBv+dh2LfuAgxMddCkYy0xbce1vj2UVXg3wuQHBVDps+x5+RWiQhPwwjMA9ds4i6Kt09cMKJeFW/+JyOBYrJm8D75PQ9GihzsW7f5KYdu5x0YkYOeio1h9ekaZ1+b6lLunHmPa1lHSXg3GGCu3+EiXKRyXxk4IfR2OqKBYiTx/py9biue+vO+2zAVM6AzUxLVDEOYXhYNrz8LW2RLa+pKv5dFxaFPRQeLIpos48dN1cZDZ/atWkJZWfeqhWu0qWPX1Hvg+CRGth/tPbo+mXRTzbKikVFKqhNqNq4mFMkv8noXh3iUvMXiaP3Q7VNWVUaepI+q3qQH3Zk6iBgpjsiY9JRNP7/jj4XUfJMQkI+xNDOq3riFaAc/aMFjham+UJgqGXz32CD/OO4JqbtaYvGoA2g9sqLDZDhRQ2zjtVwyd0x2m1kbSXh0EeYWJ/amxJdfOUShcw4QxucJHCUzh2LpY4bWnP94GxYiDvdI+sKMMk5TENJHBEheRCCPL0q2T8l9p6Wni6+8HYXb3Nfhh4h4s/HV8mRzc9hrXRlTw3zrnILbNO4yKShXR9csWkBZLOxOsPT0dx3+6hj0rTmHF6F3oPNwfI+Z0F/VW2Oehg/bq7jZiIZHBcXh49TV8noVg09wjyMvNh62TOZp2doNTnSqo4W4LZVU+W8/KHgX3qGjrk5t+eHLbD6lJGVBSqiSyoroNbSzaa8tC5oCsS32Xjt0rT+PS4fuwsjPFuKV9UMXRDIrsyEbKotNFm/4NIQvO77mBDsOaS3s1GGOsXOOACVPIwq8UNKGONrHhCTCxLpqiU1roQLvLV61x79QTXDt4F/1ndIWsqdnEEQNndMHdM09xbs9NdB5RNoGLbqNaiGkcZ3bfxC/fnRKF6np/3RbSQmeO+01oB6c6tlg7+ReRbTK25TJMWTsE7i2qS229FIGFrRF6jmoBKvObkZaF53ff4MlNH1w78Ri/rD0HFdXKaNWrLsyrGKJWIwdUdbbglsVMYmLfvhOfwWd334iCrUqVK8Kptg1a93SHezNHzn76TM9u+WHttF+REJ2ML2Z0Qp+xrRU+AOr7JAgXf72DjVfnyUQGTVZGNvyfBGP0ioHSXhVWyirQybzC0k0JKe3nY4z9iQMmTOFQlgMFS17d9YP/48BSD5iQ1gMaYfeCw2JqTu/JHcVrypqBMzrj9f032Db7IBzcbFCtdlFmgKT1HNtaFIHdNH0/di46Jg48KftEmmo2csCPV+Zi++KjuHzwPpaO2I6OQxrji5ldONukFKhrqqJRe1exUFZXVGg8nt72R5B3JA79eBW7vjsNl3pVoamtDpf6VeFSzw52NcyhpCR7fzdMPsRGvoPvi1A8vemHF/cDQONbA1M91G7sgG7DmsDB1VJklbDPQ1Mrdy0/iRd3/UXHueUHvkadZpKvhSUL2TSrx/6Mb7aNFIXTZcGtYw/h2sSJ6+owxpiU8dEqU0hO9exF+9/gV2Fo2qt+qT+/rrEOmvaqJ6blPLr4Eg1lsDYGBXFmbh+FCc2X4rc1ZzBt83AxXacsdPyiiSgGemL7VexedhzvYpPx5cJeUj1rp6Gjhmk/fIHGndxEQdiTO2/g7rnnmPj9QNRr4yK19VI0tI3NbYzEQiYs74Mg77fwfxmGx9d9cGjLFexff1EMcKkzD7VsdalvBwcXSw5esb+sKxHmHw3vpyEI9n2LR3/UImnW2Q1G5rqY8G0f1KhrI4o+s3+PgiS/rDkH70dBaNmzLsYt7Q0tPdkIHkgSBXl/mLwX7QY3RnUZat17/9xTjFw+oNSf9+CqU+g0siW0DbRK/bnZP8Q1TBiTKxUKaU/B2P+QkpICHR0dJCcnQ1tbG/Lg3unH+LbfOjjVc8APN5dI5DX8HgdiUtPFqNmsOlZfnAtZ5XXPD7O6roF7qxpYcmhSmc7d97zwAt+N2oHc7Dz0m9xeFNKThWkZVNNg59Lfcek3TzjUsoa5rRHGLO0DPSP5+HzLe30JKrr5+lEQXj8OhvfjYKhrqSLENwpW9sao27y6mO5TrZYVqlQz44Kc5VBibAr8X4TB93kYQt9EiU42NBhwcquCui2cxPQuJzdrDpCUktTkdOxecRrn998Twcseo1qgccdaKC+ObLyIML+3mLppmMzUtvF9HIhflx/HsuMzSv2553ZZieWnZ8nEtKPydpxavJ61By9HJWXVUn3u/JwsPNs/T+bfA8bkER+JMoXkWNcO+XkFIgMkNydPIoOuau5VxUKBE6pkX9XVGrLItZEjRizshYM/nMexTRfRd3LHMnvthh1qYdmhSTiw5gwOb7iAcP8ozNo+CipqypAmLV11TF03BK16e2D9tP24+SIMz2/7Yficbmg3sKHMHDQrInpvbRzNxNJ5SGNxG2ULiAHys1AkJ6Zh53enkJmWLf5ua3jYijoodjUsYe9qiSrVTKGiKt3PDysddL6Gtn3g60iE+EfD71ko/L3CYWplgJR36XCsZS26Lw2Z3B42TuYyEWxVtPf/7rkX2LPyNNJSMtG6twe+WtwL2uUgq6TYy9u+OLf3JjZcnitT3/s0HafbuHal/ryZ6VmwdDBTqGAJY4xJGmeYMIWJ3Je0qPcaBL0MxZxfJsK5YTWJvMbt4w/xw9idaNzDA9O3j4asoj/zDZN/wYV9tzH/l3Fo0tW9TF8/2DsCC/pvRHxUEtoMaIgx3/aTmVTvrIwc7F93Dm+eh4qUdMc6NpjwXX/Y15TNAFh5yUJ5GxyPN17hYioPDagDvSNR1dkc3o+CRQaKbXVz0bGjioOpCKKYVTHkAbWM18aICIxFkO9bhPhFiZoRj276IiUxHRY2RqjTzBHG5rpwqGkFO2cLaOqoS3uVFVpMRCIObbqE87/ehbGlPiavHoA6zcpXIWzqcje75zrM2jEK1dzKpsbXP5EYnYTZXb7HtofLSz2I8+z6a/g+DMDAWd2hSOQuw2SQhDJMDnCGCWOSwBkmTGFpaKsjJiQOrz39JBYwadC5DtQ0VXH94D0MX9wbBmay1WK4GJ1NGvvdAPg/C8GhH87BzMYIdmWYEWPrbIl152dh56KjuHLQE76Pg/DtwUliPaRNVV0ZI+f3QIjvW2yedRCvHwZi28Kj4oz20G+6QFu/bOq+sD/RIMHSzlgsLXu4vw/6RYcnINgnStSxoJ9vnHiCyJB4FOQXiE48NPi2on9nbyyCKha2xmIwrqnDtVHKAm2jxJhkRATHISIoDvHRSQjwihBTsKiLjVtjB9E5i7KLnOvaotPgRuJnKhrMygZlXFKr9QM/XICOgSZ6j22NIdM7lrvpTdSBZumwH9F/akeZCpaQa4c90W1MG4lkvIS8CoNrY8dSf17GGFNknGHCFCZyX9LlfbdwYc91WFUzx5Stksv+OLX9Ms78dFUUfh2xpB9kWUx4PKa3/04ciG24Nh96xmXbajM9JQPLv9yOpzd8ULdVDQz+piuq160KWUFfh9d/f4SdS6lQbQo0ddXx5bweaDegASpxxw2ZlJOdh8igWESGxCHULwphAbFITkgV9S+yM3PEY+q2qC7q1phZG8DU2kBko1CxUBMLPRia6YkCxeyfoRbScW/fISbinQhaJSemiwBWVGgCosISRBZQdFhCURaQoxnMbAxhbW8KawcTGJrq8FQAKXpywwc/LfkdOdm50DPUxvgV/WDnYonyhr7nV43ZCTNbY1FXS9YCOeMazMeWu0uhrlX6gd4FPVZj/m+TpT4ttrwepxavZ52BkskwefobZ5gwJgkcMGEKsyMqKcL/Lb6sMU1M/TgSvUNi85PTkzMwxGEyKlaqiH3+6yVykFOavB8EYFbXVWjawwOTNwwr8wOnvNw8HFh7TtQ1qayihGkbh6Fl79LvZPRfpKdk4sAP5+HzOAg+j4Nh5WCK0Yt7waNVDWmvGvucbIfYFEQGx4mpYGEBMWIgT+1oqe1xUkKaeBzVSHkbkgBDc10xoLe2NxFn2/WNtaFvog19Qy3oGmqJug7UZlVR0SA6OSEN7+LT8C4uBUnxqYiPThYL8Xseiri3SaLWhVPtKngXlwpTK31Y2hUFQsyqGIhaMxSU4uk0suVtcBx+33EdZ/feFlklXy3qhRY93WWqZkdZOrrpAh5f88ayw5NENzlZcmn/bUQGxGDEoj4S2feu/Wo7Zu0eD0UjL8epHDBhTD7J1p6CsVJk4WAGW1drqGqoiPbCdjUlk3aroaOOXpM64vmN1zj/8w30LsOiqv+Gc317zN41Bt8O2YLsjBzM3TuuTGs/0AHqF7O6QtdQE9vmHcbJHddEZsDQ2d1k5gCe2tuOXtQL4QHRItvk4eVXOLjhAo5vv4qRC3rCzsVK2qvI/gZlMhiY6IjlU7KzchAXmYT4mGQRRKGgCk0hocyJ8MAYEWyhIEmof/T7f+PezBHR4YnidloMzXRFdoqGtqoIqqioVBYdf9Q0VKChpQYV1cpiyhcFJVXVK0NZpbJEPuPUdpemumRl5ohLqheSmZGNnMxcEeBIT81Cemom8nLyRZHVtORM5OXlizoWVGSXskSobojXg0AoKVeCvqE2bJyoQ1El8f5RRk7NBnYwMteDkZkuDEy0ZW6gyf6/1KR0HNx4Cad+vik+r9T9ZvC0juU6oHX75BPcOf0MSw9OlLnPMP0dH1l3Ft+d/kYizx/wPASGMjptuNzhtsKMyRXZ2lswVsoDpirOlrhx6B5e3vCWWMCEdB7VCofWnMHbwBh0HdMayjLexaNxV3cMX9ALh9efw/6VJ/HF3B5lmipPr9VtVCvYVLfEshFb4fs4GBFvojF14zAx0JQVVvamWPLLODy75Yvf1p/Hs5u+mHhrJXqPa43Ow5rBtIqhtFeR/UvUaae4Tspfyc/PR2pSpsi2oIwUyjyizIrkd2lIfZchghI0PYWCElo6aqK+Ck1ZocXZ3RavHgW9fy5dIy0kxaWKLBWanhIflYxKShXFdQdXKxGYqVixAipULAr0UMCGsmRocEsFUgvyC8X6UKHbNy/DRS2KvNx8MfUl4FWEeA01TRXRXYjQc9dsYC8ybDS0VMXfFbVtpn+jo68BbQMNEQTR0dcUg2ldA02xjpraajxtRs7RZ+PsL3dwcONFEfx1a+qIUQt6iDbd5Znf02Bsn3cI35+cLpO1qe6ffQqnevYwNJdMUMP7/hu4lrPCvowxVho4YMIUmluLGrh74hFCvIsGFJKib6qLTiNbIuBZCK7svyt+lnX9p3dGWnIGDqw6DTUNVfSdUvaZMTUbV8PGy/OwdOiPiI1IxOS2KzB/zzhRcFWW1G7mJNb1+rGHOLHjOk7vviUuOw5pgv6T28PAVFfaq8gkoFKlSkWBBIPPG1xRoKM424O6MNHPuVm5yMnJEzVX8vPykZ2Vi/zcfJHpQdP56LEFBYUoLCgUwQ5qi160DhWBCkWX9DjKVunQr77IBKG2yxScpUwQVVVlKKsqielEtHBdlvLZXerWqaeiGPKDy69EltDoRT3LXfebT3kbHItlQ7dizq6vYGFnAllD3xkHVp7ErJ/HSuw1nl31QofhLST2/Oyfq1BYtJT2czLGJIOPqJhCq9PGVRyI3Dp6H5M2j5Ro4U6aijPSdSZiQuPQZkgTkX4vy+gs8ojFffA2KBaXDtyBjpEW2g1uUubrQVka1EFn8zcHRAedub1/wLgVA9C0e9m2Pv47NGBt068Bmnatg3O/3MbBDRfhefElLh++j05fNEHf8e3EGXrG6G9LTV1FLDqcAc8kjPZxT276YvfykwjyjoS9qxWmrBmENv3qc6ttAO/ikrF82FaMWtoHNerbQxY9vPgCplWNYe1kIZHnz83JRU5WjszXWCs3eEoOY3KF96RMoZnaGMPIUl8UZvV/8md6vCSYWBuiw4jmUNdWw9Xf7kIe0ME01TOhdV8/YTfunHoilfWgM+LTNw3HxDWDRQBl+cjt+HH2b6IQpayhM/w9x7TG7odLxdQceg9/33YVc/ptwK6lv4uCmYwxVhYZJTRdcGrXdVgyYruYbjVyQQ+sOT4F7Qc25GAJdXVKzcLC/pvQfmhTNO9VD7Ia8Pp1+XEMniW5jj2+DwNQoyG3E2aMsX+D96ZM4bXo3xiaehp4cuWlxF+r/4yuImPjlyXHRHtAeaCsWhlzd4+FXa0qOP3TVTy+4iW1s/KdhzfH+O8HwdzWGJcP3hNTdSiVWhapa6qix+hW2P3wWwyc2hFauuo4+uMVjKi3APtWnRa1LRhjTBID7Ke3fDGz1wb8tPQ4wt5Eo9/4tlh7cir6jG2tcC1j/y2a5rZl5n4xpZJqZsmqhxeew8jKAFVdrSX2Gs+uvkKtFs4Se37276fllNbCGJMcDpgwhedQ2xZp79Lx+OILib+WkaUBun7VWrTvu7zvNuQFpekuPzYNSQmpWDp4M57f9JHauti5WmHTtXnoNLQZHl99jQktl+Hm8UeQVRQoGTqrKxbsHovB0zuJuhLUsvLL+gvxw9R9CH/zZ5cVxhj7L4GS+5e8sHriL5g3cAuCvSPRors79nguxhczOpfr7jcl0T74u5HboaKujBGLekOWs4T2LTuOIXN6SPR1gr3C4FTfQaKvwRhjiooDJkzhubWsgVrNnREbnoDUd2kSf73+M7tBSUkJe5YcFd0t5IW2gSZWnpwhCuL9svw4Xtz2ldq6UEeP0Uv7YsaPI1BZtTJ2f3sca8bvFq1RZTlwMmRmF+x59C0ad3aDlq4GLv3midXjd2PpiO14/TBQDHgYY+xzUJHgO2ef4+u2K7FkxE8IfB2BAZPaY8/9Jeg/sR209TWkvYoy1553z9LjIng9fvVgme76dOfEI5hXNZZodklKYpp4T6gzGJMRdCwgiYUxJhEcMGEKT1O36GAyPiIBT8tguomesTa6jGmNjJRMnNlxFfJEz1gHy3+fJg6wFvZdjxe3pJdpQtr0a4j1F2ZD30wHVw55Yn6/jXj9IACyTENbHf0mtheBk8lrByM9NQue519gRre1WDlmF26eeCxqDTDG2N/V3zi56wa+bLwUP0zfj8z0LAyf3RXrTk7DsFldOFDyFxkb6yftQURgDGZuHynTdVwoC+bgqtMYulCyGTDPrnmhQec6En0NxhhTZLK7J2GsFDXt3QCuTZ3gdbtsAgC9JnaAQx0b7F9xAtGhcZAn1CL5+zPfoFodW3Hg+eTqK6muj5mNEVafmonh83ogOysHM7qsxo6FR5CdmQNZrw3TYXBj/HRnEeb//BUadqwlWn6uHPszRtRfINoSJ8WnSns1GWMyJi7yHXYtO4EvGy/BwU2XRDtpCpRsvTJHZJRoaHOnk78KluxYcARZ6dmYu3sMKivLdqe6i3tuopq7LSwdzCT6Og/OPkOtFjUk+hpMuvVLuI4JY5LFARNWLtRu5QKv2764cdhTpKZKmpqmKjqPao3c7Fwc/eEc5I2BqS7m7hkLFXUVLB6wUbQ8lCY6SzhgaifM3DICVV0scem3e5jdcy18Jdz5qLTWvXEnNyzcPQbrz32DFr08oKRUCdsXHMEXdeZhzYQ98H0azNN1GCvH6O//1f0ALP9qJ2b324ijW6/C1NoQE1cOwM5b89F1eDOoaahCVsja9xXt13+YuAcR/lGYuW0UlFVkO1iSkZqJcz9fx5C5PSX+vtCULgt7U4m+DmOMKTIOmLBywcrRHA271RUHDa/vlk1tjjaDm6BZ73o4ve0SvO5Irx7If5mes+rMN2jUtQ6WDtmMq4c8pb1KsHW2xIZLczB4ZmcEvAzHtI7f49dVp2U+26SYYx0bzPpxBNacnI5B0zqJuiePb/hgWpc1mND2O5z75bY4kGaMlQ80zYb+7hcP3YqZPX/AvXMvUK+Ni5h2s/7MdDTqUBOVlCpBVryLTcHKr3bC72kIZAUFBChYQoHoBb+OF9l9su7w2jOo36k2DC30Jfo6fg8DoGeiI9HXYP9CoYQWxphEcMCElRtW1czg8+ANPE8/KbPMgu7j2omfD3x3vEwyW0qbtr4mJq0fBuf6Dlg7bhfO7bkh7VWCUmUl9PiqDTZfnw/n+va4ctATY5suxtMb3pAXBma6+OKbLtj7eBkmrRoE1wb2CHoVgR2Lj2Fkg8VYN2UfF4llTIEFvgrHnhUnMdhtHjbNOoi8vAIMnNIBex99izFLeqN6XVvI2nSXa0ceYNmI7eg6sgWc3GVj/XKyc7Bs+FZkZWTj69WDZT6zhNA03Ze3fNFnSieJv9bz66/RuHtdib8O+zwVCiSzMMYko0IhH5Gzv5GSkgIdHR0kJydDW1sb8srn4RvsnH0AFStVwKpLC8qscv6OuQdwYtMFjFk9BN3GFgVQ5E1OVi52LjyCU9uvoM+kDvhySR9UrFhRJg7iz++9jV1Lj0FVXQXurWpg5MJe0DWSv88ptR++c/YZTvx0DSmJRd2VatS3E2ebW/WpD0MzXWmvImPsP0hPycS9889xevctvHkRhuoeVUVWRKehTcS0PerqIotCfCKxdc4hOLrbiuw+Wem2QkVxl36xBQ5uVTB8fk+ZysT5X74dtFEUYW07uInEX2tK0wVYe32x3Lw3in6cWryeHj2XQaly6U6xy8vNwqPj82X+PWBMHsnm3pkxCXCsa4eo4BjERyQi4FkIHOqUzRmyXhM74tzOazi46hSa9KgniqrKG0pxHvPdAOTn5uHSr3eQnpKBcaukfzaPgjadRzRHgw61cPTHSzi+9Qo8zz3H6G/7ok3/hjLdIaEkKwdTDJzSEb3HtcGDi154dO01rh6+j9cPArFnxSm06u0Bt2ZOaNTJDeqaslPLgDH21yiz8OU9f9Fi/N75F7B3tUJMWAJ6jWmFdgMbooqjOWQ5wLNv5SlkpGVh7Ir+sHW2gKxIik/B8uHbYF/TGiMW9pKJAP4/8ez6a3HZemAjib9WqE8EajRxUvhgiVySxBQaPv3NmMRwhglTmMj9P/HLkiN4fuM13FrWwNCFfcvsdS/suYHdCw/BrXkNzNk3AfKKvi5ObL2M7XMOwqVRNcz/dTx0DbQgK17c9sXWeYeQlZYNLT0NjPtuAJzr2UFeJUQn4drRh3h45ZUoCElU1Cqj0xdN4drIAe4tnaUetGKM/f/vyWDvSFw79hA3jj+GaRVD+DwKEn+vFCShrDFZ/rulIM+N3x/i8IaL6P5VK7Qf3Fimgs9RIXGY33c9unzZEj3Gti6zbNHSmD40odFCzNw5Fg61bST+er8uP4aqrlXQqJviT8mRuwyTHhLKMDnBGSaMSQIHTJjC7Ij+CW9Pf0xpthCmtsbY67ehzA606AB0SvNFePMkGMtOzkTddrUgz24cfYCjmy4gJzMXC/aPh5WE2yJ+jrzcPJzedQP7vj8lUtzrt6+FoXO6wdBMD/KKvqYDvcLFAOzO6WfIysxG6rsM0V60w5DGqNnQQWSfyPIgjDFFFxkci1snnuDGicfQ0deE1/0A2FQ3R7sBDdGsWx3RfUzWeXm+Ea15bWtYYsSCntA1lJ2AOPF7EiTWr92QJmg3qDHkyW/fnxTTW4ct6lMmr7dqxI+Y/OMoqKjJxhQqSZKX49Ti9azXXTIBk4cnOWDCmCRwwIQpzI7on6CP+8Keq5HwNhHjN4xAjYaOZfbagS9DsWbUNqQmpmP705XQ0FaHPHv94A2+HbxFtE6mTJPazZ0hS5LiUnBq53Uc/OEcKqtWxpBvuqLrly1ErRN5lp+fj1eeAbh58gnunHkGEysDBLwME8GTlr09ULuZE+o0d4aquuIfJDMmC0GSu2ee4/bpp1BWU4b3w0AYmeuh/eBGaNi+JqrWsIQ8iAiIxs7Fv0NTRw3dRrdCNbcqkDV3Tj3Blpn7MX3rl6jbygXyJDIgCvO6rcEWz2+hoSP5fX+YTyT2rziGOfsmoTyQl+NUDpgwJp84YMIUZkf0T+2a9xsOrTqJ7l+3F0GTsrR3yVHRMaff9C4YuXwg5F1MWDx2Lz6KW8cf4cslfdF7YnuZS48O8X2LHQsOIzEmWRRT/WJ2N7Qd0FAh5nXn5ebjxV0/kXVCxSSrOJnD694bMW2ndrPqaNSpFjxau8jcWWLG5BUdMtF3yr1zz0VNEhL0OhJ6RlpoP6gx3FtWh7NHVbmpqUHT/k7/fBM3jz/CF7O6okUvD5lbd3rPD/9wDj6PAjFsfi+R/SJPaP1nd/4e3ce1RaOu7mXymkfWnoZ1dQvU71QH5YHcBUy6fSuZgMmpBTL/HjAmjzhgwhRmR/RPhXqHY9PEn5Ecn4qtj1eKNrVlJTcnD6tHbsPNI55YfnoW6ratCXmXlZ6NdeN/xr0zT9FhWDOMXNIXajJYlPT5LV/sWnIMwd4RqFHfHj3GtBbFYmUtwPNv5efl49WDANw79wKeF16INOyIwFjx+zm526B5d3e4NLAXZ7wV5XdmrKy+t6nN94NLXnhw2QuZadlIik8V7cE7DGqEmo2qiY5WslTn4+8kJ6Ti961XcfnAPXT5sjl6fd1GJrPvqF3wpmm/IDIgFgv3T4C+iQ7kzfk9N/Dg7DMsOjylTL576bB+UqP5WHtjcbmZpikvx6kcMGFMPnHAhCnMjuhzjK8/F2+eBmHRselo3M2jTF878EUIJjVZCFtXaxE00ZGhoqn/Fn2NnP35Bn6c8SusHM0x75evYV1NduqafNiG+MGFl6INcURADOq0qI4BUzuhZuOym5pVVtsj6FUEPC++FIO8AK9w6BtrIzE2RQw4WvSsC6c6NnBr4iiK4zLG/n/mxZMbPqLgctDrCESFxIvbrexN0KZ/A9Rs5CCmrchaNsbfSU/NFN3ETu28gW4jW6DLyBYym4EWG56Ab4dshltzZ3wxt4fo1iZvYkLjMa/7Knx/bg4MzMumjpbvwzfwPPMUI5b2R3khL8epxetZv6tkAiYPTnPAhDFJ4IAJU5gd0ee4sOc6zmy7DCNrAyw6PL3MX//UtkvYMec31O/ohnn7JynMGf8Xt3ywado+FOTl44t5PdGybwPIajYGteylTAxqQ1yriaOYquPSwAGKKP7tOzy+7o1HV1/j2W0/2LtawsszQHzuHNys4dGyhui6U93dVi4HJYz9V1SM0+dxEB7f8MHTG97izLzv0xBUUqoIl/r2YnqbewtnWFQ1hjxKS87Aie1X4fc0RATpB8/sDDMbI8iq57d8sGXGr+j8ZQt0H9NGLveRFKBf0nc9mvauhzaDmpTZ626csAstBzSCa5PqKC/k5TiVAyaMyScOmDCF2RF9jtR3aRhgNU6cid8XsAkGZdxBhbrmzGq/HG+eBmPS5i/RugwPpiQtPuodVo36CS9v+6L/tM4Y+E1XmUz1Lu6oc/mgJ35bdw6GZrpiGsug6Z3h2qgaFHl6ge+TYDy57o2nN32RmpSO6LAEcR8FS1r1rgdjS/33Z9Cp0xBjioaCppR59eKuP57f9hNTbhxqWuH1wyAx1YYKttZqXA21mztBQ0sN8oqKX1/6zRNHNl4UnbQGTu8k04VoKchwZP15nPrpKmbt/Ao1mzhBXp3YchF+T4Lxza4xZRbwycnKwbqvtmPW3glyGWQqNwGTLhIKmJzhgAljksABE6YwO6LPtWP2r7iy/zZ6TuyEAd90L/PXjw2Lx8Lea/A2MBab7i5FleqyexD7bwJCh9edxdGN52FgqofZP49BVVdryHIQ4fqxBzj0w3lEBsWiVb8GaNOvAWo3r67wB51JCal4edcfL+744/kdPzE4fPMyTNynqaMO+5pWqFHPTixUC0VNQ/bq0zD2TwokB3iFiSDJoyuvRb2fjNQsaOtrIDc7D64NHdCgvSucPexgXc1U7v/uo0LjcXL7VVzYfxfuLZ0xeGYXmQ6UkJTENGyYtBeFKMT41YPL/ERGaQr1jsCS/uux7tpC6BqV3XHT9YN38TYoBoPn9kJ5Ii/HqcXr2aCzZAIm989ywIQxSeCACVOYHdHnennbBzNaLYGjhz023Fkqlbno9049xpJ+P6Bpr/qYseMrqCrYYPTlHV+sGv0TLKqaoF5HN/T8uq1Mz/mnQM/d009xaMN5BHqFw76mtTgj26CDm1wVdPwvYiMS8ep+AF56vhF1HB5f835/H03byUzNQvW6tnBytxWXptaGcj+4ZIqHpqBQJpX342CE+Ebi6Q1fZGfmwM7FEuEBMeKzW6tRNbg1dYRDrSpQqiz/XbPImxehOLblMu6eeYYOQ5qg0/BmsHW2gKzzvv8GK0f9hBZ96mPovB5lWoy9tNHnbHKzxRi1YkCZF3ZfNeJHDF/aD8ZWhihP5OU4lQMmjMknDpgwhdkRfS766H/b/wfcOf4Qy07NQr2OtaWyHr+uOI59S4+KaTkzd41VuMFnSkIqts06gGuH78OthTOmbh4OE2vZnTtf/NmgOgZXDnni+tGHMLMxRI8xbdBuUKNyl2FB7ZhfPwrE6/uBiI9KEoVkC/ILULFiBSgpK4npVo61q8C1ob1oa+xQ0xp6ZXhGlTHKHgnxiYTfs1D4PQtBWnKmqE9UjGoUVa5cSXSyoXok1WpXUajuIRTofXDxpSjmmpeXD5vqFug9vi0s7UwgD1OjDqw6jYv7bmPqlhFwb+UCebd3yVFkpmdj7KrBZfq6Ef5v8dOs/Vh6fCbKG7kLmHRaKpmAybmFn/0ebNmyBatXr0Z0dDRq1aqFTZs2oV69en/5+CNHjmDBggUICQmBg4MDvv/+e3Tq1On9/cOHD8fevXs/+jft27fHhQsX3l9PTEzExIkTcfr0aXESrXfv3tiwYQM0NTU/+/dmrCxwwIQpzI7o3zi97RK2TNmD9sOaY+r2MVKbDjKz3TJkpWWh69i26DyqNRQNfc1cP3wfVw/dg8+DAIxePkC0IJaH4FDQq3D8vvWKOEsdE5aAjl80RdeRLWBkoY/yiNp8+j8PQ6BXGLzuB4oBKgVVnOtVhffDIPEYqgHRqENN6BhoimkAdi5WMLLQk4vtzWRbTnYuwvyjEf4mGt6PgkRGRZB3JMyqGIrbiWNtG1jaGaN63apw9qgKa0czhcwQS32Xjov778LL8w1e3w9Ax2FN0W1USxiVUTeW/+ptYAz2rzot6qxM3zpSLlsGl3T9kCeOb76ANVfmQVlFuUxfe/fCQ3Bt4oS67WqhvJGX41RZC5gcOnQIQ4cOxbZt21C/fn2sX79eBET8/PxgbPz/C1zfu3cPzZo1w3fffYcuXbrgwIEDImDy9OlTuLi4vA+YxMTEYPfu3e//nYqKCvT0/vxe6tixI6KiorB9+3bk5uZixIgR8PDwEM/HmCzigAlTmB3Rv5GZlilaDIf7vsWu1+tg7SSd1OW4t4mY3GQhkuNS8P2FeXBRsDa3xeIiE7Fx0h48u+GNxt3dMWJRX5hWkY/U4cToJJz75TbO7rkpDuzp7G3XkS1Ro4F9uQ4E0C6EMk+o9arP42C8eRGGgJdhMDTXRdDryPePs7Q3hp6RDmyqm8O2ujmqONJiBg1t+S2oyST7uXoXm4IQ37cI9olEqG8UAl9FIMw/SmSUONSyFp81PWNt8TO1yKaWv1SoWFtfU6HfF+p0c+/sc5zaeQ16xjroM6EdWvWpBzVNVbn5Hc7vvYW9S49h4Dfd0O2rVjI9VfOfCvWJwNYZv2LKjyNhWqVssygz07MwvcVibLq/QiGDg4pynFq8ng07SiZg4nn+8wImFCShQMXmzZvfF122srIS2R+zZ8/+f4/v378/0tPTcebMmfe3NWjQAG5ubiLoUhwwSUpKwokTJz75mj4+PnB2dsajR49Qt25dcRtln1CWSkREBMzNzf/V78+YJMnvJFHGSoGaphoadHZHVlo2rh24g+FL+0tlPYzM9TF330QsH7wRuxcdwqyfv4axtXwEEj4HZWUsPToVN44+wJbp+/Dg/AuMWtYfHYc3l/mDPH1TXQz5piv6Te4g0t9P/HQVM7quhkdbVzTsUAstetWDupZ8DFhKEwWL6Iw2LfXbur4fEFEtlGDvSAS+Chdn/ikThc6E02Jua4S3wXHvs1FosKulqy4GvJb2prC0N4GekVa5DkSVt4BbREAMYiMS4P8iDGF+UQj1jxZZIhSEK+ZUxwYerV1g52oJe1crkblkYKpTLj4nVJPF8/wLnPzpmqiv1LxnXczeMRoebVxk/rvzQ7HhCVg/aQ+09DWw+vxsWDsqxuAoPSUDywZuwKjvBpV5sIRcPXAHHUe2lqvPApNcUOZDlN1BS0k5OTl48uQJ5syZ8/42Cly2adMGnp6en3xuun3atGn/b7pNyeDIjRs3RIYKZZW0atUKy5Ytg4GBwfvn0NXVfR8sIfSa9NoPHjxAz549/+VvzpjkcMCElXs9J3XE8U3ncWTtaXSf0EGcsZMGyioZtrgPfhi7E4v6rMXaqwuhLsftLP8KDW5a9m2Amk2dRG2T3zdfwMVfbmHShmFwcLOBrKPaB027uYuFpuvcOf0UOxcfxY6FR9B5RAu06OUhisWWZ7SNTawMxNKg/Z9FDzPSshDq+xaRwXEIeBGGUP8ohPpFISY8AVePPHj/OJcG9iKbwMLWCOZVjVGtlrXoZmJWxUhkJOmbaCvEGelylS0Sl4KokHhEh8aLTlT0GUiKT4X/sxBkZeSIx9m5WolggKauOqpUM0OtJtVEm2vKSrJxMhddm8oTOtvr/TAQF/bdxZ3TT0RwoUZ9e8zcOkJkaMnb73Jh7y0c3nAe7Qc3Qd8pHeW6sGvJ341qh7Qe3AT1pVALjV7//M6rWHlhXpm/NvuXKLe/tPP7/3g+yhD50KJFi7B48eL/9/D4+Hjk5+fDxOTjWkd03dfX95MvQXVOPvV4ur1Yhw4d0KtXL9ja2iIwMBBz584VU3AoUFKpUiXx2JLTfZSUlKCvr//R8zAmSxRjb8XYf2BkaYBWAxsjwj8K53ddw6A50otudxjeEiGvIxDwPATfDd2MxUemoZKSYnRvKMnAVBfz9n4Nz7NP8ePM/fh+5HbUaVUDw+b3goacDIyquliJpd+kDrh18rFozzuh1TIx8Os0rBma9/Qod4O8/0VdU1XUlaClTd/6729PeZeGyMBY0b0kIjBGFEykFqMUUKE2sPFR797XRyG1GjsgMSZFBGSMrfTF4FFTR60o08VCT3y2Kivz7q0si45SHYq4t+/ENJq3IXGICU8UgTA6iH9xxw/ZWbnisbTNxO0i40wPzvXsYFHVWGQXWTuYwsrBVEyzKQ9ZI38lKiQWVw8/wJVD98WUNergM2Z5P/F9Qn9D8obqzWycvAc5WblY/NtE2FSX7fbGn2vf0mNISUjD5C0jpfL6D849Q502NaGlp7hT0dg/Fx4e/tGUnE9ll0jSgAED3v/s6uqKmjVrws7OTmSdtG6teDX6WPnAR5SMAeg7vStG15yBMJ9IdB/fHhra0hvkUkrvt/3X4/7Zp/h5wSGMWjFQoQcPDTvXgVtzZxzfcgn7vz+F28cfYcz3g9Csp4fcZBGoaqig3aDGYhkwtRMuHbiLh5e9sG3eITTs4IY2AxqI7g+cLv1p2nqa0K6rKQIpJQfi8W/fISo0XkzhocuokDiRsRDs/VYEWAhNz6DASjFqGUvZDBQ4MTDTgV0NS1RSqii69+gaaYsMFR0DLegaaoouP4r89/VfUK2Q5IRUJMWniWyQxNgUEcyiYp0JMcmi7TQV9n109TXy8wreZwdRW2pCA32P1jVEUIQ6TVFhVppyZWKlL7KFVNXLtiimLKP39/bJJ+8zrSIDY9CylwfaDGyIanKQefcpFCA5tO4sbv7+EJ2+bI7uY9oq3Hfgtd/uIuJNlOhwJ6391cktFzB5yyipvDb7dyoUFi2l/ZyEgiX/pIaJoaGhyPigAq0fouumpqaf/Dd0++c8nlStWlW8VkBAgAiY0GNjY2M/ekxeXp7onPO/nocxaeKACWMAbGpYocOIlgj3eysOPgbN6SW1dVFSqoQ5v4zHmtHbcW7nNWjpaWDAN92hyKhY4aBZ3UQh2L1Lf8e6cbtw4sfLGPf9IDiWGETLOqq/8eXCXmKw/+KOL64c9MTRLZfxw+RfxBniFr3riS4ePEj/ezS4Kp7aQ3VOSqIpPlQrhTIbYsOLLqmwcEEBkJKYLrJVqIYKdRP5sBaGawN7eP0xqFdWrQy3po5IiEoW037o783USl9kdmnoqIkMIU1tdahrq4opchqadKkKFXUVqGkoy3xQj4IemWlZyEjPRkZqFrLSs5CanIn0lEykJ2eIzym9h6lJGUh5ly6mw/g+DkZyYppoz/vhe0Vc6tvhlecb8TMFS4wt9eHe0vl9HRuqTzNsdlexzag4sqINkEu7LgkVb71x/BHycvJEp5vazaujdf8GaNy5tvhsyqvHV7xwavsVVKqshBXHp8PYqqh+gSJ5dc9PdKZZc2WB1KbPvrrjC30zPZhVlf0W0uwDBYVFS2k/52dQVlaGu7s7rl69ih49ehQ9RUGBuD5hwoRP/puGDRuK+6dMmfL+tsuXL4vb/woVck1ISICZmdn756CisFQ/hV6fXLt2Tbw2FaFlTBZxlxw59Dk903fs2IFffvkFr169Etfpy2nFihX/s8e6vFYf/68CngVjXN3Z0DbUwi/+G6ChoyHV9UmKTcbUlksQF5GAKT+OQpvBTVEe0FfS3dNPsGPeIeib6MLUxhAjFvWR6wNuasV7/8ILXD/6UAzoqZV0sx51RQDFulrRQQSTzGeJggQJMUl4F5sq6mjQlJHc7DxEBMUgOSFNLAYmOnhx118EEYh9TSsEvPwzY4UCA/S4D+kaaSEpLhUqaspQ01CBg1sVxEQkQkW1shjo0vSShJgUVFauJKYH6Rpqic9BxUqVRLYLLRRsqVixAhV9EZeqasrIyswR6017Zk1tVRH4oaBGQX6ByHxLjE1GXk4+8nLzxGtTZgKdyc/JzhMtd7Mzc0RNkKw/Lg1NdBD9xxQYQvVgqLDqRwGQB4HiZwriubdwQnRYgvidaaFaIhUrFWfnaInno0uaNkP1fNjnoalmnude4PapJ2J7ed17I1pvtx7QAC16eEDfVL5b60aHxuGnuQcR+DIc41YNQoOOblBEYX5vsaD7Ksz9dSIc69pJbT1WDN6IAbO6o2rNKijP5OU4tXg9G7VdIpEuOfcuL/rstsLDhg0T7X1pXEBthQ8fPixqmFBtEmo5bGFhIdoIF7cVbt68OVauXInOnTvj4MGDYkxR3FY4LS0NS5YsQe/evUW2CNUw+eabb5CamgovL6/304OopgllplBnneK2wlQEltsKM1nFGSZyhr7cqEL1hz3TqUL1X/VMpzmDAwcORKNGjaCqqir6pbdr1w6vX78WX4LsT/a1bcV0HM8zT/H7xvP4YkEfqa6PrrEOVpyejR++3oG1o7dDWVUZzXorfvSdBm1NutVFvXa1cGbnNfz63QncPfUEg2d1Q+eRrcQZcHlD0z6oiw4tVK/j/vkX4qzyyR3XREeFxp3d0KRrHVg5cPCktD9LVAOCFut/8N5SNgad9adBLQVPUpMzkJ6cKQIdFLig4AtltWSkZorHUlYG3UeBicKCQvFvaNoKBTAo1fnlH5kYpGoNi4/aLCspVxKBjw9ZOZgg/E3MJ4MZpLq7LXye/JkpQ9kdsX8EaSqrVhaX9Ltq62mIbA+a8kKBxho5eVDXVBH1L6jbU7sBDYuyZ7TVRHciyqLR0tUQt3FGSOmjbXTv7DPcPftcZPhQMI4CwR0GN8b4VQPlroDrp9DfxKF153Bu9w30mdQBs3aOEQE9RRT/NhGrR27DmNVDpBos8bn/BtmZ2eU+WCKXJFj09XNQm+C4uDgsXLhQnISl9sDU4re4sGtYWNhHWZQ0lqCgxvz580UxVwcHB9Ehh4IlROz3Xr7E3r17RRYJtQimMce33377US2V/fv3iywWmqJDz08Blo0bN5bGu8CYRHCGiZz53J7pJVFFbGrzRf+eIseKFLkvDcGvwjDG7RtYOZlj7Y3F0DWU/u8b8jocM9ouQ5XqFug7vQsadKqD8oSKSR7ddAEntlyCqoYqBs7sgi6jWinEwTjVh6BaJ3dOPcXTG96o1cQRjnVs0ahzbVR1seRpO3KMdq1U14OyiSijJTcnFwX5heI7mG7Py8sXQRaxBy4sRMEH6dS02WnbU2ZHxUqUfUKXRQtlq1SuXAlKykoiS4Wu8+dEttB+2f9ZqMgqe3j5lWilS8E0E2sDdPiiCeq2rAG7mlYKsd0o++nSr3dw/+wzqGqqYOSSvnKdDfh3aHrfzLbfipMrHUe0lOq6rBuzHV3HtINDHVuUd3KXYdJGQhkmVz4vw4Qx9s9wwESOUM90dXV1HD169P18Q0LpdBTJPXny5N8+B6XFUSbKkSNH0KVLF4XaEZWWnXP24/Ca0+g1qRPGrv1nQSVJ838ajNkdlyMnMxeLjkyDR/taKG8iA6Kxd9nveBeTgrdBMRg4syvaD22mMN1Q0lIy8PS6NzzPv8DDSy9F8IQGHvXaucK1YTWF+T0ZU0Q03evZTR88uvxKFCimdsmEuv60G9RI1CZRpCAoHTo+vuyFnQsPQ01TDaOX9UONBg5QZFQLaPOUPTCvaoLBc6XXTY+8vO2D4xvPYdGR6VJdD1khL8epxevZmAImSqUcMMnLwl0OmDAmEXwELkf+Tc/0kmbNmiVS5Nq0afOXj8nOzhbLh1/w5UnXse1wYvNF+D8NQmRAFCzspT9NolodWyw7OQurRvyIrTP2ARUAj3blK2hiYW+KuXu+xpvnIdi3/AQ2T9uHR5deon5HN7Qd3ETuAwpUWLRZ97piofoU3g8D8eDiS2z55jdRiNTEUh9127igbisXUT+CMSY9NA3L90kQnlz3xtNr3mI6E9UjoVo0TnWrYtSSPqjf3hWWdopXjPPV/TfYs+QY3sUkY+S3/dCwk5vCBIL+CtUGWtxnHezcqmDQnD9PWEkrWHVp7w0MXdRXquvBGGPlhXyPMNhnoSJNVKCJ6ppQPZO/QsWdqGhTeWVSxQj9v+mGXxYfwc7ZB7DoqGycwXFu4ICZP4/D/G7fY0mfdVh4eCrqdVDMgnr/i4ObDZYemQKfhwHYv/IkNk7ei0Nrz6L/9M5oM7CxXHeWKKZUWQk1GzuKZfTSvngbHIvH116LtqM/zvoN1erYwLmePdxbOMOprq14PGNMcqhAa+DLMLy444/nt3zx+mGgaFkdE5ogasA096iLjkObio5B2vqaUER+T4Kxb8VxMTWsVb8GaDekSbn47snJysGKIZvgVN8Owxf3k3pw6O6JR6Jeha2LtVTXg/0HlNxf2gn+PGGAMYnhKTnlZErOmjVrsGzZMly5ckVUov5fPpVhQnVSylOaX3pKBqY1XwQVNRUM/7Y/6rR2hazwfRSIX5YexfPrr/HN7nFo0fev27mVB94PAnB5/x2c33NTFLTsO6UT2n/RRGptHiUtOytHtH2lmic0hUfbQBPKKkpwa1Ydbs2cxBQAWW91y5isy0rPhs/jIAR6heHxNW/4PAqCaRVDhPlFiSwS+1rWotOVs4cdqtWuItpQKyoKlJzeeQ2PL79Ev6md0fnLFgpRQ+qfZpasHL4ZOgbamLT5S6l/t1JNpO+GbBTThY2tDKW6LrJE7qbktF4smSk5VxfL/HvAmDzigIkcFn2l1l/USri4uJy1tbWoNv1XRV9XrVqF5cuX4+LFi2jQoIHC7ohK24Xd17F21DaRgrvx3nKZaqEZ+CIEc7p8Lzpi9JnaBZ1HtUJ5R1N1Dq07i5T4NAS9CkPnkS3RbUwbGJjqQpFR151Xnm/EWe83z0MR9zYRro0oO6UaajV2hJmtkdTPiDImy+gwKC7yHbwfBSLCP1pkc715ESZaOTvXsxPT4yyqGqNWU0fUblFd1BeirkKK7vX9N/j9x0t4fe+N6HzT6csWotNSeUEdjRb1WYdq7lUxYmk/qQdLyOG1p5CVls3TceT0OLV4PZu0kkzA5M41DpgwJgkcMJEzn9szndoIU7swagPWuHHj98+jqakpFkXaEZU2CkatGr4FL657o/uE9hgwS7rzlksK84vEqhFb8eZpMIYs6I0hc3vywBhAuH8Uft9yEVcO3IWje1WY2Rqj5/h2qOpihfKAOu94eb6B1z1/0WHI53EwqtetCpeGDnBtVA3W1TgDhZVvKe/SxfQa3yfB8HsaIv5mKGuL6BpqISk+FWY2RnBpYA/3Vs5wrm8v2jSXB3RI+OTqK/y25gxiQuMxeHY3tOzbQLRGL0/SktKx7ZtfoaGlhrFrvpCJfWtC1DtxTLLk+Mxytz0ULmDSUkIBk+scMGFMEjhgIoeoJfDq1avf90yn3uWUeUJatGgBGxsb7NmzR1ynn0NDQ//fcyxatAiLFy9WmB1RXEQCbhy6h9aDm0DftPQObIO9wjCu7mxYOpqJgxQLO1PI2u89v/sqaOiow8rRHBM3jigXc8r/icSYZJzdeQ2nd1xDSmIaWvVriKY964q6L5UqlZ+AQXpqpphOQGfJfR4HIjIwFlUczVCnhTOq1rAUNWHUtcrPWWNWviRGJyPAKwwhPm9FBtabF6HQM9IWwZJirg0dRNvf6vWqiik2lJ1lWE4CJB/WaLlx9AGObboIE2tDNOpSG636N5SpzMqyQsVs53ZdifqdamPYor4yESwha0dtRb2OddC0d9HxHpOv41TCARPG5BMHTJhC7IgmNZoLn/tv8PX6Eeg5qVOpPveh1Sexa+5vqNWiBlZdmi8zB08fnglb2n89Xtz0RtshTTFu7VARQGF/zkG/9ftDHN10AaHekaIOQdfRrdB6UGPoGsrm51mS8vMLEOb3FkGvI/Dq3hv4Py9qf1q3tYs4q17NrQqsHc048MbkbsAf7h8tPtfBryOQmpSOJ1e9RbtfQpkCFDwkVOfHxNoATu62cKxjC0d3m3IxxeZTKAvt7O4beHjhBSoqVUTfSR1F57HyFFT+UGRgNFZ/uQ3N+zVAz/EdICueXH6JU1svYfGx6TJ3DCIL5OE49cP1bNpikUQCJrdvLJH594AxecQBE6YQO6Iz2y/h2m93oKWviSW/f1PqA+6x7rOga6QtMli6fNUWsoYKwf0061dcPXAXBma6WHJsBswVsJ3lf0Ffda/u+Yvgydmfb4gBQZMeddFxeHO4NnYs1wehmWlZCPKOQMCLMHEWPsw/ShTNta5mBvua1rCraQUbJ3OFLmzJ5NvRzZewa8nv76/TdJpX9wOgolYZNs6WcG9ZHRZ2JqhW2wbmtkblflqa/9Ng8T344MJzuDV3Ro9xbeBU1w7lmd+jACzp/wNGrRiEVgP+nMIsbVkZ2Vg+cL04IURTTJl8HqcSDpgwJp84YMIUYkeUkpCK/uajkZebjz3+G2Fhb1aqz+/z4A2mNFkAZTVlbHu2Suam5hD6Uz646hR+WXIENZtXx4CZ3VG7lYu0V0sm0Vzwi/tui45DDy++FAOpDsObo/WARtA30ZH26skEChSG+L4VtR6iQ+MR4BWOgrwCGFvqo2GnWmjYsfy1tGay68l1b6z8aqeYZmbrbAGnulXFpaW9CQf6Phh43zr+CDePPRR/1x2HN0OnES1gZKGP8u728Yf4cepezP5lAmo1qw5Zsm36LzCw0EPfaV2lvSoySx6OUz8KmDSTUMDkFgdMGJMEDpgwhdkRbZu+B7ePPUCHL1vhi4WlX0F+z6JDeHb1FQpRiB9uLJHZg/CHF55hxZDNyM7Ixtfrh6HL6DblOnvi76anPL36Chd+uYWk2BT4PAqER1tXtBnUWKSll8f5+/8L7S5iwxOQnZUrsk8Yk6Ui3fQ9x991/1+QVxhuHnuEsz9fh00NS3Qf2wb1O9Ti77c/vtMOrT6FJ1e9MGH9cFSpbglZ8uqOL878dBkzd48vt9OkFOk4lQMmjMknDpgwhdkRPbn8ArPbL4N7u1pYcW5uqadc0xz5SY3mI9Q7AiOXD0SfaV0gq0J9IrB50h68eR6Muu1qYdq20WKKBftr7+KScf3QfVzafwcqqsqIDIpFs54eaNW/AWo0cOCBGGNMbqQmpuHOmac4t+u6yBDr+XU7NO5WB1WcLKS9ajIjMz0LG77eidjwRCw8OBm6xrKVXZiRmonJjedjweFpsObtphDHqcXr2azpQokETG7dXirz7wFj8ogDJkxhdkR0hnF6i0XijMzKi/Ph3rZWqb9GiHc4vhu8CSGvwrDqykLUau4MWZWenIE1o7fj3qnHaNClDoYu7AO7mlWkvVoyj74Sg16F4+rBe7h+5IFoM0rFIlv0qocWferD1sWKgyeMMZmsZfXsujcu/Xobjy57iS43tZtXR4POtTmbpISooFhRr6RBp9oYPK8XKivLXpFr6opTo7ETOoxoKe1VkXnycpzKARPG5BMHTJjC7IjI0XWncf23O3CqZ4+JW0ZL5DUu7L6On+cfhKWDKeYfnAp9U13IKvrzPvfzNWyZvBcVK1XEhPXD0H54Cx7w/0P5efnwuuePa4c8cff0E2hoq6OyipLIPGnawwO2NSz5vZQxq7/eDVUNFRHoosXYQh9a+hrQFoum6CDFqe1MUdCJAu8Hgbh+2BO3TzxGraZOqFbHFi37NSh3rZH/qfvnnuLourNoNbAxOo1sBVl0+/cHuLj7Br499Q3vYxQxYNJEQgGTOxwwYUwSOGDCFGZHRNKS0jDIepwoWLk3YBNMq5R+RXn6k9kwfhfObr+M+p3riDZ/st6ClYrW/jjtF0QGxoiOMFO3jRZdf9jnTcl6cdMH147cx4Pzz1GQXwDjKkZo0L4mGnWtIwYp5b3zhiycYe9mMeGj21waOuCV55v315WUlYqCJ3oa0NRRh6auOqo4mYt6NtR6VkO7aNE10oKapirUNFTEpbqWqni8Ig5e6Henz3dmahbSkjOQlpSBzIxsZCRnIuVdOlIS05D6Lh10sBATFo/UdxkoLChEq3710WloU2mvfrlD+yDfx0GigOvt449gV8saVg5maNWvAWfA/Q95uXn4Zekx3DjsiTn7JqB6PXvIoog3UVjYYxXWXl8MPRmbJiSr5C5g0niBZAImd7+V+feAMXnEAROmMDuiYr8sPoz7Z5+I2h1fLh8ksW4D3w3ZCO/7b9CiXyOMXz8csi71XRo2T94jDhbrtquJbuPaoX7H2tJeLbmUk5WLl7d9cPP4I9w/91wMJqu528KhVhU06OSGWk2rQ1mVU+ClsV0u/HoHyQlpSIpPRXJ8qgiIBL4MR8q7NKQmpqOyamWkJKR99O9cGzrA64OgCqFMFJrWVsy+ljUCX0VARU1ZtKpVUVeGY20bJEQnQ1lFSUx5oOfW0lUXBaGVKhctlNVCkYaKShVRqWJFcUmPoSAFDWwrVqTBbQXxfCgsFINh2isrKVUCjXvzCwpFcI4Weg0KbFDmE3UEo6WSUkVkZ+aKYBH9/rQu6SmZImhMxXnpkgJA8W/fITM9G1np2aKeUUx4gqiRkJGahYKCQtGGNzkuFRo6aiIwZGZjJD7DFFgSGTp6mqJluZ6Rlmjfzlk6ZYs+L94P3uDuqSe4c+oJqtawgqmNEZr18oBzfXsO1v6N6JA4rBy2GfZuNhi6qG/R36UMomOLH8b8hM6jW6NmM9md8itr5OU4lQMmjMknDpgwhdkRFYsJjcNQ+wmo4myJDfeWQ02jdHdKxaJDYjG+3lwxmBgwq4fczDOmVN91Y3cgIyUTvad0wuA5PcXgkP2HaTt3/eF57plYYsMSRFq8uraa6Ljj0a4mt+2UIRRwoMACZVHQkpqcgbycXKQlZ4pAAwUQ6G+DtmtGWpZYKMigbaCJ6NAEMaARwYjMHDEtL9ArHLn0nNlFAQsKcny4V63mVgX+z0M/Woca9ezw+mHgR7dp6amLzI1ilPUS6vtW/ExBFZpS59qoGkK8I1Hpj2AMLYZmOsjLKxQ1GChYYmJtgLyc/D8DO2rK0DPWRmXlyiJbhqYrqWuqQsdAU2TNUPCEnofJZlHS5zd94Hn2GR5ceAFHd1uYVzVGk251Ub2+PQet/qFrB+/izI4raNarPrp/3V5mM3DocPz7oZth52aDvtO5hbAiHqcWr2fzRpIJmNy8xwETxiSBAyZMYXZEH9ox+1ccXnUS434Yjl6TO0vsdbzu+GJBt+/FgGrlhXlwa1kD8iAuIgGbJu1G8KtwFOTnY9LmkZxtUgro65QGtI+veolBju/DQDh52IlBt3srF9Rt4wLnBvaiCw9TzO1PgzHKBsjLyRMZIBR4oWAHZYjk5xWImhMUValUsQIKaPdL/xUWiqlCFBgpbo1boSKgoqby/jZWPtCUp8eXvfDg4gsRLKEplNXq2KBhp9pwqG3Dn4XPkJKQiq3T9yHwZShm7R4Hu1o2kGVH1p1BfEQCxq4dyttZQY9TOWDCmHzigAlTmB3RhyL832JW+29RxdkKi3+fKdEOARd+vo5TWy8iMSYJK8/Pg00NK8gD+tO/uOcmts/6VXTPMbTQx9jVQ2SutaK8t/Z8cccX98+/wJOrr8QgmjIcXBpWQ+0WzqjT0hk2NSw5nZ6xcoqykrwfBeDRhZd4dMULidHJcKpbFfXa10S9djXFtBv27wq7bpzwM7qMbo3eUzqLTCtZ9vjSC+xecBBrri2SWFasIpO7gEnD+ZIJmHguk/n3gDF5xAETpjA7opLWfLkFF/fcwKQfR6Hr2PYSfa3dCw/hwPLfUbuVC2bu/hpGlgaQF7Hh8dg55zfcPHof5nYm6Du1Czp82YIH8ZLIPnkdgafXX+PpdW+8uucPK0czMYWHziJT8KRGo2qwdjTj954xBUXZR0Fe4fC+748HF17ilac/ajZxhJmtMeq2dUWtJk5i2hT791klP80+gLeBMRixpC9cm1aHrAt4HoLlA9dj1aUFMLKSn2MHWSIvx6kcMGFMPnHAhCnMjqikqKAY/PDVNsSExeGnF2tFeruk0J/RrrkHcOyHszC3N8W6G4uhYyg/7xW5e/IRzu++gUcXnsPRww4TN46AQ21baa+WwqJME99HgXhx2xcvb/uK6RreDwJEMUKXRg5wb+0K+1pVYFfTSua7MDHGPo2mYFGglP62KVD68o6fqHPTun9DWDmai0ApBUvYf98HU30u6gbXsn8jDFvUB6pykKlBNde2z9yHgbN7wKFOVWmvjtySl+PU4vVsUV8yAZMbDzhgwpgkcMCE/S152RF9ytape3Bu5xV8tXoouo5tJ9HXoloFS/qsQ8LbRFGUccXZOdDU1YA8yUzLwv4Vv+PRxRcI832LdkObYcSSfjxNpwxQMVG/J8Ei88Trrh/i3r5D5Jto0T2F6p9YO5mjukdVURNFx0BL2qvLGPsE6lb05nkIXnu+QWx4Aq4feYDCwgIxDa9W8+oig8TWhafhlaYI/yhsnrIHSXEpmLp1FBzr2kEeJMUlY2abb/HV90Pg0cFN2qsj1+TlOJUDJozJJw6YMIXZEX1KckIKJjeah5T4VOx5swna+pIdaGZnZmNRr7V4cukFmvdriKnbv4KGtvx1oAnzjcS2Gfvw5IoXajR2FAVhe05oD2UuVlpm8nLzROr+6/tvEB0ah7unnyI+8p24r3HXOlBWUxZdM6rVsYWdq5VEM6gYY5+WGJ0En0eB8HkYCL8nQQh4EQpVdRXUaOCAOq1d4OBmg6quVtzRRgKy0rPw2/en8ODcU7Qe3AS9JnYULb3lQXpKBma1Wya64TTv21DaqyP35C5gUm+eZAImD5fL/HvAmDzigAlTmB3RXzmx+TxuHr4H54aOGP39kDJpBblp/C5cP3gX9nVs8d25uXKXaULoq+HxxRfY9s2v4gwedQBqO6QpWg5ozAf/UhIXmQjfR0EI9Y3Ei1s+8H8WgpzMXGjoqMHYykBM4XGsW1UEUGycLcXAjTFWOqgNdeCrMPg9ChLZYP7PgmFmayRud6prh+r17OBc3160/uUuJ5LdN10/dA83j9wX33GjvhsoV3XDKJNz44RdcKpnL9ocs3IYMPGQUMDkEQdMGJMEDpgwhdkR/a+pMuPrzUbo63BsfboKNjWsy+SAaF7XlUhPzhAHdN+e/AbacjqNgjIdzv98HdcO3oW35xvYuFhh5Lf9RQoxDwqk/9kO9XuLgGchePM8VCy0ReiMN7WjtXQwhXsbV+gb64hpALY1LKFvqsvbjbG/8S42GYFe4SLLK8zvrZhiExUcC7ua1qLmCGV3ObpXhX0ta6hrqUl7dcuN157+2LfsmMgaHbN6CGo1c4Y8yUjLxIJuq9Cgizv6Tusi7dVRGPJynMoBE8bkEwdMmMLsiP4Xnwf+WDd6G7T0NLHm+uIymT9OmSYrBm7A/bNPUb9THUzZPhqG5vqQVxmpmTi2/hyeXX+F1/f84VTPDsMX9xOZJzwAl60AV0RADAJfhiHoZRjSUzPhefYZkuNTxf3UiSMnOw9VnMxhU91CtDW2cjAVxWYZK49tfSMCohH8OqJo8Y4QGXQPL3mJ7BE7V2vUqG8Hq2rmcKhdhesHSQllOf688BB8HwZg5PKBaNGvodxlOtI0nCV91qJRNw/0mNBB2qujUOTlOLV4PVvWnSuRgMn1xytk/j1gTB5xwIQpzI7o72yetAv+j4PQaVQrdPiydZm8Zk5WDjZP3o0bh+6J+ikrzs6GdXVLyPuZ16M/nMWprZdgWc1MZNAMntdLzNfnQoayKzEmGcGvwvE2OFZM5Qn1fYswvyhYVTPDm2ch0DHUgnU1M7g0qgZtfQ1Y2pvCwt4EJtaGcjcwYaykrPRshL+JQrh/NOKj3sH3MU1te4uooFgxjSYvLx+2zkVZWGJKW3ULaOjIX/0pRUOFc/cv/x0PLzxH9/Ht0OPr9nLR/aaklMQ0rBu9HTWbOaHX5M4Se52TWy7AuWG1ctdxR16OUzlgwph84oAJU5gd0T/J+BhTa4aYLvPj4+/LbM4zTZtYP24Hnl17JTIxZu0dD5fGTpB3CVHvRODk7I6rMLY2hKqGCgZ80x2Nurlz4ESOWp7GhiUg/E00wvxpMBklppFR69PkhDTxGNfGjkiOTxHTECzsjMWgkqb10Nl3Y0t9bnnMZKpVd0xYAmLD4sVnmjKtIgOjUZBfgBe3/aCmqQJLBzNUr1sVhuZ6IsuqirOF+Bzzd5ZsiYtIwKHVpxAZEANbVyv0n9FNBHXlUXxkIuZ1+Q7dJ3RAp5GSO1kT8joM22fsw7Izs1GpknwUvy23ARP3OZIJmDz5TubfA8bkEQdMmMLsiP6JV3d9sXr4Zpjbm2LFuXllNpWE/swOrTqBXXMPwqSKkUgpbjmwMRTBu5hkXNx7QxzcZqRmoXF3D3i0r4XWgxpzVx05lvouDeFvYsTgk4IpkYGxIjtFS1cdT697i8dQnRS35s4oyM8XQTMTawOYWBnA2MpQXBqY6XBAhZVqgC8pLhWxEYmIDolDdGi86CBF6DNJA1NUqADXRtWgrKIEC8qSsjOBpYOJyJiiIAlPH5RtMaFxOLLujOjQVrddLfSf0RWGFvI7lTXUJ0IUge86rp1Eu+FQW/qZrZdg9r6JsLA3Q3kjL8epHDBhTD5xwIQpzI7on/p5/m/wuuWNFv0bo/v4sp1HfHHPdRxecxphPpEYurivmMqiKGc2U9+l49yuazix5QISo5Jg5WiO5n0boOtXbaBrrCPt1WOlHEyJCokXg9ak+FRR+yEmPAExYfFIT85EUlzK+4AKZaPUbFIN+bkFMLTQE4NWIwt96JvoQN9MV1wqq1SW9q/EpIwORdKS0pEQnSzaZ1NHKFri374T3y00fSw2IgG52Xmo09JZBE5MbQxhWsUQ1tXMxWfLnLKerAw4SCengYWrB+7g9PYraDe0OfpM7Sy+J+TZy1ve2DB+JyZtGolaLWpI9G/nxym74VTfAa0HNUV5JHcBkzpzoFSplAMm+Vm4/pQDJoxJAgdMmMLsiP6pvLw8zGy1BKHeEVhzbTGq1qxS5gdRKwZthJa+JkxtjMQZIUWaK091W64euIsnV71w+9gDVFZWQo+JHdC8dwM41LGV9uqxMsgCoAKzlAVA9QfoMiMlU2Sp0ECYBsAa2moI8Yl8/2+o4GzNJo6isLCesQ50jbShb6oNPSMdkYava6Qlim3Sz/R5YvJVVJU+D0nxKUhJTBdT+RJjUkQtJKqrQ/VxqEVvQnSSeCzVEKE2vSKwZlkUXKPpYAamOiIYQoumjjpniigAOvx8dddP1MN6eu2VaLFLAXY9E/kPsJ//+RoOrz6Fxcemo4qzlWRfa9dVeN32wczd48vt34W8HKdywIQx+cQBE6YwO6LPERMWh6V91oiB3OaHK6GhXbYBCzozv7j3GgQ+D0H9zrXx5bKBZR64kTT6anl6xQsnfryIYK9wMSfdsa4duo1rhyY9PUSxWFY+5efnIyUhTWQTJEQlITE6CempWeLvggbS72JTkJuTK+oX0DSvYjUaOCDEOwLaBpoigKKlp4Eq1c2Rn1cATV11aOlqiNvofnVNVTGwpmAkBWiUVSuX28FEacjNyUN6SiYyUjLEJWV9fLjkFxQgOjhO1L6hApe0fWkbxr9NEv+eAl3O9e2QmZ4tgmKUWaRnrA3TKkZimxmY6YqFgmVcZFjxP0uep5/g8NrTSIpNwYBZ3dBqQGOFaM9MNcu2z9yHtHfpGL1qiPisSxIFSrbP2ItVVxdBXVP+3z9FP04tXs9WtWdLJGBy7dlKmX8PGJNHHDBhCrMj+lzPrnlh76JDMLIyxNz9k8t8MEVzjn/99hgOrTopBnNTt3+F1oObKuSgjgovUnFYaglJLYlpcNt5dBvRGrKqq7W0V4/JsOzMHJGhkJyQipR36WKARQNyGpinJqZBSbmSmK5RNHDPEJcG5roI94v66HmodTL9OxqUqWupikuazkEtllU1lKGmoSoKF9MgnqioKYtFVV0ZapqqUKpcCZVVKou/VeU/LikIoETLH/dJa6BPu/G83Hzk5eSJSyp+mpudi5ysPBG0oGks9D5mZeYgJzMX2ZnZQCHEe5qVkSMKYVMXGfo9I4OKglSU7UOXFlWN8fy2L7Izct6/Xs2mjgjzfQtNClD9EaSydjQT2UXUDUzHQLMoqGWoBT0jbegaakFdW00hv9vYPxcXkYhzu67i/M/XRfC85YBGaNLDQ2GmUFGw9+d5B+kvEhM3j5T4VMO3gdHYMetXjPxusChmXJ7JXcDEjQImpXvSKC8/G9eec8CEMUnggAlTmB3Rv3Fi03lsnbYHY9YMlWirv79Cf37nd17DtYN38OIG1VVphIlbRkJbTxOKiAZtt39/gHM7r6FipYrwuuMrpul0HdMWDbu6i6kZjP1X+fkFYsBPHX8oGyItOUMM/qm+CgUHioMBlBWRHJciggZZaVki+4EG+hFvosVt9HmlxcGtCrzuvfnoNRzdbcVUkg9RgIX+pispVRRdKuiyolIl2Fa3QERgjKjpUqFiBXFp7WguMmhKouALnYEvoF1zYSEKCgpFN5cgr3Dxe1HHF8qooUKmgV7hyMvJF2e1CQUkPrVLpza5VLCXijCLIJCaMuxqWonpUZTpRd1jqFUrFe2l5ysOKFGWjpaeugikUBaeho6ayNqhwBIHP9g/QUG059dfi9okT696ocOIlmg1oJEImCgSb09/rBy6Gf1mdEXnr9pI/O+DOpdRkdfR3w+BR4faKO/k5TiVAyaMyScOmDCF2RH9G/Tx373gIH5b8TvmH56G5n0kV8X+fwl6GYrlgzaIs9Z01vybPeNRs5kzFFmobyQu/3ITVw7cgZJSJdFtp36n2mg7pBnc29XkQqBMpr4nKChBGRsieyMrF9lZucjLzfsgsyMPubn5yKcARj4FMYoCG/TvKlaqIDJZKNhRWFAUBKHsFKrZURKNsyioQgOu4uAKfS8U5FMgpigIQwv9+woV6ZJuq/RnpouyElRUlVH5j0yYyipFt3OAg5UlKgB9ed8tXNp7E3ZuVVCjkaP4bpfX1sD/KyBEtUquH7yHaT99BUcP+zLJTp3Vdinaj2iFTqMk16ZYnshdwKTWLMkETF58L/PvAWPyiAMmTGF2RP8WDWjWfbUNz6+9wrzfpsC5oaNU1iMzPQt7Fx/BsXVnYOFgioZd62L40v7ijLAiy83NE7VOruy/jYCnIXgbFCPqTrTs30i0KK7VvLoYEDLGGJNdlLl17+RjXN5/G5EB0XBt4oSOI1rCtamTQgbsqI31lim7oaKmggkbR4gpapJGAdsN436CnokuRn03WOKvJy/k5TiVAyaMyScOmDCF2RH91zM237RZAnUtdXy9YQSsnSykti4Pzj7FbytP4PU9PzjVt8dXq4bAtUl1lAepSenigPv6oXuiBgN1UKAzku2Ht0Dtli6o2cxJYea7M8aYvKPMquc3vHF1/x3cO/0Y9TvWRu1WLmjWu75CdX8r6dax+9g55wAGfNMdHUe2KpOAEL3Xa0dtQ0F+Pmb9MhEVK3JxZLkNmLhKKGDixQETxiSBAyZMYXZE/1VKYqqYE5yWlI5115fAxMZYiuuShh+n7EFkQBR8HwSIdosjlg1Q6APQkhKj3+Huyce4dfQBClEIr9u+ohVzxxEtUL2BA+q0duVOO4wxJoWsTK+7vrhxyBN3Tz5CrWbOqFrTGi37N4ZZVentN8tCUlwyNk/8WRSwnbn76zIrtkrBkhWD1kNZTRkzdn3NJw7k9DiVAyaMyScOmDCF2RGVhncxSdg0YRcCngVj7Y0lMLI0kOr6PDj3DBvH74SpjREi/KMwZs0XojBseTuzlBD1Dp5nnuDuiUcimBTwLETUdWg/rIU4UK/XwQ2GFvrSXk3GGFNIOVk5eHnbVxTtvnfqMexq2aCae1U079tAdDpTxCk3H6JD5RuH7mHH7P0Y8E0PdB7Tpsy6YtF7v3bUVtHZavqucaJ4M5PP49Ti9Wzt8o1EAiZXX62S+feAMXnEAROmMDui0gyaLO61WpzJ+WbPBKkHTairx/7lx3Bk7RnY17YVHS3Grx8B23Lajpfaxj66+FwEUIJfhSPc9624vWnverCwM0PddjXh3MCB654wxth/kJKQiidXvHD31GM8vvgCltXMRGYfTbexq1VF4YMkxaKCYrB58m7RLWr4kn6wrGZepnVhqGYJtVZf/PtMzqqU8+NUDpgwJp84YMIUZkdU2mm3s9svE0XcZvz8NUylOD2nWLBXGPYuPiyyLExsjFCvY218sbAP9Ix1UF5RmjLVOaFMnFd3feH/R5vXGg2rQcdIG3Vau6BOK1eY25uUm4N7xhj7N+hwkDq2Pbr0Ek8uvxStcimLpHEPD9HBzKoMAwWygDI7jqw7g5uHPdFzYke0H9GiTLM738UmYX6XlTCraiI653FmiQIFTGrMlEzA5PVqmX8PGJNHHDBhCrMjKm1Uy+T7YZvx5kkgVl5cAJsaVjJxQEsHb9SO9+GF59A21EK/6V3QfUJHPvNErSxD4/Do0gu8uOGNp1e9kJaUAZsalkhPzoRbC2fUbV8L1evZw6SKkbRXlTHGpI7auT+79gpPrnrB/3EgEmOS4d7GFY261hXFWxWtDfA/3c96nnqMHXP2i4LrXy4fAF2jsj0xEeYbiW/7rUWt5jUwdt0wrlmiaAET5xmSCZh4r5H594AxecQBE6YwOyJJnWHaNn0vbh+7j4VHZsC1qWx0q8nOzMHvG87ixU1vPLn0EvpmehjxbX+0HtwUlZX5wIrk5xfgzZMg8R49u/5adB2iVGo6g0oBk2Z96sPS3gwuTRxhYW/KGSiMsXJxIsDrjq/4Xnx+/TXSUzJhYK6HOq1c4NG+lsgoKc/TGQOeB2Pb9F+Qm52H8RtGiPejrD25/AJ7Fx1CjUZOGL1qSLmrWabIx6kcMGFMPnHAhCnMjkhSCgoKsOObX/HmaRC6jm2H5v0aQVYkxaXgt++OizOENGWHisMOX9ofzfs15DNSnwh++T4OgtctH7y87YO8nKLpPMStZQ0xP51qn1AHnmp1qkJFjdOfGWPyjfYR3vf9RZcx+t4LehEKlyZOsHWxgluLGuIkgJaeBsq7t0Ex2LvwEHwfBmDkioFo2rtBmQfR6XD83I4r2DLpZ3y15gv0mNCpTF9fnsldwKT6dMkETHzWfvZ7sGXLFqxevRrR0dGoVasWNm3ahHr16v3l448cOYIFCxYgJCQEDg4O+P7779GpU9FnNTc3F/Pnz8e5c+cQFBQkftc2bdpg5cqVMDf/c0qfjY0NQkNDP3re7777DrNnz/5XvztjksYBE6YwOyJJO7/zKjZN2IlR3w9Bz0mdZCojgaaiHFhxHFcP3IGGtproIDN4fm+0GtQEyiqVpb16Mlv/JOB5qMg8iQ2Nw81jD0R6unlVE8SExcPW1QpOHvao0agaqtasAitH8zLrisAYY/8muB/u9xY+9wMQ6h2OhxdfIPJNNKrUsBTTa5zrO8C1iRP0TMpv3auSYiPicWD5cby86Y0uY9ugy5h2UtlnZqZnYf2Y7Xh1xxeTfhyN+p3qlPk6yDN5OU6VtYDJoUOHMHToUGzbtg3169fH+vXrRUDEz88Pxsb/v3bfvXv30KxZMxHc6NKlCw4cOCACJk+fPoWLi4t47T59+mD06NEi+PLu3TtMnjwZ+fn5ePz48UcBk5EjR4rHFdPS0oKGBgdvmWzigAlTmB1RWfD29MPi3mtEUdEZeyZAQ0sNsiQqOAYHV57Epb034FjPHlGBMeg5uSO6fNVWFLBlf42+CmNC4uD/PBiv7/jB52EAQl6FUydH5GTmQFVDRXSH0NBRh72bDezcbGBNQZRynL7OGJNuu3WaYujt+Qa+jwJQkF8An4eBcHSvCudG1UTGnHODapxB8gmx4fE4+P1JkZlJndXoJIi6lPbnFNxa/eWP4kTH5B9Ho4qz9OulyRu5C5g4Sihg4vd5ARMKknh4eGDz5s3vg65WVlaYOHHiJ7M9+vfvj/T0dJw5c+b9bQ0aNICbm5sIunzKo0ePRMYKZZRYW1u/D5hMmTJFLIzJAw6YMIXZEZWV+LeJ2LPgIHwfvMGCw9Nk8uAmNiwev288h7Pbr8C6uoU469hxZCv0mNgRZrbS7/gjL3KycxD6OhJ+TwLx5kkwUpPScf/MU+Tn5YuME8rssXGxQlVXazGVx8LOVBQH5gEKY6y00GFawtt3CHgegoAXoYjwi8TL275IjEpCkx4eIphLAXInDzsx1YanY/61CP+3OLT6lHjvnOrbi+430jqZQNv17E9X8PuGM6K+1szd46GlpymVdZF38nKcWhYBk/Dw8I/eAxUVFbGUlJOTA3V1dRw9ehQ9evR4f/uwYcOQlJSEkydP/r9/QwGPadOmfRToWLRoEU6cOIEXL158cr2uXLmCdu3aiecsXi8KmGRlZYkpPPScgwYNwtSpU6GkxN9dTDbxJ5Oxz2Roro+p28dgz8JD2Dp1L1oPaYq2XzSHLDG2NsTYNUMxaE5P0VHn4KpTOL3tMq7svw2XRo4icEJ1O2RpWpEsUlZRhkMdW7Fg9J+1UEK9I0UXA8pCoZoAN4/cx/MbPogOiROPqdnUCcpqyqhS3QJVnC1hW8MSltXMpHYGkzEmH6igd5jvW0S8iYL/4yAEeYUhKz0bfo+DxHeIXa0qqNHIEZ1GtoadWxVoaKtLe5XlAk29PLL2tHgv67Rxxdi1Q6X63r2LTcauOfvF/pnWpcdE2ZrmyySrQmGhWEr7OQlliHyIAhqLFy/+f4+Pj48XU2VMTEw+up2u+/r6fvI1qM7Jpx5Pt38KBUVmzZqFgQMHfhTEmTRpEurUqQN9fX0xzWfOnDmIiorCunXrPuM3ZqzscMCEsX+BpmGMXDEIz6+/wsovNiLkVRgGzestcwev2gZa6D21C7qOa4c7Jx7h8OpTuHfqMVIS07Bx/E50/qoN2gxpWuYtE+WZsuqfQZTWg5q8T2OlbJPgVxEIeR2O1MR00abz2bXXIhuFWhuHvI6AoYU+7N2qwMTaUAx+ihdDcz3uhMBYOQuMRAZEI8wnEqE+kaJuEgVF3gZEi2Brk+4eUFZXRpOeHiKDjRYqTM3+udycPNw+eh9nfrqC3JxcdB7VBq0GS7+u172Tj/DDV9tQpYYVfrj9rZg2xVhp+VSGiTRQ9ki/fv1EJtXWrVs/uo+yVIrVrFkTysrKGDNmjKiNIq31Zex/4YAJY/+BW0sX/PRiLTZP2oUxbjMw+9dJcGnkBFkc5Lca0Bgt+zcSZ9ou/Hwdfg8DRDrwrjkH0LhHPXQa3Rq1WtTgwqb/AgU7zGxNxNKoq/tHB+xiUOT3FuG+b8WZ48zUTFzYexPZGTniMS6NHfHmaTDM7UzEQm0stQ00i67bGsPAQg+VKnGdFMbkDQVS4yIS8TYwRmSMJMWmwO9xIML9ohATGo+azZyQlpQhMtGsnczRtKeHmFJDGYIcQP1v9UnO7bgKb09/KCkrYeCcHnBvW1Pq72liTBI2T9wlamPV61gHX28YDg0dnr5ZLlE2SGlXRPjj+ShY8k+mJRkaGopji5iYmI9up+umpqaf/Dd0+z95fHGwhOqWXLt27W/Xh2qp5OXlic47jo6Of7vujJU1DpgwVgpZHHP3T8Hdkw/xbd91aN63IUasGAQ1ddmLklPKr0tjJ7GMWjkYd44/wPGN53HziCeCX4chIzkTbb9oJrrrUC0O9t9UVlaCjbOlWEoOpBLeJolBVFxEAhxq2yAyMAah3hGICo5F0Muw9481qWIoahJQy2hTW2OYWBvApIqRyFIxtjaAvqkup3IzJiXZmdmICUtAdHAsooLjkPouDW+ehYggCd2mrq2OihUrwNLBVEynqd3SBd3GthU1kCgwwgHq0pGTk4dH557h/K5r4vuVpkJO+nEULB3MpL1qYn2oy96Fn6+JTKKJm0aKtsWsHCsopDk0pf+cn4GyOtzd3XH16tX3NUzos0rXJ0yY8Ml/07BhQ3H/hzVMLl++LG4vGSx58+YNrl+/DgMDg79dl+fPn4uA5qc68zAmC7joK1OYYlqyICUhFQe/P4G7Jx5i8tavUKe1K2QdfQV43/fHxd03cOvofVg7WcDnwRtUrVUFnUa2Qr1OdbhQbBnKzy9APJ2VDopBVFAMokPjxcCL6qNkpmWL2inFtPQ1kZ2RLab6GFsZwMjKoOhnS31xWbToiUKCHFRh7PO/G9OS0kWWSGx4AuLCE5CRlomgF2EiQyQmLA4WDmZ4dccPRpb6IqDpXN8ealqqoj05ZYmZVTWWuamaiiTIKxSXf7mFa7/dgaOHPZr3aYCmveuLrEpZQBlFB7/7HXeOPxQnIsatHwFdQz6OKq/HqcXr2cZuikSKvl4JXP/ZbYWpyOv27dtFJxtqK3z48GFRw4Rqk1DLYQsLCzFVhlC9kebNm2PlypXo3LkzDh48iBUrVrxvK0zBEmorTNepk86H9U6oXgkFaTw9PfHgwQO0bNlStBKm61TwtWPHjti7d2+pvieMlRYOmDCF2RHJEqptcmLzeahrq2H0yiHQM9GFvMyrf3zpOS7vvYWH559Bz0wPsaFxcGnihPqda6NprwawsP90qiYrG1npWWLwRgO2hKgkkZFCAznKVMlIzRS1UvJy898/nqb3UKFDAzM96JvpohoVsKWDF1Nd6JnqwshCD7pG2tA11hGPlXbaOmNlISc7F++ik0XxTepAE//2nWjTW1hQCP+nwYiPTBSLXU1r+DwIgIF5UVCyWl1bMRg3rWIoMr1MbQxhZGUo9boY5QnVi7r9+0Nc2ntDZHi6NnUSmZHmdrKzb0qMfidqhv2+/oz4nEz5aSzc29SU9mopLLkLmFSdLJmASdCGz34PqKXw6tWrReFWag+8ceNGMUWGtGjRQnS02bNnz/vHHzlyBPPnzxfTZxwcHLBq1Sp06tRJ3Ee32doWHWOURNkm9HwUTPn6669FUCY7O1s8/osvvhB1Tbh+CZNVHDBhCrMjkjVUZO7ExvN4ds1LZGl0GdNWrto9prxLg+fJx2K6TmZaFl7dKaqaTlOOqFBpw67ucHCvygNsGUMptSnxqYiLfCeCKNQ+kwaCxQNCGth5P3iD5LhU8Xjnhg7w9nwjfq5YqaIInljYm4hBIf2sY6gFE1sjqKopQ8dQWwRV6HbKbtHUpekGvP2Z9NGhDH1PJcenIjkuBUnxqchIyRTBRAqKvItNQVJsMlIS0hAXmYjUxDTx71waV0NceCIMzPVgYKYraoioqKuI7CwjC31RQ4guqdA3k57YsHgRxL+87xYi30SJ/WnddrXg3KiaTH0HZaZl4tTWS9j/7VHk5eRh+LIB6PZ1B6jK4BRdRSIvx6myGDBhjP09DpgwhdkRyap3MUnYu/AQkuJS0GVsO3GQJ29oXv6Ds89EzZOoIKqxESrm5Zvbm4oWl/U71xHTjzR0OPVcXuTl5uFdTIr4XCZGJ4nBpBhYxqQgOSEV72KSxcAzOT5FTDvwuv1xm0H72jai5ammnga09TVFAIUGlpVVKkNLT+P9Qvdr6mpAU0ddfD4oyEJ1HdQ0VHiaEPvLwAdNhaGCqLRkpmeJgqmp79LFdxFdUuAv+FW4mAZJAcLkhDTx+aLPMX036Rhpo0ZDB6QlZ0LPWBt6xjoii4qmqOkaa0PfRFdkWWnpa8jUgJv9+TmgDkJ3Tz5C4PMQPL/+Gk161UPj7h6iLbCsnXygjKXLe29g76JD4ruz29ftRYc6Wcp6UWTyFzCZBKWKpRwwKaCAyUaZfw8Yk0ccMGEKsyOSdSGvw/DzvN/E4LHXlM6wd/t02qI8TNt5dtULXrd9cO23uyJtvXoDB/g9ChSXzXo3gKOHnVhk7aCW/fuslfTkTDE4pTP4dJmemimCK9Sims7W00A2LTlDDF5pkJualI6crFxxlrUkCqLQ2X+asqahpSYuqW0qPY+6lpqoAUEtVPVMdERRTPpZVUNFXKppqIrHK6tWFmdtaVFRVxYLD3ylg1pn07SvrIxsEeygDlAU5KCaO3SdFjrUoIAcbXeaOpaRkgUNHTXRUjc9OUN8dtJTMmFpbwrfR4Hvn5syO1ybOIrsKKrFUxyIo6mBFHDTMdAU0zIoE0rHSEvUh6DPF38W5A8FHV7d9sH9s0/h/yRI1G6iDm5Ne9UT00JlcX9C63z111v49dujIguGMjDpxAh10GNlR16OUzlgwph84oAJU5gdkbzwue+PPYsOiTNPvad0hmU1c8gr+voI9gqD1x1f3Pn9gZi2Y+tqLdrk0sC3Ufe6onhsreY1xKUSp7WXK3l5+WKALAbESRl/DIzTRfCFBs00QC4eQCurKIkuIxlpWeI2GmRTIU2/J0HvWzAXo8Ka3g8C/t/rVa1pLaYhqagqQ1lN+Y/LyqJTBgV6KMhSWUVJZCdUVq0sLmmgTe2fqaMRLUp/LBSIoQG5UuVKqFS5krikzy/9TP+uQsUKYgpTJaWKYnBOPxddr1T0c8UKRY8Rlx9cr/DHQP6D5BqRaVP03x/dJul/RZdiEVcocFWIgvwCUWeDigMXFvz5c35egbgvPz+/6Oe8fHGdfjeqaUMZRXSZn5sv1oXe59zsPORm5yI3N19c0u9F7xMNAinYlZuVK36m94jaY2dl5CAnM0cETWmp4mwhCp7SaxSjoBZtO0LvtwhyaarCsW5VMS1MBMj+CJIZW+mL35cyQ6gwKgVQtHQ1iq7rqoufKVDGmUiKiT7b4X5vRav7u8cf4eUtb7HPoIy2Bp3rwM7NRmYDXzT15ur+2ziw/HfxnePR0Q1tv2iB5v0ayuw6KzK5C5jYTpRMwCR4k8y/B4zJIw6YMIXZEckbOkjcv/yYaC3Za3Jn0Z1G3tFAiTJPnl71gs/9N6LbDg3oqLgoFSt1aeSE2m1cUc29qshA4Xnd7J+ggAB1A6KMBfoc0cCdshnotqLLouuU6UCZCzlZRQP6nMxcZGfliM9ZQvS7j4IAFCyg6/qmOqKlM2XC5OXkIze3KIhAbZvDfN9+cn1oqsfrP+q+fNZ9jarh9T3/T97n0qgaXv2L+yj7wuuO3yfvo5ocVBhYBH4oEERBH+VKqO5hjxDvCBE8qqxcWdxHP1M3LMryKQosVRaXKmrKYjoLBVxEEOqPhWrc0PQqOutPQQ2R7aNRlO1DQRH6met+sE8VbKXpNS9uvEbw63BRY6lhN3e4NnaCe7taoj6SLKNMp6v7b+HgyhNIe5eOGk2c0G5oc7Qd2lwmM2DKC3k5TuWACWPyiQMmTGF2RPLK/0kgjqw5BTVNNUzbMRaKhKZuUO2L4FehuHfysZiLXr1hNby+6ycGU836NBBFRJ3qOcCpvh0s7M34bDKTGbR7pCyNogwNys4oytKg22iaUkF+oQjSUECnII+yOwpEioi4r6BQBAvp56LLQvHZFlkhH+x1i3fBFSvRfZRtIlJOxGVR5kkFMS2pKFvlg0yVP7JYKMOlkpISKtJlpUriseJnkQ1TUQRE6DbGyhp99qkOCU3XpM5xL2/5wMrRXATXarWsgdqtXVGluoVcfOf7PnyDW8fu48SGcyLLzLq6BbqObY/WQ5qKvzEmXXIXMKkyQTIBk9DNMv8eMCaPOGDCFGZHJO/Sk9OhoaMBRZaekiEOnmnqDp1pp8Hjs2uvxH00Rz3kVbjIPqnT1hVmtiZwqGMLU1tjuTigZoyx8ozqGAU8C8aru34iw5DqW9EUL7cWznCsZ49azZ1h42ItNwE8mnZz/eA9PL3yEjcP3xOdkwwt9dFnalc06VWfM6hkiNwFTKy/lkzAJOxHmX8PGJNHnD/ImIxQ9GAJoToF1E2HluIzkOG+b8XUnQj/t2IqBE3poQNvqoNCardyEbUw7GrZwKmePSwdTFGlhhVP52GMMSlJS0pD4PNQ+D8NQsLbd7h/5iliQ+NE1iAV3W09qAmcGznC1MZIrgLedA7R29MfF3ZdFYVnqSMcBe6b9q6P7hM6omYzZ7n6fRhjjP13HDBhjEkNTTGo4mwplmJUXyL0dYQ4U/nmWbA4Sym68tzygX1tW3E7HbA26uEBFBTC2tlStLi1sDOFpaO5qK/AGGPsv6OgdlRQDIK9whH8KkxMqwx8ESqKB1NghIIJzg2rie4wdrWqiNo28ijcLxLXDtzB7WP3ER0SK+oieXSojfqd66DT6DYwtTGW9ioyRVJU3bv0n5MxJhEcMGGMyRTq1EEH4bR0/OigPRYhr8LEGT86eKcin08uv8Tdk4/g2qy6mOZDgRSPDm6ixoRlNTMRiKEpPdSC1MjKUG5SwRljrCxR/R1q4xvmG4lw30hEh8bB72GguG5kZQAdAy3RAa1epzoYOLuHmFoj78HpUJ8IPLn4Ahf3XBf7k7cB0WIf0ml0a9RpUxMNu9Xl+iSMMcY4YMIYk49MFAp60NK4R72PslEi/aMQGRgtpu7QwX3FChVw79RjPL70AlVrVhEBFkItY62cLESrWlNbE1R1tYauiY44c2hqYyiK7jLGmKKi6SaJ0UmICoxBxJsoRL6JQmJ0sig8TsEC25rWImBAHduq1bVDg051YFPDSgRMFGEaCgXeqbbKvZOP4HXHBz6e/qjR2EnsI6iI69i1w9Csb0MYWRpIe1WZoiugbJBCCTwnY0wSOGDCGJPrbBQ660lLkw8CKXRgTPPq3wbGINzvLd4GRCEuIhGh3hGipWX2uWeoWtMaQS/DxOMppZwGD8bWRjCxMYR5VRMYmOnByNoQJlWMYGiuBx0jbRG4YYwxWZWRmomY0HjEhceL77+o4BjEhMSJdr4Rb6JFnSiXxo5QUVcRAWjXpo5o80VTWFUzh4G5nkIERkq2Afa65Q3P048R8jocQS+KAugujZ1E9qFbyxqYuHmkCK4r2u/OGGOsdHDAhDGmcCiwQWcJaaHODJ86y0rp59EhcWK+ek5mjhhAxIbF4cUNb9z5/eH7x1P3HpruQxkqdIBNgRPqlkABFQsHM2joqMPATFdc1zfT5UwVxphE5GTliJpO8W/fIS48AfGRCchMy0awVyhiwxKgY6SFp1e8oKWnIYLIWvqaMLM1FtNLzO1NRCDY2NoQSpWVFDpg9PzaKzy75iWKifs9DHj/HU7cWrrAqb4DmvSuj2p1qnKQhEkH1zBhTK4o7l6TMcY+gQ6QKbhBS41Gjv/vfspOSYpNEQOS2PB4pCSkiu48NFDJSstC8OtwcRCem5MH16ZO8LpddCBOdAy1kJOVCz1TXTHlBxUAXSMd6JnoiEwVdS016Bppi6ALLTSw4QN2xsovCuCmJ2cgKTYZ72iJThYB3ZTENNF1hjIkNHXV8fTqK6QmpolWtiZVDKFnoiumylBgxL1NzT+y4QxhYm0kgrjl1et7fljUc5X4maYV0fula6yDMWuGigKultXM+TuXMcbYZ+GACWOMlchO0TfVFYujh91fDnIokEKDmcSoJCRGvRM/U6AlMSYJ76KTREDl9V0/pCWli39DhWmp00+xanWrIuBZiAiyaBtqoUp1SxGs0dbXgraBpgjoqGmqirPEWnqa0BSXGmKR104UjCm6/Lx8EexITUhDalKa+E5IiU9FckIq8nLyxDSZpLhkEfygwAjdL6bJNHES3WgoEELfPRbVqFC1gQjWUjCk77SuMLQ0EIFWLl791+h9pLordNmgqztcmzqLQDVjMkWUMCntDJPSfTrG2J84YMIYY5+JzlDqGGqLpaprlf/5WAqc0NnjpLgUJMel/PFzKnKysmFXy0YEXpLjU8XgKcI/SlynLj+UiUL/phil0seGxYuflVUri+wY+jd0NllTV0MULaRWmBo6auI2GlipqCpDXVutaNFSF/fR4EFNS01MMWKM/f9gaGZaFjJSMpGZmom0pAykJaeLy9zsHFEkNe1dmrhesVJFRAZEi+BH6rs0GFsZvM84o+8I97Y1ERMWJzrMaBtqw9rJXARBHGrbiKwHXWNt6Brrigw0CpJyjaT/Tk1DFTu81kl7NRhjjCkQPmJmTI5RRsLv68/CtWl12NW2hZJSJWmvEiuBAhPF9VT+6YCNBmvJCSlIe5chBmI0ICu6LRVp79LFUlGpIipUrID0pAxEBkSJf0cFbelsNfmwQ1AxOzcbBD4Peb9edrVtxBluGmSoaamKjBZqx0yvpaqhClUNFXGbpp6GqHugqq4ibqN6L6qaKuI6BWVU1Gmh25VFq1Ee+LGy+v7LzsxBdkY2stL/XOi2rPQsEfighW6j4EbC20RkpmaJOhd0G13SZ11cpmaKIqgvb3qj4I9uExSUDHgeIqbEUBCS2pRTUJIClJTpZWChB6tqZkVZYLQYaEJbTxPaBlrQ0NXgTBDG2KdxDRPG5AoHTBiTY6Gvw7F9xi/ibCa1y6WgiUcHN1Sraw+7WlUUurifoqJtSYOzf1uHgFotU00EMRAUg8Gin9NTMkUwhTJYMlKzxNlzJWUl0U1DXE/LEhkwFPDwexwoBpR0G2W76Jvri6lHf6eyihJys/NEMEZZrSh4Qpkv72KSRVZM8ULFcum1aGoR/Ru6rbJKZTEwLSwoeh66Tuun/MfP9Jx0XalypaJLZbpdCZUrV0Yluq14UVb643rRY6mGAV3SgJlrF/w3FJTLy81HXm4eCvIKxGU+XebkiUwquhQ/0/35BaKeD30e6HNHt+dk07/LR3ZWjri96P5csX1oGgsFOnKzcsXt9DNtu+T4FPEzFWamS/ocJMYkiyAJPY6oqlcWn9diZlVNRACEgn0U1KNLGxcrEXikn2nRN9ODhsi8KsrAoqwrCoKorRwirtPfH11SJy7GGCtVBQX0Pwk8J2NMEng0xZgcy88vQKPuHqJ+BhUipe4JTy+/FAML50aOYiBKARRrJ0tU87CDvomutFeZSRgN8JSNdaBnrFMqA2QaCNPZejqzTosIpKRniYGuuJ5RdEZfDGAzc8VgWAxuaeD7xwCXpheJwXB20WCYgjVUy0EMmrP/HDzT46h7Eb1mMRrQ0uD3c1CbaG9P//93Ow3ADS30kJJAxTMriuu0UDCFrlOx3rTEdHH9/VKxgrik6RMUiKpQseg2yu4puqwoplNQUKo4ICMybCoUBb+09DU+uu990KYCxKCc7vsUuo9ej7YBzU2ny6IFYiCfkZwhMiHEbX9cUsaFurY60pPSxX0UtKD76HuikO7TUhOBCbqdAh10Wbyoa6kiOTFN1OCgYAhdFi0FIpsoOa5oqtiHqJAxtav9lOL76L0rCnhVEpdUo4O6vdDnVAS8VJVFsdKM5Ewoq1FATVlMHdM10YGmjrp4nynwJhZ1Zahqqv6R1UQZTnQ7ZTZVFt2pirOfOLODMcYYY6WFAyaMyTF7N1ssOf6NGChRtgm1UXx5y1sMFqnVZHH6uf+jQDFthwYwbq1cYG5nKupn2NexgYGZPp95Z59EnwsRgKGz7Ppl97rFgRqRlZCV8z6LoSiTIf+Py1wxoC/KeMhH/h+ZD3RJg+wuo1sjP/+P+ygA8Ec2BAUqKEAjAgPFgQMKDBQUiOAJBXnE7X8EG4oDCpXVKiMrLbvotoKi+4p/psBkblbe+6AGBTiKgwvFwSBx+x+/W3HmNAUT/ioYRFkyFJwSf5t/BF+KFvq5KPij9Efghm6n4A3dUZQ1oSqCNnQfBQ9EcKdSRTHNit4fCg4VB4Qq/XFJwQh6IbqPsnOKHkMBpYpFGT0UXHqfyfNHFg8FQSpTJlClPzOAKPPnjywfCn5w8IIxxkrgKTmMyRUOmDCmAGhwZOtaRSydRrURt1Equ+/DAIS8CoOhub4YGFLRUCosemnPDTHnnlLU6ZKKE1IBU8pCMbM1gZWTuWiHy5i0AzXc4YIxxhhjjEkLB0wYU1AUAKnfqY5YiqUkpiLUOxyBz0MRF5EA73t+CHkdDs9Tj4um8fwxlaFaXTvEhMSKWhNUg8KsqqmoAWBsZQhTW2NR9JAxxhhjjH0mzjBhTK5wwISxckRbXwuuTZzFUoymFFBb2zDvCDFlx7mh4/upEFQbpbgWhH1tGwQ8C4Gjhz2ig2NgYmMMmxqWIhPF2pkutWFgoS8KLlJWAE/zYYwxxhhjjMkzDpgwVs7RdB69vygSSsGUpNhkUaAzITIBkQExoqjnmydBiAtPwP0zT0QBTddm1eF1y0dknqQlpYvii1RsNi0pA/pmurB0MBf36ZvrwtDCQLwWFXWkdraMMcYYY+WGaF1eyhkhf7RDZ4yVPg6YMMb+ZzBF31RPLH8lMy1TdOdJpCX6HRLevkN8ZKK4Ly0pFAFPg+HzIADRgdEwrWqCtwHR4j6XJk4IehEKHSNt8TO1sNXW14S5vakoFqltoCWCLVp6mqLOCgVYqDsGY4wxxhhjjJUFDpgwxv4TaudpVY0W8//5OOpYkpKQitTEdJG1kpGSgcToZCTHpaCwsABRQbFIiksRrVTD/d8iNSENFZUqIiYkTkzvoe4i1I1Ex0gHmnrqIpBCrVepVgtdUgYLtX6l6UDUWlXbUKvoZy1VaOppiPXkaUKMMcYYkyY65qGltJ+TMSYZHDBhjJWJSpUqQc9YVyzWThb/+N9RoIWm9mSnZyH1XbqY8kPtVimwkp5MP+eIS+oKFB+RgPTUTBGMofamCZGJyEjNgnEVQwQ9DxHPV71BNUQFxUBNU1UUtc3NzoWqugpUNVVFtgt1ZlFRV0HN5s6wdbGW4DvCGGOMsXKHCrSW9hQaLvrKmMRwwIQxJvOBFh0DLcBAC8bWRv/5+aguCwVJaAoQBV6owC1dZqVnIZeK3WbmiOsVK1UslfVnjDHGGGOMyScOmDDGyl1dFhU1FbHgv8dfGGOMMcY+MxuEM0wYkxd8CpUxxhhjjDHGGGOsBA6YyKEtW7bAxsYGqqqqqF+/Ph4+fPg/H3/kyBE4OTmJx7u6uuLcuXNltq6MMcYYY4yxPxQUSGZhjEkEB0zkzKFDhzBt2jQsWrQIT58+Ra1atdC+fXvExsZ+8vH37t3DwIEDMXLkSDx79gw9evQQy6tXr8p83RljjDHGGGOMMXlRoZB6dTK5QRklHh4e2Lx58/sCllZWVpg4cSJmz579/x7fv39/pKen48yZM+9va9CgAdzc3LBt27Z/9JopKSnQ0dFBcnIytLW1S/G3YYwxxhhj7N+Tl+PU4vVsrTkIShWUS/W58wpzcDXtgMy/B4zJI84wkSM5OTl48uQJ2rRp81EBS7ru6en5yX9Dt3/4eEIZKX/1eJKdnS2+1D9cGGOMMcYYY4yx8oQDJnIkPj4e+fn5MDEx+eh2uh4dHf3Jf0O3f87jyXfffSci4MULZbAwxhhjjDHG/pvCggKJLIwxyeCACft/5syZI1L6ipfw8HBprxJjjDHGGGPyj6ohSGJhjEmEkmSelkmCoaEhKlWqhJiYmI9up+umpqaf/Dd0++c8nqioqIiFMcYYY4wxxhgrrzjDRI4oKyvD3d0dV69efX8bFX2l6w0bNvzkv6HbP3w8uXz58l8+njHGGGOMMSYhBYWSWRhjEsEZJnKGWgoPGzYMdevWRb169bB+/XrRBWfEiBHi/qFDh8LCwkLUISGTJ09G8+bNsXbtWnTu3BkHDx7E48eP8dNPP0n5N2GMMcYYY4wxxmQXB0zkDLUJjouLw8KFC0XhVmoPfOHChfeFXcPCwkTnnGKNGjXCgQMHMH/+fMydOxcODg44ceIEXFxcpPhbMMYYY4wxVg6JeiOlXKSVa5gwJjEVCgv5L4wpRn97xhhjjDFWvsjLcWrxerZS7gulCpVL9bnzCnNxLeeIzL8HjMkjzjBhjDHGGGOMsTJQWFCIwgqle76az38zJjlc9JUxxhhjjDHGGGOsBM4wYYwxxhhjjLGyUFgggRompfx8jLH3OGDCGGOMMcYYY2WAp+QwJl94Sg5jjDHGGGOMMcZYCZxhwhhjjDHGGGNlgafkMCZXOGDC/nGaH7VDY4wxxhhjTFYUH5/Ky7SUPOQChRJ4TsaYRHDAhP2t1NRUcWllZSXtVWGMMcYYY+yTx6s6OjqQVcrKyjA1NcWd6HMSeX56bnoNxljpqlAoL+FYJjUFBQV4+/YttLS0UKFChf98FoACL+Hh4dDW1i61dWSyj7d9+cTbvfzibV9+8bYvv6Sx7WkoQ8ESc3NzVKwo2+UZs7KykJOTI5HnpmCJqqqqRJ6bsfKMM0zY36Kdj6WlZak+J+1E+SCqfOJtXz7xdi+/eNuXX7zty6+y3vaynFnyIQpocFCDMfki22FYxhhjjDHGGGOMMSnggAljjDHGGGOMMcZYCRwwYWVKRUUFixYtEpesfOFtXz7xdi+/eNuXX7ztyy/e9owxRcNFXxljjDHGGGOMMcZK4AwTxhhjjDHGGGOMsRI4YMIYY4wxxhhjjDFWAgdMGGOMMcYYY4wxxkrggAljjDHGGGOMMcZYCRwwYaVuy5YtsLGxgaqqKurXr4+HDx/+z8cfOXIETk5O4vGurq44d+5cma0rk96237FjB5o2bQo9PT2xtGnT5m8/K0wx/uaLHTx4EBUqVECPHj0kvo5MNrZ9UlISxo8fDzMzM9FFo1q1avydX062/fr16+Ho6Ag1NTVYWVlh6tSpyMrKKrP1Zf/drVu30LVrV5ibm4vv7hMnTvztv7lx4wbq1Kkj/t7t7e2xZ8+eMllXxhgrLRwwYaXq0KFDmDZtmmgp9/TpU9SqVQvt27dHbGzsJx9/7949DBw4ECNHjsSzZ8/EwImWV69elfm6s7Ld9nQQRdv++vXr8PT0FAfQ7dq1Q2RkZJmvOyu77V4sJCQEM2bMEEEzVj62fU5ODtq2bSu2/dGjR+Hn5ycCpxYWFmW+7qxst/2BAwcwe/Zs8XgfHx/s2rVLPMfcuXPLfN3Zv5eeni62NQXL/ong4GB07twZLVu2xPPnzzFlyhSMGjUKFy9elPi6MsZYaeG2wqxU0VkmDw8PbN68WVwvKCgQA+GJEyeKg6WS+vfvL3bAZ86ceX9bgwYN4Obmhm3btpXpurOy3fYl5efni0wT+vdDhw4tgzVm0trutK2bNWuGL7/8Erdv3xZZB//kTCWT721P3+mrV6+Gr68vKleuLIU1ZtLa9hMmTBCBkqtXr76/bfr06Xjw4AHu3LlTpuvOSgdlmBw/fvx/ZgjOmjULZ8+e/egk2IABA8R3/oULF8poTRlj7L/hDBNWaujs4ZMnT8TUimIVK1YU1ymD4FPo9g8fT+gs1V89ninOti8pIyMDubm50NfXl+CaMlnY7kuXLoWxsbHILGPlZ9ufOnUKDRs2FFNyTExM4OLighUrVogAGlPsbd+oUSPxb4qn7QQFBYmpWJ06dSqz9WZlj4/xGGOKQEnaK8AUR3x8vDjwpQPhD9F1OqP4KdHR0Z98PN3OFHvbf+pMFM2LLnlwxRRru9PZZErHp/RsVr62PQ2Sr127hsGDB4vBckBAAL7++msRKKWpGkxxt/2gQYPEv2vSpAkosTkvLw9jx47lKTkK7q+O8VJSUpCZmSnq2TDGmKzjDBPGmNStXLlSFACl9F4qIMgUU2pqKr744gtRt8LQ0FDaq8PKGE3boMyin376Ce7u7mJK5rx583j6ZTlANasom+jHH38UNU9+//13MVXj22+/lfaqMcYYY/8TZ5iwUkMDoEqVKiEmJuaj2+m6qanpJ/8N3f45j2eKs+2LrVmzRgRMrly5gpo1a0p4TZk0t3tgYKAo+EldFj4cRBMlJSVRBNTOzq4M1pxJ42+eOuNQ7RL6d8WqV68uzkLTNA9lZWWJrzeTzrZfsGCBCJZSwU9CHfGoftlXX30lgmY0pYcpnr86xtPW1ubsEsaY3OA9FCs1dLBLZw0/LOpGgyG6TvPWP4Vu//Dx5PLly3/5eKY4256sWrVKnGGk4m9169Yto7Vl0tru1D7cy8tLTMcpXrp16/a+gwIVjWSK+zffuHFjMQ2nOEhG/P39RSCFgyWKve2pRlXJoEhx4Ix7DyguPsZjjCkE6pLDWGk5ePBgoYqKSuGePXsKvb29C7/66qtCXV3dwujoaHH/F198UTh79uz3j797926hkpJS4Zo1awp9fHwKFy1aVFi5cuVCLy8vKf4WrCy2/cqVKwuVlZULjx49WhgVFfV+SU1NleJvwSS93UsaNmxYYffu3ctwjZm0tn1YWFihlpZW4YQJEwr9/PwKz5w5U2hsbFy4bNkyKf4WrCy2Pe3badv/9ttvhUFBQYWXLl0qtLOzK+zXr58Ufwv2uWj//OzZM7HQEGLdunXi59DQUHE/bXPa9sVoW6urqxfOnDlTHONt2bKlsFKlSoUXLlyQ4m/BGGOfh6fksFJFc9Lj4uKwcOFCkWZN7YEpe6C46FdYWNhHZ5mocv6BAwcwf/58UfzNwcFBtBel7glMsbf91q1bRRp+nz59PnoeKv64ePHiMl9/VjbbnZXfbU8ZRBcvXsTUqVPF9DsLCwtMnjxZFHxmir3taR9PbWjpMjIyEkZGRmJq3vLly6X4W7DP9fjxY5ERWGzatGnictiwYdizZw+ioqLEti9ma2sratXQ3/yGDRtgaWmJnTt3ik45jDEmLypQ1ETaK8EYY4wxxhhjjDEmS/i0H2OMMcYYY4wxxlgJHDBhjDHGGGOMMcYYK4EDJowxxhhjjDHGGGMlcMCEMcYYY4wxxhhjrAQOmDDGGGOMMcYYY4yVwAETxhhjjDHGGGOMsRI4YMIYY4wxxhhjjDFWAgdMGGOMMcYYY4wxxkrggAljjDHGGGOMMcZYCRwwYYwxxhhjjDHGGCuBAyaMMcYYY4wxxhhjJXDAhDHGGGOMMcYYY6wEDpgwxhhjjDHGGGOMlcABE8YYY4wxxhhjjLESOGDCGGOMMcYYY4wxVgIHTBhjjDHGGGOMMcZK4IAJY4wxxhhjjDHGWAkcMGGMMcYYY4wxxhgrgQMmjDHGGGOMMcYYYyVwwIQxxhhjjDHGGGOsBA6YMMYYY4wxxhhjjJXAARPGGGOMMcYYY4yxEjhgwhhjjDHGGGOMMVYCB0wYY4wxxhhjjDHGSuCACWOMMcYYY4wxxlgJHDBhjDHGGGOMMcYYK4EDJowxxhhjjDHGGGMlcMCEMcYYY4wxxhhjrAQOmDDGGGOMMcYYY4yVwAETxhhjjDHGGGOMsRI4YMIYY4wxxhhjjDFWAgdMGGOMMcYYY4wxxkrggAljjDHGGGOMMcZYCRwwYYwxxhhjjDHGGCuBAyaMMcYYY4wxxhhjJXDAhDHGGGOMMcYYY6wEDpgwxhhjjDHGGGOMlcABE8YYY4wxxhhjjLESOGDCGGOMMcYYY4wxVgIHTBhjjDHGGGOMMcZK4IAJY4wxxhhjjDHGWAkcMGGMMcYYY4wxxhgrgQMmjDHGGGOMMcYYYyVwwIQxxhhjjDHGGGMMH/s/0CWHwaG/h7MAAAAASUVORK5CYII=",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -319,10 +311,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Linear  solve converged due to CONVERGED_RTOL iterations 1618\n",
+      "    Linear solve converged due to CONVERGED_RTOL iterations 960\n",
       "\n",
       "SNES iterations: 1; SNES converged reason: CONVERGED_ITS\n",
-      "   KSP iterations: 1618; KSP converged reason: CONVERGED_RTOL\n"
+      "   KSP iterations: 960; KSP converged reason: CONVERGED_RTOL\n"
      ]
     }
    ],
@@ -501,33 +493,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    Residual norms for  solve.\n",
       "    0 KSP preconditioned resid norm 5.496162170027e+00 true resid norm 7.005934058591e-04 ||r(i)||/||b|| 1.000000000000e+00\n",
-      "    1 KSP preconditioned resid norm 9.288239850616e-01 true resid norm 2.453830661658e-03 ||r(i)||/||b|| 3.502503222463e+00\n",
-      "    2 KSP preconditioned resid norm 4.322571804179e-01 true resid norm 1.513282272809e-03 ||r(i)||/||b|| 2.160000736737e+00\n",
-      "    3 KSP preconditioned resid norm 9.747752360309e-02 true resid norm 4.889677087718e-04 ||r(i)||/||b|| 6.979336440831e-01\n",
-      "    4 KSP preconditioned resid norm 2.168769655093e-02 true resid norm 2.367419610221e-04 ||r(i)||/||b|| 3.379163421211e-01\n",
-      "    5 KSP preconditioned resid norm 6.602391993960e-03 true resid norm 1.323183601885e-04 ||r(i)||/||b|| 1.888661227495e-01\n",
-      "    6 KSP preconditioned resid norm 3.201127618913e-03 true resid norm 5.559951304397e-05 ||r(i)||/||b|| 7.936059999850e-02\n",
-      "    7 KSP preconditioned resid norm 1.583402974887e-03 true resid norm 1.881712631973e-05 ||r(i)||/||b|| 2.685884018085e-02\n",
-      "    8 KSP preconditioned resid norm 5.459453643441e-04 true resid norm 3.548481386560e-06 ||r(i)||/||b|| 5.064965437705e-03\n",
-      "    9 KSP preconditioned resid norm 2.435745730827e-04 true resid norm 1.708889316683e-06 ||r(i)||/||b|| 2.439202685026e-03\n",
-      "   10 KSP preconditioned resid norm 8.264338667027e-05 true resid norm 4.816892100534e-07 ||r(i)||/||b|| 6.875445958027e-04\n",
-      "   11 KSP preconditioned resid norm 2.869421028055e-05 true resid norm 2.297141937468e-07 ||r(i)||/||b|| 3.278851782299e-04\n",
-      "   12 KSP preconditioned resid norm 1.126260471802e-05 true resid norm 1.157469218690e-07 ||r(i)||/||b|| 1.652126909860e-04\n",
-      "   13 KSP preconditioned resid norm 3.183234314886e-06 true resid norm 2.964758721483e-08 ||r(i)||/||b|| 4.231782224452e-05\n",
-      "   14 KSP preconditioned resid norm 1.315553328176e-06 true resid norm 1.114853947343e-08 ||r(i)||/||b|| 1.591299515553e-05\n",
-      "   15 KSP preconditioned resid norm 6.727350581287e-07 true resid norm 4.697218845361e-09 ||r(i)||/||b|| 6.704628970353e-06\n",
-      "   16 KSP preconditioned resid norm 2.943828880986e-07 true resid norm 2.160101650639e-09 ||r(i)||/||b|| 3.083245763626e-06\n",
-      "KSP Object: () 1 MPI process\n",
+      "    1 KSP preconditioned resid norm 9.288239850614e-01 true resid norm 2.453830661658e-03 ||r(i)||/||b|| 3.502503222463e+00\n",
+      "    2 KSP preconditioned resid norm 4.322571804187e-01 true resid norm 1.513282272808e-03 ||r(i)||/||b|| 2.160000736736e+00\n",
+      "    3 KSP preconditioned resid norm 9.747752360340e-02 true resid norm 4.889677087758e-04 ||r(i)||/||b|| 6.979336440888e-01\n",
+      "    4 KSP preconditioned resid norm 2.168769655068e-02 true resid norm 2.367419610200e-04 ||r(i)||/||b|| 3.379163421182e-01\n",
+      "    5 KSP preconditioned resid norm 6.602391994106e-03 true resid norm 1.323183601894e-04 ||r(i)||/||b|| 1.888661227509e-01\n",
+      "    6 KSP preconditioned resid norm 3.201127617659e-03 true resid norm 5.559951302511e-05 ||r(i)||/||b|| 7.936059997159e-02\n",
+      "    7 KSP preconditioned resid norm 1.583402976189e-03 true resid norm 1.881712632717e-05 ||r(i)||/||b|| 2.685884019148e-02\n",
+      "    8 KSP preconditioned resid norm 5.459453639346e-04 true resid norm 3.548481385676e-06 ||r(i)||/||b|| 5.064965436443e-03\n",
+      "    9 KSP preconditioned resid norm 2.435745734653e-04 true resid norm 1.708889316321e-06 ||r(i)||/||b|| 2.439202684509e-03\n",
+      "   10 KSP preconditioned resid norm 8.264338654907e-05 true resid norm 4.816892094520e-07 ||r(i)||/||b|| 6.875445949442e-04\n",
+      "   11 KSP preconditioned resid norm 2.869421013434e-05 true resid norm 2.297141940561e-07 ||r(i)||/||b|| 3.278851786714e-04\n",
+      "   12 KSP preconditioned resid norm 1.126260479121e-05 true resid norm 1.157469223178e-07 ||r(i)||/||b|| 1.652126916266e-04\n",
+      "   13 KSP preconditioned resid norm 3.183234318532e-06 true resid norm 2.964758722405e-08 ||r(i)||/||b|| 4.231782225768e-05\n",
+      "   14 KSP preconditioned resid norm 1.315553337784e-06 true resid norm 1.114853946562e-08 ||r(i)||/||b|| 1.591299514438e-05\n",
+      "   15 KSP preconditioned resid norm 6.727350493786e-07 true resid norm 4.697218905430e-09 ||r(i)||/||b|| 6.704629056093e-06\n",
+      "   16 KSP preconditioned resid norm 2.943828879120e-07 true resid norm 2.160101611906e-09 ||r(i)||/||b|| 3.083245708339e-06\n",
+      "KSP Object: 1 MPI process\n",
       "  type: gmres\n",
       "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
       "    happy breakdown tolerance 1e-30\n",
       "  maximum iterations=100, initial guess is zero\n",
-      "  tolerances:  relative=1e-07, absolute=1e-50, divergence=10000.\n",
+      "  tolerances: relative=1e-07, absolute=1e-50, divergence=10000.\n",
       "  left preconditioning\n",
       "  using PRECONDITIONED norm type for convergence test\n",
-      "PC Object: () 1 MPI process\n",
+      "PC Object: 1 MPI process\n",
       "  type: fieldsplit\n",
       "    FieldSplit with Schur preconditioner, factorization FULL\n",
       "    Preconditioner for the Schur complement formed from A11\n",
@@ -538,7 +529,7 @@
       "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
       "        type: preonly\n",
       "        maximum iterations=10000, initial guess is zero\n",
-      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "        left preconditioning\n",
       "        using NONE norm type for convergence test\n",
       "      PC Object: (fieldsplit_0_) 1 MPI process\n",
@@ -549,23 +540,23 @@
       "          factor fill ratio given 5., needed 5.8857\n",
       "            Factored matrix follows:\n",
       "              Mat Object: (fieldsplit_0_) 1 MPI process\n",
-      "                type: seqaij\n",
+      "                type: seqbaij\n",
       "                rows=33282, cols=33282, bs=2\n",
       "                package used to perform factorization: petsc\n",
       "                total: nonzeros=4459972, allocated nonzeros=4459972\n",
-      "                  using I-node routines: found 16529 nodes, limit used is 5\n",
+      "                    block size is 2\n",
       "        linear system matrix = precond matrix:\n",
       "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
-      "          type: seqaij\n",
+      "          type: seqbaij\n",
       "          rows=33282, cols=33282, bs=2\n",
       "          total: nonzeros=757764, allocated nonzeros=757764\n",
       "          total number of mallocs used during MatSetValues calls=0\n",
-      "            using I-node routines: found 16638 nodes, limit used is 5\n",
+      "              block size is 2\n",
       "    KSP solver for S = A11 - A10 inv(A00) A01\n",
       "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
       "        type: preonly\n",
       "        maximum iterations=10000, initial guess is zero\n",
-      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "        left preconditioning\n",
       "        using NONE norm type for convergence test\n",
       "      PC Object: (fieldsplit_1_) 1 MPI process\n",
@@ -618,25 +609,25 @@
       "            has attached null space\n",
       "            not using I-node routines\n",
       "  linear system matrix followed by preconditioner matrix:\n",
-      "  Mat Object: () 1 MPI process\n",
+      "  Mat Object: 1 MPI process\n",
       "    type: nest\n",
       "    rows=37507, cols=37507\n",
       "      has attached null space\n",
       "      Matrix object:\n",
       "        type=nest, rows=2, cols=2\n",
       "        MatNest structure:\n",
-      "        (0,0) : type=seqaij, rows=33282, cols=33282\n",
+      "        (0,0) : type=seqbaij, rows=33282, cols=33282\n",
       "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
       "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
       "        (1,1) : type=seqaij, rows=4225, cols=4225\n",
-      "  Mat Object: () 1 MPI process\n",
+      "  Mat Object: 1 MPI process\n",
       "    type: nest\n",
       "    rows=37507, cols=37507\n",
       "      has attached null space\n",
       "      Matrix object:\n",
       "        type=nest, rows=2, cols=2\n",
       "        MatNest structure:\n",
-      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqaij, rows=33282, cols=33282\n",
+      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqbaij, rows=33282, cols=33282\n",
       "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
       "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
       "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
@@ -721,15 +712,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "KSP Object: () 1 MPI process\n",
+      "KSP Object: 1 MPI process\n",
       "  type: gmres\n",
       "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
       "    happy breakdown tolerance 1e-30\n",
       "  maximum iterations=10000, initial guess is zero\n",
-      "  tolerances:  relative=1e-07, absolute=1e-50, divergence=10000.\n",
+      "  tolerances: relative=1e-07, absolute=1e-50, divergence=10000.\n",
       "  left preconditioning\n",
       "  using PRECONDITIONED norm type for convergence test\n",
-      "PC Object: () 1 MPI process\n",
+      "PC Object: 1 MPI process\n",
       "  type: fieldsplit\n",
       "    FieldSplit with Schur preconditioner, factorization FULL\n",
       "    Preconditioner for the Schur complement formed from A11\n",
@@ -740,7 +731,7 @@
       "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
       "        type: preonly\n",
       "        maximum iterations=10000, initial guess is zero\n",
-      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "        left preconditioning\n",
       "        using NONE norm type for convergence test\n",
       "      PC Object: (fieldsplit_0_) 1 MPI process\n",
@@ -751,23 +742,23 @@
       "          factor fill ratio given 5., needed 5.8857\n",
       "            Factored matrix follows:\n",
       "              Mat Object: (fieldsplit_0_) 1 MPI process\n",
-      "                type: seqaij\n",
+      "                type: seqbaij\n",
       "                rows=33282, cols=33282, bs=2\n",
       "                package used to perform factorization: petsc\n",
       "                total: nonzeros=4459972, allocated nonzeros=4459972\n",
-      "                  using I-node routines: found 16529 nodes, limit used is 5\n",
+      "                    block size is 2\n",
       "        linear system matrix = precond matrix:\n",
       "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
-      "          type: seqaij\n",
+      "          type: seqbaij\n",
       "          rows=33282, cols=33282, bs=2\n",
       "          total: nonzeros=757764, allocated nonzeros=757764\n",
       "          total number of mallocs used during MatSetValues calls=0\n",
-      "            using I-node routines: found 16638 nodes, limit used is 5\n",
+      "              block size is 2\n",
       "    KSP solver for S = A11 - A10 inv(A00) A01\n",
       "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
       "        type: preonly\n",
       "        maximum iterations=10000, initial guess is zero\n",
-      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "        left preconditioning\n",
       "        using NONE norm type for convergence test\n",
       "      PC Object: (fieldsplit_1_) 1 MPI process\n",
@@ -788,7 +779,35 @@
       "                  package used to perform factorization: petsc\n",
       "                  total: nonzeros=202291, allocated nonzeros=202291\n",
       "                    not using I-node routines\n",
-      "          linear system matrix = precond matrix:\n",
+      "          linear system matrix followed by preconditioner matrix:\n",
+      "          Mat Object: (fieldsplit_1_) 1 MPI process\n",
+      "            type: schurcomplement\n",
+      "            rows=4225, cols=4225\n",
+      "              has attached null space\n",
+      "              Schur complement A11 - A10 inv(A00) A01\n",
+      "              A11\n",
+      "                Mat Object: (fieldsplit_1_) 1 MPI process\n",
+      "                  type: seqaij\n",
+      "                  rows=4225, cols=4225\n",
+      "                  total: nonzeros=4225, allocated nonzeros=4225\n",
+      "                  total number of mallocs used during MatSetValues calls=0\n",
+      "                    has attached null space\n",
+      "                    not using I-node routines\n",
+      "              A10\n",
+      "                Mat Object: 1 MPI process\n",
+      "                  type: seqaij\n",
+      "                  rows=4225, cols=33282, rbs=1, cbs=2\n",
+      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
+      "                  total number of mallocs used during MatSetValues calls=0\n",
+      "                    not using I-node routines\n",
+      "              KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
+      "              A01\n",
+      "                Mat Object: 1 MPI process\n",
+      "                  type: seqaij\n",
+      "                  rows=33282, cols=4225, rbs=2, cbs=1\n",
+      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
+      "                  total number of mallocs used during MatSetValues calls=0\n",
+      "                    using I-node routines: found 16638 nodes, limit used is 5\n",
       "          Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
       "            type: seqaij\n",
       "            rows=4225, cols=4225\n",
@@ -806,7 +825,7 @@
       "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
       "                type: seqaij\n",
       "                rows=4225, cols=4225\n",
-      "                total: nonzeros=29057, allocated nonzeros=29057\n",
+      "                total: nonzeros=4225, allocated nonzeros=4225\n",
       "                total number of mallocs used during MatSetValues calls=0\n",
       "                  has attached null space\n",
       "                  not using I-node routines\n",
@@ -828,19 +847,19 @@
       "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
       "          type: seqaij\n",
       "          rows=4225, cols=4225\n",
-      "          total: nonzeros=29057, allocated nonzeros=29057\n",
+      "          total: nonzeros=4225, allocated nonzeros=4225\n",
       "          total number of mallocs used during MatSetValues calls=0\n",
       "            has attached null space\n",
       "            not using I-node routines\n",
       "  linear system matrix = precond matrix:\n",
-      "  Mat Object: () 1 MPI process\n",
+      "  Mat Object: 1 MPI process\n",
       "    type: nest\n",
       "    rows=37507, cols=37507\n",
       "      has attached null space\n",
       "      Matrix object:\n",
       "        type=nest, rows=2, cols=2\n",
       "        MatNest structure:\n",
-      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqaij, rows=33282, cols=33282\n",
+      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqbaij, rows=33282, cols=33282\n",
       "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
       "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
       "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
@@ -921,15 +940,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "KSP Object: () 1 MPI process\n",
+      "KSP Object: 1 MPI process\n",
       "  type: gmres\n",
       "    restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
       "    happy breakdown tolerance 1e-30\n",
       "  maximum iterations=10000, initial guess is zero\n",
-      "  tolerances:  relative=1e-07, absolute=1e-50, divergence=10000.\n",
+      "  tolerances: relative=1e-07, absolute=1e-50, divergence=10000.\n",
       "  left preconditioning\n",
       "  using PRECONDITIONED norm type for convergence test\n",
-      "PC Object: () 1 MPI process\n",
+      "PC Object: 1 MPI process\n",
       "  type: fieldsplit\n",
       "    FieldSplit with Schur preconditioner, factorization FULL\n",
       "    Preconditioner for the Schur complement formed from A11\n",
@@ -940,7 +959,7 @@
       "      KSP Object: (fieldsplit_0_) 1 MPI process\n",
       "        type: preonly\n",
       "        maximum iterations=10000, initial guess is zero\n",
-      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "        left preconditioning\n",
       "        using NONE norm type for convergence test\n",
       "      PC Object: (fieldsplit_0_) 1 MPI process\n",
@@ -952,7 +971,7 @@
       "          KSP Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
       "            type: preonly\n",
       "            maximum iterations=10000, initial guess is zero\n",
-      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "            left preconditioning\n",
       "            using NONE norm type for convergence test\n",
       "          PC Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
@@ -964,18 +983,18 @@
       "              factor fill ratio given 5., needed 2.49235\n",
       "                Factored matrix follows:\n",
       "                  Mat Object: (fieldsplit_0_mg_coarse_) 1 MPI process\n",
-      "                    type: seqaij\n",
+      "                    type: seqbaij\n",
       "                    rows=578, cols=578, bs=2\n",
       "                    package used to perform factorization: petsc\n",
       "                    total: nonzeros=30636, allocated nonzeros=30636\n",
-      "                      using I-node routines: found 271 nodes, limit used is 5\n",
+      "                        block size is 2\n",
       "            linear system matrix = precond matrix:\n",
       "            Mat Object: 1 MPI process\n",
-      "              type: seqaij\n",
+      "              type: seqbaij\n",
       "              rows=578, cols=578, bs=2\n",
       "              total: nonzeros=12292, allocated nonzeros=12292\n",
       "              total number of mallocs used during MatSetValues calls=0\n",
-      "                using I-node routines: found 286 nodes, limit used is 5\n",
+      "                  block size is 2\n",
       "        Down solver (pre-smoother) on level 1 -------------------------------\n",
       "          KSP Object: (fieldsplit_0_mg_levels_1_) 1 MPI process\n",
       "            type: chebyshev\n",
@@ -987,12 +1006,12 @@
       "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
       "                  happy breakdown tolerance 1e-30\n",
       "                maximum iterations=10, initial guess is zero\n",
-      "                tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
+      "                tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
       "                left preconditioning\n",
       "                using PRECONDITIONED norm type for convergence test\n",
-      "              estimating eigenvalues using noisy right hand side\n",
+      "              estimating eigenvalues using a noisy random number generated right-hand side\n",
       "            maximum iterations=2, nonzero initial guess\n",
-      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "            left preconditioning\n",
       "            using NONE norm type for convergence test\n",
       "          PC Object: (fieldsplit_0_mg_levels_1_) 1 MPI process\n",
@@ -1000,11 +1019,11 @@
       "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
       "            linear system matrix = precond matrix:\n",
       "            Mat Object: 1 MPI process\n",
-      "              type: seqaij\n",
+      "              type: seqbaij\n",
       "              rows=2178, cols=2178, bs=2\n",
       "              total: nonzeros=48132, allocated nonzeros=48132\n",
       "              total number of mallocs used during MatSetValues calls=0\n",
-      "                using I-node routines: found 1086 nodes, limit used is 5\n",
+      "                  block size is 2\n",
       "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
       "        Down solver (pre-smoother) on level 2 -------------------------------\n",
       "          KSP Object: (fieldsplit_0_mg_levels_2_) 1 MPI process\n",
@@ -1017,12 +1036,12 @@
       "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
       "                  happy breakdown tolerance 1e-30\n",
       "                maximum iterations=10, initial guess is zero\n",
-      "                tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
+      "                tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
       "                left preconditioning\n",
       "                using PRECONDITIONED norm type for convergence test\n",
-      "              estimating eigenvalues using noisy right hand side\n",
+      "              estimating eigenvalues using a noisy random number generated right-hand side\n",
       "            maximum iterations=2, nonzero initial guess\n",
-      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "            left preconditioning\n",
       "            using NONE norm type for convergence test\n",
       "          PC Object: (fieldsplit_0_mg_levels_2_) 1 MPI process\n",
@@ -1030,11 +1049,11 @@
       "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
       "            linear system matrix = precond matrix:\n",
       "            Mat Object: 1 MPI process\n",
-      "              type: seqaij\n",
+      "              type: seqbaij\n",
       "              rows=8450, cols=8450, bs=2\n",
       "              total: nonzeros=190468, allocated nonzeros=190468\n",
       "              total number of mallocs used during MatSetValues calls=0\n",
-      "                using I-node routines: found 4222 nodes, limit used is 5\n",
+      "                  block size is 2\n",
       "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
       "        Down solver (pre-smoother) on level 3 -------------------------------\n",
       "          KSP Object: (fieldsplit_0_mg_levels_3_) 1 MPI process\n",
@@ -1047,12 +1066,12 @@
       "                  restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
       "                  happy breakdown tolerance 1e-30\n",
       "                maximum iterations=10, initial guess is zero\n",
-      "                tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
+      "                tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
       "                left preconditioning\n",
       "                using PRECONDITIONED norm type for convergence test\n",
-      "              estimating eigenvalues using noisy right hand side\n",
+      "              estimating eigenvalues using a noisy random number generated right-hand side\n",
       "            maximum iterations=2, nonzero initial guess\n",
-      "            tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "            tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "            left preconditioning\n",
       "            using NONE norm type for convergence test\n",
       "          PC Object: (fieldsplit_0_mg_levels_3_) 1 MPI process\n",
@@ -1060,36 +1079,36 @@
       "              type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
       "            linear system matrix = precond matrix:\n",
       "            Mat Object: (fieldsplit_0_) 1 MPI process\n",
-      "              type: seqaij\n",
+      "              type: seqbaij\n",
       "              rows=33282, cols=33282, bs=2\n",
       "              total: nonzeros=757764, allocated nonzeros=757764\n",
       "              total number of mallocs used during MatSetValues calls=0\n",
-      "                using I-node routines: found 16638 nodes, limit used is 5\n",
+      "                  block size is 2\n",
       "        Up solver (post-smoother) same as down solver (pre-smoother)\n",
       "        linear system matrix = precond matrix:\n",
       "        Mat Object: (fieldsplit_0_) 1 MPI process\n",
-      "          type: seqaij\n",
+      "          type: seqbaij\n",
       "          rows=33282, cols=33282, bs=2\n",
       "          total: nonzeros=757764, allocated nonzeros=757764\n",
       "          total number of mallocs used during MatSetValues calls=0\n",
-      "            using I-node routines: found 16638 nodes, limit used is 5\n",
+      "              block size is 2\n",
       "    KSP solver for S = A11 - A10 inv(A00) A01\n",
       "      KSP Object: (fieldsplit_1_) 1 MPI process\n",
       "        type: chebyshev\n",
       "          Chebyshev polynomial of first kind\n",
-      "          eigenvalue targets used: min 0.0958741, max 1.05461\n",
-      "          eigenvalues estimated via gmres: min 0.128801, max 0.958741\n",
+      "          eigenvalue targets used: min 0.0958699, max 1.05457\n",
+      "          eigenvalues estimated via gmres: min 0.128792, max 0.958699\n",
       "          eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1]          KSP Object: (fieldsplit_1_esteig_) 1 MPI process\n",
       "            type: gmres\n",
       "              restart=30, using Classical (unmodified) Gram-Schmidt Orthogonalization with no iterative refinement\n",
       "              happy breakdown tolerance 1e-30\n",
       "            maximum iterations=10, initial guess is zero\n",
-      "            tolerances:  relative=1e-12, absolute=1e-50, divergence=10000.\n",
+      "            tolerances: relative=1e-12, absolute=1e-50, divergence=10000.\n",
       "            left preconditioning\n",
       "            using PRECONDITIONED norm type for convergence test\n",
-      "          estimating eigenvalues using noisy right hand side\n",
+      "          estimating eigenvalues using a noisy random number generated right-hand side\n",
       "        maximum iterations=2, initial guess is zero\n",
-      "        tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.\n",
+      "        tolerances: relative=1e-05, absolute=1e-50, divergence=10000.\n",
       "        left preconditioning\n",
       "        using PRECONDITIONED norm type for convergence test\n",
       "      PC Object: (fieldsplit_1_) 1 MPI process\n",
@@ -1100,7 +1119,35 @@
       "        PC Object: (fieldsplit_1_mass_) 1 MPI process\n",
       "          type: sor\n",
       "            type = local_symmetric, iterations = 1, local iterations = 1, omega = 1.\n",
-      "          linear system matrix = precond matrix:\n",
+      "          linear system matrix followed by preconditioner matrix:\n",
+      "          Mat Object: (fieldsplit_1_) 1 MPI process\n",
+      "            type: schurcomplement\n",
+      "            rows=4225, cols=4225\n",
+      "              has attached null space\n",
+      "              Schur complement A11 - A10 inv(A00) A01\n",
+      "              A11\n",
+      "                Mat Object: (fieldsplit_1_) 1 MPI process\n",
+      "                  type: seqaij\n",
+      "                  rows=4225, cols=4225\n",
+      "                  total: nonzeros=4225, allocated nonzeros=4225\n",
+      "                  total number of mallocs used during MatSetValues calls=0\n",
+      "                    has attached null space\n",
+      "                    not using I-node routines\n",
+      "              A10\n",
+      "                Mat Object: 1 MPI process\n",
+      "                  type: seqaij\n",
+      "                  rows=4225, cols=33282, rbs=1, cbs=2\n",
+      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
+      "                  total number of mallocs used during MatSetValues calls=0\n",
+      "                    not using I-node routines\n",
+      "              KSP solver for A00 block viewable with the additional option -fieldsplit_0_ksp_view\n",
+      "              A01\n",
+      "                Mat Object: 1 MPI process\n",
+      "                  type: seqaij\n",
+      "                  rows=33282, cols=4225, rbs=2, cbs=1\n",
+      "                  total: nonzeros=156930, allocated nonzeros=156930\n",
+      "                  total number of mallocs used during MatSetValues calls=0\n",
+      "                    using I-node routines: found 16638 nodes, limit used is 5\n",
       "          Mat Object: (fieldsplit_1_mass_) 1 MPI process\n",
       "            type: seqaij\n",
       "            rows=4225, cols=4225\n",
@@ -1118,7 +1165,7 @@
       "              Mat Object: (fieldsplit_1_) 1 MPI process\n",
       "                type: seqaij\n",
       "                rows=4225, cols=4225\n",
-      "                total: nonzeros=29057, allocated nonzeros=29057\n",
+      "                total: nonzeros=4225, allocated nonzeros=4225\n",
       "                total number of mallocs used during MatSetValues calls=0\n",
       "                  has attached null space\n",
       "                  not using I-node routines\n",
@@ -1140,19 +1187,19 @@
       "        Mat Object: (fieldsplit_1_) 1 MPI process\n",
       "          type: seqaij\n",
       "          rows=4225, cols=4225\n",
-      "          total: nonzeros=29057, allocated nonzeros=29057\n",
+      "          total: nonzeros=4225, allocated nonzeros=4225\n",
       "          total number of mallocs used during MatSetValues calls=0\n",
       "            has attached null space\n",
       "            not using I-node routines\n",
       "  linear system matrix = precond matrix:\n",
-      "  Mat Object: () 1 MPI process\n",
+      "  Mat Object: 1 MPI process\n",
       "    type: nest\n",
       "    rows=37507, cols=37507\n",
       "      has attached null space\n",
       "      Matrix object:\n",
       "        type=nest, rows=2, cols=2\n",
       "        MatNest structure:\n",
-      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqaij, rows=33282, cols=33282\n",
+      "        (0,0) : prefix=\"fieldsplit_0_\", type=seqbaij, rows=33282, cols=33282\n",
       "        (0,1) : type=seqaij, rows=33282, cols=4225\n",
       "        (1,0) : type=seqaij, rows=4225, cols=33282\n",
       "        (1,1) : prefix=\"fieldsplit_1_\", type=seqaij, rows=4225, cols=4225\n",
@@ -1232,7 +1279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1251,8 +1298,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/09-hybridisation.ipynb b/docs/notebooks/09-hybridisation.ipynb
index 1076e4f957..aae3fa9b95 100644
--- a/docs/notebooks/09-hybridisation.ipynb
+++ b/docs/notebooks/09-hybridisation.ipynb
@@ -114,10 +114,20 @@
      "text": [
       "Help on cython_function_or_method in module firedrake.utility_meshes:\n",
       "\n",
-      "CubedSphereMesh(radius, refinement_level=0, degree=1, reorder=None, distribution_parameters=None, comm=, name='firedrake_default', distribution_name=None, permutation_name=None)\n",
+      "CubedSphereMesh(\n",
+      "    radius,\n",
+      "    refinement_level=0,\n",
+      "    degree=1,\n",
+      "    reorder=None,\n",
+      "    distribution_parameters=None,\n",
+      "    comm=,\n",
+      "    name='firedrake_default',\n",
+      "    distribution_name=None,\n",
+      "    permutation_name=None\n",
+      ")\n",
       "    Generate an cubed approximation to the surface of the\n",
       "    sphere.\n",
-      "    \n",
+      "\n",
       "    :arg radius: The radius of the sphere to approximate.\n",
       "    :kwarg refinement_level: optional number of refinements (0 is a cube).\n",
       "    :kwarg degree: polynomial degree of coordinate space (e.g.,\n",
@@ -189,18 +199,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cebcb070884d4d30908b7c4574aa5e80",
+       "model_id": "86db3723f2584185897449a62f5cd2b4",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -483,7 +493,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "reduction in residual: 1.4316859461176471e-08\n"
+      "reduction in residual: 1.4316859543564344e-08\n"
      ]
     }
    ],
@@ -903,7 +913,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "reduction in residual: 9.161842216465896e-09\n"
+      "reduction in residual: 1.7694303099308495e-08\n"
      ]
     }
    ],
@@ -987,7 +997,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gmres iterations (trace sys) = 5, converged reason = CONVERGED_RTOL\n"
+      "gmres iterations (trace sys) = 6, converged reason = CONVERGED_RTOL\n"
      ]
     }
    ],
@@ -1007,7 +1017,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gmres iterations (trace sys) = 7, converged reason = CONVERGED_RTOL\n"
+      "gmres iterations (trace sys) = 8, converged reason = CONVERGED_RTOL\n"
      ]
     }
    ],
@@ -1027,7 +1037,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gmres iterations (trace sys) = 14, converged reason = CONVERGED_RTOL\n"
+      "gmres iterations (trace sys) = 16, converged reason = CONVERGED_RTOL\n"
      ]
     }
    ],
@@ -1097,7 +1107,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "225 ms ± 3.15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "174 ms ± 3.91 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -1123,7 +1133,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "141 ms ± 737 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "99.3 ms ± 486 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -1132,11 +1142,32 @@
     "wh.assign(0.0)\n",
     "uD_solver_hybrid.solve()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/10-sum-factorisation.ipynb b/docs/notebooks/10-sum-factorisation.ipynb
index 73acb70213..6fc82a84a8 100644
--- a/docs/notebooks/10-sum-factorisation.ipynb
+++ b/docs/notebooks/10-sum-factorisation.ipynb
@@ -296,8 +296,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n"
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
@@ -335,70 +335,70 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n"
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
@@ -430,18 +430,18 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b643618da54d41f791bdda429df36129",
+       "model_id": "d4fa8aa7c77a4fcdab3054cdf712d823",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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",
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       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
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@@ -481,38 +481,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n",
-      "/home/jack/Documents/firedrake/firedrake/src/ufl/ufl/utils/sorting.py:84: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
-      "  warnings.warn(f\"Applying str() to a metadata value of type {type(value).__name__}, \"\n"
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n",
+      "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
@@ -539,18 +539,18 @@
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        "version_major": 2,
        "version_minor": 0
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",
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       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -588,8 +588,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/11-extract-adjoint-solutions.ipynb b/docs/notebooks/11-extract-adjoint-solutions.ipynb
index 79b779a96a..39ae5c5cef 100644
--- a/docs/notebooks/11-extract-adjoint-solutions.ipynb
+++ b/docs/notebooks/11-extract-adjoint-solutions.ipynb
@@ -2,91 +2,91 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "e7b5808f-f0ae-4e40-bcca-eff67f251fc9",
    "metadata": {},
    "source": [
-    "# Solving adjoint equations using `firedrake-adjoint`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook demonstrates how to use `firedrake-adjoint` to solve an adjoint equation and extract the solution thereof.\n",
+    "# Solving adjoint problems\n",
     "\n",
-    "Suppose we have a PDE in 'residual form'\n",
+    "Firedrake has built-in capabilities for differentiating the solution of a PDE with respect to any of its inputs. These are documented in [the manual](https://www.firedrakeproject.org/adjoint.html). Here we will explore these capabilities using a the same Burgers equation example that we already explored when we learned how to solve nonlinear problems. As before, we will define the equation in one spatial dimension and avoid considering boundary conditions by selecting a periodic domain:\n",
     "\n",
-    "$$F(u)=0,\\quad u\\in V,$$\n",
+    "$$\n",
+    "\\frac{\\partial u}{\\partial t} + u \\frac{\\partial u}{\\partial x} - \\nu \\frac{\\partial^2 u}{\\partial x^2} = 0.\n",
+    "$$\n",
     "\n",
-    "for some function space $V$.\n",
-    "Given an objective functional $J:V\\rightarrow\\mathbb R$, the adjoint equation is given by\n",
+    "Applying finite element in time and implicit Euler in space, the problem becomes at each timestep find $u^{n+1}\\in V$ such that:\n",
     "\n",
-    "$$\\frac{\\partial F}{\\partial u}^T\\lambda=\\frac{\\partial J}{\\partial u}^T,\\quad\\lambda\\in V.$$\n",
+    "$$\n",
+    "\\int_\\Omega \\frac{u^{n+1} - u^n}{\\Delta t} v + u^{n+1} \\frac{\\partial u^{n+1}}{\\partial x} v + \\nu \\frac{\\partial u^{n+1}}{\\partial x}\\frac{\\partial v}{\\partial x} \\, \\mathrm{d}x = 0 \\quad \\forall v \\in V.\n",
+    "$$\n",
     "\n",
-    "We seek to compute the adjoint solution, $\\lambda$."
+    "As usual, we start by setting up plotting:"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "11709a56-6feb-4cac-9a98-3327e8c65a56",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Suppose the PDE involves a parameter $\\nu$ which is defined as a Firedrake `Constant` or `Function` (rather than a float).\n",
-    "Then `firedrake-adjoint` enables us to compute the gradient $\\mathrm dJ/\\mathrm d\\nu$.\n",
-    "Whilst we might not actually need this for our application, performing the computation endows the `SolveVarFormBlock`s with adjoint solutions.\n",
-    "Why is that?\n",
-    "\n",
-    "Taking the transpose of the adjoint equation gives\n",
-    "\n",
-    "$$\\frac{\\partial J}{\\partial u}=\\lambda^T\\frac{\\partial F}{\\partial u}.$$\n",
-    "\n",
-    "Now, expanding the gradient $\\mathrm dJ/\\mathrm d\\nu$ using the chain rule gives\n",
-    "\n",
-    "$$\n",
-    "\\frac{\\mathrm dJ}{\\mathrm d\\nu}\n",
-    "=\\frac{\\partial J}{\\partial u}\\frac{\\mathrm du}{\\mathrm d\\nu}+\\frac{\\partial J}{\\partial\\nu}\n",
-    "=\\lambda^T\\frac{\\partial F}{\\partial u}\\frac{\\mathrm du}{\\mathrm d\\nu}+\\frac{\\partial J}{\\partial\\nu}\n",
-    "=\\lambda^T\\frac{\\partial F}{\\partial\\nu}+\\frac{\\partial J}{\\partial\\nu}.\n",
-    "$$\n",
+    "# Code in this cell makes plots appear an appropriate size and resolution in the browser window\n",
+    "%matplotlib widget\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
     "\n",
-    "In order to compute the gradient $\\mathrm dJ/\\mathrm d\\nu$, it is therefore sufficient to evaluate:\n",
-    "1. the adjoint solution, $\\lambda$;\n",
-    "2. the _partial_ derivative of the PDE residual w.r.t. the parameter $\\nu$;\n",
-    "3. the _partial_ derivative of the objective functional w.r.t. $\\nu$.\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
-    "In order to compute the gradient, `firedrake-adjoint` does all three of these things."
+    "plt.rcParams['figure.figsize'] = (11, 6)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e46e290c-1b65-42c5-8f73-686331d28fdc",
    "metadata": {},
    "source": [
-    "## The forward problem"
+    "... and importing Firedrake:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "efa08a55-d8f3-422c-9e52-789fcacd2cbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from firedrake import *\n",
+    "from firedrake.__future__ import interpolate"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8047d692-358e-4f52-b678-505ec3e65533",
    "metadata": {},
    "source": [
-    "Import Firedrake with adjoint mode activated."
+    "This is where the code is slightly different because we're solving an adjoint problem. Firedrake uses [pyadjoint](https://pyadjoint.org) to deliver its adjoint capability, but the relevant parts of the Pyadjoint interface are exposed via the `firedrake.adjoint` module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
+   "id": "24e4fc04-bc5a-4f57-a80d-da3fb6be1677",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Code in this cell makes plots appear an appropriate size and resolution in the browser window\n",
-    "%matplotlib widget\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.rcParams['figure.figsize'] = (11, 6)"
+    "from firedrake.adjoint import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9cd6fc6-1d30-4eb7-8232-9b0014dd7b2a",
+   "metadata": {},
+   "source": [
+    "Executing the adjoint problem depends on having recorded all of the operations on which the results of the PDE depend. We switch on the recording thus:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
+   "id": "ae90fb73-f2dd-4026-880d-59b37f086c73",
    "metadata": {},
    "outputs": [
     {
@@ -95,118 +95,209 @@
        "True"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from firedrake import *\n",
-    "from firedrake.adjoint import *\n",
     "continue_annotation()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2725a8f3-c7b1-480e-92d2-71a605f40d84",
    "metadata": {},
    "source": [
-    "We employ the same finite element discretisation of Burgers equation in $\\mathbb P2$ space as in the [Firedrake demo](https://firedrakeproject.org/demos/burgers.py.html). Here $\\nu$ is the viscosity."
+    "We next define the mesh, initial conditions and residual for the Burgers equation as we have done in previous notebooks."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
+   "id": "d8e381b1-8d8e-4960-b04e-3b5f6edee6ba",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define a simple mesh\n",
-    "n = 32\n",
-    "mesh = UnitSquareMesh(n, n)\n",
-    "\n",
-    "# Define P2 function space and corresponding test function\n",
-    "V = VectorFunctionSpace(mesh, \"Lagrange\", 2)\n",
+    "n = 100\n",
+    "with stop_annotating():\n",
+    "    mesh = PeriodicIntervalMesh(n, length=2)\n",
+    "x = SpatialCoordinate(mesh)[0]\n",
+    "nu = Constant(1e-2)\n",
+    "V = FunctionSpace(mesh, \"Lagrange\", 2)\n",
+    "u_n1 = Function(V, name=\"u^{n+1}\")\n",
+    "u_n = Function(V, name=\"u^{n}\")\n",
     "v = TestFunction(V)\n",
-    "\n",
-    "# Create Functions for the solution and time-lagged solution\n",
-    "u = Function(V, name=\"Velocity\")\n",
-    "u_ = Function(V)\n",
-    "\n",
-    "# Assign initial condition\n",
-    "x, y = SpatialCoordinate(mesh)\n",
-    "u_ = interpolate(as_vector([sin(pi*x), 0]), V)\n",
-    "u.assign(u_)\n",
-    "\n",
-    "# Set diffusivity constant\n",
-    "R = FunctionSpace(mesh, \"R\", 0)\n",
-    "nu = Function(R)\n",
-    "nu.assign(0.0001)\n",
-    "\n",
-    "# Define nonlinear form\n",
-    "dt = 1.0/n\n",
-    "F = (inner((u - u_)/dt, v) + inner(dot(u, nabla_grad(u)), v) + nu*inner(grad(u), grad(v)))*dx"
+    "u_init = Function(V).interpolate(sin(2*pi*x))\n",
+    "u_n.assign(u_init)\n",
+    "dt = 1.0 / n\n",
+    "F = (((u_n1 - u_n)/dt) * v +\n",
+    "     u_n1 * u_n1.dx(0) * v + \n",
+    "     nu*u_n1.dx(0)*v.dx(0))*dx"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "1f9cb135-762c-428f-ad43-78c5d80fa348",
    "metadata": {},
    "source": [
-    "Having set up the residual,`F`, we are able to compute weak solutions of the PDE."
+    "Now we timestep the equations as usual:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
+   "id": "725c7b79-29d5-482e-b606-761f7ec04f9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Time step ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 50/50 [0:00:00]\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "problem = NonlinearVariationalProblem(F, u_n1)\n",
+    "solver = NonlinearVariationalSolver(problem)\n",
+    "results = [Function(u_n)]\n",
+    "timesteps = 50\n",
+    "for t in ProgressBar(\"Time step\").iter(range(timesteps)):\n",
+    "    solver.solve()\n",
+    "    u_n.assign(u_n1)\n",
+    "    results.append(Function(u_n))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37638133-5468-421c-b663-7b2f7d7128cd",
+   "metadata": {},
+   "source": [
+    "The basic form of adjoint computiation computes the derivative of a scalar outcome of a PDE with respect to one or more inputs. Here we'd just like to see how the solution of the PDE at the end of the time interval depends on the initial conditions, so we choose the squared $L^2$ norm of the final solution as our functional:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ee93684f-cb5e-4867-b36b-22084c6ca73e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Timestepping details\n",
-    "end_time = 0.5\n",
-    "timesteps_per_export = 4\n",
-    "num_timesteps = int(end_time/dt)\n",
-    "num_exports = num_timesteps//timesteps_per_export\n",
-    "\n",
-    "# Store forward solution at exports so we can plot again later\n",
-    "forward_solutions = [u.copy(deepcopy=True)]\n",
-    "\n",
-    "# Time integrate\n",
-    "i = 0\n",
-    "t = 0.0\n",
-    "while (t < end_time):\n",
-    "    solve(F == 0, u)\n",
-    "    u_.assign(u)\n",
-    "    t += dt\n",
-    "    i += 1\n",
-    "    if i % timesteps_per_export == 0:\n",
-    "        forward_solutions.append(u.copy(deepcopy=True))"
+    "J = assemble(u_n*u_n*dx)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f894db4d-af9f-429c-a3d7-c317a2ba78dc",
    "metadata": {},
    "source": [
-    "Plot solution at each export timestep."
+    "We've now completed the forward solution. We want to stop recording so that any computations we do on the results aren't part of the tape:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
+   "id": "dbed6910-f7d6-4cc0-8e03-0dc1a4bfd068",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pause_annotation()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc7cdd0d-3df2-4265-9568-e22cdc039ea5",
+   "metadata": {},
+   "source": [
+    "Just as with the forward calculation, it's helpful to have a visualisation of the execution of the model. However, the adjoint calculations will be executed automatically, so we can't directly attach a progress bar. Instead, we pass a progress bar class to the tape and let it apply it automatically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3ea75d66-9c55-4f22-9db2-7a13c8e41148",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_working_tape().progress_bar = ProgressBar\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f67d11c-5a84-4cb5-a365-d98a3e849027",
+   "metadata": {},
+   "source": [
+    "We select the initial conditions of the simulation as the control. The way we achieve this in code is by defining a [`ReducedFunctional`](https://www.firedrakeproject.org/adjoint.html#reduced-functionals) with the initial conditions as the [`Control`](https://pyadjoint.org/documentation/pyadjoint_api.html#pyadjoint.Control):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7807c438-6175-4789-9624-52307943011e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Jhat = ReducedFunctional(J, Control(u_init))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "229b7676-d483-47fa-9a45-0f7aa3b1ebd0",
+   "metadata": {},
+   "source": [
+    "The reduced functional is the core object in any adjoint calculation. For example, we can compute the derivative of a ReducedFunctional as follows. Notice the progress bar. It has 154 entries in it in contrast to the 50 timesteps. This is because the adjoint calculation records and replays individual Firedrake operations such as `solve` and `assign` rather than whole timesteps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "43496003-814b-49d4-91e8-ce38a335e60f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating adjoint ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 154/154 [0:00:00]\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "dJ = Jhat.derivative()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00b6b619-14fa-47e7-96b4-f7cce2de1559",
+   "metadata": {},
+   "source": [
+    "Let's look at the derivative we've calculated. Can you intuitively work out why this is the right derivative?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cdc905b6-48bc-43e6-89c5-18bac8ea7e1d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c8e3cdf3b5e640b6883e2aecc68e202e",
+       "model_id": "619cf53d8b6d45b4bf7d92160b197d94",
        "version_major": 2,
        "version_minor": 0
       },
-      "image/png": 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       "text/html": [
        "\n",
        "            
      \n",
        "                
      \n",
        "                    Figure\n",
        "                
      \n",
-       "                \n",
+       "                \n",
        "            
      \n",
        "        "
       ],
@@ -219,289 +310,54875 @@
     }
    ],
    "source": [
-    "from firedrake.pyplot import tricontourf\n",
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "from firedrake.pyplot import plot\n",
     "\n",
-    "fig, axs = plt.subplots(num_exports+1, sharex='col')\n",
+    "fig, axes = plt.subplots()\n",
     "\n",
-    "for i in range(len(forward_solutions)):\n",
-    "    tricontourf(forward_solutions[i], axes=axs[i])\n",
-    "    axs[i].annotate('t={:.2f}'.format(i*timesteps_per_export*dt), (0.05, 0.05), color='white');\n",
-    "axs[0].set_title('Forward solution, u');"
+    "paths=plot(dJ, axes=axes)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "3bedff1a-68b6-4bf1-b4d1-d7da00e6be07",
    "metadata": {},
    "source": [
-    "## The objective functional"
+    "## Exercise 1\n",
+    "\n",
+    "The reduced functional defines a function:\n",
+    "\n",
+    "$$\n",
+    "\\hat{J}(m) = J(u(m), m)\n",
+    "$$\n",
+    "\n",
+    "Where $m$ is the control. As well as differentiating the functional, it's possible to evaluate it for a new intial condition. This is achieved by calling the reduced functional:\n",
+    "\n",
+    "```python3\n",
+    "Jhat(u_new)\n",
+    "```\n",
+    "\n",
+    "### a.\n",
+    "Try evaluating `Jhat` for a new initial condition. You'll still need to use a periodic funcition, but you could change the frequency of the wave, or try a more adventurous but still periodic function.\n",
+    "\n",
+    "### b. \n",
+    "You can access the solution of the last solve in the reduced functional with `get_solve_blocks()[-1].get_outputs()[0].saved_output`. See how the solution changes for different values of the initial condition.\n",
+    "\n",
+    "### c.\n",
+    "The gradient returned by `Jhat.derivative()` is linearised around the solution for the last state at which the reduced functional was evaluated. Plot the gradient for different initial conditions and see if you can intuit why it looks like it does.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24821568-b9b3-4c26-a8c9-3dc948a42cc1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "679e0876-e9fc-4af5-9154-bd931dea324d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
+   "id": "48a49a11-626e-457e-9c85-7d8824566b52",
    "metadata": {},
    "source": [
-    "In order to solve the adjoint equation, we need an objective functional. For this example, consider the following integral over the 'outflow' (right hand) boundary at the final time $T$:\n",
+    "## Taylor tests\n",
     "\n",
-    "$$J(u)=\\int_{\\Gamma_2}u|_{t=T}\\cdot u|_{t=T}\\;\\mathrm ds,\n",
-    "\\quad\\quad\\Gamma_2=\\{(1,y)\\mid y\\in(0,1)\\}.$$"
+    "How do we know that the gradient we have calculated above is correct? The [Taylor test](https://www.firedrakeproject.org/adjoint.html#taylor-tests) provides us a very sensitive test of this. Almost any error in the computation of the gradient will cause its convergence rate to drop below 2. We evaluate the Taylor test at our original functional and with a perturbation of a constant function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
+   "id": "f20c41ff-de35-4cc0-b8cd-3598b4d7ebf1",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating functional ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 154/154 [0:00:00]\n",
+      "Evaluating adjoint ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 154/154 [0:00:00]\n",
+      "Evaluating functional ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 154/154 [0:00:00]\n",
+      "Evaluating functional ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢▢ 52/154 [0:00:00]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Objective value: 0.8600\n"
+      "Running Taylor test\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Evaluating functional ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 154/154 [0:00:00]\n",
+      "Evaluating functional ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 154/154 [0:00:00]\n",
+      "Evaluating functional ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 154/154 [0:00:00]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computed residuals: [0.00019655556020453503, 4.913959213387254e-05, 1.2284943066431411e-05, 3.0712385987728138e-06]\n",
+      "Computed convergence rates: [np.float64(1.999979387323389), np.float64(1.99999471149752), np.float64(1.9999986696076852)]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[?25h"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.999979387323389)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "J_form = inner(u, u)*ds(2)\n",
-    "J = assemble(J_form)\n",
-    "print(\"Objective value: {:.4f}\".format(J))"
+    "taylor_test(Jhat, u_init, assemble(interpolate(Constant(1.), V))) "
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f27e3478-3ef4-41af-bb3a-ae9ae58f05eb",
    "metadata": {},
    "source": [
-    "Having solved the forward equation, `firedrake-adjoint` has all it needs to solve the adjoint equation.\n",
-    "Therefore, we can tell it to stop annotating the tape."
+    "## Examining the adjoint calculation\n",
+    "\n",
+    "When we first studied the Burgers equation we plotted an animation of the solution over time. As a reminder:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
+   "id": "ffc4579b-f1d0-45bf-972c-7ad9ff2ad0f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
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+       "\n",
+       "\n",
+       "\n",
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+       "    
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+       "  
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+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "model_id": "ebdd4858599846d6a3390641d0b024d9",
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+      },
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+      "text/html": [
+       "\n",
+       "            
      \n",
+       "                
      \n",
+       "                    Figure\n",
+       "                
      \n",
+       "                \n",
+       "            
      \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "from firedrake.pyplot import plot\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "\n",
+    "fig, axes = plt.subplots()\n",
+    "\n",
+    "def animate(u):\n",
+    "    axes.clear()\n",
+    "    plot(u, axes=axes)\n",
+    "    axes.set_ylim((-1., 1.))\n",
+    "\n",
+    "interval = 4e3 * float(dt)\n",
+    "animation = FuncAnimation(fig, animate, frames=results, interval=interval)\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "HTML(animation.to_jshtml())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b7e37e9-5b11-4ae7-b5cd-e2cd9b4ba69a",
+   "metadata": {},
+   "source": [
+    "In order to understand how the adjoint solution works, it's helpful to do the same for the adjoint. We can extract the adjoint solutions to the PDE for each timestep as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "23fe145a-df41-4362-b24f-54199baa8299",
    "metadata": {},
    "outputs": [],
    "source": [
-    "stop_annotating();"
+    "adjoints = [block.get_dependencies()[0].adj_value for block in get_solve_blocks()[::-1]]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "748827e1-4b50-4411-bab1-c32179051faf",
+   "metadata": {},
+   "source": [
+    "We deliberately extracted the blocks in reverse order because the adjoint is solved in reverse order. The adjoint value of the control is the last solve value in the sequence so we add that:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a2cfd7de-6956-488f-a387-c83af6cb54a8",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Extracting adjoint solutions"
+    "adjoints += [Jhat.controls[0].adj_value]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8e265b61-cf41-4014-8a56-ca99eac81328",
    "metadata": {},
    "source": [
-    "Get the working tape and take a look at its blocks. These correspond to the (high level) operations we've performed during the simulation. For example, value assignment, FEM solves and functional assembly."
+    "Now, if we take a look at those variables..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
+   "id": "2a092582-a161-45f7-8ff5-e334e67ec80f",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Block  0: \n",
-      "Block  1: \n",
-      "Block  2: \n",
-      "Block  3: \n",
-      "Block  4: \n",
-      "Block  5: \n",
-      "Block  6: \n",
-      "Block  7: \n",
-      "Block  8: \n",
-      "Block  9: \n",
-      "Block 10: \n",
-      "Block 11: \n",
-      "Block 12: \n",
-      "Block 13: \n",
-      "Block 14: \n",
-      "Block 15: \n",
-      "Block 16: \n",
-      "Block 17: \n",
-      "Block 18: \n",
-      "Block 19: \n",
-      "Block 20: \n",
-      "Block 21: \n",
-      "Block 22: \n",
-      "Block 23: \n",
-      "Block 24: \n",
-      "Block 25: \n",
-      "Block 26: \n",
-      "Block 27: \n",
-      "Block 28: \n",
-      "Block 29: \n",
-      "Block 30: \n",
-      "Block 31: \n",
-      "Block 32: \n",
-      "Block 33: \n",
-      "Block 34: \n",
-      "Block 35: \n",
-      "Block 36: \n",
-      "Block 37: \n",
-      "Block 38: \n",
-      "Block 39: \n",
-      "Block 40: \n",
-      "Block 41: \n",
-      "Block 42: \n",
-      "Block 43: \n",
-      "Block 44: \n",
-      "Block 45: \n"
-     ]
+     "data": {
+      "text/plain": [
+       "[Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1427),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1438),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1449),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1460),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1471),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1482),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1493),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1504),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1515),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1526),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1537),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1548),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1559),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1570),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1581),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1592),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1603),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1614),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1625),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1636),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1647),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1658),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1669),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1680),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1691),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1702),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1713),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1724),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1735),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1746),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1757),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1768),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1779),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1790),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1801),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1812),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1823),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1834),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1845),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1856),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1867),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1878),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1889),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1900),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1911),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1922),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1933),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1944),\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1955),\n",
+       " None,\n",
+       " Cofunction(FiredrakeDualSpace(FunctionSpace(, FiniteElement('Lagrange', interval, 2), name=None), Mesh(VectorElement(FiniteElement('Discontinuous Lagrange', interval, 1, variant='equispaced'), dim=1, variant='equispaced'), 6)), 1966)]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adjoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f14c44b4-810c-4ac9-9f57-c1358172fad8",
+   "metadata": {},
+   "source": [
+    "The adjoint variables are, as expected, cofunctions. To recover the adjoint solutions in a form we can visualise, we take the Riesz represntation to recover Functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c76e7526-d1b8-4040-ba0a-3a7b4673a5f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adjoints = [a.riesz_representation(riesz_map=\"L2\") for a in adjoints if a] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5327cf8c-fe20-4022-a42f-d5bb0b6cac9e",
+   "metadata": {},
+   "source": [
+    "Now we can visualise the adjoint solution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0f3723f8-8df1-4095-a6bc-f2bd1589380e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
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+       "  \n",
+       "  
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+       "    \n",
+       "    
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+       "      \n",
+       "      \n",
+       "      \n",
+       "      \n",
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+       "      \n",
+       "      \n",
+       "      \n",
+       "    
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+       "    
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+       "      \n",
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+       "      \n",
+       "      \n",
+       "    
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+       "  
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+       "
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+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e22bed432a14ee786511a464dfaa978",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            
      \n",
+       "                
      \n",
+       "                    Figure\n",
+       "                
      \n",
+       "                \n",
+       "            
      \n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "tape = get_working_tape()\n",
-    "for i, block in enumerate(tape._blocks):\n",
-    "    print(\"Block {:2d}: {:}\".format(i, type(block)))"
+    "fig, axes = plt.subplots()\n",
+    "\n",
+    "def animate(u):\n",
+    "    axes.clear()\n",
+    "    plot(u, axes=axes)\n",
+    "    axes.set_ylim((-2.5, 2.5))\n",
+    "\n",
+    "interval = 4e3 * float(dt)\n",
+    "animation = FuncAnimation(fig, animate, frames=adjoints, interval=interval)\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "HTML(animation.to_jshtml())"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "405540f6-367e-4f2d-be39-46b88d35cd35",
    "metadata": {},
    "source": [
-    "The time integration routine is apparent, with the repeated solves (`SolveVarFormBlock`) and assignments (`FunctionAssignBlock`). Having time integrated the forward equation, we end by assembling the objective functional (`AssembleBlock`).\n",
+    "## Exercise 2\n",
     "\n",
-    "As discussed above, we get `firedrake-adjoint` to compute the gradient $\\mathrm dJ/\\mathrm d\\nu$ in order to endow the solve blocks with adjoint solutions."
+    "Go back to the Poisson equation example, and solve Poisson's equation with Dirichlet boundary conditions. Find $u$ such that:\n",
+    "$$\n",
+    "\\begin{gather*}\n",
+    "-\\nabla^2 u = f \\qquad x\\in \\Omega\\\\\n",
+    "u = 0 \\qquad x \\in \\Gamma\n",
+    "\\end{gather*}\n",
+    "$$\n",
+    "Choose a suitable forcing function $f$ and evaluate the functional:\n",
+    "$$\n",
+    "J = \\int_\\Omega u^2 + f^2\\,\\mathrm{d}x\n",
+    "$$\n",
+    "Create a reduced functional and conduct a Taylor test in order to establish that your implementation is correct.\n",
+    "Before you start, you will need to remove the recording of the Burgers equation from the tape:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
+   "id": "17eabff0-9f62-4861-a701-a059626e251b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Gradient of J w.r.t. diffusivity = -9.8526\n"
-     ]
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "g = compute_gradient(J, Control(nu))\n",
-    "print(\"Gradient of J w.r.t. diffusivity = {:.4f}\".format(*g.dat.data_ro))"
+    "get_working_tape().clear_tape()\n",
+    "continue_annotation()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ff6d0fa-49ef-4ccc-989c-071da4d3c451",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50169141-e31c-4b1d-a951-1bb2c89567ef",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
+   "id": "ce79c50c-133e-4014-9d13-8e95bbf7fa9d",
    "metadata": {},
    "source": [
-    "### Exercise: Convince yourself that the negative gradient makes sense\n",
+    "## Visualising the tape\n",
     "\n",
-    "- Hint 1: What is it that the objective functional quantifies?\n",
-    "- Hint 2: How does diffusion affect this quantity?"
+    "An exceptionally useful aid to understanding the computation of the adjoint is to produce a visual representation of the tape. To produce a managable sized figure, we'll rerun the Burgers equation with just 5 timesteps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "9c1d602e-d5f0-4f34-a08f-8cb4fa959a02",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Time step ▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣▣ 5/5 [0:00:00]\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "tape=get_working_tape()\n",
+    "continue_annotation()\n",
+    "tape.clear_tape()\n",
+    "u_n.assign(u_init)\n",
+    "timesteps = 5\n",
+    "for t in ProgressBar(\"Time step\").iter(range(timesteps)):\n",
+    "    solver.solve()\n",
+    "    u_n.assign(u_n1)\n",
+    "J = assemble(u_n*u_n*dx)\n",
+    "pause_annotation()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d0aafb5e-3f62-4b39-bc51-a9aabca49c01",
    "metadata": {},
    "source": [
-    "We are only interested in the blocks corresponding to nonlinear solves.\n",
-    "These may be extracted using Firedrake's helper function `get_solve_blocks`."
+    "We now need to install a few packages that may not be installed automatically. On Ubuntu (including Colab) run:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "id": "7c1833ed-c09b-41a4-aff5-94c44989d1d8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from firedrake.adjoint import get_solve_blocks\n",
-    "\n",
-    "solve_blocks = get_solve_blocks()\n",
-    "assert len(solve_blocks) == num_timesteps"
+    "# NBVAL_SKIP\n",
+    "!apt update\n",
+    "!apt install graphviz libgraphviz-dev poppler-utils fonts-dejavu"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "b4e4cb73-35b7-40da-ab7d-ede31699778e",
    "metadata": {},
    "source": [
-    "For each solve block, `block`, the adjoint solution is stored as the attribute `block.adj_sol`."
+    "MacOS users are likely to need:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
+   "id": "9831c497-5559-4cdc-9c81-8528517cc6d5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "for block in solve_blocks:\n",
-    "    assert block.adj_sol is not None"
+    "# NBVAL_SKIP\n",
+    "!brew install graphviz poppler"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d3ef4eae-fa43-4ce3-a478-85a4da8baa6a",
    "metadata": {},
    "source": [
-    "By accessing these, we are now able to plot the adjoint solution.\n",
-    "Remember that information propagates _backwards_ in time for the adjoint, so we should view the plots in ascending order.\n",
-    "\n",
-    "Since the objective functional is defined at the end time, its derivative provides an 'initial condition' for the adjoint."
+    "Users of all OSs will need the following, though Mac users may need to follow [specific instructions for pygraphviz](https://pygraphviz.github.io/documentation/stable/install.html#homebrew):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "id": "78e43809-5941-4ffd-a52d-c0ebe6dfb465",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# NBVAL_SKIP\n",
+    "!pip install networkx pygraphviz pdf2image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "31926022-2d1a-4726-a12a-0ea78f7a0538",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tape.visualise(\"tape.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a722ed53-7931-478f-a718-41c6a61f0810",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c4cc8e7f77b64d40a2e08d7e1007be10",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "image/png": 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",
       "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+       ""
       ]
      },
+     "execution_count": 26,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# 'Initial condition' for both adjoint\n",
-    "dJdu = assemble(derivative(J_form, u))\n",
+    "from pdf2image import convert_from_path\n",
     "\n",
-    "# Plot adjoint solutions at matching timesteps to forward\n",
-    "fig, axs = plt.subplots(num_exports+1, 2, sharex='col', figsize=(10, 5))\n",
-    "for i in range(num_exports+1):\n",
-    "    t = i*timesteps_per_export*dt\n",
-    "    tricontourf(forward_solutions[i], axes=axs[i, 0])\n",
-    "    adjoint_solution = dJdu if i == num_exports else solve_blocks[timesteps_per_export*i].adj_sol\n",
-    "    # Get the Riesz representer\n",
-    "    adjoint_solution = dJdu.riesz_representation(riesz_map=\"H1\")\n",
-    "    tricontourf(adjoint_solution, axes=axs[i, 1])\n",
-    "    axs[i, 0].annotate('t={:.2f}'.format(t), (0.05, 0.05), color='white');\n",
-    "    axs[i, 1].annotate('t={:.2f}'.format(t), (0.05, 0.05), color='white');\n",
-    "\n",
-    "# Plot formatting\n",
-    "axs[0, 0].set_title('Forward solution');\n",
-    "axs[0, 1].set_title('Adjoint solution');"
+    "images = convert_from_path(\"tape.pdf\")\n",
+    "images[0]  # first page"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f581a32-8a82-4588-aa4a-fb1f689ab381",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ac64346-bd72-4a1c-ac0c-21ec13cbf4ae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/pyproject.toml b/pyproject.toml
index 8558b4a073..bc48b19ce6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -104,7 +104,10 @@ ci = [
   "matplotlib",
   "mpi-pytest>=2025.7",
   "nbval",
+  "networkx",
   "ngsPETSc",
+  "pdf2image",
+  "pygraphviz",
   "pylit",
   "pytest",
   "pytest-split",  # needed for firedrake-run-split-tests
@@ -119,6 +122,9 @@ docker = [  # Used in firedrake-vanilla container
   "matplotlib",
   "mpi-pytest>=2025.7",
   "nbval",
+  "networkx",
+  "pdf2image",
+  "pygraphviz",
   "pylit",
   "pytest",
   "pytest-split",  # needed for firedrake-run-split-tests

From a445cd45de345767d353f75b6a245884530bd59f Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Thu, 31 Jul 2025 00:50:53 +0100
Subject: [PATCH 211/222] Add tests for 3D zany elements (#4459)

* TSFC: physical_normals

* Test 3D zany elements
---
 .../regression/test_projection_zany.py        | 31 +++++++--
 tsfc/fem.py                                   | 66 ++++++++-----------
 2 files changed, 53 insertions(+), 44 deletions(-)

diff --git a/tests/firedrake/regression/test_projection_zany.py b/tests/firedrake/regression/test_projection_zany.py
index 144ab8cb2c..dc1329dc19 100644
--- a/tests/firedrake/regression/test_projection_zany.py
+++ b/tests/firedrake/regression/test_projection_zany.py
@@ -24,11 +24,17 @@ def jrc(a, b, n):
 
 
 @pytest.fixture
-def hierarchy(request):
+def hierarchy_2d(request):
     msh = UnitSquareMesh(2, 2)
     return MeshHierarchy(msh, 4)
 
 
+@pytest.fixture
+def hierarchy_3d(request):
+    n = 2
+    return [UnitCubeMesh(n*2**r, n*2**r, n*2**r) for r in range(3)]
+
+
 def do_projection(mesh, el_type, degree):
     V = FunctionSpace(mesh, el_type, degree)
 
@@ -57,6 +63,12 @@ def do_projection(mesh, el_type, degree):
     return sqrt(assemble(inner(fh - f, fh - f) * dx))
 
 
+def run_convergence_test(mh, el, degree, convrate):
+    err = [do_projection(m, el, degree) for m in mh]
+    conv = np.diff(-np.log2(err))
+    assert (conv > convrate).all()
+
+
 @pytest.mark.parametrize(('el', 'deg', 'convrate'),
                          [('Johnson-Mercier', 1, 1.8),
                           ('Morley', 2, 2.4),
@@ -69,10 +81,15 @@ def do_projection(mesh, el_type, degree):
                           ('Bell', 5, 4.7),
                           ('Argyris', 5, 5.8),
                           ('Argyris', 6, 6.7)])
-def test_projection_zany_convergence_2d(hierarchy, el, deg, convrate):
-    diff = np.array([do_projection(m, el, deg) for m in hierarchy[2:]])
-    conv = np.log2(diff[:-1] / diff[1:])
-    assert (np.array(conv) > convrate).all()
+def test_projection_zany_convergence_2d(hierarchy_2d, el, deg, convrate):
+    run_convergence_test(hierarchy_2d[2:], el, deg, convrate)
+
+
+@pytest.mark.parametrize(('el', 'deg', 'convrate'),
+                         [('Johnson-Mercier', 1, 1.8),
+                          ('Morley', 2, 2.4)])
+def test_projection_zany_convergence_3d(hierarchy_3d, el, deg, convrate):
+    run_convergence_test(hierarchy_3d, el, deg, convrate)
 
 
 @pytest.mark.parametrize(('element', 'degree'),
@@ -81,9 +98,9 @@ def test_projection_zany_convergence_2d(hierarchy, el, deg, convrate):
                           ('HCT', 4),
                           ('Argyris', 5),
                           ('Argyris', 6)])
-def test_mass_conditioning(element, degree, hierarchy):
+def test_mass_conditioning(element, degree, hierarchy_2d):
     mass_cond = []
-    for msh in hierarchy[1:4]:
+    for msh in hierarchy_2d[1:4]:
         V = FunctionSpace(msh, element, degree)
         u = TrialFunction(V)
         v = TestFunction(V)
diff --git a/tsfc/fem.py b/tsfc/fem.py
index 421f58906c..9166b4b8f0 100644
--- a/tsfc/fem.py
+++ b/tsfc/fem.py
@@ -155,17 +155,16 @@ def config(self):
         config["interface"] = self.interface
         return config
 
-    def cell_size(self):
-        return self.interface.cell_size(self.mt.restriction)
-
-    def jacobian_at(self, point):
-        ps = PointSingleton(point)
-        expr = Jacobian(extract_unique_domain(self.mt.terminal))
-        assert ps.expression.shape == (extract_unique_domain(expr).topological_dimension(), )
+    def translate_point_expression(self, expr, point=None):
         if self.mt.restriction == '+':
             expr = PositiveRestricted(expr)
         elif self.mt.restriction == '-':
             expr = NegativeRestricted(expr)
+
+        cell = self.interface.fiat_cell
+        sd = cell.get_spatial_dimension()
+        if point is None:
+            point, = cell.make_points(sd, 0, sd+1)
         config = {"point_set": PointSingleton(point)}
         config.update(self.config)
         config.update(use_canonical_quadrature_point_ordering=False)  # quad point ordering not relevant.
@@ -173,18 +172,16 @@ def jacobian_at(self, point):
         expr = self.preprocess(expr, context)
         return map_expr_dag(context.translator, expr)
 
+    def cell_size(self):
+        return self.interface.cell_size(self.mt.restriction)
+
+    def jacobian_at(self, point):
+        expr = Jacobian(extract_unique_domain(self.mt.terminal))
+        return self.translate_point_expression(expr, point=point)
+
     def detJ_at(self, point):
         expr = JacobianDeterminant(extract_unique_domain(self.mt.terminal))
-        if self.mt.restriction == '+':
-            expr = PositiveRestricted(expr)
-        elif self.mt.restriction == '-':
-            expr = NegativeRestricted(expr)
-        config = {"point_set": PointSingleton(point)}
-        config.update(self.config)
-        config.update(use_canonical_quadrature_point_ordering=False)  # quad point ordering not relevant.
-        context = PointSetContext(**config)
-        expr = self.preprocess(expr, context)
-        return map_expr_dag(context.translator, expr)
+        return self.translate_point_expression(expr, point=point)
 
     def reference_normals(self):
         cell = self.interface.fiat_cell
@@ -210,30 +207,25 @@ def physical_tangents(self):
 
     def physical_normals(self):
         cell = self.interface.fiat_cell
-        if not (isinstance(cell, UFCSimplex) and cell.get_dimension() == 2):
+        sd = cell.get_spatial_dimension()
+        num_faces = len(cell.get_topology()[sd-1])
+        if isinstance(cell, UFCSimplex) and sd == 2:
+            pts = self.physical_tangents()
+            return gem.ListTensor([[pts[i, 1], -1*pts[i, 0]] for i in range(num_faces)])
+        elif isinstance(cell, UFCSimplex) and sd == 3:
+            t = ufl.classes.CellEdgeVectors(extract_unique_domain(self.mt.terminal))
+            edges = cell.get_connectivity()[(sd-1, 1)]
+            normalize = lambda x: x / ufl.sqrt(ufl.dot(x, x))
+            expr = ufl.as_tensor([-2.0*normalize(ufl.cross(t[edges[i][0], :], t[edges[i][1], :]))
+                                  for i in range(num_faces)])
+            return self.translate_point_expression(expr)
+        else:
             raise NotImplementedError("Can't do physical normals on that cell yet")
 
-        num_edges = len(cell.get_topology()[1])
-        pts = self.physical_tangents()
-        return gem.ListTensor([[pts[i, 1], -1*pts[i, 0]] for i in range(num_edges)])
-
     def physical_edge_lengths(self):
         expr = ufl.classes.CellEdgeVectors(extract_unique_domain(self.mt.terminal))
-        if self.mt.restriction == '+':
-            expr = PositiveRestricted(expr)
-        elif self.mt.restriction == '-':
-            expr = NegativeRestricted(expr)
-
-        cell = self.interface.fiat_cell
-        sd = cell.get_spatial_dimension()
-        num_edges = len(cell.get_topology()[1])
-        expr = ufl.as_vector([ufl.sqrt(ufl.dot(expr[i, :], expr[i, :])) for i in range(num_edges)])
-        config = {"point_set": PointSingleton(cell.make_points(sd, 0, sd+1)[0])}
-        config.update(self.config)
-        config.update(use_canonical_quadrature_point_ordering=False)  # quad point ordering not relevant.
-        context = PointSetContext(**config)
-        expr = self.preprocess(expr, context)
-        return map_expr_dag(context.translator, expr)
+        expr = ufl.as_vector([ufl.sqrt(ufl.dot(expr[i, :], expr[i, :])) for i in range(expr.ufl_shape[0])])
+        return self.translate_point_expression(expr)
 
     def physical_points(self, point_set, entity=None):
         """Converts point_set from reference to physical space"""

From 7bfd7f2fd909b036a45a633bda29e7c13b4c9ae5 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 31 Jul 2025 11:19:24 +0100
Subject: [PATCH 212/222] Dependency groups (#4463)

* Use dependency groups for internal dependencies

Dependency groups are a new feature of pyproject.tomls perfect for
describing internal dependency requirements.

See https://packaging.python.org/en/latest/specifications/dependency-groups/
---
 .github/workflows/core.yml |  5 +-
 docker/Dockerfile.vanilla  |  5 +-
 docs/source/install.rst    |  8 +++-
 pyproject.toml             | 94 +++++++++++++++++++-------------------
 scripts/check-config       |  2 +-
 5 files changed, 61 insertions(+), 53 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index df79bb4d8e..5024f1554a 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -203,7 +203,10 @@ jobs:
           pip install --verbose $EXTRA_PIP_FLAGS \
             --no-binary h5py \
             --extra-index-url https://download.pytorch.org/whl/cpu \
-            "$(echo ./firedrake-repo/dist/firedrake-*.tar.gz)[ci,docs]"
+            "$(echo ./firedrake-repo/dist/firedrake-*.tar.gz)"
+
+          pip install -U pip
+          pip install --group ./firedrake-repo/pyproject.toml:ci
 
           firedrake-clean
           pip list
diff --git a/docker/Dockerfile.vanilla b/docker/Dockerfile.vanilla
index 58c2cb92e1..b0128ac748 100644
--- a/docker/Dockerfile.vanilla
+++ b/docker/Dockerfile.vanilla
@@ -61,5 +61,6 @@ ENV CFLAGS="-mtune=generic" CPPFLAGS="-mtune=generic"
 ENV MPICC=$CC
 
 # Install Firedrake
-RUN pip install --verbose --no-binary h5py --src /opt \
-        --editable git+https://github.com/firedrakeproject/firedrake.git@release#egg=firedrake[docker]
+RUN pip install -U pip \
+    && git clone https://github.com/firedrakeproject/firedrake.git /opt/firedrake --branch release \
+    && pip install --verbose --no-binary h5py --editable /opt/firedrake --group /opt/firedrake/pyproject.toml:docker
diff --git a/docs/source/install.rst b/docs/source/install.rst
index 6e3d08e078..1d40693d1b 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -586,10 +586,14 @@ should be followed:
       $ pip install $PETSC_DIR/src/binding/petsc4py
       $ pip install -r ./firedrake/requirements-build.txt
 
-#. Install Firedrake in editable mode without build isolation::
+#. Install Firedrake in editable mode without build isolation along with
+   any developer dependencies::
 
-   $ pip install --no-build-isolation --no-binary h5py --editable './firedrake[check]'
+   $ pip install --no-build-isolation --no-binary h5py --editable './firedrake' --group ./firedrake/pyproject.toml:dev
 
+   .. note::
+      Installing the developer dependencies requires pip to be version 25.1
+      or greater. You may need to run ``pip install -U pip`` first.
 
 Editing subpackages
 -------------------
diff --git a/pyproject.toml b/pyproject.toml
index bc48b19ce6..3009a3f1d9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -61,26 +61,11 @@ firedrake-preprocess-bibtex = "firedrake.scripts.firedrake_preprocess_bibtex:mai
 firedrake-status = "firedrake.scripts.firedrake_status:main"
 pyop2-clean = "pyop2.compilation:clear_compiler_disk_cache"
 
-
 [project.optional-dependencies]
 check = [
   "mpi-pytest>=2025.7",
   "pytest",
 ]
-docs = [
-  "bibtexparser",
-  "matplotlib",  # needed to resolve API
-  "numpydoc",
-  "pylit",
-  "sphinx<8.2.0",  # https://github.com/firedrakeproject/firedrake/issues/4059
-  "sphinx-autobuild",
-  "sphinx-reredirects",
-  "sphinxcontrib-bibtex",
-  "sphinxcontrib-jquery",
-  "sphinxcontrib-svg2pdfconverter",
-  "sphinxcontrib-youtube",
-  "vtk",  # needed to resolve API
-]
 jax = [
   "jax",
 ]
@@ -97,56 +82,71 @@ vtk = [
   "vtk",
 ]
 
-# Dependencies needed to run the full test suite
-ci = [
+[build-system]
+requires = [
+  "Cython>=3.0",
+  "libsupermesh",
+  "mpi4py>3; python_version >= '3.13'",
+  "mpi4py; python_version < '3.13'",
+  "numpy",
+  "pkgconfig",
+  "pybind11",
+  "setuptools>=77.0.3",
+  "petsc4py==3.23.4",
+  "rtree>=1.2",
+]
+build-backend = "setuptools.build_meta"
+
+[dependency-groups]
+docs = [
+  "bibtexparser",
+  "matplotlib",  # needed to resolve API
+  "numpydoc",
+  "pylit",
+  "sphinx<8.2.0",  # https://github.com/firedrakeproject/firedrake/issues/4059
+  "sphinx-autobuild",
+  "sphinx-reredirects",
+  "sphinxcontrib-bibtex",
+  "sphinxcontrib-jquery",
+  "sphinxcontrib-svg2pdfconverter",
+  "sphinxcontrib-youtube",
+  "vtk",  # needed to resolve API
+]
+test = [
+  "pytest",
+  "mpi-pytest>=2025.7",
+]
+dev = [
+  {include-group = "docs"},
+  {include-group = "test"},
+  "pytest-timeout",
+  "pytest-xdist",
+]
+fulltest = [
+  {include-group = "test"},
   "ipympl",  # needed for notebook testing
   "jax",
   "matplotlib",
-  "mpi-pytest>=2025.7",
   "nbval",
   "networkx",
   "ngsPETSc",
   "pdf2image",
   "pygraphviz",
   "pylit",
-  "pytest",
-  "pytest-split",  # needed for firedrake-run-split-tests
-  "pytest-timeout",
-  "pytest-xdist",
   "slepc4py==3.23.2",
   "torch",  # requires passing '--extra-index-url' to work
   "vtk",
 ]
-docker = [  # Used in firedrake-vanilla container
-  "ipympl",  # needed for notebook testing
-  "matplotlib",
-  "mpi-pytest>=2025.7",
-  "nbval",
-  "networkx",
-  "pdf2image",
-  "pygraphviz",
-  "pylit",
-  "pytest",
+docker = [  # used in firedrake-vanilla containers
+  {include-group = "fulltest"},
   "pytest-split",  # needed for firedrake-run-split-tests
   "pytest-timeout",
   "pytest-xdist",
-  "slepc4py==3.23.2",
 ]
-
-[build-system]
-requires = [
-  "Cython>=3.0",
-  "libsupermesh",
-  "mpi4py>3; python_version >= '3.13'",
-  "mpi4py; python_version < '3.13'",
-  "numpy",
-  "pkgconfig",
-  "pybind11",
-  "setuptools>=77.0.3",
-  "petsc4py==3.23.4",
-  "rtree>=1.2",
+ci = [
+  {include-group = "docs"},
+  {include-group = "docker"},
 ]
-build-backend = "setuptools.build_meta"
 
 # TODO: Convert firedrake-zenodo to a proper entrypoint script.
 [tool.setuptools]
diff --git a/scripts/check-config b/scripts/check-config
index 8a62b5c765..a2e536c2a5 100755
--- a/scripts/check-config
+++ b/scripts/check-config
@@ -65,7 +65,7 @@ def check_slepc_version() -> None:
     check_file_contains_pattern(
         REPO_ROOT / "pyproject.toml",
         "slepc4py==(.*)",
-        3,
+        2,
     )
 
 

From 3655074de24d51288fa5f406808d98fe5b2fecf5 Mon Sep 17 00:00:00 2001
From: Pablo Brubeck 
Date: Thu, 31 Jul 2025 12:55:04 +0100
Subject: [PATCH 213/222] TensorFunctionSpace: support empty shape (#4470)

* TensorFunctionSpace: support empty shape
---
 firedrake/functionspace.py                    |  3 +-
 tests/firedrake/regression/test_assemble.py   | 43 ++++++-------------
 .../regression/test_function_spaces.py        |  6 +++
 3 files changed, 21 insertions(+), 31 deletions(-)

diff --git a/firedrake/functionspace.py b/firedrake/functionspace.py
index 6f10a4720b..cefa3bf9a4 100644
--- a/firedrake/functionspace.py
+++ b/firedrake/functionspace.py
@@ -231,7 +231,8 @@ def TensorFunctionSpace(mesh, family, degree=None, shape=None,
 
     """
     sub_element = make_scalar_element(mesh, family, degree, vfamily, vdegree, variant)
-    shape = shape or (mesh.geometric_dimension(),) * 2
+    if shape is None:
+        shape = (mesh.geometric_dimension(),) * 2
     element = finat.ufl.TensorElement(sub_element, shape=shape, symmetry=symmetry)
     return FunctionSpace(mesh, element, name=name)
 
diff --git a/tests/firedrake/regression/test_assemble.py b/tests/firedrake/regression/test_assemble.py
index 255b5f9178..94e3439c6e 100644
--- a/tests/firedrake/regression/test_assemble.py
+++ b/tests/firedrake/regression/test_assemble.py
@@ -41,41 +41,13 @@ def fs(request, mesh):
 
 @pytest.fixture
 def f(fs):
-    f = Function(fs, name="f")
-    f_split = f.subfunctions
     x = SpatialCoordinate(fs.mesh())[0]
-
-    # NOTE: interpolation of UFL expressions into mixed
-    # function spaces is not yet implemented
-    for fi in f_split:
-        fs_i = fi.function_space()
-        if fs_i.rank == 1:
-            fi.interpolate(as_vector((x,) * fs_i.value_size))
-        elif fs_i.rank == 2:
-            fi.interpolate(as_tensor([[x for i in range(fs_i.mesh().geometric_dimension())]
-                                      for j in range(fs_i.rank)]))
-        else:
-            fi.interpolate(x)
-    return f
+    return Function(fs, name="f").interpolate(as_tensor(np.full(fs.value_shape, x)))
 
 
 @pytest.fixture
 def one(fs):
-    one = Function(fs, name="one")
-    ones = one.subfunctions
-
-    # NOTE: interpolation of UFL expressions into mixed
-    # function spaces is not yet implemented
-    for fi in ones:
-        fs_i = fi.function_space()
-        if fs_i.rank == 1:
-            fi.interpolate(Constant((1.0,) * fs_i.value_size))
-        elif fs_i.rank == 2:
-            fi.interpolate(Constant([[1.0 for i in range(fs_i.mesh().geometric_dimension())]
-                                     for j in range(fs_i.rank)]))
-        else:
-            fi.interpolate(Constant(1.0))
-    return one
+    return Function(fs, name="one").interpolate(Constant(np.ones(fs.value_shape)))
 
 
 @pytest.fixture
@@ -362,3 +334,14 @@ def test_split_subdomain_ids():
     assert (a.dat[0].data == b.dat[0].data).all()
     assert b.dat[1].data[0] == 0.0
     assert b.dat[1].data[1] == a.dat[1].data[1]
+
+
+def test_assemble_tensor_empty_shape(mesh):
+    W = TensorFunctionSpace(mesh, "CG", 1, shape=())
+    w = Function(W).assign(1)
+    result = assemble(inner(w, w)*dx)
+
+    V = FunctionSpace(mesh, "CG", 1)
+    v = Function(V).assign(1)
+    expected = assemble(inner(v, v)*dx)
+    assert np.allclose(result, expected)
diff --git a/tests/firedrake/regression/test_function_spaces.py b/tests/firedrake/regression/test_function_spaces.py
index 7d0fdfff65..3445f8773a 100644
--- a/tests/firedrake/regression/test_function_spaces.py
+++ b/tests/firedrake/regression/test_function_spaces.py
@@ -149,6 +149,12 @@ def test_VV_ne_VVV():
     assert W0 != W1
 
 
+def test_tensor_function_space_empty_tuple():
+    mesh = UnitSquareMesh(1, 1)
+    W = TensorFunctionSpace(mesh, "CG", 1, shape=())
+    assert W.value_shape == ()
+
+
 def test_function_space_dir(cg1):
     dir(cg1)
 

From e17b039b34921a3c8769ecfbc97db5879a28d3a7 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 31 Jul 2025 13:14:03 +0100
Subject: [PATCH 214/222] Update .test_durations (#4464)

* Update test durations to load balance CI
* Fix make test_durations to be more robust
* Actually use the durations file during a run
---
 .github/workflows/core.yml |    4 +-
 .test_durations            | 1358 -----
 Makefile                   |   17 +-
 tests/test_durations.json  | 9623 ++++++++++++++++++++++++++++++++++++
 4 files changed, 9637 insertions(+), 1365 deletions(-)
 delete mode 100644 .test_durations
 create mode 100644 tests/test_durations.json

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index 5024f1554a..0ced60224f 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -93,7 +93,9 @@ jobs:
       FIREDRAKE_CI: 1
       PYOP2_CI_TESTS: 1
       PYOP2_SPMD_STRICT: 1
-      EXTRA_PYTEST_ARGS: --splitting-algorithm least_duration --timeout=600 --timeout-method=thread -o faulthandler_timeout=660
+      # NOTE: One should occasionally update test_durations.json by running
+      # 'make test_durations' inside a 'firedrake:latest' Docker image.
+      EXTRA_PYTEST_ARGS: --splitting-algorithm least_duration --timeout=600 --timeout-method=thread -o faulthandler_timeout=660 --durations-path=./firedrake-repo/tests/test_durations.json
       PYTEST_MPI_MAX_NPROCS: 8
     steps:
       - name: Fix HOME
diff --git a/.test_durations b/.test_durations
deleted file mode 100644
index cd1fbe815b..0000000000
--- a/.test_durations
+++ /dev/null
@@ -1,1358 +0,0 @@
-{
-    "tests/ensemble_reduced_functional/test_reduced_functional.py::test_verification": 2.0288130468688905,
-    "tests/ensemble_reduced_functional/test_reduced_functional.py::test_verification_gather_functional_Function": 0.9344081431627274,
-    "tests/ensemble_reduced_functional/test_reduced_functional.py::test_verification_gather_functional_adjfloat": 0.11416081106290221,
-    "tests/extrusion/test_extruded_cell_subdomains.py::test_subdomain_cell_integral_parallel[1]": 0.5329550113528967,
-    "tests/extrusion/test_extruded_cell_subdomains.py::test_subdomain_cell_integral_parallel[2]": 0.072725934907794,
-    "tests/extrusion/test_extruded_cell_subdomains.py::test_subdomain_cell_integral_parallel[everywhere]": 0.9353436501696706,
-    "tests/extrusion/test_extruded_periodic.py::test_extruded_periodic_annulus": 15.55769157782197,
-    "tests/extrusion/test_extruded_periodic.py::test_extruded_periodic_poisson": 3.2808128437027335,
-    "tests/extrusion/test_mixed_bcs.py::test_stokes_taylor_hood_parallel": 6.443470865488052,
-    "tests/extrusion/test_mixed_bcs.py::test_stokes_taylor_hood_parallel_monolithic": 0.46189043996855617,
-    "tests/extrusion/test_offset_computation.py::test_offset_parallel_allsame": 0.016358421184122562,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_bottom_to_top_parallel[DG]": 3.6461463570594788,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_bottom_to_top_parallel[DPC]": 3.1246388177387416,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_left_to_right_parallel[DG]": 4.959518405608833,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_left_to_right_parallel[DPC]": 3.6704856320284307,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_right_to_left_parallel[DG]": 5.781243119388819,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_right_to_left_parallel[DPC]": 4.9262388944625854,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_top_to_bottom_parallel[DG]": 4.315540089271963,
-    "tests/extrusion/test_steady_advection_2D_extr.py::test_top_to_bottom_parallel[DPC]": 3.419489706866443,
-    "tests/extrusion/test_subdomain_extruded.py::test_base_box_3d_parallel": 2.967332679312676,
-    "tests/extrusion/test_variable_layers_numbering.py::test_layer_extents_parallel": 0.287050673738122,
-    "tests/extrusion/test_variable_layers_numbering.py::test_layer_extents_parallel_vertex_owners": 0.2982264691963792,
-    "tests/firedrake/demos/test_demos_run.py::test_no_missing_demos": 0.0013349439832381904,
-    "tests/firedrake/demos/test_demos_run.py::test_parallel_demo[full_waveform_inversion/full_waveform_inversion]": 0.0010378129954915494,
-    "tests/firedrake/demos/test_demos_run.py::test_serial_demo[benney_luke/benney_luke]": 0.0007347310020122677,
-    "tests/firedrake/extrusion/test_extruded_periodic.py::test_extruded_periodic_annulus": 0.00033995299600064754,
-    "tests/firedrake/extrusion/test_extruded_periodic.py::test_extruded_periodic_poisson": 2.1182622750056908,
-    "tests/meshes/test_meshes_volume.py::test_meshes_volume_annulusmesh": 0.38126750802621245,
-    "tests/meshes/test_meshes_volume.py::test_meshes_volume_solidtorusmesh": 1.8617067653685808,
-    "tests/multigrid/test_basics.py::test_refine_interval_parallel": 0.004817801062017679,
-    "tests/multigrid/test_basics.py::test_refine_square_ncell_parallel": 0.006221716292202473,
-    "tests/multigrid/test_basics.py::test_refining_overlapped_mesh_fails_parallel": 0.0032490291632711887,
-    "tests/multigrid/test_embedded_transfer.py::test_riesz_parallel[Averaging-1-CG]": 0.4996175360865891,
-    "tests/multigrid/test_embedded_transfer.py::test_riesz_parallel[Averaging-1-N1curl]": 0.6011090106330812,
-    "tests/multigrid/test_embedded_transfer.py::test_riesz_parallel[Averaging-1-RT]": 0.6065232278779149,
-    "tests/multigrid/test_embedded_transfer.py::test_riesz_parallel[Averaging-2-CG]": 0.6251957044005394,
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-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[shiftedmesh-redistribute]": 0.043402054347097874,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[shiftedmesh-redundant]": 0.04328847490251064,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[square-nonredundant]": 0.0368351386860013,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[square-redistribute]": 0.03711844375357032,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[square-redundant]": 0.03762094955891371,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[squarequads-nonredundant]": 0.1390082435682416,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[squarequads-redistribute]": 0.13877047505229712,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[squarequads-redundant]": 0.13896663254126906,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[tetrahedron-nonredundant]": 0.055896326433867216,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[tetrahedron-redistribute]": 0.0567539120092988,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_cell_midpoints_parallel[tetrahedron-redundant]": 0.05676683783531189,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[cube-0-coords]": 0.05357308313250542,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[cube-1-coords]": 0.05287368921563029,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[cube-100-coords]": 0.057740097399801016,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[extruded-0-coords]": 0.1139101292937994,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[extruded-1-coords]": 0.11297856876626611,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[extruded-100-coords]": 0.11640832806006074,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[extrudedvariablelayers-0-coords]": 0.0001523834653198719,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[extrudedvariablelayers-1-coords]": 0.00011890940368175507,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[extrudedvariablelayers-100-coords]": 0.000407584011554718,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[immersedsphere-0-coords]": 0.04676300985738635,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[immersedsphere-1-coords]": 0.046495156828314066,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[immersedsphere-100-coords]": 0.052409997675567865,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[immersedsphereextruded-0-coords]": 0.12094306852668524,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[immersedsphereextruded-1-coords]": 0.12071698438376188,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[immersedsphereextruded-100-coords]": 0.12788908183574677,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[interval-0-coords]": 0.053596177604049444,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[interval-1-coords]": 0.05359441367909312,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[interval-100-coords]": 0.058950081933289766,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[periodicrectangle-0-coords]": 0.05105950403958559,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[periodicrectangle-1-coords]": 0.0486557362601161,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[periodicrectangle-100-coords]": 0.052294655703008175,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[shiftedmesh-0-coords]": 0.034812782891094685,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[shiftedmesh-1-coords]": 0.03496122919023037,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[shiftedmesh-100-coords]": 0.0383984437212348,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[square-0-coords]": 0.0352798723615706,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[square-1-coords]": 0.03530895709991455,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[square-100-coords]": 0.03934145299717784,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[squarequads-0-coords]": 0.12821605056524277,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[squarequads-1-coords]": 0.1282229539938271,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[squarequads-100-coords]": 0.13070066180080175,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[tetrahedron-0-coords]": 0.05173378111794591,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[tetrahedron-1-coords]": 0.0514869038015604,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_generate_random_parallel[tetrahedron-100-coords]": 0.05551104620099068,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_on_boundary_behaviour": 0.14402359584346414,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_partition_behaviour_2d_2procs": 0.4705595220439136,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_partition_behaviour_2d_3procs": 0.0805418691597879,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_pyop2_labelling": 0.062442450784146786,
-    "tests/vertexonly/test_vertex_only_mesh_generation.py::test_redistribution": 0.039744147565215826
-}
\ No newline at end of file
diff --git a/Makefile b/Makefile
index 06cca02e41..8005f48ea5 100644
--- a/Makefile
+++ b/Makefile
@@ -88,12 +88,17 @@ clean:
 	@echo "    RM tinyasm/*.so"
 	-@rm -f tinyasm/*.so > /dev/null 2>&1
 
-.PHONY: durations
-durations:
-	@echo "    Generate timings to optimise pytest-split"
-	python -m pytest --store-durations -m "parallel[1] or not parallel" tests/
+# NOTE: It is recommended to run this command from inside the 'firedrake'
+# Docker image to reduce the likelihood of test failures.
+.PHONY: test_durations
+test_durations:
+	@echo "    Regenerating test durations"
+	@echo "    Removing old durations file"
+	rm -f tests/test_durations.json
+	python3 -m pytest --store-durations --durations-path=tests/test_durations.json -m parallel[1] tests/ || true
 	# use ':' to ensure that only rank 0 writes to the durations file
 	for nprocs in 2 3 4 6 7 8; do \
-		mpiexec -n 1 python -m pytest --store-durations -m parallel[$${nprocs}] tests/ : \
-			-n $$(( $${nprocs} - 1 )) pytest -m parallel[$${nprocs}] -q tests/ ; \
+		mpiexec -n 1 python3 -m pytest --store-durations --durations-path=tests/test_durations.json -m parallel[$${nprocs}] tests/ \
+		: -n $$(( $${nprocs} - 1 )) python3 -m pytest -m parallel[$${nprocs}] -q tests/ || true ; \
 	done
+	@echo "    Test durations regenerated"
diff --git a/tests/test_durations.json b/tests/test_durations.json
new file mode 100644
index 0000000000..284c155634
--- /dev/null
+++ b/tests/test_durations.json
@@ -0,0 +1,9623 @@
+{
+    "tests/firedrake/adjoint/test_assemble.py::test_assemble_0_forms": 0.11699065286666155,
+    "tests/firedrake/adjoint/test_assemble.py::test_assemble_0_forms_mixed": 0.05080048879608512,
+    "tests/firedrake/adjoint/test_assemble.py::test_assemble_1_forms_adjoint": 0.6783476094715297,
+    "tests/firedrake/adjoint/test_assemble.py::test_assemble_1_forms_tlm": 0.16764807561412454,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_constant_scale": 0.1537756365723908,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_hessian": 0.13231402076780796,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_linear_combination": 0.07032399997115135,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_nonlin_changing": 0.19213404366746545,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_nonlincom": 0.06386978505179286,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_tlm": 0.06640525767579675,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_tlm_with_constant": 0.0425544623285532,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_vector_valued": 0.16471612267196178,
+    "tests/firedrake/adjoint/test_assignment.py::test_assign_with_constant": 0.06580564565956593,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[Mixed-NLVS]": 3.149162760935724,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[Mixed-solve]": 3.8258056547492743,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[None-NLVS]": 0.8291889880783856,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[None-solve]": 1.798611435573548,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[NoneAdjoint-NLVS]": 0.1870131134055555,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[NoneAdjoint-solve]": 0.2921231943182647,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[Revolve-NLVS]": 1.009876606054604,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[Revolve-solve]": 2.1743315076455474,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[SingleMemory-NLVS]": 0.7470649927854538,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton[SingleMemory-solve]": 1.292730525135994,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_burgers_newton_docs": 2.4443190190941095,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_checkpointing_validity[Mixed-NLVS]": 0.8957321811467409,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_checkpointing_validity[None-NLVS]": 0.35274974489584565,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_checkpointing_validity[NoneAdjoint-NLVS]": 0.3655318100936711,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_checkpointing_validity[Revolve-NLVS]": 0.43189760111272335,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_checkpointing_validity[SingleMemory-NLVS]": 0.34514879807829857,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_global_deps[False]": 0.618662356864661,
+    "tests/firedrake/adjoint/test_burgers_newton.py::test_global_deps[True]": 0.7930112918838859,
+    "tests/firedrake/adjoint/test_checkpointing_multistep.py::test_multisteps": 6.356374865397811,
+    "tests/firedrake/adjoint/test_checkpointing_multistep.py::test_validity": 1.8252300801686943,
+    "tests/firedrake/adjoint/test_disk_checkpointing.py::test_adjoint_dependencies_set": 7.8067972916178405,
+    "tests/firedrake/adjoint/test_disk_checkpointing.py::test_bcs": 1.543254959397018,
+    "tests/firedrake/adjoint/test_disk_checkpointing.py::test_disk_checkpointing": 6.200564044062048,
+    "tests/firedrake/adjoint/test_disk_checkpointing.py::test_disk_checkpointing_parallel": 0.7101251194253564,
+    "tests/firedrake/adjoint/test_disk_checkpointing.py::test_disk_checkpointing_successive_writes": 0.4635328268632293,
+    "tests/firedrake/adjoint/test_dynamic_meshes.py::test_dynamic_meshes_2D[mesh0]": 10.412232912145555,
+    "tests/firedrake/adjoint/test_dynamic_meshes.py::test_dynamic_meshes_3D[mesh0]": 16.7634016466327,
+    "tests/firedrake/adjoint/test_dynamic_meshes.py::test_dynamic_meshes_3D[mesh1]": 15.077729538083076,
+    "tests/firedrake/adjoint/test_dynamic_meshes.py::test_dynamic_meshes_3D[mesh2]": 0.6124035464599729,
+    "tests/firedrake/adjoint/test_dynamic_meshes.py::test_dynamic_meshes_3D[mesh3]": 23.52826664922759,
+    "tests/firedrake/adjoint/test_dynamic_meshes.py::test_dynamic_meshes_3D[mesh4]": 0.5357360746711493,
+    "tests/firedrake/adjoint/test_dynamic_meshes.py::test_dynamic_meshes_3D[mesh5]": 0.5243322998285294,
+    "tests/firedrake/adjoint/test_ensemble_reduced_functional.py::test_minimise": 1.3624158934690058,
+    "tests/firedrake/adjoint/test_ensemble_reduced_functional.py::test_verification": 1.0372503753751516,
+    "tests/firedrake/adjoint/test_ensemble_reduced_functional.py::test_verification_gather_functional_Function": 0.5286976504139602,
+    "tests/firedrake/adjoint/test_ensemble_reduced_functional.py::test_verification_gather_functional_adjfloat": 0.09556434908881783,
+    "tests/firedrake/adjoint/test_external_modification.py::test_external_modification": 0.7430885951034725,
+    "tests/firedrake/adjoint/test_hessian.py::test_burgers": 7.733764440286905,
+    "tests/firedrake/adjoint/test_hessian.py::test_dirichlet": 4.649521538987756,
+    "tests/firedrake/adjoint/test_hessian.py::test_function": 5.679557761177421,
+    "tests/firedrake/adjoint/test_hessian.py::test_mixed_derivatives": 4.0715289786458015,
+    "tests/firedrake/adjoint/test_hessian.py::test_nonlinear": 6.397556947078556,
+    "tests/firedrake/adjoint/test_hessian.py::test_simple_solve": 1.574312848970294,
+    "tests/firedrake/adjoint/test_optimisation.py::test_optimisation_constant_control[minimize]": 2.0876244767569005,
+    "tests/firedrake/adjoint/test_optimisation.py::test_optimisation_constant_control[minimize_tao_lmvm]": 3.0455485992133617,
+    "tests/firedrake/adjoint/test_optimisation.py::test_optimisation_constant_control[minimize_tao_nls]": 5.504181575030088,
+    "tests/firedrake/adjoint/test_optimisation.py::test_petsc_roundtrip_multiple": 0.008265864569693804,
+    "tests/firedrake/adjoint/test_optimisation.py::test_petsc_roundtrip_single": 0.20853523211553693,
+    "tests/firedrake/adjoint/test_optimisation.py::test_simple_inversion": 7.268051557708532,
+    "tests/firedrake/adjoint/test_optimisation.py::test_simple_inversion_riesz_representation[blmvm]": 6.815316422842443,
+    "tests/firedrake/adjoint/test_optimisation.py::test_simple_inversion_riesz_representation[lmvm]": 6.832113857381046,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_bounds": 1.1609799303114414,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[H1-minimize_tao_lmvm]": 32.63342834310606,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[H1-minimize_tao_nls]": 1.7475712862797081,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[L2-minimize_tao_lmvm]": 0.9486426138319075,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[L2-minimize_tao_nls]": 2.24328000145033,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[None-minimize_tao_lmvm]": 0.9439638028852642,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[None-minimize_tao_nls]": 2.231039377860725,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[l2-minimize_tao_lmvm]": 4.129450380336493,
+    "tests/firedrake/adjoint/test_optimisation.py::test_tao_simple_inversion[l2-minimize_tao_nls]": 1.6673743296414614,
+    "tests/firedrake/adjoint/test_projection.py::test_project_hessian": 0.4369850237853825,
+    "tests/firedrake/adjoint/test_projection.py::test_project_nonlin_changing": 1.2080176370218396,
+    "tests/firedrake/adjoint/test_projection.py::test_project_nonlincom": 2.0952208270318806,
+    "tests/firedrake/adjoint/test_projection.py::test_project_tlm": 0.0856841173954308,
+    "tests/firedrake/adjoint/test_projection.py::test_project_to_function_space": 1.6493415390141308,
+    "tests/firedrake/adjoint/test_projection.py::test_project_vector_valued": 1.9253910896368325,
+    "tests/firedrake/adjoint/test_projection.py::test_self_project": 0.8555903863161802,
+    "tests/firedrake/adjoint/test_projection.py::test_self_project_function": 1.269624792970717,
+    "tests/firedrake/adjoint/test_reduced_functional.py::test_assemble_recompute": 0.48612361680716276,
+    "tests/firedrake/adjoint/test_reduced_functional.py::test_constant": 1.3460850026458502,
+    "tests/firedrake/adjoint/test_reduced_functional.py::test_function": 0.6532919486053288,
+    "tests/firedrake/adjoint/test_reduced_functional.py::test_mixed_boundary": 2.4485798059031367,
+    "tests/firedrake/adjoint/test_reduced_functional.py::test_time_dependent": 2.791299310978502,
+    "tests/firedrake/adjoint/test_reduced_functional.py::test_wrt_function_dirichlet_boundary[dirichlet]": 2.167655209545046,
+    "tests/firedrake/adjoint/test_reduced_functional.py::test_wrt_function_dirichlet_boundary[neumann]": 0.7305823159404099,
+    "tests/firedrake/adjoint/test_shape_derivatives.py::test_PDE_hessian_dirichlet": 10.227813831530511,
+    "tests/firedrake/adjoint/test_shape_derivatives.py::test_PDE_hessian_neumann": 8.110230156220496,
+    "tests/firedrake/adjoint/test_shape_derivatives.py::test_multiple_assignments": 5.250948273576796,
+    "tests/firedrake/adjoint/test_shape_derivatives.py::test_shape_hessian": 2.7723169601522386,
+    "tests/firedrake/adjoint/test_shape_derivatives.py::test_sin_weak_spatial": 1.0733468257822096,
+    "tests/firedrake/adjoint/test_shape_derivatives.py::test_tlm_assemble": 1.6717797806486487,
+    "tests/firedrake/adjoint/test_solving.py::test_linear_problem": 1.9647090048529208,
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+    "tests/firedrake/adjoint/test_solving.py::test_nonlinear_problem": 5.57521973317489,
+    "tests/firedrake/adjoint/test_solving.py::test_singular_linear_problem": 3.0968201239593327,
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+    "tests/firedrake/adjoint/test_solving.py::test_wrt_function_neumann_boundary": 2.408797298092395,
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+    "tests/firedrake/adjoint/test_split_and_subfunctions.py::test_split[non-mixed]": 0.5955226379446685,
+    "tests/firedrake/adjoint/test_split_and_subfunctions.py::test_split_subvariables_update[mixed]": 0.23465487640351057,
+    "tests/firedrake/adjoint/test_split_and_subfunctions.py::test_split_subvariables_update[non-mixed]": 0.0068154470063745975,
+    "tests/firedrake/adjoint/test_split_and_subfunctions.py::test_subfunctions_always_create_blocks": 0.3977146139368415,
+    "tests/firedrake/adjoint/test_split_and_subfunctions.py::test_writing_to_subfunctions": 0.1795627106912434,
+    "tests/firedrake/adjoint/test_tag.py::test_tags[constant assign]": 0.0009369412437081337,
+    "tests/firedrake/adjoint/test_tag.py::test_tags[function assign]": 0.0037202094681560993,
+    "tests/firedrake/adjoint/test_tag.py::test_tags[interpolate_method]": 0.19737727707251906,
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+    "tests/tsfc/test_underintegration.py::test_mass_action[cell2-3-mass_cg]": 0.3206823240034282,
+    "tests/tsfc/test_underintegration.py::test_mass_action[cell2-3-mass_dg]": 0.319683110807091
+}
\ No newline at end of file

From 5a824db0296dfc66e065cda444f3f46d66e99304 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 31 Jul 2025 13:16:15 +0100
Subject: [PATCH 215/222] Change master to main (1) (#4444)

* Change refs to master to main

* Fix notebook links
---
 .github/workflows/core.yml    |  8 ++++----
 .github/workflows/push.yml    |  2 +-
 docs/source/citing.rst        |  2 +-
 docs/source/conf.py           |  6 +++---
 docs/source/documentation.rst |  8 ++++----
 docs/source/index.rst         |  2 +-
 docs/source/notebooks.rst     | 24 ++++++++++++------------
 7 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/.github/workflows/core.yml b/.github/workflows/core.yml
index 0ced60224f..185f6678d3 100644
--- a/.github/workflows/core.yml
+++ b/.github/workflows/core.yml
@@ -8,7 +8,7 @@ on:
         type: string
         required: true
       target_branch:
-        description: The target branch (usually 'master' or 'release')
+        description: The target branch (usually 'main' or 'release')
         type: string
         required: true
       run_tests:
@@ -39,7 +39,7 @@ on:
         type: string
         required: true
       target_branch:
-        description: The target branch (usually 'master' or 'release')
+        description: The target branch (usually 'main' or 'release')
         type: string
         required: true
       run_tests:
@@ -547,12 +547,12 @@ jobs:
   deploy_website:
     name: Deploy GitHub pages (optional)
     needs: test_linux
-    # Only deploy the website for master here, the release branch is managed
+    # Only deploy the website for main here, the release branch is managed
     # by https://github.com/firedrakeproject/firedrakeproject.github.io
     if: |
       always() &&
       inputs.deploy_website &&
-      inputs.target_branch == 'master' &&
+      inputs.target_branch == 'main' &&
       needs.test_linux.outputs.docs_conclusion == 'success'
     permissions:
       pages: write
diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index f6a06f9834..45573bd8f5 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -3,7 +3,7 @@ name: Deploy after push
 on:
   push:
     branches:
-      - master
+      - main
       - release
 
 jobs:
diff --git a/docs/source/citing.rst b/docs/source/citing.rst
index 3f1f60a834..074dd7a4e5 100644
--- a/docs/source/citing.rst
+++ b/docs/source/citing.rst
@@ -56,7 +56,7 @@ preconditioning, please cite :cite:`Kirby2017`.
 If you would like to help us to keep track of research directly
 benefitting from Firedrake, please feel free to add your paper in
 bibtex format in the `bibliography for firedrake applications
-`_.
+`_.
 
 Citing other packages
 ~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/source/conf.py b/docs/source/conf.py
index ba2ea1bcb8..70eb88e260 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -250,10 +250,10 @@
 
 # Optional header warning about docs version
 rst_prolog = """
-.. only:: master
+.. only:: main
 
     .. warning::
-        You are reading a version of the website built against the unstable ``master`` branch. This content is liable to change without notice and may be inappropriate for your use case.
+        You are reading a version of the website built against the unstable ``main`` branch. This content is liable to change without notice and may be inappropriate for your use case.
 """
 
 # -- Options for LaTeX output --------------------------------------------
@@ -427,7 +427,7 @@
 
 #  -- Options for sphinx.ext.extlinks ------------------------------------
 extlinks = {}
-if tags.has('master'):
+if tags.has('main'):
     extlinks['demo'] = (
         'https://firedrakeproject.org/firedrake/demos/%s', None
     )
diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst
index 48de7143d5..9fccfa43f3 100644
--- a/docs/source/documentation.rst
+++ b/docs/source/documentation.rst
@@ -16,14 +16,14 @@
 
       .. only:: not release
 
-         Latest Firedrake **master** branch status:
+         Latest Firedrake **main** branch status:
 
-         |firedrakemasterbuild|_
+         |firedrakemainbuild|_
 
       .. |firedrakereleasebuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml/badge.svg?branch=release
       .. _firedrakereleasebuild: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml?branch=release
-      .. |firedrakemasterbuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml/badge.svg?query=branch%3Amaster
-      .. _firedrakemasterbuild: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml?query=branch%3Amaster
+      .. |firedrakemainbuild| image:: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml/badge.svg?query=branch%3Amain
+      .. _firedrakemainbuild: https://github.com/firedrakeproject/firedrake/actions/workflows/push.yml?query=branch%3Amain
 
       Firedrake and its components are developed on `GitHub
       `__.
diff --git a/docs/source/index.rst b/docs/source/index.rst
index ddebc3ea9b..46318e89b2 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -2,7 +2,7 @@
 
 .. only:: html
 
-   .. sidebar:: Latest commits to the Firedrake master branch on Github
+   .. sidebar:: Latest commits to the Firedrake main branch on Github
 
       .. raw:: html
 
diff --git a/docs/source/notebooks.rst b/docs/source/notebooks.rst
index 4372c4e318..7fdd1ef33e 100644
--- a/docs/source/notebooks.rst
+++ b/docs/source/notebooks.rst
@@ -37,7 +37,7 @@ A first example
 In this notebook, we solve the symmetric positive definite "Helmholtz"
 equation, and learn about meshes and function spaces.  A rendered
 version of this notebook is available `here
-`__
+`__
 and there is a version `on Colab `__
 
 
@@ -47,7 +47,7 @@ Incorporating strong boundary conditions
 Next, we modify the problem slightly and solve the Poisson equation.
 We introduce strong (Dirichlet) boundary conditions and `how to use
 them
-`__.
+`__.
 You can run this notebook yourself `on Colab `__
 
 
@@ -58,7 +58,7 @@ Moving on from scalar problems, we look at our first vector-valued problem,
 namely the equations of linear elasticity.  In this notebook, we learn about
 some of UFL's support for tensor algebra, and start looking at `configuring
 linear solvers
-`__.
+`__.
 You can run this notebook yourself `on Colab
 `__
 
@@ -70,7 +70,7 @@ This notebook looks at a simple nonlinear problem, the viscous
 Burgers' equation, and also treats simple timestepping schemes.  We
 learn about formulating nonlinear, as opposed to linear problems, and
 also a little bit about how to write `efficient Firedrake code
-`__.
+`__.
 You can run this notebook yourself `on Colab
 `__
 
@@ -79,7 +79,7 @@ A mixed formulation of the Poisson equation
 ===========================================
 
 `In this notebook
-`__.,
+`__.,
 we look at our first mixed finite element problem.  A dual formulation of the
 Poisson equation.  This equation also appears in the context of flow in porous
 media, as Darcy flow.  We introduce mixed function spaces and how to work with
@@ -95,7 +95,7 @@ PDE-constrained optimisation with `dolfin-adjoint `__
+`__
 introduces the use of `dolfin-adjoint `__ to
 solve PDE constrained optimisation problems.  We solve the Stokes equations and
 minimise energy loss due to heat, controlling inflow/outflow in a pipe. You can
@@ -109,7 +109,7 @@ Geometric multigrid
 The next notebook looks a little bit at the support Firedrake has for
 geometric multigrid, and how you can configure complex multilevel
 solvers purely using `PETSc options
-`__.
+`__.
 You can run this notebook yourself `on Colab
 `__
 
@@ -121,7 +121,7 @@ We next dive a little deeper into the advanced ways in which Firedrake
 and PETSc enable solvers and preconditioners to be composed in
 arbitrarily complex ways to `create an optimal solution strategy for a
 particular problem
-`__.
+`__.
 You can run this notebook yourself `on Colab
 `__
 
@@ -132,7 +132,7 @@ Hybridisation
 Building on the theme of composable solvers, we now explore
 `Firedrake's capabilities in the area of static condensation and
 hybridisation
-`__.
+`__.
 You can run this notebook yourself `on Colab
 `__
 
@@ -143,7 +143,7 @@ Sum Factorisation
 In this notebook, we take a look under the hood at the sorts of performance
 optimisation that Firedrake's compilers can generate. In this case, we focus on
 `sum factorisation for tensor product elements
-`__.
+`__.
 You can run this notebook yourself `on Colab
 `__
 
@@ -154,7 +154,7 @@ Solving adjoint problems
 In some cases, it can be useful to extract adjoint solution data which is
 written to tape by `dolfin-adjoint`. This notebook shows how to do that:
 `solving adjoint equations using firedrake-adjoint
-`__.
+`__.
 You can run this notebook yourself `on Colab
 `__
 
@@ -165,7 +165,7 @@ Running on HPC
 When it comes to running Firedrake on a high performance computer
 there are a range of different techniques to get the best performance
 from your code. The `HPC demonstration
-`__
+`__
 notebook builds up a multigrid solver for an elliptic problem
 specifically designed for solving very large problems using Firedrake on
 HPC. You can run this notebook yourself `on Colab

From ac5e2912ae6ada9675970a7ccaaba31aef9b794a Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 31 Jul 2025 13:17:19 +0100
Subject: [PATCH 216/222] Reference main not master in install link (#4445)

---
 docs/source/install.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/install.rst b/docs/source/install.rst
index 6d45e288c5..191720425b 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -54,7 +54,7 @@ firedrake-configure
 To simplify the installation process, Firedrake provides a utility script called
 ``firedrake-configure``. This script can be downloaded by executing::
 
-  $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/master/scripts/firedrake-configure
+  $ curl -O https://raw.githubusercontent.com/firedrakeproject/firedrake/main/scripts/firedrake-configure
 
 Note that ``firedrake-configure`` **does not install Firedrake for you**. It
 is simply a helper script that emits the configuration options that Firedrake

From 237c17e85af89c41a128b873a7ef9e93f3296310 Mon Sep 17 00:00:00 2001
From: Josh Hope-Collins 
Date: Thu, 31 Jul 2025 18:24:01 +0100
Subject: [PATCH 217/222] Use petsctools in tests (#4447)

* use petsctools for the OptionsManager in adjoint test
---
 tests/firedrake/adjoint/test_optimisation.py | 29 ++++++++++++++------
 1 file changed, 20 insertions(+), 9 deletions(-)

diff --git a/tests/firedrake/adjoint/test_optimisation.py b/tests/firedrake/adjoint/test_optimisation.py
index 8fdac40ad1..c4521c0ee7 100644
--- a/tests/firedrake/adjoint/test_optimisation.py
+++ b/tests/firedrake/adjoint/test_optimisation.py
@@ -6,7 +6,8 @@
 from firedrake import *
 from firedrake.adjoint import *
 from pyadjoint import Block, MinimizationProblem, TAOSolver, get_working_tape
-from pyadjoint.optimization.tao_solver import OptionsManager, PETScVecInterface
+from pyadjoint.optimization.tao_solver import PETScVecInterface
+import petsctools
 
 
 @pytest.fixture(autouse=True)
@@ -68,7 +69,9 @@ def test_petsc_roundtrip_multiple():
 def minimize_tao_lmvm(rf, *, convert_options=None):
     problem = MinimizationProblem(rf)
     solver = TAOSolver(problem, {"tao_type": "lmvm",
-                                 "tao_gatol": 1.0e-7,
+                                 "tao_monitor": None,
+                                 "tao_converged_reason": None,
+                                 "tao_gatol": 1.0e-5,
                                  "tao_grtol": 0.0,
                                  "tao_gttol": 0.0},
                        convert_options=convert_options)
@@ -78,7 +81,9 @@ def minimize_tao_lmvm(rf, *, convert_options=None):
 def minimize_tao_nls(rf, *, convert_options=None):
     problem = MinimizationProblem(rf)
     solver = TAOSolver(problem, {"tao_type": "nls",
-                                 "tao_gatol": 1.0e-7,
+                                 "tao_monitor": None,
+                                 "tao_converged_reason": None,
+                                 "tao_gatol": 1.0e-5,
                                  "tao_grtol": 0.0,
                                  "tao_gttol": 0.0},
                        convert_options=convert_options)
@@ -218,11 +223,13 @@ def mult(self, A, x, y):
         M_mat.setUp()
 
         mfn = SLEPc.MFN().create(comm=comm)
-        options = OptionsManager(mfn_parameters, None)
-        options.set_default_parameter("fn_type", "sqrt")
+        petsctools.attach_options(
+            mfn, parameters=mfn_parameters,
+            options_prefix=None)
+        petsctools.set_default_parameter(mfn, "fn_type", "sqrt")
         mfn.setOperator(M_mat)
 
-        options.set_from_options(mfn)
+        petsctools.set_from_options(mfn)
         mfn.setUp()
         if mfn.getFN().getType() != SLEPc.FN.Type.SQRT:
             raise ValueError("Invalid FN type")
@@ -234,7 +241,8 @@ def mult(self, A, x, y):
         if y.norm(PETSc.NormType.NORM_INFINITY) == 0:
             x.zeroEntries()
         else:
-            mfn.solve(y, x)
+            with petsctools.inserted_options(mfn):
+                mfn.solve(y, x)
             if mfn.getConvergedReason() <= 0:
                 raise RuntimeError("Convergence failure")
 
@@ -281,10 +289,11 @@ def test_simple_inversion_riesz_representation(tao_type):
     mfn_parameters = {"mfn_type": "krylov",
                       "mfn_tol": 1.0e-12}
     tao_parameters = {"tao_type": tao_type,
+                      "tao_monitor": None,
+                      "tao_converged_reason": None,
                       "tao_gatol": 1.0e-5,
                       "tao_grtol": 0.0,
-                      "tao_gttol": 0.0,
-                      "tao_monitor": None}
+                      "tao_gttol": 0.0}
 
     with stop_annotating():
         mesh = UnitIntervalMesh(10)
@@ -355,6 +364,8 @@ def test_tao_bounds():
     lb = 0.5 - 7.0 / 11.0
     problem = MinimizationProblem(rf, bounds=(lb, None))
     solver = TAOSolver(problem, {"tao_type": "bnls",
+                                 "tao_monitor": None,
+                                 "tao_converged_reason": None,
                                  "tao_gatol": 1.0e-7,
                                  "tao_grtol": 0.0,
                                  "tao_gttol": 0.0})

From bd78e5e049091c8d9fda07b3a1d12f46170526c9 Mon Sep 17 00:00:00 2001
From: Connor Ward 
Date: Thu, 31 Jul 2025 13:39:23 +0100
Subject: [PATCH 218/222] Merge fixups

---
 docs/notebooks/07-geometric-multigrid.ipynb | 5 -----
 pyproject.toml                              | 3 ++-
 2 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/docs/notebooks/07-geometric-multigrid.ipynb b/docs/notebooks/07-geometric-multigrid.ipynb
index e08f7e36e4..52a31fce49 100644
--- a/docs/notebooks/07-geometric-multigrid.ipynb
+++ b/docs/notebooks/07-geometric-multigrid.ipynb
@@ -510,13 +510,8 @@
     "                       1, \n",
     "                       0)\n",
     "\n",
-<<<<<<< HEAD
-    "    value = as_vector([gbar*(1 - (12*t)**2), 0])\n",
-    "    bcs = [DirichletBC(W.sub(0), assemble(interpolate(value, V)), (1, 2)),\n",
-=======
     "    value = assemble(interpolate(as_vector([gbar*(1 - (12*t)**2), 0]), V))\n",
     "    bcs = [DirichletBC(W.sub(0), value, (1, 2)),\n",
->>>>>>> origin/release
     "           DirichletBC(W.sub(0), zero(2), (3, 4))]\n",
     "    \n",
     "    a = (nu*inner(grad(u), grad(v)) - p*div(v) + q*div(u))*dx\n",
diff --git a/pyproject.toml b/pyproject.toml
index ea102453a0..93cd3c06e9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -96,7 +96,8 @@ requires = [
   "pkgconfig",
   "pybind11",
   "setuptools>=77.0.3",
-  "petsc4py==3.23.4",
+  # TODO RELEASE
+  # "petsc4py==3.23.4",
   "rtree>=1.2",
 ]
 build-backend = "setuptools.build_meta"

From c2d2940a987e4f256404403a7432fa2f8b6a9664 Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Tue, 5 Aug 2025 11:17:00 +0100
Subject: [PATCH 219/222] adjoint test fixes

---
 tests/firedrake/adjoint/test_optimisation.py           |  2 +-
 .../vertexonly/test_poisson_inverse_conductivity.py    | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/tests/firedrake/adjoint/test_optimisation.py b/tests/firedrake/adjoint/test_optimisation.py
index a81ca4c633..2189a7882b 100644
--- a/tests/firedrake/adjoint/test_optimisation.py
+++ b/tests/firedrake/adjoint/test_optimisation.py
@@ -156,7 +156,7 @@ def test_simple_inversion(riesz_representation):
 @pytest.mark.parametrize("riesz_representation", [None, "l2", "L2", "H1"])
 @pytest.mark.skipcomplex
 def test_tao_simple_inversion(minimize, riesz_representation):
-    """Test inversion of source term in helmholze eqn using TAO."""
+    """Test inversion of source term in helmholtz eqn using TAO."""
     mesh = UnitIntervalMesh(10)
     V = FunctionSpace(mesh, "CG", 1)
     source_ref = Function(V)
diff --git a/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py b/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py
index 9975aa6dd1..5ed29951e6 100644
--- a/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py
+++ b/tests/firedrake/vertexonly/test_poisson_inverse_conductivity.py
@@ -59,8 +59,8 @@ def test_poisson_inverse_conductivity(num_points):
     # Compute the true solution of the PDE.
     u_true = Function(V)
     v = TestFunction(V)
-    f = Constant(1.0, domain=m)
-    k0 = Constant(0.5, domain=m)
+    f = Constant(1.0)
+    k0 = Constant(0.5)
     bc = DirichletBC(V, 0, 'on_boundary')
     F = (k0 * exp(q_true) * inner(grad(u_true), grad(v)) - f * v) * dx
     solve(F == 0, u_true, bc)
@@ -79,7 +79,7 @@ def test_poisson_inverse_conductivity(num_points):
     signal_to_noise = 20
     U = u_true.dat.data_ro[:]
     u_range = U.max() - U.min()
-    σ = Constant(u_range / signal_to_noise, domain=point_cloud)
+    σ = Constant(u_range / signal_to_noise)
     ζ = generator.standard_normal(len(xs))
     u_obs_vals = np.array(u_true.at(xs)) + float(σ) * ζ
 
@@ -102,7 +102,7 @@ def test_poisson_inverse_conductivity(num_points):
 
     # Two terms in the functional
     misfit_expr = 0.5 * ((u_obs - assemble(interpolate(u, P0DG))) / σ)**2
-    α = Constant(0.5, domain=m)
+    α = Constant(0.5)
     regularisation_expr = 0.5 * α**2 * inner(grad(q), grad(q))
 
     # Form functional and reduced functional
@@ -111,7 +111,7 @@ def test_poisson_inverse_conductivity(num_points):
     Ĵ = ReducedFunctional(J, q̂)
 
     # Estimate q using Newton-CG which evaluates the hessian action
-    minimize(Ĵ, method='Newton-CG', options={'maxiter': 3, 'disp': True})
+    minimize(Ĵ, method='Newton-CG', options={'maxiter': 10, 'xtol': 1e-1, 'disp': True})
 
 
 @pytest.mark.skipcomplex  # Taping for complex-valued 0-forms not yet done

From 7bf2d474f9e6c82cd3689916f9e9699eee87de88 Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Tue, 5 Aug 2025 14:49:04 +0100
Subject: [PATCH 220/222] set riesz map correctly in shape optimisation demo

---
 demos/shape_optimization/shape_optimization.py.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/demos/shape_optimization/shape_optimization.py.rst b/demos/shape_optimization/shape_optimization.py.rst
index 69c0776451..ee30704900 100644
--- a/demos/shape_optimization/shape_optimization.py.rst
+++ b/demos/shape_optimization/shape_optimization.py.rst
@@ -70,9 +70,10 @@ and evaluate the objective function::
 
 We now turn the objective function into a reduced function so that pyadjoint
 (and UFL shape differentiation capability) can automatically compute shape
-gradients, that is, directions of steepest ascent::
+gradients, that is, directions of steepest ascent. We also set the relevant
+Riesz map for this problem::
 
-  Jred = ReducedFunctional(J, Control(dT))
+  Jred = ReducedFunctional(J, Control(dT, riesz_map="H1"))
   stop_annotating()
 
 We now have all the ingredients to implement a basic steepest descent shape
@@ -84,8 +85,7 @@ optimization algorithm with fixed step size.::
     File.write(mesh.coordinates)
 
     # compute the gradient (steepest ascent)
-    opts = {"riesz_representation": "H1"}
-    gradJ = Jred.derivative(options=opts)
+    gradJ = Jred.derivative(apply_riesz=True)
 
     # update domain
     dT -= 0.2*gradJ

From b6bff26f1f56c580c361712639ff89f84c15277c Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Tue, 5 Aug 2025 16:51:39 +0100
Subject: [PATCH 221/222] update fwi demo tolerance

---
 .../full_waveform_inversion/full_waveform_inversion.py.rst  | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/demos/full_waveform_inversion/full_waveform_inversion.py.rst b/demos/full_waveform_inversion/full_waveform_inversion.py.rst
index 07da946b59..a16ec65782 100644
--- a/demos/full_waveform_inversion/full_waveform_inversion.py.rst
+++ b/demos/full_waveform_inversion/full_waveform_inversion.py.rst
@@ -296,7 +296,7 @@ we have a custom ``ReducedFunctional``, we need to do this ourselves::
     from pyadjoint.reduced_functional_numpy import ReducedFunctionalNumPy
     Jnumpy = ReducedFunctionalNumPy(J_hat)
 
-    c_optimised = minimize(Jnumpy, method="L-BFGS-B", options={"disp": True, "maxiter": 1},
+    c_optimised = minimize(Jnumpy, method="L-BFGS-B", options={"disp": True, "maxiter": 1, "ftol": 0.9},
                            bounds=(1.5, 2.0))
 
 The ``minimize`` function executes the optimisation algorithm until the stopping criterion (``maxiter``) is met.
@@ -310,9 +310,7 @@ For 20 iterations, the predicted velocity model is shown in the following figure
 .. warning::
 
     The ``minimize`` function uses the SciPy library for optimisation. However, for scenarios that require higher
-    levels of spatial parallelism, you should assess whether SciPy is the most suitable option for your problem.
-    SciPy's optimisation algorithm is not inner-product-aware. Therefore, we configure the options with
-    ``derivative_options={"riesz_representation": 'l2'}`` to account for this requirement.
+    levels of spatial parallelism, you should assess whether SciPy is the most suitable option for your problem such as the pyadjoint's TAOSolver.
 
 .. note::
 

From e6d6731d5589868c46c7ae86e5bfee3d2e491e85 Mon Sep 17 00:00:00 2001
From: JHopeCollins 
Date: Wed, 6 Aug 2025 14:04:39 +0100
Subject: [PATCH 222/222] Trigger CI


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