From 7f90703db5b622b29df3a13f4ff4fe0c7a35f6f3 Mon Sep 17 00:00:00 2001
From: Markus Holzer <markus.holzer@fau.de>
Date: Sun, 27 Mar 2022 16:59:00 +0200
Subject: [PATCH 1/6] Minor changes
---
lbmpy/creationfunctions.py | 5 +++--
lbmpy/methods/abstractlbmethod.py | 2 +-
lbmpy/methods/creationfunctions.py | 6 +++---
lbmpy/methods/momentbased/momentbasedmethod.py | 7 ++++---
lbmpy_tests/test_momentbased_equivalences.py | 2 +-
setup.py | 2 +-
6 files changed, 13 insertions(+), 11 deletions(-)
diff --git a/lbmpy/creationfunctions.py b/lbmpy/creationfunctions.py
index 44cbfe15..43d82f7c 100644
--- a/lbmpy/creationfunctions.py
+++ b/lbmpy/creationfunctions.py
@@ -151,7 +151,7 @@ class LBMConfig:
If this argument is not provided, Gram-Schmidt orthogonalization of the default modes is performed.
"""
- force_model: Union[Type[lbmpy.forcemodels.AbstractForceModel], ForceModel] = None
+ force_model: Union[lbmpy.forcemodels.AbstractForceModel, ForceModel] = None
"""
Force model to determine how forcing terms enter the collision rule.
Possibilities are defined in :class: `lbmpy.enums.ForceModel`
@@ -177,7 +177,8 @@ class LBMConfig:
Special correction for D3Q27 cumulant LBMs. For Details see
:mod:`lbmpy.methods.centeredcumulant.galilean_correction`
"""
- moment_transform_class: Type[lbmpy.moment_transforms.AbstractMomentTransform] = PdfsToMomentsByChimeraTransform
+ moment_transform_class: Union[Type[lbmpy.moment_transforms.AbstractMomentTransform], None] = \
+ PdfsToMomentsByChimeraTransform
"""
Python class that determines how PDFs are transformed to the moment space. Usually, the chimera transform is
the best choice (see :cite:`geier2015`). However, for the SRT and TRT methods it defaults to `None`, since
diff --git a/lbmpy/methods/abstractlbmethod.py b/lbmpy/methods/abstractlbmethod.py
index 07b46b74..0aecd696 100644
--- a/lbmpy/methods/abstractlbmethod.py
+++ b/lbmpy/methods/abstractlbmethod.py
@@ -108,7 +108,7 @@ class AbstractLbMethod(abc.ABC):
relaxation_rate = sp.sympify(relaxation_rate)
# special treatment for zero, sp.Zero would be an integer ..
if isinstance(relaxation_rate, Zero):
- relaxation_rate = 0.0
+ relaxation_rate = sp.Number(0)
if not isinstance(relaxation_rate, sp.Symbol):
rt_symbol = sp.Symbol(f"rr_{len(subexpressions)}")
subexpressions[relaxation_rate] = rt_symbol
diff --git a/lbmpy/methods/creationfunctions.py b/lbmpy/methods/creationfunctions.py
index f05bef51..5edf517b 100644
--- a/lbmpy/methods/creationfunctions.py
+++ b/lbmpy/methods/creationfunctions.py
@@ -605,11 +605,11 @@ def _get_relaxation_info_dict(relaxation_rates, nested_moments, dim):
for group in nested_moments:
for moment in group:
if get_order(moment) <= 1:
- result[moment] = 0.0
+ result[moment] = sp.Number(0)
elif is_shear_moment(moment, dim):
result[moment] = relaxation_rates[0]
else:
- result[moment] = 1.0
+ result[moment] = sp.Number(1)
# if relaxation rate for each moment is specified they are all used
if len(relaxation_rates) == number_of_moments:
@@ -634,7 +634,7 @@ def _get_relaxation_info_dict(relaxation_rates, nested_moments, dim):
next_rr = False
for moment in group:
if get_order(moment) <= 1:
- result[moment] = 0.0
+ result[moment] = sp.Number(0)
elif is_shear_moment(moment, dim):
result[moment] = shear_rr
elif is_bulk_moment(moment, dim):
diff --git a/lbmpy/methods/momentbased/momentbasedmethod.py b/lbmpy/methods/momentbased/momentbasedmethod.py
index 93a3542e..2cb2553c 100644
--- a/lbmpy/methods/momentbased/momentbasedmethod.py
+++ b/lbmpy/methods/momentbased/momentbasedmethod.py
@@ -248,7 +248,7 @@ class MomentBasedLbMethod(AbstractLbMethod):
m_eq = sp.Matrix(self.moment_equilibrium_values)
if self._moment_transform_class:
- # Derive equations in moment space if a transform is given
+ # Derive equations in moment space if a transform is given
pdf_to_m_transform = self._moment_transform_class(self.stencil, moment_polynomials, rho, u,
conserved_quantity_equations=cqe)
@@ -276,8 +276,9 @@ class MomentBasedLbMethod(AbstractLbMethod):
subexpressions += m_post_to_f_post_eqs.subexpressions
main_assignments = m_post_to_f_post_eqs.main_assignments
else:
- # For SRT, TRT by default, and whenever customly required, derive equations entirely in
- # population space
+ # TODO: This is confusing since we still transform with the moment matrix.
+ # TODO: Furthermore, the inverse is not a good idea even symbolically
+ # For SRT, TRT by default, and whenever custom required, derive equations entirely in population space
pdf_to_moment = self.moment_matrix
collision_rule = f + pdf_to_moment.inv() * d * (m_eq - pdf_to_moment * f)
collision_eqs = [Assignment(lhs, rhs) for lhs, rhs in zip(self.post_collision_pdf_symbols, collision_rule)]
diff --git a/lbmpy_tests/test_momentbased_equivalences.py b/lbmpy_tests/test_momentbased_equivalences.py
index 90bee891..8e8f72db 100644
--- a/lbmpy_tests/test_momentbased_equivalences.py
+++ b/lbmpy_tests/test_momentbased_equivalences.py
@@ -81,7 +81,7 @@ setups = [
@pytest.mark.parametrize('setup', setups)
-def test_population_and_moment_space_equivalence(setup):
+def test_population_and_moment_space_equivalence(setup: tuple):
stencil = LBStencil(setup[0])
method = setup[1]
nested_moments = setup[2]
diff --git a/setup.py b/setup.py
index ebc9acdf..51ecf66e 100644
--- a/setup.py
+++ b/setup.py
@@ -89,7 +89,7 @@ setup(name='lbmpy',
author_email='cs10-codegen@fau.de',
url='https://i10git.cs.fau.de/pycodegen/lbmpy/',
packages=['lbmpy'] + ['lbmpy.' + s for s in find_packages('lbmpy')],
- install_requires=[f'pystencils>=0.4.0,<={major_version}', 'sympy>=1.5.1,<=1.9', 'numpy>=1.11.0'],
+ install_requires=[f'pystencils>=0.4.0,<={major_version}', 'sympy>=1.5.1,<=1.10.1', 'numpy>=1.11.0'],
package_data={'lbmpy': ['phasefield/simplex_projection.pyx', 'phasefield/simplex_projection.c']},
ext_modules=cython_extensions("lbmpy.phasefield.simplex_projection"),
classifiers=[
--
GitLab
From 3be3644db6b86126f3cb16ee7002229868559294 Mon Sep 17 00:00:00 2001
From: Markus Holzer <markus.holzer@fau.de>
Date: Tue, 29 Mar 2022 08:19:11 +0200
Subject: [PATCH 2/6] Use SymPy solution
---
.../momentbased/momentbasedsimplifications.py | 39 ++++++++++++++++++-
1 file changed, 37 insertions(+), 2 deletions(-)
diff --git a/lbmpy/methods/momentbased/momentbasedsimplifications.py b/lbmpy/methods/momentbased/momentbasedsimplifications.py
index e5a7d921..a6de88c4 100644
--- a/lbmpy/methods/momentbased/momentbasedsimplifications.py
+++ b/lbmpy/methods/momentbased/momentbasedsimplifications.py
@@ -140,6 +140,35 @@ def replace_common_quadratic_and_constant_term(cr: LbmCollisionRule):
return cr
+def find_subadd(exprs, replacement_symbol_generator):
+ exprs = sp.Tuple(*exprs)
+ adds = sp.Tuple(*exprs.atoms(sp.Add))
+ arg_set = set([i for e in exprs for a in adds for i in a.args])
+ d = next(replacement_symbol_generator)
+ reps = {}
+ while 1:
+ e = None
+ best = adds.count_ops()
+ for i in sp.subsets(list(sp.ordered(arg_set))):
+ if len(i) < 2:
+ continue
+ a = -sp.Add(*i)
+ for s in range(2):
+ a *= -1
+ new = adds.subs(a, d)
+ hit = new.count(d)
+ if hit > 1:
+ op = new.subs(a, d).count_ops()
+ if op < best:
+ best = op
+ e = a
+ if e is None:
+ break
+ reps[e] = next(replacement_symbol_generator)
+ adds = adds.subs(e, 0) # get rid of subpattern
+ return reps
+
+
def cse_in_opposing_directions(cr: LbmCollisionRule):
"""
Looks for common subexpressions in terms for opposing directions (e.g. north & south, top & bottom )
@@ -200,8 +229,13 @@ def cse_in_opposing_directions(cr: LbmCollisionRule):
new_coefficient = relaxation_rate
handled_terms = terms
+ subadd_terms = find_subadd(handled_terms, replacement_symbol_generator)
+ handled_terms = [handled_term.subs(subadd_terms) for handled_term in handled_terms]
+
found_subexpressions, new_terms = sp.cse(handled_terms, symbols=replacement_symbol_generator,
order='None', optimizations=[])
+
+ substitutions += [Assignment(subadd_terms[f], f) for f in subadd_terms]
substitutions += [Assignment(f[0], f[1]) for f in found_subexpressions]
update_rules = [Assignment(ur.lhs, ur.rhs.subs(relaxation_rate * old_term, new_coefficient * new_term))
@@ -248,13 +282,13 @@ def substitute_moments_in_conserved_quantity_equations(ac: AssignmentCollection)
for cq_sym, moment_sym in cq_symbols_to_moments.items():
moment_eq = reduced_assignments[moment_sym]
assert moment_eq.count(cq_sym) == 0, "Expressing conserved quantity " \
- f"{cq_sym} using moment {moment_sym} would introduce a circular dependency."
+ f"{cq_sym} using moment {moment_sym} would introduce a circular dependency."
cq_eq = subs_additive(reduced_assignments[cq_sym], moment_sym, moment_eq)
if cq_sym in main_asm_dict:
main_asm_dict[cq_sym] = cq_eq
else:
assert moment_sym in subexp_dict, f"Cannot express subexpression {cq_sym}" \
- f" using main assignment {moment_sym}!"
+ f" using main assignment {moment_sym}!"
subexp_dict[cq_sym] = cq_eq
main_assignments = [Assignment(lhs, rhs) for lhs, rhs in main_asm_dict.items()]
@@ -316,6 +350,7 @@ def split_pdf_main_assignments_by_symmetry(ac: AssignmentCollection):
main_assignments = [Assignment(lhs, rhs) for lhs, rhs in asm_dict.items()]
return ac.copy(main_assignments=main_assignments, subexpressions=subexpressions)
+
# -------------------------------------- Helper Functions --------------------------------------------------------------
--
GitLab
From 054e4ea3328ffdc9b2860d56942ab6473c9d50be Mon Sep 17 00:00:00 2001
From: Markus Holzer <markus.holzer@fau.de>
Date: Tue, 29 Mar 2022 08:25:51 +0200
Subject: [PATCH 3/6] Minor clean up
---
lbmpy/methods/momentbased/momentbasedsimplifications.py | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/lbmpy/methods/momentbased/momentbasedsimplifications.py b/lbmpy/methods/momentbased/momentbasedsimplifications.py
index a6de88c4..880b7ba3 100644
--- a/lbmpy/methods/momentbased/momentbasedsimplifications.py
+++ b/lbmpy/methods/momentbased/momentbasedsimplifications.py
@@ -140,6 +140,7 @@ def replace_common_quadratic_and_constant_term(cr: LbmCollisionRule):
return cr
+# TODO: This solution is from https://github.com/sympy/sympy/issues/23297. Can be removed if in SymPy master
def find_subadd(exprs, replacement_symbol_generator):
exprs = sp.Tuple(*exprs)
adds = sp.Tuple(*exprs.atoms(sp.Add))
@@ -281,8 +282,8 @@ def substitute_moments_in_conserved_quantity_equations(ac: AssignmentCollection)
for cq_sym, moment_sym in cq_symbols_to_moments.items():
moment_eq = reduced_assignments[moment_sym]
- assert moment_eq.count(cq_sym) == 0, "Expressing conserved quantity " \
- f"{cq_sym} using moment {moment_sym} would introduce a circular dependency."
+ assert moment_eq.count(cq_sym) == 0, f"Expressing conserved quantity {cq_sym} using moment {moment_sym} " \
+ "would introduce a circular dependency."
cq_eq = subs_additive(reduced_assignments[cq_sym], moment_sym, moment_eq)
if cq_sym in main_asm_dict:
main_asm_dict[cq_sym] = cq_eq
--
GitLab
From ae586dd6af938a186dc3777b4176693c28a40fb4 Mon Sep 17 00:00:00 2001
From: Markus Holzer <markus.holzer@fau.de>
Date: Tue, 29 Mar 2022 09:17:39 +0200
Subject: [PATCH 4/6] Removed hacked function
---
.../momentbased/momentbasedsimplifications.py | 34 -------------------
lbmpy_tests/test_srt_trt_simplifications.py | 8 ++---
2 files changed, 4 insertions(+), 38 deletions(-)
diff --git a/lbmpy/methods/momentbased/momentbasedsimplifications.py b/lbmpy/methods/momentbased/momentbasedsimplifications.py
index 880b7ba3..dea5f06b 100644
--- a/lbmpy/methods/momentbased/momentbasedsimplifications.py
+++ b/lbmpy/methods/momentbased/momentbasedsimplifications.py
@@ -140,36 +140,6 @@ def replace_common_quadratic_and_constant_term(cr: LbmCollisionRule):
return cr
-# TODO: This solution is from https://github.com/sympy/sympy/issues/23297. Can be removed if in SymPy master
-def find_subadd(exprs, replacement_symbol_generator):
- exprs = sp.Tuple(*exprs)
- adds = sp.Tuple(*exprs.atoms(sp.Add))
- arg_set = set([i for e in exprs for a in adds for i in a.args])
- d = next(replacement_symbol_generator)
- reps = {}
- while 1:
- e = None
- best = adds.count_ops()
- for i in sp.subsets(list(sp.ordered(arg_set))):
- if len(i) < 2:
- continue
- a = -sp.Add(*i)
- for s in range(2):
- a *= -1
- new = adds.subs(a, d)
- hit = new.count(d)
- if hit > 1:
- op = new.subs(a, d).count_ops()
- if op < best:
- best = op
- e = a
- if e is None:
- break
- reps[e] = next(replacement_symbol_generator)
- adds = adds.subs(e, 0) # get rid of subpattern
- return reps
-
-
def cse_in_opposing_directions(cr: LbmCollisionRule):
"""
Looks for common subexpressions in terms for opposing directions (e.g. north & south, top & bottom )
@@ -230,13 +200,9 @@ def cse_in_opposing_directions(cr: LbmCollisionRule):
new_coefficient = relaxation_rate
handled_terms = terms
- subadd_terms = find_subadd(handled_terms, replacement_symbol_generator)
- handled_terms = [handled_term.subs(subadd_terms) for handled_term in handled_terms]
-
found_subexpressions, new_terms = sp.cse(handled_terms, symbols=replacement_symbol_generator,
order='None', optimizations=[])
- substitutions += [Assignment(subadd_terms[f], f) for f in subadd_terms]
substitutions += [Assignment(f[0], f[1]) for f in found_subexpressions]
update_rules = [Assignment(ur.lhs, ur.rhs.subs(relaxation_rate * old_term, new_coefficient * new_term))
diff --git a/lbmpy_tests/test_srt_trt_simplifications.py b/lbmpy_tests/test_srt_trt_simplifications.py
index d40a02f2..24dc98da 100644
--- a/lbmpy_tests/test_srt_trt_simplifications.py
+++ b/lbmpy_tests/test_srt_trt_simplifications.py
@@ -34,7 +34,7 @@ def test_simplifications_srt_d2q9_incompressible():
omega = sp.symbols('omega')
method = create_srt(LBStencil(Stencil.D2Q9), omega, compressible=False,
equilibrium_order=2, moment_transform_class=None)
- check_method(method, [53, 46, 0], [49, 30, 0])
+ check_method(method, [53, 46, 0], [57, 38, 0])
def test_simplifications_srt_d2q9_compressible():
@@ -53,18 +53,18 @@ def test_simplifications_trt_d2q9_incompressible():
def test_simplifications_trt_d2q9_compressible():
o1, o2 = sp.symbols("omega_1 omega_2")
method = create_trt(LBStencil(Stencil.D2Q9), o1, o2, compressible=True)
- check_method(method, [77, 106, 1], [65, 56, 1])
+ check_method(method, [77, 106, 1], [65, 50, 1])
def test_simplifications_trt_d3q19_force_incompressible():
o1, o2 = sp.symbols("omega_1 omega_2")
force_model = Luo([sp.Rational(1, 3), sp.Rational(1, 2), sp.Rational(1, 5)])
method = create_trt(LBStencil(Stencil.D3Q19), o1, o2, compressible=False, force_model=force_model)
- check_method(method, [268, 281, 0], [241, 175, 1])
+ check_method(method, [246, 243, 0], [219, 137, 1])
def test_simplifications_trt_d3q19_force_compressible():
o1, o2 = sp.symbols("omega_1 omega_2")
force_model = Luo([sp.Rational(1, 3), sp.Rational(1, 2), sp.Rational(1, 5)])
method = create_trt_with_magic_number(LBStencil(Stencil.D3Q19), o1, compressible=False, force_model=force_model)
- check_method(method, [270, 284, 1], [243, 178, 1])
+ check_method(method, [248, 246, 1], [221, 140, 1])
--
GitLab
From ad081175be602c456a8f7caf5b959146c303b7a7 Mon Sep 17 00:00:00 2001
From: Markus Holzer <markus.holzer@fau.de>
Date: Tue, 29 Mar 2022 11:09:50 +0200
Subject: [PATCH 5/6] Rewrote tests a little bit
---
lbmpy_tests/test_free_slip.ipynb | 322 -------------------------------
lbmpy_tests/test_free_slip.py | 80 ++++++++
lbmpy_tests/test_oldroydb.py | 156 ++++++---------
3 files changed, 142 insertions(+), 416 deletions(-)
delete mode 100644 lbmpy_tests/test_free_slip.ipynb
create mode 100644 lbmpy_tests/test_free_slip.py
diff --git a/lbmpy_tests/test_free_slip.ipynb b/lbmpy_tests/test_free_slip.ipynb
deleted file mode 100644
index 08069b40..00000000
--- a/lbmpy_tests/test_free_slip.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this test case a domain is tested with shear inflow profile at west, NoSlip walls at south, top and bottom, a FreeSlip wall north and an Outflow boundary at the eastern border.\n",
- "\n",
- "Due to the FreeSlip wall at the northern boundary an almost zero velocity gradient should occure near the north boundary. This is tested here."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "from dataclasses import replace\n",
- "\n",
- "import pystencils as ps\n",
- "import lbmpy as lp\n",
- "from pystencils.slicing import slice_from_direction, make_slice\n",
- "\n",
- "from lbmpy.boundaries.boundaryhandling import LatticeBoltzmannBoundaryHandling\n",
- "from lbmpy.boundaries.boundaryconditions import FreeSlip, NoSlip, ExtrapolationOutflow, UBB\n",
- "\n",
- "\n",
- "from lbmpy.creationfunctions import create_lb_method, create_lb_update_rule\n",
- "from lbmpy.macroscopic_value_kernels import pdf_initialization_assignments\n",
- "\n",
- "import lbmpy.plot as plt\n",
- "\n",
- "import numpy as np"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "gpu = False\n",
- "target = ps.Target.CPU"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "stencil = lp.LBStencil(lp.Stencil.D3Q27)\n",
- "domain_size = (30, 15, 30)\n",
- "dim = len(domain_size)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "dh = ps.create_data_handling(domain_size=domain_size, periodicity=(False, False, False))\n",
- "\n",
- "src = dh.add_array('src', values_per_cell=stencil.Q, alignment=True)\n",
- "dh.fill('src', 0.0, ghost_layers=True)\n",
- "dst = dh.add_array('dst', values_per_cell=stencil.Q, alignment=True)\n",
- "dh.fill('dst', 0.0, ghost_layers=True)\n",
- "\n",
- "velField = dh.add_array('velField', values_per_cell=stencil.D, alignment=True)\n",
- "dh.fill('velField', 0.0, ghost_layers=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "lbm_config = lp.LBMConfig(stencil=stencil, method=lp.Method.SRT, relaxation_rate=1.8,\n",
- " output={'velocity': velField}, kernel_type='stream_pull_collide')\n",
- "method = create_lb_method(lbm_config=lbm_config)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Initialisation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "init = pdf_initialization_assignments(method, 1.0, (0, 0, 0), src.center_vector)\n",
- "\n",
- "config = ps.CreateKernelConfig(target=dh.default_target)\n",
- "ast_init = ps.create_kernel(init, config=config)\n",
- "kernel_init = ast_init.compile()\n",
- "\n",
- "dh.run_kernel(kernel_init)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Update Rules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "lbm_opt = lp.LBMOptimisation(symbolic_field=src, symbolic_temporary_field=dst)\n",
- "lbm_config = replace(lbm_config, lb_method=method)\n",
- "\n",
- "update = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)\n",
- "\n",
- "ast_kernel = ps.create_kernel(update, config=config)\n",
- "kernel = ast_kernel.compile()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Boundary Handling"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "def velocity_info_callback(boundary_data, **_):\n",
- " boundary_data['vel_1'] = 0\n",
- " boundary_data['vel_2'] = 0\n",
- " u_max = 0.1\n",
- " x, y = boundary_data.link_positions(0), boundary_data.link_positions(1)\n",
- " dist = (domain_size[1] - y) / domain_size[1]\n",
- " boundary_data['vel_0'] = u_max * (1 - dist)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1200x800 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "bh = LatticeBoltzmannBoundaryHandling(method, dh, 'src', name=\"bh\")\n",
- "\n",
- "inflow = UBB(velocity_info_callback, dim=dim)\n",
- "outflow = ExtrapolationOutflow(stencil[4], method)\n",
- "wall = NoSlip(\"wall\")\n",
- "freeslip = FreeSlip(stencil, (0, -1, 0))\n",
- "\n",
- "bh.set_boundary(inflow, slice_from_direction('W', dim))\n",
- "bh.set_boundary(outflow, slice_from_direction('E', dim))\n",
- "bh.set_boundary(wall, slice_from_direction('S', dim))\n",
- "bh.set_boundary(wall, slice_from_direction('T', dim))\n",
- "bh.set_boundary(wall, slice_from_direction('B', dim))\n",
- "bh.set_boundary(freeslip, slice_from_direction('N', dim))\n",
- "\n",
- "plt.figure(dpi=200)\n",
- "plt.boundary_handling(bh, make_slice[:, :, domain_size[2]//2])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "def timeloop(timeSteps):\n",
- " for i in range(timeSteps):\n",
- " bh()\n",
- " dh.run_kernel(kernel)\n",
- " dh.swap(\"src\", \"dst\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "timeloop(5000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "vel_profile = dh.gather_array('velField')[-2, :, domain_size[2] // 2, 0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.07369491639715217"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.max(vel_profile)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[<matplotlib.lines.Line2D at 0x7f35955717f0>]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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uJ0eWVTnGzBoCicB+dy909/0A7r4M2AycWfkB3H26u4fcPZSUpHOOyPG5O396ewtXT3sfd3jl1nOYfH4vlbzICUSzRZ8OpJlZT8oLfQJwbaUxC4AbgfeBccCb7u5mlgTkuHupmfUC0oAt1ZZe6pUDR4q4+5VVLP5oL189uxOPfGMQic11nhqRkzlp0Ufm3KcCi4AE4Bl3X2tmDwBhd18APA3MMrMMIIfyFwOAC4AHzKwYKANudfecmngiEt/C23K4bfYK9ucVcf8VZ3PDOT20FS8SJXP3oDN8SigU8nA4HHQMiRFlZc4f3trMr1/fSHLbZvzumqEMSE4MOpZIzDGzZe4eqmqd3hkrMWtfXiHfm7uSdzbt42sDu/CrqwbQSqcUFvncVPQSk97fvJ875qzg4NFiHvz6AK4ZkaKpGpFTpKKXmFJa5vz2zU38ZvEmUju0YObNIzirS+ugY4nUaSp6iRl7DxVw59yVvLd5P18f0o1fXtlf13AVqQb6LZKY8M6mbL43dyV5hSU8Mm4gV+tkZCLVRkUvgSopLeOJNzbx1L8z6J3Ukpe+PYozO7UKOpZIXFHRS2D25BZw++wVLN2WwzdDydx/RX+aNU4IOpZI3FHRSyBWZx1k8swweYUlPD5+EF8fkhx0JJG4paKXWvf31bu46+VVdGjZhPnf+QJ9O+uoGpGapKKXWuPu/GZxBo+/sZFhPdryx+uH0aFlk6BjicQ9Fb3UioLiUu6Zt5q/rdrFVUO78aurBtCkoebjRWqDil5q3N5DBXz7+TCrd+bywzF9ufWLOq2wSG1S0UuN+nBnLt9+PszB/GKmTRzGV8/uHHQkkXpHRS81ZtHaPdw5ZyVtmjdi3n+dw9ldddZJkSCo6KXauZefWviR1zYwKKUNf7p+GB1bNw06lki9paKXalVYUsp9r65h/oqdXD6oK4+OG0jTRtrpKhIkFb1Um315hdwyaxnLth/g+xefyW0X9tZOV5EYoKKXavHRnkNMei7M/iOFPHXtUC4b2CXoSCISoaKX07Z4/cfcPnsFLZs25OVbzmFgcpugI4lIBSp6OWXuzox3tvLgP9dzdtfWzLhhOJ0TtdNVJNY0iGaQmY0xsw1mlmFm91axvomZzY2sX2JmqZXWdzezPDO7u5pyS8CKSsr44aur+Z9/rOeS/p155ZYvqORFYtRJt+jNLAF4CrgYyALSzWyBu6+rMGwScMDde5vZBOBhYHyF9b8G/ll9sSVIOUeKuPWFZSzdmsNtF/bmexedSYMG2ukqEquimboZAWS4+xYAM5sDjAUqFv1Y4OeR2/OA35mZubub2ZXAVuBIdYWW4Gz6+DCTZobZc6iAJycMZuzgbkFHEpGTiGbqphuQWeF+VmRZlWPcvQTIBdqbWUvgh8D9J3oAM5tiZmEzC2dnZ0ebXWrZvzfs5arfv0d+USlzpoxSyYvUEVHN0Z+GnwOPu3veiQa5+3R3D7l7KCkpqYYjyefl7jz37lZufi6d5HbN+evUcxnavW3QsUQkStFM3ewEUircT44sq2pMlpk1BBKB/cBIYJyZPQK0AcrMrMDdf3e6waV2uDsPvfYRf3xrCxed1YknJwymRRMdrCVSl0TzG5sOpJlZT8oLfQJwbaUxC4AbgfeBccCb7u7A+ccGmNnPgTyVfN1RVub8bMFaZn2wnetGdueBsf1J0E5XkTrnpEXv7iVmNhVYBCQAz7j7WjN7AAi7+wLgaWCWmWUAOZS/GEgdVlJaxg9eXc385Tu55YJe3HtJX53OQKSOsvIN79gRCoU8HA4HHaNeKywp5Y7ZK3lt7R7uuvhMpuqcNSIxz8yWuXuoqnWabJVPOVpUyq0vLOOtjdn85Gv9mHRez6AjichpUtHLJw4XFDNpZpj0bTk8dNUAJozoHnQkEakGKnoB4MCRIm56dilrdx3iyQlDuGJQ16AjiUg1UdELew8XcP2MpWzdf4RpE4dxUb9OQUcSkWqkoq/ndh48ysQZS9iTW8CzNw3n3N4dgo4kItVMRV+Pbd13hIkzlnCooJgXJo9gWI92QUcSkRqgoq+nPtpziIkzllLmzuxvj6J/t8SgI4lIDVHR10OrMg9y47NLadKwAbMnjSKtU6ugI4lIDVLR1zNLtuxn0swwbZo34qXJo+jevnnQkUSkhqno65G3NmZzy6ww3do048XJo3RFKJF6QkVfT7z24W5um72CtI6teH7SCDq0bBJ0JBGpJSr6emD+8izumbeaQcmJPPutESQ2axR0JBGpRTV94REJ2KwPtvP9l1cxsmc7Zk0aqZIXqYe0RR/H/vjWZn71z48Y3bcjT103lKaNEoKOJCIBUNHHIXfn169v5LdvZvC1gV14fPxgGiXojzeR+kpFH2fcnQf+vo5n393GN0PJ/OqqgboqlEg9p6KPI2Vlzo/+vIY56Zl869xUfnJZPxqo5EXqPRV9nDi2JT8nPZPvfvkM7v5KH10VSkQAHXUTNx5/fSPPvbeNyef1VMmLyKeo6OPAn97ewm/ezGB8KIX/vuwslbyIfEpURW9mY8xsg5llmNm9VaxvYmZzI+uXmFlqZPkIM1sZ+VhlZl+v5vz13tz0HfzPP9Zz6YDOPHjVAJW8iHzGSYvezBKAp4BLgH7ANWbWr9KwScABd+8NPA48HFn+IRBy98HAGOCPZqb9AtVk4erd3Dd/DRecmcTj4wfr6BoRqVI0W/QjgAx33+LuRcAcYGylMWOBmZHb84DRZmbunu/uJZHlTQGvjtAC/96wlzvnrmBo97ZMmziUJg31ZigRqVo0Rd8NyKxwPyuyrMoxkWLPBdoDmNlIM1sLrAFurVD8nzCzKWYWNrNwdnb2538W9Uz6thxufWEZaR1b8fRNw2neWH8kicjx1fjOWHdf4u5nA8OB+8zsM+fGdffp7h5y91BSUlJNR6rTPtyZy83PptM1sRnPT9IJykTk5KIp+p1ASoX7yZFlVY6JzMEnAvsrDnD39UAe0P9Uw9Z3GXvzuOGZpbRu1ogXJo/UqYZFJCrRFH06kGZmPc2sMTABWFBpzALgxsjtccCb7u6Rz2kIYGY9gL7AtmpJXs9kHcjn+qeX0MBg1qQRdG3TLOhIIlJHnHRy191LzGwqsAhIAJ5x97Vm9gAQdvcFwNPALDPLAHIofzEAOA+418yKgTLgO+6+ryaeSDzLPlzIxBlLyCssYe6Uc+iV1DLoSCJSh5h7bB0IEwqFPBwOBx0jZuTmFzN++vts35/PC5NHMKxHu6AjiUgMMrNl7h6qap3eGRvDjhSW8K3nlrIl+wjTbximkheRU6Kij1GFJaXc+sIyVmYe5DfXDOb8NB2NJCKnRgdgx6CS0jLumL2Sdzbt45FxAxnTv0vQkUSkDtMWfYwpK3Punb+G19bu4adf68c3Qykn/yQRkRNQ0ceQY+eUn7csizsvSuPm83oGHUlE4oCKPoY88cYmnntvGzef25M7RqcFHUdE4oSKPkbMeGcLTy7exNXDkvmxzikvItVIRR8DXk7P5JcL13NJ/8786qoBus6riFQrFX3A/rFmN/fOX835aR14YsJgGibov0REqpdaJUBvbczmjjkrGNK9LX+8fpjOKS8iNUJFH5DwthxumRUmrWMrntE55UWkBqnoA7Bt3xEmPx+mS2IzZt6sc8qLSM1S0dey3KPFTJqZDsCzNw0nqZXOKS8iNUtFX4tKSsuY+tJyduTkM23iMFI7tAg6kojUA5oYrkX3/21d+flrvjGQUb3aBx1HROoJbdHXkpnvbWPWB9uZckEvvjlc568Rkdqjoq8Fb23M5v6/reWiszrxwzF9g44jIvWMir6Gbfr4MFNfXM6ZnVrx5ITBJOhdryJSy1T0NSjnSBGTZoZp0iiBp28aTosm2iUiIrVPzVNDCktKuXXWMvYcKmDOlFF0a9Ms6EgiUk9FtUVvZmPMbIOZZZjZvVWsb2JmcyPrl5hZamT5xWa2zMzWRP69sJrzxyR358d//pCl23J4dNxAhnZvG3QkEanHTlr0ZpYAPAVcAvQDrjGzfpWGTQIOuHtv4HHg4cjyfcDl7j4AuBGYVV3BY9n0t7fwyrIsbh+dxtjB3YKOIyL1XDRb9COADHff4u5FwBxgbKUxY4GZkdvzgNFmZu6+wt13RZavBZqZWVy/FfT1dR/z0GsfcdmALtypi4eISAyIpui7AZkV7mdFllU5xt1LgFyg8juCvgEsd/fCyg9gZlPMLGxm4ezs7Gizx5y1u3K5Y84KBnRL5LGrB+m88iISE2rlqBszO5vy6Zxbqlrv7tPdPeTuoaSkpNqIVO32Hi7g2zPDtG7aiBk3hGjWWKccFpHYEE3R7wQqvpUzObKsyjFm1hBIBPZH7icDfwZucPfNpxs4FhUUl/Lt55dxIL+YGTeG6Ni6adCRREQ+EU3RpwNpZtbTzBoDE4AFlcYsoHxnK8A44E13dzNrAywE7nX3d6spc0xxd+6Zt5pVmQd5fPxg+ndLDDqSiMinnLToI3PuU4FFwHrgZXdfa2YPmNkVkWFPA+3NLAP4PnDsEMypQG/gp2a2MvLRsdqfRYCeXLyJv63axQ/G9GFM/85BxxER+Qxz96AzfEooFPJwOBx0jKj8bdUubpu9gquGduN/rx6EmXa+ikgwzGyZu4eqWqdTIJyilZkHufuVVQxPbcuvrhqgkheRmKWiPwW7Dh7l28+H6di6CdMm6qLeIhLbdK6bz+lIYQmTZ4Y5WlTKi5NH0r5lXL//S0TigLboP4eyMud7c1fy0Z5D/PbaIZzZqVXQkURETkpF/zk8smgD/1r3MT++rB9f7hNXBw+JSBxT0UfplXAm097azLUju/Otc1ODjiMiEjUVfRSWbs3hR39ew7m923P/FWfrCBsRqVNU9CeRmZPPLbPCpLRtzu+vHUajBH3LRKRuUWudQEFxKbe+sIzSMufpm4aT2LxR0JFERD43HV55Ar/4+zrW7jrE0zeG6NmhRdBxREROibboj+OvK3fy4pId3PLFXow+q1PQcURETpmKvgoZew9z3/w1DE9ty91f6RN0HBGR06KiryS/qITvvLicZo0S+O01Q7XzVUTqPM3RV+Du/PgvH7Jpbx7P3zyCzom6gIiI1H3aXK3g5XAm85fv5PYL0zg/rW5e0lBEpDIVfcS6XYf46V/Xcl7vDtw+Oi3oOCIi1UZFDxwuKOa7Ly0nsVkjnpgwmIQGeueriMSPej9H7+7c++oaduTk89LkkXTQaYdFJM7U+y3659/fzsI1u7n7K30Y2at90HFERKpdvS76lZkH+eXCdYzu25FbLugVdBwRkRoRVdGb2Rgz22BmGWZ2bxXrm5jZ3Mj6JWaWGlne3sz+z8zyzOx31Zz9tBzML+K7Ly6nY6um/O83B9FA8/IiEqdOWvRmlgA8BVwC9AOuMbN+lYZNAg64e2/gceDhyPIC4CfA3dWWuBqUlTl3vbyKvYcLeOq6obRp3jjoSCIiNSaaLfoRQIa7b3H3ImAOMLbSmLHAzMjtecBoMzN3P+Lu/6G88GPG9He2sPijvfz3pWcxOKVN0HFERGpUNEXfDciscD8rsqzKMe5eAuQCUe/ZNLMpZhY2s3B2dna0n3ZKlm7N4dFFG7h0QGdu/EJqjT6WiEgsiImdse4+3d1D7h5KSqq5d6TuyyvkttnLSWnbjIe+MVBXihKReiGaot8JpFS4nxxZVuUYM2sIJAL7qyNgdSktc+6cs5ID+cX8/rphtG6qi4iISP0QTdGnA2lm1tPMGgMTgAWVxiwAbozcHge86e5efTFP32/f3MR/MvbxwBVn069r66DjiIjUmpO+M9bdS8xsKrAISACecfe1ZvYAEHb3BcDTwCwzywByKH8xAMDMtgGtgcZmdiXwFXdfV+3P5AT+s2kfTy7exFVDujF+eMrJP0FEJI5YjG14EwqFPBwOV9vX25NbwGW/eYd2LRrz16nn0rxxvT/rg4jEITNb5u6hqtbFdeuVlJZx2+zl5BeVMveWoSp5EamX4rr5HvvXRtK3HeDx8YPo3bFV0HFERAIRE4dX1oTF6z9m2lubuWZEd74+JDnoOCIigYnLos/Myef7L6+iX5fW/OzyymdrEBGpX+Ku6ItKypj60nLKypzfXzeUpo0Sgo4kIhKouJujf/Af61mVlcu0iUNJ7dAi6DgiIoGLqy36hat389x727j53J6M6d8l6DgiIjEhbop+674j/PDV1QxOacO9l/QNOo6ISMyIm6Jv2MAY0r0NT103lMYN4+ZpiYictriZo09p15xZk0YGHUNEJOZo01dEJM6p6EVE4pyKXkQkzqnoRUTinIpeRCTOqehFROKcil5EJM6p6EVE4lzMXUrQzLKB7afxJToA+6opTk2rS1mhbuVV1ppTl/LWpaxwenl7uHtSVStiruhPl5mFj3fdxFhTl7JC3cqrrDWnLuWtS1mh5vJq6kZEJM6p6EVE4lw8Fv30oAN8DnUpK9StvMpac+pS3rqUFWoob9zN0YuIyKfF4xa9iIhUoKIXEYlzcVP0ZjbGzDaYWYaZ3Rt0nhMxsxQz+z8zW2dma83sjqAznYyZJZjZCjP7e9BZTsbM2pjZPDP7yMzWm9k5QWc6HjP7XuRn4EMzm21mTYPOVJGZPWNme83swwrL2pnZ62a2KfJv2yAzHnOcrI9Gfg5Wm9mfzaxNgBE/paq8FdbdZWZuZh2q47HioujNLAF4CrgE6AdcY2b9gk11QiXAXe7eDxgFfDfG8wLcAawPOkSUngRec/e+wCBiNLeZdQNuB0Lu3h9IACYEm+ozngPGVFp2L7DY3dOAxZH7seA5Ppv1daC/uw8ENgL31XaoE3iOz+bFzFKArwA7quuB4qLogRFAhrtvcfciYA4wNuBMx+Xuu919eeT2YcqLqFuwqY7PzJKBy4AZQWc5GTNLBC4AngZw9yJ3PxhoqBNrCDQzs4ZAc2BXwHk+xd3fBnIqLR4LzIzcnglcWZuZjqeqrO7+L3cvidz9AEiu9WDHcZzvLcDjwA+AajtSJl6KvhuQWeF+FjFcnBWZWSowBFgScJQTeYLyH7yygHNEoyeQDTwbmWqaYWYtgg5VFXffCTxG+ZbbbiDX3f8VbKqodHL33ZHbe4BOQYb5HG4G/hl0iBMxs7HATndfVZ1fN16Kvk4ys5bAq8Cd7n4o6DxVMbOvAXvdfVnQWaLUEBgK/MHdhwBHiJ2phU+JzG2PpfzFqSvQwswmBpvq8/Hy47Nj/hhtM/tvyqdMXww6y/GYWXPgR8BPq/trx0vR7wRSKtxPjiyLWWbWiPKSf9Hd5wed5wTOBa4ws22UT4ldaGYvBBvphLKALHc/9hfSPMqLPxZdBGx192x3LwbmA18IOFM0PjazLgCRf/cGnOeEzOwm4GvAdR7bbxw6g/IX/VWR37dkYLmZdT7dLxwvRZ8OpJlZTzNrTPkOrQUBZzouMzPK55DXu/uvg85zIu5+n7snu3sq5d/XN909Zrc63X0PkGlmfSKLRgPrAox0IjuAUWbWPPIzMZoY3XFcyQLgxsjtG4G/BpjlhMxsDOXTjle4e37QeU7E3de4e0d3T438vmUBQyM/06clLoo+srNlKrCI8l+Ul919bbCpTuhc4HrKt45XRj4uDTpUHLkNeNHMVgODgQeDjVO1yF8d84DlwBrKfx9j6i37ZjYbeB/oY2ZZZjYJeAi42Mw2Uf5XyUNBZjzmOFl/B7QCXo/8nk0LNGQFx8lbM48V23/JiIjI6YqLLXoRETk+Fb2ISJxT0YuIxDkVvYhInFPRi4jEORW9iEicU9GLiMS5/wd7wkQUMnwNkwAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(vel_profile)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "At the FreeSlip wall the gradient of the velocity should be almost zero"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "np.testing.assert_almost_equal(np.gradient(vel_profile)[-1], 0, decimal=3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/lbmpy_tests/test_free_slip.py b/lbmpy_tests/test_free_slip.py
new file mode 100644
index 00000000..0aa16180
--- /dev/null
+++ b/lbmpy_tests/test_free_slip.py
@@ -0,0 +1,80 @@
+from dataclasses import replace
+
+import pystencils as ps
+import lbmpy as lp
+from pystencils.slicing import slice_from_direction
+
+from lbmpy.boundaries.boundaryhandling import LatticeBoltzmannBoundaryHandling
+from lbmpy.boundaries.boundaryconditions import FreeSlip, NoSlip, ExtrapolationOutflow, UBB
+
+from lbmpy.creationfunctions import create_lb_method, create_lb_update_rule
+from lbmpy.macroscopic_value_kernels import pdf_initialization_assignments
+
+import numpy as np
+
+
+def velocity_info_callback(boundary_data, **_):
+ boundary_data['vel_1'] = 0
+ boundary_data['vel_2'] = 0
+ u_max = 0.1
+ x, y = boundary_data.link_positions(0), boundary_data.link_positions(1)
+ dist = (15 - y) / 15
+ boundary_data['vel_0'] = u_max * (1 - dist)
+
+
+def test_free_slip():
+ stencil = lp.LBStencil(lp.Stencil.D3Q27)
+ domain_size = (30, 15, 30)
+ dim = len(domain_size)
+
+ dh = ps.create_data_handling(domain_size=domain_size)
+
+ src = dh.add_array('src', values_per_cell=stencil.Q)
+ dh.fill('src', 0.0, ghost_layers=True)
+ dst = dh.add_array('dst', values_per_cell=stencil.Q)
+ dh.fill('dst', 0.0, ghost_layers=True)
+
+ velField = dh.add_array('velField', values_per_cell=stencil.D)
+ dh.fill('velField', 0.0, ghost_layers=True)
+
+ lbm_config = lp.LBMConfig(stencil=stencil, method=lp.Method.SRT, relaxation_rate=1.8,
+ output={'velocity': velField}, kernel_type='stream_pull_collide')
+ method = create_lb_method(lbm_config=lbm_config)
+
+ init = pdf_initialization_assignments(method, 1.0, (0, 0, 0), src.center_vector)
+
+ config = ps.CreateKernelConfig(target=dh.default_target)
+ ast_init = ps.create_kernel(init, config=config)
+ kernel_init = ast_init.compile()
+
+ dh.run_kernel(kernel_init)
+
+ lbm_opt = lp.LBMOptimisation(symbolic_field=src, symbolic_temporary_field=dst)
+ lbm_config = replace(lbm_config, lb_method=method)
+
+ update = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
+
+ ast_kernel = ps.create_kernel(update, config=config)
+ kernel = ast_kernel.compile()
+
+ bh = LatticeBoltzmannBoundaryHandling(method, dh, 'src', name="bh")
+
+ inflow = UBB(velocity_info_callback, dim=dim)
+ outflow = ExtrapolationOutflow(stencil[4], method)
+ wall = NoSlip("wall")
+ freeslip = FreeSlip(stencil, (0, -1, 0))
+
+ bh.set_boundary(inflow, slice_from_direction('W', dim))
+ bh.set_boundary(outflow, slice_from_direction('E', dim))
+ bh.set_boundary(wall, slice_from_direction('S', dim))
+ bh.set_boundary(wall, slice_from_direction('T', dim))
+ bh.set_boundary(wall, slice_from_direction('B', dim))
+ bh.set_boundary(freeslip, slice_from_direction('N', dim))
+
+ for i in range(2000):
+ bh()
+ dh.run_kernel(kernel)
+ dh.swap("src", "dst")
+
+ vel_profile = dh.gather_array('velField')[-2, :, domain_size[2] // 2, 0]
+ np.testing.assert_almost_equal(np.gradient(vel_profile)[-1], 0, decimal=3)
diff --git a/lbmpy_tests/test_oldroydb.py b/lbmpy_tests/test_oldroydb.py
index a4a52286..663923a9 100755
--- a/lbmpy_tests/test_oldroydb.py
+++ b/lbmpy_tests/test_oldroydb.py
@@ -1,50 +1,42 @@
-import pystencils as ps
-from lbmpy.stencils import get_stencil
from lbmpy.updatekernels import create_stream_pull_with_output_kernel
from lbmpy import create_lb_update_rule, relaxation_rate_from_lattice_viscosity, ForceModel, Method, LBStencil
from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
from pystencils.boundaries.boundaryhandling import BoundaryHandling
-from pystencils.boundaries.boundaryconditions import Boundary, Neumann, Dirichlet
+from pystencils.boundaries.boundaryconditions import Neumann, Dirichlet
from lbmpy.boundaries.boundaryhandling import LatticeBoltzmannBoundaryHandling
from lbmpy.boundaries import NoSlip
from lbmpy.oldroydb import *
import pytest
-
# # Lattice Boltzmann with Finite-Volume Oldroyd-B
# # taken from the electronic supplement of https://doi.org/10.1140/epje/s10189-020-00005-6,
# # available at https://doi.org/10.24416/UU01-2AFZSW
pytest.importorskip('scipy.optimize')
+
+
def test_oldroydb():
import scipy.optimize
# ## Definitions
- # In[2]:
-
-
L = (34, 34)
lambda_p = sp.Symbol("lambda_p")
eta_p = sp.Symbol("eta_p")
lb_stencil = LBStencil("D2Q9")
fv_stencil = LBStencil("D2Q9")
- eta = 1-eta_p
+ eta = 1 - eta_p
omega = relaxation_rate_from_lattice_viscosity(eta)
f_pre = 0.00001
-
# ## Data structures
- # In[3]:
-
-
dh = ps.create_data_handling(L, periodicity=(True, False), default_target=ps.Target.CPU)
- opts = {'cpu_openmp': True,
+ opts = {'cpu_openmp': False,
'cpu_vectorize_info': None,
'target': dh.default_target}
@@ -52,11 +44,11 @@ def test_oldroydb():
dst = dh.add_array_like('dst', 'src')
Ï = dh.add_array('rho', layout='c', latex_name='\\rho')
u = dh.add_array('u', values_per_cell=dh.dim, layout='c')
- tauface = dh.add_array('tau_face', values_per_cell=(len(fv_stencil)//2, dh.dim, dh.dim), latex_name='\\tau_f',
+ tauface = dh.add_array('tau_face', values_per_cell=(len(fv_stencil) // 2, dh.dim, dh.dim), latex_name='\\tau_f',
field_type=ps.FieldType.STAGGERED, layout='c')
tau = dh.add_array('tau', values_per_cell=(dh.dim, dh.dim), layout='c', latex_name='\\tau')
- tauflux = dh.add_array('j_tau', values_per_cell=(len(fv_stencil)//2, dh.dim, dh.dim), latex_name='j_\\tau',
+ tauflux = dh.add_array('j_tau', values_per_cell=(len(fv_stencil) // 2, dh.dim, dh.dim), latex_name='j_\\tau',
field_type=ps.FieldType.STAGGERED_FLUX, layout='c')
F = dh.add_array('F', values_per_cell=dh.dim, layout='c')
@@ -67,18 +59,14 @@ def test_oldroydb():
taufacebh = BoundaryHandling(dh, tauface.name, fv_stencil, name="tauface_boundary_handling",
openmp=opts['cpu_openmp'], target=dh.default_target)
-
# ## Solver
- # In[4]:
-
-
collision = create_lb_update_rule(stencil=lb_stencil,
method=Method.TRT,
- relaxation_rate=omega,
+ relaxation_rate=omega,
compressible=True,
- force_model=ForceModel.GUO,
- force=F.center_vector+sp.Matrix([f_pre,0]),
+ force_model=ForceModel.GUO,
+ force=F.center_vector + sp.Matrix([f_pre, 0]),
kernel_type='collide_only',
optimization={'symbolic_field': src})
@@ -90,23 +78,15 @@ def test_oldroydb():
stream_kernel = ps.create_kernel(stream, **opts).compile()
collision_kernel = ps.create_kernel(collision, **opts).compile()
-
- # In[5]:
-
-
ob = OldroydB(dh.dim, u, tau, F, tauflux, tauface, lambda_p, eta_p)
flux_kernel = ps.create_staggered_kernel(ob.flux(), **opts).compile()
tauface_kernel = ps.create_staggered_kernel(ob.tauface(), **opts).compile()
continuity_kernel = ps.create_kernel(ob.continuity(), **opts).compile()
force_kernel = ps.create_kernel(ob.force(), **opts).compile()
-
# ## Set up the simulation
- # In[6]:
-
-
- init = macroscopic_values_setter(collision.method, velocity=(0,)*dh.dim,
+ init = macroscopic_values_setter(collision.method, velocity=(0,) * dh.dim,
pdfs=src.center_vector, density=Ï.center)
init_kernel = ps.create_kernel(init, ghost_layers=0).compile()
@@ -116,8 +96,8 @@ def test_oldroydb():
nostressdiff = Flux(fv_stencil, tau.center_vector)
# put some good values into the boundaries so we can take derivatives
- noforce = Neumann() # put the same stress into the boundary cells that is in the nearest fluid cell
- noflow = Dirichlet((0,)*dh.dim) # put zero velocity into the boundary cells
+ noforce = Neumann() # put the same stress into the boundary cells that is in the nearest fluid cell
+ noflow = Dirichlet((0,) * dh.dim) # put zero velocity into the boundary cells
lbbh.set_boundary(noslip, ps.make_slice[:, :4])
lbbh.set_boundary(noslip, ps.make_slice[:, -4:])
@@ -141,141 +121,129 @@ def test_oldroydb():
dh.fill(tauflux.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
dh.fill(tauface.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
dh.fill(F.name, np.nan, ghost_layers=True, inner_ghost_layers=True)
- dh.fill(F.name, 0) # needed for LB initialization
-
- sync_tau() # force calculation inside the initialization needs neighbor taus
+ dh.fill(F.name, 0) # needed for LB initialization
+
+ sync_tau() # force calculation inside the initialization needs neighbor taus
dh.run_kernel(init_kernel)
dh.fill(F.name, np.nan)
-
- # In[7]:
-
-
- sync_pdfs = dh.synchronization_function([src.name]) # needed before stream, but after collision
- sync_u = dh.synchronization_function([u.name]) # needed before continuity, but after stream
- sync_tau = dh.synchronization_function([tau.name]) # needed before flux and tauface, but after continuity
+ sync_pdfs = dh.synchronization_function([src.name]) # needed before stream, but after collision
+ sync_u = dh.synchronization_function([u.name]) # needed before continuity, but after stream
+ sync_tau = dh.synchronization_function([tau.name]) # needed before flux and tauface, but after continuity
def time_loop(steps, lambda_p_val, eta_p_val):
dh.all_to_gpu()
- vmid = np.empty((2,steps//10+1))
+ vmid = np.empty((2, steps // 10 + 1))
sync_tau()
sync_u()
ubh()
i = -1
for i in range(steps):
dh.run_kernel(flux_kernel)
- fluxbh() # zero the fluxes into/out of boundaries
+ fluxbh() # zero the fluxes into/out of boundaries
dh.run_kernel(continuity_kernel, **{lambda_p.name: lambda_p_val, eta_p.name: eta_p_val})
-
+
sync_tau()
- dh.run_kernel(tauface_kernel) # needed for force
+ dh.run_kernel(tauface_kernel) # needed for force
taufacebh()
dh.run_kernel(force_kernel)
-
+
dh.run_kernel(collision_kernel, **{eta_p.name: eta_p_val})
sync_pdfs()
- lbbh() # bounce-back populations into boundaries
+ lbbh() # bounce-back populations into boundaries
dh.run_kernel(stream_kernel)
sync_u()
- ubh() # need neighboring us for flux and continuity
-
+ ubh() # need neighboring us for flux and continuity
+
dh.swap(src.name, dst.name)
-
+
if i % 10 == 0:
if u.name in dh.gpu_arrays:
dh.to_cpu(u.name)
uu = dh.gather_array(u.name)
- uu = uu[L[0]//2-1:L[0]//2+1, L[1]//2-1:L[1]//2+1, 0].mean()
+ uu = uu[L[0] // 2 - 1:L[0] // 2 + 1, L[1] // 2 - 1:L[1] // 2 + 1, 0].mean()
if np.isnan(uu):
raise Exception(f"NaN encountered after {i} steps")
- vmid[:, i//10] = [i, uu]
+ vmid[:, i // 10] = [i, uu]
sync_u()
dh.all_to_cpu()
-
- return vmid[:,:i//10+1]
+ return vmid[:, :i // 10 + 1]
# ## Analytical solution
#
# comes from Waters and King, Unsteady flow of an elastico-viscous liquid, Rheologica Acta 9, 345–355 (1970).
- # In[9]:
-
-
def N(n):
- return (2*n-1)*np.pi
+ return (2 * n - 1) * np.pi
def Alpha_n(N, El, eta_p):
- return 1+(1-eta_p)*El*N*N/4
+ return 1 + (1 - eta_p) * El * N * N / 4
- def Beta_n(alpha_n,N, El):
- return np.sqrt(np.abs(alpha_n*alpha_n - El*N*N))
+ def Beta_n(alpha_n, N, El):
+ return np.sqrt(np.abs(alpha_n * alpha_n - El * N * N))
def Gamma_n(N, El, eta_p):
- return 1-(1+eta_p)*El*N*N/4
+ return 1 - (1 + eta_p) * El * N * N / 4
- def G(alpha_n,beta_n,gamma_n,flag,T):
- if(flag):
- return ((1.0 - gamma_n/beta_n)*np.exp(-(alpha_n+beta_n)*T/2) +
- (1.0 + gamma_n/beta_n)*np.exp((beta_n-alpha_n)*T/2))
+ def G(alpha_n, beta_n, gamma_n, flag, T):
+ if (flag):
+ return ((1.0 - gamma_n / beta_n) * np.exp(-(alpha_n + beta_n) * T / 2) +
+ (1.0 + gamma_n / beta_n) * np.exp((beta_n - alpha_n) * T / 2))
else:
- return 2*np.exp(-alpha_n*T/2)*(np.cos(beta_n*T/2) + (gamma_n/beta_n)*np.sin(beta_n*T/2))
+ return 2 * np.exp(-alpha_n * T / 2) * (np.cos(beta_n * T / 2) + (gamma_n / beta_n) * np.sin(beta_n * T / 2))
def W(T, El, eta_p):
W_ = 1.5
- for n in range(1,1000):
+ for n in range(1, 1000):
N_ = N(n)
alpha_n = Alpha_n(N_, El, eta_p)
-
- if(alpha_n*alpha_n - El*N_*N_ < 0):
+
+ if alpha_n * alpha_n - El * N_ * N_ < 0:
flag_ = False
else:
flag_ = True
- beta_n = Beta_n(alpha_n,N_, El)
+ beta_n = Beta_n(alpha_n, N_, El)
gamma_n = Gamma_n(N_, El, eta_p)
- G_=G(alpha_n,beta_n,gamma_n,flag_,T)
+ G_ = G(alpha_n, beta_n, gamma_n, flag_, T)
- W_ -= 24*(np.sin(N_/2)/(N_*N_*N_))*G_
+ W_ -= 24 * (np.sin(N_ / 2) / (N_ * N_ * N_)) * G_
return W_
-
# ## Run the simulation
- # In[11]:
-
-
lambda_p_val = 3000
eta_p_val = 0.9
init()
- vmid = time_loop(lambda_p_val*4, lambda_p_val, eta_p_val)
+ vmid = time_loop(lambda_p_val * 4, lambda_p_val, eta_p_val)
- actual_width = sum(dh.gather_array(lbbh.flag_array_name)[L[0]//2,:] == 1)
- uref = float(f_pre*actual_width**2/(8*(eta+eta_p)))
+ actual_width = sum(dh.gather_array(lbbh.flag_array_name)[L[0] // 2, :] == 1)
+ uref = float(f_pre * actual_width ** 2 / (8 * (eta + eta_p)))
- Wi = lambda_p_val*uref/(actual_width/2)
- Re = uref*(actual_width/2)/(eta+eta_p)
- El = float(Wi/Re)
+ Wi = lambda_p_val * uref / (actual_width / 2)
+ Re = uref * (actual_width / 2) / (eta + eta_p)
+ El = float(Wi / Re)
- pref = 1/W(vmid[0,-1]/lambda_p_val, El, eta_p_val)
+ pref = 1 / W(vmid[0, -1] / lambda_p_val, El, eta_p_val)
- El_measured, pref_measured = scipy.optimize.curve_fit(lambda a, b, c: W(a, b, eta_p_val)*c,
- vmid[0,:]/lambda_p_val, vmid[1,:]/vmid[1,-1],
+ El_measured, pref_measured = scipy.optimize.curve_fit(lambda a, b, c: W(a, b, eta_p_val) * c,
+ vmid[0, :] / lambda_p_val, vmid[1, :] / vmid[1, -1],
p0=(El, pref))[0]
- measured_width = np.sqrt(4*lambda_p_val*float(eta+eta_p)/El_measured)
-
+ measured_width = np.sqrt(4 * lambda_p_val * float(eta + eta_p) / El_measured)
+
print(f"El={El}, El_measured={El_measured}")
print(f"L={actual_width}, L_measured={measured_width}")
assert abs(measured_width - actual_width) < 1, "effective channel width differs significantly from defined width"
- an = W(vmid[0,:]/lambda_p_val, El, eta_p_val)*pref
- an_measured = W(vmid[0,:]/lambda_p_val, El_measured, eta_p_val)*pref_measured
+ an = W(vmid[0, :] / lambda_p_val, El, eta_p_val) * pref
+ an_measured = W(vmid[0, :] / lambda_p_val, El_measured, eta_p_val) * pref_measured
- diff = abs(vmid[1,:]/vmid[1,-1]-an_measured)/an_measured
- assert diff[lambda_p_val//5:].max() < 0.03, "maximum velocity deviation is too large"
+ diff = abs(vmid[1, :] / vmid[1, -1] - an_measured) / an_measured
+ assert diff[lambda_p_val // 5:].max() < 0.03, "maximum velocity deviation is too large"
# from pystencils import plot as plt
#
--
GitLab
From 2d62dcaac59c77e179a73c443c143bde499b0398 Mon Sep 17 00:00:00 2001
From: Markus Holzer <markus.holzer@fau.de>
Date: Tue, 29 Mar 2022 11:40:39 +0200
Subject: [PATCH 6/6] Minor changes
---
lbmpy/boundaries/boundaryhandling.py | 2 +-
.../phasefield/test_entropic_model.ipynb | 36 ++---
.../test_n_phase_boyer_noncoupled.ipynb | 143 +++++-------------
lbmpy_tests/test_fluctuating_lb.py | 2 +-
lbmpy_tests/test_force.py | 2 +-
lbmpy_tests/test_free_slip.py | 2 +-
pytest.ini | 2 +-
7 files changed, 60 insertions(+), 129 deletions(-)
diff --git a/lbmpy/boundaries/boundaryhandling.py b/lbmpy/boundaries/boundaryhandling.py
index 60c00dfe..865e081b 100644
--- a/lbmpy/boundaries/boundaryhandling.py
+++ b/lbmpy/boundaries/boundaryhandling.py
@@ -18,7 +18,7 @@ class LatticeBoltzmannBoundaryHandling(BoundaryHandling):
"""
def __init__(self, lb_method, data_handling, pdf_field_name, streaming_pattern='pull',
- name="boundary_handling", flag_interface=None, target=Target.CPU, openmp=True):
+ name="boundary_handling", flag_interface=None, target=Target.CPU, openmp=False):
self._lb_method = lb_method
self._streaming_pattern = streaming_pattern
self._inplace = is_inplace(streaming_pattern)
diff --git a/lbmpy_tests/phasefield/test_entropic_model.ipynb b/lbmpy_tests/phasefield/test_entropic_model.ipynb
index 1d33d24e..7dc3c29d 100644
--- a/lbmpy_tests/phasefield/test_entropic_model.ipynb
+++ b/lbmpy_tests/phasefield/test_entropic_model.ipynb
@@ -47,7 +47,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/latex": [
"$\\displaystyle - \\frac{A \\omega}{2} + A + B \\omega + eq \\omega - fq \\omega + fq$"
],
@@ -108,7 +108,7 @@
"\n",
"fd_discretization = fd_stencils_isotropic_high_density_code\n",
"target = ps.Target.CPU\n",
- "threads = 4"
+ "threads = False"
]
},
{
@@ -129,7 +129,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
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\n",
"text/latex": [
"$\\displaystyle - 0.037 \\rho^{2} + \\frac{0.042578305 \\rho \\left(- 0.000125 \\rho^{3} + 0.0025 \\rho^{2} + 0.05 \\rho + 1\\right)}{\\left(1 - 0.05 \\rho\\right)^{3}}$"
],
@@ -170,7 +170,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
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\n",
"text/latex": [
"$\\displaystyle \\left( 0.0695273860315274, \\ 8.02904149705209, \\ 115.480272671423\\right)$"
],
@@ -202,20 +202,20 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/latex": [
- "$\\displaystyle 0.5 c_{l1}^{2} \\left(1 - c_{l1}\\right)^{2} + 0.5 c_{l2}^{2} \\left(1 - c_{l2}\\right)^{2} + 0.3 \\rho \\left(- 0.037 \\rho - \\frac{1.0 \\left(1.7031322 \\rho - 51.0939660000001\\right)}{1.0 \\rho^{2} - 40.0 \\rho + 400.0} + 0.042578305 \\log{\\left(1.0 \\rho \\right)} - 0.0530922164415325\\right) + 0.5 {\\partial c_{l1}}^{2} + 0.5 {\\partial c_{l2}}^{2} + 0.005 {\\partial \\rho}^{2} + 0.00085206629489322$"
+ "$\\displaystyle 0.5 c_{l1}^{2} \\left(1 - c_{l1}\\right)^{2} + 0.5 c_{l2}^{2} \\left(1 - c_{l2}\\right)^{2} + 0.3 \\rho \\left(- 0.037 \\rho - \\frac{1.0 \\cdot \\left(1.7031322 \\rho - 51.0939660000001\\right)}{1.0 \\rho^{2} - 40.0 \\rho + 400.0} + 0.042578305 \\log{\\left(1.0 \\rho \\right)} - 0.0530922164415325\\right) + 0.5 {\\partial c_{l1}}^{2} + 0.5 {\\partial c_{l2}}^{2} + 0.005 {\\partial \\rho}^{2} + 0.00085206629489322$"
],
"text/plain": [
- " 2 2 2 2 ⎛ 1.0â‹…(1.7031322â‹…Ï - 51.0939660000001) \n",
- "0.5â‹…câ‚—â‚ â‹…(1 - câ‚—â‚) + 0.5â‹…câ‚—â‚‚ â‹…(1 - câ‚—â‚‚) + 0.3â‹…Ï⋅⎜-0.037â‹…Ï - ──────────────────────────────────── + 0.042578305â‹…log(1.0â‹…Ï\n",
- " ⎜ 2 \n",
- " ⎠1.0â‹…Ï - 40.0â‹…Ï + 400.0 \n",
+ " 2 2 2 2 ⎛ 1.7031322â‹…Ï - 51.0939660000001 \n",
+ "0.5â‹…câ‚—â‚ â‹…(1 - câ‚—â‚) + 0.5â‹…câ‚—â‚‚ â‹…(1 - câ‚—â‚‚) + 0.3â‹…Ï⋅⎜-0.037â‹…Ï - ────────────────────────────── + 0.042578305â‹…log(1.0â‹…Ï) - 0.\n",
+ " ⎜ 2 \n",
+ " ⎠1.0â‹…Ï - 40.0â‹…Ï + 400.0 \n",
"\n",
- " ⎞ 2 2 2 \n",
- ") - 0.0530922164415325⎟ + 0.5⋅D(c_l1) + 0.5⋅D(c_l2) + 0.005⋅D(rho) + 0.00085206629489322\n",
- " ⎟ \n",
- " ⎠"
+ " ⎞ 2 2 2 \n",
+ "0530922164415325⎟ + 0.5⋅D(c_l1) + 0.5⋅D(c_l2) + 0.005⋅D(rho) + 0.00085206629489322\n",
+ " ⎟ \n",
+ " ⎠"
]
},
"execution_count": 7,
@@ -262,7 +262,7 @@
"outputs": [
{
"data": {
- "image/png": "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\n",
+ "image/png": "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\n",
"text/latex": [
"$\\displaystyle \\left\\{\\phi, \\rho\\right\\}$"
],
@@ -379,9 +379,9 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/latex": [
- "$\\displaystyle {{\\mu_{\\phi}}_{(0,0)}} \\leftarrow 0.0004 \\phi^{3} + 0.000473530700220886 \\phi \\rho^{2} - 0.00760399528440326 \\phi \\rho + 0.0205263968409311 \\phi - 0.02 {\\partial {\\partial \\phi}}$"
+ "$\\displaystyle {\\mu_{\\phi}}_{(0,0)} \\leftarrow 0.0004 \\phi^{3} + 0.000473530700220886 \\phi \\rho^{2} - 0.00760399528440326 \\phi \\rho + 0.0205263968409311 \\phi - 0.02 {\\partial {\\partial \\phi}}$"
],
"text/plain": [
" 3 2 \n",
@@ -816,7 +816,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.9"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb b/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb
index 81b6bd26..6958e868 100644
--- a/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb
+++ b/lbmpy_tests/phasefield/test_n_phase_boyer_noncoupled.ipynb
@@ -6,14 +6,16 @@
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "<module 'pycuda' from '/home/markus/miniconda3/envs/pystencils/lib/python3.8/site-packages/pycuda/__init__.py'>"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "Skipped",
+ "evalue": "could not import 'pycuda': No module named 'pycuda'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mSkipped\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/07/0d7kq8fd0sx24cs53zz90_qc0000gp/T/ipykernel_16968/622163826.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpytest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpytest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimportorskip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pycuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/_pytest/outcomes.py\u001b[0m in \u001b[0;36mimportorskip\u001b[0;34m(modname, minversion, reason)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"could not import {modname!r}: {exc}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mSkipped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_module_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mminversion\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mSkipped\u001b[0m: could not import 'pycuda': No module named 'pycuda'"
+ ]
}
],
"source": [
@@ -23,7 +25,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -49,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -69,7 +71,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -85,7 +87,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -103,7 +105,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -112,7 +114,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -123,7 +125,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -132,7 +134,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -142,7 +144,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -155,7 +157,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -164,15 +166,15 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"μ_sync = dh.synchronization_function(μ.name)\n",
"c_sync = dh.synchronization_function(c.name)\n",
- "optimization = {'cpu_openmp': 4, 'cpu_vectorize_info': None}\n",
+ "optimization = {'cpu_openmp': False, 'cpu_vectorize_info': None}\n",
"\n",
- "config = ps.CreateKernelConfig(cpu_openmp=4, target=dh.default_target)\n",
+ "config = ps.CreateKernelConfig(cpu_openmp=False, target=dh.default_target)\n",
"\n",
"μ_kernel = create_kernel(μ_assignments, config=config).compile()\n",
"c_kernel = create_kernel(c_assignments, config=config).compile()\n",
@@ -202,7 +204,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -215,7 +217,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -224,72 +226,27 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.phase_plot(dh.gather_array(c.name))"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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//tcO3U58YYH9sLAwyJ69Ggt8qIV2et/laljtRHYLjFlg9+Mxe+3oK7VAvLMQb+U/0QI9e+HwSnnvtHYL7BbYLbBbYLfAboELWUBngfg/Q7zs++T/IiSoD8TeysQAAAAASUVORK5CYII=\n",
- "text/latex": [
- "$\\displaystyle \\left[ 146.442690238079, \\ 117.818139284654, \\ 95.7391704772668\\right]$"
- ],
- "text/plain": [
- "[146.44269023807928, 117.81813928465394, 95.73917047726678]"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"neumann_angles_from_surface_tensions(lambda i, j: float(σ[i, j]))"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0 0.336687609999899 [75.97322089603733, 106.74183196592085, 177.28494713804187]\n",
- "1 0.26290948200039566 [75.82906362273266, 106.85098913683895, 177.3199472404283]\n",
- "2 0.2779139079993911 [75.63430957957638, 107.00770302824824, 177.3579873921755]\n",
- "3 0.26703732999976637 [75.41332394977111, 107.18964653942447, 177.39702951080454]\n",
- "4 0.26817269299954205 [75.1826458194294, 107.38183806648352, 177.43551611408705]\n",
- "5 0.26151279999976396 [74.95847110720459, 107.5698167786542, 177.47171211414116]\n",
- "6 0.2587056009997468 [74.75276623427364, 107.74235960736014, 177.5048741583663]\n",
- "7 0.2577263920002224 [74.58423615358463, 107.88207793021803, 177.53368591619756]\n",
- "8 0.2616421119992083 [74.4468008687682, 107.99262930599151, 177.56056982524032]\n",
- "9 0.2715399830003662 [64.84732273461297, 118.54619924713631, 176.60647801825087]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import time\n",
"for i in range(10):\n",
@@ -305,22 +262,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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J1zMnAH8AbB9ZtiXCNBy6GhieScYqG9bhzWaklTGu7eSxqaf8/tN7rRCu+4EXM/rnx5V5phlHt6pM63Fty/G5UH2UqPXGVXn4VI9ekrOYr64+vc5yGHPZm1j2jpLhk56u5q8/Tr0O9biy1LLd9V5UU36z19RWZ56unzf1CEz2Vh1Tne2CIAhCmfYJ170zwMZFyXhJoX1sX5a8X3jEKFwfOjsEAFx+cLCPRgnz4uBGsoTR2b1wyZasDhQz+ueSyZmajnFrKuYWmX/RtjShzbYsos5VOo+rZEsTVuk4BUEQmiFjXFeX0TlgsLNsK4Rpyc7d8Kxx97lh4pnaGbTv0hSA7YEPADg3FOE6gQEKY8UzUz2ZjFOFU9xiF8dZLu42Wj/GurGgdfmqW5mVunrKXsmirXrfmmstljN5Dot+R8/gUc0pemPr8pe9u8U8tvGXWSue8Tj1I8qPy5scX5KvPFa02H/VY1OLnmS97qrjzPqimF8/i8XjdRmTqtZpGgOLSS71U3FbPK6CIMwTBlq1FusstE8dDM8CAwlBbC3ZubMK12Sum+0WTOwjlDkwSDyu58TjOoGY4YfxnGttetPLhU/duGme1zHU1TPr/qoyVWVt+9jhs0v+WdNMx1CVVldvXZn83fy4waVNW7/XbVfVWb/NYO2WsgvfP0EQVomI1yOuo33qYHgW6ItwbS39Go/rXojtvg/PW48vYNcQj6uF9D7VPKIvzzLNVW/3I+kj+BbTnl6X3p5rnbpt2fZ0Htmyvbb2TP01r/bM57qcWvbJmT/pvl2TH1QvV/Zqmv3ELjbox2cqV+9lhVZaz2NLy8+KPU8xDSj3j3nbZrupB/Tjrm4PSDziahuCIAjzhEEyOdPKMjwLHDy6bCuEaRkcTN5H54y7zw9D7Gy077IUEgLfw0bPw3kRrgXI8tmWZ5q69XfX9poKNLUd0w39NMdRZbPtuKate97t6UK3ri/y0FU9H2nbpk/Fki7lMGmvuFe3w62usqgv21VuL6P8UKD+7Kj5WGm1yvYq+6v71Gy7Lu9NZV0/mc6/IAiC4Eb7FMLwrIxxbTOTMa5njLvPDUMZ39pydgYBzopwLcJ6qO6CmjHUXdferLY0a2+RR78cmglddfSonmpLc5lBt5knr7q9ekzH0KS96ocp9rrN5Uz5zXWY7Z7GC2qvw7b+a7UNgiAIsxHL5EwryuicjHFtM5MxrmaPqwjX9rMzCMTjWsG0sq1K8pm8fbO250Lz9lZPtM5qkct5KbdWDNSt86aaPKTldtyPxMV761rLtO3Nq257/iaxDfNrr+ghtp2XfBooQRCEWWHIOq6ry/BsPk5SaB+9LYC8ismZJFS47WwPApmcaQFUeTZdbrvrfJ2z+ELdyy7f4zpvC5p5s/NgWZqkmNZlNXnqyvkz+VOsASjPgFuu02UtWdXTq9cJZZ+pTpunWD9OmzfZvC6t3Vtt8xjbZhtWj1Pvl/JxopSn2OO2OpN307ldvUc4giC0FQbJ5EwrSTQGwr18nKTQPoiSybUsY1zP7YW4ZHtrn40S5snOIJDJmQxUTfrTVEw18/IlUE2eWX7yXMs2Gd9nn8ynroZynrLIr84zTat1+YveN330I5VSbPaRJU8eglpVl55HbUO/Qkw+RHOqahtrR6luc0U96j51JGm5DtM1ZB9XW/Zcm/IVrcofB9jzVO3XbbKfD/G4CoIgNKFdwjXz0skY13YzOCAe1w6zMwjwpTN7yzZjpbB5R6tCfG11VAlgvX4bukCoq7MJVXWWVzytt7FYNznYaRMv5hQXi2b1RKvzzOqewTpPYfauHnn2v7ms7o21WVYcR1v2ZBbbqKqTlLqKZ4gt+8rHbpPQ6rbpePV99WN1cy+oPlux+v8sorLKFlnDVRCERSDruK4iE+EqY1xbzWBHJmfqMDsbAc49JB7XKqoFa1FK6XnN/jhTyWJ6VsbkfSwultKEsjSz2Zf5+apFsp5q8tFV21nVBybPX9kX6YYqRvVSdjvLHsRyzqJ1pl4lbZ9ui837WK5dtaXqGExew2Ktup36+S5ebaosrFuoR7fJfFy5nfWPNfRjzrb1fjXh/qDDfBUU2xcBKwjC7DADkUzOtIJk4aUiXNvN4IBxciZmTpbDEeHaamRypjI2D5JLbrMAdGunLr1K5rgF5Vbf5Kt7ihKhWn7Yt+tx6YOm/VSdt75UUTCrIq84DrI8ZU9x3GRxPGgu/8iQxzQrMZcsUesDirUXrTDVWcxn9s4WvZcmYalL2eIjBltd+vjS8njUvF/VnjGPLy0fj23cb25jEdfxvQRd5AuCIMwKIV6TvyntUgiZx1UmZ2o3/R1jqPAwjBHGLKHCLWdnEOCsTM5UYJ4huPvFYtqa3otVxzzDnfU6Zy1f9JoXJbTNy2f3glZLcLW+okQqe02rPK36J/2Rgy2ffixlOalL17JnUv1kO2bdavODBPexw+b67f1rpun5EwRBmB2GeFxXk8xLJ5MztZvBAeDs/aXkbEIf8bi2m51BgGEYYxzF6Pnr8Ye0jipvpu1zXR3umGXCbHW6HUP5szkU1GTHtKJxnrKgqq4m5zDfNoWGFj2SukdQ92I2W2/VZvt0dVV5Us2fizWosyDXh8kWvat1djYdO1o1vtfFJpN9ppHIEg4sCIIwP9qlELJxkTI5U7sZHDR6XLMlVES4tpvt9PydH4Y4tNVfsjWriU20uYiu5sJs8b5T98/uPqemVu+3H6vp8dtS6krpXsx5eO2mrava4+tyJU93tbvYOe2xNO9P+1VNhtT6xx+CIAizI+u4riIyxrUbDHaMY1wzj+u2CNdWk4V6nxPhujDmFcI6T2YN1V1kKPQi+ms+k1kVvXfm9Vd1qsvra5bmpUzjNdXy+oRTVV7Mctn8+LJPqpdf91JWl6/3COv1UKm83rZ9zGp5TG3ZbhOmYyh6xrM6zGv0SsiwIAjzgUGIZR3XFUTGuHaDwYHEe84MUP5Fy4TrARGurSbzmMtarmXmdbs6qzC0hbXOYp+Lf8mlfBXLCh+e3+2A3kt13rtyy6zsLQpO0yzDprbtaaR9qvZGmtrJ02w153ns9rh5Q+v60FynTnGG57q1WW026OVtdc76LREEQTCzLh7Xdh2lLIfTDfo7ABgYnS8kZ6HC4nFtNxPhKhM0WagPD9RzsCWtSWtVsqXK99XUummDH/P2WEvNt1VvYLmc3UJTuqvFtqPV+8pcS9muqlfinVNLlvuD0rRsfz6eklHsC70Ghun4i/3AKLaZb6t1V/V5cZZhvd7iMbi2Z2qxfEymMub61XqoUFZvzXQ+bFaV2yvayEaL9gsiejMRPUhEH1fS/hMRnSCiO9LXS5V9P0FEdxPRp4joG/bZXEEQGsAAYvZmetVBRDemfw/uJqLXGfY/j4g+TEQhEX2bkv51yt+YO4hoj4i+Od33FiL6nLLvhjo72qUQhmeB3hbg+cu2RJiF7MHD6FxhvPL5UTrGVWYVbjXb4nG14jqeruzHqc/jWpdrHruteZrJczabx7W6blMLdh+bvWRReFZbPNu5sNtl84oWfXL21k1eyWIZvYZib5Z9f9VHVvbmmutTa647Bpf2TFtqSvn4qryatqvadCxFj3J9D9t83CYv7P4LVwBvAfDfALxNS38jM/9fagIRXQ/gFQCeCuCxAN5LRE9i5mg/DBUEYbUgIh/AmwC8CMBxALcR0S3MfJeS7QsAXg3g36tlmfkvAdyQ1nMxgLsB/IWS5ceY+Q9cbWmfx1W8re0nO4faBE1nZXKmTnBgQ4TrutBcpLrfri8ymHJZgZqqJ6/sJS17B8te4XL+on+vqm4Y8hfrdGlPPw6zl9StvWpPq71/6uydV3uklalvr+qcmureP5j5rwE86pj95QDewcxDZv4ckhvNZy3MOEEQZoQQzfiq4VkA7mbme5h5BOAdSP5OTGDme5n5owDiinq+DcCfMfOFaY+0XQphdE7Gt3YBi3A9L8vhdAJ1VmHBjtnjli+p4Vq+HPZobkPNT7C1b7cta8e1jMku9djm1UaTY3HpA9UGl/pd28wxe4zLZdTlWspeTHUPK2lqvSY/ZdGnmU8cpI/1NL+rx1i+TilNV49LTzd5YtUy5v4we0STfVB6yuxTVutEKV/RlrJtZe9uvuyNef1Yex3F9OK3ZGm8loi+G8DtAH6UmU8COArgg0qe42laCSK6CcBNALCBLbx48F0LNlcQus0BuvgZTcsw4BTuOwNHAdynbB8H8Owp6nkFgF/W0n6WiF4P4H0AXsfMw6oKxOMq7D8W4XpuGIII2OpLKHibyR48nJUxrpWYhJCLaNXFHUG94be3US9Fqm2ztaGXUe3Ty+QzvNa3Ybqlt8kNXcDbyrj0AVnyNZEYNtFqHtupy06bp6+YptZFWj1UKKOWNbdLWj6z1zEfT6vnh2aL2r7JtroymZTlijJ5nZjkV+syH0v5HJjqVvtOt0stU11Wz286/0vnVwE8AUkY3/0AfqlpBcx8MzMfY+ZjPdqYs3mCILgyB4/rESK6XXndNE/7iOgKAF8O4N1K8k8A+DIAzwRwMYAfr6unXa4tEa7dIPOaj4pL4pwbhtjpByCquj0WVp3t9MGDhAqbqfLeuVz5Tbyxy6C63bIPcLp6Zs/flGk8ruUythl/1X2mUroFthrM/9u9fy5lm+Wx2exy5puWsXuI7XW6nEm979yv2jqb9fO/XPHKzA9kn4no1wH8Sbp5AsBVStYr0zRBELrLw8x8zLJvHn8TvgPAu5h5nCUw8/3pxyER/U9o42NNtMzjek6EaxeweVz3QpmYqQMEvofNni+hwhb2Q1C6trH/t80r4WVaIrlnENq7GpbMSprtnbW8Ju+qnmLzEpo9hXY79ZxN7MyvgHLb5mNyfZnsrLJhvv3q+iqe/+WSekAyvgVANuPwLQBeQUQDIroWwHUA/nG/7RMEwQ1mWvSswrcBuI6IriWiPpKQ31samvlKAG9XE7K/QZR4rL4Z+d8gK+1SCcMzIly7QEWosCyF0w12NgLxuM5Ance0an8Tb6stpHXa+upwHb/rVtd8bZ6lPn1/lY+w6FXNA1zL5fVxlObZacvly+NfzeMrdcuKZROBVvQ2csnyYg2qvaZzXTUmNN9X9le7nE9Tv+Y2FVsz96dp/HVxFmXdFlu/6vaq2zav+qIhorcDeD6ScMDjAH4KwPPT5ScYwL0Avg8AmPlOInongLsAhAB+UGYUFoTVJlrgGFdmDonotUjCfH0Ab07/TrwBwO3MfAsRPRPAuwAcBvAviOg/M/NTAYCIrkHisf0rrerfIaJLkfyJvAPA99fZ0i6VIJMzdYMK4SoTM3WDnUGAc0O5z5mWutvaqv3lG3U7tpBW0825qU7XtDy9uLdJeZ15C+26Pq1qx709Nua1id5EzOW+0mIZNpZJtmnS3/XBuVxKKX5ibW9xm7VPuWwtUhSmxWPLay7mKR6NWib/P0sjg21Q9tcdt5pW1Qe5nfbzUpbkxVaWATO/0pD8mxX5fxbAzy7OIkEQ5gUDiBf814WZbwVwq5b2euXzbUhCiE1l74Vhgjdm/vqmdrRLJcgY124QbADki3DtMDuDAOf2xvUZhYXQ1OPqkj5LWp6u3+S7l3dlv4TBdO00K0Wl/93rspVqYpNeR/U5rJZn5lpMddmvCtP/xTLlVurqrEurs7P+vFSdg9UIGRYEoe3QQj2uq0R7jjIcAtEIGIjHtfUQJQ8gtMmZzotw7QzbAx/nxeNqRb9dZUNa3f6mt7x1+W37Z0k3jQU0jUgs5lmMXa7755G/2jb7uElzP5V7yTQOs5zfPAJTPwdly822qEfgYksxv24dG/KbrgaXfmFj/vJxTtPn9nqn7/O6b7sgCIJgoz0qYZiKnMHB5dohzIfBQZmcqcPsDHo4cWp32WasJEmIY5FpQoPrwleb5m9SziU9q7tok3mlzUW0r+JynDbmmb/soyuv1Jql2zyZ5XNs9otm76b8Zhv305aiJ3S2finWbbvW7F5Z97qL+Vzz2/IKgiDMBwYQ83r8bWmPShilIkfGuHaDwY6ECneYnYGPc0MJFTbh8tNiFn2u6KXsteipSU51Up5mrZrqLcqb6iOy1dHUBr3vXOty7zn3Osp7i+SiTR2PqR4BG/JntenjN9V+zlLKdZjRz7nqqzSNEzWJVrPt+TjUehuya3D6BwamvnPvg/JVaBo/a+8TWNLt+QVBEGYnalEQ7Sy0RyWM95L3nixw3Ql6W8DofCHpwijCVroGqNBuNno+huN42Wa0ltm8g039ueXyTb251a26+FVd9tbj3pJb+/Oow32vbbymvYz5fJvSZjsSezu2MsV39/LN8rrb0aROW99V+5ar67DlFyErCMLsMEg8ritHNEze/cFy7RDmQzBIxiynRDEjjBmDQIRrF+gHHkaRCNdpmc7Tuti6mnotXdudzbs8PcvpY92bXfRIFrF5CU3puncxb0tNqfZAFtPUsF5TXr01k8e4uK3bYDqufL+5rmJbeSnzsVTnUS0qz3hcPMpynup69Hy691kQBGG+xOJxXTHCVOQEIlw7QTAA9s5MNkdhInL6wXp88bpO3/cm51TIcQ2anWd4alVdixCLdX5Cvc1pvMuzhvDO+7ib+LSLIp0sn+tqtnlZi2LJ5vdTPxcFaFMPYgYjP7a69VxJKYHSZ71PzMeiB++a+8OtL6t8y9WfbPXkvVHeu4yHNIIgCF2hPcJ14nHtL9cOYT74g/ycQoRr1+gHIlxNqDet+yWcZhXITW+y68Z41vmbXNqbLfC17P/bPxHBlbaY7SgLUp78b8rTLPzULEnLa5FW25S3qz+IKItHvQ/K3mbX82HzCJfHCtfZ3oT681GU1eX2RLQKgjBPmIFoTUKFnVQCEd1IRJ8ioruJ6HWG/VcT0V8S0UeI6KNE9NK5WxqmIkc8rt0g6OdedADDKFk6RYRrN+gHHsKYEcerPYaLiN5MRA8S0ceVtF8kok+mf8veRUSH0vRriGiXiO5IX//DtZ3q6XNsEqA+zWXfrD9lTctX56+vbb9/ehfRnv180hQvvZz6vy3PLC+XustpPEXdbK3fXJ+5DZT2qb3kYvtiz0dVe4IgCPMhZprp1RZqVQIR+QDeBOAlAK4H8Eoiul7L9h8BvJOZnw7gFQD++7wNnYyHFI9rN9A8rtlEPgNfhGsXyB5AtGCc61sA3KilvQfA05j5KwB8GsBPKPs+y8w3pK/vd23E9JNAFfvr8jfZNy2LeeSg1Uo0n9eKoVqkh8Ty5FMxJf9cXCfUlEetpzgq1V4HG/JY2yOkfQvji4nAk/5P0kjPU3Ocqt2m4ySjjfY6yXYshnoW2nc157PYL4IgCLOTTM7kzfRqCy6WPgvA3cx8DzOPALwDwMu1PAwgW2D1IgBfnJ+JKeJx7RaaxzUTOOJx7Qb99AHEcMXDhZn5rwE8qqX9BTOH6eYHAVw5czsoB3KaPK5Veery1pWbV55ZymXyQs/japNtf50MqCqr7nfpb1OOuvOm1m32BiZ9k3kOszzZO2t5VF8fIe9bVTkW99u9j+WxqGVPZtnTqYrxYh7W2i8eW15nlqK2r+ZRvazFAPNiHj2/2sv68er1oPDZ3v/F81Dt3dXPlW5TMb8gCILQBJcxrkcB3KdsHwfwbC3PfwLwF0T0QwC2AbzQVBER3QTgJgC4+uqrm1k68biKcO0EMsa10wwyj+uKC1cH/g2A31O2ryWijwA4A+A/MvPfmAoV/tZlaZb3QrmKPJnnTp/Cx+S5dfHOZoInu81Wa57Wu0tIvHFmu9Wt6e027qvxuk72sll+uvaXLYc9jz6vrl3+ulwX+VnLxWjdmqeZBUyeoX1PyaVam+ers8c6PpWSGnPr2HCMad+xaUxtUXKr5UxpVXmKrZnrzizMxXT5XBW/O+Y1d3OxW+5DkauCICyKaE3+wsxLJbwSwFuY+UoALwXwW0RUqpuZb2bmY8x87NJLL23WwsTjKqHCnSAYFD2uqcAZiHDtBC0KFbZCRD8JIATwO2nS/QCuTodE/AiA3yWig6ayhb91qL9hdfm50aXqPH6iFlunabtOvnUjiNLer7rnstmLlDqoUHt9GXNelOpsZlNV/rzdZvUC9XVP+6rqi7q+sp0LUxlX2wVBEGaHsT5jXF08ricAXKVsX5mmqbwG6TgxZv4AEW0AOALgwXkYCUDWce0afh8I9yabEircLfot97gS0asBfBOAFzAnbjpmHgIYpp8/RESfBfAkALfPq13df7NoqtpxsqHx+NKk1qpS+r659kWFvczVnsv5UBVwbFonNLkaTOuKGstTdvXAUEYNVs7z1NWZ1aH7RIt1oGBnVR4jVNxIv3AF20zt5N+VYjtcrKnm2Mx22Y6lZGep/WI+l3MnCIIwG9Sqcaqz4CJcbwNwHRFdi0SwvgLAv9TyfAHACwC8hYieAmADwEPzNDRfx1U8rp0gGADxGIhjwMuXTunL5EydoO/7ANopXInoRgD/XwBfy8wXlPRLATzKzBERPR7AdQDumWvb2jtbPrsyS/lSXoPoa25TMbdL+ar9s/ZPsSGH47OEGtso22RaqzXbU/6kblM6+5FreXt9epq919Q6yrl08aivUetqS7mfCEA+25OSxuUHH1T6v7698h7z0dmD8s3t6Eer1qPK3zyHiFlBEOZDvCZRHLUqIZ2k5LUA3g3gE0hmD76TiN5ARC9Ls/0ogO8lon8C8HYAr868FHNDPK7dIpsdOh27LGNcu0VbPK5E9HYAHwDwZCI6TkSvAfDfABwA8B5t2ZvnAfgoEd0B4A8AfD8zP2qqd1rKPh3z56oy9eWrxy9OVz8XPlX5udTcTX5mTXVW9U/TH6CqMY2u9tTVmXsHy6+8Z9Q+4kkZriwPYz16ncV09Thc8pvyJC99XGz9cUHLn+chQxu6RVV9V94PY56iNeZ69JmKq15qnXp+UtKL508QBEFogovHFcx8K4BbtbTXK5/vAvDc+ZqmIbMKd4tgI3mPhkBvYzL7rAjXbpCdx2EYLdmSapj5lYbk37Tk/UMAf7hIe6Z5Xtq8jGMJMvmP6uuszt+kTlsLi8nvVIJcj9Ocq+rozb5JfdxlVctlr1+5vWrPZHV+u1fRZoV92yWPISdpY0uZS/nN9pFlb7EnXP63UfZOV9uQHkBlnYIgCC4wA1GLxqnOgpNwXQnCIUA+4PnLtkSYB9kDiDQEPBvjKpMzdYMs5HvVPa6rhCnsVX835XVNq/vcpHydzTDktddSLGPO4Y7peFw+V1loSzPZXGV/2bZ8BtvyWEi9N/OlY9RyLnmryuvjWIv7i5/Nx1klvkjLa7pOisfiboPqwQRM/WErbz/eYl3q8jnlc+Zy7GprxdT1uMUUBGG/kDGuq0Y0yr10QvuZhAonnvR8jKs8mOgCg17qcW3xrMKLwkUY6R6xsu+mTF2a6bPaLgHQx3mqN9lV9ZfqsWLea7NTp8kNf92x29pyTVPT9TptDwPK+es8qvlnfQ3SsvdOHf9qqs9cvpxm/2wW5/WPKGzXSeYvrfZQmm0AKL1c09Jc7u3a8qXP5f/zHOZy1ddk3VkVBEGYHUa7ZgaehfYI13AoEzN1iYnHVROu4nHtBOJxtWO61XX1wtTlqfMMlm2xl9UFq62eabzBpu06e2xM470qlnGvwUWgl/Po/kYbRQ+dnpcs6cU8+X59Bdn6sNTqus325XXb1jVV85NWV1nWFdNUj2rxmGBIs/dd9bGU08rHYi9Xvg5MZeBgkyAIwvSsy+RM7RGu0VAmZuoS2uRM2VhIEa7dYNCSyZmWQ73XsVnJ6v2VZSrGbNZt1+WrEnkudc/qDa3C5ktrVs41TxNfm0tetzpMHtl6pq+7mGY68/qjgvqzqbdj94TS5Fqe1F07P6S9/ex/myfYRL0X2pQmYlYQBKEJ7RGu4Ug8rl1CPK6dpi2zCq8ljddenbIZY+riR/et9jNnu/euavyqaTxpfX67R3H69kzjSc2eUDK2Zx6Ta7K3ao1Zc11FLy6TcnyMxv2jplW1V8wLY357e4IgCLPDgIQKrxzice0W2bnUl8ORdVw7wUS4yhhXANMGo7rnqCo1L6noEhJszq9+MvvZqltaDE37bBar1LGnRczBsmZPY/KpPObYll/3cNrrNO/L5Za5ZnOqzfOYyUqCubx+bZjqLB57Obdah157se9sZ1Pfo/qGTd7jJv25HjeVgiAsB5mcadUIR7IUTpfIvOfhHoBE4BABPV9+3LuAjHEt4npVuwg71yDEfOd8pKJbwKRpMieT7KpqNxUhqYPK1UHcVFhW5W3Sx836zhYamnvvSNt2taNsV16TzXPpWkvZ+0nIlmrPzk+WpyhCq72wxX1Vx8DWPC59l9k4Kc9l24q1ZTXZ7XWlynMsCIIwF1gmZ1o9omE+LlJoP762HE4Yo+97oH0KYxQWi4QK25nVn2iayKiqBZe25vmtq/ZUUinvZCii/t2fCCK1MjZmNbU7T6r63Cy4qkSWjaaPBsx7spLlnrZT6X1OK2XSnuYrY0r1vEUxq+N2pnQfaTXlvpvle6b3Xf13rq6uqnMpCIIguNIe4RoOxePaJYLicjjDMJbxrR1CQoXtNPX0Nc+j5ChMWFNGD8FcBMX6UznCuW11DRdsU8VShYidN1VNmPquWnLm40QBVRbVo6/Naht1me21r12qe0LL7eSaUa3VZI+Srj9sYAbItoZpcS1ZfU1Zm5fadEy1dulklXN5zG557uO8Hfvat9U26MeptyMIgjALDJlVePWQdVy7RXYus8mZongyE63QfrJQ4aF4XAFUjw+tGk/ZZFyp+5jTnKpASJMNZgFSrt92TMyUCCFqVtaYjxQBw1wQsFUesjrv2TTno25MbPGzOs4zscRkk1p3tk+XukWhqtdhemiR57IeS/ZQodCfut8wH/up79MfNMQAKD0/ui2kvKveWd1TW/09KC9cY7bWXg9ppVjJoY6aLR9vuddNNqj/l8+/IAjC7Eio8KoRDoGNi5ZthTAvtOVwslBhoRsQEfq+J6HCKa4euZnLWdyPNoFaJRLLt+T2z+q2sc5UDBE1D42sanNSPVFBvFYJ8ro+tAsbe17TfpPdJm+ryQtHSl6T/89Ud70nNZ/d1rROafJQIStU75HNylfNJjzxNGZe22R630m7puOtOw53W8r9VbKN8jrAbOjzYl/UzZ5sX2d20or2SRAEYXYYIlxXj2gkY1y7hGE5HAkV7hb9QIRrEbNMLAoI1fdUF8ir7CdPuWE2lzP5n8ztOLRX8V4WFkAebmp+J2P5una08pSlxKX91e/249T7hxzL1bWnnhWT/w6GLXuauqf6xiXLo3sLk4he95sem7fRnENpj3LxXL5W7D0xvS31tk3+p1S1V6z/arfN5SyaPgmtw5vy7MXNH9p1GunHubMuwrU9SkHGuHYLw3I4Ily7RT/wMIqiZZuxQrj67Wzv7Sin3lYkniV1vymoVfdHNbWzWI4nP2uzH6fur3MtZ39PAnuz8N7scx7wW9ynpmGSlvexns/8AvS61VriVFDqdRfbLtdZtKH6eNRjyPbErNemHyc71+3aL+V68u3qvtT7xXaOzDabjlNuv1uAR+XXKtTVNqQfOwER3UhEnyKiu4nodYb9zyOiDxNRSETfpu2LiOiO9HWLkn4tEf1DWufvEVGth7I9SiEayTquXWKyHE4+xlWEa7eQUGEz+g3/1BDlr2XbopBZwwYhW+8xnI8ttlDM5YuFRMjncl71fpb3qWl5eV0SZ1MZ5e/FV7ldtZyt7mLbet02ea6K/mJ5PR+lg17VYzW3Xe6r8gMRc79kL7Mtee5CDeTlL0PdxWvY4ThLtcgY15VmPwVRl8XXMvpxjWEky+HM8qqCiHwAbwLwEgDXA3glEV2vZfsCgFcD+F1DFbvMfEP6epmS/gsA3sjMTwRwEsBr6o61PUohHOZiR2g/E49rNqtwJGNcO4aECpup9tsVvUBVuIswe11lG2aXdok3ybaEiK3+PN2ev9o21SOW2aAzjTie/TyYfHW61033zOVpJu+p7s0j5YjLHkyzhzfLbfIc6p5PKG2U8xV9qVR419vXjpey9WDNHlfbi9K8ZMxbrMNUp9lbWvSM6n5VexvlPqguqdslrATLFj/Lbn8eLFuId6EPZyAGzfSq4VkA7mbme5h5BOAdAF6uZmDme5n5owCcbvwoGZ/y9QD+IE16K4BvrivXojGuQ5lVuEtk45VDCRXuKoPAk+VwGlP0Apmz6B6g+jrr8pq9SdNSZZ+t/lSC6GuFmnKy+ZpSj0H1+pqWKnE9Spd89XnMPk3S8phktrkny949vY3izLVU6hs1nLrKn+hyXia9y2zs6+pjA0AeOJ1Yq8l1WJ/XfmS2PaVrKD1uYoa5Z8reW1Mb5pLCSrBqQke1py3jOaUPlw/PZYzrESK6Xdm+mZlvTj8fBXCfsu84gGc3qHsjrTsE8PPM/L8AXALgFDOHSp1H6ypqj3ANZXKmTuF5gNcDwj0AiXA9tCXnt0uIx7UZ6q2x+lNr+ilSvZnlW2p7Xpc8Lvlt6E9tq+zL0pm82uMtlFNEVCZizXIpa8c002t9WxP7KvKa7K47H7nfLZ9AKv/fll78v3y16PvNR5D5TvVSqk1Zjngy4ZfJPkPL2eRG4PS8FK/mYliy6gdVZ/dFKY9Lf7j1lt4fTeq226T3r/1MmPtaWBKrJrZMZDauqvhqSx+uav/NEcZchOvDzHxsDuaYeBwznyCixwP430T0MQCnp6moHS4u5tTjKmNcO0UwmEzONBSPa+foB56s46pg/+nM5EQebkjp9kRiZO4oosJ+1sqZ3gv1WN6z+lzzm1JjFMfz6T4oPT+IUhFaPF5bq6Y9TB7idDyiPtKzkK8gXfPjNB9JcT9pudRPpvaKIbWm40hKlT1yVel5Wf1qMe1Xy5XHg6qf1HRCNraTyZvsL9ZHWmsmKwlMvvJAIh9Lq/eBerzmyblMXmdbD9l7znisFX1d+n8ynjy71kz1VZ21cv4W3PJ3lzYILpVVs7dtIblts3c1OQHgKmX7yjTNCWY+kb7fA+D9AJ4O4BEAh4goc6I61dkOpRCHAMcyOVPX8PsyOVOH6fsiXDN0gVkUseUAS9UzVrWfavbPWn/VfvU2POaq4yseP4ETsWmpvzh+sejTMtmX7edUcFXZXxRH9f1XFCIm6WorXxQzugDmQl63bdLSqGa/SqzJa73+WOs7c380sxdAIoKVfVSXX5H9pv315du3LewzbRUwyx4/qtrQVtpsuwOLnJwJwG0ArktnAe4DeAWAW2rKAACI6DARDdLPRwA8F8BdnExw8JcAshmIXwXgj+rqa4dSSMWNTM7UMYKNyeRMozDGQCZn6hQSKpyT+0nz7YyC6Eg9O6R4WJls4xDLcoxr9mdbqgDI2i/bZy5v3K/MbFz2rnGh/pj8yv1Z/Zx6ZDn1dLHyQpquemyh1K/u148vRiKaOU1TvYJTH78hl+34ckmLuW+rM//y5JgMXlKtL+v227Zd7Mtn6J3/8S5tW5nRm7Lv65T1CftE20WXisx8PD1dOQ6NRc8qnI5DfS2AdwP4BIB3MvOdRPQGInoZABDRM4noOIBvB/BrRHRnWvwpAG4non9CIlR/npnvSvf9OIAfIaK7kYx5/c26Y23HGNc0nFQ8rh0j6MvkTB1mEHh4RIRrAqniQZWZ6li5aspetUwgmgWRnq9kkiLpyrfW7vXogo2048onCmIw+dZj0vfbjrfUfsF7q4h3jpT9qkhngFmpryi6UUgrLxljKpf1Ixf2c6m+eXra3K6aNK9lciWXybBsV4le3jZplpqXODbXA/VbYZ5Qa5406TvnOmmK8F9xve4PHRUrABY3Dlb6rHXw7GNca+rnWwHcqqW9Xvl8G5JwX73c3wP4ckud9yCZsdiZdghX8bh2E3+Qe1wlVLhz9GVWYQ1WbsrtQlEVpJng0wUuK+l6PbrAy/ORtl1EnXwnS6mqR2/HLOjycrn3k8tlU89pfmx5LZmHNheCRZmTzZCr98/EI8hRoZy68Ewx3d67cSFFFaQmYeder406cZdtu6B6ndU0tdYqm4pe+QRdpDKAfAbefMKs8tkyt1Oou5TPpYayzbb+s9VQt7+uHbO9brYJC6TLAkzFdJwuwmxd+sfEmkzc1DXaIVxTcSMe146he1wlVLhT9H0JFVbRBakaUmsSiFUCV8/PUGVl0dOpWlB/y6zPlmry42bBtUnYbXG6IPOtvxrSq3okM0GrU/DkTTyxSf54EtqqCMl0PwHAZEbbbL8P4uK6nzRZO7RstU2Axkq6XoK1d3W/TcCZqMujp7ncchXH/Ra31bMUa8JTzZF5SiefNU+rLorVtMKVQB6YzSsG6o87ilLWnMtGVf22Gur2u7ZTV8May4T9ZwGiTA0LdyEZxrckWiBKW9WfK47DWqydoB3CNRU3Mqtwx1A9rhIq3DlkjGsRuyDVRQRpJcy51HzqbX4eOqwLz2JNukfVtr9sbdFSPRyWtO1cKJVDKYuilYqeUkWQTupI3+N0NuHMPk5nFVbb0JfK8TjKLS7c/KTlUkGlC1fdy1m1X20PWr7svc6bqi8k4+prVL2eBQ+p9mAgF/56/6flVNGZiViO4RlCgXMRy0jWcc3b8GyeV/LShwvmY7BJ1nx/c4+mrb66MqYHDtN4Y6etQ5iBOYu2pgJLLyeCK2favlTLzq0/O+J1ZZ7LcjitoB3CdeJxlVDhThEMgHCEKGaEMYtw7RgSKlyNTcjWhyzW+1GLnk1o+TWPp7ZdbkEvX/Q82n2Wuhwv5tc9f9k6oJlgZQIY+aRA2bI35XeCaZ7BXLZHeeucjkJVPK56aGtZHpmPq2n+WSWLXrqqNlWQTj6TOruy/W8tIZ54qP04TIQsx4mnVHkVynAMEE0EKcHsea07BjUvESV3Y6X9zfvQ7Xo2l2nu760utx63lt1hFpGl1yPidUX7szPidT3+urRDuIrHtZv4fWB0buKVE+HaLfq+Lx7XFLOEM2+XpVEu+Yr15alV27pnUBcQdomZC82kPBXzE2UuPevxZN64onTLwn15Ul+2rXpUkxmV/YloZc9H7PXARIgoAHs+mHzEnp/MFKyKNdX+NMzVSwUsMQMcK/ujSf8URWydNNX7tz5/8WFC+YEFlJQ6L6FO7nlG3ofApF+YaCJYTeHCmbfVS4WrF48RMcOLQ3gcgeIQHsfwUjGLVJR6mdc1fc89s1zwvBZsNXhddQEZc/FxhEv/VIp5Q3lqmL/YXr3nt768sBDm4G2dl8Ay1bmuAnbefTrX/my9eHVa0qYTtEO4ise1mwQD4MIjuXCVMa6dQkKFc3QfqW1ao+ptm0fU5kMl49669kytmDyJ+b2CrT69vGqRkjudjZUVgQXyEkHq9RB7AWLPR+T1EPkDhH4fkddH5AcIvUGyn4Jc7E7ansjkxNPKUdI32RjYiUjLx8Sqnlh1OxOm6lhbuyd1MR5XcHksqc4kfFoTqUw0Efec3expXldCDC+O4HEEj0P40Rh+PEYQDeHHo2Q7GsGPx6A4hJ+urx5nXlgkwjcXq/l5JcUTm7cHrf3m2/ptZvks2KMI6s5EvSCmin3u5YU5s6KiVa9/3cTrIvt03R8IrBvtEK6TWYXF49op/D4QDjGMkmUrBj2/poDQJrJQYWZe+I1AG7GF8taXs9/ymm6Wc4+PW3uqhDWtA5rl0W8RTONBTZTDWLnwHnkBQB4iv4fY6yH0+wiDDYT+AONgE2N/I3/3NxF6vUTIkp96aIs+NUq9q5SO0yREIMZEyIIZHlJhpYYQqyHGSn3q44EqwWoPHXaHULPEDNS+zAVpJlwz0ZqlxeRPPhfaYQYhhh+H8OMxfB6jF+2hF+6l77voRbsIwiGCaIgg2gMiL/HGxqFWV1wQq1bbHfLUUdWbZrm6qHamLy+sFlP9Vnnp9zCWB7UmGvXpMvuy5V5XCRVeJWQd124SJJMzZV65gXhcO8UgDf0eRTEGgTyUyMluqfVQUTe/q+pLrG5FD9Att4dCPeUgyqowyroxtER5MLBqgdoCkAvZmHywl4QAx34foddDGGxi2NvGKNjCqLeD3f4BDIMD2OvtYOhvYehvYeRtJHmphxh+KtSKPedzDCCGz3HiWUzfCZxO2pSJt8xiexhrnfycZn/V2E8X2cvwJsecCdWYvFywwgcjE65U6iOPY3iI4HOIgMfoR3vox3vYCM+jH53H5vgsNsZnsTE6i354Hv1xgID2EKTRUGbxSpNJsKrWcK3ql2mZZ12ztl91boU5M6O31VlgeZZ7FT29Qnyto9fViGtfArVidp09rwyZnGm1EI9rN0knZ5Ixrt1kIlxDEa5FbIGD5ZGixbGpeqhv3Rg7V49unTSyCWyzfeoNu1oiGQ6rrNeqeV6TMawBIr+feFd7GxgFO9gdXITd/iFc6F+E8/1DONc7jAv+QZyjLdx55vP40CMfwP0X7sPp4SMYhrsIeYw4E6PkwYMH3wvgUw89f4C+10PP38DAG6DnDdD3B+h7fQReHz3Ph08+PPLhkQePvMLRAvlNUTZylxHP9UYpu/ki5ONV87TcFmZGxBFijhBxjJBDhPEY43iEUTTCKB5iGO1hHA0xikcI4yHCeIyIQ8QcgZnB4PQ4Awz8TRzoH8ZlW4/FV1x8A55x+Kk4ONjFVngW2+FpbI9PYTs4ic3RaWx6pzGgs5OJs4JwbyJeix7XYr809lwaJmhy6kM0e9hgw7Wcnm+WEGVhSvZDtNpEVl3+NfbEWvu1aV+qZRwE7NR/k9vqdeWp/lS2knYI10gmZ+ok6XI42cyzIly7RV8RrkIVxR92fSIl263uZNzlJL8qLvW85ZBhu+dL985SoZxaPxn3qyNtGbqnOJNfMQjq7MBMXiJcvR7GvU0M+wdxoX8I5zYuwdnBpTg1OILTvSP4yJkv4l33vgnHz3wKALDZvwQ7gytx0c4xbPQfg75/CJ63AQCIeYyY9xBG5xGGZzCOzmIcncMw3sX58S5iPo8oGib5EIE5AnMMRly4CzAvfKP0OdV7wF3IxHHyZr4LKdhClApcApEPIh8+9eB5ffjeAL63iaB3Mbb9HfT8Hfj+geTd24ZHvUkfjcNT2Bs/iPPD+/DJk5/Ahx94P97MEY5sXYUXXfkyvPCyr8LB3sM4FGzjQLCZjD+m/GFUFvLrxWEhBFj1ulYftx5Wbd7rVk85HmG6M+P2gMhcP8EunMXnumosRLTqZeclXk2ialHrteptzaudWfoyK7/GDwNsyDquq0QokzN1Et3jKqHCnSI7n7Ikjo5d6JkpTrmU16KHAOtenmK5svSqrle/wdbbizX7y0dZvl3PPjH5qagFQDSZgCn0Bxj1trDXvwgXBodwZvNynNp4DE4OLsfHh3v4Hx99Ax7ZPYEjO9fjuU/+FRy56GUY8QBh7CGMfURMYM5DknOPJcOjdFQqpYG1ynb2DpSPhFC8ebOfp8Wh26DbMQnBTo9df4/ZM6Yndef9E1CMwIsx8EOcvfABfPK+N+Idn/7v+KPPHcArnvR9eP6hJ2DobyUTZXm93D5lmRzVk56EB6er2FaMaS1fZea9dVQ96GkOae/Tlc4+232wwkrjKrIy8Wt7UGMQW408g1VeQHXfLOKyztM4r3aqcI2yqBGva+l1XRPaIVwjCRXuJH6/MMZVPK7dQjyuOaqnURdVKG0Xl58pe2+KYaPmGtQctpBjMm7rPiuebNnlBBc+6/7itDwlI0cnx5EudcPkIyYvCREONjDqbWG3fwBnNy7DqY3H4KGNK/E7J/4O7773d3HJzlPxkhvejtj/Mpwf93D8XIBR6CGMPERxJsry+57sXpIoF2j6Z/VYJuK14T3ZPEO0ZmlbFaRJP6h9kn9Wy2XteQT4HsP3GP0gwiB4IZ7y+K/FDfQQPn7vv8Ob7/xF3HbkufjhJ70qqQeAF4/TWYgjECezDOvrvOrXifodUPPoV2HpOLX61DrqusyUt87vOe/2XNoU9p9Kb2uVaLWV09PVL2hTT+E04mm/BFfWTlMBq/epqR9NaaY/suJ5nZA9kFwH2iFcs3VcxePaLYIBEI0wGifj0US4dgsRrjmqZ7K4SiqUPcWwWvU2P5O85TBelXJ580y/ZclbtKgYIqmHOJaX18mOsZiflf22oNc4m/U2CxEONjHsH8D5jSM4tXk5Htq4Ejff++f4mxN/jGc+/idx+SWvxZnRBs6d72F37GM09jCOCHGcTExhu4chQkGkqgJVvUdq++TXuijNBCuQ3diU96vi3iPA9xmB76PnMy70e9jqPxZPufa38ITHvAd/dder8YaPn8Trn/bvAABBPIYfR/DjUTrDcAROw4Wz5XtMnlbdn6/7tM0efPu2PSy3nN8mkE111IlVW7v6dyZLKz7gEebGorx/Jpr+kZhynHZrPH5NBKwqWufVjxXidb28rrKO62ohHtdukj6IGI33AIhw7RpZqPBQhKsRfQIXl8mUivvrgyr1/MUxe9MFL9YHc5r3T1qfrNfqJV7YdOmbyO+n3tZDODs4glP9y/C7J/4Wf3Pij/E1T/5/sL3znXjg/CbODQNcGPoYjQnjkBBFiWhVPa2Ftqn4rtqbCVh9f1vRva+Tz9p+Wz8l4pXh+4n3dbfv4ULfx944wKHNl+CFX/HneO9HX4Jf/OSv4Sef8j3YiC6gH+6iH15AEA7hRyPEXph4XSkJT7Z5X22fp/FJ2uty22/L0+T7aM5r+h5Ue5aFFcHkbXX1zupCShVdLl7CVgmmlJhne4DgMiPzFOJ1nZDJmVaJUJbD6STpg4jxMHkwIWNcu0U/WP0xrkT0ZgDfBOBBZn5amnYxgN8DcA2AewF8BzOfpCSm7L8CeCmACwBezcwfdm3LtrxN7iW1TwGUWmusJ0kzeZCqgnvr7ayzN2/XLI+z48nyqVbGmXhFOr6VAoz9DQx7O7jQP4Qz/SP4+GiMP7/37XjW438S2zvfiUd3N3F6t4cLex6GYw/jMSGKgSiCVbROjtziVTWFCc5DwDa5gVhUe3palWhVbSEieB7g+4QwYoQRIYqSxxWHN5+OFz7tnXj3x74Ff/rQM/FtF1+LjfA8BuE59MPz6Rqvw8I417kf2JKZNdxXBOvq0Gh9UVNel6VcMkHl6nlto2h1weZtrevDqocADsiyQ92jHcI1GgLkAX47zBUcSR9ERKnHdSAe107RklDhtwD4bwDepqS9DsD7mPnnieh16faPA3gJgOvS17MB/Gr67oRtbGnV0hk65SBjKOLQNslSM8Fatqto2ySdCODyjXweCp2XMIVdxtlMwuQj8vuI/D6GwQ4u9C7Cmd7F+NWP/Swu2bkejznyg/jS+Q2c2e3h3K6PvWHiaQ3D5J4mu6+x3Zvo95uuQ6rajE202rZVJp5XD4iCNAw79gD0QAAu2/lqfNkV34U/vPvNeOFz3oQDvVPY6p3Cpn8aPX8XsRcUZhde9rjOWdovxybMfizL7o/OsYgwYZcJmbQ8qgguCKUm3sC2i1YXr+tkUH2D/gOKfWgSr+J1lTGuK0W4J97WLhIkocLRaBcAZK3PjpE9iFjlUGFm/msiukZLfjmA56ef3wrg/UiE68sBvI2TX9UPEtEhIrqCme93bg/qmDi7RzQfi1ocFVdcyCYXwOrYV315nHySmbLgJa0e8367NDUHP6b1MSP/HVVtUSQweWDPy8e39rZxoXcQHz57Px7e/SK+8em/i9OjDZzd6+HC0MPekDAaJ6I187TW3ausg1CtwsUbq0OU9Ks6RtbzPPh+gI3eAE++8udw9wO/j3fcdyt+4LHPxk7vAHaCTUThAPFk0i1PE6+sCUDzyNYs1dOfdmj5XCkKz3LpqvpsZZuLz+nC8oXF0Wh9UT1vhejSt5k5F1WZ4Eq3O+kJbBgybI56Sb9nVQ8Aph073FHUv9VdpyXCdTQROUKHyDyuozRUWDyunaLvJw8iVtzjauJyRYx+CcDl6eejAO5T8h1P00rClYhuAnATAFydpukjTMseUf3mNr+VN4Xk2m6GdSFbFrR6DeYQZXtrqS+Vs9Z0gZvL2qIUUY8GyaRM5CFG4nEN/QH2gm1cCA7iDz95My7duR4UPBkXzvewO/QxHHkTT2sUlYWVjWQCIi6lTWzq6G+97Z7O7eaGtT4ieCMg8D1cGPVwvreJ6y7/Nvz9/bfgVVe/GMNgB6NgC6HXR+wFxSVxkF+lBbGqGejq0dQ9oC6Q4ZM9pb5s80umoxfZOqKI1kahxnW03duqUide0z6s6z8J823GukzO1A6lEA3F49pF0jGu8TjxuIpw7RYtCRWuJPWuNv7lZOabmfkYMx+7NEkp3Zjb1gPVc6pzCqumlGtU81cFN+YeVlLqV8vnLZkkgiJFJrvM7RXDoLMaKRU2qXD1AkRegLG/iZG/jfPYwn1nP4Prr/w+nA/7uDDyMRwTxiEKojWOyfkJM2fru2ovIJ/UqXsv2zG791ecjiEOQyCMCMMRYXfk4/yojycf/VHshmdx9+6jGPpbCP0+olS0RhPxypMrtsmXiBMjtHLFByCl/JN3e7nK9kqf7eVMx9T4+IR2YBmPWRBdHllfZAmNXUuUPsv7xd53hXyA9GENs/5mtIV2XAXhSGYU7iLpOY1C8bh2kXxypmjJljTmASK6AgDS9wfT9BMArlLyXZmmOaB6HnNfJKDfjrMi8LJULuyFlq7faBdDeJtQtCQXs3o9XEjR28vtRvpryJPqWbM7Jg+gZIKm0Oth5G/g4+c+DwC47KJ/gWHoYxx5iCJCFJPiZdUsmvLHuErgtf1Vh2ufZeHYUQREMWEcEUaRB/iPwVbvYvzNgx/AyNvA2N9A5CceV5tvclJ1Gj6sC79sW3XWuPg5VS8sK1cuoH6jNBu08npdVd+hab5datvr4RdZEwoXq5e8hOaY+s4kXquYt7Ddz2WWBGfa8Q2LhrKGaxdJveg8llmFu0iLPa63AHhV+vlVAP5ISf9uSngOgNNNxreWUYVmduNeFKZFiauG5Jpqyj7rowfz+tTc+Q0+F8qZSxfTc0crl2Jt862y4C3kJALIA3s+Ii+ZnGnsDfCPj3wEW/1LMMIAo8hPwoOj4uzBk7VJncVpu54oL4Kmgj55zz3ScQyEISGMCKPQxzAKcHj7yfjMqU9g5G8g9AaIKUhDwNOrhfIQdM/wYEQXjIT0clIMNJladX3b4wNycVuHLpZtYle3v85OuQ3uMKroouoxsHXpa0MmDCv6rlxG7hNtLPph56rQjisgHIrHtYuk45bjVLj2/PZ8cYR6sgcRqyxciejtAD4A4MlEdJyIXgPg5wG8iIg+A+CF6TYA3ArgHgB3A/h1AD/QpC1baO/EFsPtsXrbbNMapAtCreaqcub2bKkmSZrI3eL9lyaMufr8x/DARIi8ACH18MXzJ3Bg4yjC2EcYeamnlUR8LpGJ4AUQRYQwJoxjD4e3r8ep4SMIqYeY/OQFD4x0cib1imJOxV6zk2i6JquvU4P9FfW7WDPtL5P8onWTQpgrIF5WnVk9lYb+NIr8dRf+CozFR+msCu2YnCmSUOFOknpc43CIfuDJ08eOMei1YlbhV1p2vcCQlwH84LRtqf7MXJCSMoa1ag3XMrb8pPmV9G29PX0ZHLUFNQhT3Vuw1+B1NbWrtq9lREzJWq6RF+DU6BFcfOBZCGMvDw/GfDynWdl1+lMzL7E/8bxyMr44Yg+Htq7HXvRbiNJzF3v+5KbT2sUVBune1nlhflRkTxOEeeI0wZBH3ZqgCQ4e5apJnMgDah56lv5WrPtyOMs2YJ9ox2OiUCZn6iSpx5XDIQYSJtw5Jh7XaL1/THKKoblVHldT0KPdi0qlMGOd8pjUPBRZt8fmGdZHCqpjCMG519VkR1G0K8dJqWQmQoxkhuFhuIfN/mWImRBPxqBaD03YRwrilQlbG49DFIeIKEi8rNktBeXXjDp2VX8vfE5Fa5a/6Sl3zV8OYJ9PvW2AiN5MRA8S0ceVtIuJ6D1E9Jn0/XCaTkT0K0R0NxF9lIi+anmWt4w6wdVlHL2tBTHfpL/WXJxamfxWdt/j2g618LRvBb7yO5dthTBv/Ey4jtCTiZk6R9/38H1f+3jccNWhZZuyEpRHiBbHuOb58mVzqmpQx6yax9fZprXJBapp6hx9LKypvuIUNpMBkcmwVUM7SCdp8gw3KJN607GRIY/RDw4jTkOfFuESW5ew40UdI6fCdbP3GDDiJEwtW3tRDRHm/BEMA8bzD6TDnVm/vqe0rWa//t2qa2caOyoF+nJ5C4AbtbTXAXgfM18H4H3pNgC8BMB16esmAL+6TzZ2h5hzgeYiuLo+GZDpD1LmZdb/NmjbsiyOkNGOUOFnvGrZFgiLIBWuFI1lfGsH8TzCT7zkKcs2Y0VQF6kph+5CScnfTaG25VvuatlqkgB6vbbvnnqLX1wPNk/T7ErFa/L0VvUbq3bk63mWfc4E5giBt5HXLPcrK4V6PjxvK0nLvK2ke9XZeK7Vzx4hvUm1Bcyb1xnO96p16t+mPE+xfVOsgXt75dHetu8gFT5X5d4PmPmviegaLfnlAJ6ffn4rgPcD+PE0/W3pEIkPEtEhIrpitgnpVgdmNoeyxnFxAiB1KES6b1I2C3XVRZce8puJ1q7/MXMV3q792ATxxK7M07FF0w7hKnQTL7n8OBqjJ6HCQqdRZag5BFj1tBbDgrNSRU9tls8kFUjJp+bU2+XCZ5MH1iyfqbC/aHd2n5d4WtUycSJuOAY4/b4ry+WY/bzF8ahEad0z/kCvyxjXefSVqc5SGmIQkrWKPE7eaSJGCcTmJbHyMa3Vo1CrTpc9oL4q1TYCe/b2zN9z0zd/ZbhcEaNfAnB5+vkogPuUfMfTtIJwJaKbkHhksYGtxVq6X1SJ10mSIrqA0hjVSg9hlcjq4FjXAllfmsQrYDx+o9e66w8BpqBN4b6zIGpBWB7ZEkexCFdhfbCNsTPNtloUlm7Yx6yWa9e9UGY/qLl+fTmdwns2gyzlR6HfZ2SzDRMYxAyPo1QGe4jiUbKPGCjfM+Z10PqI0P2krk+zkHDmvfq6LCHxi5qIyZU6D25dWtN620DqXW10Upj5ZmY+xszHerRRX2CFmCr8VBFPhfI20bou3lYDxn5QSdOY1ZBqi2idhw0dp2ptbpdXWxC1ICwPvwdAQoWF9cB6A6+NhMsFpF7OXF9SqrwNpT2zN1P3AtnWiTX/opXbK9ar/xKqk0HpAc8T3y0zfPIQxbuTdT8zEVX0vLboV7blqA8Hip8Z4+gMAILHMYg58bxy6n1F/hADANSJl0yitckZNV2h6guGd1fqAutd6rN9g4rvK3MNP0BEVwBA+v5gmn4CwFVKvivTtPVAF1oWEZaJLvU1yWMSrTbhu07M2pcym/BSIKIbiehT6YRtrzPsfx4RfZiIQiL6NiX9BiL6ABHdmU709p3KvrcQ0eeI6I70dUOdHU7Ctc7YNM93ENFdqWG/61KvsOakwhXxGIEsKi2sGbonNF/lMg9VJEM+E+ZQXn273KLpnS25q3xJ1nq12Yaz45mIUs7DS4ljeIjgU4BxdAZEPPHYzvpYKxNc6+ihneXY1QcEpZBtAKPwNCgdvZmIViU/yjeYhGxMa/mGvYlppitTfcHwPi3u34IEUwiy2aaVuRBvAfCq9POrAPyRkv7d6ezCzwFwuivjW1UahfSqoikTU7aXWsZW3xpQ8rqaBOg0fanXV9f2NLQoZJuBhc4qTEQ+gDchmbTtegCvJKLrtWxfAPBqALoGvADgu5n5qUgmh/u/ieiQsv/HmPmG9HVH3bHWjnFVjH0RkjEOtxHRLcx8l5LnOgA/AeC5zHySiC6rq1cQ4KUe13iMXk+Eq7BelNdbtaVn28X0add9rStX9ji5tZPnK96eT4aHpVGIhPRJOufiNRkjmXhce34f4/BMKnJy4auKr3V1VCybyTlAImpH44fhkQ+PoyTUe3Iu0eqTZBKfrqyMHDVARG8H8HwAR4joOICfAvDzAN5JRK8B8HkA35FmvxXASwHcjeTG81/vu8H7hHWiJiAXRvqYV8D+JMg4e+76iVYnpulLoLY/186bzQAWO8b1WQDuZuZ7AICI3oFkAreJFmTme9N9hZPDzJ9WPn+RiB4EcCmAU9MY4jI5U62xAL4XwJuY+WRq2IOlWgRBJ5tVOB6j1/Vp4AWhRO7ZzEN0Xb4H6thSKgnLskCtrrOcP6m3LKSrhXM5X7EeBiXhpJSHDE/GuaaT+Xgcoe9tYBidmYjW5FXdN+o9T9190LozTV8VQoQBeB7DI8Ze+Ah8L4CHSAkVNnlalbBv5pmEoY151tnVS4eZX2nZ9QJDXgbwg4u1aHWoFK9AecKmpJBb5QaRZRVWLfLyuVLq26qHAXU4PABYO9GasuDDNk3W9uymlRDRswD0AXxWSf5ZIno90uW4mHlYVYeLm8s2s5zKkwA8iYj+jog+SET6OmGZwTcR0e1EdPtDDz3k0LTQadJQYU9mFRbWBHW0avYbQ1p6tk9ddkbNydp+86RKbPlcbk8fc8qK1FS3WbOPCvm5ZG8hP3PeLmv2KqLVQ4QNf4BxeDYRrrCLKhGm+0MhRBjJQ4QkJJexN34EAQXwEKce12w8azbWlQshw1wSrfloT9s9VzndnGK7HEz1mtrTx8hW1zA763lrvQDmKPRqBU9dSGtdqCvycZydpOJcGI95xr40tdHZvnWBZ3wlkRi3K6+b5mleOob+twD8a+bJ2kc/AeDLADwTwMVIluKqZF7L4QRIFql+PpIB/H9NRF/OzKfUTMx8M4CbAeDYsWNrfHUJANLH+D7AIQKZnElYA0yeVZM/k0p78hp0z6a6HI0pnynUV7cj29bnGQZyT2rerukoynap8pcAgBhgZUqnTNwoL49jbASbOBmeg0dxLpKUMGEgCxc2yxURtO5U9VX1TM6Jx3UYPoqePyidx0lRzftqOFuW9EqrjSnmq6F49RevYJeai6muXt06IV1lg7BcMuFT6X2dsW4rHfS2qiyqb9darM6Xh5n5mGXfTJO1EdFBAH8K4CeZ+YNZujJufkhE/xPAv6+ry8XN5WLscQC3MPOYmT8H4NNIhKwgVOP34cVj9MXjKqwpttmG9VwmT6ZenlCux3QzX91Kcb/J46rm05fH0T3KJrzUG5fXF8NL1wLdCrYxis4nIanEyiRNaV65498nyv1OSvi2R4zR+CR63gBe6jHPZxUun/1pz5trsTrRWVeXSzuz2dKsDmG5GGe3tex3rWctcBDfs/Zb3blZT2abmMlhDdjbAFxHRNcSUR/AK5BM4FZvWZL/XQDexsx/oO3LZjQnAN8M4ON19bmoBRdj/xcSbyuI6AiS0OF7HOoW1h2/By8Wj6uwTuhepnJIsEsYoy4o1XdW3rPwYtPyNS4/+bp1ebninizkWLWzYC8XQ5wZNPG6ghmIYxBH2A62EEa7hTGugAjWZaKfg+wxyig6g4G/AUpFqxoSrIegA9XX2zS3n/p1Vo58KwfNm/a5hyk3s8uUXhUWLUzJPngqq8SSaRmXxqKqS95Wx2Nx7belCNU2no/ZQ4XtVTOHAF4L4N0APgHgncx8JxG9gYheBgBE9Mx00rdvB/BrRHRnWvw7ADwPwKsNy978DhF9DMDHABwB8DN1h1kbKszMIRFlxvoA3pwZC+B2Zr4l3fdiIroLQIRkauNH6uoWBPg9eOMxAvG4CmuDHhycy8pc0pk8RXoorh6sq6frocB6fUWLsvDecutle7OcrNlh1pZluxiceliRjo9MPK4+x9gOthHFwyRUOC1hE68yw/D+oZ6DbHKmcXgem/4G/HRyJk8NFWYtNqDmRE0Tglt+5FNfr16+Kqy3FO5eYUtVm67tCWtIGwWSC9lxycSb+wPDxWs6WxPMtyKZbVxNe73y+TYkUbl6ud8G8NuWOr++qR1OY1wdjGUAP5K+BMEdryehwsJaULccjS5E3ZevMd/O2+RvOcUUelyUyuVjMY/TK0rwculsXdbE25p65NLJmbLXTrCFKB7DA09ChfOyNkuEeWILzZ4shQPAoxhhfAGb/e1EtCJKZ4lWZovWvPxNxJ9pX9WZdrkSXITtPMuZ65JrdiHEvByRNK1A66pg1Zm1f5Z5ToWVZF6TMwnCdPh9eBwhkKdywppgv1EvClXdg6pOwlT9bTGXq5KhpEgMNX9xYht1iZyy3VXphdYUMzyOJ5+zsZE7wRYiDtPxlMVwYWG/KN64FcUrTybNCuM9DPyLE9Eap2u4qp5VzgPWm4q/pqd8VlG7aOriEoSWoYsbfdt0T7POgkg99iZ9s98Cts3nqMWmN0GEq7Bc/AA+j9ELxOMqrAemG1jVU0mTEFyzv9TmNy2jBzrq7ZlsUj1CbjcK5dmGqVR64oOdLIuj9AInggdgeBxhO9gEc1SoozxJUFrvmvxQrwLqzM5J+DYjikbY8DcKa7hmS+AUJt9Kn7g09bC6oJaz1VEVNdC0bVu5ujps3zdhjixS5DQVNG0WQItmmr5xeTAwK60/Z+vxV0WEq7Bc/D48DtETj6vQcXJBmqF7RHWJWSUvzZhupE1yVQ9BztozLx6S21/n17KVL4V8cnHiHkI6PhIxtoJNxIjz5XBS0WPzvMo41/ni4t3OPK4Rp8IVyvqK2RI4kzVd2XrZzCrgbGNem4QNu9pg+j5R4fvhVj7LK5fsAplH2HDrRUzHmaeQ7cq57shh1CHCVVguXg8Bh+jJGFdhDTAJUtt6q3VjW9WcJoFYJzRtY2qbYhLAtnbVG/aJZy6bbhgEYmDT2wDSCX4y756wHIwPCibvjJhDDPx+4mnlfGxriWz9Rktdpn1O9jVMnwW9zqbfl6pjFxZAXWiqLa/QTkzncN3Oe8cOx4YIV2G5+D34HMqswkLnaSpMqyZdSrb04N5ibWVPbjGU1zb5U126vm0XwDb70m3VKwcAqejZ8gel399kgiZ7kCkRidd1DmSTZ+Wfi/vUmYWJGMwR+t4gfQARJ+eVysvg6DQJsbVP9TU9TeusjmSw5xVWhK4JFMENOe+dRISrsFTY78HHOfRlHVeh4+hjQcseGLdb3vINv3naJrU9l5v08nI6WTv2WZD1lnkyGU+eyqTlSd9V31w2MRMhRs8L0laL4ySF5WALHSYAMccIPG9ynjwkDyO8TPxazp86gVOhekNjZMo3JbrodK2zKhzZFq4Mw766dEEQhKlgAAteDmdVEOEqLBcvQA+ReFyFTmO+UbX7bsyjW+1jTnWPq8kDmwvNcj2myaB0O8r2mGcT1kuo/tbsOPLjUSfwSdJ9zy/Wli2HU7KuaKl4XWejamxraVbhyRbDgw9Wx7MCpe08t1KPId1V5FX73+35Tem2dk31qnmrxK8aDl98CFTtrRUEQZiWdfn9E+EqLJXY66EHGeMqdJ+yX9QeLGkeD1fMbxtbWl7+xj5JkmpX2WOb5yjbp93GM6PcXvrOaR2Uytc0KfOpTlrh6cbYFuyi9fnxXgzNO68sxBh5CLmW1yBkC+WZSwra5MGsjhxwS8vSq4+4+rtTXSIvJYJVEISFsya/fSJchaUSewECROhJqLDQYcyhhLrQNPtwcsFbPYupbcyp7Xa5LlyyLFeL9po8W+ZbfHOAZlG25ulRHFosSnNR8bMI1dlpuk6uGugbIQKT+uDRvbKmf/UX8StRXWfzFpsIZ6Gj2CYpAwBa44f0Vf0CrHffzAMJFRaExcPURw8hAlkOR+g4emCvHkqbyQF9TKnruqpZyG85SDhr3+bRNI9pLXuNcmFsCjm2e5TzVmwweakX1sP5aIxM1jII7PxjnIYki5htRC5a2ZBWJjsfDMAjH8NonJwn8nIBa6mgaYjvatPM4vYdnzChTnBNW9e6CDXX/mvSz+vSd0IJEa7CUonTMa69QP4ICeuCPeRX90GaSmXYPZ7mW+SyJ9aWryxloQnVUilK9heX7LSNyWUY550lD0zAhfEFEAgxJ2NWWalNaa5SnIp4daOppzWDGYjZg0897EZ7iCmZhCt5AOEVruNsHd5aW2YxSLUN0wnE5uWqvm223EKrmKdgtdXfZQG2yP7L6u5y/zWE1uQ3T864sFQiCpIxrp5cisK6kdzRZ2MB82VueDIGVMX2m8STG+ji+/TlzCG8ajmTmMy9sMXjMb1nRw4ixOSB0/eIenhkdAYeBYhV8drI65paLUqhkmL/uN3xZA8SOD03vr+Bc+NzYPITwapUmp3fQpuzm222aw5t7Gc5Vv4XVpRFi1a1nf1qaz+R/ttfeA6vliAeV2GpxBQgoAi9QO4yhW6TeRz1kOE8EFj3aLr5cvRlbKpmH25SLsdeXymkmbxJftLyq+8xvMQrmoX2ko/YCxCTj4dHJxF4fcTsgTWvK9DMkyqe1/mgLrnLDMRxIlwHwUGcGZ1GRAFiLxOvnv2pwYKeJrTt18MStyCsAssSQV3yvi6jD7vUf1NBMsZVEPaDZFbhCIF4XIUOE5Mq5RgxqLCdjU3FJEUNKM5vc3UNxsb9ttvioscT1vxFOwBKZ4PN/UQE/caE0+PIamDoR5HtJ9UTR4QYieiJvABjr4cvXXgAg+AihOwh4nyMqy5A7aJURhPWMY23FchChAkxAxF72OpfjlPDezGmPiLqIfYCsOcjVsRrdub167d4DeZpbNivB7hndZvy2ALiTXnUtlwC6V3acS2fH4ywMjgILo6rTxqt+3wdM4jWmft27cXreiDCVVgqEQJZDkfoPLoHtbhd7V8l4623aX9VriKTGqnoCS7akc9jnIeAZmNUPZgkAXFZTU72UirW0wfDMREizwN7HkLqIfJ6CGmAL+3ej63BZQhjH1GciKRZvKbidS0yy7jW7BXFhDD2cHDrOtxz9sMIvT5Cv4/IS8UrEWICvPTyoKxRLo/izq4NoOgwKIahWwSsZZuRj/dSr1ygPq34rTTYqnxWbTIel26ToU6hHdSJKlu+RkK27cJrCtHq2q96Xmu/tr0PZ2FNfudEuApLJaIAG7IcjtBx2NNvb9Vb42LwcLY/hjoLsBp4a1p3tRiYi0I53ZOb+kxJzV+sV/UAs6Gd4hI+NBEJPMmiC13K0ykRwux7AHmIPB+x30fob2DsDfDI3oO48pIbEcZeKlwJcVwOV50coVGYikxwo9mdThImnApX9nDpwa/GnSd+EyNvA2NvA5HfR0ge+p4P9n1E8OGr3nmuaVERsFUeUtX6ygB3NqTZjk1rzxWTTVVtFJCheatBheCqFFYOMwRzzOslXh2pFaw1ky9V9uua9GEJEa6CsHgiCtAXj6vQYdgjRH3fcEOrysh8uy7dVl7Pb0u3vTvZwWr95nzMbLRj8p9HiMlH7PkI+z1EQQ/jYIA9fwu7NMDZ0aM4evGNGKUe1ygqitZpEK9rgou3Vc+jelqZkwcJUUwYRx4uP/A1iDnCZ88/hMcGmxj5A8S9PiIOEMMHPA8U+PanDjWGFbyoFcYv6tQu/PFHWL1msbBcmngDq4RWY/HacZwfBmTbIl7dWJPfOBGuwlIJqYcAIQLxuAodhQkF4WoTsHWCctpyNsE7VTm2lyfmVJMkHtbJXko/EZIbCQJi8hEFPqLAQxj0MQ4GGPub+PCZzwIgHDrwNfjiBR9h5KUzC9MUwlO8rvMkmyQrihKP6zj2EWILW71L8JcP/A2eeeVXY9TbRNjvIaQAvu8jCH1MYr0V4WpctoGoem6RLk4TfWHZBgg2b6tNXGVCySq+LIJJxKsDpnOR9iV5ZOxz6dcUBmRyJkHYByLy4ROjvy4LUAlrB3seRjt9ZIKuGMyrC8AsTZWReZpWM8xC05xe53mdCEwwwOosw5b3dMImBsHjLDSz4CNL6s1u9CidssmnJEQ46GHk9zH2AwwHm7jQO4D33PsuHN66BsN4A6PQR5h5XLMa5c/EHGnemXGcTM4URoRR6GEv6uGxh5+Ljz3yAVy45sUYbWxgjB68qAdEPYQhw4s9IObkGsm88XFxrCsDSMZAZxOZKY8dJoLV/F1Q79Wm/Rmpf8Th/hiovk5lzylRrktlGtHq+6AgAIZDcOwBcZTuJLWC2cRrG72FjuNbKwV/YTvJR37a574PjENwFFnrLPWtrPXaSUS4CkslhA8A6FH5j5EgLAsiejKA31OSHg/g9QAOAfheAA+l6f+BmW+tqisOCBcu3oCLpxOTT6Z8CTZBanrP6ssFc4UQrfH0TkQHK+mpeCXWp3jK4TREmL1ExHPgIfI8RL0AIfUx9PvYoz7O8w4+e+oufPlV/xa7UQ+jyEMYFce4AtURp1VIuPD0qOHC2RjXUehhd9zDdY/9t7j7wVvwAICLD2zhwk4PUdTHOB4gCD14UQQvYlDMoCi5kaSJ514le8CRbSrXvCGtExxftgFriqtg1cawUhCAdraBQwdBJ0+Dd/cQ7w3N9bVRfC4LU/95yb2ht7MN2t4G+j3EDz4MjJCIV4MotT4YWJNzsS7+HxGuwlIZowcA6EHG+girAzN/CsANAEBEPoATAN4F4F8DeCMz/1+udUU9D6ceu12QgkUhmbapbFVLUrPvSa8tXyZH/WzLbxfE2UywzJrzi7P9cS5cFWXIAIgSTy57HmKPwD4hChLxOg58hL0+xh7h7G4ft33uXozjIa667DV4eNTDKEyFqzYxkwkRpfXMqvkKwjVCIlxDH4cPfCV6/jb+1xffi6uf8TScOkDoRz30wk0EYR/+KIYXxfDCVLzGaaiwaX0jpCJVF62qM0vNC7O303oMDfO51q9/dwGUvt96fgDARx2MEZaDUUx5oF4P0eEdxJceRPDQGXgPPAzeG+aewGWtA9sCnMYMkwfyKHlIsDFAfN3VYJ9Awwj08KNZRWqlk3JZG2srXtfkN1CEq7BUMo9r35M/9sLK8gIAn2Xmz9MUd//jDR8PXncQrh7UutvmOo9rffBiOXRYXwtWC8ZMRCrn28RJTorz2pL9RbuZEuHBHiHyCBwQwp6HuOdhPPAwCnoY93u4/Y4v4H9+8u/gewFGg4MYDQnjGIhjRqzEgoo4XT4MQsyMkIExPOx6Pg5sPxYf+OJf4qrrx3jxswmDaBP90EMwitMXJ17XiOHFDOLsGtLqJmUJpkmIuXKlaqJ2VXERyAwAf754WwQNF29rQRhlIR5RIk77PexduoEHv6qHw5/exEUfIeBLDwGqeBWmhjwC9fuJl/XSw7jvhQew8Qjj0GdH6EfpOcjOiW1St3UWr2uACFdhqYTpJdjj8ZItEQQrrwDwdmX7tUT03QBuB/CjzHyyqvB4w8N9X7ZllJNNPDqoyDOtJ6luvxp6VAhDSscokipoFTGSlY8JgAfEPiH2gDAgjDzG5x/YxZ2fegSf+eRp3PfZcxjtxXjGi78Kd/7VCZwc343YP4aYMBGtIlhXg8TryvlETQCGQYQzF+7D13/ni/GuP38v3vnXIxy9dgdP/LKL8NQvO4QnPGEHWyD4EeCNGR4zvBiF6wVIr/H0nnIyzjXdzV46OZenOBX04Wxo7n3NqjGVKYfc2+txldJN8gqrB49G4JOnsfX5LfzkG9+L3//SMdz5gcfjSTfH4IcfRby7B44gXtdpIQ/wfXiHD+HClx/FvS8j/NqLfx3f9xf/Go957yMI94b5mGJACwNSq7F8yzouWiVUWBD2gbGMcRVWGCLqA3gZgJ9Ik34VwE8juQf9aQC/BODfGMrdBOAmALjo8gG+98f/AVHYvpsZokRBpMNUQUTJxMDpu0eUPCGffM7LJmGljChkROMY4ThGNGbEURJj3N/2sfOYDVzx9Rdj4+Al+Kc/+SQ8OoBRcCbxfngE8uRWf5UgSu8TPYA9AEGMaDBCFId4/x+9F0962RMRB4/ioY+cxvv/4gT+4g8/P3Fy+D0PQd9DEBD8wIPnU3E+G048JXH6zjGnYeIMxIpoBiZqklv6RKO/4ePf/7/PXbYZQlOSP3Rgn/CuB5+Oj933WOzcL+MU5oL64xHHCM6NsXXfFn77oX+G/kkf7HvpzMJqGfltWEdEuApLJR/j2r6bemEteAmADzPzAwCQvQMAEf06gD8xFWLmmwHcDACXfPkOP/1Hr0Ic6hN/wM39aUufohx5VNyvjh9Mx61mS9ogTj1rIYPj5D0OGRwxeJQI0HgUIw4Z8ThNj9IwrVTwen1CMPDQOxRgcGkfW1f1ceCpmwgu62HsxxgTMI580IPXwr/66finn/k9hBsRPGKQz4kHjtL6NGRc63LIxKvnM7jPCLdicBzh6m/8Olzxw1cguvIuXOVH6IHRizzgdITTd17A7ueH2Lt/hNHZCNH5CNEoE6RpWB8BXkCggOAFBK9P8AIPXi9JoyC5fr0epQ81kMw4CkUAZ/e+rL1jtUSu3/fw2cdJlFHbII+AIAD3A9zxj0/Egc97OPKxPWA0Ll5fHffsLRoOI/QeOofLPtLD3116PS66D+DNPtKnptP/4e96qLAshyMIiydM7zR6kB9xYSV5JZQwYSK6gpnvTze/BcDH6yoY+TEG37PTrNW62EWXmWKUfNwwSNGWMwvtVIYd5mkG2zltmSkZ7xqB8Yg3RkwjhIgRRz5ovIGeF2Prq54AgHHu3O04fOBr4fcZng94Xi5MRKwuj4m3lZIJP70e4A9iDKO7AQIu/bpjOO99ASM/Avf2QEGIgAj+tgfvMT762MYGH0hDzKl8jbE+npWz5DyLIRau2bWdjaCtzTabo9+h/HGcn6EBYd+hZMIg+D5oFOKJ7zgP7/QF4OQZxHt7wFjuYaam4G1l8IULoPEIm6fO4LqHLgVigMIY6PcAjovjXA2s5dqu6YPndUCEq7BURpxcgj4kVFhYLYhoG8CLAHyfkvxfiOgGJD8R92r7jIR+hC9tnlmEiftK8YY/HZ+oiVcTyYQ7ioBlIPZicOQB1EMwCrBJIQa9CJuXH8XJL/4VjnzlD8PvxfADhucpoklYCuqDA88DyOPk/GxEePSBd8Pvb4Au28QuQuxyjNAfIu7vwfOS68QDTcQqpZ9tqOJUF6XMLJHjwuIhLx+nOrn4vSQ6YDiEd/IcACSzCV+4AETRSnn0W0/Wn1EM/4FTyTmI4+LkVxU/CGsnWjPW5BIU4SoslckYV1kOR1gxmPk8gEu0tH/VtJ4IMU4H58temKYzJenptnyzzFJTA4FK+Zs0kUlYZoA8Dz6S8e0+hfCDMS667kl46B8/BG8Qwu/H8PxEJBGJ13X+qFMPOeSmXLj6PuD3GMEgxKkv/g02LzuKqLeH0AsxYsYIIULam8zelYV6k5u/02gVr9pd2TReWRmyvXxUUVpIpuLMwqZ8MYPHIXDhQvI5DHORlQzOXrDx7abUx4b9QPqAKopAcQw+d27ikZ08HNDPjWv4b5fDhNcIEa7CUsk8roEIV6GjxMS44O01F5Q6TQWsLZ+tXmO+4k3GRLjaJm3UPGkMxUOmhhAz4EUBeugDiBB6IeLeCBc/6wZ88a/eC68fwgsieH7icUWNaDULWVEIKpYJOBtBlDxI8HyGH8SgXoTzJ+/GZc/5Z4h6I4TBGCNEiXBFiJgiwLLUWelaqZkS0yxc5RwLU9BEvKpwDB6N8z84cQyOivVUibO19QQ2Je0/9gCMlBDsKMr71iBCK/t3DUTromcVJqIbAfxXAD6A32Dmn9f2Pw/A/w3gKwC8gpn/QNn3KgD/Md38GWZ+a5r+DABvAbAJ4FYAP8w14QsiXIWlMuLE4+rHMj5E6CYxYowwKu+oWo/DJA6bblel6xM0NRG7BiFa3E2FPJz8BxDy8GIm+J4H4ggeYkR+iMgf4sBTrwM4xt6FT8DvPRuez+l8HMUJmqbxuoqXdjay8a3ZxExeL4bXDzHePYWLn/40xL0hIi9ERCEixBhzhBghmCMkzzuU60C9RpB6UtTryXWMtylawOUBkV7O1HaTMeW6DaY8ep3CcnEVrwrJ2Ml48eu1tlFkWfqznK3a61ogZnA2jKymzLqLVgAL/btCRD6ANyEZPnUcwG1EdAsz36Vk+wKAVwP491rZiwH8FIBjqZUfSsueRLJSw/cC+AckwvVGAH9WZYsIV2GpjDj5g0KxeFyF7hIhKgpF9T37bLsxn8U7aytfJXpd22xS78RjmoaMkgcggufFiL0YcepxpUEIv7+JUw/8PQ4feGbqcS2GCgvzpF5F6eOLkzGugOfHCMMvARzjwPXX4IL/CcTBGBHFiIkRE6fXfYx8lHOFGSazbN8RWzlTmklYVl3/pu+ny4McF0x1CytHrbhKl+sqiakK8Sbe1moq+yfzwE7jyV4X0Qos+oHYswDczcz3AAARvQPAywFMhCsz35vu078E3wDgPcz8aLr/PQBuJKL3AzjIzB9M098G4JshwlVYZbJQYUTicRW6SfLM2OEJvU3QNmWe5atwFLj6qMZ8RtkYMWJEiBD7IWJ/DA5G6O0cxPmTH8clh+NUtKIwvnW+B7FezBIunJyHZMyq5zG8IMaZR/4B5PeAQYQ4GCH2QjDFUP8xoly0ZqHiLndYLqLUpfysl8I8LiW5HFeTGqFpFa8cA7FdEOllRbQWaep1BSAPBGpI/zTPyhEiul3Zvjld2g8AjgK4T9l3HMCzHes1lT2avo4b0isR4SoslSxUGBIqLHQYJi4LvSaTJ9WF+zapz6Udlx9Ax7ZYX4KEAHDqg6MYnHlcvTFiP0Tv4CEMz38R5MdpqDCXvH5NkTDhKope1ypxq84qDI+xe+bT8AebQJA8eIi9EOzFyXnV/qnMdaIlW0ixKzJhkmBhMlmQq8gylJ2i4HTlVgHHcOEka/P+afxAoM19uRweZuZjyzaiDhGuwlLZi1PhGhnGAApC13AZu1cXDlkV/lsVblwndEkRFC5j+2x1c9nLWhYqaegXpeKVYrAfgf0xBhcdwoXjj4D85BFynWAti1JRIYsge3iQTNCUhArvnbsPweYWYm8M9iIw0nNJ7mK1MIFXFTUh9JNrbp6nfxGiVsa5rhYOYmvfvHoitGpxPhfr2JcVy4zNgRMArlK2r0zTXMs+Xyv7/jT9yqZ1ruGZFVaJbIwrIhnjKnSXxLuo+aCo5t32z7SfK+qoq4/zzxbji+/qZ4uATgRpsW01dFNtbyJcvQjsxegdPIhodA7kxenETLOFCIu3tcjMMzCnIcPkMcbDRxFsbicPHbwoP5cUl66DyXknLqUnhtW3W3lcjte40/epbh9bPjfZv+gpQAV31lHkLIpZ+lLOw2zwjK9qbgNwHRFdS0R9AK8AcIujZe8G8GIiOkxEhwG8GMC7mfl+AGeI6DmUTIDx3QD+qK4yuUqEpTKMU6e/hAoL68A0D0RtPygV3qdGttRNOuMy9rau/cybq9ZFiWgFiuK1d9EBRONdwON0TGVaRGtDPK3TM6t4pTRcOBqdQbC5BVAMpuTBw+SBRHbhqm1VNeHi3W9u6nT5m9RnEt513xW5VFePeYmmaevpkmhb9rEsu/0lkY1znfZVBTOHAF6LRIR+AsA7mflOInoDEb0MAIjomUR0HMC3A/g1IrozLfsogJ9GIn5vA/CGbKImAD8A4DcA3A3gs6iZmAmQUGFhyQwnHlcJFRYEAPYbYJcZgpvMfmoKWZznmFm9raz+9OluvlQb5146L0Zvewsch+mvaerdq7XDnEG8rU0xx7Cqk2Ml5yO504nGu+gNDiYe1zTsG3qYcFplycNo89q7XOcmbKHEprHbTa9zl3K2cei2fcLq4TpGc97CaE2FViOWdW7axIJ/75j5ViRL1qhpr1c+34Zi6K+a780A3mxIvx3A05rYscZnWFgF9qJsjKt4XIWOYx7mWYaUFwzvVfVN63mdVihUbev1k2V7ki8RPv7mBjiOcoFkarqQLKJ1Guz94zqGDIijIfzBAEgFa0m06lXqD0maXndV59QmIptGB9jqttVTZ5OIVqGKrgqtpsc1j37oal8KBcTjKiyV3OMqwlXoOHU30DWTz1jry/LqkyQ1sWca0au316R8TGmz+Vi/JJSY4W30wRyl3taGNqWIaHVjmuVx8kmaGMwhvH4v97bq9YOTczEv8TZrPdM8oHGtcxETOQn7TyZ+HGfHnUtbXaXBLMNzaWudcQj37QoiXIWlciGW5XAEAUBRCNrCDutuvG1eWVcR7IrJzqaiueB9y8Ur+f5EeTKaCysRrc0wiVc9Te/TSZB3HIE837LT0JjtXC5a9FXVP6+2RcB2C5uAdRFIdYJtnUTWoh8ErFNf1rEmv30iXIWlMoxljKuwJqhjSl3Hobrs0+ur84K62FHFPMYgVuD56c+Sw+NjEalLhACAE+Fa8Jqr+5vUtUCm+V4toi2hfch41vlQJWBd+2Rd+86VNfk9lKtAWCp7kSyHI6wJ+g29649MXb6qCZbq7KjyjtnaN9mvTqdfNdaVFbHJKNcBzPSrJEJ2OmaaobmJS7zJdTZPZm23Lk8T212+c0L7UUVWMg23CC9A+kGYGfG4CktlHAMhfATicRXWDVU4zuqtnDUMssoD6+IdrptdVW9HzceUvLLtmGurMSGidTZs411r+5UZ2cL3xGp8sSHvPDyf047HzsrqbdVNBuUywZIe8VBXr8t+of2IQLMjfTN31mWMq1w5wlIZRzEiCmSMq9BtTF6WJjfFTWZatXlf64RElfjV302e0ql+NEnbIsRxlFSXiiARpKtHGiUMKOdrskMVbjwndVYVUdDk+qgaP27L3/T6q/q+6hOwCYIgCI0Qj6uwVMKIE+EqswoLXcY07m9eHiZ9n+nGfpYZhG226/XY2rW1FxMINHlKTEg9r2Gcu/5qb+7FbTVPXGYZZiXcm8gDx3F+7oCCMCPQfPSZy/U/y6RI+zUGdprvvyAIggtr8jBMhKuwVCYeVxGuwrpRJ+xsHlnXcq4e3bp6bfXVCeSGZGIoGo1B5BV+hDOxZPO+ild2/thmG84/E8gLwGE4EamTsG9g/4WZCEFBENYVllBhQdgXxnGMiHoSKix0m6ow3boxpKbwYpdytkmbTIKzql5bfVX2VInqSXZF4KTeV8SEOAwB8sBM6ctQj7Av2B8UUHKj5PmJcI29YliwYawrpWWKFRk+66HoVuNqtqvSFzHZ0rS2CIIgCM6Ix1VYKmHEiMXjKnSdWbxBthBdFw+n68RMKnr9TcWpnk//rK2GwDGBKPPUEeLhKPW4ZtK22EhpTVERA3OlbpKmfOwxgbw+ovEIyWMISs9Z/hlA9YOWuvDzKupC5OvS6+puMrGZa5viFRYEYVGsyW+hCFdhqYyjGLEnwlUQStTN8lt3Ezxt6O68BIHWPqkzB2efWRE87IHYQ7Q3BHkBsqVzTCHCVWHDwvzJRWuWAIAJntcHj0MQe8VwYUN5p1Vz9kvYLeq7s991CoIgZKzJb6IIV2GpjCNGHPQAWQ5HWAf0WUVtHsxpfoDqJktqalPTMaxN21dmn1W9dPHuMAlBTUOFJ6atyY/yypM9NGDA8/uIxhfSsa3FcOHJ0jhM9sFX+y0Qm3pRXfIvok5hOXjpiYlr/th4FSewrqxQzbR963ruOgpBxrgWIKIbiehTRHQ3Eb2uIt+3EhET0bH5mSh0mXEUJ6HCcbhsUwRhcajj9vQxpaZtQlnEuowPdNlXRdUNtS6y9XodbsY5hiJulJBSzr2u0XAPnt9P04reVRGv+0kWEozSe/ZQwQs2EY9GBY/55JymHvUCVdfMrOdWeRBiHSdrGqddhe1Bklp/VXumsiJal49H5lfdfj1fk7pdyq4Di+rbujzrAM/4agm1wpWIfABvAvASANcDeCURXW/IdwDADwP4h3kbKXSXMGLEnnhchY5DKIpR3TPpIhj1m2hbHerYVD2fzizhxE3LKWUmftRsTGScip7YQ7ibCFeOqTYkWITsYqidvTm90fGDbcTjESj2i2HfILtwtGHy8DfB9Ts17fWubzdpb9oHScL8WbaIWSchlbEKAnLZ7Qtzw8Xj+iwAdzPzPcw8AvAOAC835PtpAL8AYG+O9gkdhpkximKwjHEV1ol53aDrnlhdqLqGODZt3+YBdi2XoopXTDyvHqLdPXj+RuptzWcWFpG6PIrL4GShwgS/t4N4NAQxwWN/4nmdnFMo57kpXbrH1EWusP+smnBZNXsWxaod46rZMy84CRWe5dUWXITrUQD3KdvH07QJRPRVAK5i5j+tqoiIbiKi24no9oceeqixsUK3iNKxCOz1RLgK64vr76gt5NAmVPXwZL0emye3jjoPkqGeXKRm5XNRM/kX+aDIT0KFe5vg2DOGMOUiqqM3ICtEWbDS5D2OCUH/AOJwDMR+eUkc5OedmMCxOu4V+bv+2fU61PNOE1Jvq9Phmnaqz9Z+i24SO8ECxAoRGV9T2dZFMTXlcc2lTxdk28ojocJuEJEH4JcB/GhdXma+mZmPMfOxSy+9dNamhZYzjhThKuu4CuuKSZC6hFnW/dBUhSab6mjyO26ru6IeTvcVPK3q+Na00mhvF36wnXhc4ySvPruweF8XT90armBC0DuMOByn41uVca7ZWNcMU2itnt40/NyWv+p6NOXXvwOmem3lXOy1HbewP8xZoNSJqYWLro7h0l8LFbBdQoTrhBMArlK2r0zTMg4AeBqA9xPRvQCeA+AWmaBJqGMcJws6soxxFdYB2w+Dy823qZ55/OY2qWPKcXqqgDGFjWZCB+yBYh/RaIigv1M7xlXE6/7A2nmnNI1jQjA4BI7C1Nuaj1NO8ioPJjLIfA0UcLkmqx7sTBt637Sszc4mHuMVgYjuJaKPEdEdRHR7mnYxEb2HiD6Tvh9etp2NmaMwmVY8OZfpkohyOJZp+lMeCAiAm3C9DcB1RHQtEfUBvALALdlOZj7NzEeY+RpmvgbABwG8jJlvX4jFQmcYh4lwhR8AkcwqLHScuhvrulBHm2CtC2ucJcxRRfccTXvjnU3IBJp45nLxSohHI/i9A9r4VqoVqqpXVn8JOdP0k+7tzryuvcHFiKMQFPuTCZqM4pRzz/lsxqfvVfets9zTzuthkMtxrt6999cx8w3MnDkdXgfgfcx8HYD3pdvtYU5C0CqUPM/+cq2ji9T0e2VfOPZnbT1N6NADAxnjmsLMIYDXAng3gE8AeCcz30lEbyCily3aQKG7hJMxrn0JFRbWh7qwQ1uYou33Vb9RNm1Xtaenu1LnceJUwKiiSK0/DQNGnIYKR4n4icNUuMZaWYgQ3X+KJzmfmCn1uPYvATiGuhxOvrSR4QLhrFb7vso0V6/qvK+Rpu1141745QDemn5+K4BvXp4py6FSYFVRIWDXmdoHADZqBOzMdEW8rkmocOCSiZlvBXCrlvZ6S97nz26WsA6MVI/rUEKFhQ5TNx5uljF0pvJ15apE8KykdWQ3FGzamXnm2EuenKfvYAKHIYLgwES0TsZUVhjq4o0tlO7IfYoLTYR+ljfrH+bi5+w9Oy9B7yIwx0l4MCse9KSWyT9Ot5h4UmeSrhjXJGS+6cOfJpi+d1Xt6fmbfG9XBwbwF0TEAH6NmW8GcDkz35/u/xKAy00FiegmADcBwAa29sPWeuYgRBqJVjWv+oXzPCAdEqXWy/L0LaFJX1YgfYrWic9ZcBKugrAIMo8r/L6ECgvdxnXcXt1EMi3BOJbVOmtTKm4Y6eyzEbxgE/oMtbrnVU8T5ocuWI39zYAXHEgzFL2tPJk92HzOreJ1Fub1fWn6sKjpQ6PV5GuY+QQRXQbgPUT0SXUnM3MqakukIvdmADjoXdKJb6SzaDXl05/0iHgFYOjTOfWlWv9MfeoRELf7nLQp3HcWZp5VeD84vTvGo+fFI9c1wij9AySTM3UTZuDsl4DRhWVbshrYflRs4/bU8Fq2pNvy19kxS4hQVbup+JzsMtStLpGSl6XEaxd7YGZ4fj8tT5PJgITVYXIf6W8oicU86uzRSci4+UFEo3Veq66Daa8Rl3JN6m7htcrMJ9L3BwG8C8CzADxARFcAQPr+4PIs3D+mEq2mUNa6/euM2hdEzftSWGtacQV832/dju//7Q8t2wxhzowy4er3Rbh2kTgEfunJwAfftGxLlk/V+D2XsF6TJ9Z1LKxp/B0ZPpvscxEKSl2UuNHMY1FZEzKgSXhw0Zai63kRY1zXZazs4o/Rzz9OTqNBqNo8r3FexgnXyAWXtCZ1mr5Ttjpb5nElom0iOpB9BvBiAB9HMgnnq9JsrwLwR8uxsCHLHK+oi651GpMwD9S+qxvzamDdxxDLGNcVoh/4OLMrk/d0jWyMKwUDEa5dJBwm7/5guXasAqYb33mO05tmvJ3LuFoypFvKJSLU8vuXedcyARPbjIpB5IHjIUAMIm6dEFgHKH3IEEfn80RDVIAuSInT0GDlWmLOnlkkY2ALddgewlRdl1VjZV3qNLWvf7+a2KV/ttm4PC4H8K70pj8A8LvM/OdEdBuAdxLRawB8HsB3LNHGfcFZ+NR5ANWQVqL1eEqWEXPp4QHZ+itLt4nUitDghdDycOF1CRVuh3D1vXwiH6EzTIRrr5+LHKE7ZA8jgnYK13Rd6rMAIgAhMx8joosB/B6AawDcC+A7mPlk88phvol2EZN19c5Sn22MrUkMKzfj5omYlOJEQIz8uAs7GYwYTDHYY5AfIBydTj245UiyebAuD+YXdc+cnBNGOHoEIAJ7EdiL03PJRQFaLGm8u+KsTnXMa5UAtW3XGm5q2CFv3cOkKrtsn1cEZr4HwFca0h8B8IL9t2gFmSY81SS6lLR1HOdaoOkfYcc/Zmvdr2ty2K0IFR4EXh5WKnSG7Jx6wQbAERBHS7ZImCsTj2t/uXbMxuLWNqz73TZ5cdT3JnXpeZuM6zOFGpuyV4YVU9quEhYMTEQKe3EifvwQ/mATo70H4fkxyEu9rliMgBWaQZTewxNAHmO4+3mQFwDZ+aP632lbWPBUY15nZRnX05rcXAoKLn+4WuzpK9HkWKYdsypjXYvMGibcosuvFWe+H3gYhiJqusZwrIQKA+J17RrhXvLeUo+rhZdjlrUNDeGUhW2XsbDzuNluMq7PllcRGlzxw1daz1PLx5R66lLh2j94EfbOHQcFMTyf4XlIBWxa3wzHv47idx7HrHq+yWN4PoOCGBdOfQrBYBMcjCbCdSJep4hbc3aULOIma79u3Nbs+ttXljG+tSqUVcTVbOxnmLBQCxHdSESfIqK7iaj00J6IBkT0e+n+fyCia9L07yKiO5RXTEQ3pPven9aZ7buszo5WfKskVLibTDyuvVTYRCJcO0UWKtxejysjWdvwQ+lahYDj2obGmoDqsEEt/NYaTtykvUWWS0M76z24ZN1kxGDEiL0QsT8GByNsPuYK7J09Aa8XwQs4F68GAeYiyhYVbtw2CuLToc+M5b3kXPhBDL8X4fypu9C/6DDiYJycPy9KBGsaMgzkHtRkwiZYJ2rKSMa81hlYvXsqbGH109Ai74UwBa5PWCqE19qGs06L3l8iagvQHF6V9RP5AN4E4CUArgfwSiK6Xsv2GgAnmfmJAN4I4BcAgJl/J41cuwHAvwLwOWa+Qyn3Xdn+dGbzStohXAMRrl0kO6d+XzyunSQ7n+31uH4NM38Vkj/UP0hEz1N3Mtv9jER0ExHdTkS34yG43RSrITsmb6ftPsdUz7TbJhFa5QVGej/hICQmnlfO3pLvfxYmHPtjRP09XPyVT8Xo3IPwN8YI+hH8HsPz8wk7s/GV+4Eu9pYhgJdpAxFP+t3301ePEfRj+BtjnH/0k9i55gmI+3uIgxFif4zYCyde13TEa1JZFi6OBYUDN7kkXPMusp9Ft7SHOpEUx+U86raIVDumvjX1p1DPYkOFnwXgbma+h5lHAN6BJAJNRY1I+wMAL6DyjGevTMtOjQhXYWlMhGsvXQtQhGu3mHhc2ylcZ1nbkJlvZuZjzHwMl1oa0Meu2h591s1EWuW5te1vsq3bUZVHy18QKAWbGKBctEb+KBE+/T0cevaTwXGEs6f/HsFGiN4gQtBj+AFr4lWpziLubAIvE791L9svvEvZeb1mtcGGS59l254HeD4Q9Bi9foxgIwT1zmB47gFc9txnIhpcQNTbS7yuWbhw1cOFeXhdSwe0oLzT0CQUX+gOmeAS0VWg1rus9pup70T4O5EGu0z9AnAke+Cevm5Sqj8K4D5l+3iaBlMeZg4BnAZwiZbnOwG8XUv7n2mY8P9pELol2iNcZXKmzjGMMo9rKlxlSZxuMfG4ti9UeO5rG7p4MavKTTv2r66cyxNXixd2MrbVZgMhFyhZiGjmfOM0nJRixBQh9sYIg12E/Qvgg3vYuuwojn/8VxBsj9DbjNAfxAh6jCBIvH6JeK0WZhPzDSI1EX7V4q0Lr+T43cVs3mcMLwsL9oEgAHo9Rq/P6G1G6G2P8cXP3gwv6OPA069MhGuwlzyA8MI0BNzQju0609NdPqvbNVEBtfnqytelVXWput/NsyG0AVcxpeYTQZszTf8Ji+bh7IF7+rp5npUT0bMBXGDmjyvJ38XMXw7gn6evf1VXTyuWwxkEHsYRI44Z3jIXlxbmSuZxDXqbSYJ4XLtFNmY52FiuHdMx37UNq7yaVX/Smvy5M4Xsupavy2eqV7+fIEteNb/iZU6WTUnDhIMxot4Q4cZZhFtncOVLX4pPv/U3QM95EIOdPkbnA8QRIY7ySqIouQ/Mnubr9ze2cZouaW1nMkMv2dKr+ywTvploDXqM3iDGYCvCYHuM/sFdnPir/4kjX/ksRFtnMd44i7C/Oxnnmi2NYyRro2rpG/V6cYkeUNOrytmuzQo7raH7VeX176JaXu7DF4dhDdH51R2XJ1uyfdHUfetI1TlQ+3Ha/pOxw2YWe+gnAFylbF+ZppnyHCeiAMBFAB5R9r8CmrdViWw7S0S/iySy7W1VhrRCuPaD5CIfRTE2PH/J1gjzYiJcJx5XEa6dImzv5Ez7urahSXBOk1dPd6l32ns8NvxGau2VZhMutZsKV8TJ+NbeHsaD8xhvncKRb7gen/v9A7j79h/BdTe8FYMLPcQRIYqAJFCIQJRHlmX3Kuo9i2kip6pt130qi7xHmocN+r66bdVb63m5aB1sMDa2Ygx2QvQv2sODJ34d4fAsHv8934bR9gmEG+cSj2swxGRN3vT8FhtQGye7uIXlHqzJNd3ke+VSn6sdNtFaVZ+wEjAzHCIVTQXr89R5Wz3q1pI4TWjyh1S81nYWe/ncBuA6IroWiUB9BYB/qeXJItI+AODbAPzvdC4QEJGH5EH/P88yp+L2EDM/TEQ9AN8E4L11hrRDuPqKcO2JcO0KJeEaSqhwp4haPznT/qDfaOvvel6XfKZ6bbju1z1PsZZeqqP6BjATNrEfIorGCIM9jAfnMNo+hd7BR/DEV70an/jvv4IrnvwX2Lz4G9NS/SRU2Cf4Y0IU0kS42u5n9DGbdZ/bjk2cqum2+0TV0+r5jKAH9AcxBlsxNg+OsXnxLmjzHtzzwZ/D0a97Cfjy8xhvn8Ro4wzC/m4SKuyHxWVx5kmT8zTPc1onRuu8skI3MHldXcutCy4e7wX147p7Wxc5ZyEzh0T0WgDvBuADeDMz30lEbwBwOzPfAuA3AfwWEd0N4FEk4jbjeQDuS50CGQMA705Fq49EtP56nS2tEK6DzOMqEzR1ilEUwfcIviyH003Cdk/OtG/YhJ+rZ1WvZ9b8tvI2IdzUW8wAU5yEiqYCJ/bHiIJhIly3TqF38GEcfMHjcOQDz8adf/F9uOGb/gibFz8DAOD5PXi+j9HQQzgG4ixkOE4MsXpdlTGf6r4mnthVxiZMc+GaHphByALpcRPgeclYYs9n9AaMwWaEjQNjbBzaQ3Dwfnz4T78JGxdfisd974tw4aJ7Mdw+ifHgHKJgWJpVeFYK4cTLZFGRC8LKUet1zcSTq/BqIlrb7nVtEqbdVLzuh/hvc98DCx+GwMy3ArhVS3u98nkPwLdbyr4fwHO0tPMAntHUjlYI174I104yCuPEm5555MTj2i2i9k7OtDDqvKguZZoKXRumcXdNPLPTtMcAezGIPTAYxABThNgLEfkjhL1djDbOINh5BP5oE0/4sW/H6P88jTv+5OX48pe+FTtXPA/9rU30z/Ux3vUxHvqIxpSMf42BOM7FGZFyWMSl8Zs2ITvVsalMc/Mwx/aYiyJV9bgW9mVllX5IZg/mZBbnXoz+VoT+1hj9i/aAjc/hw3/yjYAPfMXP/RiGhx/A8MDDGG2eRti/gDDYm4jWSbiwLmCrogTmFdpbhWsb87ZlP45NSFjkONdCOzUCdtpxmJntbRNR0/S52kdT9KPKWntb1wwRrsLSGIZxcm6zMZDice0W2WRb4nHNqfKiunpMdc/ntGP6qsIbqyZ6mtqDyyV3Jnsx4hggLwT5HsLeLrwowN72I/CiAGDCU376e/CZn/0dfPSPvwtXPPXbcc1X/gyC7QMIz/cR7gUIRz7ikBCF3kSoqeJsIlqp/HnyjqKYVcuWjqJmKZdF4GRHQaBS2hdU7JOCkNX6KBWtfhDD70cIBlEyq/POHh6472bc84H/go2LL8VX/NyPIbzqIewd+hL2dh7GcPMUxv3zicc1E65e/ls9Ea+MfB2DJpEB88Aazm5h3raIaG0Vjca6rlMYsA0H0ersyRamZp+WN1867RCufjKuVZbE6RajTLhOPK4iXDtFtryReFyLHk2bd1PdV1dXldBUvVqmdurqrBs7a9uvljfUy8SFCZuY4omYiTkEkZeGC3sg9uHFwaSS697wHTj5x0/H3b/z23jgU/8Llz/l5bjqqT+CDboC0V4P8dgHhx7iKBGvhXVCM4HqcS5WvWRAUEG8Eia//C5LxlTRRNzOra2Cl5WS0OlMrMZUErOT/EqfeEEMrxfBG4xBwRmc+MzNOPG+tyHcO4ujL7gRV3/vCzE69CB2D92P4c4jubfVT7yt2VI4IE7eVbyK42zy0GXaMdmm8nXX+DQ2NqlTWFmmnqjJoV4n2hI2vAIrfYi3NWVNuqEdwlU8rp1kFMbJ+OXM4yrCtVuIxzXHNQw1u+FmLc1WF2D/sWoyLk//PM1NdgMPMFMSLpxN8sRejBghIowSx+wg+ZufiaGov4eLvvVKPOtFP4cTv/tXuP/978b9H/t99LYOYfuS67B98ZOxfegp2Ni5Cr2Ny0B+usRWPEY0PoNwfBbh6CTC0SmEo7MIR6cRjc4hii4gGu8ijobgaIQ4DsEcAXEE5hjVdwJZzHEyFtMaa2y7+S3cbBUHnvLENVpzJ0IeiHyQ54O8AJ7XhxcM4Acb8IJN+L0DCPoHEPQPwu8dQm9wGEH/IgS9A4A3ABGBo12Mh6cwPP8FnH/oUzh/8tM4//AnMTz7ELz+AEdueCau/d5vAV92HrsHP4/hgYewt/MIRhtnMNo4g3HvQjK2NVsKJ51RWD/3nE1F7eptdck7TWRA3XexiY22+qvsEhbPnMKF5y1eGwusVQ8bbtjHS+/PKla1jxsgHtcVIhOuQxGunWIYaR5XCRXuFhOPqwjXRrh4OXVvki50XbxKTWyoar9JG1o+pjgJH+VEpBayZmu8+mOE/QsYb55B/8AhXP6DX46jr34uhvecw4P/+zac/uSn8dDn/hT3774THIVgPeQsjQEmzwP5Pjw/AAUBPL8H6gXwgl6SHgSgngfyfMDz4HkeQJSK0tz+iQBThGUicoGS0Ky7kSj0pSKCs01KxH3BBqQ3bKkNHI/AUYQ4jhCGEeJhCA5DxOEYcRgmfRKFiNOFb5mV9YMmdnjwfB/+YBP9Q4dx+Ian4sjXPB0Hnv5YhJtnMdz+AsabpzHaPoXR5ulEsPbPT0Rr5I8QU4SYosLY1kKYsH691PWHy/Vky1Pl9awrW1eHy/6m3x9hZVmU57X1TPlgYF79KV7W9aUdwtXPhGu0ZEuEeZJPziTL4XSScA8gH5C1l4tMI/p0L1KdN8fFw2v63a+7Ea8Sy8YGqLCZTcwE5F5XdQIfphiRP5rUyRQnHtdgiLC/i/HGWfS2TyE4uI3g4m085iuejqPj54DCPigKkhcTkLaBzPvnMYAY7CcTB8FPxRVxkoeS0OHCZEKmx9dWQTTHG9umj83Ttok95TMBsT9Jo9gvvk/Cir28j/woWcomGCMOhogGuzg/+CzCwXmMB+cm7+P+eYS9XYTpuq2ZV7w0m3B2HKaQ3SqvaOGSmfLm1FVQVuWp88Y2CSV2OW5hZZlVbM0sslYtbHhGb/bS+1Nnlfp2WhwCdLpCO4SrhAp3klKosHhcu0U4FG+riVUJIVxwu/qY1rr2MpGqClt1tuFx/zyCzdPwxxvwwwGC8Sa8sA8v6sGLeqDYT17sFepM2uZJGGsmVDPhWvIOtjHeShWvyEUssTcR8mra5H1SPOmLLNw3W9om6iXiNOztIgqGycsf5V7WVKyqonXynt1FEWBcHqeN/Vx1DYsY7TSqWGoiuuYmslYlbHhOY1qzfllKX3aVNemeVghXWce1m5QnZxKPa6eIRvlDCaGeacNvXfO7him6tKsPz3S1m5XxrcpnphgU597XbJKfLOw0WzIn9sfweruJWA178OM+KE4ncpoINS9vy1PFFBe2Jx5XFIWVdQmXqr6oOv55hYVW1KOK0Pz4adLPBK8kWE0CPxP32XqsE4Gavmeh25E3LgjWkmi1reOqHEPJm7pK4bOrZIswHQtcFmepAmqZ3tcF9OfSxeiyHwTMCUI7nwVOQyuE68TjKrMKd4pRFGOj5wFeAIDE49o1xOPajFnuCaYJN7aVcwynpHSYZXWYMudhtHpbqYieiFev+Ped/WQ8ZiZaiUeI4mEiTiMfHgcToVoUrNkY0fxXfCKuSmIWpc+FNMtYYVX0LQRV4FWIQIJXSkvOTVGc2t7zEGeeeEbVV+yFiSdW86zqL9VO3d5i+HDFMa+SUFwlWwRBEFwQ4bo6ZGNcxePaLUZhjIMbAUCUCByZVbhbRCOZUVilasIYkyfUdQzdLJ5UWzmTYNPSOBOlFe0y0nBhTSxnHlZVnKkhwmq7sQcAEYi9RMDGHsj3kvd0ZuKSICNOnuRn4lgVq2m9VoE1rwl0FuV5rUAX1AVPbFzsKz2PLuSrBKpxAiZU9KmGVYwDeT/tp/tgmu+OTLS0+izQ67pUluF17WI/Cq2kHcJVQoU7yTCMJucW/iCfhVboBuFQ1nBVMXjuCuk26m6MXe8nbDfeNRPkzNQmkAoQRbxO5gWKjeGqpgmbJuHE7AEeQCAgJqMQU3ESqtbCRXudWSFBUylk0zBik40mMVrlWbV6rKcyerpiMzHN9S4TLbWDrorX/aSr/deRMOEMWnbY9T7RLuEqocKdIhnjms44G/TF49o1xOOaUzUbqb5fTcvyW7yeVjHsIp6qvK2qnVXtZqJUbU8tCxQnaWJ9n+J55TwNDBB5yQzASr5JODED5CuiTGvXNfRXrc/qHa3yPtvEbd35MJWvOp9VdVsoCFMthLiQL1bStPb1Pit4pWHu52S3ZZyw2uflQmU71LJNqIpOMLVhKut6Pur210UvCMI0rNpMw8JyYRT/vnWYdglX8bh2islyOIB4XLuIeFwncDYgtEpEIvUkammFz3qazWvq6g1y9baabLGJMQMlgaqK11TITMZrZqKJ40l+gjYGlhRxxIA+1DNr03QcEzHmob7/1HxQ2qnrX114eSjaaTufps9qPbaHCep+3cOctqt7soE0zXf4XdUeNlSdZ6vNpv16G6Yw4WlErMt5qStbdz60Mtale+q+x4Ig7D8dFP0yOdMKka/jKsK1S4yiGINeeicX9JN1P4XuEA3F45pRN95x2u06j+s+bE88qjX5J6HBFtE1CRWGFsKqilhT/Vp9Jc+qkr80UVBd/9nES5P+yuzzlH1qfa7bujjX69P3a+Jo0v9K/RPvdnZeTO3r9RnsK4hZdb/iVda9tXX5p9rfxm1h8Ui48PRIv7UHEa6rg0zO1E2GusdVQoW7RTgSj2uGLtZsIjD75dH2l9a6qxIVVR5ENY8eNmmzr2a7tAyfyQObbls9r7qnUK1fFVVa+/naq4b2s/2611T3AoJBNaLPadvkDTSJ62nqbmwLGferfZtPZGUQ9Np+U3vGvrdsV84srG97XL1/BbYL3tVZ6xOEaZFw4emQPms1rRCunkfo+56Mce0YozCerNGLQEKFO0c0BDYOLtuK1UEXlzYhYyjHYJDLHa/LTbFJTM065k5d9kYXv9pxFjyvJruahJcaKI2hLHk+2bjbXFldBktFpjKLFCyla0q7MeNy46aw4ar9zn1hC1l2Kspu3dT0up31+p5Uw+rGfL47wv4gXleh40io8IrRDzzxuHYIZsYoivNZhWU5nO4RjgBfPK4lbF5Nnbob4iY3zLa8i7iPcxDm6gzC87Cpas3VvF6u3m+tvCbvtHXZQkj1NFPdutfc9DCiUF6ND9bq1ATmZLKmOk9hXUixKlpNeQ1pzqe+qWg1pZm89KpNddeTS7t151YQZmWRXtcuCv0ue1s7fGgqIlyFpRDGDGYoocJ98bh2jWiYPJAQpkO7Z9AnfymFDzeoq/F+pzZSr6urMEcmOMnNm2wrP2kf5s+ZbajYb6OJB7tpfS4hpLa6q/rY9NBgki8V75ZQYkDxWNedl4pdJS+ry7E1dRfMcg4rxLaaZgzdrypja2O/PfCCma56XbNjmpco62IfdR1eH4+rfQG8FaPvexiG0bLNEOZE9hBCPK4dJpTJmRrB2meG/Qmquq/ux6qqHlt9U/8Actn2qmMAAGIw4uRVsw4dU1x4FerSX0iFh+nX3LVP6vKazpNr39nKuHyuq68uTzbLtWk/5xuTvobW19pnRnoO9fPiYh8jP0c2m2b9XHcNun6uSptXHYIwLR4VX4suJwhLQDyuwlIoCVdZDqd7yHI4zajz0GRkoZ3pnS+RIfzTVq+lLqe2XcIls/GVFR49Y10T7xWXPMvO7ZqSXfqkLoy6aX+4hvna+nzaEPKG55nJMG66wqbJzM4mj27WRpWttvBedcyx6bw09Wg26UtTXu37ZbR7FpvqjksQ5oWI0PViTR6GtcfjGsjkTF1iWPK49sXj2jVkOZwiuvdn2ndTfVSRv+q9Sbkmx6F69Paz3fSdi27XudfvVM62v2losV7OJob1clXbSES9ce1R283PNA8P6uyoim1zFefzvlmra7eJZ9u1fmF/ECEndJTsmfEsr7bQHo+rLx7XLjHxuKrL4UQiXDuFLIdTRBce075rcCYSCcX1VOvG8S363UvGsU/G4u5Tu0xc9LTu93E3PH+1NCmnPhTQPb8GbycRwKx5Xl3am8f9/7zulOZkr1HEL7A9YZ8R0SoA3V5CqGaoTVdolcd1KMK1M4yiZLzyoOcnCeJx7R7icV0MJiGkiLbSDfgS79esXr0FwWB7ePCq4OLJtXlvbZhEsq2sVvdU58hke52nOdvkBo/3m3rFq8pam7CEpzdtz95Add3C4hHRKqh0dCzvunhcWyVcxePaHYYmj6sI1+4QhQDHMquwiotQWRZVN/4mUeJSPoXSGCYGux9nnegy5CmI1lXuTxePrClNP/a6PmrgAc7OUWPUuvS+N7TDzCAP1f1jqr/qmAyeZL3dxrhGLbh8N+rqEBaDTDYk1CHXRytpTajwIPBwbhgu2wxhTmQPIQbqrMIyOVN3yMK+ZR3XnDqhYgrvnJLEq5XUMwnVNdWZpVXd+FfZYSpvsV317JUmBdLL2No0eZlTwUp6Plsfzti3xjpc7Lfld7XHVL8aBmxrV6eqPUrDu2G5ZqquF0vd+SRiqUCeRmjWlWty/IpNFRnMdrlep/UGiHCdFREcwrxwuZZWPbzY5SFaR2iNcJUxrt1ClsPpONm5FI9rPVVioInAUfdXefFQkVYlbk2YBEWFjXkYL0+G4xB01VlvS+a9JU/JUuHpM/aNjSb9XJW3rt9Mn13qqDvPdfXU5E3OUXp+qELE1tiqnqNiAzW2Wupz8q4u4qGEqR2XMtPU0wVEUApdpAXXtW0lsq7RHuEqocKdIpshurAcDkdAHAGev0TLhLmQec/F41qmSujpIrZK4DgKRquXs6ktNqYUgiYRW1svKWX1/fO6r2hyvFVi0OVBhCmfmu5yPqY5hyZPrVZPHnadi9jKY1HqmHjAqbzP6MW15bXZbCtf5X3l9LtQddy2zy593rTONfGOCIKwT6zJ35R2CVdZDqczlGYVzmafDYdAf2tJVglzQzyudqqEURPxNQ+hNi9bXNuoFbELYpUeluvCyibaq4SQWt5UL1vSbe2peW0itsqrqQs8W4ixzS625NHtZUOaqYxe3vXhSp2N2WeTfSYRb7KvYVizIAiCC22aYGkW2jM5k4QKd4pSqHA2+6wsidMNJh5XEa6NqLppn7E8a/8WZlPV/nndrNfZNE0/zrGvrZDyMqVVedttnlgypNvqbFLeNb/tZSrf9LNLezX2ZjNtM7H78dnaqCtv835X9Y8gCEILIKIbiehTRHQ3Eb3OsH9ARL+X7v8HIromTb+GiHaJ6I709T+UMs8goo+lZX6FqP4xdnuEq4QKd4phaYxr5nGVCZo6wcTjKqHCjbB5shZQ3lnENrVpP27K62yaph8X2NdTYxJBi6p/lWlgp/MDmjaeL0EQBBMMgHm2VwVE5AN4E4CXALgewCuJ6Hot22sAnGTmJwJ4I4BfUPZ9lplvSF/fr6T/KoDvBXBd+rqx7lBFuApLoRQqLB7XbjGZVVg8rk4swiO4LKa1sy3H54Lr+XQ9ZhePsl6fKay2qm6XfNNGBJjyzVLWlO7Sxqx1LboNQRCEKVnwOq7PAnA3M9/DzCMA7wDwci3PywG8Nf38BwBeUOVBJaIrABxk5g8yMwN4G4BvrjOkVcJ1KGNcO0N2LgvL4QDice0K2XkUj2sZ25jGpnnmaEull6qpqJ7WTpdyixTF8xQSrh5e175y8e5WhdgC9uNT89WFebvY4Xp9T3vshvQsFLi0VrCrvdP0r22sqimPqy2CIAjTwjO+qjkK4D5l+3iaZszDzCGA0wAuSfddS0QfIaK/IqJ/ruQ/XlNnidZMzjRIx7gyMxxCoIUVJ1/HNZ1BOJt9NtxbkkXCXMnOo3hci6g3yVU377P+iatrJ0sjy349j0s7VWkuuPSNq/CYxY5543rOXfJl6apo0vOylu5Sl4tNtvbVfWraLMdgar+uTf16tdmu2+DSdzZxW3fOXPIIgiCsBkeI6HZl+2ZmvnkO9d4P4GpmfoSIngHgfxHRU6etrDXCNRsLOYriXOwIrcW4jisgocJdIZucqaWzChPRVUjCVi5Hcrt5MzP/VyL6T0jGYzyUZv0PzHyre8WGz4u4ma0ba1ezf7JsCADjMjp62XmM7Zu2Dhfxv4gxrimNxghXtaHssz6cretvNuQxlXGpy7WM3q6tbJN2Temah9W5jqq66o7Plq/GvsZ5BEEQZoCAecwq/DAzH7PsOwHgKmX7yjTNlOc4EQUALgLwSBoGPAQAZv4QEX0WwJPS/FfW1FmiVaHCAGSca0cozyoskzN1imxypvau4xoC+FFmvh7AcwD8oDIRwRuVSQbcRStr7/q+WcfKsZZma8+1Xptt07TnmjZNeyZ7XdLr+meW8+FyPt3Cs8x22dqo847Pu72qdt1D0OZTVrdT9eruR3vAdP0rCIIwK7NOzFQzOROA2wBcR0TXElEfwCsA3KLluQXAq9LP3wbgfzMzE9Gl6eROIKLHI5mE6R5mvh/AGSJ6TjoW9rsB/FGdIU7C1WEK5B8horuI6KNE9D4iepxLvU3IvKwiXLvBKIrgewTfS+94go3kXTyu3WDicd1Yrh1Twsz3M/OH089nAXwCDmMvKqny8ughufqNN2vpevmqdLVOkyDU0dKZ8vGDk2VF9ONQ7ddDO1Vb2JBfzWPbV9WexW4nwVvRb4VjBpe3039O51OptyCs9LSJWco/1tqn4ssZW79m/aC+Z/n14zGdW70eUx36fttLtxco9kHW5yZMdrp6OG3nzKVc9q5+Z21iVj//giAIc2KRkzOlY1ZfC+DdSO6H3snMdxLRG4joZWm23wRwCRHdDeBHAGR68XkAPkpEdyCZtOn7mfnRdN8PAPgNAHcD+CyAP6s7ztpQYWUK5BchGTh7GxHdwsx3Kdk+AuAYM18gon8L4L8A+M66upughgoL7WcUxvmMwoBMztQ1OrQcTroW2dMB/AOA5wJ4LRF9N4DbkXhlTxrK3ATgJgDA1bM0bnm35TOV45o8trYq0MVDIZxYvylvKiJ0mogPl3clf6UIAuYvMGyCvKqdmv3GybQoK+povOmhQ1VRm4e17ry7Xluu170LTc6hKj5dQ4/rjlNChQVB2A8WHMWRRpjdqqW9Xvm8B+DbDeX+EMAfWuq8HcDTmtjh4nGtnQKZmf+SmS+kmx9EMWZ5LmQiRzyu3WAUxnmYMJCHlIrHtRt0ZDkcItpB8gf33zHzGSRrjj0BwA1IJhz4JVM5Zr6ZmY8x8zFcqu+saNAmMuvKVbHfN8vzEBuzMM8f70Ucg8m+Ok9mk7DXedg8TR2rIsosnlsn5nHt1pWVEGFBEISpcRGuLlMgq7wGFlcvEd1ERLcT0e0PPfSQKYsVGePaLYa6cJ14XEW4doKw3ZMzAQAR9ZCI1t9h5v8XAJj5AWaOmDkG8OtIHuxNT5ObbJN4WZGbYHMQbUVYZ17QLa0Jaohpzb9GmMJZ6+w1nS9VpNaFbleF3er7DfVNwmzZcPTZmCaTDTa75nS+rGfENM5q1jZNoch15V36xFana4jwinx3BUHoBgtex3VlmOvkTET0fwA4BuAXTftVL8Sll+puiGoykTMU4doJSqHCE4+rhAp3gqjdkzOlEwX8JoBPMPMvK+lXKNm+BcDHm1du+dy0rCmc0fXmfN4/UlXiwjTeT72JN4n3/bDPZZ+632Xsbd2+qnG6pjGkdWJVD+21hYZX2d0kvNfl+Kfta1t4btPrwdTnpr5uUp8uOKtCq237ZrFBEAShCgYQ82yvluCyHI7LFMggohcC+EkAX8vMc3ebyRjXbjGMYgzE49pd2u9xfS6AfwXgY+mEAgDwHwC8kohuQPIzcS+A75tbi3Xj6mxjCGFJMwmKpmMZq7CVVW/UgWQCoSoxZRLjruMMm9ppqtN1LGOTOmfFxfPuKi4r7Gdm83FUCec6bP1S108O11LBNpc6q1AfnNTZqeZz/Q7Ny05BEAQX2qM9Z8JFuE6mQEYiWF8B4F+qGYjo6QB+DcCNzPzg3K0EMJAxrp2iPMY1W8dVPK6dIBoCIMBrzVLRBZj5b2G+1XRf/qYpdTfCVUK0Sf16edd6bGLJVVC7tDeNHU3yTSMe6uqZt4B1EY11DzhcyleJ7ybHZGvP9SGMix02mjzo0dNt16LNg9z0mtUfAgiCICyQNoX7zkJtqLDjFMi/CGAHwO8T0R1EpK/tMzMyxrVbjELd45qt47q3HIOE+RIOE28riZvBGdMNshpmC+3zvLrWtR7XG3f9x9P2Y1qXb1pxq4Yhu+Svw1beZm9dOKweOq1v6x5Gl/K6TS7HnAk31j5Pe/NTdVymtk2f9bpMx+n60MJWl5rW5NqwfQ/r+kwVybP2sSAIwprj5A5xmAL5hXO2q4QI125h9bjKcjjdIBq1fkbhfafOyzrvcEOTF3Qebdg8WLZ3Pd+sNs2zj6psmEZY19XR1MPYICzY2XPcxJ4m5VzOZd21UVW/6bpxLVtF3fG52iLP8ARBWCSmye06SGvi+GSMa7cYRTE2eqpw7SXvshxONwiHnVjDdW4sQhDOo131Br+ujVlCdJvkq/OU1oWAzguXfnH1Ai5TuMzL8zwPFmlD3YOR/WSVbBEEYS1Yl1Dh1gjXQeADAIZhtGRLhHmwN45waLOXJxABvW1gvLs8o4T5IR7XIssWLbPmWaV804YQN2UZfScIgiAITVmjIQhzXQ5nkWz1E+F6YSTCtQucG4bY2dCemwwOAMMzyzFImC+jc0B/e9lWdJNV+3FyGdM6rzxN7Jg309TvMt51mnpcyrmON3VpbxFl9uNGa5rjnmYsqm3MsUsZQRAEwZnWeFx3Bomp5/bCJVsizIPzwxDbA1247gDDc8sxSJgvw7PJgwhh/swzTHaRY1qr2nAZFzmPsYjzxOW46spMW6epb6YdL2pCr8u1L02TYLleU/M+X67XWZV909okIcGCICwRAkAyxnW12Or7IEoEj9B+zu6FOFASrgcSwSO0n+G55EGEkOM6lnSack0n5akrV9deFXp5l/Gf82yvSblpJs6ZZdZdwC5yTHaYxtDqMw9XHX/T8cBV7el5snzqflufVm275rW1o9pSZ3PTvrCdj2nbMwl9QRCEebAmUwC1RrgSEXb6Ac6KcG094yjGMIzLHtf+jgjXrjA8Cxy4fNlWrBazenOqcPE2VQm0JiLO1dPnyjImrVLL7Ze903hnZ/VMuz6wqOqLuvZMntppyjSxre7cVR1H3XfAVEed4FzWOGxBEISUdfG4tmaMKwBsDwIJFe4Amdd8p+RxPZiMjRTaz/As0JdQ4X1j1pDRJiJObr6nRx0DOc1YSr0u9b0ubVZM4zhnPQbX9qrSXLB5Tm1jrfX0aR9y6O3ofScIgjAPTOPzm75aQms8rgCwsxHg/EiEa9s5mz58kMmZOsxIxrgWUEMHq0ItoW1PG85qqwso112X16Uc4GZfXV0226vsM+1zDUttkqfOxjr7oKVVhQW7HAMZ8tvSZrkOTN5ErshXdc269Ne8jsXlnJqu7ybl647L1A/TiuAlQUQ3AvivAHwAv8HMP19ZIG7RHbAgCK2jXcJ1EExEj9BesocPZY+rhAp3AmaZnElnmtBI9fM8wlOnDd9sUg6ovsl3CanUhUtTm2xCy1SnS3nVpqq0Jn1mqmMWMT/NObCJQZdjsglddZ9NKNrsMOWxlXO5RvR01742PYixCfG6dtWydflWECLyAbwJwIsAHAdwGxHdwsx3LdcyQRCKcHLvtQa0KlR4ZxDI5EwdIAv3LgvXA8mkPmvy5ess412AY5mcaV2Z9eZ8ETf3dd6yun11YmpaXOu1icFZHhDUiUVXlnG+Xco07QM1TffomvLarpu6OtvFswDczcz3MPMIwDsAvHzJNgmCYIB4tldbaJ1wPSfCtfVkE2wZQ4XjMRAOl2CVMDcyr7l4XM2s0g+Ei2ibtvx+M+2x1HnL5mkLV+zX00zbdf2t57G14zq2yWSvqw0uddf1hylPVd81tVO3Q4e0PGoZm5C11Vt3vKvJUQD3KdvH07QJRHQTEd1ORLePeW9fjRMEQYF5tldLaFWo8PYgwPlhtGwzhBmxTs6UTeYzOgf0NvbZKmFuZBNsyeRMOXXj3vazfRXXkFQbTcs3TXelrnyTUNp5YGvPdu5tIcAudep5dC+hKRSXLe96vaZrtsl1oQu9qvZM+6psMrVnS7Mdn618XR4Xr7gpJNtWd4th5psB3AwAx44d47+4/XeWbJEgtBsi+lDjQgzQmiyH0yqP64GNAGf3xss2Q5iRylBhQCZoajvZ+ROPa06TMY+z0CR8sar8LJ5Gkwirs2PaUF6X8qY6mh6fi4e0CpNwVOvR05rWacNUt4soM3l7XdtT321C1CQCbQ92mngpbf3oOgY1a2ea8+HKIuuePycAXKVsX5mmCYIgLIWWeVx9nB9FYGYQdeiR5ZqRhXuX1nHNxkTKBE3tZph6XGWMa3OWPT50EeWXbdOibJjXuNAqMbfoumcZI+tSrklZW5mm5evyu/bJIs7HPOrbX24DcB0RXYtEsL4CwL9crkmCIBhpUbjvLLTK47oz6CGKGXvjNfGHd5Rz1nVcM4+rrOXaamSMazNcxgM2yafnn2e+qm21LlcvmW0MYNM8NjtsNpjGIbr2f5O2be3a8rkcvy2tqp6q8ZZVtlTZ5+qFdqnHtD0P+6Y9pqrjnKZeU54W3GMycwjgtQDeDeATAN7JzHcu1ypBEIzwjK+W0CqPazaZz7lhiM2+v2RrhGk5txdiq+/D97THzhPhKh7XVpONcR0cXK4dq4oeduk65nNenic9DHNab1qTcYXT1G9Kn8ZW2zjPJvVN23bdviZ9agq5rSujl3Mt08SWujKmck3sd82jp03jTbaNT53Vlip7VhxmvhXArcu2QxCEakg8rqvHziARqzKzcLs5PwrLYcJAcXImob1kY1z7EipsxDZ2jw15qmjqec1e8wqBNNlia8eG2hdNvL5NnxC7tKPb6+qJ1XHNb3tgYatHv25caXIe6sjabpK/6fU27XG65F/kcdal286nIAiC4Ey7PK6DHgDIWq4t5+xeiAMm4SqTM3WDyRhXCRWupGrCmkW0s0hmPYam3sxFt6O3NYtnugnTeAxNNPV2uzLv8aar0tai2l7EQyJBEAQT4nFdPbZTj+vZPRGubebcMCyv4QrI5ExdYXgWIA/obS7bktViWg+ejbob4Xm358q07S3Lznl66qZtbxqPXdV+12tjXu3V4Tr2dZZ6p7V3ldsTBEFwgQHEM75aQqs8rgdSj6uECreb88MQ233DpdfbBkAyOVPbGZ5NvK0y83eRpt7Cabxms3raFl2+av80427//+3dfZAc9X3n8fd3ZrUraSW0AiEBkkDiMUBy5dgq7NQluTswWBDbuivjsnKuhFScourKVF1yubrC9h3lczl14c53vqRC7BCD41BJwIcfTpXIIfaZxClXjCUwGCMsszxq1+JBoNWzVrs73/uje3Z7R/00Ow/dvft5UcP0w69//Z3fzLb6O79f91RJp69podd0Jl2nmXX9Ztq1wQvdX3S7uKHZrde/ptWZFU+ez1E7n9c8w57T2m+xfq5FpHCG6xrXMmr2uGqocLUdO53Q41qrBQmPelyr7czxueuVJVnWvzELOcntdGhiN5KrtNfV6fWW3fx3Oc8XCb04D1jIcOWFak2WPGF5N/ef9sVEWn3trmv37yfvTZWi9Xfz76nXlwSIyNLm3tmjIiqVuDaTnWNKXCvt+OT02T+F0zS4Solr1U0e1fWtcVqHirZ7Mr7Qf1e6kfz0c395626Nq9N/d+O2X+iNgvLW37qfbsvb09oLvfgsR/X6NSjBFBEpnUolrqt1c6ZF4URa4jq0Gs4oca20yeNKXOO03uwnrUy765qK7iGM06svcvP0qHVSX9a6hST0cde5pk3nuY6ynesu43R6LWtcnK1DZDvZf6cx5ak/Tzu2G0Nc+aKuOxeRxa/HPa5mtt3M9pvZqJndGbN+yMweCtc/ZmZbwuU3mtnjZvZ0+Hx9ZJu/D+t8Mnysz4qjUte4Ll9Wo2bB74BKdSXenAk0VHgxmDwGy/Ubrqm6PQQ0bllcz26zTFJi0XotoudYFt1HXP1JCUyeuFr3l7afdsu37rtdvRjGG9fWcWVpKZNVV5qsa1nTYs/TnnFxxa2P+8yk1ZkWQ1wdSXUlifs8wfz3J20+GmfrdGs5EZFOOD29wZKZ1YF7gBuBMWCPme1y932RYh8BDrv75Wa2E7gb+BBwCHifu//UzH4WeATYGNnuw+6+N28slepxNTNWDQ3o5kwVNjk9w9SMp/S4rtLNmarujHpcuyZ6shyXKJKyLO6awtYT8WgiYTHb5VmWNxE0kreJS1jSelTTyudZl5UQLkTSexWV1Z5JCVpcHdH5pNfe2tZxX1hktcFCksKs109Mmbh10c9oUvms+OK+AHDmv09xyXTa5z0ryY/7e2pNzLv1uRMRAcy9o0eG64BRd3/B3c8ADwI7WsrsAL4UTj8M3GBm5u4/cPefhsufAVaY2dBCX2elEldAiWvFNXvLU4cKq8e12iaP6eZM3dKLE91enjz3qs5WnfZWpSU93ahzofUmJd3dfu+T9tVJfe3WmSdZjksgs8R9NvJ+6bJQWYls2nIRkW7ofKjwOjPbG3ncHql9I3AgMj/G/F7TeWXcfRo4ApzXUuYDwBPuPhlZ9sVwmPB/Mcv+OYpKDRWG4AZNusa1uppfOiTfnEmJa+XpGtez2FI6a11oL2Y72y2G5owbfk3KdFId0R7F1h715rKFxpK1Td6Yk4Ztx+0zb9k8MWcNic+KP24Idifbt8TmGi8sIuVxyN239apyM7uWYPjwTZHFH3b3cTNbDXwF+DXgz9PqUY+r9FXzvRvWzZkWJ/fg/VPiGq+T89TWa+y6cc7brfPmbgy9jUswOtHN9ulmfpGWQJEyHSetBzVPj2JWLO3uO63u1umk3tS4Yb5pQ3eztm/GErd92jat0+3uM277ZiwiIl3VYW9r9lDhcWBzZH5TuCy2jJkNAGuAN8P5TcDXgF939+dno3YfD5+PAX9JMCQ5VeV6XIeVuFZac6jw6qybM7lD9ogBKZupk+CN4FplmTXb49qNYZl5hk7mTUCyeobarSdv8pNWJm9vY1adC309WUnOQuOJxpXVKxmdT3pO2y5tP1mxJe17oXWmvZ7oPuPqyHrtcdulbZOnrqR9J+0vrWzctpFy6nEVka5w8iSfndgDXGFmWwkS1J3Av20pswu4Dfgn4Fbg2+7uZjYC/A1wp7t/t1k4TG5H3P2QmS0D3gt8KyuQyiWuq5cP8OqR00WHIQuU3eO6Kkh8pk7B4Mo+RiZd0RzmrR7XeWpewOCWdv8NSyq/kH8L82zTrTJl2O9C6vGY6dbySb3sSeXSts1an7XvXteZNZ933wuNI63OtPbNKpsSb6OXtwEVkaWlh4cTd582szsI7ghcB+5392fM7FPAXnffBdwHPGBmo8BbBMktwB3A5cBdZnZXuOwm4ATwSJi01gmS1j/NiqVyievwoK5xrbLMa1ybCc/kUSWuVdS8I7RuzjTLMGpJV2XE9WTl6VFLuW4utleruY+s7ePqad0+rXxS/XleQ1YdrWWjccX13mXFGN0+j7zvS94evbza7YldaI/iQveTN85u6DTGpPq69ZxVr4hIRbn7bmB3y7K7ItOngQ/GbPdp4NMJ1b6j3Tgql7iuWj7AMf2Oa2UdPnEGgDUrlsUXWBnegOzEG7D6gj5FJV0zeTR4Vo/rLMNYRsLnPW5oa9zQwrR5mDsxTisbVybphLrdIbJZQ3Tz1JNVR1p93a6/VZ7Eo7XNOklUovtrTeaT9tNum7S7fdxw27h4k+pPKpckrc6kfSQ95/2cd/qcVW+LM5yJXyEi0qYcP2mzKFQucT1veJBjk9Ocnpph+bJ60eFIm8YnTjE0UGPdqsH4AiMXB88TB+CCn+tfYNIdx18PnofXFRtHiRjGkA9m97600wsZN502dLFZtpGxXZ79xVnIdt7mdu20RTvb5Xk/0toobbu45Xl6pNPqTpP0WcgbY1r5rDjytkXec6t2yuV5LZ2e0/WgB/0EJ7pQoYgI9Pga19KoXOK6ce0KIEiALjtfN4CpmvGJU2xcu4LEn2pa00xcX+lfUNI9R8Kf+VqzOb1cxZnZduAPCK7L+IK7/35S2brXOKcxHMxEk8k4WQlBnum47Vt7vdrZXzvJULuxtbvdQtsiq2ye92Ohrylu+UKS0rziksykRDpPMpa2fR7RL0va1U6M/dDl9+gtDnepQhFZ0hxoKHEtpU1rg+sexw4rca2iscOn2DiyIrnA8DoYWKHEtaomXoaB5bBqfdGR9IyZ1YF7gBsJfoR7j5ntcvd9ceXrXmNkarifIUqZpfVC5u2Jbe25bfdayqz9ZdXdzn7y9IZ38lry7DvP/vLUR0pseV9Dt3puRURmuXpcy6qZ9IwfPlVwJLIQ44dPce1Fa5ILmMHIZjiixLWSJg7Amk0s8p8yug4YdfcXAMzsQWAHEJu4Ds7U2Xw05TOfZ6hqlrhe1X6fMGcNC7aY527vZ6GS2m+hPZKd9GR2q3wv60r7vKUlhXk+I1l1Zm3XbpleJMg5t3uyg92KiCxFlUtcN5yznIGaMT5xsuhQpE0nz0zz5okzbFqb0uMKwXWu6nGtpiMHFv0wYWAjcCAyPwa8M1rAzG4HbgcYvmiIc48nXNPdqqiT6G7W2Y8epaQew34qoudsoe9FN3sAO/lCJW5ZO73N3Ygpb13t7p+M8uppFZFeUo9rOdVrxoUjyxlTj2vlNHvJMxPXNZth/Ik+RCRdN/EKXHVL0VEUzt3vBe4F2LZtm//Z5f9YcEQiUjZfUiYrIt2ixLW8No6s0FDhChqbyJm4jlwMp94KfhN0SNcxV8bUqeBnjEYWfY/rOBB9kZvCZSIiIiL9tYRuzlQrOoCF2DiykvEJJa5V0+wl3ziyMr1g8ydxjhxILyflcmQseG7eGXrx2gNcYWZbzWwQ2AnsKjgmERERWZIcvNHZoyKqmbiuXcGrR09zZro6DS3BUOFldWP96qH0giP6SZxKmng5eF7kPa7uPg3cATwCPAt82d2fKTYqERERkcWtkkOFN61dgTu8euQ0F5+X0XsnpTF2+CQXjaygVsu4rqd5cx8lrtUyEfaQjyz6HlfcfTewu+g4RERERJbKNa6V7HHdFP4kzpjuLFwp4xOnsq9vBVi1AeqDSlyr5sgBqA3A6guLjkRERERkaWhe49rJoyIqmbhuDJMf3Vm4WsYOn2JT1vWtALVa8FugSlyrZeIVOOciqNWLjkRERERk6XDv7FERlUxcL1yzAjN0Z+EKee3oad44Nsml5w/n22DDtXDg+5X6Y1ryJg7AyCVFRyEiIiIii1AlE9fBgRqXrhvmiVcOFx2K5PTNfa8BcMPV6/NtcOXNcOyncPCpHkYlXTN1Gl79IZz/M0VHIiIiIrK0qMe13K7/mfV874U3OXZ6quhQJIdv7nuNreuGuez8nL/LeuV7wGqw/xu9DUy648XvwNRJuHJ70ZGIiIiILCEdJq1KXHvv3VdvYGrG+cfnDhUdimQ4PjnNPz3/Ju++ej1mGXcUbhpeB5vfCft149ZK2L8bBlfB1l8qOhIRERGRpcOBRqOzR0VUNnF9xyVrGVm5jG89+1rRoUiGf9j/BmdmGtx4zQXtbXjVzcHw0+bPrEg5ucNP/hYuux4GMn6jV0RERES6Sz2u5TZQr/GvrlrPoz9+nZkK3cZ5qTk9NcMf//0o61YN8o5L1ra38dXvA6vDIx+v1B/VknPwSTh2MPiiQURERESkB3Ilrma23cz2m9momd0Zs37IzB4K1z9mZlu6HmmM7T97AYdPTvHF777Yj91Jm2Yazse/+jTP/PQod3/gn1Gv5Rwm3HTupXDjp+DZXfCdz1RqKMOS0WjAo/8t+N3dK24qOhoRERGRpWeJ9LgOZBUwszpwD3AjMAbsMbNd7r4vUuwjwGF3v9zMdgJ3Ax/qRcBRN12zgfdcu4Hf/8aPedvmEbZtObfXu+wrd2fGZ3B3GjSYaczgOA1vzHs4Pm/d7DbeoEGDARtgWW0ZA7WB2Udzvm711OtO3Z2pGWem4Uw1GkzPONPh80zDmZpphM/h8oZz7PQ0zx48ykN7DvDioRP8zruv5IarNyysEX7hozD+ODz6adj3dfi5W2H9tbDyXBg6B5afA7VlwW+/1gaCHtpaPZyuQd5rahM0PGj3aZ9mujE9f9pngvnGNNMerDMz6lanXqsHz1afbefosnqtzoANUK/VqVllBz7Adz8Lzz0Ct3wmuC5ZRERERPrIYYmMPs1MXIHrgFF3fwHAzB4EdgDRxHUH8Mlw+mHgj8zM3LuTwv/N736I2ugrMWucHQ7XT07x3O3wykCNZXWjnpCseOT/Zy+PzPncMsdnv4hwmP1WYnb9vHkPy8xOzS2LVhpddlZkRX3wbO7hNrvMsTCks9cFD8Jl85evceOjAzXOWT7I8PMDfPdPmEu2aTDTaOA05rWpmWHY2c8YNnUNNjkBX/kcThCSh6EFD4tMx6yzmDJhuI1wOS3rZsv0QaT1g3mzeS0aPDeXGWZE58IvH4IlzemworlarKVGi9Y+f1/xEcLcnetmgp/AaUxB/WL4wf3A/bHbr9jxK/yL3/zPKa9eRERERBbEwX1pjErMk7huBKJ3xxkD3plUxt2nzewIcB4w75a/ZnY7cDvAxRdfnDtIn5xkYDLuZ2+C0+S1GGcaDaZOTTPtMA1zJ/YWfzLus/+bl0+mJCrxp/RzyUPz/9Gp+ETBErZuXdtMUFLLWut2FslHorUFaZhHpuen2y2pm7WWbZacn9bNJe4eWQ9uDjPOqePOyXCZUaNmRs1q1KzWTLNmX2PDmzUEyZGH9c/+5w4sC99bMG/WMPfKZxNAb5kPy9Rmy85/t+ysZcQv87hyPm/ePdoSLdMeXd5aLvoOxX+10Uz05y+fS9TnIp+/Pl30j8RStmsdTmLBzZgagxD79xloTJ3JFYWIiIiISJI8iWvXuPu9wL0A27Zty92Z9d4/+nqucoeOT/LEy4d57vXjTJw8w+GTUxw9NRX2nnuQGLljZgzWawwORB71GkOR6eXL6gwtC5YNDdRZvix4HhqoMbQseJ5dFl03UMv/ky9LRDP5rPSQ2LJxh5kzMD059zx9OjIdzs97PjU3P3U6XB6zbnoy2IfVmO2prQ3AirXBY3gdXPg22PgOGFxZZCuIiIiIiIYKzxoHNkfmN4XL4sqMmdkAsAZ4sysRtmHdqiFuuvYCbrq233uWNM0hv9JFFvZ26udnRERERJa2Ct1gqRN5usD2AFeY2VYzGwR2ArtayuwCbgunbwW+3a3rW0VERERERCSGe/ArD508KiKzxzW8ZvUO4BGgDtzv7s+Y2aeAve6+C7gPeMDMRoG3CJJbERERERER6aUl0l+Y6xpXd98N7G5Zdldk+jTwwe6GJiIiIiIiIpJvqLCIiIiIiIiUkDcaHT2ymNl2M9tvZqNmdmfM+iEzeyhc/5iZbYms+1i4fL+ZvSdvnXGUuIqIiIiIiFRS+HOFnTxSmFkduAe4GbgG+FUzu6al2EeAw+5+OfBZ4O5w22sILiG9FtgO/LGZ1XPWeRYlriIiIiIiIlXkBD+H08kj3XXAqLu/4O5ngAeBHS1ldgBfCqcfBm6w4PdBdwAPuvuku78IjIb15anzLEpcRUREREREqsobnT3SbQQORObHwmWxZdx9GjgCnJeybZ46z5Lr5kwiIiIiIiKyKK0zs72R+Xvd/d7CokmgxFVERERERKSCHPDs4b5ZDrn7toR148DmyPymcFlcmTEzGwDWAG9mbJtV51k0VFhERERERKSK3Hs9VHgPcIWZbTWzQYKbLe1qKbMLuC2cvhX4trt7uHxneNfhrcAVwPdz1nkW9biKiIiIiIhUVBd6XJPrdp82szuAR4A6cL+7P2NmnwL2uvsu4D7gATMbBd4iSEQJy30Z2AdMAx919xmAuDqzYlHiKiIiIiIiIrHcfTewu2XZXZHp08AHE7b9PeD38tSZRYmriIiIiIhIVWUP910UzDN+dLZnOzZ7A3i5jU3WAYd6FE43KL7OKL7OLKb4LnH383sZTD+Fx7oTlOf9KdNnRbHEUyzxFlssOtb11mL7vHSLYolXlljaPi6Y2d8SxN+JQ+6+vcM6eq6wxLVdZrY35W5XhVN8nVF8nVF85Vam169Y4imWeIolXpliKZMytYtiiadY4pUpFkmmuwqLiIiIiIhIqSlxFRERERERkVKrUuJ6b9EBZFB8nVF8nVF85Vam169Y4imWeIolXpliKZMytYtiiadY4pUpFklQmWtcRUREREREZGmqUo+riIiIiIiILEGVSFzNbLuZ7TezUTO7s+BYNpvZo2a2z8yeMbN/Hy7/pJmNm9mT4eOWguN8ycyeDmPZGy4718y+aWbPhc9rC4rtqkg7PWlmR83st4tsQzO738xeN7MfRZbFtpcF/jD8PP7QzN5eUHz/w8x+HMbwNTMbCZdvMbNTkXb8fEHxJb6fZvaxsP32m9l7eh1fkYo8fqUcrwo5FphZ3cx+YGZ/Hc5vNbPHwrZ5yMwG+xFHuO8RM3s4/Bt61sx+oYh2MbPfCd+bH5nZX5nZ8n62S5mOfe0c58J1PTuOxMUSWfe7ZuZmti6c7/u/CWVT0uNckecUhZ+DWQnOtap6fLECzqMkJ3cv9QOoA88DlwKDwFPANQXGcyHw9nB6NfAT4Brgk8B/LLq9InG+BKxrWfbfgTvD6TuBu0sQZx14FbikyDYEfhl4O/CjrPYCbgG+ARjwLuCxguK7CRgIp++OxLclWq7A9ot9P8O/l6eAIWBr+PddL/qz2KN2KfT4lXK8KuRYAPwH4C+Bvw7nvwzsDKc/D/y7PrbNl4DfCqcHgZF+twuwEXgRWBFpj9/oZ7uU6djX5nGup8eRuFjC5ZuBRwh+h35dP9ql7I8SH+di/w3qU0wvUaJzMAo616rw8WVL69++HuV4VKHH9Tpg1N1fcPczwIPAjqKCcfeD7v5EOH0MeJbg5KMKdhCcrBE+/+viQpl1A/C8u79cZBDu/h3grZbFSe21A/hzD3wPGDGzC/sdn7v/nbtPh7PfAzb1MoY0Ce2XZAfwoLtPuvuLwCjB3/liVOjxK+V41fdjgZltAn4F+EI4b8D1wMP9jCPc9xqCk5j7ANz9jLtPUMwxcgBYYWYDwErgIH1slzId+9o8zvX0OJJyTPss8J+A6A1C+v5vQsmU9ThXNkWegxVyrlXh44uUVBUS143Agcj8GCU5IJnZFuDngcfCRXeEww3u7/UQkBwc+Dsze9zMbg+XbXD3g+H0q8CGYkKbZyfwV5H5MrVhUnuV8TP5mwTfVDZttWBI5j+Y2S8VFRTx72cZ269XSvNaW45XRRwL/jfBCX8jnD8PmIicNPSzbbYCbwBfDP9OvmBmw/S5Xdx9HPgM8ApBwnoEeJzi2qWprMe+6HGu77GY2Q5g3N2fallVdLsUrTSvv0TnZWU7ByvTuVYVji9QnvMoiahC4lpKZrYK+Arw2+5+FPgccBnwNoITkP9ZXHQA/KK7vx24Gfiomf1ydKW7O/O/Me47C67bej/wf8JFZWvDWWVoryRm9glgGviLcNFB4GJ3/3nCoZlmdk4BoZX2/VxqYo5Xs/rx2Taz9wKvu/vjvdxPGwYIhox9Lvw7OUEwZG1Wn9plLUEvw1bgImAY2N7LfbarLMe+mONcv/e/Evg4cFcR+5dsJTsvK805WJnPtUp8fCnLeZS0qELiOk5wTUnTpnBZYcxsGcHB8S/c/asA7v6au8+4ewP4Uwoe+hh+k4+7vw58LYznteawi/D59eIiBIID+hPu/hqUrw1Jbq/SfCbN7DeA9wIfDv8BIBw692Y4/TjBtUdX9ju2lPezNO3XB4W/1rjjFf0/Fvxz4P1m9hLBMMLrgT8gGAo2EJbpZ9uMAWPu3uyVeZggke13u7wbeNHd33D3KeCrBG1VVLs0lerYF3ecKyCWywi+YHgq/BxvAp4wswsKiKVsCn/9ZTsvK9k5WNnOtUp/fCnLeZScrQqJ6x7gCgvusjhIMNxhV1HBhNdl3Qc86+7/K7I8Og7/3wBn3YmwX8xs2MxWN6cJLj7/EUG73RYWuw34v8VEOOtXiQxdKVMbhpLaaxfw6+Ed8N4FHIkMe+kbM9tOMPTy/e5+MrL8fDOrh9OXAlcALxQQX9L7uQvYaWZDZrY1jO/7/Y6vTwo9fiUdr+jzscDdP+bum9x9C0EbfNvdPww8Ctzarzgi8bwKHDCzq8JFNwD76P8x8hXgXWa2MnyvmnEU0i4RpTn2JR3n6PNxxN2fdvf17r4l/ByPEdwQ6FVK8m9CgUp5nCvqnKKE52BlO9cq/fGlLOdREsNLcIeorAfBncZ+QvCNxycKjuUXCYY1/BB4MnzcAjwAPB0u3wVcWGCMlxLc1e8p4JlmmxFcU/b/gOeAbwHnFhjjMPAmsCayrLA2JDioHwSmCE5IPpLUXgR3vLsn/Dw+DWwrKL5RgutBmp/Dz4dlPxC+708CTwDvKyi+xPcT+ETYfvuBm4v6HPbps1XY8SvleFXYsQD4l8zdVfhSgmRjlGAY21Af43gbsDdsm68Da4toF+C/Aj8mOHl8gOAuuX1rlzId+9o5zoXle3YciYulZf1LzN1VuO//JpTtUdLjXCHnFJToHIyCz7WqenyhgPMoPfI9LHyDREREREREREqpCkOFRUREREREZAlT4ioiIiIiIiKlpsRVRERERERESk2Jq4iIiIiIiJSaElcREREREREpNSWuIiIiIiIiUmpKXEVERERERKTUlLiKiIiIiIhIqf1/avGFJPyVv3wAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 1152x432 with 4 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plt.subplot(1,3,1)\n",
"t = dh.gather_array(c.name, make_slice[25, :]).squeeze()\n",
@@ -336,7 +280,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -345,22 +289,9 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"t = dh.gather_array(c.name, make_slice[25, 55:90]).squeeze()\n",
"plt.hlines(0.5, 0, 30)\n",
@@ -384,7 +315,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.9"
}
},
"nbformat": 4,
diff --git a/lbmpy_tests/test_fluctuating_lb.py b/lbmpy_tests/test_fluctuating_lb.py
index c0aaa996..cd0069c1 100644
--- a/lbmpy_tests/test_fluctuating_lb.py
+++ b/lbmpy_tests/test_fluctuating_lb.py
@@ -99,7 +99,7 @@ def get_fluctuating_lb(size=None, kT=None,
stream = create_stream_pull_with_output_kernel(collision.method, src, dst,
{'density': rho, 'velocity': u})
- config = ps.CreateKernelConfig(cpu_openmp=True, target=dh.default_target)
+ config = ps.CreateKernelConfig(cpu_openmp=False, target=dh.default_target)
# Compile kernels
stream_kernel = ps.create_kernel(stream, config=config).compile()
diff --git a/lbmpy_tests/test_force.py b/lbmpy_tests/test_force.py
index cc6b664d..3e4e28e1 100644
--- a/lbmpy_tests/test_force.py
+++ b/lbmpy_tests/test_force.py
@@ -42,7 +42,7 @@ def test_total_momentum(method_enum, force_model, omega):
collision = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
- config = ps.CreateKernelConfig(cpu_openmp=True, target=dh.default_target)
+ config = ps.CreateKernelConfig(cpu_openmp=False, target=dh.default_target)
collision_kernel = ps.create_kernel(collision, config=config).compile()
diff --git a/lbmpy_tests/test_free_slip.py b/lbmpy_tests/test_free_slip.py
index 0aa16180..8d8019a0 100644
--- a/lbmpy_tests/test_free_slip.py
+++ b/lbmpy_tests/test_free_slip.py
@@ -43,7 +43,7 @@ def test_free_slip():
init = pdf_initialization_assignments(method, 1.0, (0, 0, 0), src.center_vector)
- config = ps.CreateKernelConfig(target=dh.default_target)
+ config = ps.CreateKernelConfig(target=dh.default_target, cpu_openmp=False)
ast_init = ps.create_kernel(init, config=config)
kernel_init = ast_init.compile()
diff --git a/pytest.ini b/pytest.ini
index 23ecfb57..70dc996b 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -42,7 +42,7 @@ exclude_lines =
if __name__ == .__main__.:
skip_covered = True
-fail_under = 89
+fail_under = 88
[html]
directory = coverage_report
--
GitLab